From 58c808ea69410cd7a8f0ff8f1a5433d77cf09d6a Mon Sep 17 00:00:00 2001 From: nuwangeek Date: Tue, 14 Oct 2025 08:55:35 +0530 Subject: [PATCH 01/11] initial diff identifier setup --- DSL/CronManager/DSL/data_resync.yml | 5 + .../DSL/initiate_vector_indexer.yml | 5 + DSL/CronManager/script/agency_data_resync.sh | 19 ++ .../script/vector_indexer_pipeline.sh | 12 + .../rag-search-script-v1-llm-connections.sql | 17 ++ DSL/Resql/rag-search/POST/get-agency-id.sql | 7 + .../rag-search/POST/mock-get-data-from-kb.sql | 20 ++ .../POST/ckb/agency_data_import.yml | 33 +++ .../POST/ckb/agency-data-import.yml | 33 +++ .../rag-search/POST/data/update.yml | 67 +++++ docker-compose.yml | 230 +++++++++--------- migrate.sh | 2 +- 12 files changed, 334 insertions(+), 116 deletions(-) create mode 100644 DSL/CronManager/DSL/data_resync.yml create mode 100644 DSL/CronManager/DSL/initiate_vector_indexer.yml create mode 100644 DSL/CronManager/script/agency_data_resync.sh create mode 100644 DSL/CronManager/script/vector_indexer_pipeline.sh create mode 100644 DSL/Resql/rag-search/POST/get-agency-id.sql create mode 100644 DSL/Resql/rag-search/POST/mock-get-data-from-kb.sql create mode 100644 DSL/Ruuter.private/rag-search/POST/ckb/agency_data_import.yml create mode 100644 DSL/Ruuter.public/rag-search/POST/ckb/agency-data-import.yml create mode 100644 DSL/Ruuter.public/rag-search/POST/data/update.yml diff --git a/DSL/CronManager/DSL/data_resync.yml b/DSL/CronManager/DSL/data_resync.yml new file mode 100644 index 0000000..059818d --- /dev/null +++ b/DSL/CronManager/DSL/data_resync.yml @@ -0,0 +1,5 @@ +agency_data_resync: + trigger: "0 0/1 * * * ?" + # trigger: off + type: exec + command: "../app/scripts/agency_data_resync.sh -s 10" \ No newline at end of file diff --git a/DSL/CronManager/DSL/initiate_vector_indexer.yml b/DSL/CronManager/DSL/initiate_vector_indexer.yml new file mode 100644 index 0000000..82b858b --- /dev/null +++ b/DSL/CronManager/DSL/initiate_vector_indexer.yml @@ -0,0 +1,5 @@ +vector_indexer: + trigger: off + type: exec + command: "../app/scripts/vector_indexer_pipeline_s3.sh" + allowedEnvs: ['signedUrls', 'datasetId', 'majorVersion', 'minorVersion'] \ No newline at end of file diff --git a/DSL/CronManager/script/agency_data_resync.sh b/DSL/CronManager/script/agency_data_resync.sh new file mode 100644 index 0000000..33ae952 --- /dev/null +++ b/DSL/CronManager/script/agency_data_resync.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +# DEFINING ENDPOINTS + +CHECK_RESYNC_DATA_AVAILABILITY_ENDPOINT=http://ruuter-public:8086/rag-search/data/update + +# Construct payload to update training status using cat +payload=$(cat < 0 THEN ARRAY_AGG(agency_id ORDER BY agency_id) + ELSE NULL + END as agency_ids, + COUNT(*) > 0 as has_data +FROM public.agency_sync; \ No newline at end of file diff --git a/DSL/Resql/rag-search/POST/mock-get-data-from-kb.sql b/DSL/Resql/rag-search/POST/mock-get-data-from-kb.sql new file mode 100644 index 0000000..313f430 --- /dev/null +++ b/DSL/Resql/rag-search/POST/mock-get-data-from-kb.sql @@ -0,0 +1,20 @@ +WITH parsed_ids AS ( + SELECT unnest(string_to_array(:agencyIds, ' ')) AS agency_id +) +SELECT + mock_ckb.agency_id, + mock_ckb.agency_data_hash, + mock_ckb.data_url, + CASE + WHEN mock_ckb.agency_data_hash = agency_sync.agency_data_hash THEN true + ELSE false + END AS hash_match +FROM + public.mock_ckb +JOIN + parsed_ids ON mock_ckb.agency_id = parsed_ids.agency_id +LEFT JOIN + public.agency_sync ON mock_ckb.agency_id = agency_sync.agency_id +WHERE + mock_ckb.agency_data_hash IS NOT NULL + AND mock_ckb.data_url IS NOT NULL; diff --git a/DSL/Ruuter.private/rag-search/POST/ckb/agency_data_import.yml b/DSL/Ruuter.private/rag-search/POST/ckb/agency_data_import.yml new file mode 100644 index 0000000..ba892e5 --- /dev/null +++ b/DSL/Ruuter.private/rag-search/POST/ckb/agency_data_import.yml @@ -0,0 +1,33 @@ +declaration: + call: declare + version: 0.1 + description: "Get agency data information by agency IDs" + method: post + accepts: json + returns: json + namespace: rag-search + allowlist: + body: + - field: agencyIds + type: array + description: "Array of unique institution IDs" + +extractRequestData: + assign: + agencyIds: ${incoming.body.agencyIds || []} + log: "Received request for agency data: ${agencyIds}" + +get_agency_data: + call: http.post + args: + url: "[#GLOBAL_CLASSIFIER_RESQL]/mock-get-data-from-kb" + headers: + type: json + body: + agencyIds: ${agencyIds} + result: agency_data_info + next: return_result + +return_result: + return: ${agency_data_info.response.body} + next: end \ No newline at end of file diff --git a/DSL/Ruuter.public/rag-search/POST/ckb/agency-data-import.yml b/DSL/Ruuter.public/rag-search/POST/ckb/agency-data-import.yml new file mode 100644 index 0000000..9905b27 --- /dev/null +++ b/DSL/Ruuter.public/rag-search/POST/ckb/agency-data-import.yml @@ -0,0 +1,33 @@ +declaration: + call: declare + version: 0.1 + description: "Get agency data information by agency IDs" + method: post + accepts: json + returns: json + namespace: rag-search + allowlist: + body: + - field: agencyIds + type: array + description: "Array of unique institution IDs" + +extractRequestData: + assign: + agencyIds: ${incoming.body.agencyIds || []} + log: "Received request for agency data: ${agencyIds}" + +get_agency_data: + call: http.post + args: + url: "[#RAG_SEARCH_RESQL]/mock-get-data-from-kb" + headers: + type: json + body: + agencyIds: ${agencyIds} + result: agency_data_info + next: return_result + +return_result: + return: ${agency_data_info.response.body} + next: end \ No newline at end of file diff --git a/DSL/Ruuter.public/rag-search/POST/data/update.yml b/DSL/Ruuter.public/rag-search/POST/data/update.yml new file mode 100644 index 0000000..dddf8d9 --- /dev/null +++ b/DSL/Ruuter.public/rag-search/POST/data/update.yml @@ -0,0 +1,67 @@ +declaration: + call: declare + version: 0.1 + description: "Resync new data from KB" + method: post + accepts: json + returns: json + namespace: rag-search + +getAgencyId: + call: http.post + args: + url: "[#RAG_SEARCH_RESQL]/get-agency-id" + result: get_agency_id_result + next: log_result + +log_result: + log: ${get_agency_id_result.response.body[0].agencyIds} + next: checkSyncStatus + +checkSyncStatus: + switch: + - condition: ${get_agency_id_result.response.body[0].hasData} + next: importAgencyData + - condition: true + next: noAgencyData + +importAgencyData: + call: http.post + args: + url: "[#RAG_SEARCH_RUUTER_PUBLIC]/ckb/agency-data-import" + body: + agencyIds: ${get_agency_id_result.response.body[0].agencyIds} + result: importResult + next: logImportAgencyDataResponse + +logImportAgencyDataResponse: + log: ${JSON.stringify(importResult.response)} + next: checkHashMatch + +checkHashMatch: + switch: + - condition: ${importResult.response.body.response[0].hashMatch} + next: noAgencyData + - condition: true + next: logNewDataPresent + +executeCronManager: + call: http.post + url: "[#RAG_SEARCH_CRON_MANAGER]/data-resync" + +# logNewDataPresent: +# log: "New data present - synchronization required" +# next: end + +assignNoAgencyResponse: + assign: + no_agency_response: + success: false + message: "No agency data available for sync" + next: noAgencyData + +noAgencyData: + status: 200 + return: ${no_agency_response} + next: end + \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml index d8d1224..4c285b9 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,125 +1,125 @@ services: - # ruuter-public: - # container_name: ruuter-public - # image: ruuter - # environment: - # - application.cors.allowedOrigins=http://localhost:8086,http://localhost:3001,http://localhost:3003,http://localhost:3004,http://localhost:8080,http://localhost:8000,http://localhost:8090 - # - application.httpCodesAllowList=200,201,202,204,400,401,403,500 - # - application.internalRequests.allowedIPs=127.0.0.1 - # - application.logging.displayRequestContent=true - # - application.logging.displayResponseContent=true - # - application.logging.printStackTrace=true - # - application.internalRequests.disabled=true - # - server.port=8086 - # volumes: - # - ./DSL/Ruuter.public:/DSL - # - ./constants.ini:/app/constants.ini - # ports: - # - 8086:8086 - # networks: - # - bykstack - # cpus: "0.5" - # mem_limit: "512M" + ruuter-public: + container_name: ruuter-public + image: ruuter + environment: + - application.cors.allowedOrigins=http://localhost:8086,http://localhost:3001,http://localhost:3003,http://localhost:3004,http://localhost:8080,http://localhost:8000,http://localhost:8090 + - application.httpCodesAllowList=200,201,202,204,400,401,403,500 + - application.internalRequests.allowedIPs=127.0.0.1 + - application.logging.displayRequestContent=true + - application.logging.displayResponseContent=true + - application.logging.printStackTrace=true + - application.internalRequests.disabled=true + - server.port=8086 + volumes: + - ./DSL/Ruuter.public:/DSL + - ./constants.ini:/app/constants.ini + ports: + - 8086:8086 + networks: + - bykstack + cpus: "0.5" + mem_limit: "512M" - # ruuter-private: - # container_name: ruuter-private - # image: ruuter - # environment: - # - application.cors.allowedOrigins=http://localhost:3001,http://localhost:3003,http://localhost:8088,http://localhost:3002,http://localhost:3004,http://localhost:8000 - # - application.httpCodesAllowList=200,201,202,400,401,403,500 - # - application.internalRequests.allowedIPs=127.0.0.1 - # - application.logging.displayRequestContent=true - # - application.logging.displayResponseContent=true - # - application.logging.printStackTrace=true - # - application.internalRequests.disabled=true - # - server.port=8088 - # volumes: - # - ./DSL/Ruuter.private:/DSL - # - ./constants.ini:/app/constants.ini - # ports: - # - 8088:8088 - # networks: - # - bykstack - # cpus: "0.5" - # mem_limit: "512M" + ruuter-private: + container_name: ruuter-private + image: ruuter + environment: + - application.cors.allowedOrigins=http://localhost:3001,http://localhost:3003,http://localhost:8088,http://localhost:3002,http://localhost:3004,http://localhost:8000 + - application.httpCodesAllowList=200,201,202,400,401,403,500 + - application.internalRequests.allowedIPs=127.0.0.1 + - application.logging.displayRequestContent=true + - application.logging.displayResponseContent=true + - application.logging.printStackTrace=true + - application.internalRequests.disabled=true + - server.port=8088 + volumes: + - ./DSL/Ruuter.private:/DSL + - ./constants.ini:/app/constants.ini + ports: + - 8088:8088 + networks: + - bykstack + cpus: "0.5" + mem_limit: "512M" - # data-mapper: - # container_name: data-mapper - # image: data-mapper - # environment: - # - PORT=3000 - # - CONTENT_FOLDER=/data - # volumes: - # - ./DSL:/data - # - ./DSL/DMapper/rag-search/hbs:/workspace/app/views/rag-search - # - ./DSL/DMapper/rag-search/lib:/workspace/app/lib - # ports: - # - 3000:3000 - # networks: - # - bykstack + data-mapper: + container_name: data-mapper + image: data-mapper + environment: + - PORT=3000 + - CONTENT_FOLDER=/data + volumes: + - ./DSL:/data + - ./DSL/DMapper/rag-search/hbs:/workspace/app/views/rag-search + - ./DSL/DMapper/rag-search/lib:/workspace/app/lib + ports: + - 3001:3000 + networks: + - bykstack - # tim: - # container_name: tim - # image: tim - # depends_on: - # tim-postgresql: - # condition: service_started - # environment: - # - SECURITY_ALLOWLIST_JWT=ruuter-private,ruuter-public,data-mapper,resql,tim,tim-postgresql,chat-widget,authentication-layer,127.0.0.1,::1 - # - KEY_PASS=ppjjpp - # ports: - # - 8085:8085 - # networks: - # - bykstack - # extra_hosts: - # - "host.docker.internal:host-gateway" - # cpus: "0.5" - # mem_limit: "512M" + tim: + container_name: tim + image: tim + depends_on: + tim-postgresql: + condition: service_started + environment: + - SECURITY_ALLOWLIST_JWT=ruuter-private,ruuter-public,data-mapper,resql,tim,tim-postgresql,chat-widget,authentication-layer,127.0.0.1,::1 + - KEY_PASS=ppjjpp + ports: + - 8085:8085 + networks: + - bykstack + extra_hosts: + - "host.docker.internal:host-gateway" + cpus: "0.5" + mem_limit: "512M" - # tim-postgresql: - # container_name: tim-postgresql - # image: postgres:14.1 - # environment: - # - POSTGRES_USER=tim - # - POSTGRES_PASSWORD=123 - # - POSTGRES_DB=tim - # # - POSTGRES_HOST_AUTH_METHOD=trust - # volumes: - # - ./tim-db:/var/lib/postgresql/data - # ports: - # - 9876:5432 - # networks: - # - bykstack + tim-postgresql: + container_name: tim-postgresql + image: postgres:14.1 + environment: + - POSTGRES_USER=tim + - POSTGRES_PASSWORD=123 + - POSTGRES_DB=tim + # - POSTGRES_HOST_AUTH_METHOD=trust + volumes: + - ./tim-db:/var/lib/postgresql/data + ports: + - 9876:5432 + networks: + - bykstack - # authentication-layer: - # container_name: authentication-layer - # image: authentication-layer - # ports: - # - 3004:3004 - # networks: - # - bykstack + authentication-layer: + container_name: authentication-layer + image: authentication-layer + ports: + - 3004:3004 + networks: + - bykstack - # resql: - # container_name: resql - # image: resql - # depends_on: - # rag_search_db: - # condition: service_started - # environment: - # - sqlms.datasources.[0].name=byk - # - sqlms.datasources.[0].jdbcUrl=jdbc:postgresql://rag_search_db:5432/rag-search #For LocalDb Use - # # sqlms.datasources.[0].jdbcUrl=jdbc:postgresql://171.22.247.13:5435/byk?sslmode=require - # - sqlms.datasources.[0].username=postgres - # - sqlms.datasources.[0].password=dbadmin - # - logging.level.org.springframework.boot=INFO - # ports: - # - 8082:8082 - # volumes: - # - ./DSL/Resql:/DSL - # - ./shared:/shared - # - ./DSL/DatasetGenerator/output_datasets:/app/output_datasets - # networks: - # - bykstack + resql: + container_name: resql + image: resql + depends_on: + rag_search_db: + condition: service_started + environment: + - sqlms.datasources.[0].name=byk + - sqlms.datasources.[0].jdbcUrl=jdbc:postgresql://rag_search_db:5432/rag-search #For LocalDb Use + # sqlms.datasources.[0].jdbcUrl=jdbc:postgresql://171.22.247.13:5435/byk?sslmode=require + - sqlms.datasources.[0].username=postgres + - sqlms.datasources.[0].password=dbadmin + - logging.level.org.springframework.boot=INFO + ports: + - 8082:8082 + volumes: + - ./DSL/Resql:/DSL + - ./shared:/shared + - ./DSL/DatasetGenerator/output_datasets:/app/output_datasets + networks: + - bykstack # gui: # container_name: gui diff --git a/migrate.sh b/migrate.sh index 3a03cdc..c156698 100644 --- a/migrate.sh +++ b/migrate.sh @@ -12,4 +12,4 @@ INI_FILE="constants.ini" DB_PASSWORD=$(get_ini_value "$INI_FILE" "DB_PASSWORD") -docker run --rm --network bykstack -v `pwd`/DSL/Liquibase/changelog:/liquibase/changelog -v `pwd`/DSL/Liquibase/master.yml:/liquibase/master.yml -v `pwd`/DSL/Liquibase/data:/liquibase/data liquibase/liquibase --defaultsFile=/liquibase/changelog/liquibase.properties --changelog-file=master.yml --url=jdbc:postgresql://rag_search_db:5432/rag-search?user=postgres --password=$DB_PASSWORD update +docker run --rm --network bykstack -v `pwd`/DSL/Liquibase/changelog:/liquibase/changelog -v `pwd`/DSL/Liquibase/master.yml:/liquibase/master.yml -v `pwd`/DSL/Liquibase/data:/liquibase/data liquibase/liquibase:4.33 --defaultsFile=/liquibase/changelog/liquibase.properties --changelog-file=master.yml --url=jdbc:postgresql://rag_search_db:5432/rag-search?user=postgres --password=$DB_PASSWORD update From c423bf669ae565017cf0922eb822a423e4c72d85 Mon Sep 17 00:00:00 2001 From: nuwangeek Date: Tue, 14 Oct 2025 19:55:41 +0530 Subject: [PATCH 02/11] added get-configuration.sqpl and updated llmconnections.ts --- .../rag-search/POST/get-configuration.sql | 5 ++ GUI/src/services/llmConnections.ts | 1 + docker-compose.yml | 64 +++++++++---------- 3 files changed, 38 insertions(+), 32 deletions(-) create mode 100644 DSL/Resql/rag-search/POST/get-configuration.sql diff --git a/DSL/Resql/rag-search/POST/get-configuration.sql b/DSL/Resql/rag-search/POST/get-configuration.sql new file mode 100644 index 0000000..f03b322 --- /dev/null +++ b/DSL/Resql/rag-search/POST/get-configuration.sql @@ -0,0 +1,5 @@ +SELECT id, key, value +FROM configuration +WHERE key=:key +AND id IN (SELECT max(id) from configuration GROUP BY key) +AND NOT deleted; diff --git a/GUI/src/services/llmConnections.ts b/GUI/src/services/llmConnections.ts index b385aaf..96d37e9 100644 --- a/GUI/src/services/llmConnections.ts +++ b/GUI/src/services/llmConnections.ts @@ -197,6 +197,7 @@ export async function checkBudgetStatus(): Promise { // Return null if no production connection found (404) or other errors return null; } +} export async function updateLLMConnectionStatus( id: string | number, diff --git a/docker-compose.yml b/docker-compose.yml index c61e6c9..788b316 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -292,7 +292,7 @@ services: - langfuse-worker - rag_search_db ports: - - 3000:3000 + - 3005:3000 env_file: - .env environment: @@ -449,37 +449,37 @@ services: # LLM Orchestration Service - # llm-orchestration-service: - # build: - # context: . - # dockerfile: Dockerfile.llm_orchestration_service - # container_name: llm-orchestration-service - # restart: always - # ports: - # - "8100:8100" - # env_file: - # - .env - # environment: - # - ENVIRONMENT=production - # - VAULT_ADDR=http://vault:8200 - # - VAULT_TOKEN=/agent/out/token - # volumes: - # # Mount configuration files - # - ./src/llm_config_module/config:/app/src/llm_config_module/config:ro - # # Mount logs directory for persistence - # - llm_orchestration_logs:/app/logs - # - ./vault/agent-out:/agent/out:ro - # networks: - # - bykstack - # depends_on: - # - vault - # - vault-agent-llm - # healthcheck: - # test: ["CMD", "curl", "-f", "http://llm-orchestration-service:8100/health"] - # interval: 30s - # timeout: 10s - # start_period: 40s - # retries: 3 + llm-orchestration-service: + build: + context: . + dockerfile: Dockerfile.llm_orchestration_service + container_name: llm-orchestration-service + restart: always + ports: + - "8100:8100" + env_file: + - .env + environment: + - ENVIRONMENT=production + - VAULT_ADDR=http://vault:8200 + - VAULT_TOKEN=/agent/out/token + volumes: + # Mount configuration files + - ./src/llm_config_module/config:/app/src/llm_config_module/config:ro + # Mount logs directory for persistence + - llm_orchestration_logs:/app/logs + - ./vault/agent-out:/agent/out:ro + networks: + - bykstack + depends_on: + - vault + - vault-agent-llm + healthcheck: + test: ["CMD", "curl", "-f", "http://llm-orchestration-service:8100/health"] + interval: 30s + timeout: 10s + start_period: 40s + retries: 3 volumes: loki-data: From 89018bc9c940e26feaee2b8fe0f04dc9ab196bce Mon Sep 17 00:00:00 2001 From: nuwangeek Date: Wed, 15 Oct 2025 19:06:26 +0530 Subject: [PATCH 03/11] added initil diff identifier functionality --- .../diff_identifier/_init__py => 3.55.2 | 0 DSL/CronManager/DSL/data_resync.yml | 4 +- .../DSL/initiate_vector_indexer.yml | 4 +- .../script/vector_indexer_pipeline.sh | 64 +- .../rag-search-script-v1-llm-connections.sql | 6 +- DSL/Resql/rag-search/POST/get-agency-id.sql | 7 +- .../rag-search/POST/mock-get-data-from-kb.sql | 23 +- .../rag-search/POST/data/update.yml | 70 +- docker-compose.yml | 40 + pyproject.toml | 1 + .../config/vector_indexer_config.yaml | 16 +- src/vector_indexer/constants.py | 21 + .../diff_identifier/DIFF_IDENTIFIER_FLOW.md | 1224 +++++++++++++++++ .../diff_identifier/__init__.py | 16 + .../diff_identifier/diff_detector.py | 228 +++ .../diff_identifier/diff_models.py | 63 + .../diff_identifier/s3_ferry_client.py | 199 +++ .../diff_identifier/version_manager.py | 308 +++++ src/vector_indexer/main_indexer.py | 137 +- uv.lock | 775 ++++++++++- 20 files changed, 3134 insertions(+), 72 deletions(-) rename src/vector_indexer/diff_identifier/_init__py => 3.55.2 (100%) create mode 100644 src/vector_indexer/diff_identifier/DIFF_IDENTIFIER_FLOW.md create mode 100644 src/vector_indexer/diff_identifier/__init__.py create mode 100644 src/vector_indexer/diff_identifier/s3_ferry_client.py diff --git a/src/vector_indexer/diff_identifier/_init__py b/3.55.2 similarity index 100% rename from src/vector_indexer/diff_identifier/_init__py rename to 3.55.2 diff --git a/DSL/CronManager/DSL/data_resync.yml b/DSL/CronManager/DSL/data_resync.yml index 059818d..c5fb58d 100644 --- a/DSL/CronManager/DSL/data_resync.yml +++ b/DSL/CronManager/DSL/data_resync.yml @@ -1,5 +1,5 @@ agency_data_resync: - trigger: "0 0/1 * * * ?" - # trigger: off + # trigger: "0 0/1 * * * ?" + trigger: off type: exec command: "../app/scripts/agency_data_resync.sh -s 10" \ No newline at end of file diff --git a/DSL/CronManager/DSL/initiate_vector_indexer.yml b/DSL/CronManager/DSL/initiate_vector_indexer.yml index 82b858b..561f787 100644 --- a/DSL/CronManager/DSL/initiate_vector_indexer.yml +++ b/DSL/CronManager/DSL/initiate_vector_indexer.yml @@ -1,5 +1,5 @@ vector_indexer: trigger: off type: exec - command: "../app/scripts/vector_indexer_pipeline_s3.sh" - allowedEnvs: ['signedUrls', 'datasetId', 'majorVersion', 'minorVersion'] \ No newline at end of file + command: "../app/scripts/vector_indexer_pipeline.sh" + allowedEnvs: ['signedUrl', 'clientDataHash'] \ No newline at end of file diff --git a/DSL/CronManager/script/vector_indexer_pipeline.sh b/DSL/CronManager/script/vector_indexer_pipeline.sh index 259c060..487fefe 100644 --- a/DSL/CronManager/script/vector_indexer_pipeline.sh +++ b/DSL/CronManager/script/vector_indexer_pipeline.sh @@ -1,12 +1,62 @@ #!/bin/bash -# Check if environment variable is set -if [ -z "$signedUrls" ] || [ -z "$datasetId" ] || [ -z "$majorVersion" ] || [ -z "$minorVersion" ]; then - echo "Please set the signedUrls, datasetId, majorVersion, minorVersion environment variables." +echo "Starting vector indexer pipeline..." + +if [ -z "$signedUrl" ] || [ -z "$clientDataHash" ]; then + echo "Please set the signedS3Url and clientDataHash environment variables." exit 1 fi -# Logging function -log() { - echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1" -} \ No newline at end of file +PYTHON_SCRIPT="/app/src/vector_indexer/main_indexer.py" + +echo "Using signedUrl: $signedUrl" +echo "Using clientDataHash: $clientDataHash" + +# Install uv if not found +UV_BIN="/root/.local/bin/uv" +if [ ! -f "$UV_BIN" ]; then + echo "[UV] Installing uv..." + curl -LsSf https://astral.sh/uv/install.sh | sh || { + echo "[ERROR] Failed to install uv" + exit 1 + } +fi + +# Activate Python virtual environment +VENV_PATH="/app/python_virtual_env" +echo "[VENV] Activating virtual environment at: $VENV_PATH" +source "$VENV_PATH/bin/activate" || { + echo "[ERROR] Failed to activate virtual environment" + exit 1 +} + +# Install required packages +echo "[PACKAGES] Installing required packages..." + +"$UV_BIN" pip install --python "$VENV_PATH/bin/python3" "numpy>=1.21.0,<2.0" || exit 1 +"$UV_BIN" pip install --python "$VENV_PATH/bin/python3" "requests>=2.32.5" || exit 1 +"$UV_BIN" pip install --python "$VENV_PATH/bin/python3" "pydantic>=2.11.7" || exit 1 +"$UV_BIN" pip install --python "$VENV_PATH/bin/python3" "qdrant-client>=1.15.1" || exit 1 +"$UV_BIN" pip install --python "$VENV_PATH/bin/python3" "rank-bm25>=0.2.2" || exit 1 +"$UV_BIN" pip install --python "$VENV_PATH/bin/python3" "tiktoken>=0.11.0" || exit 1 +"$UV_BIN" pip install --python "$VENV_PATH/bin/python3" "dvc[s3]>=3.55.2" || exit 1 + +echo "[PACKAGES] All packages installed successfully" + +export PYTHONPATH="/app:/app/src:/app/src/vector_indexer:$PYTHONPATH" + +[ ! -f "$PYTHON_SCRIPT" ] && { echo "[ERROR] Python script not found"; exit 1; } + +echo "[FOUND] Python script at: $PYTHON_SCRIPT" + +# Run vector indexer with signed URL parameter +echo "[STARTING] Vector indexer processing..." +if [ -n "$signedUrl" ]; then + echo "[SIGNED_URL] Using signed URL for dataset processing" + python3 "$PYTHON_SCRIPT" --signed-url "$signedUrl" +else + echo "[NO_URL] Running without signed URL" + python3 "$PYTHON_SCRIPT" +fi + +echo "[COMPLETED] Vector indexer pipeline finished" \ No newline at end of file diff --git a/DSL/Liquibase/changelog/rag-search-script-v1-llm-connections.sql b/DSL/Liquibase/changelog/rag-search-script-v1-llm-connections.sql index 155832d..3664915 100644 --- a/DSL/Liquibase/changelog/rag-search-script-v1-llm-connections.sql +++ b/DSL/Liquibase/changelog/rag-search-script-v1-llm-connections.sql @@ -130,8 +130,8 @@ INSERT INTO public.agency_sync (agency_id, created_at) VALUES ('AGENCY001', NOW()); CREATE TABLE public.mock_ckb ( - agency_id VARCHAR(50) PRIMARY KEY, - agency_data_hash VARCHAR(255) NOT NULL, - data_url TEXT NOT NULL, + client_id VARCHAR(50) PRIMARY KEY, + client_data_hash VARCHAR(255) NOT NULL, + signed_s3_url TEXT NOT NULL, created_at TIMESTAMP NOT NULL DEFAULT NOW() ); \ No newline at end of file diff --git a/DSL/Resql/rag-search/POST/get-agency-id.sql b/DSL/Resql/rag-search/POST/get-agency-id.sql index cfbd10c..a2bf5b0 100644 --- a/DSL/Resql/rag-search/POST/get-agency-id.sql +++ b/DSL/Resql/rag-search/POST/get-agency-id.sql @@ -1,7 +1,4 @@ SELECT - CASE - WHEN COUNT(*) > 0 THEN ARRAY_AGG(agency_id ORDER BY agency_id) - ELSE NULL - END as agency_ids, - COUNT(*) > 0 as has_data + agency_id, + agency_data_hash FROM public.agency_sync; \ No newline at end of file diff --git a/DSL/Resql/rag-search/POST/mock-get-data-from-kb.sql b/DSL/Resql/rag-search/POST/mock-get-data-from-kb.sql index 313f430..9c9dc1b 100644 --- a/DSL/Resql/rag-search/POST/mock-get-data-from-kb.sql +++ b/DSL/Resql/rag-search/POST/mock-get-data-from-kb.sql @@ -1,20 +1,5 @@ -WITH parsed_ids AS ( - SELECT unnest(string_to_array(:agencyIds, ' ')) AS agency_id -) SELECT - mock_ckb.agency_id, - mock_ckb.agency_data_hash, - mock_ckb.data_url, - CASE - WHEN mock_ckb.agency_data_hash = agency_sync.agency_data_hash THEN true - ELSE false - END AS hash_match -FROM - public.mock_ckb -JOIN - parsed_ids ON mock_ckb.agency_id = parsed_ids.agency_id -LEFT JOIN - public.agency_sync ON mock_ckb.agency_id = agency_sync.agency_id -WHERE - mock_ckb.agency_data_hash IS NOT NULL - AND mock_ckb.data_url IS NOT NULL; + client_id, + client_data_hash, + signed_s3_url +FROM public.mock_ckb; diff --git a/DSL/Ruuter.public/rag-search/POST/data/update.yml b/DSL/Ruuter.public/rag-search/POST/data/update.yml index dddf8d9..9c81d79 100644 --- a/DSL/Ruuter.public/rag-search/POST/data/update.yml +++ b/DSL/Ruuter.public/rag-search/POST/data/update.yml @@ -7,7 +7,7 @@ declaration: returns: json namespace: rag-search -getAgencyId: +get_agency_id: call: http.post args: url: "[#RAG_SEARCH_RESQL]/get-agency-id" @@ -15,43 +15,67 @@ getAgencyId: next: log_result log_result: - log: ${get_agency_id_result.response.body[0].agencyIds} - next: checkSyncStatus + log: ${get_agency_id_result.response.body[0].agencyId} + next: extract_params -checkSyncStatus: - switch: - - condition: ${get_agency_id_result.response.body[0].hasData} - next: importAgencyData - - condition: true - next: noAgencyData +extract_params: + assign: + single_agency_id: ${get_agency_id_result.response.body[0].agencyId} + agency_ids: + - ${single_agency_id} + agency_data_hash: ${get_agency_id_result.response.body[0].agencyDataHash} + next: logs_params + +logs_params: + log: "Agency ID: ${agency_ids}, Agency Data Hash: ${agency_data_hash}" + next: import_agency_data -importAgencyData: +# check_sync_status: +# switch: +# - condition: ${get_agency_id_result.response.body[0].hasData} +# next: importAgencyData +# - condition: true +# next: noAgencyData + +import_agency_data: call: http.post args: url: "[#RAG_SEARCH_RUUTER_PUBLIC]/ckb/agency-data-import" body: - agencyIds: ${get_agency_id_result.response.body[0].agencyIds} + agencyIds: ${agency_ids} result: importResult next: logImportAgencyDataResponse logImportAgencyDataResponse: log: ${JSON.stringify(importResult.response)} - next: checkHashMatch + next: assign_import_agency_data -checkHashMatch: +assign_import_agency_data: + assign: + client_data_hash: ${importResult.response.body.response[0].clientDataHash} + signed_s3_url: ${importResult.response.body.response[0].signedS3Url} + next: check_has_match + +check_has_match: switch: - - condition: ${importResult.response.body.response[0].hashMatch} + - condition: ${agency_data_hash === importResult.response.body.response[0].clientDataHash} next: noAgencyData - condition: true - next: logNewDataPresent + next: execute_cron_manager -executeCronManager: +execute_cron_manager: call: http.post - url: "[#RAG_SEARCH_CRON_MANAGER]/data-resync" + args: + url: "[#RAG_SEARCH_CRON_MANAGER]/execute/initiate_vector_indexer/vector_indexer" + query: + signedUrl: ${signed_s3_url} + clientDataHash: ${client_data_hash} + result: res + next: log_new_data_present -# logNewDataPresent: -# log: "New data present - synchronization required" -# next: end +log_new_data_present: + log: "New data present - synchronization required" + next: end assignNoAgencyResponse: assign: @@ -61,7 +85,11 @@ assignNoAgencyResponse: next: noAgencyData noAgencyData: + assign: + response_data: + success: true + message: "No sync required - data is up to date" status: 200 - return: ${no_agency_response} + return: ${response_data} next: end \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml index 4c285b9..55ccee3 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -156,6 +156,42 @@ services: # mem_limit: "1G" # restart: unless-stopped + rag-s3-ferry: + image: s3-ferry:latest + container_name: rag-s3-ferry + volumes: + - shared-volume:/app/shared + - cron_data:/app/data + - ./datasets:/app/datasets # Access to datasets folder for diff identifier operations + env_file: + - .env + ports: + - "3006:3000" + networks: + - bykstack + depends_on: + minio: + condition: service_started + + cron-manager: + container_name: cron-manager + image: cron-manager-python:latest + user: "root" + volumes: + - ./DSL/CronManager/DSL:/DSL + - ./DSL/CronManager/script:/app/scripts + - ./src/vector_indexer:/app/src/vector_indexer + - cron_data:/app/data + - shared-volume:/app/shared # Access to shared resources for cross-container coordination + - ./datasets:/app/datasets # Direct access to datasets folder for diff identifier operations + environment: + - server.port=9010 + - PYTHONPATH=/app:/app/src/vector_indexer + ports: + - 9010:8080 + networks: + - bykstack + qdrant: image: qdrant/qdrant:v1.15.1 restart: always @@ -487,6 +523,10 @@ volumes: name: vault-data vault-agent-out: name: vault-agent-out + shared-volume: + name: shared-volume + cron_data: + name: cron_data networks: bykstack: diff --git a/pyproject.toml b/pyproject.toml index be030f6..9dc039e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,6 +28,7 @@ dependencies = [ "nemoguardrails>=0.16.0", "rerankers[transformers]>=0.10.0", "tiktoken>=0.11.0", + "dvc[s3]>=3.55.2", ] [tool.pyright] diff --git a/src/vector_indexer/config/vector_indexer_config.yaml b/src/vector_indexer/config/vector_indexer_config.yaml index 5d09cf9..9d9fbdd 100644 --- a/src/vector_indexer/config/vector_indexer_config.yaml +++ b/src/vector_indexer/config/vector_indexer_config.yaml @@ -92,4 +92,18 @@ vector_indexer: # File validation min_file_size_bytes: 1 - max_file_size_bytes: 50000000 # 50MB \ No newline at end of file + max_file_size_bytes: 50000000 # 50MB + + # Diff Identifier Configuration + diff_identifier: + # Dataset tracking + datasets_path: "datasets" + metadata_filename: "processed-metadata.json" + + # Retry configuration + max_retries: 3 + max_delay_seconds: 8 + + # S3Ferry configuration (uses environment variables) + # S3_DATA_BUCKET_NAME, S3_DATA_BUCKET_PATH, S3_ENDPOINT_URL + # S3_ACCESS_KEY_ID, S3_SECRET_ACCESS_KEY \ No newline at end of file diff --git a/src/vector_indexer/constants.py b/src/vector_indexer/constants.py index 2b9e796..f878748 100644 --- a/src/vector_indexer/constants.py +++ b/src/vector_indexer/constants.py @@ -110,3 +110,24 @@ class LoggingConstants: # Progress reporting PROGRESS_REPORT_INTERVAL = 10 # Report every N documents + + +def GET_S3_FERRY_PAYLOAD(destinationFilePath: str, destinationStorageType: str, sourceFilePath: str, sourceStorageType: str) -> dict[str, str]: # noqa: N802 + """ + Generate S3Ferry payload for file transfer operations. + + Args: + destinationFilePath: Path where file should be stored + destinationStorageType: "S3" or "FS" (filesystem) + sourceFilePath: Path of source file + sourceStorageType: "S3" or "FS" (filesystem) + + Returns: + dict: Payload for S3Ferry API + """ + return { + "destinationFilePath": destinationFilePath, + "destinationStorageType": destinationStorageType, + "sourceFilePath": sourceFilePath, + "sourceStorageType": sourceStorageType + } diff --git a/src/vector_indexer/diff_identifier/DIFF_IDENTIFIER_FLOW.md b/src/vector_indexer/diff_identifier/DIFF_IDENTIFIER_FLOW.md new file mode 100644 index 0000000..6f097f1 --- /dev/null +++ b/src/vector_indexer/diff_identifier/DIFF_IDENTIFIER_FLOW.md @@ -0,0 +1,1224 @@ +# Vector Indexer Diff Identifier + +## Overview + +The **Diff Identifier** is a sophisticated change detection system that forms the first critical step in the Vector Indexer pipeline. It intelligently identifies which files have changed between dataset downloads using **Data Version Control (DVC)** and **content hashing**, ensuring that only new or modified content is processed for vector generation. This eliminates unnecessary reprocessing and can reduce processing time by up to 90% for incremental updates. + +## System Architecture + +### Component Structure + +``` +src/vector_indexer/diff_identifier/ +├── __init__.py # Module exports and public API +├── diff_detector.py # Main orchestrator and entry point +├── version_manager.py # DVC operations & file version tracking +├── s3_ferry_client.py # S3Ferry service integration for metadata transfer +└── diff_models.py # Pydantic data models and configuration classes +``` + +### Core Components Deep Dive + +#### 1. **DiffDetector** (`diff_detector.py`) +**Primary Role:** Main orchestrator that coordinates the entire diff identification workflow. + +**Key Responsibilities:** +- Initialize and manage component lifecycle +- Coordinate between VersionManager and S3FerryClient +- Handle fallback scenarios when diff identification fails +- Provide simplified interface to main_indexer.py + +**Public Interface:** +```python +class DiffDetector: + async def get_changed_files() -> DiffResult + async def mark_files_processed(file_paths: List[str]) -> bool +``` + +**Implementation Details:** +- Uses factory pattern to create VersionManager and S3FerryClient +- Implements graceful degradation (falls back to all files if diff fails) +- Handles both first-time setup and incremental change detection +- Manages cross-container file operations via shared volumes + +#### 2. **VersionManager** (`version_manager.py`) +**Primary Role:** Handles DVC operations and file content tracking for change detection. + +**Key Responsibilities:** +- Initialize DVC repository with MinIO S3 remote configuration +- Perform recursive file scanning with content hash calculation +- Compare current file state with previously processed file metadata +- Generate comprehensive change reports with statistics + +**Core Operations:** +```python +class VersionManager: + def initialize_dvc() -> bool # Set up DVC with S3 remote + def scan_current_files() -> Dict[str, str] # Hash all current files + def identify_changed_files() -> Set[str] # Compare with previous state + def get_processed_files_metadata() -> Dict # Load metadata via S3Ferry +``` + +**Change Detection Algorithm:** +1. **File Discovery:** Recursively scan `datasets/` folder for all files +2. **Content Hashing:** Calculate SHA-256 hash for each file's content +3. **Metadata Comparison:** Compare current hashes with stored metadata +4. **Delta Calculation:** Identify new, modified, or deleted files +5. **Result Packaging:** Return structured change report + +#### 3. **S3FerryClient** (`s3_ferry_client.py`) +**Primary Role:** Manages metadata transfer operations between local filesystem and MinIO S3 storage via S3Ferry service. + +**Key Responsibilities:** +- Upload/download processing metadata to/from S3 +- Handle temporary file operations for S3Ferry API compatibility +- Implement retry logic with exponential backoff for resilience +- Manage S3Ferry API payload generation and response handling + +**S3Ferry Integration Pattern:** +```python +# S3Ferry API Usage Pattern +def transfer_file(self, destinationFilePath, destinationStorageType, + sourceFilePath, sourceStorageType) -> requests.Response: + payload = GET_S3_FERRY_PAYLOAD(destinationFilePath, destinationStorageType, + sourceFilePath, sourceStorageType) + return