From f24581051c13e88830d3ef625425a0ef0f3ad1f8 Mon Sep 17 00:00:00 2001 From: thomashebrard Date: Thu, 15 Jan 2026 13:19:37 +0100 Subject: [PATCH 1/3] bump to Chicago --- .env.example | 2 +- .pipelex/inference/backends.toml | 36 +- .pipelex/inference/backends/anthropic.toml | 28 +- .pipelex/inference/backends/azure_openai.toml | 98 ++++- .pipelex/inference/backends/bedrock.toml | 25 +- .pipelex/inference/backends/blackboxai.toml | 32 +- .pipelex/inference/backends/fal.toml | 50 +++ .pipelex/inference/backends/google.toml | 41 +- .pipelex/inference/backends/groq.toml | 16 +- .pipelex/inference/backends/huggingface.toml | 43 ++ .pipelex/inference/backends/internal.toml | 10 +- .pipelex/inference/backends/mistral.toml | 31 +- .pipelex/inference/backends/openai.toml | 134 +++--- .../inference/backends/pipelex_gateway.toml | 41 ++ .../inference/backends/pipelex_inference.toml | 55 ++- .pipelex/inference/backends/portkey.toml | 263 ++++++++++++ .pipelex/inference/backends/scaleway.toml | 67 +++ .pipelex/inference/deck/base_deck.toml | 65 ++- .pipelex/inference/routing_profiles.toml | 74 ++-- .pipelex/pipelex.toml | 146 +++++-- .pipelex/pipelex_service.toml | 19 + .pipelex/telemetry.toml | 95 ++++- CHANGELOG.md | 6 + Makefiles/Makefile.local.mk | 2 +- README.md | 35 +- api/routes/helpers.py | 2 +- api/routes/pipelex/pipe_builder.py | 2 +- api/security.py | 28 +- docker-compose.yml | 3 +- docs/index.md | 62 ++- pyproject.toml | 9 +- uv.lock | 403 +++++++----------- 32 files changed, 1405 insertions(+), 518 deletions(-) create mode 100644 .pipelex/inference/backends/huggingface.toml create mode 100644 .pipelex/inference/backends/pipelex_gateway.toml create mode 100644 .pipelex/inference/backends/portkey.toml create mode 100644 .pipelex/inference/backends/scaleway.toml create mode 100644 .pipelex/pipelex_service.toml diff --git a/.env.example b/.env.example index 43dcd75..0bb5543 100644 --- a/.env.example +++ b/.env.example @@ -1,2 +1,2 @@ API_KEY= -PIPELEX_INFERENCE_API_KEY= \ No newline at end of file +PIPELEX_GATEWAY_API_KEY= \ No newline at end of file diff --git a/.pipelex/inference/backends.toml b/.pipelex/inference/backends.toml index c87d1ef..360a73d 100644 --- a/.pipelex/inference/backends.toml +++ b/.pipelex/inference/backends.toml @@ -10,16 +10,14 @@ # #################################################################################################### -[pipelex_inference] -display_name = "⭐ Pipelex Inference" -enabled = true -endpoint = "https://inference.pipelex.com/v1" -api_key = "${PIPELEX_INFERENCE_API_KEY}" +[pipelex_gateway] +display_name = "⭐ Pipelex Gateway" +enabled = true # Enable after accepting terms via `pipelex init config` +api_key = "${PIPELEX_GATEWAY_API_KEY}" [anthropic] enabled = false api_key = "${ANTHROPIC_API_KEY}" -claude_4_tokens_limit = 8192 [azure_openai] display_name = "Azure OpenAI" @@ -55,6 +53,11 @@ enabled = false endpoint = "https://api.groq.com/openai/v1" api_key = "${GROQ_API_KEY}" +[huggingface] +display_name = "Hugging Face" +enabled = false +api_key = "${HF_TOKEN}" + [mistral] display_name = "Mistral AI" enabled = false @@ -69,9 +72,21 @@ display_name = "OpenAI" enabled = false api_key = "${OPENAI_API_KEY}" +[portkey] +display_name = "Portkey" +enabled = false +endpoint = "https://api.portkey.ai/v1" +api_key = "${PORTKEY_API_KEY}" + +[scaleway] +display_name = "Scaleway" +enabled = false +endpoint = "${SCALEWAY_ENDPOINT}" +api_key = "${SCALEWAY_API_KEY}" + [vertexai] display_name = "Google Vertex AI" -enabled = false +enabled = false # This is the only one we disable beacuse setting it up requires internet access just to get credentials so it fails in CI sandboxes gcp_project_id = "${GCP_PROJECT_ID}" gcp_location = "${GCP_LOCATION}" gcp_credentials_file_path = "${GCP_CREDENTIALS_FILE_PATH}" @@ -84,3 +99,10 @@ api_key = "${XAI_API_KEY}" [internal] # software-only backend, runs internally, without AI enabled = true + +# Deprecated +[pipelex_inference] +display_name = "🛑 Legacy Pipelex Inference" +enabled = false +endpoint = "https://inference.pipelex.com/v1" +api_key = "${PIPELEX_INFERENCE_API_KEY}" diff --git a/.pipelex/inference/backends/anthropic.toml b/.pipelex/inference/backends/anthropic.toml index 6b9c912..2c94cd3 100644 --- a/.pipelex/inference/backends/anthropic.toml +++ b/.pipelex/inference/backends/anthropic.toml @@ -39,19 +39,11 @@ outputs = ["text", "structured"] max_prompt_images = 100 costs = { input = 0.25, output = 1.25 } -[claude-3-opus] -model_id = "claude-3-opus-20240229" -max_tokens = 4096 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 