requests.post(self.s3_ferry_url, json=payload) +``` + +**Storage Operations:** +- **Upload Metadata:** Creates temp file → transfers FS to S3 via S3Ferry → cleanup +- **Download Metadata:** Transfers S3 to FS via S3Ferry → reads from temp file → cleanup +- **Error Handling:** Graceful handling of file not found (expected on first run) +- **Retry Mechanism:** Exponential backoff for network resilience + +#### 4. **Data Models** (`diff_models.py`) +**Primary Role:** Type-safe data structures using Pydantic for configuration and results. + +**Model Classes:** +```python +@dataclass +class ProcessedFileInfo: + content_hash: str # SHA-256 of file content + original_path: str # Relative path from datasets folder + file_size: int # File size in bytes + processed_at: str # ISO timestamp of processing + +class DiffResult(BaseModel): + new_files: List[str] # Files requiring processing + total_files_scanned: int # Total files discovered + previously_processed_count: int # Files already processed + is_first_run: bool # First-time execution flag + +class DiffConfig(BaseModel): + # S3 Configuration (from environment - no defaults for error detection) + s3_bucket_name: str + s3_bucket_path: str + s3_endpoint_url: str + s3_access_key_id: str + s3_secret_access_key: str + + # Service URLs + s3_ferry_url: str # S3Ferry service endpoint + + # Paths + datasets_path: str # Path to datasets folder + metadata_filename: str = "processed-metadata.json" + + # Retry Configuration + max_retries: int = 3 + max_delay_seconds: int = 8 +``` + +## Comprehensive Flow Analysis + +### High-Level Processing Pipeline + +``` +Dataset Download → Diff Identification → Selective Processing → Vector Generation → Metadata Update → Cleanup + ↓ ↓ ↓ ↓ ↓ ↓ + [Future Step] [Current Focus] [Filtered Docs] [Unchanged] [S3 Upload] [Volume Cleanup] +``` + +### Detailed Component Interaction Flow + +#### Phase 1: Initialization & Setup +```python +# 1. Configuration Bootstrap (main_indexer.py) +diff_config = create_diff_config() # Load from environment variables +diff_detector = DiffDetector(diff_config) + +# 2. Component Initialization (diff_detector.py) +version_manager = VersionManager(config) # DVC operations handler +s3_ferry_client = S3FerryClient(config) # S3 metadata operations +``` + +**What Happens Internally:** +1. **Environment Validation:** Checks for all required S3 and service configuration +2. **Service Discovery:** Validates S3Ferry service availability +3. **Directory Validation:** Ensures datasets folder exists and is accessible +4. **Component Wiring:** Creates fully configured component instances + +#### Phase 2: Version State Analysis +```python +# 3. DVC State Detection (version_manager.py) +is_first_run = not version_manager._is_dvc_initialized() + +if is_first_run: + version_manager.initialize_dvc() # Set up DVC with S3 remote + return DiffResult(new_files=all_files, is_first_run=True) +``` + +**First Run Scenario:** +1. **DVC Detection:** Checks for `.dvc/` folder existence in datasets directory +2. **Repository Setup:** Initializes DVC repository with `dvc init` +3. **Remote Configuration:** Configures MinIO S3 as DVC remote storage +4. **Baseline Creation:** Marks this as initial state for future comparisons +5. **Full Processing:** Returns all discovered files for complete indexing + +**Subsequent Run Detection:** +1. **DVC Validation:** Verifies existing DVC configuration integrity +2. **Remote Connectivity:** Tests connection to MinIO S3 remote +3. **Metadata Availability:** Checks for previous processing metadata +4. **Change Detection Mode:** Proceeds to differential analysis + +#### Phase 3: Current State Scanning +```python +# 4. Comprehensive File Discovery (version_manager.py) +current_files = version_manager.scan_current_files() +# Returns: Dict[content_hash, file_path] for all discovered files + +def scan_current_files(self) -> Dict[str, str]: + file_hash_map = {} + for root, _, files in os.walk(self.config.datasets_path): + for file in files: + file_path = os.path.join(root, file) + relative_path = os.path.relpath(file_path, self.config.datasets_path) + + # Calculate content hash for change detection + content_hash = self._calculate_file_hash(file_path) + file_hash_map[content_hash] = relative_path + + return file_hash_map +``` + +**File Discovery Process:** +1. **Recursive Traversal:** Walks entire datasets directory tree +2. **Content Hashing:** Calculates SHA-256 hash for each file's content +3. **Path Normalization:** Converts to relative paths for portability +4. **Hash Mapping:** Creates hash-to-path mapping for efficient lookup +5. **Metadata Collection:** Gathers file size and modification timestamps + +#### Phase 4: Historical State Retrieval +```python +# 5. Previous State Download (s3_ferry_client.py) +processed_metadata = await s3_ferry_client.download_metadata() +# Downloads from: s3://rag-search/resources/datasets/processed-metadata.json + +def download_metadata(self) -> Optional[Dict[str, Any]]: + # Create temporary file for S3Ferry transfer + with tempfile.NamedTemporaryFile(suffix='.json', delete=False) as temp_file: + temp_file_path = temp_file.name + + # Transfer S3 → FS via S3Ferry API + response = self._retry_with_backoff( + lambda: self.s3_ferry.transfer_file( + destinationFilePath=temp_file_path, + destinationStorageType="FS", + sourceFilePath=self.config.metadata_s3_path, + sourceStorageType="S3" + ) + ) + + if response.status_code == 200: + with open(temp_file_path, 'r') as f: + return json.load(f) + elif response.status_code == 404: + return None # First run - no metadata exists yet +``` + +**Metadata Retrieval Process:** +1. **Temporary File Creation:** Creates secure temp file for S3Ferry operations +2. **S3Ferry Transfer:** Uses standardized payload format for S3 → FS transfer +3. **Response Handling:** Distinguishes between success, not found, and errors +4. **JSON Parsing:** Loads structured metadata into memory +5. **Cleanup Operations:** Ensures temporary files are properly removed + +#### Phase 5: Differential Analysis +```python +# 6. Change Detection Algorithm (version_manager.py) +changed_files = version_manager.identify_changed_files(current_files, processed_metadata) + +def identify_changed_files(self, current_files: Dict[str, str], + processed_state: Optional[Dict]) -> Set[str]: + if not processed_state: + return set(current_files.values()) # All files are "new" + + processed_hashes = set(processed_state.get('processed_files', {}).keys()) + current_hashes = set(current_files.keys()) + + # Identify new and modified files + new_or_changed_hashes = current_hashes - processed_hashes + + # Convert hashes back to file paths + return {current_files[hash_val] for hash_val in new_or_changed_hashes} +``` + +**Change Detection Logic:** +1. **Hash Set Operations:** Uses set mathematics for efficient comparison +2. **New File Detection:** Identifies hashes present in current but not in processed +3. **Modification Detection:** Content hash changes indicate file modifications +4. **Deletion Handling:** Processed files no longer present are ignored (graceful) +5. **Path Resolution:** Converts hash differences back to actionable file paths + +#### Phase 6: Result Compilation & Statistics +```python +# 7. Comprehensive Result Generation (diff_detector.py) +return DiffResult( + new_files=list(changed_files), + total_files_scanned=len(current_files), + previously_processed_count=len(processed_state.get('processed_files', {})), + is_first_run=is_first_run +) +``` + +**Statistical Analysis:** +- **Processing Efficiency:** Calculate percentage of files requiring processing +- **Change Rate Metrics:** Track how much content changes between runs +- **Performance Insights:** Measure time savings from selective processing +- **Trend Analysis:** Historical view of dataset evolution patterns + +### Container Integration & Deployment Architecture + +#### Docker Volume Configuration +```yaml +# docker-compose.yml - Updated for diff identifier support + +rag-s3-ferry: + volumes: + - shared-volume:/app/shared # Cross-container communication + - cron_data:/app/data # Persistent operation data + - ./datasets:/app/datasets # Direct datasets access for S3Ferry operations + +cron-manager: + volumes: + - ./src/vector_indexer:/app/src/vector_indexer # Source code mounting + - cron_data:/app/data # Shared operational data + - shared-volume:/app/shared # Cross-container coordination + - ./datasets:/app/datasets # Direct datasets access +``` + +**Volume Strategy Rationale:** +1. **`shared-volume`:** Enables cross-container file coordination and temporary data exchange +2. **`./datasets`:** Direct mount ensures both containers see the same dataset state +3. **`cron_data`:** Persistent storage for operational metadata and logs +4. **Separation of Concerns:** S3Ferry handles transfers, cron-manager handles processing + +#### Cross-Container Communication Flow +``` +Dataset Download → [shared-volume] → diff_identifier → [datasets mount] → S3Ferry → MinIO S3 + ↓ ↓ ↓ +[Future Step] [Change Detection] [Metadata Storage] + ↓ ↓ ↓ + Processing ← [datasets mount] ← Filtered Files ← [Version Manager] ← [S3 Metadata] +``` + +### Phase 7: Selective Document Processing +```python +# 8. Document Filtering Integration (main_indexer.py) +if diff_result.new_files: + # Process only changed files + documents = self._filter_documents_by_paths(diff_result.new_files) + logger.info(f"Processing {len(documents)} documents from {len(diff_result.new_files)} changed files") +else: + # No changes detected - skip processing entirely + logger.info("No changes detected. Skipping processing phase.") + return ProcessingResult(processed_count=0, skipped_count=diff_result.total_files_scanned) + +# Continue with existing vector generation pipeline... +``` + +**Document Filtering Process:** +1. **Path-Based Selection:** Filter discovered documents by changed file paths +2. **Content Preservation:** Maintain document structure and metadata +3. **Processing Optimization:** Skip unchanged content while preserving relationships +4. **Quality Assurance:** Ensure filtered subset maintains processing integrity + +### Phase 8: Post-Processing State Update +```python +# 9. Metadata Update & Persistence (diff_detector.py) +async def mark_files_processed(self, file_paths: List[str]) -> bool: + # Update processed files metadata + new_metadata = self._create_updated_metadata(file_paths) + + # Upload to S3 via S3Ferry + success = await self.s3_ferry_client.upload_metadata(new_metadata) + + # Commit DVC state (optional - for advanced versioning) + if success: + self.version_manager.commit_dvc_state(f"Processed {len(file_paths)} files") + + return success + +def _create_updated_metadata(self, file_paths: List[str]) -> Dict[str, Any]: + current_files = self.version_manager.scan_current_files() + + metadata = { + "last_updated": datetime.utcnow().isoformat(), + "total_processed": len(file_paths), + "processed_files": {} + } + + # Add file metadata for each processed file + for file_path in file_paths: + file_hash = self._get_file_hash(file_path) + metadata["processed_files"][file_hash] = ProcessedFileInfo( + content_hash=file_hash, + original_path=file_path, + file_size=os.path.getsize(file_path), + processed_at=datetime.utcnow().isoformat() + ).dict() + + return metadata +``` + +**State Persistence Strategy:** +1. **Incremental Updates:** Merge new processed files with existing metadata +2. **Atomic Operations:** Ensure metadata consistency during concurrent access +3. **Timestamp Tracking:** Maintain processing history for audit and debugging +4. **Hash-Based Keys:** Use content hashes as stable identifiers across runs +5. **Rollback Safety:** Preserve previous state until new state is confirmed + +## Multi-Tier Storage Architecture + +### Layer 1: DVC Version Control Storage (Content-Addressed) +- **Location**: `s3://rag-search/resources/datasets/dvc-cache/` +- **Purpose**: Immutable file content storage with deduplication +- **Format**: Content-addressed storage (SHA-256 hashes as keys) +- **Benefits**: Automatic deduplication, integrity verification, version history + +**DVC Storage Structure:** +``` +s3://rag-search/resources/datasets/dvc-cache/ +├── ab/ # First 2 chars of content hash +│ └── cdef123...890 # Remaining hash - actual file content +├── cd/ +│ └── ef456...123 +└── .dvcignore # DVC configuration files +``` + +### Layer 2: Processing Metadata Storage (State Tracking) +- **Location**: `s3://rag-search/resources/datasets/processed-metadata.json` +- **Purpose**: Track processing state and enable incremental operations +- **Format**: Structured JSON with comprehensive file metadata +- **Access Pattern**: Download → Process → Upload (atomic updates) + +**Enhanced Metadata Structure:** +```json +{ + "schema_version": "1.0", + "last_updated": "2024-10-15T10:30:00Z", + "processing_session_id": "session_20241015_103000", + "total_processed": 150, + "total_files_scanned": 152, + "processing_statistics": { + "new_files_count": 5, + "modified_files_count": 2, + "unchanged_files_count": 145, + "processing_time_seconds": 45.7, + "efficiency_ratio": 0.95 + }, + "processed_files": { + "sha256:abc123def456...": { + "content_hash": "sha256:abc123def456...", + "original_path": "datasets/collection1/abc123/cleaned.txt", + "file_size": 1024, + "processed_at": "2024-10-15T10:30:00Z", + "processing_duration_ms": 150, + "document_count": 1, + "vector_count": 25 + }, + "sha256:def789ghi012...": { + "content_hash": "sha256:def789ghi012...", + "original_path": "datasets/collection2/def789/cleaned.txt", + "file_size": 2048, + "processed_at": "2024-10-15T10:30:15Z", + "processing_duration_ms": 280, + "document_count": 3, + "vector_count": 67 + } + }, + "system_metadata": { + "diff_identifier_version": "1.0.0", + "dvc_version": "3.55.2", + "container_id": "cron-manager-abc123", + "environment": "production" + } +} +``` + +### Layer 3: Temporary Cross-Container Storage +- **Location**: `shared-volume:/app/shared/` +- **Purpose**: Facilitate communication between rag-s3-ferry and cron-manager containers +- **Lifecycle**: Ephemeral files created during operations, cleaned up after completion +- **Use Cases**: Temporary S3Ferry payloads, processing locks, status files + +## Configuration Management + +### Environment Variables (Required - No Defaults Policy) + +The diff identifier follows a **"fail-fast"** configuration philosophy where missing environment variables cause immediate startup failure rather than silent defaults. This prevents production issues from misconfiguration. + +#### Core S3 Configuration +```bash +# MinIO S3 Backend Configuration +S3_DATA_BUCKET_NAME=rag-search # Target bucket for all data operations +S3_DATA_BUCKET_PATH=resources # Prefix path within bucket +S3_ENDPOINT_URL=http://minio:9000 # MinIO service endpoint (container network) +S3_ACCESS_KEY_ID=minioadmin # S3 access credentials +S3_SECRET_ACCESS_KEY=minioadmin # S3 secret credentials + +# S3Ferry Service Integration +S3_FERRY_URL=http://rag-s3-ferry:3000 # S3Ferry service endpoint +``` + +#### Service Discovery & Networking +```bash +# Container Network Configuration +PYTHONPATH=/app:/app/src/vector_indexer # Python module path for imports +DATASETS_PATH=/app/datasets # Mounted datasets directory path + +# Optional Performance Tuning +MAX_RETRIES=3 # S3Ferry operation retry attempts +MAX_DELAY_SECONDS=8 # Maximum backoff delay for retries +``` + +### Advanced Configuration Schema + +#### DVC Configuration (Auto-Generated) +```yaml +# .dvc/config (Created automatically during initialization) +[core] + remote = minio-s3 + +['remote "minio-s3"'] + url = s3://rag-search/resources/datasets/dvc-cache + endpointurl = http://minio:9000 + access_key_id = minioadmin + secret_access_key = minioadmin + ssl_verify = false # For local MinIO development +``` + +#### Vector Indexer Integration Configuration +```yaml +# src/vector_indexer/config/vector_indexer_config.yaml +vector_indexer: + diff_identifier: + enabled: true # Enable/disable diff identification + datasets_path: "datasets" # Relative path to datasets folder + metadata_filename: "processed-metadata.json" # S3 metadata file name + + # Performance Configuration + max_retries: 3 # Retry attempts for operations + max_delay_seconds: 8 # Exponential backoff maximum delay + + # Operational Configuration + cleanup_on_completion: true # Clean datasets folder after processing + fallback_on_error: true # Process all files if diff fails + + # Logging Configuration + log_level: "INFO" # DEBUG for detailed file operations + log_statistics: true # Include processing statistics in logs + log_file_operations: false # Log individual file operations (verbose) +``` + +### Configuration Validation & Error Handling + +#### Startup Validation Process +```python +# Configuration validation on startup +def validate_diff_config(config: DiffConfig) -> List[str]: + errors = [] + + # Required S3 configuration + if not config.s3_bucket_name: + errors.append("S3_DATA_BUCKET_NAME is required") + if not config.s3_endpoint_url: + errors.append("S3_ENDPOINT_URL is required") + + # Service connectivity validation + try: + response = requests.get(f"{config.s3_ferry_url}/health", timeout=5) + if response.status_code != 200: + errors.append(f"S3Ferry service unavailable at {config.s3_ferry_url}") + except requests.RequestException: + errors.append(f"Cannot connect to S3Ferry service at {config.s3_ferry_url}") + + return errors +``` + +#### Configuration Error Examples +```bash +# Missing Environment Variable Error +[ERROR] Missing required environment variables: S3_ACCESS_KEY_ID, S3_SECRET_ACCESS_KEY +[ERROR] Diff identifier cannot start without complete configuration +[ERROR] System will fall back to processing all files + +# Service Connectivity Error +[ERROR] S3Ferry service not responding at http://rag-s3-ferry:3000 +[WARN] Falling back to direct S3 operations (reduced functionality) + +# Invalid Configuration Error +[ERROR] Invalid S3 endpoint URL: invalid-url-format +[ERROR] Configuration validation failed - check .env file +``` + +## Usage Patterns & Integration + +### Production Deployment via CronManager + +#### Pipeline Script Execution +```bash +# DSL/CronManager/script/vector_indexer_pipeline.sh +export signedUrl="https://s3.amazonaws.com/datasets/daily-export.zip?signed-params" +export ENVIRONMENT="production" +export LOG_LEVEL="INFO" + +# Execute pipeline with diff identifier integration +./vector_indexer_pipeline.sh +``` + +**Pipeline Script Responsibilities:** +1. **Environment Setup:** Validates and exports required environment variables +2. **Dependency Management:** Ensures DVC v3.55.2 is installed and available +3. **Parameter Passing:** Forwards signed URL to main_indexer.py with `--signed-url` flag +4. **Error Handling:** Captures and logs any initialization or processing failures +5. **Resource Cleanup:** Ensures containers clean up temporary files and datasets + +#### Advanced Pipeline Configuration +```bash +# Enhanced pipeline execution with monitoring +export ENABLE_DIFF_IDENTIFIER="true" +export DIFF_IDENTIFIER_LOG_LEVEL="DEBUG" +export PROCESSING_TIMEOUT_MINUTES="30" +export CLEANUP_ON_FAILURE="true" + +# Execute with enhanced monitoring +./vector_indexer_pipeline.sh --enable-monitoring --diff-stats +``` + +### Development & Testing Modes + +#### Direct Python Execution (Development) +```bash +# Container execution context +cd /app +export PYTHONPATH=/app:/app/src/vector_indexer + +# Basic execution +python3 src/vector_indexer/main_indexer.py --signed-url "https://example.com/dataset.zip" + +# Debug mode with verbose logging +python3 src/vector_indexer/main_indexer.py \ + --signed-url "https://example.com/dataset.zip" \ + --log-level DEBUG \ + --enable-diff-stats + +# Dry-run mode (identify changes without processing) +python3 src/vector_indexer/main_indexer.py \ + --signed-url "https://example.com/dataset.zip" \ + --dry-run \ + --diff-only +``` + +#### Manual Component Testing +```python +# Test diff identifier components independently +from src.vector_indexer.diff_identifier import DiffDetector, create_diff_config + +# Initialize for testing +config = create_diff_config() +detector = DiffDetector(config) + +# Test change detection +diff_result = await detector.get_changed_files() +print(f"Found {len(diff_result.new_files)} changed files") + +# Test metadata operations +success = await detector.mark_files_processed(diff_result.new_files) +print(f"Metadata update successful: {success}") +``` + +### API Integration Patterns + +#### Programmatic Usage +```python +# Integration with external orchestration systems +class VectorIndexerOrchestrator: + def __init__(self): + self.diff_config = create_diff_config() + self.detector = DiffDetector(self.diff_config) + + async def process_dataset_update(self, dataset_url: str) -> ProcessingResult: + # Step 1: Download dataset (future implementation) + await self.download_dataset(dataset_url) + + # Step 2: Identify changes + diff_result = await self.detector.get_changed_files() + + if not diff_result.new_files: + return ProcessingResult(message="No changes detected", processed_count=0) + + # Step 3: Selective processing + processing_result = await self.process_files(diff_result.new_files) + + # Step 4: Update metadata + await self.detector.mark_files_processed(processing_result.processed_files) + + return processing_result +``` + +## Technical Implementation Details + +### DiffConfig Usage & Flow + +#### Configuration Object Creation +```python +# main_indexer.py - Entry point +diff_config = create_diff_config() # Creates config from environment variables +diff_detector = DiffDetector(diff_config) # Passes to main orchestrator + +# diff_detector.py - Configuration factory +config = DiffConfig( + s3_ferry_url=s3_ferry_url, # → Used by S3FerryClient + metadata_s3_path=metadata_s3_path, # → Used for S3Ferry operations + datasets_path=datasets_path, # → Used for file scanning + metadata_filename=metadata_filename, # → Used to build paths + dvc_remote_url=dvc_remote_url, # → Used by DVC setup + s3_endpoint_url=str(s3_endpoint_url), # → Used by DVC S3 config + s3_access_key_id=str(s3_access_key_id), # → Used by DVC authentication + s3_secret_access_key=str(s3_secret_access_key) # → Used by DVC authentication +) +``` + +#### Configuration Flow Through System +``` +main_indexer.py + ↓ create_diff_config() +DiffConfig Object + ↓ passed to +DiffDetector(config) + ↓ self.config = config + ↓ VersionManager(config) + ↓ Uses: datasets_path, dvc_remote_url, s3_endpoint_url, s3_access_key_id, s3_secret_access_key + ↓ S3FerryClient(config) + ↓ Uses: s3_ferry_url, metadata_s3_path, max_retries, max_delay_seconds +``` + +#### Config Properties Usage Map +| **Property** | **Component** | **Specific Usage** | +|-------------|---------------|-------------------| +| `s3_ferry_url` | S3FerryClient | `S3Ferry(config.s3_ferry_url)` | +| `metadata_s3_path` | S3FerryClient | Upload/download destination path | +| `datasets_path` | VersionManager | `Path(config.datasets_path)` for file scanning | +| `metadata_filename` | DiffConfig | Used to build `metadata_s3_path` | +| `dvc_remote_url` | VersionManager | `dvc remote add rag-storage {url}` | +| `s3_endpoint_url` | VersionManager | `dvc remote modify endpointurl` | +| `s3_access_key_id` | VersionManager | `dvc remote modify access_key_id` | +| `s3_secret_access_key` | VersionManager | `dvc remote modify secret_access_key` | +| `max_retries` | S3FerryClient | Retry loop iterations | +| `max_delay_seconds` | S3FerryClient | Exponential backoff cap | + +### S3 Transfer Operations & Payloads + +#### 1. Metadata Upload (FS → S3) +**Location:** `s3_ferry_client.py:79-84` +**Trigger:** After processing files completion + +```python +# S3Ferry API Call +response = self.s3_ferry.transfer_file( + destinationFilePath="resources/datasets/processed-metadata.json", + destinationStorageType="S3", + sourceFilePath="/tmp/tmpABC123.json", # Temporary file + sourceStorageType="FS" +) +``` + +**HTTP Payload sent to S3Ferry:** +```json +POST http://rag-s3-ferry:3000 +Content-Type: application/json + +{ + "destinationFilePath": "resources/datasets/processed-metadata.json", + "destinationStorageType": "S3", + "sourceFilePath": "/tmp/tmpABC123.json", + "sourceStorageType": "FS" +} +``` + +#### 2. Metadata Download (S3 → FS) +**Location:** `s3_ferry_client.py:123-128` +**Trigger:** At start of processing to get previous state + +```python +# S3Ferry API Call +response = self.s3_ferry.transfer_file( + destinationFilePath="/tmp/tmpDEF456.json", # Temporary file + destinationStorageType="FS", + sourceFilePath="resources/datasets/processed-metadata.json", + sourceStorageType="S3" +) +``` + +**HTTP Payload sent to S3Ferry:** +```json +POST http://rag-s3-ferry:3000 +Content-Type: application/json + +{ + "destinationFilePath": "/tmp/tmpDEF456.json", + "destinationStorageType": "FS", + "sourceFilePath": "resources/datasets/processed-metadata.json", + "sourceStorageType": "S3" +} +``` + +### DVC S3 Operations & Commands + +#### DVC Initialization (First Run) +**Location:** `version_manager.py:54-70` + +```bash +# 1. Initialize DVC repository +dvc init --no-scm + +# 2. Add S3 remote storage +dvc remote add -d rag-storage s3://rag-search/resources/datasets/dvc-cache + +# 3. Configure S3 endpoint +dvc remote modify rag-storage endpointurl http://minio:9000 + +# 4. Configure S3 credentials +dvc remote modify rag-storage access_key_id minioadmin +dvc remote modify rag-storage secret_access_key minioadmin +``` + +**DVC Config File Created:** +```ini +# datasets/.dvc/config +[core] + remote = rag-storage + +['remote "rag-storage"'] + url = s3://rag-search/resources/datasets/dvc-cache + endpointurl = http://minio:9000 + access_key_id = minioadmin + secret_access_key = minioadmin +``` + +#### DVC Content Operations (After Processing) +**Location:** `version_manager.py:253-258` + +```bash +# 1. Track all files in datasets folder +dvc add . + +# 2. Upload content to S3 remote +dvc push +``` + +#### Underlying S3 API Calls Made by DVC +When `dvc push` executes, DVC makes direct S3 API calls: + +**Content Upload (PUT):** +```http +PUT /rag-search/resources/datasets/dvc-cache/ab/cdef1234567890abcdef1234567890abcdef12 HTTP/1.1 +Host: minio:9000 +Authorization: AWS4-HMAC-SHA256 Credential=minioadmin/20241015/us-east-1/s3/aws4_request, SignedHeaders=host;x-amz-date, Signature=... +Content-Type: application/octet-stream +Content-Length: 1024 + +[Binary file content] +``` + +**Existence Check (HEAD):** +```http +HEAD /rag-search/resources/datasets/dvc-cache/ab/cdef1234567890abcdef1234567890abcdef12 HTTP/1.1 +Host: minio:9000 +Authorization: AWS4-HMAC-SHA256 Credential=minioadmin/... +``` + +**Remote Listing (GET):** +```http +GET /rag-search/resources/datasets/dvc-cache?prefix=ab/ HTTP/1.1 +Host: minio:9000 +Authorization: AWS4-HMAC-SHA256 Credential=minioadmin/... +``` + +### S3 Storage Architecture + +#### Complete S3 Bucket Structure +``` +s3://rag-search/resources/datasets/ +├── dvc-cache/ # DVC content-addressed storage +│ ├── ab/ # First 2 chars of SHA-256 hash +│ │ └── cdef1234567890abcdef12... # Remaining hash - actual file content +│ ├── cd/ +│ │ └── ef567890abcdef1234567890... +│ └── ... +└── processed-metadata.json # Processing state metadata (via S3Ferry) +``` + +#### Dual Access Pattern +- **DVC Operations**: Direct AWS S3 API calls with full authentication +- **Metadata Operations**: S3Ferry service with simple payloads +- **Content Deduplication**: Same file content = same hash = single storage + +### System Integration Flow + +#### Complete Processing Pipeline +``` +Environment Variables → create_diff_config() → DiffConfig + ↓ +DiffDetector(config) → VersionManager(config) + S3FerryClient(config) + ↓ ↓ ↓ +Change Detection DVC Operations Metadata Operations + ↓ ↓ ↓ +File Filtering Direct S3 API S3Ferry HTTP API + ↓ ↓ ↓ +Processing Content Storage State Tracking +``` + +## Real-World Processing Scenarios + +### Scenario 1: Initial System Deployment (First Run) + +**Context:** Fresh deployment with no previous processing history. + +**Execution Flow:** +``` +1. DiffDetector initializes and detects no .dvc/ folder in datasets/ +2. Calls VersionManager.initialize_dvc() to set up version control +3. Configures MinIO S3 as DVC remote storage backend +4. Scans all files in datasets/ folder (50 files discovered) +5. Returns ALL files for processing (expected behavior) +6. Post-processing: Creates initial metadata and uploads to S3 +``` + +**Detailed Logs:** +``` +[INFO] 2024-10-15 10:00:00 - Starting diff identification process... +[INFO] 2024-10-15 10:00:01 - DVC repository not found in datasets/ +[INFO] 2024-10-15 10:00:01 - Initializing DVC for first run... +[INFO] 2024-10-15 10:00:02 - DVC initialized successfully +[INFO] 2024-10-15 10:00:02 - Configuring MinIO S3 remote: s3://rag-search/resources/datasets/dvc-cache +[INFO] 2024-10-15 10:00:03 - DVC remote configured successfully +[INFO] 2024-10-15 10:00:03 - Scanning datasets folder for files... +[INFO] 2024-10-15 10:00:05 - File discovery complete: 50 files found +[INFO] 2024-10-15 10:00:05 - First run setup complete: processing all 50 files +[INFO] 2024-10-15 10:00:05 - Estimated processing time: ~15 minutes + +# ... processing occurs ... + +[INFO] 2024-10-15 10:14:32 - Processing completed: 50 files, 1,250 documents, 31,750 vectors +[INFO] 2024-10-15 10:14:33 - Uploading initial metadata to S3... +[INFO] 2024-10-15 10:14:35 - Metadata uploaded successfully: processed-metadata.json +[INFO] 2024-10-15 10:14:35 - First run baseline established for future comparisons +``` + +**Performance Metrics:** +- **Files Processed:** 50/50 (100%) +- **Processing Time:** 14m 32s +- **Efficiency Ratio:** N/A (baseline establishment) + +### Scenario 2: Daily Incremental Update (Typical Production) + +**Context:** Daily dataset update with minimal changes (5% change rate). + +**Execution Flow:** +``` +1. DiffDetector finds existing .dvc/ folder (previous run detected) +2. Downloads processed-metadata.json from S3 via S3Ferry +3. Scans current dataset: 52 files (2 new files added) +4. Compares file hashes: 50 unchanged, 2 new files +5. Returns only 2 changed files for processing +6. Processes 2 files instead of 52 (96% time savings) +``` + +**Detailed Logs:** +``` +[INFO] 2024-10-16 10:00:00 - Starting diff identification process... +[INFO] 2024-10-16 10:00:00 - Existing DVC repository detected +[INFO] 2024-10-16 10:00:01 - Downloading previous processing metadata... +[INFO] 2024-10-16 10:00:02 - Metadata downloaded: 50 previously processed files +[INFO] 2024-10-16 10:00:02 - Scanning current dataset files... +[INFO] 2024-10-16 10:00:04 - Current scan complete: 52 files found +[INFO] 2024-10-16 10:00:04 - Performing hash-based change detection... +[INFO] 2024-10-16 10:00:05 - Change analysis complete: 2 new/modified files identified +[INFO] 2024-10-16 10:00:05 - Processing efficiency: 96.1% (processing 2/52 files) + +# ... selective processing occurs ... + +[INFO] 2024-10-16 10:00:45 - Processing completed: 2 files, 48 documents, 1,240 vectors +[INFO] 2024-10-16 10:00:46 - Updating metadata with newly processed files... +[INFO] 2024-10-16 10:00:47 - Metadata updated successfully: 52 total processed files +[INFO] 2024-10-16 10:00:47 - Processing complete with 96% time savings +``` + +**Performance Metrics:** +- **Files Processed:** 2/52 (3.8%) +- **Processing Time:** 47s (vs. 15m estimated for full processing) +- **Efficiency Gain:** 96.1% time savings +- **Change Rate:** 3.8% (2 new files) + +### Scenario 3: No Changes Detected (Optimal Efficiency) + +**Context:** Dataset downloaded but no actual content changes occurred. + +**Execution Flow:** +``` +1. Normal diff identification process initiated +2. All current file hashes match processed metadata exactly +3. Zero files identified for processing +4. Skips entire processing pipeline +5. Cleans up datasets folder and exits +``` + +**Detailed Logs:** +``` +[INFO] 2024-10-17 10:00:00 - Starting diff identification process... +[INFO] 2024-10-17 10:00:01 - Downloading previous processing metadata... +[INFO] 2024-10-17 10:00:02 - Metadata downloaded: 52 previously processed files +[INFO] 2024-10-17 10:00:03 - Scanning current dataset files... +[INFO] 2024-10-17 10:00:05 - Current scan complete: 52 files found +[INFO] 2024-10-17 10:00:05 - Performing hash-based change detection... +[INFO] 2024-10-17 10:00:06 - No changes detected: all files match previous state +[INFO] 2024-10-17 10:00:06 - Processing efficiency: 100% (0 files need processing) +[INFO] 2024-10-17 10:00:06 - Skipping processing pipeline entirely +[INFO] 2024-10-17 10:00:07 - Cleaning up datasets folder... +[INFO] 2024-10-17 10:00:08 - Processing complete: no changes detected +``` + +**Performance Metrics:** +- **Files Processed:** 0/52 (0%) +- **Processing Time:** 8s (vs. 15m for full processing) +- **Efficiency Gain:** 99.9% time savings +- **Change Rate:** 0% (no changes) + +### Scenario 4: Large Dataset Update (Batch Changes) + +**Context:** Weekly comprehensive update with significant changes (30% change rate). + +**Execution Flow:** +``` +1. Dataset download includes substantial content updates +2. Hash comparison identifies 16 changed files out of 52 total +3. Processes substantial subset but still more efficient than full reprocessing +4. Updates metadata with batch of changes +``` + +**Detailed Logs:** +``` +[INFO] 2024-10-20 02:00:00 - Starting diff identification process... +[INFO] 2024-10-20 02:00:01 - Downloading previous processing metadata... +[INFO] 2024-10-20 02:00:03 - Metadata downloaded: 52 previously processed files +[INFO] 2024-10-20 02:00:03 - Scanning current dataset files... +[INFO] 2024-10-20 02:00:08 - Current scan complete: 52 files found +[INFO] 2024-10-20 02:00:08 - Performing hash-based change detection... +[INFO] 2024-10-20 02:00:10 - Change analysis complete: 16 modified files identified +[INFO] 2024-10-20 02:00:10 - Processing efficiency: 69.2% (processing 16/52 files) +[INFO] 2024-10-20 02:00:10 - Estimated processing time: ~5 minutes + +# ... batch processing occurs ... + +[INFO] 2024-10-20 02:04:45 - Processing completed: 16 files, 410 documents, 10,750 vectors +[INFO] 2024-10-20 02:04:46 - Updating metadata with batch changes... +[INFO] 2024-10-20 02:04:48 - Metadata updated successfully: 52 total processed files +[INFO] 2024-10-20 02:04:48 - Processing complete with 69% time savings +``` + +**Performance Metrics:** +- **Files Processed:** 16/52 (30.8%) +- **Processing Time:** 4m 48s (vs. 15m for full processing) +- **Efficiency Gain:** 68% time savings +- **Change Rate:** 30.8% (significant but manageable) + +### Scenario 5: Error Recovery & Fallback + +**Context:** S3Ferry service unavailable, diff identification fails gracefully. + +**Execution Flow:** +``` +1. DiffDetector attempts to download metadata via S3Ferry +2. S3Ferry service connection fails (network/service issue) +3. Graceful fallback: processes all files for safety +4. Logs failure but continues operation +5. System remains operational despite component failure +``` + +**Detailed Logs:** +``` +[INFO] 2024-10-18 10:00:00 - Starting diff identification process... +[ERROR] 2024-10-18 10:00:02 - S3Ferry connection failed: Connection refused to rag-s3-ferry:3000 +[ERROR] 2024-10-18 10:00:02 - Retry attempt 1/3 failed +[ERROR] 2024-10-18 10:00:04 - Retry attempt 2/3 failed +[ERROR] 2024-10-18 10:00:08 - Retry attempt 3/3 failed +[WARN] 2024-10-18 10:00:08 - Diff identification failed: unable to download metadata +[WARN] 2024-10-18 10:00:08 - Falling back to processing all files for safety +[INFO] 2024-10-18 10:00:09 - Fallback mode: scanning all files for processing +[INFO] 2024-10-18 10:00:11 - Fallback scan complete: 52 files will be processed + +# ... full processing occurs ... + +[INFO] 2024-10-18 10:14:50 - Processing completed in fallback mode: 52 files processed +[WARN] 2024-10-18 10:14:50 - Metadata update skipped due to S3Ferry unavailability +[INFO] 2024-10-18 10:14:50 - Processing complete despite diff identifier failure +``` + +**Performance Metrics:** +- **Files Processed:** 52/52 (100% - fallback mode) +- **Processing Time:** 14m 50s (full processing time) +- **Efficiency Gain:** 0% (fallback negates optimization) +- **Reliability:** 100% (system continues operation despite component failure) + +## Error Handling + +### Graceful Degradation + +If diff identification fails for any reason, the system falls back to processing all files: + +```python +try: + diff_result = await diff_detector.get_changed_files() +except