15.0, output = 75.0 } - # --- Claude 3.7 Series -------------------------------------------------------- ["claude-3.7-sonnet"] model_id = "claude-3-7-sonnet-20250219" max_tokens = 8192 -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] max_prompt_images = 100 costs = { input = 3.0, output = 15.0 } @@ -60,7 +52,7 @@ costs = { input = 3.0, output = 15.0 } [claude-4-sonnet] model_id = "claude-sonnet-4-20250514" max_tokens = 64000 -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] max_prompt_images = 100 costs = { input = 3.0, output = 15.0 } @@ -68,7 +60,7 @@ costs = { input = 3.0, output = 15.0 } [claude-4-opus] model_id = "claude-opus-4-20250514" max_tokens = 32000 -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] max_prompt_images = 100 costs = { input = 3.0, output = 15.0 } @@ -77,7 +69,7 @@ costs = { input = 3.0, output = 15.0 } ["claude-4.1-opus"] model_id = "claude-opus-4-1-20250805" max_tokens = 32000 -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] max_prompt_images = 100 costs = { input = 3.0, output = 15.0 } @@ -86,7 +78,7 @@ costs = { input = 3.0, output = 15.0 } ["claude-4.5-sonnet"] model_id = "claude-sonnet-4-5-20250929" max_tokens = 64000 -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] max_prompt_images = 100 costs = { input = 3.0, output = 15.0 } @@ -94,7 +86,15 @@ costs = { input = 3.0, output = 15.0 } ["claude-4.5-haiku"] model_id = "claude-haiku-4-5-20251001" max_tokens = 64000 -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] max_prompt_images = 100 costs = { input = 1.0, output = 5.0 } + +["claude-4.5-opus"] +model_id = "claude-opus-4-5-20251101" +max_tokens = 64000 +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 5.0, output = 25.0 } diff --git a/.pipelex/inference/backends/azure_openai.toml b/.pipelex/inference/backends/azure_openai.toml index 6054b12..8a89898 100644 --- a/.pipelex/inference/backends/azure_openai.toml +++ b/.pipelex/inference/backends/azure_openai.toml @@ -22,9 +22,9 @@ [defaults] model_type = "llm" -sdk = "azure_openai" +sdk = "azure_openai_responses" prompting_target = "openai" -structure_method = "instructor/openai_tools" +structure_method = "instructor/openai_responses_tools" ################################################################################ # LANGUAGE MODELS @@ -68,28 +68,28 @@ model_id = "o1-mini-2024-09-12" inputs = ["text"] outputs = ["text", "structured"] costs = { input = 3.0, output = 12.0 } -constraints = ["temperature_must_be_1"] +valued_constraints = { fixed_temperature = 1 } [o1] model_id = "o1-2024-12-17" inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 15.0, output = 60.0 } -constraints = ["temperature_must_be_1"] +valued_constraints = { fixed_temperature = 1 } [o3-mini] model_id = "o3-mini-2025-01-31" inputs = ["text"] outputs = ["text", "structured"] costs = { input = 1.1, output = 4.4 } -constraints = ["temperature_must_be_1"] +valued_constraints = { fixed_temperature = 1 } [o3] model_id = "o3-2025-04-16" inputs = ["text"] outputs = ["text", "structured"] costs = { input = 2, output = 8 } -constraints = ["temperature_must_be_1"] +valued_constraints = { fixed_temperature = 1 } # --- GPT-5 Series ------------------------------------------------------------- [gpt-5-mini] @@ -97,24 +97,64 @@ model_id = "gpt-5-mini-2025-08-07" inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 0.25, output = 2.0 } +valued_constraints = { fixed_temperature = 1 } [gpt-5-nano] model_id = "gpt-5-nano-2025-08-07" inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 0.05, output = 0.4 } +valued_constraints = { fixed_temperature = 1 } [gpt-5-chat] model_id = "gpt-5-chat-2025-08-07" inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } [gpt-5] model_id = "gpt-5-2025-08-07" inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +# --- GPT-5.1 Series ------------------------------------------------------------- +["gpt-5.1"] +model_id = "gpt-5.1-2025-11-13" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +["gpt-5.1-chat"] +model_id = "gpt-5.1-chat-2025-11-13" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +["gpt-5.1-codex"] +model_id = "gpt-5.1-codex-2025-11-13" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +# --- GPT-5.2 Series ------------------------------------------------------------- +["gpt-5.2"] +model_id = "gpt-5.2-2025-12-11" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.75, output = 14.0 } + +["gpt-5.2-chat"] +model_id = "gpt-5.2-chat-2025-12-11" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } ################################################################################ # IMAGE GENERATION MODELS @@ -122,8 +162,52 @@ costs = { input = 1.25, output = 10.0 } # --- OpenAI Image Generation -------------------------------------------------- [gpt-image-1] +sdk = "azure_rest_img_gen" model_type = "img_gen" model_id = "gpt-image-1-2025-04-15" inputs = ["text"] outputs = ["image"] -costs = { input = 0.04, output = 0.0 } +costs = { input = 10, output = 40 } + +[gpt-image-1.rules] +prompt = "positive_only" +num_images = "gpt" +aspect_ratio = "gpt" +background = "gpt" +inference = "gpt" +safety_checker = "unavailable" +output_format = "gpt" + +[gpt-image-1-mini] +sdk = "azure_rest_img_gen" +model_type = "img_gen" +model_id = "gpt-image-1-mini-2025-10-06" +inputs = ["text"] +outputs = ["image"] +costs = { input = 2.5, output = 8 } + +[gpt-image-1-mini.rules] +prompt = "positive_only" +num_images = "gpt" +aspect_ratio = "gpt" +background = "gpt" +inference = "gpt" +safety_checker = "unavailable" +output_format = "gpt" + +["gpt-image-1.5"] +sdk = "azure_rest_img_gen" +model_type = "img_gen" +model_id = "gpt-image-1.5-2025-12-16" +inputs = ["text"] +outputs = ["image"] +costs = { input = 8, output = 32 } + +["gpt-image-1.5".rules] +prompt = "positive_only" +num_images = "gpt" +aspect_ratio = "gpt" +background = "gpt" +inference = "gpt" +safety_checker = "unavailable" +output_format = "gpt" diff --git a/.pipelex/inference/backends/bedrock.toml b/.pipelex/inference/backends/bedrock.toml index cea9134..dc3a566 100644 --- a/.pipelex/inference/backends/bedrock.toml +++ b/.pipelex/inference/backends/bedrock.toml @@ -60,7 +60,7 @@ costs = { input = 3.0, output = 15.0 } sdk = "bedrock_anthropic" model_id = "us.anthropic.claude-3-7-sonnet-20250219-v1:0" max_tokens = 8192 -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] max_prompt_images = 100 costs = { input = 3.0, output = 15.0 } @@ -69,7 +69,7 @@ costs = { input = 3.0, output = 15.0 } sdk = "bedrock_anthropic" model_id = "us.anthropic.claude-sonnet-4-20250514-v1:0" max_tokens = 64000 -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] max_prompt_images = 100 costs = { input = 3.0, output = 15.0 } @@ -78,7 +78,7 @@ costs = { input = 3.0, output = 15.0 } sdk = "bedrock_anthropic" model_id = "us.anthropic.claude-opus-4-20250514-v1:0" max_tokens = 32000 -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] max_prompt_images = 100 costs = { input = 3.0, output = 15.0 } @@ -87,7 +87,7 @@ costs = { input = 3.0, output = 15.0 } sdk = "bedrock_anthropic" model_id = "us.anthropic.claude-opus-4-1-20250805-v1:0" max_tokens = 32000 -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] max_prompt_images = 100 costs = { input = 3.0, output = 15.0 } @@ -95,8 +95,8 @@ costs = { input = 3.0, output = 15.0 } ["claude-4.5-sonnet"] sdk = "bedrock_anthropic" model_id = "us.anthropic.claude-sonnet-4-5-20250929-v1:0" -max_tokens = 8192 -inputs = ["text", "images"] +max_tokens = 64000 +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] max_prompt_images = 100 costs = { input = 3.0, output = 15.0 } @@ -104,8 +104,17 @@ costs = { input = 3.0, output = 15.0 } ["claude-4.5-haiku"] sdk = "bedrock_anthropic" model_id = "us.anthropic.claude-haiku-4-5-20251001-v1:0" -max_tokens = 8192 -inputs = ["text", "images"] +max_tokens = 64000 +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] max_prompt_images = 100 costs = { input = 1.0, output = 5.0 } + +["claude-4.5-opus"] +sdk = "bedrock_anthropic" +model_id = "global.anthropic.claude-opus-4-5-20251101-v1:0" +max_tokens = 64000 +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 5.0, output = 25.0 } diff --git a/.pipelex/inference/backends/blackboxai.toml b/.pipelex/inference/backends/blackboxai.toml index 405675b..ee8a105 100644 --- a/.pipelex/inference/backends/blackboxai.toml +++ b/.pipelex/inference/backends/blackboxai.toml @@ -73,7 +73,7 @@ inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 3.00, output = 15.00 } -[claude-opus-4] +[claude-4-opus] model_id = "blackboxai/anthropic/claude-opus-4" inputs = ["text", "images"] outputs = ["text", "structured"] @@ -104,12 +104,6 @@ inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 1.25, output = 10.00 } -["gemini-flash-1.5-8b"] -model_id = "blackboxai/google/gemini-flash-1.5-8b" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.04, output = 0.15 } - # --- Mistral Models ----------------------------------------------------------- [mistral-large] model_id = "blackboxai/mistralai/mistral-large" @@ -167,13 +161,13 @@ costs = { input = 0.06, output = 0.24 } ################################################################################ # --- DeepSeek Free Models ----------------------------------------------------- -[deepseek-chat-free] +[deepseek-chat] model_id = "blackboxai/deepseek/deepseek-chat:free" inputs = ["text"] outputs = ["text", "structured"] costs = { input = 0.00, output = 0.00 } -[deepseek-r1-free] +[deepseek-r1] model_id = "blackboxai/deepseek/deepseek-r1:free" inputs = ["text"] outputs = ["text", "structured"] @@ -193,7 +187,7 @@ costs = { input = 0.00, output = 0.00 } [flux-pro] model_type = "img_gen" -sdk = "openai_alt_img_gen" +sdk = "blackboxai_img_gen" model_id = "blackboxai/black-forest-labs/flux-pro" inputs = ["text"] outputs = ["image"] @@ -201,7 +195,7 @@ costs = { input = 0.0, output = 0.04 } ["flux-pro/v1.1"] model_type = "img_gen" -sdk = "openai_alt_img_gen" +sdk = "blackboxai_img_gen" model_id = "blackboxai/black-forest-labs/flux-1.1-pro" inputs = ["text"] outputs = ["image"] @@ -209,15 +203,15 @@ costs = { input = 0.0, output = 0.04 } ["flux-pro/v1.1-ultra"] model_type = "img_gen" -sdk = "openai_alt_img_gen" +sdk = "blackboxai_img_gen" model_id = "blackboxai/black-forest-labs/flux-1.1-pro-ultra" inputs = ["text"] outputs = ["image"] costs = { input = 0.0, output = 0.06 } -["fast-lightning-sdxl"] +[fast-lightning-sdxl] model_type = "img_gen" -sdk = "openai_alt_img_gen" +sdk = "blackboxai_img_gen" model_id = "blackboxai/bytedance/sdxl-lightning-4step" inputs = ["text"] outputs = ["image"] @@ -225,8 +219,16 @@ costs = { input = 0.0, output = 0.0014 } [nano-banana] model_type = "img_gen" -sdk = "openai_alt_img_gen" +sdk = "blackboxai_img_gen" model_id = "blackboxai/google/nano-banana" inputs = ["text"] outputs = ["image"] costs = { input = 0.0, output = 0.039 } + +[nano-banana-pro] +model_type = "img_gen" +sdk = "blackboxai_img_gen" +model_id = "blackboxai/google/nano-banana-pro" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.0, output = 0.039 } diff --git a/.pipelex/inference/backends/fal.toml b/.pipelex/inference/backends/fal.toml index d54a25f..3433f99 100644 --- a/.pipelex/inference/backends/fal.toml +++ b/.pipelex/inference/backends/fal.toml @@ -36,18 +36,60 @@ inputs = ["text"] outputs = ["image"] costs = { input = 0.05, output = 0.0 } +[flux-pro.rules] +prompt = "positive_only" +num_images = "fal" +aspect_ratio = "flux" +inference = "flux" +safety_checker = "available" +output_format = "flux_1" +specific = "fal" + ["flux-pro/v1.1"] model_id = "fal-ai/flux-pro/v1.1" inputs = ["text"] outputs = ["image"] costs = { input = 0.05, output = 0.0 } +["flux-pro/v1.1".rules] +prompt = "positive_only" +num_images = "fal" +aspect_ratio = "flux" +inference = "flux" +safety_checker = "available" +output_format = "flux_1" +specific = "fal" + ["flux-pro/v1.1-ultra"] model_id = "fal-ai/flux-pro/v1.1-ultra" inputs = ["text"] outputs = ["image"] costs = { input = 0.06, output = 0.0 } +["flux-pro/v1.1-ultra".rules] +prompt = "positive_only" +num_images = "fal" +aspect_ratio = "flux_11_ultra" +inference = "flux_11_ultra" +safety_checker = "available" +output_format = "flux_1" +specific = "fal" + +[flux-2] +model_id = "fal-ai/flux-2" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.05, output = 0.0 } + +[flux-2.rules] +prompt = "positive_only" +num_images = "fal" +aspect_ratio = "flux" +inference = "flux" +safety_checker = "available" +output_format = "flux_2" +specific = "fal" + # --- SDXL models -------------------------------------------------------------- [fast-lightning-sdxl] model_id = "fal-ai/fast-lightning-sdxl" @@ -55,3 +97,11 @@ inputs = ["text"] outputs = ["image"] costs = { input = 0.0003, output = 0.0 } +[fast-lightning-sdxl.rules] +prompt = "positive_only" +num_images = "fal" +aspect_ratio = "flux" +inference = "sdxl_lightning" +safety_checker = "unavailable" +output_format = "sdxl" +specific = "fal" diff --git a/.pipelex/inference/backends/google.toml b/.pipelex/inference/backends/google.toml index ff0040a..de001aa 100644 --- a/.pipelex/inference/backends/google.toml +++ b/.pipelex/inference/backends/google.toml @@ -33,7 +33,7 @@ structure_method = "instructor/genai_tools" # --- Gemini 2.0 Series ---------------------------------------- ["gemini-2.0-flash"] model_id = "gemini-2.0-flash" -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] max_prompt_images = 3000 costs = { input = 0.10, output = 0.40 } @@ -41,21 +41,54 @@ costs = { input = 0.10, output = 0.40 } # --- Gemini 2.5 Series ---------------------------------------- ["gemini-2.5-pro"] model_id = "gemini-2.5-pro" -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] max_prompt_images = 3000 costs = { input = 1.25, output = 10.0 } ["gemini-2.5-flash"] model_id = "gemini-2.5-flash" -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] max_prompt_images = 3000 costs = { input = 0.30, output = 2.50 } ["gemini-2.5-flash-lite"] model_id = "gemini-2.5-flash-lite" -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] max_prompt_images = 3000 costs = { input = 0.10, output = 0.40 } + +# --- Gemini 3.0 Series ---------------------------------------- +["gemini-3.0-pro"] +model_id = "gemini-3-pro-preview" +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 2, output = 12.0 } + +["gemini-3.0-flash-preview"] +model_id = "gemini-3-flash-preview" +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 0.5, output = 3.0 } + +################################################################################ +# IMAGE GENERATION MODELS (Nano Banana) +################################################################################ + +[nano-banana] +model_type = "img_gen" +model_id = "gemini-2.5-flash-image" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.0, output = 0.039 } + +[nano-banana-pro] +model_type = "img_gen" +model_id = "gemini-3-pro-image-preview" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.0, output = 0.039 } diff --git a/.pipelex/inference/backends/groq.toml b/.pipelex/inference/backends/groq.toml index 72fc0b8..72bdae3 100644 --- a/.pipelex/inference/backends/groq.toml +++ b/.pipelex/inference/backends/groq.toml @@ -46,7 +46,7 @@ outputs = ["text", "structured"] costs = { input = 0.59, output = 0.79 } # --- Meta Llama Guard --------------------------------------------------------- -["meta-llama/llama-guard-4-12b"] +[llama-guard-4-12b] model_id = "meta-llama/llama-guard-4-12b" max_tokens = 1024 inputs = ["text"] @@ -54,14 +54,14 @@ outputs = ["text", "structured"] costs = { input = 0.20, output = 0.20 } # --- OpenAI GPT-OSS Models ---------------------------------------------------- -["openai/gpt-oss-20b"] +[gpt-oss-20b] model_id = "openai/gpt-oss-20b" max_tokens = 65536 inputs = ["text"] outputs = ["text", "structured"] costs = { input = 0.075, output = 0.30 } -["openai/gpt-oss-120b"] +[gpt-oss-120b] model_id = "openai/gpt-oss-120b" max_tokens = 65536 inputs = ["text"] @@ -88,7 +88,7 @@ costs = { input = 0.10, output = 0.30 } ################################################################################ # --- Meta Llama 4 Vision Models (Preview) ------------------------------------- -["meta-llama/llama-4-scout-17b-16e-instruct"] +[llama-4-scout-17b-16e-instruct] model_id = "meta-llama/llama-4-scout-17b-16e-instruct" max_tokens = 8192 inputs = ["text", "images"] @@ -96,7 +96,7 @@ outputs = ["text", "structured"] max_prompt_images = 5 costs = { input = 0.11, output = 0.34 } -["meta-llama/llama-4-maverick-17b-128e-instruct"] +[llama-4-maverick-17b-128e-instruct] model_id = "meta-llama/llama-4-maverick-17b-128e-instruct" max_tokens = 8192 inputs = ["text", "images"] @@ -105,7 +105,7 @@ max_prompt_images = 5 costs = { input = 0.20, output = 0.60 } # --- Moonshot Kimi K2 --------------------------------------------------------- -["moonshotai/kimi-k2-instruct-0905"] +[kimi-k2-instruct-0905] model_id = "moonshotai/kimi-k2-instruct-0905" max_tokens = 16384 inputs = ["text"] @@ -113,7 +113,7 @@ outputs = ["text", "structured"] costs = { input = 1.00, output = 3.00 } # --- OpenAI Safety Model ------------------------------------------------------ -["openai/gpt-oss-safeguard-20b"] +[gpt-oss-safeguard-20b] model_id = "openai/gpt-oss-safeguard-20b" max_tokens = 65536 inputs = ["text"] @@ -121,7 +121,7 @@ outputs = ["text", "structured"] costs = { input = 0.075, output = 0.30 } # --- Qwen 3 ------------------------------------------------------------------- -["qwen/qwen3-32b"] +[qwen3-32b] model_id = "qwen/qwen3-32b" max_tokens = 40960 inputs = ["text"] diff --git a/.pipelex/inference/backends/huggingface.toml b/.pipelex/inference/backends/huggingface.toml new file mode 100644 index 0000000..1a79638 --- /dev/null +++ b/.pipelex/inference/backends/huggingface.toml @@ -0,0 +1,43 @@ +################################################################################ +# Hugging Face Backend Configuration +################################################################################ +# +# This file defines the model specifications for Hugging Face models. +# It contains model definitions for various image generation models +# accessible through the Hugging Face Inference API with provider="auto". +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots or slashes must be quoted (e.g., ["stabilityai/stable-diffusion-2-1"]) +# - Model costs are in USD per million tokens (input/output) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "img_gen" +sdk = "huggingface_img_gen" + +################################################################################ +# IMAGE GENERATION MODELS +################################################################################ + +# --- Qwen Image Models -------------------------------------------------- +[qwen-image] +model_id = "Qwen/Qwen-Image" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.0, output = 0.0 } +variant = "fal-ai" +# variant = "replicate" + +[qwen-image.rules] +prompt = "with_negative" +aspect_ratio = "qwen_image" +inference = "qwen_image" diff --git a/.pipelex/inference/backends/internal.toml b/.pipelex/inference/backends/internal.toml index 5bb9683..e44b222 100644 --- a/.pipelex/inference/backends/internal.toml +++ b/.pipelex/inference/backends/internal.toml @@ -19,7 +19,7 @@ ################################################################################ # --- PyPDFium2 Text Extractor ------------------------------------------------- -[pypdfium2-extract-text] +[pypdfium2-extract-pdf] model_type = "text_extractor" sdk = "pypdfium2" model_id = "extract-text" @@ -27,3 +27,11 @@ inputs = ["pdf"] outputs = ["pages"] costs = {} +# --- Docling Text Extractor --------------------------------------------------- +[docling-extract-text] +model_type = "text_extractor" +sdk = "docling_sdk" +model_id = "extract-text" +inputs = ["pdf", "image"] +outputs = ["pages"] +costs = {} diff --git a/.pipelex/inference/backends/mistral.toml b/.pipelex/inference/backends/mistral.toml index c4b49e3..e131f8e 100644 --- a/.pipelex/inference/backends/mistral.toml +++ b/.pipelex/inference/backends/mistral.toml @@ -118,14 +118,14 @@ costs = { input = 2.0, output = 6.0 } [mistral-medium] model_id = "mistral-medium-latest" max_tokens = 128000 -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] costs = { input = 0.4, output = 2.0 } [mistral-medium-2508] model_id = "mistral-medium-2508" max_tokens = 128000 -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] costs = { input = 0.4, output = 2.0 } @@ -133,11 +133,32 @@ costs = { input = 0.4, output = 2.0 } # EXTRACTION MODELS ################################################################################ -# --- OCR Models --------------------------------------------------------------- +# TODO: add support to pricing per page + +[mistral-ocr-2503] +model_type = "text_extractor" +model_id = "mistral-ocr-2503" +max_tokens = 16384 +inputs = ["pdf", "image"] +outputs = ["pages"] + +[mistral-ocr-2505] +model_type = "text_extractor" +model_id = "mistral-ocr-2505" +max_tokens = 16384 +inputs = ["pdf", "image"] +outputs = ["pages"] + +[mistral-ocr-2512] +model_type = "text_extractor" +model_id = "mistral-ocr-2512" +max_tokens = 16384 +inputs = ["pdf", "image"] +outputs = ["pages"] + [mistral-ocr] model_type = "text_extractor" model_id = "mistral-ocr-latest" -max_tokens = 131072 +max_tokens = 16384 inputs = ["pdf", "image"] outputs = ["pages"] -costs = { input = 0.4, output = 2.0 } diff --git a/.pipelex/inference/backends/openai.toml b/.pipelex/inference/backends/openai.toml index 4853332..0f0b1dc 100644 --- a/.pipelex/inference/backends/openai.toml +++ b/.pipelex/inference/backends/openai.toml @@ -22,9 +22,9 @@ [defaults] model_type = "llm" -sdk = "openai" +sdk = "openai_responses" prompting_target = "openai" -structure_method = "instructor/openai_tools" +structure_method = "instructor/openai_responses_tools" ################################################################################ # LANGUAGE MODELS @@ -39,123 +39,146 @@ costs = { input = 0.5, output = 1.5 } # --- GPT-4 Series ------------------------------------------------------------- [gpt-4] -model_id = "gpt-4" +inputs = ["text"] +outputs = ["text"] costs = { input = 30.0, output = 60.0 } [gpt-4-turbo] -model_id = "gpt-4-turbo" inputs = ["text"] outputs = ["text", "structured"] costs = { input = 10.0, output = 30.0 } # --- GPT-4o Series ------------------------------------------------------------ [gpt-4o-2024-11-20] -model_id = "gpt-4o-2024-11-20" -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] costs = { input = 2.5, output = 10.0 } [gpt-4o] -model_id = "gpt-4o" -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] costs = { input = 2.5, output = 10.0 } [gpt-4o-mini-2024-07-18] -model_id = "gpt-4o-mini-2024-07-18" -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] costs = { input = 