DiffError as e: + logger.error(f"Diff identification failed: {e}") + logger.info("Falling back to processing all files") + # Process all files as safety measure +``` + +### Retry Logic + +All S3Ferry operations use exponential backoff: + +```python +# Retry delays: 0.5s, 1s, 2s, 4s, 8s (max) +await self._retry_with_backoff(operation, max_retries=3, max_delay=8) +``` + +### Missing Environment Variables + +System fails fast if required environment variables are missing: + +``` +[ERROR] Missing required environment variables: S3_ACCESS_KEY_ID, S3_SECRET_ACCESS_KEY +``` + +## Performance Benefits + +### Efficiency Gains + +- **First Run**: Processes all files (expected) +- **Incremental Runs**: Only processes changed files (potentially 90%+ reduction) +- **No Changes**: Skips processing entirely (near-instant completion) + +### Resource Optimization + +- **Network**: Only downloads small metadata file (vs. full dataset comparison) +- **CPU**: File hashing is single-pass and efficient +- **Storage**: Content-addressed DVC storage eliminates duplicates + +## Monitoring & Logging + +### Key Log Messages + +```bash +# Diff identification +[INFO] Starting diff identification process... +[INFO] Found 5 new/changed files out of 100 total + +# First run detection +[INFO] DVC not initialized - setting up for first run + +# No changes +[INFO] No new or changed files detected. Processing complete. + +# Fallback behavior +[ERROR] Diff identification failed: connection timeout +[INFO] Falling back to processing all files +``` + +### Statistics + +Each run provides comprehensive statistics: + +```python +DiffResult( + new_files=["datasets/collection1/abc123/cleaned.txt"], + total_files_scanned=100, + previously_processed_count=99, + is_first_run=False +) +``` + +## Troubleshooting + +### Common Issues + +1. **Missing Environment Variables** + - Check `.env` file has all required S3 variables + - Restart containers after environment changes + +2. **S3Ferry Connection Failed** + - Verify S3Ferry service is running: `docker ps | grep s3-ferry` + - Check S3Ferry logs: `docker logs rag-s3-ferry` + +3. **DVC Initialization Failed** + - Check datasets folder permissions + - Verify MinIO is accessible from container + +4. **Metadata Download Failed** + - Normal on first run (no metadata exists yet) + - Check S3 bucket permissions and credentials + +### Debug Mode + +Enable debug logging for detailed information: + +```bash +# In vector_indexer_config.yaml +logging: + level: "DEBUG" +``` + +This provides detailed file-by-file processing information and DVC command outputs. + +## Integration Points + +### Main Indexer Integration + +The diff identifier is seamlessly integrated as the first step in `main_indexer.py`: + +1. **Before**: Document discovery → Processing → Storage +2. **After**: Diff identification → Filtered document discovery → Processing → Tracking update → Storage → Cleanup + +### Document Loader Compatibility + +The existing `DocumentLoader` continues to work unchanged: +- If diff result available: Filter to specific paths +- If diff unavailable: Use existing `discover_all_documents()` + +### Future Enhancements + +- **Dataset Download**: Integration point ready for signed URL download implementation +- **Parallel Processing**: DVC operations can be parallelized for large datasets +- **Delta Sync**: Potential for incremental dataset synchronization + +## Conclusion + +The Diff Identifier transforms the Vector Indexer from a batch processing system to an efficient incremental system, providing: + +- **Performance**: Only process what changed +- **Reliability**: Graceful fallback ensures robustness +- **Scalability**: Efficient handling of large, frequently updated datasets +- **Transparency**: Comprehensive logging and statistics \ No newline at end of file diff --git a/src/vector_indexer/diff_identifier/__init__.py b/src/vector_indexer/diff_identifier/__init__.py new file mode 100644 index 0000000..93b6f6f --- /dev/null +++ b/src/vector_indexer/diff_identifier/__init__.py @@ -0,0 +1,16 @@ +"""Diff identifier module for detecting dataset changes.""" + +from diff_identifier.diff_detector import DiffDetector, create_diff_config +from diff_identifier.diff_models import DiffConfig, DiffResult, DiffError +from diff_identifier.version_manager import VersionManager +from diff_identifier.s3_ferry_client import S3FerryClient + +__all__ = [ + "DiffDetector", + "create_diff_config", + "DiffConfig", + "DiffResult", + "DiffError", + "VersionManager", + "S3FerryClient" +] \ No newline at end of file diff --git a/src/vector_indexer/diff_identifier/diff_detector.py b/src/vector_indexer/diff_identifier/diff_detector.py index e69de29..c939412 100644 --- a/src/vector_indexer/diff_identifier/diff_detector.py +++ b/src/vector_indexer/diff_identifier/diff_detector.py @@ -0,0 +1,228 @@ +"""Main diff detector for identifying changed files.""" + +import os +from pathlib import Path +from typing import List +from loguru import logger + +from diff_identifier.diff_models import DiffConfig, DiffError, DiffResult +from diff_identifier.version_manager import VersionManager + + +class DiffDetector: + """Main orchestrator for diff identification.""" + + def __init__(self, config: DiffConfig): + self.config = config + self.version_manager = VersionManager(config) + + async def get_changed_files(self) -> DiffResult: + """ + Get list of files that need processing. + + Returns: + DiffResult with files to process and metadata + + Raises: + DiffError: If diff detection fails critically + """ + try: + logger.info("Starting diff identification process...") + + # Check if DVC is initialized + if not self.version_manager.is_dvc_initialized(): + logger.info("DVC not initialized - setting up for first run") + return await self._handle_first_run() + + # Get previously processed files + logger.info("Loading processed files metadata...") + processed_state = await self.version_manager.get_processed_files_metadata() + + # Scan current files + logger.info("Scanning current dataset files...") + current_files = self.version_manager.scan_current_files() + + if not current_files: + logger.info("No files found in datasets directory") + return DiffResult( + new_files=[], + total_files_scanned=0, + previously_processed_count=0 if processed_state is None else processed_state.total_processed, + is_first_run=False + ) + + # Identify changed files + changed_file_paths = self.version_manager.identify_changed_files(current_files, processed_state) + + result = DiffResult( + new_files=list(changed_file_paths), + total_files_scanned=len(current_files), + previously_processed_count=0 if processed_state is None else processed_state.total_processed, + is_first_run=processed_state is None + ) + + logger.info(f"Diff identification complete: {len(result.new_files)} files need processing") + return result + + except Exception as e: + # Log error but don't fail - fall back to processing all files + logger.error(f"Diff identification failed: {e}") + logger.info("Falling back to processing all files as safety measure") + + try: + # Get all files as fallback + current_files = self.version_manager.scan_current_files() + return DiffResult( + new_files=list(current_files.values()), + total_files_scanned=len(current_files), + previously_processed_count=0, + is_first_run=True + ) + except Exception as fallback_error: + raise DiffError(f"Both diff identification and fallback failed: {fallback_error}", e) + + async def mark_files_processed(self, processed_file_paths: List[str]) -> None: + """ + Mark files as successfully processed. + + Args: + processed_file_paths: List of file paths that were processed successfully + + Raises: + DiffError: If marking files fails + """ + try: + if not processed_file_paths: + logger.info("No files to mark as processed") + return + + logger.info(f"Marking {len(processed_file_paths)} files as processed...") + + # Calculate hashes for processed files + processed_files = {} + for file_path in processed_file_paths: + try: + full_path = Path(file_path) + if full_path.exists(): + content = full_path.read_bytes() + import hashlib + file_hash = hashlib.sha256(content).hexdigest() + processed_files[file_hash] = file_path + logger.debug(f"Processed: {file_path} -> {file_hash[:12]}...") + else: + logger.warning(f"Processed file not found: {file_path}") + except Exception as e: + logger.warning(f"Failed to hash processed file {file_path}: {e}") + + if not processed_files: + logger.warning("No valid processed files to record") + return + + # Update metadata + await self.version_manager.update_processed_files_metadata(processed_files) + + # Commit to DVC if initialized + if self.version_manager.is_dvc_initialized(): + await self.version_manager.commit_dvc_changes() + + logger.info(f"Successfully marked {len(processed_files)} files as processed") + + except Exception as e: + raise DiffError(f"Failed to mark files as processed: {str(e)}", e) + + async def _handle_first_run(self) -> DiffResult: + """ + Handle first run setup. + + Returns: + DiffResult for first run + + Raises: + DiffError: If first run setup fails + """ + try: + logger.info("Setting up DVC for first run...") + + # Initialize DVC + await self.version_manager.initialize_dvc() + + # Get all files for processing + current_files = self.version_manager.scan_current_files() + + logger.info(f"First run setup complete: {len(current_files)} files to process") + + return DiffResult( + new_files=list(current_files.values()), + total_files_scanned=len(current_files), + previously_processed_count=0, + is_first_run=True + ) + + except Exception as e: + raise DiffError(f"First run setup failed: {str(e)}", e) + + +def create_diff_config() -> DiffConfig: + """ + Create DiffConfig from environment variables. + + Hybrid approach: + - S3Ferry handles metadata operations (processed files tracking) + - DVC needs direct S3 access for version control operations + + Returns: + DiffConfig instance + + Raises: + DiffError: If required environment variables are missing + """ + try: + # S3Ferry Configuration + s3_ferry_url = os.getenv("S3_FERRY_URL", "http://rag-s3-ferry:3000") + + # Path configurations + datasets_path = os.getenv("DATASETS_PATH", "datasets") + metadata_filename = os.getenv("METADATA_FILENAME", "processed-metadata.json") + + # S3 configuration (required for DVC operations) + s3_bucket_name = os.getenv("S3_DATA_BUCKET_NAME") + s3_bucket_path = os.getenv("S3_DATA_BUCKET_PATH", "resources") + s3_endpoint_url = os.getenv("S3_ENDPOINT_URL") + s3_access_key_id = os.getenv("S3_ACCESS_KEY_ID") + s3_secret_access_key = os.getenv("S3_SECRET_ACCESS_KEY") + + # Validate required S3 credentials for DVC + if not all([s3_bucket_name, s3_endpoint_url, s3_access_key_id, s3_secret_access_key]): + missing = [var for var, val in [ + ("S3_DATA_BUCKET_NAME", s3_bucket_name), + ("S3_ENDPOINT_URL", s3_endpoint_url), + ("S3_ACCESS_KEY_ID", s3_access_key_id), + ("S3_SECRET_ACCESS_KEY", s3_secret_access_key) + ] if not val] + raise DiffError(f"Missing required S3 environment variables for DVC: {', '.join(missing)}") + + # Build paths + metadata_s3_path = f"{s3_bucket_path}/datasets/{metadata_filename}" + dvc_remote_url = f"s3://{s3_bucket_name}/{s3_bucket_path}/datasets/dvc-cache" + + config = DiffConfig( + s3_ferry_url=s3_ferry_url, + metadata_s3_path=metadata_s3_path, + datasets_path=datasets_path, + metadata_filename=metadata_filename, + dvc_remote_url=dvc_remote_url, + s3_endpoint_url=str(s3_endpoint_url), + s3_access_key_id=str(s3_access_key_id), + s3_secret_access_key=str(s3_secret_access_key) + ) + + logger.info("Diff configuration loaded successfully") + logger.debug(f"S3Ferry URL: {config.s3_ferry_url}") + logger.debug(f"Metadata S3 Path: {config.metadata_s3_path}") + logger.debug(f"DVC Remote URL: {config.dvc_remote_url}") + logger.debug(f"Datasets Path: {config.datasets_path}") + + return config + + except Exception as e: + raise DiffError(f"Failed to create diff configuration: {str(e)}", e) diff --git a/src/vector_indexer/diff_identifier/diff_models.py b/src/vector_indexer/diff_identifier/diff_models.py index e69de29..754d8b3 100644 --- a/src/vector_indexer/diff_identifier/diff_models.py +++ b/src/vector_indexer/diff_identifier/diff_models.py @@ -0,0 +1,63 @@ +"""Data models for diff identifier.""" + +from typing import Dict, List, Optional +from pydantic import BaseModel, Field + + +class ProcessedFileInfo(BaseModel): + """Information about a processed file.""" + + content_hash: str = Field(..., description="SHA256 hash of file content") + original_path: str = Field(..., description="Original path in datasets folder") + file_size: int = Field(..., description="File size in bytes") + processed_at: str = Field(..., description="ISO timestamp when file was processed") + + +class DiffResult(BaseModel): + """Result of diff identification process.""" + + new_files: List[str] = Field(..., description="List of new file paths to process") + total_files_scanned: int = Field(..., description="Total files found in current scan") + previously_processed_count: int = Field(..., description="Number of previously processed files") + is_first_run: bool = Field(..., description="Whether this is the first time running") + + +class VersionState(BaseModel): + """Version state information.""" + + last_updated: str = Field(..., description="ISO timestamp of last update") + processed_files: Dict[str, ProcessedFileInfo] = Field(..., description="Map of hash to file info") + total_processed: int = Field(..., description="Total number of processed files") + + +class DiffConfig(BaseModel): + """Configuration for diff identifier.""" + + # S3Ferry Configuration (handles metadata operations) + s3_ferry_url: str = Field(..., description="S3Ferry service URL") + + # Metadata Configuration + metadata_s3_path: str = Field(..., description="Full S3 path for metadata file") + + # DVC Configuration (requires direct S3 access for version control) + datasets_path: str = Field(..., description="Path to datasets folder") + metadata_filename: str = Field(default="processed-metadata.json", description="Metadata file name") + + # DVC S3 Remote Configuration (minimal - only for DVC operations) + dvc_remote_url: str = Field(..., description="DVC S3 remote URL") + s3_endpoint_url: str = Field(..., description="S3 endpoint URL for DVC") + s3_access_key_id: str = Field(..., description="S3 access key for DVC") + s3_secret_access_key: str = Field(..., description="S3 secret key for DVC") + + # Retry Configuration + max_retries: int = Field(default=3, description="Maximum retry attempts") + max_delay_seconds: int = Field(default=8, description="Maximum delay between retries") + + +class DiffError(Exception): + """Custom exception for diff identification errors.""" + + def __init__(self, message: str, cause: Optional[Exception] = None): + self.message = message + self.cause = cause + super().__init__(self.message) diff --git a/src/vector_indexer/diff_identifier/s3_ferry_client.py b/src/vector_indexer/diff_identifier/s3_ferry_client.py new file mode 100644 index 0000000..1991dc2 --- /dev/null +++ b/src/vector_indexer/diff_identifier/s3_ferry_client.py @@ -0,0 +1,199 @@ +"""S3Ferry client for file transfer operations.""" + +import json +import tempfile +import time +from typing import Any, Dict, Optional +import requests +from loguru import logger + +from diff_identifier.diff_models import DiffConfig, DiffError +from constants import GET_S3_FERRY_PAYLOAD + + +class S3Ferry: + """Client for interacting with S3Ferry service.""" + + def __init__(self, url: str): + self.url = url + + def transfer_file(self, destinationFilePath: str, destinationStorageType: str, sourceFilePath: str, sourceStorageType: str) -> requests.Response: # noqa: N803 + """ + Transfer file using S3Ferry service. + + Args: + destinationFilePath: Path where file should be stored + destinationStorageType: "S3" or "FS" (filesystem) + sourceFilePath: Path of source file + sourceStorageType: "S3" or "FS" (filesystem) + + Returns: + requests.Response: Response from S3Ferry service + """ + payload = GET_S3_FERRY_PAYLOAD(destinationFilePath, destinationStorageType, sourceFilePath, sourceStorageType) + response = requests.post(self.url, json=payload) + return response + + +class S3FerryClient: + """High-level client for S3Ferry operations with metadata handling. + + S3Ferry service handles all S3 configuration internally. + This client only needs to know the S3Ferry URL and metadata paths. + """ + + def __init__(self, config: DiffConfig): + self.config = config + self.s3_ferry = S3Ferry(config.s3_ferry_url) + + async def __aenter__(self): + """Async context manager entry.""" + return self + + async def __aexit__(self, exc_type: Optional[type], exc_val: Optional[BaseException], exc_tb: Optional[object]) -> None: + """Async context manager exit.""" + pass + + def upload_metadata(self, metadata: Dict[str, Any]) -> bool: + """ + Upload metadata to S3 via S3Ferry. + + Args: + metadata: Dictionary containing metadata to upload + + Returns: + True if successful, False otherwise + + Raises: + DiffError: If upload fails + """ + try: + # Create temporary file with metadata + with tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as temp_file: + json.dump(metadata, temp_file, indent=2) + temp_file_path = temp_file.name + + try: + # Transfer from FS to S3 using S3Ferry + response = self._retry_with_backoff( + lambda: self.s3_ferry.transfer_file( + destinationFilePath=self.config.metadata_s3_path, + destinationStorageType="S3", + sourceFilePath=temp_file_path, + sourceStorageType="FS" + ) + ) + + if response.status_code == 200: + logger.info(f"Metadata uploaded successfully to {self.config.metadata_s3_path}") + return True + else: + logger.error(f"S3Ferry upload failed: {response.status_code} - {response.text}") + return False + + finally: + # Clean up temporary file + import os + try: + os.unlink(temp_file_path) + except Exception as cleanup_error: + logger.warning(f"Failed to cleanup temp file {temp_file_path}: {cleanup_error}") + + except Exception as e: + raise DiffError(f"Failed to upload metadata: {str(e)}", e) + + def download_metadata(self) -> Optional[Dict[str, Any]]: + """ + Download metadata from S3 via S3Ferry. + + Returns: + Dict containing metadata or None if not found + + Raises: + DiffError: If download fails (except for file not found) + """ + try: + # Create temporary file for download + with tempfile.NamedTemporaryFile(mode='w+', suffix='.json', delete=False) as temp_file: + temp_file_path = temp_file.name + + try: + # Transfer from S3 to FS using S3Ferry + response = self._retry_with_backoff( + lambda: self.s3_ferry.transfer_file( + destinationFilePath=temp_file_path, + destinationStorageType="FS", + sourceFilePath=self.config.metadata_s3_path, + sourceStorageType="S3" + ) + ) + + if response.status_code == 200: + # Read metadata from downloaded file + with open(temp_file_path, 'r') as f: + metadata = json.load(f) + logger.info(f"Metadata downloaded successfully from {self.config.metadata_s3_path}") + return metadata + elif response.status_code == 404: + logger.info("No previous metadata found - this appears to be the first run") + return None + else: + logger.error(f"S3Ferry download failed: {response.status_code} - {response.text}") + return None + + finally: + # Clean up temporary file + import os + try: + os.unlink(temp_file_path) + except Exception as cleanup_error: + logger.warning(f"Failed to cleanup temp file {temp_file_path}: {cleanup_error}") + + except json.JSONDecodeError as e: + raise DiffError(f"Failed to parse downloaded metadata JSON: {str(e)}", e) + except Exception as e: + # Don't raise for file not found - it's expected on first run + logger.warning(f"Failed to download metadata (may be first run): {str(e)}") + return None + + def _retry_with_backoff(self, operation: Any) -> requests.Response: + """ + Retry an operation with exponential backoff. + + Args: + operation: Operation to retry + + Returns: + Response from the operation + + Raises: + DiffError: If all retries fail + """ + last_exception = None + + for attempt in range(self.config.max_retries): + try: + response = operation() + + # Consider non-2xx responses as failures for retry purposes + if response.status_code >= 400: + if attempt == self.config.max_retries - 1: + return response # Last attempt - return the error response + + delay = min(1 * (2 ** attempt), self.config.max_delay_seconds) + time.sleep(delay) + continue + + return response + + except Exception as e: + last_exception = e + + if attempt == self.config.max_retries - 1: + raise DiffError(f"Operation failed after {self.config.max_retries} attempts: {str(e)}", e) + + delay = min(1 * (2 ** attempt), self.config.max_delay_seconds) + time.sleep(delay) + + # Should not reach here, but just in case + raise DiffError(f"Operation failed after {self.config.max_retries} attempts: {str(last_exception)}", last_exception) \ No newline at end of file diff --git a/src/vector_indexer/diff_identifier/version_manager.py b/src/vector_indexer/diff_identifier/version_manager.py index e69de29..f0a5a1f 100644 --- a/src/vector_indexer/diff_identifier/version_manager.py +++ b/src/vector_indexer/diff_identifier/version_manager.py @@ -0,0 +1,308 @@ +"""Version manager for DVC operations and metadata handling.""" + +import asyncio +import hashlib +import os +from datetime import datetime +from pathlib import Path +from typing import Dict, List, Optional, Set +from loguru import logger + +from diff_identifier.diff_models import DiffConfig, DiffError, ProcessedFileInfo, VersionState +from diff_identifier.s3_ferry_client import S3FerryClient + + +class VersionManager: + """Manages DVC operations and version tracking.""" + + def __init__(self, config: DiffConfig): + self.config = config + self.datasets_path = Path(config.datasets_path) + + async def __aenter__(self): + """Async context manager entry.""" + return self + + async def __aexit__(self, exc_type: Optional[type], exc_val: Optional[BaseException], exc_tb: Optional[object]) -> None: + """Async context manager exit.""" + pass + + def is_dvc_initialized(self) -> bool: + """Check if DVC is initialized in datasets directory.""" + dvc_dir = self.datasets_path / ".dvc" + return dvc_dir.exists() and dvc_dir.is_dir() + + async def initialize_dvc(self) -> None: + """ + Initialize DVC in datasets directory with S3 remote. + + Raises: + DiffError: If DVC initialization fails + """ + try: + logger.info("Initializing DVC in datasets directory...") + + # Ensure datasets directory exists + self.datasets_path.mkdir(parents=True, exist_ok=True) + + # Change to datasets directory for DVC operations + original_cwd = os.getcwd() + os.chdir(str(self.datasets_path)) + + try: + # Initialize DVC (no SCM integration) + await self._run_dvc_command(["dvc", "init", "--no-scm"]) + + # Add S3 remote + remote_url = self.config.dvc_remote_url + logger.info(f"Adding DVC remote: {remote_url}") + await self._run_dvc_command(["dvc", "remote", "add", "-d", "rag-storage", remote_url]) + + # Configure S3 credentials + await self._run_dvc_command([ + "dvc", "remote", "modify", "rag-storage", "endpointurl", self.config.s3_endpoint_url + ]) + await self._run_dvc_command([ + "dvc", "remote", "modify", "rag-storage", "access_key_id", self.config.s3_access_key_id + ]) + await self._run_dvc_command([ + "dvc", "remote", "modify", "rag-storage", "secret_access_key", self.config.s3_secret_access_key + ]) + + logger.info("DVC initialized successfully") + + finally: + os.chdir(original_cwd) + + except Exception as e: + raise DiffError(f"Failed to initialize DVC: {str(e)}", e) + + async def get_processed_files_metadata(self) -> Optional[VersionState]: + """ + Download and parse processed files metadata from S3. + + Returns: + VersionState if metadata exists, None if first run + + Raises: + DiffError: If metadata exists but cannot be parsed + """ + try: + async with S3FerryClient(self.config) as s3_client: + metadata_dict = await s3_client.download_metadata() + + if metadata_dict is None: + return None + + # Parse metadata into VersionState + return VersionState( + last_updated=metadata_dict["last_updated"], + processed_files={ + file_hash: ProcessedFileInfo(**file_info) + for file_hash, file_info in metadata_dict["processed_files"].items() + }, + total_processed=metadata_dict.get("total_processed", len(metadata_dict["processed_files"])) + ) + + except Exception as e: + raise DiffError(f"Failed to get processed files metadata: {str(e)}", e) + + async def update_processed_files_metadata(self, processed_files: Dict[str, str]) -> None: + """ + Update processed files metadata and upload to S3. + + Args: + processed_files: Dict mapping file hash to file path for newly processed files + + Raises: + DiffError: If metadata update fails + """ + try: + # Get existing metadata or create new + existing_state = await self.get_processed_files_metadata() + + if existing_state: + processed_files_dict = existing_state.processed_files + else: + processed_files_dict = {} + + # Add new processed files + current_time = datetime.now().isoformat() + for file_hash, file_path in processed_files.items(): + file_stats = Path(file_path).stat() + processed_files_dict[file_hash] = ProcessedFileInfo( + content_hash=file_hash, + original_path=file_path, + file_size=file_stats.st_size, + processed_at=current_time + ) + + # Create new version state + new_state = VersionState( + last_updated=current_time, + processed_files=processed_files_dict, + total_processed=len(processed_files_dict) + ) + + # Convert to dict for JSON serialization + metadata_dict = { + "last_updated": new_state.last_updated, + "total_processed": new_state.total_processed, + "processed_files": { + file_hash: { + "content_hash": file_info.content_hash, + "original_path": file_info.original_path, + "file_size": file_info.file_size, + "processed_at": file_info.processed_at + } + for file_hash, file_info in new_state.processed_files.items() + } + } + + # Upload to S3 + async with S3FerryClient(self.config) as s3_client: + success = await s3_client.upload_metadata(metadata_dict) + + if not success: + raise DiffError("Failed to upload metadata to S3") + + logger.info(f"Updated processed files metadata: {len(processed_files)} new files") + + except Exception as e: + raise DiffError(f"Failed to update processed files metadata: {str(e)}", e) + + def scan_current_files(self) -> Dict[str, str]: + """ + Scan datasets directory and calculate file hashes. + + Returns: + Dict mapping file hash to file path + + Raises: + DiffError: If file scanning fails + """ + try: + files_map = {} + + if not self.datasets_path.exists(): + logger.warning(f"Datasets path does not exist: {self.datasets_path}") + return files_map + + # Find all cleaned.txt files + cleaned_files = list(self.datasets_path.glob("**/cleaned.txt")) + logger.info(f"Found {len(cleaned_files)} files to scan") + + for cleaned_file in cleaned_files: + try: + # Calculate file hash + content = cleaned_file.read_bytes() + file_hash = hashlib.sha256(content).hexdigest() + + # Store relative path from datasets directory + relative_path = str(cleaned_file.relative_to(self.datasets_path.parent)) + files_map[file_hash] = relative_path + + logger.debug(f"Scanned file: {relative_path} -> {file_hash[:12]}...") + + except Exception as e: + logger.warning(f"Failed to process file {cleaned_file}: {e}") + continue + + logger.info(f"Successfully scanned {len(files_map)} files") + return files_map + + except Exception as e: + raise DiffError(f"Failed to scan current files: {str(e)}", e) + + def identify_changed_files(self, current_files: Dict[str, str], processed_state: Optional[VersionState]) -> Set[str]: + """ + Identify files that have changed or are new. + + Args: + current_files: Current files map (hash -> path) + processed_state: Previously processed state + + Returns: + Set of file paths that need processing + """ + if processed_state is None: + # First run - all files are new + logger.info("First run detected - all files need processing") + return set(current_files.values()) + + current_hashes = set(current_files.keys()) + processed_hashes = set(processed_state.processed_files.keys()) + + # Find new files (hashes not previously processed) + new_hashes = current_hashes - processed_hashes + new_file_paths = {current_files[file_hash] for file_hash in new_hashes} + + logger.info(f"Found {len(new_file_paths)} new/changed files out of {len(current_files)} total") + + return new_file_paths + + async def commit_dvc_changes(self) -> None: + """ + Commit current datasets state to DVC and push to remote. + + Raises: + DiffError: If DVC operations fail + """ + try: + original_cwd = os.getcwd() + os.chdir(str(self.datasets_path)) + + try: + # Add all files to DVC tracking + logger.info("Adding files to DVC tracking...") + await self._run_dvc_command(["dvc", "add", "."]) + + # Push to remote storage + logger.info("Pushing to DVC remote storage...") + await self._run_dvc_command(["dvc", "push"]) + + logger.info("DVC commit completed successfully") + + finally: + os.chdir(original_cwd) + + except Exception as e: + raise DiffError(f"Failed to commit DVC changes: {str(e)}", e) + + async def _run_dvc_command(self, command: List[str]) -> str: + """ + Run DVC command asynchronously. + + Args: + command: DVC command as list of strings + + Returns: + Command output + + Raises: + DiffError: If command fails + """ + try: + logger.debug(f"Running DVC command: {' '.join(command)}") + + process = await asyncio.create_subprocess_exec( + *command, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE + ) + + stdout, stderr = await process.communicate() + + if process.returncode != 0: + error_msg = stderr.decode().strip() if stderr else "Unknown error" + raise DiffError(f"DVC command failed: {' '.join(command)} - {error_msg}") + + output = stdout.decode().strip() + logger.debug(f"DVC command output: {output}") + + return output + + except Exception as e: + if isinstance(e, DiffError): + raise + raise DiffError(f"Failed to run DVC command {' '.join(command)}: {str(e)}", e) diff --git a/src/vector_indexer/main_indexer.py b/src/vector_indexer/main_indexer.py index ac3be23..2d709f9 100644 --- a/src/vector_indexer/main_indexer.py +++ b/src/vector_indexer/main_indexer.py @@ -1,6 +1,8 @@ """Main vector indexer script for processing documents with contextual retrieval.""" +import argparse import asyncio +import shutil import sys from pathlib import Path from datetime import datetime @@ -16,17 +18,21 @@ from vector_indexer.qdrant_manager import QdrantManager from vector_indexer.error_logger import ErrorLogger from vector_indexer.models import ProcessingStats, DocumentInfo +from vector_indexer.diff_identifier import DiffDetector, create_diff_config, DiffError class VectorIndexer: """Main vector indexer orchestrating the full pipeline.""" - def __init__(self, config_path: Optional[str] = None): + def __init__(self, config_path: Optional[str] = None, signed_url: Optional[str] = None): # Load configuration self.config_path = ( config_path or "src/vector_indexer/config/vector_indexer_config.yaml" ) self.config = ConfigLoader.load_config(self.config_path) + + # Store signed URL for future dataset download implementation + self.signed_url = signed_url # Initialize components self.document_loader = DocumentLoader(self.config) @@ -51,6 +57,9 @@ def __init__(self, config_path: Optional[str] = None): logger.info( f"Max concurrent chunks: {self.config.max_concurrent_chunks_per_doc}" ) + + if self.signed_url: + logger.info(f"Signed URL provided: {self.signed_url[:50]}...") # Log first 50 chars only async def process_all_documents(self) -> ProcessingStats: """ @@ -66,16 +75,52 @@ async def process_all_documents(self) -> ProcessingStats: self.stats.start_time = datetime.now() try: + # Step 1: Dataset download (future implementation) + if self.signed_url: + logger.info("Dataset download URL provided - download logic to be implemented") + # TODO: Implement dataset download and extraction + # await self._download_and_extract_dataset(self.signed_url) + + # Step 2: Diff identification - determine what files need processing + logger.info("Step 1: Identifying changed files...") + try: + diff_config = create_diff_config() + diff_detector = DiffDetector(diff_config) + diff_result = await diff_detector.get_changed_files() + + logger.info("Diff identification complete:") + logger.info(f" • Total files scanned: {diff_result.total_files_scanned}") + logger.info(f" • Previously processed: {diff_result.previously_processed_count}") + logger.info(f" • Files needing processing: {len(diff_result.new_files)}") + logger.info(f" • Is first run: {diff_result.is_first_run}") + + if not diff_result.new_files: + logger.info("No new or changed files detected. Processing complete.") + self._cleanup_datasets() + return self.stats + + except DiffError as e: + logger.error(f"Diff identification failed: {e}") + logger.info("Continuing with full document discovery as fallback") + diff_result = None + diff_detector = None + # Initialize Qdrant collections async with QdrantManager(self.config) as qdrant_manager: await qdrant_manager.ensure_collections_exist() - # Discover all documents - logger.info("Discovering documents...") - documents = self.document_loader.discover_all_documents() + # Step 3: Document discovery (filtered by diff results if available) + logger.info("Step 2: Discovering documents...") + if diff_result and diff_result.new_files: + # Filter documents to only those identified as changed + documents = self._filter_documents_by_paths(diff_result.new_files) + else: + # Fallback: discover all documents + documents = self.document_loader.discover_all_documents() if not documents: logger.warning("No documents found to process") + self._cleanup_datasets() return self.stats logger.info(f"Found {len(documents)} documents to process") @@ -119,10 +164,23 @@ async def process_all_documents(self) -> ProcessingStats: # Calculate final statistics self.stats.end_time = datetime.now() + # Step 4: Update processed files tracking + if diff_detector and documents: + try: + processed_paths = [doc.cleaned_txt_path for doc in documents] + if processed_paths: + await diff_detector.mark_files_processed(processed_paths) + logger.info("Updated processed files tracking") + except Exception as e: + logger.warning(f"Failed to update processed files tracking: {e}") + # Log final statistics self.error_logger.log_processing_stats(self.stats) self._log_final_summary() + # Step 5: Cleanup datasets folder after successful processing + self._cleanup_datasets() + return self.stats except Exception as e: @@ -299,10 +357,77 @@ async def cleanup(self): logger.debug("API client closed successfully") except Exception as e: logger.warning(f"Error closing API client: {e}") + + def _filter_documents_by_paths(self, file_paths: List[str]) -> List[DocumentInfo]: + """ + Filter documents by specific file paths. + + Args: + file_paths: List of file paths to process + + Returns: + List of DocumentInfo for matching files + """ + documents = [] + + for file_path in file_paths: + try: + file_path_obj = Path(file_path) + + # Ensure this is a cleaned.txt file + if file_path_obj.name != "cleaned.txt": + logger.debug(f"Skipping non-cleaned.txt file: {file_path}") + continue + + # Get hash directory and collection directory + hash_dir = file_path_obj.parent + collection_dir = hash_dir.parent + + # Check if metadata file exists + metadata_file = hash_dir / self.config.metadata_file + if not metadata_file.exists(): + logger.warning(f"Skipping file without metadata: {file_path}") + continue + + # Create DocumentInfo + doc_info = DocumentInfo( + document_hash=hash_dir.name, + cleaned_txt_path=str(file_path_obj), + source_meta_path=str(metadata_file), + dataset_collection=collection_dir.name + ) + + documents.append(doc_info) + logger.debug(f"Added document: {doc_info.document_hash}") + + except Exception as e: + logger.warning(f"Failed to process file path {file_path}: {e}") + continue + + logger.info(f"Filtered to {len(documents)} documents from {len(file_paths)} paths") + return documents + + def _cleanup_datasets(self): + """Remove datasets folder after processing.""" + try: + datasets_path = Path(self.config.dataset_base_path) + if datasets_path.exists(): + shutil.rmtree(str(datasets_path)) + logger.info(f"Datasets folder cleaned up: {datasets_path}") + else: + logger.debug(f"Datasets folder does not exist: {datasets_path}") + except Exception as e: + logger.warning(f"Failed to cleanup datasets folder: {e}") + # Non-critical error - don't fail the entire process async def main(): """Main entry point for the vector indexer.""" + + # Parse command line arguments + parser = argparse.ArgumentParser(description="Vector Indexer with Diff Identification") + parser.add_argument("--signed-url", help="Signed URL for dataset download") + args = parser.parse_args() # Configure logging logger.remove() # Remove default handler @@ -323,8 +448,8 @@ async def main(): indexer = None try: - # Initialize vector indexer - indexer = VectorIndexer() + # Initialize vector indexer with signed URL + indexer = VectorIndexer(signed_url=args.signed_url) # Run health check first logger.info("Performing pre-processing health check...") diff --git a/uv.lock b/uv.lock index a2475a7..2677629 100644 --- a/uv.lock +++ b/uv.lock @@ -2,6 +2,29 @@ version = 1 revision = 3 requires-python = "==3.12.10" +[[package]] +name = "aiobotocore" +version = "2.25.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "aiohttp" }, + { name = "aioitertools" }, + { name = "botocore" }, + { name = "jmespath" }, + { name = "multidict" }, + { name = "python-dateutil" }, + { name 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"https://files.pythonhosted.org/packages/10/9a/2fef89272d98b799e4daa50201c5582ec76bdd4e92a1a7e3deb74c52b7fa/zc_lockfile-4.0.tar.gz", hash = "sha256:d3ab0f53974296a806db3219b9191ba0e6d5cbbd1daa2e0d17208cb9b29d2102", size = 10956, upload-time = "2025-09-18T07:32:34.412Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3b/7f/3a614b65bc4b181578b1d50a78663ee02d5d2d3b859712f3d3597c8afe6f/zc_lockfile-4.0-py3-none-any.whl", hash = "sha256:aa3aa295257bebaa09ea9ad5cb288bf9f98f88de6932f96b6659f62715d83581", size = 9143, upload-time = "2025-09-18T07:32:33.517Z" }, +] + [[package]] name = "zipp" version = "3.23.0" From d989ceda9faa5c9a085e76ee479600faf2d5d81a Mon Sep 17 00:00:00 2001 From: nuwangeek Date: Thu, 16 Oct 2025 04:12:00 +0530 Subject: [PATCH 04/11] test phase1 --- .../rag-search/POST/inference/production.yml | 11 +++- .../rag-search/POST/inference/test.yml | 9 +-- docker-compose.yml | 62 +++++++++---------- migrate.sh | 2 +- 4 files changed, 45 insertions(+), 39 deletions(-) diff --git a/DSL/Ruuter.private/rag-search/POST/inference/production.yml b/DSL/Ruuter.private/rag-search/POST/inference/production.yml index e9b90ee..39fbf7e 100644 --- a/DSL/Ruuter.private/rag-search/POST/inference/production.yml +++ b/DSL/Ruuter.private/rag-search/POST/inference/production.yml @@ -85,10 +85,15 @@ call_orchestrate_endpoint: headers: Content-Type: "application/json" result: orchestrate_result + next: assign_response + +assign_response: + assign: + response: "${orchestrate_result.response.body}" next: return_orchestrate_response return_orchestrate_response: - return: ${orchestrate_result.response.body} + return: ${response} next: end assign_disconnected_response: @@ -96,7 +101,7 @@ assign_disconnected_response: disconnected_response: { chatId: "${chatId}", - content: "The LLM connection is currently unavailable. Your request couldn’t be processed. Please retry shortly.", + content: "The LLM connection is currently unavailable. Your request couldn't be processed. Please retry shortly.", status: 400 } next: return_connection_disconnected @@ -113,4 +118,4 @@ return_budget_check_error: return_no_production_connection: status: 404 return: "No production connection found" - next: end + next: end \ No newline at end of file diff --git a/DSL/Ruuter.private/rag-search/POST/inference/test.yml b/DSL/Ruuter.private/rag-search/POST/inference/test.yml index 28f346a..d936c66 100644 --- a/DSL/Ruuter.private/rag-search/POST/inference/test.yml +++ b/DSL/Ruuter.private/rag-search/POST/inference/test.yml @@ -24,8 +24,9 @@ extract_request_data: get_connection: call: http.post args: - url: "[#RAG_SEARCH_RESQL]/get-production-connection" - body: {} + url: "[#RAG_SEARCH_RESQL]/get-llm-connection" + body: + connection_id: ${connectionId} result: connection_result next: validate_connection @@ -57,7 +58,7 @@ validate_budget_status: call_orchestrate_endpoint: call: http.post args: - url: "[#RAG_SEARCH_LLM_ORCHESTRATOR]" + url: "[#RAG_SEARCH_LLM_ORCHESTRATOR]/test" body: connectionId: ${connectionId} message: ${message} @@ -68,7 +69,7 @@ call_orchestrate_endpoint: next: return_orchestrate_response return_orchestrate_response: - return: ${orchestrate_result.response.body} + return: ${orchestrate_result.response} next: end assign_disconnected_response: diff --git a/docker-compose.yml b/docker-compose.yml index c4e521a..406b11d 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -446,37 +446,37 @@ services: restart: unless-stopped # LLM Orchestration Service - llm-orchestration-service: - build: - context: . - dockerfile: Dockerfile.llm_orchestration_service - container_name: llm-orchestration-service - restart: always - ports: - - "8100:8100" - env_file: - - .env - environment: - - ENVIRONMENT=production - - VAULT_ADDR=http://vault:8200 - - VAULT_TOKEN=/agent/out/token - volumes: - # Mount configuration files - - ./src/llm_config_module/config:/app/src/llm_config_module/config:ro - # Mount logs directory for persistence - - llm_orchestration_logs:/app/logs - - ./vault/agent-out:/agent/out:ro - networks: - - bykstack - depends_on: - - vault - - vault-agent-llm - healthcheck: - test: ["CMD", "curl", "-f", "http://llm-orchestration-service:8100/health"] - interval: 30s - timeout: 10s - start_period: 40s - retries: 3 + # llm-orchestration-service: + # build: + # context: . + # dockerfile: Dockerfile.llm_orchestration_service + # container_name: llm-orchestration-service + # restart: always + # ports: + # - "8100:8100" + # env_file: + # - .env + # environment: + # - ENVIRONMENT=production + # - VAULT_ADDR=http://vault:8200 + # - VAULT_TOKEN=/agent/out/token + # volumes: + # # Mount configuration files + # - ./src/llm_config_module/config:/app/src/llm_config_module/config:ro + # # Mount logs directory for persistence + # - llm_orchestration_logs:/app/logs + # - ./vault/agent-out:/agent/out:ro + # networks: + # - bykstack + # depends_on: + # - vault + # - vault-agent-llm + # healthcheck: + # test: ["CMD", "curl", "-f", "http://llm-orchestration-service:8100/health"] + # interval: 30s + # timeout: 10s + # start_period: 40s + # retries: 3 volumes: loki-data: diff --git a/migrate.sh b/migrate.sh index 3a03cdc..c156698 100644 --- a/migrate.sh +++ b/migrate.sh @@ -12,4 +12,4 @@ INI_FILE="constants.ini" DB_PASSWORD=$(get_ini_value "$INI_FILE" "DB_PASSWORD") -docker run --rm --network bykstack -v `pwd`/DSL/Liquibase/changelog:/liquibase/changelog -v `pwd`/DSL/Liquibase/master.yml:/liquibase/master.yml -v `pwd`/DSL/Liquibase/data:/liquibase/data liquibase/liquibase --defaultsFile=/liquibase/changelog/liquibase.properties --changelog-file=master.yml --url=jdbc:postgresql://rag_search_db:5432/rag-search?user=postgres --password=$DB_PASSWORD update +docker run --rm --network bykstack -v `pwd`/DSL/Liquibase/changelog:/liquibase/changelog -v `pwd`/DSL/Liquibase/master.yml:/liquibase/master.yml -v `pwd`/DSL/Liquibase/data:/liquibase/data liquibase/liquibase:4.33 --defaultsFile=/liquibase/changelog/liquibase.properties --changelog-file=master.yml --url=jdbc:postgresql://rag_search_db:5432/rag-search?user=postgres --password=$DB_PASSWORD update From 2092f4c169cff7b89b4ccb8f2b3e07ddf730613d Mon Sep 17 00:00:00 2001 From: nuwangeek Date: Thu, 16 Oct 2025 16:33:59 +0530 Subject: [PATCH 05/11] diff fixes --- .../script/vector_indexer_pipeline.sh | 55 +- docker-compose.yml | 7 + grafana-configs/loki_logger.py | 50 +- src/vector_indexer/config/config_loader.py | 5 +- .../config/vector_indexer_config.yaml | 2 +- .../diff_identifier/diff_detector.py | 15 +- .../diff_identifier/s3_ferry_client.py | 113 +++- src/vector_indexer/loki_logger.py | 0 src/vector_indexer/main_indexer.py | 541 +++++++++++++++++- vault/agent-out/pidfile | 1 - 10 files changed, 690 insertions(+), 99 deletions(-) create mode 100644 src/vector_indexer/loki_logger.py diff --git a/DSL/CronManager/script/vector_indexer_pipeline.sh b/DSL/CronManager/script/vector_indexer_pipeline.sh index 487fefe..1146123 100644 --- a/DSL/CronManager/script/vector_indexer_pipeline.sh +++ b/DSL/CronManager/script/vector_indexer_pipeline.sh @@ -3,7 +3,7 @@ echo "Starting vector indexer pipeline..." if [ -z "$signedUrl" ] || [ -z "$clientDataHash" ]; then - echo "Please set the signedS3Url and clientDataHash environment variables." + echo "Please set the signedUrl and clientDataHash environment variables." exit 1 fi @@ -40,6 +40,7 @@ echo "[PACKAGES] Installing required packages..." "$UV_BIN" pip install --python "$VENV_PATH/bin/python3" "rank-bm25>=0.2.2" || exit 1 "$UV_BIN" pip install --python "$VENV_PATH/bin/python3" "tiktoken>=0.11.0" || exit 1 "$UV_BIN" pip install --python "$VENV_PATH/bin/python3" "dvc[s3]>=3.55.2" || exit 1 +"$UV_BIN" pip install --python "$VENV_PATH/bin/python3" "loguru>=0.7.3" || exit 1 echo "[PACKAGES] All packages installed successfully" @@ -51,12 +52,58 @@ echo "[FOUND] Python script at: $PYTHON_SCRIPT" # Run vector indexer with signed URL parameter echo "[STARTING] Vector indexer processing..." + +# Add debugging before Python execution +echo "[DEBUG] Testing basic Python execution..." +python3 --version || echo "[ERROR] Python version check failed" + +echo "[DEBUG] Testing Python imports..." +python3 -c " +import sys +print(f'[DEBUG] Python executable: {sys.executable}') +print(f'[DEBUG] Python version: {sys.version}') +try: + from pathlib import Path + print('[DEBUG] ✓ pathlib import OK') + from loguru import logger + print('[DEBUG] ✓ loguru import OK') + import argparse + print('[DEBUG] ✓ argparse import OK') +except Exception as e: + print(f'[DEBUG] ✗ Import failed: {e}') + import traceback + traceback.print_exc() +" 2>&1 + +echo "[DEBUG] Testing main_indexer.py syntax..." +python3 -m py_compile "$PYTHON_SCRIPT" 2>&1 || echo "[ERROR] Syntax check failed" + +echo "[DEBUG] About to execute main_indexer.py..." if [ -n "$signedUrl" ]; then echo "[SIGNED_URL] Using signed URL for dataset processing" - python3 "$PYTHON_SCRIPT" --signed-url "$signedUrl" + echo "[COMMAND] python3 -u $PYTHON_SCRIPT --signed-url $signedUrl" + python3 -u "$PYTHON_SCRIPT" --signed-url "$signedUrl" 2>&1 + PYTHON_EXIT_CODE=$? else echo "[NO_URL] Running without signed URL" - python3 "$PYTHON_SCRIPT" + echo "[COMMAND] python3 -u $PYTHON_SCRIPT" + python3 -u "$PYTHON_SCRIPT" 2>&1 + PYTHON_EXIT_CODE=$? fi -echo "[COMPLETED] Vector indexer pipeline finished" \ No newline at end of file +echo "[DEBUG] Python execution completed with exit code: $PYTHON_EXIT_CODE" + +# Handle exit codes +if [ $PYTHON_EXIT_CODE -eq 0 ]; then + echo "[SUCCESS] Vector indexer completed successfully" + exit 0 +elif [ $PYTHON_EXIT_CODE -eq 2 ]; then + echo "[WARNING] Vector indexer completed with some failures" + exit 2 +elif [ $PYTHON_EXIT_CODE -eq 130 ]; then + echo "[INTERRUPTED] Vector indexer was interrupted by user" + exit 130 +else + echo "[ERROR] Vector indexer failed with exit code: $PYTHON_EXIT_CODE" + exit $PYTHON_EXIT_CODE +fi \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml index 55ccee3..4cabcd7 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -184,9 +184,16 @@ services: - cron_data:/app/data - shared-volume:/app/shared # Access to shared resources for cross-container coordination - ./datasets:/app/datasets # Direct access to datasets folder for diff identifier operations + - ./grafana-configs/loki_logger.py:/app/src/vector_indexer/loki_logger.py + env_file: + - .env environment: - server.port=9010 - PYTHONPATH=/app:/app/src/vector_indexer + - S3_DATA_BUCKET_NAME=rag-search + - S3_ENDPOINT_URL=http://minio:9000 + - S3_ACCESS_KEY_ID=minioadmin + - S3_SECRET_ACCESS_KEY=minioadmin ports: - 9010:8080 networks: diff --git a/grafana-configs/loki_logger.py b/grafana-configs/loki_logger.py index 444db47..e25b340 100644 --- a/grafana-configs/loki_logger.py +++ b/grafana-configs/loki_logger.py @@ -32,7 +32,7 @@ def __init__( # Set default timeout for all requests self.timeout = 5 - def _send_to_loki(self, level: str, message: str, **extra_fields): + def _send_to_loki(self, level: str, message: str): """Send log entry directly to Loki API""" try: # Create timestamp in nanoseconds (Loki requirement) @@ -45,14 +45,6 @@ def _send_to_loki(self, level: str, message: str, **extra_fields): "hostname": self.hostname, } - # Add extra fields as labels, filtering out None values except for model_id - for key, value in extra_fields.items(): - if key == "model_id": - # Always include model_id, default to "None" if not provided - labels[key] = str(value) if value is not None else "None" - elif value is not None: - labels[key] = str(value) - # Create log entry log_entry = { "timestamp": datetime.now().isoformat(), @@ -60,7 +52,6 @@ def _send_to_loki(self, level: str, message: str, **extra_fields): "message": message, "hostname": self.hostname, "service": self.service_name, - **extra_fields, } # Prepare Loki payload @@ -87,29 +78,16 @@ def _send_to_loki(self, level: str, message: str, **extra_fields): # Also print to console for immediate feedback timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") - model_info = ( - f" [Model: {extra_fields.get('model_id', 'N/A')}]" - if extra_fields.get("model_id") - else "" - ) - print(f"[{timestamp}] {level: <8}{model_info} | {message}") - - def info(self, message: str, model_id: str | None = None, **extra_fields): - if model_id: - extra_fields["model_id"] = model_id - self._send_to_loki("INFO", message, **extra_fields) - - def error(self, message: str, model_id: str | None = None, **extra_fields): - if model_id: - extra_fields["model_id"] = model_id - self._send_to_loki("ERROR", message, **extra_fields) - - def warning(self, message: str, model_id: str | None = None, **extra_fields): - if model_id: - extra_fields["model_id"] = model_id - self._send_to_loki("WARNING", message, **extra_fields) - - def debug(self, message: str, model_id: str | None = None, **extra_fields): - if model_id: - extra_fields["model_id"] = model_id - self._send_to_loki("DEBUG", message, **extra_fields) + print(f"[{timestamp}] {level: <8} | {message}") + + def info(self, message: str): + self._send_to_loki("INFO", message) + + def error(self, message: str): + self._send_to_loki("ERROR", message) + + def warning(self, message: str): + self._send_to_loki("WARNING", message) + + def debug(self, message: str): + self._send_to_loki("DEBUG", message) diff --git a/src/vector_indexer/config/config_loader.py b/src/vector_indexer/config/config_loader.py index 34a21d7..6e39cda 100644 --- a/src/vector_indexer/config/config_loader.py +++ b/src/vector_indexer/config/config_loader.py @@ -77,7 +77,8 @@ class VectorIndexerConfig(BaseModel): """Configuration model for vector indexer.""" # API Configuration - api_base_url: str = "http://localhost:8100" + api_base_url: str = "http://llm-orchestration-service:8100" + qdrant_url: str = "http://qdrant:6333" api_timeout: int = 300 # Processing Configuration @@ -210,7 +211,7 @@ def load_config( # API config api_config = indexer_config.get("api", {}) flattened_config["api_base_url"] = api_config.get( - "base_url", "http://localhost:8100" + "base_url" ) flattened_config["api_timeout"] = api_config.get("timeout", 300) diff --git a/src/vector_indexer/config/vector_indexer_config.yaml b/src/vector_indexer/config/vector_indexer_config.yaml index 9d9fbdd..6a7d583 100644 --- a/src/vector_indexer/config/vector_indexer_config.yaml +++ b/src/vector_indexer/config/vector_indexer_config.yaml @@ -2,7 +2,7 @@ vector_indexer: # API Configuration api: - base_url: "http://localhost:8100" + base_url: "http://llm-orchestration-service:8100" qdrant_url: "http://qdrant:6333" timeout: 300 # seconds diff --git a/src/vector_indexer/diff_identifier/diff_detector.py b/src/vector_indexer/diff_identifier/diff_detector.py index c939412..6256eb5 100644 --- a/src/vector_indexer/diff_identifier/diff_detector.py +++ b/src/vector_indexer/diff_identifier/diff_detector.py @@ -178,18 +178,18 @@ def create_diff_config() -> DiffConfig: """ try: # S3Ferry Configuration - s3_ferry_url = os.getenv("S3_FERRY_URL", "http://rag-s3-ferry:3000") + s3_ferry_url = os.getenv("S3_FERRY_URL", "http://rag-s3-ferry:3000/v1/files/copy") # Path configurations datasets_path = os.getenv("DATASETS_PATH", "datasets") metadata_filename = os.getenv("METADATA_FILENAME", "processed-metadata.json") # S3 configuration (required for DVC operations) - s3_bucket_name = os.getenv("S3_DATA_BUCKET_NAME") - s3_bucket_path = os.getenv("S3_DATA_BUCKET_PATH", "resources") - s3_endpoint_url = os.getenv("S3_ENDPOINT_URL") - s3_access_key_id = os.getenv("S3_ACCESS_KEY_ID") - s3_secret_access_key = os.getenv("S3_SECRET_ACCESS_KEY") + s3_bucket_name = "rag-search" + s3_bucket_path = "resources" + s3_endpoint_url = "http://minio:9000" + s3_access_key_id = "minioadmin" + s3_secret_access_key = "minioadmin" # Validate required S3 credentials for DVC if not all([s3_bucket_name, s3_endpoint_url, s3_access_key_id, s3_secret_access_key]): @@ -202,7 +202,8 @@ def create_diff_config() -> DiffConfig: raise DiffError(f"Missing required S3 environment variables for DVC: {', '.join(missing)}") # Build paths - metadata_s3_path = f"{s3_bucket_path}/datasets/{metadata_filename}" + # S3Ferry is already configured with bucket context, so no need for s3_bucket_path prefix + metadata_s3_path = f"datasets/{metadata_filename}" dvc_remote_url = f"s3://{s3_bucket_name}/{s3_bucket_path}/datasets/dvc-cache" config = DiffConfig( diff --git a/src/vector_indexer/diff_identifier/s3_ferry_client.py b/src/vector_indexer/diff_identifier/s3_ferry_client.py index 1991dc2..c887e68 100644 --- a/src/vector_indexer/diff_identifier/s3_ferry_client.py +++ b/src/vector_indexer/diff_identifier/s3_ferry_client.py @@ -1,7 +1,7 @@ """S3Ferry client for file transfer operations.""" +import asyncio import json -import tempfile import time from typing import Any, Dict, Optional import requests @@ -31,7 +31,22 @@ def transfer_file(self, destinationFilePath: str, destinationStorageType: str, s requests.Response: Response from S3Ferry service """ payload = GET_S3_FERRY_PAYLOAD(destinationFilePath, destinationStorageType, sourceFilePath, sourceStorageType) + + # Debug logging for S3Ferry request + logger.debug("S3Ferry Request Details:") + logger.debug(f" URL: {self.url}") + logger.debug(" Method: POST") + logger.debug(" Headers: Content-Type: application/json") + logger.debug(f" Payload: {payload}") + response = requests.post(self.url, json=payload) + + # Debug logging for S3Ferry response + logger.debug("S3Ferry Response Details:") + logger.debug(f" Status Code: {response.status_code}") + logger.debug(f" Response Headers: {dict(response.headers)}") + logger.debug(f" Response Body: {response.text}") + return response @@ -54,7 +69,7 @@ async def __aexit__(self, exc_type: Optional[type], exc_val: Optional[BaseExcept """Async context manager exit.""" pass - def upload_metadata(self, metadata: Dict[str, Any]) -> bool: + async def upload_metadata(self, metadata: Dict[str, Any]) -> bool: """ Upload metadata to S3 via S3Ferry. @@ -68,14 +83,13 @@ def upload_metadata(self, metadata: Dict[str, Any]) -> bool: DiffError: If upload fails """ try: - # Create temporary file with metadata - with tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as temp_file: - json.dump(metadata, temp_file, indent=2) - temp_file_path = temp_file.name + # Create temporary file with metadata (run in thread pool) + temp_file_path = await asyncio.to_thread(self._create_temp_metadata_file, metadata) try: - # Transfer from FS to S3 using S3Ferry - response = self._retry_with_backoff( + # Transfer from FS to S3 using S3Ferry (run in thread pool) + response = await asyncio.to_thread( + self._retry_with_backoff, lambda: self.s3_ferry.transfer_file( destinationFilePath=self.config.metadata_s3_path, destinationStorageType="S3", @@ -92,17 +106,14 @@ def upload_metadata(self, metadata: Dict[str, Any]) -> bool: return False finally: - # Clean up temporary file - import os - try: - os.unlink(temp_file_path) - except Exception as cleanup_error: - logger.warning(f"Failed to cleanup temp file {temp_file_path}: {cleanup_error}") + # Clean up temporary file (run in thread pool) + # await asyncio.to_thread(self._cleanup_temp_file, temp_file_path) # Disabled for debugging + pass except Exception as e: raise DiffError(f"Failed to upload metadata: {str(e)}", e) - def download_metadata(self) -> Optional[Dict[str, Any]]: + async def download_metadata(self) -> Optional[Dict[str, Any]]: """ Download metadata from S3 via S3Ferry. @@ -113,13 +124,13 @@ def download_metadata(self) -> Optional[Dict[str, Any]]: DiffError: If download fails (except for file not found) """ try: - # Create temporary file for download - with tempfile.NamedTemporaryFile(mode='w+', suffix='.json', delete=False) as temp_file: - temp_file_path = temp_file.name + # Create temporary file for download (run in thread pool) + temp_file_path = await asyncio.to_thread(self._create_temp_file) try: - # Transfer from S3 to FS using S3Ferry - response = self._retry_with_backoff( + # Transfer from S3 to FS using S3Ferry (run in thread pool) + response = await asyncio.to_thread( + self._retry_with_backoff, lambda: self.s3_ferry.transfer_file( destinationFilePath=temp_file_path, destinationStorageType="FS", @@ -129,9 +140,8 @@ def download_metadata(self) -> Optional[Dict[str, Any]]: ) if response.status_code == 200: - # Read metadata from downloaded file - with open(temp_file_path, 'r') as f: - metadata = json.load(f) + # Read metadata from downloaded file (run in thread pool) + metadata = await asyncio.to_thread(self._read_metadata_from_file, temp_file_path) logger.info(f"Metadata downloaded successfully from {self.config.metadata_s3_path}") return metadata elif response.status_code == 404: @@ -142,12 +152,9 @@ def download_metadata(self) -> Optional[Dict[str, Any]]: return None finally: - # Clean up temporary file - import os - try: - os.unlink(temp_file_path) - except Exception as cleanup_error: - logger.warning(f"Failed to cleanup temp file {temp_file_path}: {cleanup_error}") + # Clean up temporary file (run in thread pool) + # await asyncio.to_thread(self._cleanup_temp_file, temp_file_path) # Disabled for debugging + pass except json.JSONDecodeError as e: raise DiffError(f"Failed to parse downloaded metadata JSON: {str(e)}", e) @@ -156,6 +163,54 @@ def download_metadata(self) -> Optional[Dict[str, Any]]: logger.warning(f"Failed to download metadata (may be first run): {str(e)}") return None + def _create_temp_metadata_file(self, metadata: Dict[str, Any]) -> str: + """Create a temporary file with metadata content in shared folder.""" + import os + import uuid + + # Create temp file in shared folder accessible by both containers + shared_dir = "/app/shared" + os.makedirs(shared_dir, exist_ok=True) + + temp_filename = f"temp_metadata_{uuid.uuid4().hex[:8]}.json" + temp_file_path = os.path.join(shared_dir, temp_filename) + + with open(temp_file_path, 'w') as temp_file: + json.dump(metadata, temp_file, indent=2) + + return temp_file_path + + def _create_temp_file(self) -> str: + """Create an empty temporary file in shared folder.""" + import os + import uuid + + # Create temp file in shared folder accessible by both containers + shared_dir = "/app/shared" + os.makedirs(shared_dir, exist_ok=True) + + temp_filename = f"temp_download_{uuid.uuid4().hex[:8]}.json" + temp_file_path = os.path.join(shared_dir, temp_filename) + + # Create empty file + with open(temp_file_path, 'w'): + pass # Create empty file + + return temp_file_path + + def _read_metadata_from_file(self, file_path: str) -> Dict[str, Any]: + """Read metadata from a file.""" + with open(file_path, 'r') as f: + return json.load(f) + + def _cleanup_temp_file(self, file_path: str) -> None: + """Clean up a temporary file.""" + import os + try: + os.unlink(file_path) + except Exception as cleanup_error: + logger.warning(f"Failed to cleanup temp file {file_path}: {cleanup_error}") + def _retry_with_backoff(self, operation: Any) -> requests.Response: """ Retry an operation with exponential backoff. diff --git a/src/vector_indexer/loki_logger.py b/src/vector_indexer/loki_logger.py new file mode 100644 index 0000000..e69de29 diff --git a/src/vector_indexer/main_indexer.py b/src/vector_indexer/main_indexer.py index 2d709f9..572fcb3 100644 --- a/src/vector_indexer/main_indexer.py +++ b/src/vector_indexer/main_indexer.py @@ -1,3 +1,499 @@ +# """Main vector indexer script for processing documents with contextual retrieval.""" + +# import argparse +# import asyncio +# import shutil +# import sys +# from pathlib import Path +# from datetime import datetime +# from typing import List, Optional +# from loguru import logger + +# from loki_logger import LokiLogger +# logger = LokiLogger(service_name="RAG Module Orchestrator") + +# # Add src to path for imports +# sys.path.append(str(Path(__file__).parent.parent)) + +# from vector_indexer.config.config_loader import ConfigLoader +# from vector_indexer.document_loader import DocumentLoader +# from vector_indexer.contextual_processor import ContextualProcessor +# from vector_indexer.qdrant_manager import QdrantManager +# from vector_indexer.error_logger import ErrorLogger +# from vector_indexer.models import ProcessingStats, DocumentInfo +# from vector_indexer.diff_identifier import DiffDetector, create_diff_config, DiffError + + +# class VectorIndexer: +# """Main vector indexer orchestrating the full pipeline.""" + +# def __init__(self, config_path: Optional[str] = None, signed_url: Optional[str] = None): +# # Load configuration +# self.config_path = ( +# config_path or "src/vector_indexer/config/vector_indexer_config.yaml" +# ) +# self.config = ConfigLoader.load_config(self.config_path) + +# # Store signed URL for future dataset download implementation +# self.signed_url = signed_url + +# # Initialize components +# self.document_loader = DocumentLoader(self.config) +# self.error_logger = ErrorLogger(self.config) + +# # Initialize API client +# from vector_indexer.api_client import LLMOrchestrationAPIClient + +# self.api_client = LLMOrchestrationAPIClient(self.config) + +# # Initialize contextual processor with all required arguments +# self.contextual_processor = ContextualProcessor( +# self.api_client, self.config, self.error_logger +# ) + +# # Processing statistics +# self.stats = ProcessingStats() + +# logger.info(f"Vector Indexer initialized with config: {self.config_path}") +# logger.info(f"Dataset path: {self.config.dataset_base_path}") +# logger.info(f"Max concurrent documents: {self.config.max_concurrent_documents}") +# logger.info( +# f"Max concurrent chunks: {self.config.max_concurrent_chunks_per_doc}" +# ) + +# if self.signed_url: +# logger.info(f"Signed URL provided: {self.signed_url[:50]}...") # Log first 50 chars only + +# async def process_all_documents(self) -> ProcessingStats: +# """ +# Process all documents in the dataset with contextual retrieval. + +# Returns: +# ProcessingStats: Overall processing statistics +# """ +# logger.info("=" * 60) +# logger.info("Starting Vector Indexer - Contextual Retrieval Pipeline") +# logger.info("=" * 60) + +# self.stats.start_time = datetime.now() + +# try: +# # Step 1: Dataset download (future implementation) +# if self.signed_url: +# logger.info("Dataset download URL provided - download logic to be implemented") +# # TODO: Implement dataset download and extraction +# # await self._download_and_extract_dataset(self.signed_url) + +# # Step 2: Diff identification - determine what files need processing +# logger.info("Step 1: Identifying changed files...") +# try: +# diff_config = create_diff_config() +# diff_detector = DiffDetector(diff_config) +# diff_result = await diff_detector.get_changed_files() + +# logger.info("Diff identification complete:") +# logger.info(f" • Total files scanned: {diff_result.total_files_scanned}") +# logger.info(f" • Previously processed: {diff_result.previously_processed_count}") +# logger.info(f" • Files needing processing: {len(diff_result.new_files)}") +# logger.info(f" • Is first run: {diff_result.is_first_run}") + +# if not diff_result.new_files: +# logger.info("No new or changed files detected. Processing complete.") +# self._cleanup_datasets() +# return self.stats + +# except DiffError as e: +# logger.error(f"Diff identification failed: {e}") +# logger.info("Continuing with full document discovery as fallback") +# diff_result = None +# diff_detector = None + +# # Initialize Qdrant collections +# async with QdrantManager(self.config) as qdrant_manager: +# await qdrant_manager.ensure_collections_exist() + +# # Step 3: Document discovery (filtered by diff results if available) +# logger.info("Step 2: Discovering documents...") +# if diff_result and diff_result.new_files: +# # Filter documents to only those identified as changed +# documents = self._filter_documents_by_paths(diff_result.new_files) +# else: +# # Fallback: discover all documents +# documents = self.document_loader.discover_all_documents() + +# if not documents: +# logger.warning("No documents found to process") +# self._cleanup_datasets() +# return self.stats + +# logger.info(f"Found {len(documents)} documents to process") +# self.stats.total_documents = len(documents) + +# # Process documents with controlled concurrency +# semaphore = asyncio.Semaphore(self.config.max_concurrent_documents) +# tasks: List[asyncio.Task[int]] = [] + +# for doc_info in documents: +# task = asyncio.create_task( +# self._process_single_document( +# doc_info, qdrant_manager, semaphore +# ) +# ) +# tasks.append(task) + +# # Execute all document processing tasks +# logger.info( +# f"Processing {len(tasks)} documents with max {self.config.max_concurrent_documents} concurrent" +# ) +# results = await asyncio.gather(*tasks, return_exceptions=True) + +# # Collect results and handle exceptions +# for i, result in enumerate(results): +# if isinstance(result, Exception): +# doc_info = documents[i] +# logger.error( +# f"Document processing failed: {doc_info.document_hash} - {result}" +# ) +# self.stats.documents_failed += 1 +# self.error_logger.log_document_failure( +# doc_info.document_hash, str(result) +# ) +# else: +# # Result should be number of chunks processed +# self.stats.documents_processed += 1 +# if isinstance(result, int): +# self.stats.total_chunks_processed += result + +# # Calculate final statistics +# self.stats.end_time = datetime.now() + +# # Step 4: Update processed files tracking +# if diff_detector and documents: +# try: +# processed_paths = [doc.cleaned_txt_path for doc in documents] +# if processed_paths: +# await diff_detector.mark_files_processed(processed_paths) +# logger.info("Updated processed files tracking") +# except Exception as e: +# logger.warning(f"Failed to update processed files tracking: {e}") + +# # Log final statistics +# self.error_logger.log_processing_stats(self.stats) +# self._log_final_summary() + +# # Step 5: Cleanup datasets folder after successful processing +# self._cleanup_datasets() + +# return self.stats + +# except Exception as e: +# logger.error(f"Critical error in vector indexer: {e}") +# self.stats.end_time = datetime.now() +# self.error_logger.log_processing_stats(self.stats) +# raise +# finally: +# # Clean up API client AFTER all processing is complete +# try: +# await self.api_client.close() +# except Exception as e: +# logger.warning(f"Error closing API client: {e}") + +# async def _process_single_document( +# self, +# doc_info: DocumentInfo, +# qdrant_manager: QdrantManager, +# semaphore: asyncio.Semaphore, +# ) -> int: +# """ +# Process a single document with contextual retrieval. + +# Args: +# doc_info: Document information +# qdrant_manager: Qdrant manager instance +# semaphore: Concurrency control semaphore + +# Returns: +# int: Number of chunks processed +# """ +# async with semaphore: +# logger.info(f"Processing document: {doc_info.document_hash}") + +# try: +# # Load document content +# document = self.document_loader.load_document(doc_info) + +# if not document: +# logger.warning(f"Could not load document: {doc_info.document_hash}") +# return 0 + +# # Process document with contextual retrieval +# contextual_chunks = await self.contextual_processor.process_document( +# document +# ) + +# if not contextual_chunks: +# logger.warning( +# f"No chunks created for document: {doc_info.document_hash}" +# ) +# return 0 + +# # Store chunks in Qdrant +# await qdrant_manager.store_chunks(contextual_chunks) + +# logger.info( +# f"Successfully processed document {doc_info.document_hash}: " +# f"{len(contextual_chunks)} chunks" +# ) + +# return len(contextual_chunks) + +# except Exception as e: +# logger.error(f"Error processing document {doc_info.document_hash}: {e}") +# self.error_logger.log_document_failure(doc_info.document_hash, str(e)) +# raise + +# def _log_final_summary(self): +# """Log final processing summary.""" + +# logger.info("VECTOR INDEXER PROCESSING COMPLETE") + +# logger.info("Processing Statistics:") +# logger.info(f" • Total Documents: {self.stats.total_documents}") +# logger.info(f" • Successful Documents: {self.stats.documents_processed}") +# logger.info(f" • Failed Documents: {self.stats.documents_failed}") +# logger.info(f" • Total Chunks: {self.stats.total_chunks_processed}") +# logger.info(f" • Failed Chunks: {self.stats.total_chunks_failed}") + +# if self.stats.total_documents > 0: +# success_rate = ( +# self.stats.documents_processed / self.stats.total_documents +# ) * 100 +# logger.info(f" • Success Rate: {success_rate:.1f}%") + +# logger.info(f" • Processing Duration: {self.stats.duration}") + +# if self.stats.documents_failed > 0: +# logger.warning( +# f" {self.stats.documents_failed} documents failed processing" +# ) +# logger.info(" Check failure logs for details") + +# async def run_health_check(self) -> bool: +# """ +# Run health check on all components. + +# Returns: +# bool: True if all components are healthy +# """ +# logger.info("Running Vector Indexer health check...") + +# try: +# # Check Qdrant connection +# async with QdrantManager(self.config) as qdrant_manager: +# # Test basic Qdrant connectivity by trying to list collections +# try: +# qdrant_url = getattr( +# self.config, "qdrant_url", "http://localhost:6333" +# ) +# response = await qdrant_manager.client.get( +# f"{qdrant_url}/collections" +# ) +# if response.status_code == 200: +# logger.info(" Qdrant server: Connected") + +# # Check if collections exist, create them if they don't +# collections_info = {} +# for collection_name in qdrant_manager.collections_config.keys(): +# info = await qdrant_manager.get_collection_info( +# collection_name +# ) +# if info: +# count = await qdrant_manager.count_points( +# collection_name +# ) +# collections_info[collection_name] = count +# logger.info( +# f" Qdrant collection '{collection_name}': {count} points" +# ) +# else: +# logger.info( +# f" Qdrant collection '{collection_name}': Not found (will be created automatically)" +# ) +# else: +# logger.error( +# f" Qdrant server not accessible: {response.status_code}" +# ) +# return False +# except Exception as e: +# logger.error(f" Qdrant connection failed: {e}") +# return False + +# # Check API client connectivity +# api_healthy = await self.api_client.health_check() +# if api_healthy: +# logger.info(" LLM Orchestration Service API: Connected") +# else: +# logger.error(" LLM Orchestration Service API: Not accessible") +# return False + +# # Check dataset path +# if Path(self.config.dataset_base_path).exists(): +# logger.info(f" Dataset path: {self.config.dataset_base_path}") +# else: +# logger.error( +# f" Dataset path not found: {self.config.dataset_base_path}" +# ) +# return False + +# logger.info(" All health checks passed!") +# return True + +# except Exception as e: +# logger.error(f" Health check failed: {e}") +# return False +# # NOTE: Don't close API client here - it will be used by main processing + +# async def cleanup(self): +# """Clean up resources.""" +# try: +# await self.api_client.close() +# logger.debug("API client closed successfully") +# except Exception as e: +# logger.warning(f"Error closing API client: {e}") + +# def _filter_documents_by_paths(self, file_paths: List[str]) -> List[DocumentInfo]: +# """ +# Filter documents by specific file paths. + +# Args: +# file_paths: List of file paths to process + +# Returns: +# List of DocumentInfo for matching files +# """ +# documents = [] + +# for file_path in file_paths: +# try: +# file_path_obj = Path(file_path) + +# # Ensure this is a cleaned.txt file +# if file_path_obj.name != "cleaned.txt": +# logger.debug(f"Skipping non-cleaned.txt file: {file_path}") +# continue + +# # Get hash directory and collection directory +# hash_dir = file_path_obj.parent +# collection_dir = hash_dir.parent + +# # Check if metadata file exists +# metadata_file = hash_dir / self.config.metadata_file +# if not metadata_file.exists(): +# logger.warning(f"Skipping file without metadata: {file_path}") +# continue + +# # Create DocumentInfo +# doc_info = DocumentInfo( +# document_hash=hash_dir.name, +# cleaned_txt_path=str(file_path_obj), +# source_meta_path=str(metadata_file), +# dataset_collection=collection_dir.name +# ) + +# documents.append(doc_info) +# logger.debug(f"Added document: {doc_info.document_hash}") + +# except Exception as e: +# logger.warning(f"Failed to process file path {file_path}: {e}") +# continue + +# logger.info(f"Filtered to {len(documents)} documents from {len(file_paths)} paths") +# return documents + +# def _cleanup_datasets(self): +# """Remove datasets folder after processing.""" +# try: +# datasets_path = Path(self.config.dataset_base_path) +# if datasets_path.exists(): +# shutil.rmtree(str(datasets_path)) +# logger.info(f"Datasets folder cleaned up: {datasets_path}") +# else: +# logger.debug(f"Datasets folder does not exist: {datasets_path}") +# except Exception as e: +# logger.warning(f"Failed to cleanup datasets folder: {e}") +# # Non-critical error - don't fail the entire process + + +# async def main(): +# """Main entry point for the vector indexer.""" + +# # Parse command line arguments +# parser = argparse.ArgumentParser(description="Vector Indexer with Diff Identification") +# parser.add_argument("--signed-url", help="Signed URL for dataset download") +# args = parser.parse_args() + +# # Configure logging +# logger.remove() # Remove default handler +# logger.add( +# sys.stdout, +# format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}", +# level="INFO", +# ) + +# # Add file logging +# logger.add( +# "vector_indexer.log", +# format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}", +# level="DEBUG", +# rotation="10 MB", +# retention="7 days", +# ) + +# indexer = None +# try: +# # Initialize vector indexer with signed URL +# indexer = VectorIndexer(signed_url=args.signed_url) + +# # Run health check first +# logger.info("Performing pre-processing health check...") +# health_ok = await indexer.run_health_check() + +# if not health_ok: +# logger.error("Health check failed. Aborting processing.") +# await indexer.cleanup() +# sys.exit(1) + +# # Process all documents +# logger.info("Health check passed. Starting document processing...") +# stats = await indexer.process_all_documents() + +# # Exit with appropriate code +# if stats.documents_failed > 0: +# logger.warning( +# f"Processing completed with {stats.documents_failed} failures" +# ) +# return 2 # Partial success +# else: +# logger.info("Processing completed successfully") +# return 0 + +# except KeyboardInterrupt: +# logger.info("Processing interrupted by user") +# return 130 +# except Exception as e: +# logger.error(f"Fatal error: {e}") +# return 1 +# finally: +# # Ensure cleanup happens +# if indexer: +# await indexer.cleanup() + + +# if __name__ == "__main__": +# # Run the async main function and exit with the returned code +# exit_code = asyncio.run(main()) +# sys.exit(exit_code) + """Main vector indexer script for processing documents with contextual retrieval.""" import argparse @@ -9,6 +505,9 @@ from typing import List, Optional from loguru import logger +# from loki_logger import LokiLogger +# logger = LokiLogger(service_name="RAG Module Orchestrator") + # Add src to path for imports sys.path.append(str(Path(__file__).parent.parent)) @@ -96,7 +595,7 @@ async def process_all_documents(self) -> ProcessingStats: if not diff_result.new_files: logger.info("No new or changed files detected. Processing complete.") - self._cleanup_datasets() + # self._cleanup_datasets() return self.stats except DiffError as e: @@ -120,7 +619,7 @@ async def process_all_documents(self) -> ProcessingStats: if not documents: logger.warning("No documents found to process") - self._cleanup_datasets() + # self._cleanup_datasets() return self.stats logger.info(f"Found {len(documents)} documents to process") @@ -179,7 +678,7 @@ async def process_all_documents(self) -> ProcessingStats: self._log_final_summary() # Step 5: Cleanup datasets folder after successful processing - self._cleanup_datasets() + # self._cleanup_datasets() return self.stats @@ -290,13 +789,13 @@ async def run_health_check(self) -> bool: # Test basic Qdrant connectivity by trying to list collections try: qdrant_url = getattr( - self.config, "qdrant_url", "http://localhost:6333" + self.config, "qdrant_url" ) response = await qdrant_manager.client.get( f"{qdrant_url}/collections" ) if response.status_code == 200: - logger.info(" Qdrant server: Connected") + logger.info("✓ Qdrant server: Connected") # Check if collections exist, create them if they don't collections_info = {} @@ -310,43 +809,43 @@ async def run_health_check(self) -> bool: ) collections_info[collection_name] = count logger.info( - f" Qdrant collection '{collection_name}': {count} points" + f"✓ Qdrant collection '{collection_name}': {count} points" ) else: logger.info( - f" Qdrant collection '{collection_name}': Not found (will be created automatically)" + f"✓ Qdrant collection '{collection_name}': Not found (will be created automatically)" ) else: logger.error( - f" Qdrant server not accessible: {response.status_code}" + f"✗ Qdrant server not accessible: {response.status_code}" ) return False except Exception as e: - logger.error(f" Qdrant connection failed: {e}") + logger.error(f"✗ Qdrant connection failed: {e}") return False # Check API client connectivity api_healthy = await self.api_client.health_check() if api_healthy: - logger.info(" LLM Orchestration Service API: Connected") + logger.info("✓ LLM Orchestration Service API: Connected") else: - logger.error(" LLM Orchestration Service API: Not accessible") + logger.error("✗ LLM Orchestration Service API: Not accessible") return False # Check dataset path if Path(self.config.dataset_base_path).exists(): - logger.info(f" Dataset path: {self.config.dataset_base_path}") + logger.info(f"✓ Dataset path: {self.config.dataset_base_path}") else: logger.error( - f" Dataset path not found: {self.config.dataset_base_path}" + f"✗ Dataset path not found: {self.config.dataset_base_path}" ) return False - logger.info(" All health checks passed!") + logger.info("✓ All health checks passed!") return True except Exception as e: - logger.error(f" Health check failed: {e}") + logger.error(f"✗ Health check failed: {e}") return False # NOTE: Don't close API client here - it will be used by main processing @@ -457,8 +956,7 @@ async def main(): if not health_ok: logger.error("Health check failed. Aborting processing.") - await indexer.cleanup() - sys.exit(1) + return 1 # Return exit code instead of sys.exit() # Process all documents logger.info("Health check passed. Starting document processing...") @@ -479,14 +977,19 @@ async def main(): return 130 except Exception as e: logger.error(f"Fatal error: {e}") + import traceback + logger.error(traceback.format_exc()) return 1 finally: # Ensure cleanup happens if indexer: - await indexer.cleanup() + try: + await indexer.cleanup() + except Exception as e: + logger.error(f"Error during cleanup: {e}") if __name__ == "__main__": # Run the async main function and exit with the returned code exit_code = asyncio.run(main()) - sys.exit(exit_code) + sys.exit(exit_code) \ No newline at end of file diff --git a/vault/agent-out/pidfile b/vault/agent-out/pidfile index c793025..e69de29 100644 --- a/vault/agent-out/pidfile +++ b/vault/agent-out/pidfile @@ -1 +0,0 @@ -7 \ No newline at end of file From d7b6d8e74844071bfdea79adb22ae70c341061b0 Mon Sep 17 00:00:00 2001 From: nuwangeek Date: Thu, 16 Oct 2025 22:02:15 +0530 Subject: [PATCH 06/11] uncomment llm orchestration service in docker compose file --- docker-compose.yml | 62 +++++++++++++++++++++++----------------------- 1 file changed, 31 insertions(+), 31 deletions(-) diff --git a/docker-compose.yml b/docker-compose.yml index bec62f8..90333a7 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -446,37 +446,37 @@ services: restart: unless-stopped # LLM Orchestration Service - # llm-orchestration-service: - # build: - # context: . - # dockerfile: Dockerfile.llm_orchestration_service - # container_name: llm-orchestration-service - # restart: always - # ports: - # - "8100:8100" - # env_file: - # - .env - # environment: - # - ENVIRONMENT=production - # - VAULT_ADDR=http://vault:8200 - # - VAULT_TOKEN=/agent/out/token - # volumes: - # # Mount configuration files - # - ./src/llm_config_module/config:/app/src/llm_config_module/config:ro - # # Mount logs directory for persistence - # - llm_orchestration_logs:/app/logs - # - ./vault/agent-out:/agent/out:ro - # networks: - # - bykstack - # depends_on: - # - vault - # - vault-agent-llm - # healthcheck: - # test: ["CMD", "curl", "-f", "http://llm-orchestration-service:8100/health"] - # interval: 30s - # timeout: 10s - # start_period: 40s - # retries: 3 + llm-orchestration-service: + build: + context: . + dockerfile: Dockerfile.llm_orchestration_service + container_name: llm-orchestration-service + restart: always + ports: + - "8100:8100" + env_file: + - .env + environment: + - ENVIRONMENT=production + - VAULT_ADDR=http://vault:8200 + - VAULT_TOKEN=/agent/out/token + volumes: + # Mount configuration files + - ./src/llm_config_module/config:/app/src/llm_config_module/config:ro + # Mount logs directory for persistence + - llm_orchestration_logs:/app/logs + - ./vault/agent-out:/agent/out:ro + networks: + - bykstack + depends_on: + - vault + - vault-agent-llm + healthcheck: + test: ["CMD", "curl", "-f", "http://llm-orchestration-service:8100/health"] + interval: 30s + timeout: 10s + start_period: 40s + retries: 3 volumes: loki-data: From 31d6155fd7d09ba79a8323998af3d78dfbefe1a1 Mon Sep 17 00:00:00 2001 From: nuwangeek Date: Sat, 18 Oct 2025 06:07:13 +0530 Subject: [PATCH 07/11] complete vector indexer --- .../script/vector_indexer_pipeline.sh | 25 - .../rag-search/POST/data/update.yml | 11 +- ....timestamp-1760669278597-b5a7f003c52fe.mjs | 0 docker-compose.yml | 7 +- generate_presigned_url.py | 63 ++ pyproject.toml | 1 + run_vector_indexer.py | 179 ---- src/llm_orchestrator_config/config/schema.py | 2 +- src/vector_indexer/config/config_loader.py | 4 +- src/vector_indexer/constants.py | 13 +- src/vector_indexer/dataset_download.py | 91 ++ .../diff_identifier/DIFF_IDENTIFIER_FLOW.md | 500 ++++++++-- .../diff_identifier/__init__.py | 6 +- .../diff_identifier/diff_detector.py | 273 ++++-- .../diff_identifier/diff_models.py | 81 +- .../diff_identifier/s3_ferry_client.py | 235 +++-- .../diff_identifier/version_manager.py | 514 +++++++--- src/vector_indexer/document_loader.py | 56 +- src/vector_indexer/main_indexer.py | 913 ++++++------------ src/vector_indexer/qdrant_manager.py | 263 +++++ uv.lock | 2 + 21 files changed, 1979 insertions(+), 1260 deletions(-) rename 3.55.2 => GUI/vite.config.ts.timestamp-1760669278597-b5a7f003c52fe.mjs (100%) create mode 100644 generate_presigned_url.py delete mode 100644 run_vector_indexer.py create mode 100644 src/vector_indexer/dataset_download.py diff --git a/DSL/CronManager/script/vector_indexer_pipeline.sh b/DSL/CronManager/script/vector_indexer_pipeline.sh index 1146123..035179a 100644 --- a/DSL/CronManager/script/vector_indexer_pipeline.sh +++ b/DSL/CronManager/script/vector_indexer_pipeline.sh @@ -53,31 +53,6 @@ echo "[FOUND] Python script at: $PYTHON_SCRIPT" # Run vector indexer with signed URL parameter echo "[STARTING] Vector indexer processing..." -# Add debugging before Python execution -echo "[DEBUG] Testing basic Python execution..." -python3 --version || echo "[ERROR] Python version check failed" - -echo "[DEBUG] Testing Python imports..." -python3 -c " -import sys -print(f'[DEBUG] Python executable: {sys.executable}') -print(f'[DEBUG] Python version: {sys.version}') -try: - from pathlib import Path - print('[DEBUG] ✓ pathlib import OK') - from loguru import logger - print('[DEBUG] ✓ loguru import OK') - import argparse - print('[DEBUG] ✓ argparse import OK') -except Exception as e: - print(f'[DEBUG] ✗ Import failed: {e}') - import traceback - traceback.print_exc() -" 2>&1 - -echo "[DEBUG] Testing main_indexer.py syntax..." -python3 -m py_compile "$PYTHON_SCRIPT" 2>&1 || echo "[ERROR] Syntax check failed" - echo "[DEBUG] About to execute main_indexer.py..." if [ -n "$signedUrl" ]; then echo "[SIGNED_URL] Using signed URL for dataset processing" diff --git a/DSL/Ruuter.public/rag-search/POST/data/update.yml b/DSL/Ruuter.public/rag-search/POST/data/update.yml index 9c81d79..7ba211f 100644 --- a/DSL/Ruuter.public/rag-search/POST/data/update.yml +++ b/DSL/Ruuter.public/rag-search/POST/data/update.yml @@ -30,13 +30,6 @@ logs_params: log: "Agency ID: ${agency_ids}, Agency Data Hash: ${agency_data_hash}" next: import_agency_data -# check_sync_status: -# switch: -# - condition: ${get_agency_id_result.response.body[0].hasData} -# next: importAgencyData -# - condition: true -# next: noAgencyData - import_agency_data: call: http.post args: @@ -44,9 +37,9 @@ import_agency_data: body: agencyIds: ${agency_ids} result: importResult - next: logImportAgencyDataResponse + next: log_import_agency_data_response -logImportAgencyDataResponse: +log_import_agency_data_response: log: ${JSON.stringify(importResult.response)} next: assign_import_agency_data diff --git a/3.55.2 b/GUI/vite.config.ts.timestamp-1760669278597-b5a7f003c52fe.mjs similarity index 100% rename from 3.55.2 rename to GUI/vite.config.ts.timestamp-1760669278597-b5a7f003c52fe.mjs diff --git a/docker-compose.yml b/docker-compose.yml index 4ecd0e2..9519977 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -183,15 +183,10 @@ services: - shared-volume:/app/shared # Access to shared resources for cross-container coordination - ./datasets:/app/datasets # Direct access to datasets folder for diff identifier operations - ./grafana-configs/loki_logger.py:/app/src/vector_indexer/loki_logger.py - env_file: - - .env + - ./.env:/app/.env:ro environment: - server.port=9010 - PYTHONPATH=/app:/app/src/vector_indexer - - S3_DATA_BUCKET_NAME=rag-search - - S3_ENDPOINT_URL=http://minio:9000 - - S3_ACCESS_KEY_ID=minioadmin - - S3_SECRET_ACCESS_KEY=minioadmin ports: - 9010:8080 networks: diff --git a/generate_presigned_url.py b/generate_presigned_url.py new file mode 100644 index 0000000..4f4d417 --- /dev/null +++ b/generate_presigned_url.py @@ -0,0 +1,63 @@ +import boto3 +from botocore.client import Config +from typing import List, Dict + +# Create S3 client for MinIO +s3_client = boto3.client( + "s3", + endpoint_url="http://minio:9000", # Replace with your MinIO URL + aws_access_key_id="", # Replace with your access key + aws_secret_access_key="", # Replace with your secret key + config=Config(signature_version="s3v4"), # Hardcoded signature version + region_name="us-east-1", # MinIO usually works with any region +) + +# List of files to process +files_to_process: List[Dict[str, str]] = [ + {"bucket": "ckb", "key": "sm_someuuid/sm_someuuid.zip"}, +] + +# Generate presigned URLs +presigned_urls: List[str] = [] + +print("Generating presigned URLs...") +for file_info in files_to_process: + try: + url = s3_client.generate_presigned_url( + ClientMethod="get_object", + Params={"Bucket": file_info["bucket"], "Key": file_info["key"]}, + ExpiresIn=24 * 3600, # 4 hours in seconds + ) + presigned_urls.append(url) + print(f":white_check_mark: Generated URL for: {file_info['key']}") + print(f" URL: {url}") + except Exception as e: + print(f":x: Failed to generate URL for: {file_info['key']}") + print(f" Error: {str(e)}") + +output_file: str = "minio_presigned_urls.txt" + +try: + with open(output_file, "w") as f: + # Write URLs separated by ||| delimiter (for your script) + url_string: str = "|||".join(presigned_urls) + f.write(url_string) + f.write("\n\n") + + # Also write each URL on separate lines for readability + f.write("Individual URLs:\n") + f.write("=" * 50 + "\n") + for i, url in enumerate(presigned_urls, 1): + f.write(f"URL {i}:\n{url}\n\n") + + print(f"\n:white_check_mark: Presigned URLs saved to: {output_file}") + print(f"Total URLs generated: {len(presigned_urls)}") + + # Display the combined URL string for easy copying + if presigned_urls: + print("\nCombined URL string (for signedUrls environment variable):") + print("=" * 60) + print("|||".join(presigned_urls)) + +except Exception as e: + print(f":x: Failed to save URLs to file: {str(e)}") diff --git a/pyproject.toml b/pyproject.toml index 9dc039e..7b4c375 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,6 +29,7 @@ dependencies = [ "rerankers[transformers]>=0.10.0", "tiktoken>=0.11.0", "dvc[s3]>=3.55.2", + "aiohttp>=3.13.0", ] [tool.pyright] diff --git a/run_vector_indexer.py b/run_vector_indexer.py deleted file mode 100644 index b01150f..0000000 --- a/run_vector_indexer.py +++ /dev/null @@ -1,179 +0,0 @@ -#!/usr/bin/env python3 -""" -Entry point script for Vector Indexer - Contextual Retrieval Pipeline - -This script can be run directly or called by cron jobs for automated processing. - -Usage: - python run_vector_indexer.py [--config CONFIG_PATH] [--health-check] [--dry-run] - -Examples: - # Run with default config - python run_vector_indexer.py - - # Run with custom config - python run_vector_indexer.py --config /path/to/config.yaml - - # Health check only - python run_vector_indexer.py --health-check - - # Dry run (validate without processing) - python run_vector_indexer.py --dry-run -""" - -import argparse -import asyncio -import sys -from pathlib import Path - -# Add src to Python path -sys.path.insert(0, str(Path(__file__).parent / "src")) - -from src.vector_indexer.main_indexer import VectorIndexer - - -async def main(): - """Main entry point with command line argument parsing.""" - - parser = argparse.ArgumentParser( - description="Vector Indexer - Contextual Retrieval Pipeline", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=__doc__, - ) - - parser.add_argument( - "--config", - type=str, - default="src/vector_indexer/config/vector_indexer_config.yaml", - help="Path to configuration file (default: src/vector_indexer/config/vector_indexer_config.yaml)", - ) - - parser.add_argument( - "--health-check", action="store_true", help="Run health check only and exit" - ) - - parser.add_argument( - "--dry-run", - action="store_true", - help="Validate configuration and connectivity without processing documents", - ) - - parser.add_argument( - "--verbose", "-v", action="store_true", help="Enable verbose logging" - ) - - parser.add_argument( - "--quiet", "-q", action="store_true", help="Suppress non-error output" - ) - - args = parser.parse_args() - - # Configure logging level based on arguments - log_level = "INFO" - if args.verbose: - log_level = "DEBUG" - elif args.quiet: - log_level = "ERROR" - - try: - # Initialize vector indexer with specified config - indexer = VectorIndexer(config_path=args.config) - - if args.health_check: - # Health check only - print("🔍 Running health check...") - health_ok = await indexer.run_health_check() - - if health_ok: - print("✅ Health check passed!") - return 0 - else: - print("❌ Health check failed!") - return 1 - - elif args.dry_run: - # Dry run - validate without processing - print("🧪 Running dry run validation...") - - health_ok = await indexer.run_health_check() - if not health_ok: - print("❌ Validation failed!") - return 1 - - # Discover documents but don't process - documents = indexer.document_loader.discover_all_documents() - print(f"📄 Found {len(documents)} documents ready for processing") - print("✅ Dry run validation passed!") - return 0 - - else: - # Full processing run - print("🚀 Starting Vector Indexer processing...") - - # Health check first - health_ok = await indexer.run_health_check() - if not health_ok: - print("❌ Pre-processing health check failed!") - return 1 - - # Process all documents - stats = await indexer.process_all_documents() - - # Return appropriate exit code - if stats.documents_failed > 0: - print(f"⚠️ Processing completed with {stats.documents_failed} failures") - return 2 # Partial success - else: - print("✅ Processing completed successfully!") - return 0 - - except KeyboardInterrupt: - print("\n⏹️ Processing interrupted by user") - return 130 - except FileNotFoundError as e: - print(f"❌ Configuration file not found: {e}") - return 1 - except Exception as e: - print(f"💥 Fatal error: {e}") - return 1 - - -def cron_entry_point(): - """ - Entry point specifically designed for cron jobs. - - This function: - - Uses minimal output suitable for cron logs - - Returns appropriate exit codes for monitoring - - Handles errors gracefully for automated systems - """ - import logging - - # Configure minimal logging for cron - logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - Vector Indexer - %(levelname)s - %(message)s", - ) - - try: - # Run with default configuration - result = asyncio.run(main()) - - if result == 0: - logging.info("Vector indexer completed successfully") - elif result == 2: - logging.warning("Vector indexer completed with some failures") - else: - logging.error("Vector indexer failed") - - return result - - except Exception as e: - logging.error(f"Vector indexer fatal error: {e}") - return 1 - - -if __name__ == "__main__": - # Run the async main function - exit_code = asyncio.run(main()) - sys.exit(exit_code) diff --git a/src/llm_orchestrator_config/config/schema.py b/src/llm_orchestrator_config/config/schema.py index e7c656a..8a2b525 100644 --- a/src/llm_orchestrator_config/config/schema.py +++ b/src/llm_orchestrator_config/config/schema.py @@ -8,7 +8,7 @@ class VaultConfig(BaseModel): """Configuration for HashiCorp Vault integration.""" - url: str = "http://localhost:8200" + url: str = "http://vault:8200" token: str = "" enabled: bool = True diff --git a/src/vector_indexer/config/config_loader.py b/src/vector_indexer/config/config_loader.py index 6e39cda..2d644c7 100644 --- a/src/vector_indexer/config/config_loader.py +++ b/src/vector_indexer/config/config_loader.py @@ -210,9 +210,7 @@ def load_config( # API config api_config = indexer_config.get("api", {}) - flattened_config["api_base_url"] = api_config.get( - "base_url" - ) + flattened_config["api_base_url"] = api_config.get("base_url") flattened_config["api_timeout"] = api_config.get("timeout", 300) # Processing config diff --git a/src/vector_indexer/constants.py b/src/vector_indexer/constants.py index f878748..b13ed43 100644 --- a/src/vector_indexer/constants.py +++ b/src/vector_indexer/constants.py @@ -112,16 +112,21 @@ class LoggingConstants: PROGRESS_REPORT_INTERVAL = 10 # Report every N documents -def GET_S3_FERRY_PAYLOAD(destinationFilePath: str, destinationStorageType: str, sourceFilePath: str, sourceStorageType: str) -> dict[str, str]: # noqa: N802 +def GET_S3_FERRY_PAYLOAD( + destinationFilePath: str, + destinationStorageType: str, + sourceFilePath: str, + sourceStorageType: str, +) -> dict[str, str]: # noqa: N802 """ Generate S3Ferry payload for file transfer operations. - + Args: destinationFilePath: Path where file should be stored destinationStorageType: "S3" or "FS" (filesystem) sourceFilePath: Path of source file sourceStorageType: "S3" or "FS" (filesystem) - + Returns: dict: Payload for S3Ferry API """ @@ -129,5 +134,5 @@ def GET_S3_FERRY_PAYLOAD(destinationFilePath: str, destinationStorageType: str, "destinationFilePath": destinationFilePath, "destinationStorageType": destinationStorageType, "sourceFilePath": sourceFilePath, - "sourceStorageType": sourceStorageType + "sourceStorageType": sourceStorageType, } diff --git a/src/vector_indexer/dataset_download.py b/src/vector_indexer/dataset_download.py new file mode 100644 index 0000000..ebd9590 --- /dev/null +++ b/src/vector_indexer/dataset_download.py @@ -0,0 +1,91 @@ +"""Simple dataset download utility using requests.""" + +import zipfile +import tempfile +from pathlib import Path +import requests +from loguru import logger + + +def download_and_extract_dataset(signed_url: str) -> tuple[str, int]: + """ + Download ZIP from signed URL and extract it to datasets folder. + + Args: + signed_url: URL to download ZIP from + + Returns: + tuple: (extraction_path, files_extracted_count) + + Raises: + requests.RequestException: If download fails + zipfile.BadZipFile: If ZIP file is corrupted + IOError: If extraction fails + """ + if not signed_url: + raise ValueError("signed_url cannot be empty") + + logger.info("Starting dataset download...") + logger.debug(f"Download URL (first 100 chars): {signed_url[:100]}...") + + # Create datasets folder + datasets_path = Path("/app/datasets") + datasets_path.mkdir(parents=True, exist_ok=True) + logger.debug(f"Dataset directory ready: {datasets_path}") + + # Download ZIP to temp file + with tempfile.NamedTemporaryFile(delete=False, suffix=".zip") as temp_file: + temp_zip_path = Path(temp_file.name) + + try: + # Download file with progress logging + logger.info("Downloading ZIP file...") + response = requests.get( + signed_url, stream=True, timeout=300, allow_redirects=True + ) + response.raise_for_status() + + # Write to temp file + with open(temp_zip_path, "wb") as f: + for chunk in response.iter_content(chunk_size=8192): + if chunk: + f.write(chunk) + + file_size_mb = temp_zip_path.stat().st_size / (1024 * 1024) + logger.info(f"✓ Downloaded {file_size_mb:.1f} MB") + + # Extract ZIP + logger.info("Extracting files...") + files_count = 0 + with zipfile.ZipFile(temp_zip_path, "r") as zip_ref: + files_count = len(zip_ref.namelist()) + zip_ref.extractall(datasets_path) + + logger.info(f"Extracted {files_count} files to {datasets_path}") + logger.info("Cleaning up temporary files...") + + return str(datasets_path), files_count + + except requests.exceptions.HTTPError as e: + logger.error(f"Download failed with HTTP error {e.response.status_code}") + raise + except requests.exceptions.Timeout: + logger.error("Download timed out after 300 seconds") + raise + except requests.RequestException as e: + logger.error(f"Download request failed: {e}") + raise + except zipfile.BadZipFile as e: + logger.error(f"Invalid or corrupted ZIP file: {e}") + raise + except Exception as e: + logger.error(f"Extraction failed: {e}") + raise + finally: + # Always clean up temp file + if temp_zip_path.exists(): + try: + temp_zip_path.unlink() + logger.debug("Temporary ZIP file cleaned up") + except Exception as e: + logger.warning(f"Failed to clean up temp file: {e}") diff --git a/src/vector_indexer/diff_identifier/DIFF_IDENTIFIER_FLOW.md b/src/vector_indexer/diff_identifier/DIFF_IDENTIFIER_FLOW.md index 6f097f1..57a48d2 100644 --- a/src/vector_indexer/diff_identifier/DIFF_IDENTIFIER_FLOW.md +++ b/src/vector_indexer/diff_identifier/DIFF_IDENTIFIER_FLOW.md @@ -1,103 +1,481 @@ -# Vector Indexer Diff Identifier +# Vector Indexer Diff Identifier with Automatic Cleanup ## Overview -The **Diff Identifier** is a sophisticated change detection system that forms the first critical step in the Vector Indexer pipeline. It intelligently identifies which files have changed between dataset downloads using **Data Version Control (DVC)** and **content hashing**, ensuring that only new or modified content is processed for vector generation. This eliminates unnecessary reprocessing and can reduce processing time by up to 90% for incremental updates. +The **Diff Identifier** is a sophisticated change detection and cleanup system that forms the first critical step in the Vector Indexer pipeline. It intelligently identifies which files have changed between dataset downloads using **Data Version Control (DVC)** and **content hashing**, and automatically manages **vector chunk cleanup** for deleted and modified files. This ensures that only new or modified content is processed for vector generation while maintaining vector store consistency by removing orphaned chunks. + +## Key Features + +✅ **Incremental Processing**: Only process new or changed files +✅ **Automatic Cleanup**: Delete chunks for removed/modified files +✅ **Comprehensive Change Detection**: New, modified, deleted, unchanged files +✅ **Vector Store Consistency**: Prevent orphaned chunks in Qdrant +✅ **Performance Optimization**: Reduce processing time by up to 90% +✅ **Robust Fallback**: Graceful degradation when diff detection fails ## System Architecture -### Component Structure +### Enhanced Component Structure ``` src/vector_indexer/diff_identifier/ ├── __init__.py # Module exports and public API -├── diff_detector.py # Main orchestrator and entry point -├── version_manager.py # DVC operations & file version tracking +├── diff_detector.py # Main orchestrator with cleanup coordination +├── version_manager.py # DVC operations & comprehensive diff analysis ├── s3_ferry_client.py # S3Ferry service integration for metadata transfer -└── diff_models.py # Pydantic data models and configuration classes +└── diff_models.py # Enhanced data models with cleanup metadata ``` ### Core Components Deep Dive #### 1. **DiffDetector** (`diff_detector.py`) -**Primary Role:** Main orchestrator that coordinates the entire diff identification workflow. +**Primary Role:** Main orchestrator that coordinates diff identification and cleanup workflow. -**Key Responsibilities:** -- Initialize and manage component lifecycle -- Coordinate between VersionManager and S3FerryClient -- Handle fallback scenarios when diff identification fails -- Provide simplified interface to main_indexer.py +**Enhanced Responsibilities:** +- Orchestrate comprehensive change detection (new, modified, deleted, unchanged) +- Coordinate automatic vector chunk cleanup operations +- Provide detailed logging for cleanup operations +- Handle both incremental and first-run scenarios -**Public Interface:** +**Enhanced Public Interface:** ```python class DiffDetector: - async def get_changed_files() -> DiffResult - async def mark_files_processed(file_paths: List[str]) -> bool + async def get_changed_files() -> DiffResult # Now includes cleanup metadata + async def mark_files_processed(file_paths: List[str], chunks_info: Optional[Dict] = None) -> bool ``` -**Implementation Details:** -- Uses factory pattern to create VersionManager and S3FerryClient -- Implements graceful degradation (falls back to all files if diff fails) -- Handles both first-time setup and incremental change detection -- Manages cross-container file operations via shared volumes - -#### 2. **VersionManager** (`version_manager.py`) -**Primary Role:** Handles DVC operations and file content tracking for change detection. +**Cleanup Integration:** +```python +# New comprehensive flow +1. Detect all file changes (new, modified, deleted, unchanged) +2. Generate cleanup metadata (chunks to delete) +3. Return enhanced DiffResult with cleanup information +4. Main indexer executes cleanup before processing +``` -**Key Responsibilities:** -- Initialize DVC repository with MinIO S3 remote configuration -- Perform recursive file scanning with content hash calculation -- Compare current file state with previously processed file metadata -- Generate comprehensive change reports with statistics +#### 2. **Enhanced VersionManager** (`version_manager.py`) +**Primary Role:** Advanced change detection with cleanup metadata generation. -**Core Operations:** +**Enhanced Capabilities:** ```python class VersionManager: - def initialize_dvc() -> bool # Set up DVC with S3 remote - def scan_current_files() -> Dict[str, str] # Hash all current files - def identify_changed_files() -> Set[str] # Compare with previous state - def get_processed_files_metadata() -> Dict # Load metadata via S3Ferry + # Core DVC and detection methods + def initialize_dvc() -> bool + def scan_current_files() -> Dict[str, str] + + # NEW: Comprehensive change analysis + def identify_comprehensive_changes() -> Dict[str, Any] # Returns all change types + cleanup info + + # Enhanced metadata management + async def update_processed_files_metadata( + processed_files: Dict[str, str], + chunks_info: Optional[Dict[str, Dict[str, Any]]] = None + ) -> None ``` -**Change Detection Algorithm:** +**Advanced Change Detection Algorithm:** 1. **File Discovery:** Recursively scan `datasets/` folder for all files 2. **Content Hashing:** Calculate SHA-256 hash for each file's content -3. **Metadata Comparison:** Compare current hashes with stored metadata -4. **Delta Calculation:** Identify new, modified, or deleted files -5. **Result Packaging:** Return structured change report +3. **Comprehensive Comparison:** Identify 4 file types: + - **New files**: Not in previous metadata + - **Modified files**: Same path, different content hash + - **Deleted files**: In metadata but not in current scan + - **Unchanged files**: Same content hash (skip processing) +4. **Cleanup Metadata Generation:** Map deleted/modified files to their chunk IDs +5. **Statistics Calculation:** Provide detailed change statistics + +**Enhanced Metadata Structure:** +```python +{ + "last_updated": "2025-10-17T00:00:46Z", + "total_processed": 3, + "processing_stats": { + "last_run_new_files": 2, + "last_run_modified_files": 1, + "last_run_deleted_files": 1, + "last_cleanup_deleted_chunks": 15, + "last_run_timestamp": "2025-10-17T00:00:46Z" + }, + "processed_files": { + "sha256_hash": { + "content_hash": "sha256_hash", + "original_path": "datasets/doc1/cleaned.txt", + "file_size": 15234, + "processed_at": "2025-10-17T00:00:46Z", + "chunk_count": 5, # Track chunk count for validation + "chunk_ids": ["uuid1", "uuid2", "uuid3", "uuid4", "uuid5"] # Track exact chunks + } + } +} +``` + +#### 3. **Enhanced QdrantManager Integration** +**New Cleanup Capabilities:** + +```python +# NEW: Vector chunk deletion methods +async def delete_chunks_by_document_hash(collection_name: str, document_hash: str) -> int +async def delete_chunks_by_file_path(collection_name: str, file_path: str) -> int # Fallback +async def get_chunks_for_document(collection_name: str, document_hash: str) -> List[Dict] + +# Efficient deletion using Qdrant filters +delete_payload = { + "filter": { + "must": [{"key": "document_hash", "match": {"value": document_hash}}] + } +} +``` + +#### 4. **Enhanced Data Models** (`diff_models.py`) +**Enhanced with Cleanup Support:** + +```python +class ProcessedFileInfo(BaseModel): + content_hash: str + original_path: str + file_size: int + processed_at: str + chunk_count: int = 0 # NEW: Track number of chunks + chunk_ids: List[str] = Field(default_factory=list) # NEW: Track chunk IDs + +class DiffResult(BaseModel): + # File change detection + new_files: List[str] = Field(..., description="Files to process for first time") + modified_files: List[str] = Field(default_factory=list, description="Files with changed content") + deleted_files: List[str] = Field(default_factory=list, description="Files removed from dataset") + unchanged_files: List[str] = Field(default_factory=list, description="Files with same content") + + # Statistics + total_files_scanned: int + previously_processed_count: int + is_first_run: bool + + # NEW: Cleanup metadata + chunks_to_delete: Dict[str, List[str]] = Field(default_factory=dict) # document_hash -> chunk_ids + estimated_cleanup_count: int = Field(default=0) # Total chunks to be removed + +class VersionState(BaseModel): + last_updated: str + processed_files: Dict[str, ProcessedFileInfo] + total_processed: int + processing_stats: Dict[str, Any] = Field(default_factory=dict) # NEW: Enhanced stats +``` + +## Enhanced Processing Flow + +### Comprehensive Workflow + +```mermaid +graph TD + A[Start Vector Indexer] --> B[Check Existing Metadata] + B --> C{Metadata Exists?} + C -->|No| D[First Run: All Files New] + C -->|Yes| E[Comprehensive Diff Analysis] + + E --> F[Identify File Changes] + F --> G[New Files] + F --> H[Modified Files] + F --> I[Deleted Files] + F --> J[Unchanged Files] + + G --> K[Mark for Processing] + H --> L[Mark for Processing + Cleanup] + I --> M[Mark for Cleanup Only] + J --> N[Skip Processing] + + K --> O[Execute Cleanup Operations] + L --> O + M --> O + N --> P[Document Discovery] + + O --> Q{Chunks to Delete?} + Q -->|Yes| R[Delete Chunks from Qdrant] + Q -->|No| P + R --> S[Log Cleanup Results] + S --> P + + P --> T[Filter Documents] + T --> U[Process Documents] + U --> V[Store New Chunks] + V --> W[Update Metadata] + W --> X[Commit to DVC] + X --> Y[Complete] +``` -#### 3. **S3FerryClient** (`s3_ferry_client.py`) -**Primary Role:** Manages metadata transfer operations between local filesystem and MinIO S3 storage via S3Ferry service. +### Detailed Processing Steps -**Key Responsibilities:** -- Upload/download processing metadata to/from S3 -- Handle temporary file operations for S3Ferry API compatibility -- Implement retry logic with exponential backoff for resilience -- Manage S3Ferry API payload generation and response handling +#### Step 1: Enhanced Diff Detection +```python +# NEW: Comprehensive change detection +diff_result = await diff_detector.get_changed_files() + +# Enhanced logging output: +🔍 COMPREHENSIVE DIFF ANALYSIS COMPLETE: + 📄 New files: 2 + 🔄 Modified files: 1 + 🗑️ Deleted files: 1 + ⏭️ Unchanged files: 5 + 🧹 Total chunks to cleanup: 8 +``` -**S3Ferry Integration Pattern:** +#### Step 2: Automatic Cleanup Execution ```python -# S3Ferry API Usage Pattern -def transfer_file(self, destinationFilePath, destinationStorageType, - sourceFilePath, sourceStorageType) -> requests.Response: - payload = GET_S3_FERRY_PAYLOAD(destinationFilePath, destinationStorageType, - sourceFilePath, sourceStorageType) - return requests.post(self.s3_ferry_url, json=payload) +# NEW: Execute cleanup before processing +if diff_result.chunks_to_delete: + await main_indexer._execute_cleanup_operations(qdrant_manager, diff_result) + +# Cleanup logging output: +🧹 STARTING CLEANUP: 2 documents with chunks to delete +🗑️ DELETING 5 chunks for document abc123... + ✅ Deleted 5 chunks from contextual_chunks_azure + ✅ Deleted 0 chunks from contextual_chunks_aws + 📊 Total deleted for document abc123...: 5 chunks +🧹 CLEANUP COMPLETED: 8 total chunks removed from 2 documents ``` -**Storage Operations:** -- **Upload Metadata:** Creates temp file → transfers FS to S3 via S3Ferry → cleanup -- **Download Metadata:** Transfers S3 to FS via S3Ferry → reads from temp file → cleanup -- **Error Handling:** Graceful handling of file not found (expected on first run) -- **Retry Mechanism:** Exponential backoff for network resilience +#### Step 3: Selective Processing +```python +# Only process new and modified files +files_to_process = diff_result.new_files + diff_result.modified_files -#### 4. **Data Models** (`diff_models.py`) -**Primary Role:** Type-safe data structures using Pydantic for configuration and results. +if not files_to_process: + logger.info("No new or changed files detected. Processing complete.") + return self.stats # Early exit - no processing needed +``` -**Model Classes:** +#### Step 4: Enhanced Metadata Tracking ```python -@dataclass -class ProcessedFileInfo: +# NEW: Track chunk information in metadata +await diff_detector.mark_files_processed( + processed_paths, + chunks_info=collected_chunk_information # Future enhancement +) +``` + +## Change Detection Logic + +### File Change Classification + +| File State | Detection Logic | Action Required | +|------------|----------------|-----------------| +| **New** | Hash not in metadata | ✅ Process + Store chunks | +| **Modified** | Same path, different hash | ✅ Delete old chunks + Process + Store new chunks | +| **Deleted** | In metadata, not in current scan | ✅ Delete chunks only | +| **Unchanged** | Same hash as metadata | ⏭️ Skip processing | + +### Cleanup Target Identification + +```python +# Efficient chunk identification for cleanup +chunks_to_delete = { + "document_hash_123": ["chunk_uuid_1", "chunk_uuid_2", "chunk_uuid_3"], + "document_hash_456": ["chunk_uuid_4", "chunk_uuid_5"] +} + +# Cleanup execution per collection +for document_hash, chunk_ids in chunks_to_delete.items(): + for collection_name in ["contextual_chunks_azure", "contextual_chunks_aws"]: + deleted_count = await qdrant_manager.delete_chunks_by_document_hash( + collection_name, document_hash + ) +``` + +## Performance Optimizations + +### Efficient Vector Deletion +- **Filter-based deletion**: Single API call per document using Qdrant filters +- **Batch operations**: Process multiple documents in parallel +- **Collection targeting**: Only clean collections that contain chunks +- **Validation counting**: Pre-count chunks before deletion for accurate logging + +### Metadata Optimizations +- **Incremental updates**: Only update changed file records +- **Batch metadata operations**: Single S3Ferry call per operation type +- **Minimal Qdrant queries**: Use metadata as source of truth, not live queries + +### Example Performance Gains + +| Dataset Size | Traditional Approach | With Diff + Cleanup | Performance Gain | +|--------------|---------------------|----------------------|------------------| +| 100 files, 10 changed | Process all 100 | Process 10 + cleanup 5 | **85% reduction** | +| 1000 files, 50 modified | Process all 1000 | Process 50 + cleanup 25 | **92% reduction** | +| 10 files, 2 deleted | Process all 10 | Process 0 + cleanup 2 | **100% processing skip** | + +## Configuration + +### Environment Variables +```bash +# Core S3Ferry Configuration +S3_FERRY_URL=http://rag-s3-ferry:3000/v1/files/copy +DATASETS_PATH=/app/datasets +METADATA_FILENAME=processed-metadata.json + +# DVC S3 Configuration +S3_ENDPOINT_URL=http://minio:9000 +S3_ACCESS_KEY_ID=minioadmin +S3_SECRET_ACCESS_KEY=minioadmin +``` + +### Enhanced Logging Levels +```yaml +# Enable detailed cleanup logging +logging: + level: "INFO" # Standard level shows cleanup summaries + level: "DEBUG" # Detailed level shows individual chunk operations +``` + +## Error Handling and Recovery + +### Cleanup Failure Scenarios + +1. **Partial Cleanup Failure** + ```python + # Continue processing even if some chunks fail to delete + try: + deleted_count = await delete_chunks_by_document_hash(collection, doc_hash) + except Exception as e: + logger.error(f"Failed to delete chunks from {collection}: {e}") + continue # Continue with other collections/documents + ``` + +2. **Qdrant Connection Issues** + ```python + # Fallback: Process files but skip cleanup + if cleanup_failed: + logger.warning("Cleanup failed - proceeding with processing only") + # Processing continues, cleanup will be attempted in next run + ``` + +3. **Metadata Consistency** + ```python + # Validate metadata against actual vector store state + if chunk_count_mismatch: + logger.warning("Metadata chunk count doesn't match actual chunks") + # Cleanup based on document_hash filter (more reliable than chunk IDs) + ``` + +### Recovery Mechanisms + +- **Graceful Degradation**: If cleanup fails, processing continues +- **Next-Run Recovery**: Failed cleanups are retried in subsequent runs +- **Metadata Validation**: Cross-check metadata against vector store state +- **Manual Cleanup**: Provide tools for manual cleanup if needed + +## Troubleshooting + +### Common Issues + +1. **Cleanup Operations Failing** + ```bash + # Check Qdrant connectivity + curl http://qdrant:6333/collections + + # Check for orphaned chunks + # Look for document_hash values that no longer exist in datasets + ``` + +2. **Inconsistent Chunk Counts** + ```python + # Symptoms: Metadata shows N chunks but Qdrant has different count + # Cause: Processing interruption or partial failures + # Solution: Run manual cleanup or reset metadata + ``` + +3. **Performance Degradation** + ```python + # Too many small cleanup operations + # Solution: Batch cleanup operations, optimize Qdrant filters + ``` + +### Debug Commands + +```python +# Enable comprehensive diff logging +diff_result = await diff_detector.get_changed_files() +logger.info(f"Cleanup metadata: {diff_result.chunks_to_delete}") + +# Test cleanup operations +cleanup_count = await main_indexer._execute_cleanup_operations(qdrant_manager, diff_result) +logger.info(f"Total cleanup: {cleanup_count} chunks") +``` + +## Integration Points + +### Enhanced Main Indexer Integration + +```python +# NEW: Comprehensive processing flow +async def process_all_documents(self) -> ProcessingStats: + # 1. Enhanced diff detection + diff_result = await diff_detector.get_changed_files() + + # 2. NEW: Automatic cleanup execution + if diff_result.chunks_to_delete: + cleanup_count = await self._execute_cleanup_operations(qdrant_manager, diff_result) + + # 3. Selective document processing + files_to_process = diff_result.new_files + diff_result.modified_files + if not files_to_process: + return self.stats # Early exit + + # 4. Standard processing pipeline + documents = self._filter_documents_by_paths(files_to_process) + results = await self._process_documents(documents) + + # 5. Enhanced metadata update + await diff_detector.mark_files_processed(processed_paths, chunks_info) +``` + +### Vector Store Consistency + +- **Before Processing**: Clean up orphaned chunks from deleted/modified files +- **During Processing**: Generate new chunks with consistent IDs +- **After Processing**: Update metadata with chunk tracking information +- **Validation**: Periodic consistency checks between metadata and vector store + +## Future Enhancements + +### Planned Improvements + +1. **Chunk ID Collection During Processing** + ```python + # Collect actual chunk IDs during document processing + chunk_info = await process_document_with_tracking(document) + # Update metadata with actual chunk IDs for precise cleanup + ``` + +2. **Advanced Cleanup Strategies** + ```python + # Age-based cleanup: Remove chunks older than X days + # Size-based cleanup: Remove largest chunks first if storage limit reached + # Performance-based cleanup: Batch multiple cleanup operations + ``` + +3. **Cleanup Verification** + ```python + # Post-cleanup validation + remaining_chunks = await qdrant_manager.count_chunks_by_document_hash(doc_hash) + assert remaining_chunks == 0, "Cleanup incomplete" + ``` + +4. **Rollback Capability** + ```python + # Optional: Backup chunks before deletion for potential rollback + # Useful for testing or when unsure about cleanup operations + ``` + +## Conclusion + +The enhanced Diff Identifier with automatic cleanup transforms the Vector Indexer into a fully consistent, efficient incremental processing system, providing: + +- **Performance**: Only process what changed (up to 92% reduction) +- **Consistency**: Automatic vector store cleanup prevents orphaned chunks +- **Reliability**: Graceful fallback and error recovery mechanisms +- **Scalability**: Efficient handling of large, frequently updated datasets +- **Transparency**: Comprehensive logging and statistics for all operations +- **Maintainability**: Clean separation of concerns and robust error handling + +The system now ensures that the vector store always accurately reflects the current dataset state, with no orphaned chunks and optimal processing efficiency. content_hash: str # SHA-256 of file content original_path: str # Relative path from datasets folder file_size: int # File size in bytes diff --git a/src/vector_indexer/diff_identifier/__init__.py b/src/vector_indexer/diff_identifier/__init__.py index 93b6f6f..ac1db30 100644 --- a/src/vector_indexer/diff_identifier/__init__.py +++ b/src/vector_indexer/diff_identifier/__init__.py @@ -7,10 +7,10 @@ __all__ = [ "DiffDetector", - "create_diff_config", + "create_diff_config", "DiffConfig", "DiffResult", "DiffError", "VersionManager", - "S3FerryClient" -] \ No newline at end of file + "S3FerryClient", +] diff --git a/src/vector_indexer/diff_identifier/diff_detector.py b/src/vector_indexer/diff_identifier/diff_detector.py index 6256eb5..a59b0af 100644 --- a/src/vector_indexer/diff_identifier/diff_detector.py +++ b/src/vector_indexer/diff_identifier/diff_detector.py @@ -2,73 +2,101 @@ import os from pathlib import Path -from typing import List +from typing import List, Optional, Dict, Any from loguru import logger +import hashlib from diff_identifier.diff_models import DiffConfig, DiffError, DiffResult from diff_identifier.version_manager import VersionManager +from dotenv import load_dotenv + +load_dotenv(".env") class DiffDetector: """Main orchestrator for diff identification.""" - + def __init__(self, config: DiffConfig): self.config = config self.version_manager = VersionManager(config) - + async def get_changed_files(self) -> DiffResult: """ Get list of files that need processing. - + Returns: DiffResult with files to process and metadata - + Raises: DiffError: If diff detection fails critically """ try: logger.info("Starting diff identification process...") - - # Check if DVC is initialized - if not self.version_manager.is_dvc_initialized(): - logger.info("DVC not initialized - setting up for first run") - return await self._handle_first_run() - - # Get previously processed files - logger.info("Loading processed files metadata...") + + # First, check for existing processed files metadata (this is the source of truth) + logger.info("Checking for existing processed files metadata...") processed_state = await self.version_manager.get_processed_files_metadata() - + + # Initialize DVC if needed (but don't rely on it for first-run detection) + if not self.version_manager.is_dvc_initialized(): + logger.info("DVC not initialized - initializing now") + await self.version_manager.initialize_dvc() + # Scan current files logger.info("Scanning current dataset files...") current_files = self.version_manager.scan_current_files() - + if not current_files: logger.info("No files found in datasets directory") return DiffResult( new_files=[], total_files_scanned=0, - previously_processed_count=0 if processed_state is None else processed_state.total_processed, - is_first_run=False + previously_processed_count=0 + if processed_state is None + else processed_state.total_processed, + is_first_run=processed_state is None, + ) + + # Determine if this is truly a first run based on metadata existence + if processed_state is None: + logger.info("No previous metadata found - this is a first run") + return DiffResult( + new_files=list(current_files.values()), + total_files_scanned=len(current_files), + previously_processed_count=0, + is_first_run=True, ) - - # Identify changed files - changed_file_paths = self.version_manager.identify_changed_files(current_files, processed_state) - + + # This is an incremental run - identify all types of changes + logger.info( + f"Previous metadata found with {processed_state.total_processed} processed files" + ) + changes = self.version_manager.identify_comprehensive_changes( + current_files, processed_state + ) + result = DiffResult( - new_files=list(changed_file_paths), + new_files=changes["new_files"], + modified_files=changes["modified_files"], + deleted_files=changes["deleted_files"], + unchanged_files=changes["unchanged_files"], total_files_scanned=len(current_files), - previously_processed_count=0 if processed_state is None else processed_state.total_processed, - is_first_run=processed_state is None + previously_processed_count=processed_state.total_processed, + is_first_run=False, + chunks_to_delete=changes["chunks_to_delete"], + estimated_cleanup_count=changes["estimated_cleanup_count"], + ) + + logger.info( + f"Diff identification complete: {len(result.new_files)} files need processing" ) - - logger.info(f"Diff identification complete: {len(result.new_files)} files need processing") return result - + except Exception as e: # Log error but don't fail - fall back to processing all files logger.error(f"Diff identification failed: {e}") logger.info("Falling back to processing all files as safety measure") - + try: # Get all files as fallback current_files = self.version_manager.scan_current_files() @@ -76,88 +104,141 @@ async def get_changed_files(self) -> DiffResult: new_files=list(current_files.values()), total_files_scanned=len(current_files), previously_processed_count=0, - is_first_run=True + is_first_run=True, ) except Exception as fallback_error: - raise DiffError(f"Both diff identification and fallback failed: {fallback_error}", e) - - async def mark_files_processed(self, processed_file_paths: List[str]) -> None: + raise DiffError( + f"Both diff identification and fallback failed: {fallback_error}", e + ) + + async def mark_files_processed( + self, + processed_file_paths: List[str], + force_metadata_update: bool = False, + chunks_info: Optional[Dict[str, Dict[str, Any]]] = None, + ) -> None: """ - Mark files as successfully processed. - + Mark files as successfully processed and update metadata. + Args: processed_file_paths: List of file paths that were processed successfully - + force_metadata_update: Force metadata update even if no new files processed (for cleanup operations) + chunks_info: Optional dict mapping document_hash to {"chunk_count": int} + Raises: DiffError: If marking files fails """ try: + # Handle cleanup-only scenarios (no new files processed) + if not processed_file_paths and force_metadata_update: + logger.info( + "Updating metadata for cleanup operations (no new files processed)..." + ) + await self.version_manager.update_processed_files_metadata({}) + logger.info("Metadata updated for cleanup operations") + return + if not processed_file_paths: logger.info("No files to mark as processed") return - + logger.info(f"Marking {len(processed_file_paths)} files as processed...") - - # Calculate hashes for processed files - processed_files = {} + + # Log chunks_info received + if chunks_info: + logger.info(f"RECEIVED CHUNKS INFO: {len(chunks_info)} documents") + for doc_hash, info in chunks_info.items(): + logger.info( + f" {doc_hash[:12]}... -> {info.get('chunk_count', 0)} chunks" + ) + else: + logger.warning("No chunks_info provided to mark_files_processed") + + # Calculate hashes for processed files + processed_files: Dict[str, str] = {} for file_path in processed_file_paths: try: full_path = Path(file_path) if full_path.exists(): - content = full_path.read_bytes() - import hashlib - file_hash = hashlib.sha256(content).hexdigest() + # IMPORTANT: Read file exactly the same way as document_loader.py + with open(full_path, "r", encoding="utf-8") as f: + content = f.read().strip() # Match document_loader exactly + + file_hash = hashlib.sha256(content.encode("utf-8")).hexdigest() processed_files[file_hash] = file_path - logger.debug(f"Processed: {file_path} -> {file_hash[:12]}...") + logger.debug( + f"PROCESSED FILE: {file_path} -> {file_hash[:12]}..." + ) + + # Debug: Check if this file_hash exists in chunks_info + if chunks_info and file_hash in chunks_info: + chunk_count = chunks_info[file_hash].get("chunk_count", 0) + logger.info( + f"MATCHED CHUNK INFO: {file_hash[:12]}... has {chunk_count} chunks" + ) + elif chunks_info: + logger.warning( + f"NO MATCH: {file_hash[:12]}... not found in chunks_info" + ) + logger.info( + f" Available chunks_info keys: {[k[:12] + '...' for k in chunks_info.keys()]}" + ) + else: logger.warning(f"Processed file not found: {file_path}") except Exception as e: logger.warning(f"Failed to hash processed file {file_path}: {e}") - + if not processed_files: logger.warning("No valid processed files to record") return - + # Update metadata - await self.version_manager.update_processed_files_metadata(processed_files) - + await self.version_manager.update_processed_files_metadata( + processed_files, chunks_info + ) + # Commit to DVC if initialized if self.version_manager.is_dvc_initialized(): await self.version_manager.commit_dvc_changes() - - logger.info(f"Successfully marked {len(processed_files)} files as processed") - + + logger.info( + f"Successfully marked {len(processed_files)} files as processed" + ) + except Exception as e: raise DiffError(f"Failed to mark files as processed: {str(e)}", e) - + async def _handle_first_run(self) -> DiffResult: """ Handle first run setup. - + Returns: DiffResult for first run - + Raises: DiffError: If first run setup fails """ try: logger.info("Setting up DVC for first run...") - + # Initialize DVC await self.version_manager.initialize_dvc() - + # Get all files for processing current_files = self.version_manager.scan_current_files() - - logger.info(f"First run setup complete: {len(current_files)} files to process") - + + logger.info( + f"First run setup complete: {len(current_files)} files to process" + ) + return DiffResult( new_files=list(current_files.values()), total_files_scanned=len(current_files), previously_processed_count=0, - is_first_run=True + is_first_run=True, ) - + except Exception as e: raise DiffError(f"First run setup failed: {str(e)}", e) @@ -165,47 +246,61 @@ async def _handle_first_run(self) -> DiffResult: def create_diff_config() -> DiffConfig: """ Create DiffConfig from environment variables. - + Hybrid approach: - S3Ferry handles metadata operations (processed files tracking) - DVC needs direct S3 access for version control operations - + Returns: DiffConfig instance - + Raises: DiffError: If required environment variables are missing """ try: # S3Ferry Configuration - s3_ferry_url = os.getenv("S3_FERRY_URL", "http://rag-s3-ferry:3000/v1/files/copy") - + s3_ferry_url = os.getenv("S3_FERRY_URL") + if not s3_ferry_url: + raise DiffError("Missing required environment variable: S3_FERRY_URL") + # Path configurations - datasets_path = os.getenv("DATASETS_PATH", "datasets") - metadata_filename = os.getenv("METADATA_FILENAME", "processed-metadata.json") - + datasets_path = os.getenv("DATASETS_PATH") + if not datasets_path: + raise DiffError("Missing required environment variable: DATASETS_PATH") + metadata_filename = os.getenv("METADATA_FILENAME") + if not metadata_filename: + raise DiffError("Missing required environment variable: METADATA_FILENAME") + # S3 configuration (required for DVC operations) - s3_bucket_name = "rag-search" - s3_bucket_path = "resources" - s3_endpoint_url = "http://minio:9000" - s3_access_key_id = "minioadmin" - s3_secret_access_key = "minioadmin" - + s3_bucket_name = os.getenv("S3_DATA_BUCKET_NAME") + s3_bucket_path = os.getenv("S3_BUCKET_PATH") + s3_endpoint_url = os.getenv("S3_ENDPOINT_URL") + s3_access_key_id = os.getenv("S3_ACCESS_KEY_ID") + s3_secret_access_key = os.getenv("S3_SECRET_ACCESS_KEY") + # Validate required S3 credentials for DVC - if not all([s3_bucket_name, s3_endpoint_url, s3_access_key_id, s3_secret_access_key]): - missing = [var for var, val in [ - ("S3_DATA_BUCKET_NAME", s3_bucket_name), - ("S3_ENDPOINT_URL", s3_endpoint_url), - ("S3_ACCESS_KEY_ID", s3_access_key_id), - ("S3_SECRET_ACCESS_KEY", s3_secret_access_key) - ] if not val] - raise DiffError(f"Missing required S3 environment variables for DVC: {', '.join(missing)}") - + if not all( + [s3_bucket_name, s3_endpoint_url, s3_access_key_id, s3_secret_access_key] + ): + missing = [ + var + for var, val in [ + ("S3_DATA_BUCKET_NAME", s3_bucket_name), + ("S3_ENDPOINT_URL", s3_endpoint_url), + ("S3_ACCESS_KEY_ID", s3_access_key_id), + ("S3_SECRET_ACCESS_KEY", s3_secret_access_key), + ] + if not val + ] + raise DiffError( + f"Missing required S3 environment variables for DVC: {', '.join(missing)}" + ) + # Build paths # S3Ferry is already configured with bucket context, so no need for s3_bucket_path prefix metadata_s3_path = f"datasets/{metadata_filename}" dvc_remote_url = f"s3://{s3_bucket_name}/{s3_bucket_path}/datasets/dvc-cache" - + config = DiffConfig( s3_ferry_url=s3_ferry_url, metadata_s3_path=metadata_s3_path, @@ -214,16 +309,16 @@ def create_diff_config() -> DiffConfig: dvc_remote_url=dvc_remote_url, s3_endpoint_url=str(s3_endpoint_url), s3_access_key_id=str(s3_access_key_id), - s3_secret_access_key=str(s3_secret_access_key) + s3_secret_access_key=str(s3_secret_access_key), ) - + logger.info("Diff configuration loaded successfully") logger.debug(f"S3Ferry URL: {config.s3_ferry_url}") - logger.debug(f"Metadata S3 Path: {config.metadata_s3_path}") + logger.debug(f"Metadata S3 Path: {config.metadata_s3_path}") logger.debug(f"DVC Remote URL: {config.dvc_remote_url}") logger.debug(f"Datasets Path: {config.datasets_path}") - + return config - + except Exception as e: raise DiffError(f"Failed to create diff configuration: {str(e)}", e) diff --git a/src/vector_indexer/diff_identifier/diff_models.py b/src/vector_indexer/diff_identifier/diff_models.py index 754d8b3..6ec3161 100644 --- a/src/vector_indexer/diff_identifier/diff_models.py +++ b/src/vector_indexer/diff_identifier/diff_models.py @@ -1,62 +1,101 @@ """Data models for diff identifier.""" -from typing import Dict, List, Optional +from typing import Dict, List, Optional, Any from pydantic import BaseModel, Field class ProcessedFileInfo(BaseModel): """Information about a processed file.""" - + content_hash: str = Field(..., description="SHA256 hash of file content") original_path: str = Field(..., description="Original path in datasets folder") file_size: int = Field(..., description="File size in bytes") processed_at: str = Field(..., description="ISO timestamp when file was processed") + chunk_count: int = Field( + default=0, description="Number of chunks created from this file" + ) class DiffResult(BaseModel): """Result of diff identification process.""" - + new_files: List[str] = Field(..., description="List of new file paths to process") - total_files_scanned: int = Field(..., description="Total files found in current scan") - previously_processed_count: int = Field(..., description="Number of previously processed files") - is_first_run: bool = Field(..., description="Whether this is the first time running") - - + modified_files: List[str] = Field( + default_factory=list, description="List of modified file paths to reprocess" + ) + deleted_files: List[str] = Field( + default_factory=list, + description="List of deleted file paths (chunks to remove)", + ) + unchanged_files: List[str] = Field( + default_factory=list, + description="List of unchanged file paths (skip processing)", + ) + + total_files_scanned: int = Field( + ..., description="Total files found in current scan" + ) + previously_processed_count: int = Field( + ..., description="Number of previously processed files" + ) + is_first_run: bool = Field( + ..., description="Whether this is the first time running" + ) + + # Cleanup metadata + chunks_to_delete: Dict[str, str] = Field( + default_factory=dict, + description="Map of document_hash to original_path for deletion", + ) + estimated_cleanup_count: int = Field( + default=0, description="Total estimated chunks to be removed" + ) + + class VersionState(BaseModel): """Version state information.""" - + last_updated: str = Field(..., description="ISO timestamp of last update") - processed_files: Dict[str, ProcessedFileInfo] = Field(..., description="Map of hash to file info") + processed_files: Dict[str, ProcessedFileInfo] = Field( + ..., description="Map of hash to file info" + ) total_processed: int = Field(..., description="Total number of processed files") - + processing_stats: Dict[str, Any] = Field( + default_factory=dict, description="Statistics from last processing run" + ) + class DiffConfig(BaseModel): """Configuration for diff identifier.""" - + # S3Ferry Configuration (handles metadata operations) s3_ferry_url: str = Field(..., description="S3Ferry service URL") - - # Metadata Configuration + + # Metadata Configuration metadata_s3_path: str = Field(..., description="Full S3 path for metadata file") - + # DVC Configuration (requires direct S3 access for version control) datasets_path: str = Field(..., description="Path to datasets folder") - metadata_filename: str = Field(default="processed-metadata.json", description="Metadata file name") - + metadata_filename: str = Field( + default="processed-metadata.json", description="Metadata file name" + ) + # DVC S3 Remote Configuration (minimal - only for DVC operations) dvc_remote_url: str = Field(..., description="DVC S3 remote URL") s3_endpoint_url: str = Field(..., description="S3 endpoint URL for DVC") - s3_access_key_id: str = Field(..., description="S3 access key for DVC") + s3_access_key_id: str = Field(..., description="S3 access key for DVC") s3_secret_access_key: str = Field(..., description="S3 secret key for DVC") - + # Retry Configuration max_retries: int = Field(default=3, description="Maximum retry attempts") - max_delay_seconds: int = Field(default=8, description="Maximum delay between retries") + max_delay_seconds: int = Field( + default=8, description="Maximum delay between retries" + ) class DiffError(Exception): """Custom exception for diff identification errors.""" - + def __init__(self, message: str, cause: Optional[Exception] = None): self.message = message self.cause = cause diff --git a/src/vector_indexer/diff_identifier/s3_ferry_client.py b/src/vector_indexer/diff_identifier/s3_ferry_client.py index c887e68..28481f5 100644 --- a/src/vector_indexer/diff_identifier/s3_ferry_client.py +++ b/src/vector_indexer/diff_identifier/s3_ferry_client.py @@ -13,242 +13,291 @@ class S3Ferry: """Client for interacting with S3Ferry service.""" - + def __init__(self, url: str): self.url = url - - def transfer_file(self, destinationFilePath: str, destinationStorageType: str, sourceFilePath: str, sourceStorageType: str) -> requests.Response: # noqa: N803 + + def transfer_file( + self, + destinationFilePath: str, + destinationStorageType: str, + sourceFilePath: str, + sourceStorageType: str, + ) -> requests.Response: """ Transfer file using S3Ferry service. - + Args: destinationFilePath: Path where file should be stored destinationStorageType: "S3" or "FS" (filesystem) - sourceFilePath: Path of source file + sourceFilePath: Path of source file sourceStorageType: "S3" or "FS" (filesystem) - + Returns: requests.Response: Response from S3Ferry service """ - payload = GET_S3_FERRY_PAYLOAD(destinationFilePath, destinationStorageType, sourceFilePath, sourceStorageType) - - # Debug logging for S3Ferry request - logger.debug("S3Ferry Request Details:") - logger.debug(f" URL: {self.url}") - logger.debug(" Method: POST") - logger.debug(" Headers: Content-Type: application/json") - logger.debug(f" Payload: {payload}") - + payload = GET_S3_FERRY_PAYLOAD( + destinationFilePath, + destinationStorageType, + sourceFilePath, + sourceStorageType, + ) + response = requests.post(self.url, json=payload) - - # Debug logging for S3Ferry response - logger.debug("S3Ferry Response Details:") - logger.debug(f" Status Code: {response.status_code}") - logger.debug(f" Response Headers: {dict(response.headers)}") - logger.debug(f" Response Body: {response.text}") - + return response class S3FerryClient: """High-level client for S3Ferry operations with metadata handling. - + S3Ferry service handles all S3 configuration internally. This client only needs to know the S3Ferry URL and metadata paths. """ - + def __init__(self, config: DiffConfig): self.config = config self.s3_ferry = S3Ferry(config.s3_ferry_url) - + async def __aenter__(self): """Async context manager entry.""" return self - - async def __aexit__(self, exc_type: Optional[type], exc_val: Optional[BaseException], exc_tb: Optional[object]) -> None: + + async def __aexit__( + self, + exc_type: Optional[type], + exc_val: Optional[BaseException], + exc_tb: Optional[object], + ) -> None: """Async context manager exit.""" pass - + async def upload_metadata(self, metadata: Dict[str, Any]) -> bool: """ Upload metadata to S3 via S3Ferry. - + Args: metadata: Dictionary containing metadata to upload - + Returns: True if successful, False otherwise - + Raises: DiffError: If upload fails """ try: # Create temporary file with metadata (run in thread pool) - temp_file_path = await asyncio.to_thread(self._create_temp_metadata_file, metadata) - + temp_file_path = await asyncio.to_thread( + self._create_temp_metadata_file, metadata + ) + try: # Transfer from FS to S3 using S3Ferry (run in thread pool) + # Convert absolute path to S3Ferry-relative path + s3ferry_source_path = self._convert_to_s3ferry_path(temp_file_path) + response = await asyncio.to_thread( self._retry_with_backoff, lambda: self.s3_ferry.transfer_file( destinationFilePath=self.config.metadata_s3_path, destinationStorageType="S3", - sourceFilePath=temp_file_path, - sourceStorageType="FS" - ) + sourceFilePath=s3ferry_source_path, + sourceStorageType="FS", + ), ) - - if response.status_code == 200: - logger.info(f"Metadata uploaded successfully to {self.config.metadata_s3_path}") + + if response.status_code in [ + 200, + 201, + ]: # Accept both 200 OK and 201 Created + logger.info( + f"Metadata uploaded successfully to {self.config.metadata_s3_path} (status: {response.status_code})" + ) return True else: - logger.error(f"S3Ferry upload failed: {response.status_code} - {response.text}") + logger.error( + f"S3Ferry upload failed: {response.status_code} - {response.text}" + ) return False - + finally: # Clean up temporary file (run in thread pool) - # await asyncio.to_thread(self._cleanup_temp_file, temp_file_path) # Disabled for debugging - pass - + await asyncio.to_thread(self._cleanup_temp_file, temp_file_path) + except Exception as e: raise DiffError(f"Failed to upload metadata: {str(e)}", e) - + async def download_metadata(self) -> Optional[Dict[str, Any]]: """ Download metadata from S3 via S3Ferry. - + Returns: Dict containing metadata or None if not found - + Raises: DiffError: If download fails (except for file not found) """ try: # Create temporary file for download (run in thread pool) temp_file_path = await asyncio.to_thread(self._create_temp_file) - + try: # Transfer from S3 to FS using S3Ferry (run in thread pool) + # Convert absolute path to S3Ferry-relative path + s3ferry_dest_path = self._convert_to_s3ferry_path(temp_file_path) + response = await asyncio.to_thread( self._retry_with_backoff, lambda: self.s3_ferry.transfer_file( - destinationFilePath=temp_file_path, + destinationFilePath=s3ferry_dest_path, destinationStorageType="FS", sourceFilePath=self.config.metadata_s3_path, - sourceStorageType="S3" - ) + sourceStorageType="S3", + ), ) - - if response.status_code == 200: + + if response.status_code in [ + 200, + 201, + ]: # Accept both 200 OK and 201 Created # Read metadata from downloaded file (run in thread pool) - metadata = await asyncio.to_thread(self._read_metadata_from_file, temp_file_path) - logger.info(f"Metadata downloaded successfully from {self.config.metadata_s3_path}") + metadata = await asyncio.to_thread( + self._read_metadata_from_file, temp_file_path + ) + logger.info( + f"Metadata downloaded successfully from {self.config.metadata_s3_path} (status: {response.status_code})" + ) return metadata elif response.status_code == 404: - logger.info("No previous metadata found - this appears to be the first run") + logger.info( + "No previous metadata found - this appears to be the first run" + ) return None else: - logger.error(f"S3Ferry download failed: {response.status_code} - {response.text}") + logger.error( + f"S3Ferry download failed: {response.status_code} - {response.text}" + ) return None - + finally: # Clean up temporary file (run in thread pool) - # await asyncio.to_thread(self._cleanup_temp_file, temp_file_path) # Disabled for debugging - pass - + await asyncio.to_thread(self._cleanup_temp_file, temp_file_path) + except json.JSONDecodeError as e: raise DiffError(f"Failed to parse downloaded metadata JSON: {str(e)}", e) except Exception as e: # Don't raise for file not found - it's expected on first run logger.warning(f"Failed to download metadata (may be first run): {str(e)}") return None - + def _create_temp_metadata_file(self, metadata: Dict[str, Any]) -> str: """Create a temporary file with metadata content in shared folder.""" import os import uuid - + # Create temp file in shared folder accessible by both containers shared_dir = "/app/shared" os.makedirs(shared_dir, exist_ok=True) - + temp_filename = f"temp_metadata_{uuid.uuid4().hex[:8]}.json" temp_file_path = os.path.join(shared_dir, temp_filename) - - with open(temp_file_path, 'w') as temp_file: + + with open(temp_file_path, "w") as temp_file: json.dump(metadata, temp_file, indent=2) - + + # Set broad permissions so S3Ferry can read the file + os.chmod(temp_file_path, 0o666) # rw-rw-rw- + return temp_file_path - + def _create_temp_file(self) -> str: """Create an empty temporary file in shared folder.""" import os import uuid - + # Create temp file in shared folder accessible by both containers shared_dir = "/app/shared" os.makedirs(shared_dir, exist_ok=True) - + temp_filename = f"temp_download_{uuid.uuid4().hex[:8]}.json" temp_file_path = os.path.join(shared_dir, temp_filename) - + # Create empty file - with open(temp_file_path, 'w'): + with open(temp_file_path, "w"): pass # Create empty file - + + # Set broad permissions so S3Ferry can write to the file + os.chmod(temp_file_path, 0o666) # rw-rw-rw- + return temp_file_path - + def _read_metadata_from_file(self, file_path: str) -> Dict[str, Any]: """Read metadata from a file.""" - with open(file_path, 'r') as f: + with open(file_path, "r") as f: return json.load(f) - + + def _convert_to_s3ferry_path(self, absolute_path: str) -> str: + """Convert absolute path to S3Ferry-relative path. + + S3Ferry expects paths relative to /app/ working directory. + Converts: /app/shared/filename.json -> shared/filename.json + """ + if absolute_path.startswith("/app/"): + return absolute_path[5:] # Remove '/app/' prefix + return absolute_path + def _cleanup_temp_file(self, file_path: str) -> None: """Clean up a temporary file.""" import os + try: os.unlink(file_path) except Exception as cleanup_error: logger.warning(f"Failed to cleanup temp file {file_path}: {cleanup_error}") - + def _retry_with_backoff(self, operation: Any) -> requests.Response: """ Retry an operation with exponential backoff. - + Args: operation: Operation to retry - + Returns: Response from the operation - + Raises: DiffError: If all retries fail """ last_exception = None - + for attempt in range(self.config.max_retries): try: response = operation() - + # Consider non-2xx responses as failures for retry purposes if response.status_code >= 400: if attempt == self.config.max_retries - 1: return response # Last attempt - return the error response - - delay = min(1 * (2 ** attempt), self.config.max_delay_seconds) + + delay = min(1 * (2**attempt), self.config.max_delay_seconds) time.sleep(delay) continue - + return response - + except Exception as e: last_exception = e - + if attempt == self.config.max_retries - 1: - raise DiffError(f"Operation failed after {self.config.max_retries} attempts: {str(e)}", e) - - delay = min(1 * (2 ** attempt), self.config.max_delay_seconds) + raise DiffError( + f"Operation failed after {self.config.max_retries} attempts: {str(e)}", + e, + ) + + delay = min(1 * (2**attempt), self.config.max_delay_seconds) time.sleep(delay) - - # Should not reach here, but just in case - raise DiffError(f"Operation failed after {self.config.max_retries} attempts: {str(last_exception)}", last_exception) \ No newline at end of file + + raise DiffError( + f"Operation failed after {self.config.max_retries} attempts: {str(last_exception)}", + last_exception, + ) diff --git a/src/vector_indexer/diff_identifier/version_manager.py b/src/vector_indexer/diff_identifier/version_manager.py index f0a5a1f..8ef23db 100644 --- a/src/vector_indexer/diff_identifier/version_manager.py +++ b/src/vector_indexer/diff_identifier/version_manager.py @@ -2,307 +2,521 @@ import asyncio import hashlib -import os from datetime import datetime from pathlib import Path -from typing import Dict, List, Optional, Set +from typing import Dict, List, Optional, Set, Any from loguru import logger -from diff_identifier.diff_models import DiffConfig, DiffError, ProcessedFileInfo, VersionState +from diff_identifier.diff_models import ( + DiffConfig, + DiffError, + ProcessedFileInfo, + VersionState, +) from diff_identifier.s3_ferry_client import S3FerryClient class VersionManager: """Manages DVC operations and version tracking.""" - + def __init__(self, config: DiffConfig): self.config = config self.datasets_path = Path(config.datasets_path) - + async def __aenter__(self): """Async context manager entry.""" return self - - async def __aexit__(self, exc_type: Optional[type], exc_val: Optional[BaseException], exc_tb: Optional[object]) -> None: + + async def __aexit__( + self, + exc_type: Optional[type], + exc_val: Optional[BaseException], + exc_tb: Optional[object], + ) -> None: """Async context manager exit.""" pass - + def is_dvc_initialized(self) -> bool: """Check if DVC is initialized in datasets directory.""" dvc_dir = self.datasets_path / ".dvc" return dvc_dir.exists() and dvc_dir.is_dir() - + async def initialize_dvc(self) -> None: """ Initialize DVC in datasets directory with S3 remote. - + Raises: DiffError: If DVC initialization fails """ try: logger.info("Initializing DVC in datasets directory...") - + # Ensure datasets directory exists self.datasets_path.mkdir(parents=True, exist_ok=True) - - # Change to datasets directory for DVC operations - original_cwd = os.getcwd() - os.chdir(str(self.datasets_path)) - - try: - # Initialize DVC (no SCM integration) - await self._run_dvc_command(["dvc", "init", "--no-scm"]) - - # Add S3 remote - remote_url = self.config.dvc_remote_url - logger.info(f"Adding DVC remote: {remote_url}") - await self._run_dvc_command(["dvc", "remote", "add", "-d", "rag-storage", remote_url]) - - # Configure S3 credentials - await self._run_dvc_command([ - "dvc", "remote", "modify", "rag-storage", "endpointurl", self.config.s3_endpoint_url - ]) - await self._run_dvc_command([ - "dvc", "remote", "modify", "rag-storage", "access_key_id", self.config.s3_access_key_id - ]) - await self._run_dvc_command([ - "dvc", "remote", "modify", "rag-storage", "secret_access_key", self.config.s3_secret_access_key - ]) - - logger.info("DVC initialized successfully") - - finally: - os.chdir(original_cwd) - + + # Initialize DVC (no SCM integration) + await self._run_dvc_command(["dvc", "init", "--no-scm"]) + + # Add S3 remote + remote_url = self.config.dvc_remote_url + logger.info(f"Adding DVC remote: {remote_url}") + await self._run_dvc_command( + ["dvc", "remote", "add", "-d", "rag-storage", remote_url] + ) + + # Configure S3 credentials + await self._run_dvc_command( + [ + "dvc", + "remote", + "modify", + "rag-storage", + "endpointurl", + self.config.s3_endpoint_url, + ] + ) + await self._run_dvc_command( + [ + "dvc", + "remote", + "modify", + "rag-storage", + "access_key_id", + self.config.s3_access_key_id, + ] + ) + await self._run_dvc_command( + [ + "dvc", + "remote", + "modify", + "rag-storage", + "secret_access_key", + self.config.s3_secret_access_key, + ] + ) + + logger.info("DVC initialized successfully") + except Exception as e: raise DiffError(f"Failed to initialize DVC: {str(e)}", e) - + async def get_processed_files_metadata(self) -> Optional[VersionState]: """ Download and parse processed files metadata from S3. - + Returns: VersionState if metadata exists, None if first run - + Raises: DiffError: If metadata exists but cannot be parsed """ try: async with S3FerryClient(self.config) as s3_client: metadata_dict = await s3_client.download_metadata() - + if metadata_dict is None: return None - + # Parse metadata into VersionState return VersionState( last_updated=metadata_dict["last_updated"], processed_files={ - file_hash: ProcessedFileInfo(**file_info) - for file_hash, file_info in metadata_dict["processed_files"].items() + file_hash: ProcessedFileInfo(**file_info) + for file_hash, file_info in metadata_dict[ + "processed_files" + ].items() }, - total_processed=metadata_dict.get("total_processed", len(metadata_dict["processed_files"])) + total_processed=metadata_dict.get( + "total_processed", len(metadata_dict["processed_files"]) + ), ) - + except Exception as e: raise DiffError(f"Failed to get processed files metadata: {str(e)}", e) - - async def update_processed_files_metadata(self, processed_files: Dict[str, str]) -> None: + + async def update_processed_files_metadata( + self, + processed_files: Dict[str, str], + chunks_info: Optional[Dict[str, Dict[str, Any]]] = None, + ) -> None: """ Update processed files metadata and upload to S3. - + Args: processed_files: Dict mapping file hash to file path for newly processed files - + chunks_info: Optional dict mapping file_hash to {"chunk_count": int} + Raises: DiffError: If metadata update fails """ try: # Get existing metadata or create new existing_state = await self.get_processed_files_metadata() - - if existing_state: - processed_files_dict = existing_state.processed_files - else: - processed_files_dict = {} - - # Add new processed files + processed_files_dict: Dict[str, ProcessedFileInfo] = ( + existing_state.processed_files.copy() if existing_state else {} + ) + processing_stats: Dict[str, Any] = ( + existing_state.processing_stats.copy() + if existing_state and existing_state.processing_stats + else {} + ) + + # Handle cleanup-only operation + if not processed_files and existing_state: + current_files = self.scan_current_files() + current_hashes: Set[str] = set(current_files.keys()) + deleted_count = sum( + 1 for h in processed_files_dict if h not in current_hashes + ) + processed_files_dict = { + h: info + for h, info in processed_files_dict.items() + if h in current_hashes + } + if deleted_count > 0: + logger.info(f"Removed {deleted_count} deleted files from metadata") + processing_stats["last_run_deleted_files"] = deleted_count + + # Build path-to-hash map for deduplication + path_to_hash: Dict[str, str] = { + info.original_path: h for h, info in processed_files_dict.items() + } current_time = datetime.now().isoformat() + + # Add/update new and modified files for file_hash, file_path in processed_files.items(): file_stats = Path(file_path).stat() + + # Remove old entry if file was modified + if file_path in path_to_hash and path_to_hash[file_path] != file_hash: + old_hash = path_to_hash[file_path] + del processed_files_dict[old_hash] + logger.info( + f"DEDUPLICATING: {file_path} (old: {old_hash[:12]}..., new: {file_hash[:12]}...)" + ) + + # Get chunk count + chunk_count = ( + chunks_info.get(file_hash, {}).get("chunk_count", 0) + if chunks_info + else 0 + ) + if chunks_info and file_hash in chunks_info: + logger.info(f"Found {chunk_count} chunks for {file_hash[:12]}...") + + # Add/update file entry processed_files_dict[file_hash] = ProcessedFileInfo( content_hash=file_hash, original_path=file_path, file_size=file_stats.st_size, - processed_at=current_time + processed_at=current_time, + chunk_count=chunk_count, ) - - # Create new version state + path_to_hash[file_path] = file_hash + + # Update stats and create new state + if processed_files: + processing_stats["last_run_new_files"] = len(processed_files) + processing_stats["last_run_timestamp"] = current_time + new_state = VersionState( last_updated=current_time, processed_files=processed_files_dict, - total_processed=len(processed_files_dict) + total_processed=len(processed_files_dict), + processing_stats=processing_stats, ) - - # Convert to dict for JSON serialization + + # Upload to S3 metadata_dict = { "last_updated": new_state.last_updated, "total_processed": new_state.total_processed, + "processing_stats": new_state.processing_stats, "processed_files": { - file_hash: { - "content_hash": file_info.content_hash, - "original_path": file_info.original_path, - "file_size": file_info.file_size, - "processed_at": file_info.processed_at + fh: { + "content_hash": fi.content_hash, + "original_path": fi.original_path, + "file_size": fi.file_size, + "processed_at": fi.processed_at, + "chunk_count": fi.chunk_count, } - for file_hash, file_info in new_state.processed_files.items() - } + for fh, fi in new_state.processed_files.items() + }, } - - # Upload to S3 + async with S3FerryClient(self.config) as s3_client: - success = await s3_client.upload_metadata(metadata_dict) - - if not success: + if not await s3_client.upload_metadata(metadata_dict): raise DiffError("Failed to upload metadata to S3") - - logger.info(f"Updated processed files metadata: {len(processed_files)} new files") - + + logger.info( + f"Updated processed files metadata: {len(processed_files)} new files" + ) + + except DiffError: + raise except Exception as e: raise DiffError(f"Failed to update processed files metadata: {str(e)}", e) - + def scan_current_files(self) -> Dict[str, str]: """ Scan datasets directory and calculate file hashes. - + Returns: Dict mapping file hash to file path - + Raises: DiffError: If file scanning fails """ try: - files_map = {} - + files_map: Dict[str, str] = {} + if not self.datasets_path.exists(): logger.warning(f"Datasets path does not exist: {self.datasets_path}") return files_map - + # Find all cleaned.txt files cleaned_files = list(self.datasets_path.glob("**/cleaned.txt")) logger.info(f"Found {len(cleaned_files)} files to scan") - + for cleaned_file in cleaned_files: try: - # Calculate file hash - content = cleaned_file.read_bytes() - file_hash = hashlib.sha256(content).hexdigest() - + # Calculate file hash consistently with document_loader.py + # Use text mode and encode to match document processing pipeline + with open(cleaned_file, "r", encoding="utf-8") as f: + content = f.read().strip() + file_hash = hashlib.sha256(content.encode("utf-8")).hexdigest() + # Store relative path from datasets directory - relative_path = str(cleaned_file.relative_to(self.datasets_path.parent)) + relative_path = str( + cleaned_file.relative_to(self.datasets_path.parent) + ) files_map[file_hash] = relative_path - - logger.debug(f"Scanned file: {relative_path} -> {file_hash[:12]}...") - + + logger.debug( + f"Scanned file: {relative_path} -> {file_hash[:12]}..." + ) + except Exception as e: logger.warning(f"Failed to process file {cleaned_file}: {e}") continue - + logger.info(f"Successfully scanned {len(files_map)} files") return files_map - + except Exception as e: raise DiffError(f"Failed to scan current files: {str(e)}", e) - - def identify_changed_files(self, current_files: Dict[str, str], processed_state: Optional[VersionState]) -> Set[str]: + + def identify_comprehensive_changes( + self, current_files: Dict[str, str], processed_state: Optional[VersionState] + ) -> Dict[str, Any]: """ - Identify files that have changed or are new. - + Identify all types of file changes: new, modified, deleted, unchanged. + Args: current_files: Current files map (hash -> path) processed_state: Previously processed state - + Returns: - Set of file paths that need processing + Dict with lists of different file change types and cleanup information """ if processed_state is None: # First run - all files are new - logger.info("First run detected - all files need processing") - return set(current_files.values()) - - current_hashes = set(current_files.keys()) - processed_hashes = set(processed_state.processed_files.keys()) - - # Find new files (hashes not previously processed) - new_hashes = current_hashes - processed_hashes - new_file_paths = {current_files[file_hash] for file_hash in new_hashes} - - logger.info(f"Found {len(new_file_paths)} new/changed files out of {len(current_files)} total") - - return new_file_paths - + logger.info("First run detected - all files are new") + return { + "new_files": list(current_files.values()), + "modified_files": [], + "deleted_files": [], + "unchanged_files": [], + "chunks_to_delete": {}, + "estimated_cleanup_count": 0, + } + + # Initialize result lists + new_files: List[str] = [] + modified_files: List[str] = [] + deleted_files: List[str] = [] + unchanged_files: List[str] = [] + chunks_to_delete: Dict[str, str] = {} + total_chunks_to_delete = 0 + + # Create lookup maps for efficient searching + current_hash_to_path: Dict[str, str] = current_files # hash -> path + processed_hash_to_info: Dict[str, ProcessedFileInfo] = ( + processed_state.processed_files + ) # hash -> ProcessedFileInfo + processed_path_to_hash: Dict[str, str] = { + info.original_path: hash + for hash, info in processed_state.processed_files.items() + } # path -> hash + + # 1. Find deleted files (in processed_state but not in current scan) + logger.debug("Identifying deleted files...") + for old_hash, old_info in processed_hash_to_info.items(): + if old_hash not in current_hash_to_path: + deleted_files.append(old_info.original_path) + # Use content hash (old_hash) as document_hash for cleanup - now they match! + chunks_to_delete[old_hash] = old_info.original_path + # Estimate chunks to delete (use chunk_count if available, otherwise assume some exist) + estimated_chunks = max( + old_info.chunk_count, 1 + ) # Assume at least 1 chunk if processed before + total_chunks_to_delete += estimated_chunks + logger.debug( + f"Deleted file: {old_info.original_path} (content_hash/document_hash: {old_hash[:12]}..., estimated chunks: {estimated_chunks})" + ) + + # 2. Find new, modified, and unchanged files + logger.debug("Identifying new, modified, and unchanged files...") + for current_hash, current_path in current_hash_to_path.items(): + if current_hash in processed_hash_to_info: + # File exists with same content hash - unchanged + unchanged_files.append(current_path) + logger.debug(f"Unchanged file: {current_path}") + else: + # Check if this is a modified file (same path, different hash) + if current_path in processed_path_to_hash: + old_hash = processed_path_to_hash[current_path] + old_info = processed_hash_to_info[old_hash] + modified_files.append(current_path) + # Mark old chunks for deletion - use content hash (old_hash) as document_hash + chunks_to_delete[old_hash] = old_info.original_path + total_chunks_to_delete += max(old_info.chunk_count, 1) + logger.debug( + f"Modified file: {current_path} (old_content_hash/document_hash: {old_hash[:12]}..., new_content_hash: {current_hash[:12]}..., estimated old chunks: {max(old_info.chunk_count, 1)})" + ) + else: + # Completely new file + new_files.append(current_path) + logger.debug(f"New file: {current_path}") + + # Log summary + logger.info("COMPREHENSIVE DIFF ANALYSIS COMPLETE:") + logger.info(f"New files: {len(new_files)}") + logger.info(f"Modified files: {len(modified_files)}") + logger.info(f"Deleted files: {len(deleted_files)}") + logger.info(f"Unchanged files: {len(unchanged_files)}") + logger.info(f"Total chunks to cleanup: {total_chunks_to_delete}") + + return { + "new_files": new_files, + "modified_files": modified_files, + "deleted_files": deleted_files, + "unchanged_files": unchanged_files, + "chunks_to_delete": chunks_to_delete, + "estimated_cleanup_count": total_chunks_to_delete, + } + + def identify_changed_files( + self, current_files: Dict[str, str], processed_state: Optional[VersionState] + ) -> Set[str]: + """ + Legacy method - kept for backward compatibility. + Use identify_comprehensive_changes for new functionality. + + Args: + current_files: Current files map (hash -> path) + processed_state: Previously processed state + + Returns: + Set of file paths that need processing + """ + changes = self.identify_comprehensive_changes(current_files, processed_state) + # Return new + modified files (files that need processing) + all_changed: List[str] = changes["new_files"] + changes["modified_files"] + return set(all_changed) + async def commit_dvc_changes(self) -> None: """ Commit current datasets state to DVC and push to remote. - + Raises: DiffError: If DVC operations fail """ try: - original_cwd = os.getcwd() - os.chdir(str(self.datasets_path)) - - try: - # Add all files to DVC tracking - logger.info("Adding files to DVC tracking...") - await self._run_dvc_command(["dvc", "add", "."]) - - # Push to remote storage - logger.info("Pushing to DVC remote storage...") - await self._run_dvc_command(["dvc", "push"]) - - logger.info("DVC commit completed successfully") - - finally: - os.chdir(original_cwd) - + # Add all cleaned.txt files to DVC tracking instead of using "." + logger.info("Adding files to DVC tracking...") + + # Find all cleaned.txt files relative to datasets directory + cleaned_files = list(self.datasets_path.glob("**/cleaned.txt")) + if cleaned_files: + # Add each file individually using relative paths + for cleaned_file in cleaned_files: + try: + # Get relative path from datasets directory + relative_path = cleaned_file.relative_to(self.datasets_path) + logger.debug(f"Adding file to DVC: {relative_path}") + await self._run_dvc_command(["dvc", "add", str(relative_path)]) + except Exception as e: + logger.warning(f"Failed to add {cleaned_file} to DVC: {e}") + # Continue with other files + continue + + logger.info(f"Added {len(cleaned_files)} files to DVC tracking") + else: + logger.warning("No cleaned.txt files found to add to DVC") + + # Push to remote storage + logger.info("Pushing to DVC remote storage...") + await self._run_dvc_command(["dvc", "push"]) + + logger.info("DVC commit completed successfully") + except Exception as e: raise DiffError(f"Failed to commit DVC changes: {str(e)}", e) - + async def _run_dvc_command(self, command: List[str]) -> str: """ Run DVC command asynchronously. - + Args: command: DVC command as list of strings - + Returns: Command output - + Raises: DiffError: If command fails """ try: logger.debug(f"Running DVC command: {' '.join(command)}") - + + # Ensure DVC commands run from the datasets directory + cwd = str(self.datasets_path.resolve()) + logger.debug(f"Running DVC command in directory: {cwd}") + logger.debug(f"datasets_path: {self.datasets_path}") + logger.debug(f"datasets_path.resolve(): {self.datasets_path.resolve()}") + logger.debug(f"datasets_path exists: {self.datasets_path.exists()}") + process = await asyncio.create_subprocess_exec( *command, stdout=asyncio.subprocess.PIPE, - stderr=asyncio.subprocess.PIPE + stderr=asyncio.subprocess.PIPE, + cwd=cwd, ) - + stdout, stderr = await process.communicate() - + if process.returncode != 0: error_msg = stderr.decode().strip() if stderr else "Unknown error" - raise DiffError(f"DVC command failed: {' '.join(command)} - {error_msg}") - + logger.error( + f"DVC command failed with return code {process.returncode}" + ) + logger.error(f"Command: {' '.join(command)}") + logger.error(f"Working directory: {cwd}") + logger.error(f"Stdout: {stdout.decode().strip()}") + logger.error(f"Stderr: {error_msg}") + raise DiffError( + f"DVC command failed: {' '.join(command)} - {error_msg}" + ) + output = stdout.decode().strip() logger.debug(f"DVC command output: {output}") - + return output - + except Exception as e: if isinstance(e, DiffError): raise - raise DiffError(f"Failed to run DVC command {' '.join(command)}: {str(e)}", e) + raise DiffError( + f"Failed to run DVC command {' '.join(command)}: {str(e)}", e + ) diff --git a/src/vector_indexer/document_loader.py b/src/vector_indexer/document_loader.py index 39ed7ba..a77142b 100644 --- a/src/vector_indexer/document_loader.py +++ b/src/vector_indexer/document_loader.py @@ -1,5 +1,6 @@ """Document loader for scanning and loading documents from datasets folder.""" +import hashlib import json from pathlib import Path from typing import List @@ -69,25 +70,39 @@ def discover_all_documents(self) -> List[DocumentInfo]: else: collection_name = collection_dir.name - document_hash = hash_dir.name + # This ensures document_hash is always the SHA-256 of file content + try: + with open(cleaned_file, "r", encoding="utf-8") as f: + content = f.read().strip() + + # Calculate SHA-256 hash of content (same method used everywhere) + content_hash = hashlib.sha256(content.encode("utf-8")).hexdigest() + + logger.debug( + f"Calculated content hash for {cleaned_file.name}: {content_hash[:12]}..." + ) + + except Exception as e: + logger.warning(f"Failed to calculate hash for {cleaned_file}: {e}") + continue # Check metadata file exists metadata_file = hash_dir / self.config.metadata_file if metadata_file.exists(): documents.append( DocumentInfo( - document_hash=document_hash, + document_hash=content_hash, # Use content hash consistently cleaned_txt_path=str(cleaned_file), source_meta_path=str(metadata_file), dataset_collection=collection_name, ) ) logger.debug( - f"Found document: {document_hash} in collection: {collection_name}" + f"Found document: {content_hash[:12]}... in collection: {collection_name}" ) else: logger.warning( - f"Skipping document {document_hash}: missing {self.config.metadata_file}" + f"Skipping document in {hash_dir.name}: missing {self.config.metadata_file}" ) logger.info(f"Discovered {len(documents)} documents for processing") @@ -98,7 +113,7 @@ def load_document(self, doc_info: DocumentInfo) -> ProcessingDocument: Load document content and metadata. Args: - doc_info: Document information + doc_info: Document information with content hash as document_hash Returns: ProcessingDocument with content and metadata @@ -122,24 +137,28 @@ def load_document(self, doc_info: DocumentInfo) -> ProcessingDocument: metadata["dataset_collection"] = doc_info.dataset_collection logger.debug( - f"Loaded document {doc_info.document_hash}: {len(content)} characters" + f"Loaded document {doc_info.document_hash[:12]}...: {len(content)} characters" ) + # It's already the content hash (calculated in discover_all_documents) + # No need to recalculate here - keeps the hash consistent return ProcessingDocument( - content=content, metadata=metadata, document_hash=doc_info.document_hash + content=content, + metadata=metadata, + document_hash=doc_info.document_hash, # Already the content hash ) except Exception as e: - error_msg = f"Failed to load document {doc_info.document_hash}: {e}" + error_msg = f"Failed to load document {doc_info.document_hash[:12]}...: {e}" logger.error(error_msg) raise DocumentLoadError(error_msg) from e def get_document_by_hash(self, document_hash: str) -> DocumentInfo: """ - Find document by hash. + Find document by content hash. Args: - document_hash: Document hash to find + document_hash: Document content hash to find Returns: DocumentInfo object @@ -153,7 +172,7 @@ def get_document_by_hash(self, document_hash: str) -> DocumentInfo: if doc_info.document_hash == document_hash: return doc_info - raise ValueError(f"Document not found: {document_hash}") + raise ValueError(f"Document not found with hash: {document_hash[:12]}...") def validate_document_structure(self, doc_info: DocumentInfo) -> bool: """ @@ -168,11 +187,15 @@ def validate_document_structure(self, doc_info: DocumentInfo) -> bool: try: # Check files exist if not Path(doc_info.cleaned_txt_path).exists(): - logger.error(f"Missing cleaned.txt for {doc_info.document_hash}") + logger.error( + f"Missing cleaned.txt for document {doc_info.document_hash[:12]}..." + ) return False if not Path(doc_info.source_meta_path).exists(): - logger.error(f"Missing source.meta.json for {doc_info.document_hash}") + logger.error( + f"Missing source.meta.json for document {doc_info.document_hash[:12]}..." + ) return False # Try to load content with configurable validation @@ -182,7 +205,8 @@ def validate_document_structure(self, doc_info: DocumentInfo) -> bool: content = f.read().strip() if len(content) < DocumentConstants.MIN_CONTENT_LENGTH: logger.error( - f"Content too short for {doc_info.document_hash}: {len(content)} chars (min: {DocumentConstants.MIN_CONTENT_LENGTH})" + f"Content too short for document {doc_info.document_hash[:12]}...: " + f"{len(content)} chars (min: {DocumentConstants.MIN_CONTENT_LENGTH})" ) return False @@ -191,7 +215,7 @@ def validate_document_structure(self, doc_info: DocumentInfo) -> bool: metadata = json.load(f) if not isinstance(metadata, dict): logger.error( - f"Invalid metadata format for {doc_info.document_hash}" + f"Invalid metadata format for document {doc_info.document_hash[:12]}..." ) return False @@ -199,6 +223,6 @@ def validate_document_structure(self, doc_info: DocumentInfo) -> bool: except Exception as e: logger.error( - f"Document validation failed for {doc_info.document_hash}: {e}" + f"Document validation failed for {doc_info.document_hash[:12]}...: {e}" ) return False diff --git a/src/vector_indexer/main_indexer.py b/src/vector_indexer/main_indexer.py index 572fcb3..02e722e 100644 --- a/src/vector_indexer/main_indexer.py +++ b/src/vector_indexer/main_indexer.py @@ -1,499 +1,3 @@ -# """Main vector indexer script for processing documents with contextual retrieval.""" - -# import argparse -# import asyncio -# import shutil -# import sys -# from pathlib import Path -# from datetime import datetime -# from typing import List, Optional -# from loguru import logger - -# from loki_logger import LokiLogger -# logger = LokiLogger(service_name="RAG Module Orchestrator") - -# # Add src to path for imports -# sys.path.append(str(Path(__file__).parent.parent)) - -# from vector_indexer.config.config_loader import ConfigLoader -# from vector_indexer.document_loader import DocumentLoader -# from vector_indexer.contextual_processor import ContextualProcessor -# from vector_indexer.qdrant_manager import QdrantManager -# from vector_indexer.error_logger import ErrorLogger -# from vector_indexer.models import ProcessingStats, DocumentInfo -# from vector_indexer.diff_identifier import DiffDetector, create_diff_config, DiffError - - -# class VectorIndexer: -# """Main vector indexer orchestrating the full pipeline.""" - -# def __init__(self, config_path: Optional[str] = None, signed_url: Optional[str] = None): -# # Load configuration -# self.config_path = ( -# config_path or "src/vector_indexer/config/vector_indexer_config.yaml" -# ) -# self.config = ConfigLoader.load_config(self.config_path) - -# # Store signed URL for future dataset download implementation -# self.signed_url = signed_url - -# # Initialize components -# self.document_loader = DocumentLoader(self.config) -# self.error_logger = ErrorLogger(self.config) - -# # Initialize API client -# from vector_indexer.api_client import LLMOrchestrationAPIClient - -# self.api_client = LLMOrchestrationAPIClient(self.config) - -# # Initialize contextual processor with all required arguments -# self.contextual_processor = ContextualProcessor( -# self.api_client, self.config, self.error_logger -# ) - -# # Processing statistics -# self.stats = ProcessingStats() - -# logger.info(f"Vector Indexer initialized with config: {self.config_path}") -# logger.info(f"Dataset path: {self.config.dataset_base_path}") -# logger.info(f"Max concurrent documents: {self.config.max_concurrent_documents}") -# logger.info( -# f"Max concurrent chunks: {self.config.max_concurrent_chunks_per_doc}" -# ) - -# if self.signed_url: -# logger.info(f"Signed URL provided: {self.signed_url[:50]}...") # Log first 50 chars only - -# async def process_all_documents(self) -> ProcessingStats: -# """ -# Process all documents in the dataset with contextual retrieval. - -# Returns: -# ProcessingStats: Overall processing statistics -# """ -# logger.info("=" * 60) -# logger.info("Starting Vector Indexer - Contextual Retrieval Pipeline") -# logger.info("=" * 60) - -# self.stats.start_time = datetime.now() - -# try: -# # Step 1: Dataset download (future implementation) -# if self.signed_url: -# logger.info("Dataset download URL provided - download logic to be implemented") -# # TODO: Implement dataset download and extraction -# # await self._download_and_extract_dataset(self.signed_url) - -# # Step 2: Diff identification - determine what files need processing -# logger.info("Step 1: Identifying changed files...") -# try: -# diff_config = create_diff_config() -# diff_detector = DiffDetector(diff_config) -# diff_result = await diff_detector.get_changed_files() - -# logger.info("Diff identification complete:") -# logger.info(f" • Total files scanned: {diff_result.total_files_scanned}") -# logger.info(f" • Previously processed: {diff_result.previously_processed_count}") -# logger.info(f" • Files needing processing: {len(diff_result.new_files)}") -# logger.info(f" • Is first run: {diff_result.is_first_run}") - -# if not diff_result.new_files: -# logger.info("No new or changed files detected. Processing complete.") -# self._cleanup_datasets() -# return self.stats - -# except DiffError as e: -# logger.error(f"Diff identification failed: {e}") -# logger.info("Continuing with full document discovery as fallback") -# diff_result = None -# diff_detector = None - -# # Initialize Qdrant collections -# async with QdrantManager(self.config) as qdrant_manager: -# await qdrant_manager.ensure_collections_exist() - -# # Step 3: Document discovery (filtered by diff results if available) -# logger.info("Step 2: Discovering documents...") -# if diff_result and diff_result.new_files: -# # Filter documents to only those identified as changed -# documents = self._filter_documents_by_paths(diff_result.new_files) -# else: -# # Fallback: discover all documents -# documents = self.document_loader.discover_all_documents() - -# if not documents: -# logger.warning("No documents found to process") -# self._cleanup_datasets() -# return self.stats - -# logger.info(f"Found {len(documents)} documents to process") -# self.stats.total_documents = len(documents) - -# # Process documents with controlled concurrency -# semaphore = asyncio.Semaphore(self.config.max_concurrent_documents) -# tasks: List[asyncio.Task[int]] = [] - -# for doc_info in documents: -# task = asyncio.create_task( -# self._process_single_document( -# doc_info, qdrant_manager, semaphore -# ) -# ) -# tasks.append(task) - -# # Execute all document processing tasks -# logger.info( -# f"Processing {len(tasks)} documents with max {self.config.max_concurrent_documents} concurrent" -# ) -# results = await asyncio.gather(*tasks, return_exceptions=True) - -# # Collect results and handle exceptions -# for i, result in enumerate(results): -# if isinstance(result, Exception): -# doc_info = documents[i] -# logger.error( -# f"Document processing failed: {doc_info.document_hash} - {result}" -# ) -# self.stats.documents_failed += 1 -# self.error_logger.log_document_failure( -# doc_info.document_hash, str(result) -# ) -# else: -# # Result should be number of chunks processed -# self.stats.documents_processed += 1 -# if isinstance(result, int): -# self.stats.total_chunks_processed += result - -# # Calculate final statistics -# self.stats.end_time = datetime.now() - -# # Step 4: Update processed files tracking -# if diff_detector and documents: -# try: -# processed_paths = [doc.cleaned_txt_path for doc in documents] -# if processed_paths: -# await diff_detector.mark_files_processed(processed_paths) -# logger.info("Updated processed files tracking") -# except Exception as e: -# logger.warning(f"Failed to update processed files tracking: {e}") - -# # Log final statistics -# self.error_logger.log_processing_stats(self.stats) -# self._log_final_summary() - -# # Step 5: Cleanup datasets folder after successful processing -# self._cleanup_datasets() - -# return self.stats - -# except Exception as e: -# logger.error(f"Critical error in vector indexer: {e}") -# self.stats.end_time = datetime.now() -# self.error_logger.log_processing_stats(self.stats) -# raise -# finally: -# # Clean up API client AFTER all processing is complete -# try: -# await self.api_client.close() -# except Exception as e: -# logger.warning(f"Error closing API client: {e}") - -# async def _process_single_document( -# self, -# doc_info: DocumentInfo, -# qdrant_manager: QdrantManager, -# semaphore: asyncio.Semaphore, -# ) -> int: -# """ -# Process a single document with contextual retrieval. - -# Args: -# doc_info: Document information -# qdrant_manager: Qdrant manager instance -# semaphore: Concurrency control semaphore - -# Returns: -# int: Number of chunks processed -# """ -# async with semaphore: -# logger.info(f"Processing document: {doc_info.document_hash}") - -# try: -# # Load document content -# document = self.document_loader.load_document(doc_info) - -# if not document: -# logger.warning(f"Could not load document: {doc_info.document_hash}") -# return 0 - -# # Process document with contextual retrieval -# contextual_chunks = await self.contextual_processor.process_document( -# document -# ) - -# if not contextual_chunks: -# logger.warning( -# f"No chunks created for document: {doc_info.document_hash}" -# ) -# return 0 - -# # Store chunks in Qdrant -# await qdrant_manager.store_chunks(contextual_chunks) - -# logger.info( -# f"Successfully processed document {doc_info.document_hash}: " -# f"{len(contextual_chunks)} chunks" -# ) - -# return len(contextual_chunks) - -# except Exception as e: -# logger.error(f"Error processing document {doc_info.document_hash}: {e}") -# self.error_logger.log_document_failure(doc_info.document_hash, str(e)) -# raise - -# def _log_final_summary(self): -# """Log final processing summary.""" - -# logger.info("VECTOR INDEXER PROCESSING COMPLETE") - -# logger.info("Processing Statistics:") -# logger.info(f" • Total Documents: {self.stats.total_documents}") -# logger.info(f" • Successful Documents: {self.stats.documents_processed}") -# logger.info(f" • Failed Documents: {self.stats.documents_failed}") -# logger.info(f" • Total Chunks: {self.stats.total_chunks_processed}") -# logger.info(f" • Failed Chunks: {self.stats.total_chunks_failed}") - -# if self.stats.total_documents > 0: -# success_rate = ( -# self.stats.documents_processed / self.stats.total_documents -# ) * 100 -# logger.info(f" • Success Rate: {success_rate:.1f}%") - -# logger.info(f" • Processing Duration: {self.stats.duration}") - -# if self.stats.documents_failed > 0: -# logger.warning( -# f" {self.stats.documents_failed} documents failed processing" -# ) -# logger.info(" Check failure logs for details") - -# async def run_health_check(self) -> bool: -# """ -# Run health check on all components. - -# Returns: -# bool: True if all components are healthy -# """ -# logger.info("Running Vector Indexer health check...") - -# try: -# # Check Qdrant connection -# async with QdrantManager(self.config) as qdrant_manager: -# # Test basic Qdrant connectivity by trying to list collections -# try: -# qdrant_url = getattr( -# self.config, "qdrant_url", "http://localhost:6333" -# ) -# response = await qdrant_manager.client.get( -# f"{qdrant_url}/collections" -# ) -# if response.status_code == 200: -# logger.info(" Qdrant server: Connected") - -# # Check if collections exist, create them if they don't -# collections_info = {} -# for collection_name in qdrant_manager.collections_config.keys(): -# info = await qdrant_manager.get_collection_info( -# collection_name -# ) -# if info: -# count = await qdrant_manager.count_points( -# collection_name -# ) -# collections_info[collection_name] = count -# logger.info( -# f" Qdrant collection '{collection_name}': {count} points" -# ) -# else: -# logger.info( -# f" Qdrant collection '{collection_name}': Not found (will be created automatically)" -# ) -# else: -# logger.error( -# f" Qdrant server not accessible: {response.status_code}" -# ) -# return False -# except Exception as e: -# logger.error(f" Qdrant connection failed: {e}") -# return False - -# # Check API client connectivity -# api_healthy = await self.api_client.health_check() -# if api_healthy: -# logger.info(" LLM Orchestration Service API: Connected") -# else: -# logger.error(" LLM Orchestration Service API: Not accessible") -# return False - -# # Check dataset path -# if Path(self.config.dataset_base_path).exists(): -# logger.info(f" Dataset path: {self.config.dataset_base_path}") -# else: -# logger.error( -# f" Dataset path not found: {self.config.dataset_base_path}" -# ) -# return False - -# logger.info(" All health checks passed!") -# return True - -# except Exception as e: -# logger.error(f" Health check failed: {e}") -# return False -# # NOTE: Don't close API client here - it will be used by main processing - -# async def cleanup(self): -# """Clean up resources.""" -# try: -# await self.api_client.close() -# logger.debug("API client closed successfully") -# except Exception as e: -# logger.warning(f"Error closing API client: {e}") - -# def _filter_documents_by_paths(self, file_paths: List[str]) -> List[DocumentInfo]: -# """ -# Filter documents by specific file paths. - -# Args: -# file_paths: List of file paths to process - -# Returns: -# List of DocumentInfo for matching files -# """ -# documents = [] - -# for file_path in file_paths: -# try: -# file_path_obj = Path(file_path) - -# # Ensure this is a cleaned.txt file -# if file_path_obj.name != "cleaned.txt": -# logger.debug(f"Skipping non-cleaned.txt file: {file_path}") -# continue - -# # Get hash directory and collection directory -# hash_dir = file_path_obj.parent -# collection_dir = hash_dir.parent - -# # Check if metadata file exists -# metadata_file = hash_dir / self.config.metadata_file -# if not metadata_file.exists(): -# logger.warning(f"Skipping file without metadata: {file_path}") -# continue - -# # Create DocumentInfo -# doc_info = DocumentInfo( -# document_hash=hash_dir.name, -# cleaned_txt_path=str(file_path_obj), -# source_meta_path=str(metadata_file), -# dataset_collection=collection_dir.name -# ) - -# documents.append(doc_info) -# logger.debug(f"Added document: {doc_info.document_hash}") - -# except Exception as e: -# logger.warning(f"Failed to process file path {file_path}: {e}") -# continue - -# logger.info(f"Filtered to {len(documents)} documents from {len(file_paths)} paths") -# return documents - -# def _cleanup_datasets(self): -# """Remove datasets folder after processing.""" -# try: -# datasets_path = Path(self.config.dataset_base_path) -# if datasets_path.exists(): -# shutil.rmtree(str(datasets_path)) -# logger.info(f"Datasets folder cleaned up: {datasets_path}") -# else: -# logger.debug(f"Datasets folder does not exist: {datasets_path}") -# except Exception as e: -# logger.warning(f"Failed to cleanup datasets folder: {e}") -# # Non-critical error - don't fail the entire process - - -# async def main(): -# """Main entry point for the vector indexer.""" - -# # Parse command line arguments -# parser = argparse.ArgumentParser(description="Vector Indexer with Diff Identification") -# parser.add_argument("--signed-url", help="Signed URL for dataset download") -# args = parser.parse_args() - -# # Configure logging -# logger.remove() # Remove default handler -# logger.add( -# sys.stdout, -# format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}", -# level="INFO", -# ) - -# # Add file logging -# logger.add( -# "vector_indexer.log", -# format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}", -# level="DEBUG", -# rotation="10 MB", -# retention="7 days", -# ) - -# indexer = None -# try: -# # Initialize vector indexer with signed URL -# indexer = VectorIndexer(signed_url=args.signed_url) - -# # Run health check first -# logger.info("Performing pre-processing health check...") -# health_ok = await indexer.run_health_check() - -# if not health_ok: -# logger.error("Health check failed. Aborting processing.") -# await indexer.cleanup() -# sys.exit(1) - -# # Process all documents -# logger.info("Health check passed. Starting document processing...") -# stats = await indexer.process_all_documents() - -# # Exit with appropriate code -# if stats.documents_failed > 0: -# logger.warning( -# f"Processing completed with {stats.documents_failed} failures" -# ) -# return 2 # Partial success -# else: -# logger.info("Processing completed successfully") -# return 0 - -# except KeyboardInterrupt: -# logger.info("Processing interrupted by user") -# return 130 -# except Exception as e: -# logger.error(f"Fatal error: {e}") -# return 1 -# finally: -# # Ensure cleanup happens -# if indexer: -# await indexer.cleanup() - - -# if __name__ == "__main__": -# # Run the async main function and exit with the returned code -# exit_code = asyncio.run(main()) -# sys.exit(exit_code) - """Main vector indexer script for processing documents with contextual retrieval.""" import argparse @@ -502,11 +6,9 @@ import sys from pathlib import Path from datetime import datetime -from typing import List, Optional +from typing import List, Optional, Dict, Any from loguru import logger -# from loki_logger import LokiLogger -# logger = LokiLogger(service_name="RAG Module Orchestrator") # Add src to path for imports sys.path.append(str(Path(__file__).parent.parent)) @@ -518,18 +20,22 @@ from vector_indexer.error_logger import ErrorLogger from vector_indexer.models import ProcessingStats, DocumentInfo from vector_indexer.diff_identifier import DiffDetector, create_diff_config, DiffError +from vector_indexer.diff_identifier.diff_models import DiffResult +from src.vector_indexer.dataset_download import download_and_extract_dataset class VectorIndexer: """Main vector indexer orchestrating the full pipeline.""" - def __init__(self, config_path: Optional[str] = None, signed_url: Optional[str] = None): + def __init__( + self, config_path: Optional[str] = None, signed_url: Optional[str] = None + ): # Load configuration self.config_path = ( config_path or "src/vector_indexer/config/vector_indexer_config.yaml" ) self.config = ConfigLoader.load_config(self.config_path) - + # Store signed URL for future dataset download implementation self.signed_url = signed_url @@ -556,9 +62,9 @@ def __init__(self, config_path: Optional[str] = None, signed_url: Optional[str] logger.info( f"Max concurrent chunks: {self.config.max_concurrent_chunks_per_doc}" ) - + if self.signed_url: - logger.info(f"Signed URL provided: {self.signed_url[:50]}...") # Log first 50 chars only + logger.info(f"Signed URL provided: {self.signed_url[:50]}...") async def process_all_documents(self) -> ProcessingStats: """ @@ -567,59 +73,94 @@ async def process_all_documents(self) -> ProcessingStats: Returns: ProcessingStats: Overall processing statistics """ - logger.info("=" * 60) logger.info("Starting Vector Indexer - Contextual Retrieval Pipeline") - logger.info("=" * 60) self.stats.start_time = datetime.now() try: - # Step 1: Dataset download (future implementation) + # Step 1: Dataset download if self.signed_url: - logger.info("Dataset download URL provided - download logic to be implemented") - # TODO: Implement dataset download and extraction - # await self._download_and_extract_dataset(self.signed_url) - + logger.info("Dataset download URL provided - starting download") + try: + extraction_path, files_count = download_and_extract_dataset( + self.signed_url + ) + logger.info( + f"Dataset extracted: {files_count} files to {extraction_path}" + ) + # Update config to use the downloaded dataset + self.config.dataset_base_path = extraction_path + except Exception as e: + logger.error(f"Dataset download failed: {e}") + raise + # Step 2: Diff identification - determine what files need processing logger.info("Step 1: Identifying changed files...") try: diff_config = create_diff_config() diff_detector = DiffDetector(diff_config) diff_result = await diff_detector.get_changed_files() - + logger.info("Diff identification complete:") - logger.info(f" • Total files scanned: {diff_result.total_files_scanned}") - logger.info(f" • Previously processed: {diff_result.previously_processed_count}") - logger.info(f" • Files needing processing: {len(diff_result.new_files)}") + logger.info( + f" • Total files scanned: {diff_result.total_files_scanned}" + ) + logger.info( + f" • Previously processed: {diff_result.previously_processed_count}" + ) + logger.info(f" • New files: {len(diff_result.new_files)}") + logger.info(f" • Modified files: {len(diff_result.modified_files)}") + logger.info(f" • Deleted files: {len(diff_result.deleted_files)}") + logger.info(f" • Unchanged files: {len(diff_result.unchanged_files)}") logger.info(f" • Is first run: {diff_result.is_first_run}") - - if not diff_result.new_files: - logger.info("No new or changed files detected. Processing complete.") - # self._cleanup_datasets() - return self.stats - + + files_to_process = diff_result.new_files + diff_result.modified_files + except DiffError as e: logger.error(f"Diff identification failed: {e}") logger.info("Continuing with full document discovery as fallback") diff_result = None diff_detector = None + files_to_process = [] # Initialize Qdrant collections async with QdrantManager(self.config) as qdrant_manager: await qdrant_manager.ensure_collections_exist() + # Step 2.5: Execute cleanup operations for deleted/modified files + if diff_result and diff_result.chunks_to_delete: + logger.info("EXECUTING CLEANUP OPERATIONS...") + await self._execute_cleanup_operations(qdrant_manager, diff_result) + + # Early exit check AFTER cleanup operations + # Only exit if there's nothing to process AND no cleanup was needed + if diff_result and not files_to_process: + logger.info("No new or modified files to process.") + # ALWAYS update metadata when there were deletions or modifications + if diff_detector and ( + diff_result.deleted_files or diff_result.modified_files + ): + logger.info("Updating metadata to reflect file changes...") + await diff_detector.mark_files_processed( + [], force_metadata_update=True + ) + logger.info("Metadata updated successfully.") + else: + logger.info("No changes detected - no metadata update needed.") + return self.stats + # Step 3: Document discovery (filtered by diff results if available) logger.info("Step 2: Discovering documents...") - if diff_result and diff_result.new_files: + if diff_result and files_to_process: # Filter documents to only those identified as changed - documents = self._filter_documents_by_paths(diff_result.new_files) + documents = self._filter_documents_by_paths(files_to_process) else: # Fallback: discover all documents documents = self.document_loader.discover_all_documents() if not documents: logger.warning("No documents found to process") - # self._cleanup_datasets() + self._cleanup_datasets() return self.stats logger.info(f"Found {len(documents)} documents to process") @@ -627,7 +168,7 @@ async def process_all_documents(self) -> ProcessingStats: # Process documents with controlled concurrency semaphore = asyncio.Semaphore(self.config.max_concurrent_documents) - tasks: List[asyncio.Task[int]] = [] + tasks: List[asyncio.Task[tuple[int, str]]] = [] for doc_info in documents: task = asyncio.create_task( @@ -644,6 +185,9 @@ async def process_all_documents(self) -> ProcessingStats: results = await asyncio.gather(*tasks, return_exceptions=True) # Collect results and handle exceptions + chunks_info: Dict[ + str, Dict[str, Any] + ] = {} # Track chunk counts for metadata update for i, result in enumerate(results): if isinstance(result, Exception): doc_info = documents[i] @@ -655,23 +199,69 @@ async def process_all_documents(self) -> ProcessingStats: doc_info.document_hash, str(result) ) else: - # Result should be number of chunks processed + # Result should be tuple of (chunk_count, content_hash) + doc_info = documents[i] self.stats.documents_processed += 1 - if isinstance(result, int): - self.stats.total_chunks_processed += result + if isinstance(result, tuple) and len(result) == 2: + chunk_count, content_hash = result + self.stats.total_chunks_processed += chunk_count + # Track chunk count using content_hash (not directory hash) + chunks_info[content_hash] = {"chunk_count": chunk_count} + logger.info( + f"CHUNK COUNT: Document {doc_info.document_hash[:12]}... (content: {content_hash[:12]}...) -> {chunk_count} chunks" + ) + + # Log the complete chunks_info dictionary + logger.info( + f"CHUNKS INFO SUMMARY: {len(chunks_info)} documents tracked" + ) + for doc_hash, info in chunks_info.items(): + logger.info( + f" {doc_hash[:12]}... -> {info['chunk_count']} chunks" + ) # Calculate final statistics self.stats.end_time = datetime.now() - # Step 4: Update processed files tracking - if diff_detector and documents: + # Step 4: Update processed files tracking (even if no new documents processed) + if diff_detector: try: - processed_paths = [doc.cleaned_txt_path for doc in documents] - if processed_paths: - await diff_detector.mark_files_processed(processed_paths) - logger.info("Updated processed files tracking") + # Update metadata for newly processed files + if documents: + processed_paths = [ + doc.cleaned_txt_path for doc in documents + ] + if processed_paths: + logger.debug( + f"Passing chunks_info with keys: {[k[:12] + '...' for k in chunks_info.keys()]} to mark_files_processed" + ) + await diff_detector.mark_files_processed( + processed_paths, chunks_info=chunks_info + ) + logger.info( + "Updated processed files tracking for new documents" + ) + + # CRITICAL: Update metadata even when only cleanup operations occurred + # This ensures deleted files are removed from metadata + elif diff_result and ( + diff_result.deleted_files or diff_result.modified_files + ): + logger.info( + "Updating metadata to reflect file deletions/modifications..." + ) + # Force metadata update for cleanup operations + await diff_detector.mark_files_processed( + [], force_metadata_update=True + ) + logger.info( + "Updated processed files tracking for cleanup operations" + ) + except Exception as e: - logger.warning(f"Failed to update processed files tracking: {e}") + logger.warning( + f"Failed to update processed files tracking: {e}" + ) # Log final statistics self.error_logger.log_processing_stats(self.stats) @@ -699,7 +289,7 @@ async def _process_single_document( doc_info: DocumentInfo, qdrant_manager: QdrantManager, semaphore: asyncio.Semaphore, - ) -> int: + ) -> tuple[int, str]: """ Process a single document with contextual retrieval. @@ -709,7 +299,7 @@ async def _process_single_document( semaphore: Concurrency control semaphore Returns: - int: Number of chunks processed + tuple: (chunk_count: int, content_hash: str) or Exception on error """ async with semaphore: logger.info(f"Processing document: {doc_info.document_hash}") @@ -720,7 +310,7 @@ async def _process_single_document( if not document: logger.warning(f"Could not load document: {doc_info.document_hash}") - return 0 + return (0, doc_info.document_hash) # Process document with contextual retrieval contextual_chunks = await self.contextual_processor.process_document( @@ -731,7 +321,7 @@ async def _process_single_document( logger.warning( f"No chunks created for document: {doc_info.document_hash}" ) - return 0 + return (0, document.document_hash) # Store chunks in Qdrant await qdrant_manager.store_chunks(contextual_chunks) @@ -741,7 +331,7 @@ async def _process_single_document( f"{len(contextual_chunks)} chunks" ) - return len(contextual_chunks) + return (len(contextual_chunks), document.document_hash) except Exception as e: logger.error(f"Error processing document {doc_info.document_hash}: {e}") @@ -764,15 +354,15 @@ def _log_final_summary(self): success_rate = ( self.stats.documents_processed / self.stats.total_documents ) * 100 - logger.info(f" • Success Rate: {success_rate:.1f}%") + logger.info(f"Success Rate: {success_rate:.1f}%") - logger.info(f" • Processing Duration: {self.stats.duration}") + logger.info(f"Processing Duration: {self.stats.duration}") if self.stats.documents_failed > 0: logger.warning( f" {self.stats.documents_failed} documents failed processing" ) - logger.info(" Check failure logs for details") + logger.info("Check failure logs for details") async def run_health_check(self) -> bool: """ @@ -788,14 +378,12 @@ async def run_health_check(self) -> bool: async with QdrantManager(self.config) as qdrant_manager: # Test basic Qdrant connectivity by trying to list collections try: - qdrant_url = getattr( - self.config, "qdrant_url" - ) + qdrant_url = getattr(self.config, "qdrant_url") response = await qdrant_manager.client.get( f"{qdrant_url}/collections" ) if response.status_code == 200: - logger.info("✓ Qdrant server: Connected") + logger.info("Qdrant server: Connected") # Check if collections exist, create them if they don't collections_info = {} @@ -809,43 +397,41 @@ async def run_health_check(self) -> bool: ) collections_info[collection_name] = count logger.info( - f"✓ Qdrant collection '{collection_name}': {count} points" + f"Qdrant collection '{collection_name}': {count} points" ) else: logger.info( - f"✓ Qdrant collection '{collection_name}': Not found (will be created automatically)" + f"Qdrant collection '{collection_name}': Not found (will be created automatically)" ) else: logger.error( - f"✗ Qdrant server not accessible: {response.status_code}" + f"Qdrant server not accessible: {response.status_code}" ) return False except Exception as e: - logger.error(f"✗ Qdrant connection failed: {e}") + logger.error(f"Qdrant connection failed: {e}") return False # Check API client connectivity api_healthy = await self.api_client.health_check() if api_healthy: - logger.info("✓ LLM Orchestration Service API: Connected") + logger.info("LLM Orchestration Service API: Connected") else: - logger.error("✗ LLM Orchestration Service API: Not accessible") + logger.error("LLM Orchestration Service API: Not accessible") return False # Check dataset path if Path(self.config.dataset_base_path).exists(): - logger.info(f"✓ Dataset path: {self.config.dataset_base_path}") + logger.info(f"Dataset path: {self.config.dataset_base_path}") else: - logger.error( - f"✗ Dataset path not found: {self.config.dataset_base_path}" - ) + logger.error(f"Dataset path not found: {self.config.dataset_base_path}") return False - logger.info("✓ All health checks passed!") + logger.info("All health checks passed!") return True except Exception as e: - logger.error(f"✗ Health check failed: {e}") + logger.error(f"Health check failed: {e}") return False # NOTE: Don't close API client here - it will be used by main processing @@ -856,56 +442,180 @@ async def cleanup(self): logger.debug("API client closed successfully") except Exception as e: logger.warning(f"Error closing API client: {e}") - + def _filter_documents_by_paths(self, file_paths: List[str]) -> List[DocumentInfo]: """ Filter documents by specific file paths. - + + IMPORTANT: This method now uses discover_all_documents() to get the correct + content hashes that were already calculated, instead of recalculating them. + This ensures consistency throughout the pipeline. + Args: file_paths: List of file paths to process - + Returns: List of DocumentInfo for matching files """ - documents = [] - + documents: List[DocumentInfo] = [] + + # FIX: Discover ALL documents first to get their content hashes + # This ensures we use the same hash that was calculated in discover_all_documents() + logger.debug("Discovering all documents to get content hashes...") + all_documents = self.document_loader.discover_all_documents() + + # Create a lookup map: file_path -> DocumentInfo + path_to_doc_map: Dict[str, DocumentInfo] = { + doc.cleaned_txt_path: doc for doc in all_documents + } + logger.debug(f"Created path lookup map with {len(path_to_doc_map)} documents") + for file_path in file_paths: - try: - file_path_obj = Path(file_path) - - # Ensure this is a cleaned.txt file - if file_path_obj.name != "cleaned.txt": - logger.debug(f"Skipping non-cleaned.txt file: {file_path}") - continue - - # Get hash directory and collection directory - hash_dir = file_path_obj.parent - collection_dir = hash_dir.parent - - # Check if metadata file exists - metadata_file = hash_dir / self.config.metadata_file - if not metadata_file.exists(): - logger.warning(f"Skipping file without metadata: {file_path}") - continue - - # Create DocumentInfo - doc_info = DocumentInfo( - document_hash=hash_dir.name, - cleaned_txt_path=str(file_path_obj), - source_meta_path=str(metadata_file), - dataset_collection=collection_dir.name - ) - + # Check if this file path exists in our discovered documents + if file_path in path_to_doc_map: + # Use the DocumentInfo that was already discovered (with correct content hash) + doc_info = path_to_doc_map[file_path] documents.append(doc_info) - logger.debug(f"Added document: {doc_info.document_hash}") - - except Exception as e: - logger.warning(f"Failed to process file path {file_path}: {e}") - continue - - logger.info(f"Filtered to {len(documents)} documents from {len(file_paths)} paths") + logger.debug( + f"Added document: {doc_info.document_hash[:12]}... from {file_path}" + ) + else: + logger.warning( + f"File path {file_path} not found in discovered documents" + ) + + logger.info( + f"Filtered to {len(documents)} documents from {len(file_paths)} paths" + ) return documents - + + async def _execute_cleanup_operations( + self, qdrant_manager: QdrantManager, diff_result: "DiffResult" + ) -> int: + """ + Execute cleanup operations for deleted and modified files. + + Args: + qdrant_manager: Qdrant manager instance + diff_result: DiffResult containing cleanup information + + Returns: + Total number of chunks deleted + """ + total_deleted = 0 + + if not diff_result.chunks_to_delete: + logger.info("No cleanup operations needed") + return total_deleted + + logger.info( + f"STARTING CLEANUP: {len(diff_result.chunks_to_delete)} documents with chunks to delete" + ) + + # Get appropriate collection names + collections_to_clean = ["contextual_chunks_azure", "contextual_chunks_aws"] + + for document_hash, original_path in diff_result.chunks_to_delete.items(): + logger.info( + f"ATTEMPTING CLEANUP for document {document_hash[:12]}... (path: {original_path})" + ) + logger.debug(f"DEBUG: Full document_hash for deletion: {document_hash}") + logger.info( + "DEBUG: This could be a retry if chunks were deleted in a previous run but metadata wasn't updated" + ) + + chunks_deleted_for_doc = 0 + fallback_hash = None + + for collection_name in collections_to_clean: + try: + # Try with current document_hash first + deleted_count = await qdrant_manager.delete_chunks_by_document_hash( + collection_name, document_hash + ) + chunks_deleted_for_doc += deleted_count + + if deleted_count > 0: + logger.info( + f"Deleted {deleted_count} chunks from {collection_name}" + ) + else: + # If no chunks found with current hash, try fallback with old hash calculation method + if fallback_hash is None and Path(original_path).exists(): + try: + # Calculate hash using old method (read_bytes) for backward compatibility + import hashlib + + file_content = Path(original_path).read_bytes() + fallback_hash = hashlib.sha256(file_content).hexdigest() + logger.info( + f"Trying fallback hash calculation for backward compatibility: {fallback_hash[:12]}..." + ) + except Exception as fallback_error: + logger.warning( + f"Could not calculate fallback hash: {fallback_error}" + ) + fallback_hash = "FAILED" + + if ( + fallback_hash + and fallback_hash != "FAILED" + and fallback_hash != document_hash + ): + fallback_deleted = ( + await qdrant_manager.delete_chunks_by_document_hash( + collection_name, fallback_hash + ) + ) + chunks_deleted_for_doc += fallback_deleted + if fallback_deleted > 0: + logger.info( + f" ✅ Deleted {fallback_deleted} chunks from {collection_name} using fallback hash" + ) + + except Exception as e: + logger.error(f"Failed to delete chunks from {collection_name}: {e}") + continue + + total_deleted += chunks_deleted_for_doc + if chunks_deleted_for_doc > 0: + logger.info( + f"Total deleted for document {document_hash[:12]}...: {chunks_deleted_for_doc} chunks" + ) + else: + if ( + fallback_hash + and fallback_hash != "FAILED" + and fallback_hash != document_hash + ): + logger.info( + f"No chunks found for document {document_hash[:12]}... or fallback hash {fallback_hash[:12]}... (may have been deleted previously or stored with different hash)" + ) + else: + logger.info( + f"No chunks found for document {document_hash[:12]}... (file tracked in metadata but chunks not in vector store)" + ) + + if total_deleted > 0: + logger.info( + f"CLEANUP COMPLETED: {total_deleted} total chunks removed from {len(diff_result.chunks_to_delete)} documents" + ) + else: + logger.info( + f"CLEANUP COMPLETED: No chunks removed (0 chunks found in vector store for {len(diff_result.chunks_to_delete)} tracked documents)" + ) + + # Log cleanup summary by file type + deleted_files = diff_result.deleted_files + modified_files = diff_result.modified_files + + if deleted_files: + logger.info(f"Processed cleanup for {len(deleted_files)} deleted files") + if modified_files: + logger.info(f"Processed cleanup for {len(modified_files)} modified files") + + return total_deleted + def _cleanup_datasets(self): """Remove datasets folder after processing.""" try: @@ -922,9 +632,11 @@ def _cleanup_datasets(self): async def main(): """Main entry point for the vector indexer.""" - + # Parse command line arguments - parser = argparse.ArgumentParser(description="Vector Indexer with Diff Identification") + parser = argparse.ArgumentParser( + description="Vector Indexer with Diff Identification" + ) parser.add_argument("--signed-url", help="Signed URL for dataset download") args = parser.parse_args() @@ -978,6 +690,7 @@ async def main(): except Exception as e: logger.error(f"Fatal error: {e}") import traceback + logger.error(traceback.format_exc()) return 1 finally: @@ -992,4 +705,4 @@ async def main(): if __name__ == "__main__": # Run the async main function and exit with the returned code exit_code = asyncio.run(main()) - sys.exit(exit_code) \ No newline at end of file + sys.exit(exit_code) diff --git a/src/vector_indexer/qdrant_manager.py b/src/vector_indexer/qdrant_manager.py index 93aacd8..be9dc92 100644 --- a/src/vector_indexer/qdrant_manager.py +++ b/src/vector_indexer/qdrant_manager.py @@ -307,6 +307,269 @@ async def count_points(self, collection_name: str) -> int: logger.error(f"Error counting points in {collection_name}: {e}") return 0 + async def delete_chunks_by_document_hash( + self, collection_name: str, document_hash: str + ) -> int: + """ + Delete all chunks associated with a specific document hash. + + Args: + collection_name: Name of the Qdrant collection + document_hash: SHA256 hash of the document to delete chunks for + + Returns: + Number of chunks deleted (estimated as 1 if deletion successful, 0 if nothing to delete) + + Raises: + QdrantOperationError: If deletion fails + """ + try: + logger.info( + f"🗑️ Attempting to delete chunks for document: {document_hash[:12]}... from {collection_name}" + ) + + # Step 1: Check if chunks exist BEFORE deletion (for accurate reporting) + pre_check_payload = { + "filter": { + "must": [ + {"key": "document_hash", "match": {"value": document_hash}} + ] + }, + "limit": 100, # Get up to 100 to count + "with_payload": False, + "with_vector": False, + } + + pre_check_response = await self.client.post( + f"{self.qdrant_url}/collections/{collection_name}/points/scroll", + json=pre_check_payload, + ) + + chunks_found_before = 0 + if pre_check_response.status_code == 200: + pre_check_data = pre_check_response.json() + chunks_found_before = len( + pre_check_data.get("result", {}).get("points", []) + ) + logger.info(f"🔍 Found {chunks_found_before} chunks to delete") + else: + logger.warning( + f"⚠️ Pre-check query failed with status {pre_check_response.status_code}" + ) + + # Step 2: Execute deletion using filter + delete_payload = { + "filter": { + "must": [ + {"key": "document_hash", "match": {"value": document_hash}} + ] + } + } + + logger.debug(f"🔍 Executing delete with filter: {delete_payload}") + + response = await self.client.post( + f"{self.qdrant_url}/collections/{collection_name}/points/delete", + json=delete_payload, + ) + + if response.status_code in [200, 201]: + result = response.json() + + if result.get("status") == "ok": + # Step 3: Verify deletion by checking if chunks still exist + verify_payload = { + "filter": { + "must": [ + { + "key": "document_hash", + "match": {"value": document_hash}, + } + ] + }, + "limit": 1, + "with_payload": False, + "with_vector": False, + } + + verify_response = await self.client.post( + f"{self.qdrant_url}/collections/{collection_name}/points/scroll", + json=verify_payload, + ) + + if verify_response.status_code == 200: + verify_data = verify_response.json() + remaining_chunks = len( + verify_data.get("result", {}).get("points", []) + ) + + if remaining_chunks == 0: + if chunks_found_before > 0: + logger.info( + f"✅ Successfully deleted {chunks_found_before} chunk(s) from {collection_name}" + ) + return chunks_found_before + else: + logger.info( + f"ℹ️ No chunks found for document {document_hash[:12]}... in {collection_name}" + ) + return 0 + else: + logger.error( + f"❌ Delete verification failed: {remaining_chunks} chunk(s) still exist!" + ) + return 0 + else: + # Verification query failed, but delete was accepted + # Assume success based on pre-check count + if chunks_found_before > 0: + logger.warning( + f"⚠️ Delete succeeded but verification failed - assuming {chunks_found_before} chunks deleted" + ) + return chunks_found_before + else: + logger.info( + f"ℹ️ Delete completed (verification query failed, found {chunks_found_before} before)" + ) + return 0 + else: + raise QdrantOperationError( + f"Qdrant delete returned error status: {result}" + ) + else: + raise QdrantOperationError( + f"Delete request failed with HTTP {response.status_code}: {response.text}" + ) + + except QdrantOperationError: + # Re-raise QdrantOperationError as-is + raise + except Exception as e: + logger.error( + f"❌ Failed to delete chunks for document {document_hash[:12]}...: {e}" + ) + raise QdrantOperationError( + f"Failed to delete chunks by document hash: {str(e)}" + ) + + async def delete_chunks_by_file_path( + self, collection_name: str, file_path: str + ) -> int: + """ + Delete all chunks associated with a specific file path (fallback method). + + Args: + collection_name: Name of the Qdrant collection + file_path: Original file path to delete chunks for + + Returns: + Number of chunks deleted + + Raises: + QdrantOperationError: If deletion fails + """ + try: + logger.info( + f"🗑️ Deleting chunks for file path: {file_path} from {collection_name}" + ) + + # Count chunks first + scroll_payload = { + "filter": { + "must": [{"key": "document_url", "match": {"value": file_path}}] + }, + "limit": 1000, + "with_payload": False, + "with_vector": False, + } + + scroll_response = await self.client.post( + f"{self.qdrant_url}/collections/{collection_name}/points/scroll", + json=scroll_payload, + ) + + chunks_to_delete = 0 + if scroll_response.status_code == 200: + scroll_data = scroll_response.json() + chunks_to_delete = len(scroll_data.get("result", {}).get("points", [])) + + # Delete chunks using filter + delete_payload = { + "filter": { + "must": [{"key": "document_url", "match": {"value": file_path}}] + } + } + + response = await self.client.post( + f"{self.qdrant_url}/collections/{collection_name}/points/delete", + json=delete_payload, + ) + + if response.status_code in [200, 201]: + result = response.json() + if result.get("status") == "ok": + logger.info( + f"✅ Successfully deleted {chunks_to_delete} chunks for file {file_path}" + ) + return chunks_to_delete + else: + raise QdrantOperationError(f"Qdrant returned error: {result}") + else: + raise QdrantOperationError( + f"HTTP {response.status_code}: {response.text}" + ) + + except Exception as e: + logger.error(f"Failed to delete chunks for file {file_path}: {e}") + raise QdrantOperationError( + f"Failed to delete chunks by file path: {str(e)}" + ) + + async def get_chunks_for_document( + self, collection_name: str, document_hash: str + ) -> List[Dict[str, Any]]: + """ + Get all chunks associated with a specific document hash. + + Args: + collection_name: Name of the Qdrant collection + document_hash: SHA256 hash of the document + + Returns: + List of chunk records with their metadata + """ + try: + scroll_payload = { + "filter": { + "must": [ + {"key": "document_hash", "match": {"value": document_hash}} + ] + }, + "limit": 1000, + "with_payload": True, + "with_vector": False, + } + + response = await self.client.post( + f"{self.qdrant_url}/collections/{collection_name}/points/scroll", + json=scroll_payload, + ) + + if response.status_code == 200: + result = response.json() + return result.get("result", {}).get("points", []) + else: + logger.warning( + f"Failed to get chunks for document {document_hash[:12]}...: HTTP {response.status_code}" + ) + return [] + + except Exception as e: + logger.warning( + f"Error getting chunks for document {document_hash[:12]}...: {e}" + ) + return [] + async def delete_collection(self, collection_name: str) -> bool: """Delete a collection (for cleanup/testing).""" diff --git a/uv.lock b/uv.lock index 2677629..9770552 100644 --- a/uv.lock +++ b/uv.lock @@ -2436,6 +2436,7 @@ name = "rag-module" version = "0.1.0" source = { virtual = "." } dependencies = [ + { name = "aiohttp" }, { name = "azure-identity" }, { name = "boto3" }, { name = "dspy" }, @@ -2464,6 +2465,7 @@ dependencies = [ [package.metadata] requires-dist = [ + { name = "aiohttp", specifier = ">=3.13.0" }, { name = "azure-identity", specifier = ">=1.24.0" }, { name = "boto3", specifier = ">=1.40.25" }, { name = "dspy", specifier = ">=3.0.3" }, From 6330738790a2107ca23369dee008febf4ad6e12e Mon Sep 17 00:00:00 2001 From: nuwangeek Date: Tue, 21 Oct 2025 10:52:42 +0530 Subject: [PATCH 08/11] fixed issue --- src/vector_indexer/main_indexer.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/vector_indexer/main_indexer.py b/src/vector_indexer/main_indexer.py index 02e722e..805f276 100644 --- a/src/vector_indexer/main_indexer.py +++ b/src/vector_indexer/main_indexer.py @@ -8,6 +8,7 @@ from datetime import datetime from typing import List, Optional, Dict, Any from loguru import logger +import hashlib # Add src to path for imports @@ -267,8 +268,8 @@ async def process_all_documents(self) -> ProcessingStats: self.error_logger.log_processing_stats(self.stats) self._log_final_summary() - # Step 5: Cleanup datasets folder after successful processing - # self._cleanup_datasets() + #Step 5: Cleanup datasets folder after successful processing + self._cleanup_datasets() return self.stats @@ -544,7 +545,6 @@ async def _execute_cleanup_operations( if fallback_hash is None and Path(original_path).exists(): try: # Calculate hash using old method (read_bytes) for backward compatibility - import hashlib file_content = Path(original_path).read_bytes() fallback_hash = hashlib.sha256(file_content).hexdigest() From b8c36eb664f5c63fb7010aaea522bad27a2b145d Mon Sep 17 00:00:00 2001 From: nuwangeek Date: Tue, 21 Oct 2025 11:09:04 +0530 Subject: [PATCH 09/11] fixed merge conflicts --- pyproject.toml | 6 + uv.lock | 950 ++++++++++++++++++++++++++++++++++++------------- 2 files 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