0.15, output = 0.6 } [gpt-4o-mini] -model_id = "gpt-4o-mini" -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] costs = { input = 0.15, output = 0.6 } # --- GPT-4.1 Series ----------------------------------------------------------- ["gpt-4.1"] -model_id = "gpt-4.1" -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] costs = { input = 2, output = 8 } ["gpt-4.1-mini"] -model_id = "gpt-4.1-mini" -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] costs = { input = 0.4, output = 1.6 } ["gpt-4.1-nano"] -model_id = "gpt-4.1-nano" -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] costs = { input = 0.1, output = 0.4 } # --- o Series ---------------------------------------------------------------- -[o1-mini] -model_id = "o1-mini" -inputs = ["text"] -outputs = ["text"] -costs = { input = 3.0, output = 12.0 } -constraints = ["temperature_must_be_1"] - [o1] -model_id = "o1" -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] costs = { input = 15.0, output = 60.0 } -constraints = ["temperature_must_be_1"] +valued_constraints = { fixed_temperature = 1 } [o3-mini] -model_id = "o3-mini" inputs = ["text"] outputs = ["text", "structured"] costs = { input = 1.1, output = 4.4 } -constraints = ["temperature_must_be_1"] +valued_constraints = { fixed_temperature = 1 } [o3] -model_id = "o3" -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] costs = { input = 10.0, output = 40.0 } -constraints = ["temperature_must_be_1"] +valued_constraints = { fixed_temperature = 1 } [o4-mini] -model_id = "o4-mini" inputs = ["text"] outputs = ["text", "structured"] costs = { input = 1.1, output = 4.4 } -constraints = ["temperature_must_be_1"] +valued_constraints = { fixed_temperature = 1 } # --- GPT-5 Series ------------------------------------------------------------- [gpt-5] -model_id = "gpt-5" -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] costs = { input = 1.25, output = 10.0 } -constraints = ["temperature_must_be_1"] +valued_constraints = { fixed_temperature = 1 } [gpt-5-mini] -model_id = "gpt-5-mini" -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] costs = { input = 0.25, output = 2.0 } -constraints = ["temperature_must_be_1"] +valued_constraints = { fixed_temperature = 1 } [gpt-5-nano] -model_id = "gpt-5-nano" -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text", "structured"] costs = { input = 0.05, output = 0.4 } -constraints = ["temperature_must_be_1"] +valued_constraints = { fixed_temperature = 1 } [gpt-5-chat] model_id = "gpt-5-chat-latest" -inputs = ["text", "images"] +inputs = ["text", "images", "pdf"] outputs = ["text"] costs = { input = 1.25, output = 10.0 } -constraints = ["temperature_must_be_1"] +valued_constraints = { fixed_temperature = 1 } + +[gpt-5-codex] +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +# --- GPT-5.1 Series ------------------------------------------------------------- +["gpt-5.1"] +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } + +["gpt-5.1-chat"] +model_id = "gpt-5.1-chat-latest" +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +["gpt-5.1-codex"] +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +["gpt-5.1-codex-max"] +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +# --- GPT-5.2 Series ------------------------------------------------------------- +["gpt-5.2"] +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 1.75, output = 14.0 } + +["gpt-5.2-chat"] +model_id = "gpt-5.2-chat-latest" +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 1.75, output = 14.0 } +valued_constraints = { fixed_temperature = 1 } ################################################################################ # IMAGE GENERATION MODELS @@ -163,8 +186,23 @@ constraints = ["temperature_must_be_1"] # --- OpenAI Image Generation -------------------------------------------------- [gpt-image-1] +sdk = "openai_img_gen" +model_type = "img_gen" +inputs = ["text"] +outputs = ["image"] +costs = { input = 10, output = 40 } + +[gpt-image-1-mini] +sdk = "openai_img_gen" +model_type = "img_gen" +inputs = ["text"] +outputs = ["image"] +costs = { input = 2.5, output = 8 } + +["gpt-image-1.5"] +sdk = "openai_img_gen" model_type = "img_gen" -model_id = "gpt-image-1" +model_id = "gpt-image-1.5" inputs = ["text"] outputs = ["image"] -costs = { input = 0.04, output = 0.0 } +costs = { input = 8, output = 32 } diff --git a/.pipelex/inference/backends/pipelex_gateway.toml b/.pipelex/inference/backends/pipelex_gateway.toml new file mode 100644 index 0000000..bca075b --- /dev/null +++ b/.pipelex/inference/backends/pipelex_gateway.toml @@ -0,0 +1,41 @@ +################################################################################ +# Pipelex Gateway Local Overrides +################################################################################ +# +# TELEMETRY NOTICE: +# +# Using Pipelex Gateway enables identified telemetry tied to your API key +# (hashed for security). This is independent from your telemetry.toml settings. +# +# We collect only technical data (model names, token counts, latency, error rates). +# We do NOT collect prompts, completions, pipe codes, or business data. +# +# This allows us to monitor service quality, enforce fair usage, and support you. +# +################################################################################ +# +# WARNING: USE AT YOUR OWN RISK! +# +# The actual model configuration is fetched remotely from Pipelex servers. +# Any override in this file may cause unexpected behavior or failures, +# as the remote configuration may change at any time. +# +# If you must override, you may ONLY use these keys per model: +# - sdk +# - structure_method +# +# All other keys will be ignored. +# +# If you need custom configurations, consider using your own API keys +# with direct provider backends (openai, anthropic, etc.) instead. +# +# Documentation: +# https://docs.pipelex.com/home/7-configuration/config-technical/inference-backend-config/ +# Support: https://go.pipelex.com/discord +# +################################################################################ + +# Per-model overrides example: +# [gpt-4o] +# sdk = "gateway_completions" +# structure_method = "instructor/openai_tools" diff --git a/.pipelex/inference/backends/pipelex_inference.toml b/.pipelex/inference/backends/pipelex_inference.toml index 655105a..751c570 100644 --- a/.pipelex/inference/backends/pipelex_inference.toml +++ b/.pipelex/inference/backends/pipelex_inference.toml @@ -36,54 +36,88 @@ model_id = "pipelex/gpt-4o" inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 2.75, output = 11.00 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" [gpt-4o-mini] model_id = "pipelex/gpt-4o-mini" inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 0.17, output = 0.66 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" ["gpt-4.1"] model_id = "pipelex/gpt-4.1" inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 2, output = 8 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" ["gpt-4.1-mini"] model_id = "pipelex/gpt-4.1-mini" inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 0.4, output = 1.6 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" ["gpt-4.1-nano"] model_id = "pipelex/gpt-4.1-nano" inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 0.1, output = 0.4 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" [gpt-5-nano] model_id = "pipelex/gpt-5-nano" inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 0.05, output = 0.40 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" [gpt-5-mini] model_id = "pipelex/gpt-5-mini" inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 0.25, output = 2.00 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" [gpt-5-chat] model_id = "pipelex/gpt-5-chat" inputs = ["text", "images"] outputs = ["text"] costs = { input = 1.25, output = 10.00 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" [gpt-5] model_id = "pipelex/gpt-5" inputs = ["text", "images"] outputs = ["text"] costs = { input = 1.25, output = 10.00 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" + +["gpt-5.1"] +model_id = "pipelex/gpt-5.1" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.00 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" + +["gpt-5.1-chat"] +model_id = "pipelex/gpt-5.1-chat" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.00 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" # --- Claude LLMs -------------------------------------------------------------- ["claude-4-sonnet"] @@ -110,32 +144,45 @@ inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 1, output = 5 } +["claude-4.5-opus"] +model_id = "pipelex/claude-4.5-opus" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 5, output = 25 } + # --- Gemini LLMs -------------------------------------------------------------- ["gemini-2.0-flash"] -model_id = "gemini/gemini-2.0-flash" +model_id = "pipelex/gemini-2.0-flash" inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 0.10, output = 0.40 } ["gemini-2.5-pro"] -model_id = "gemini/gemini-2.5-pro" +model_id = "pipelex/gemini-2.5-pro" inputs = ["text", "images"] outputs = ["text", "structured"] max_prompt_images = 3000 costs = { input = 1.25, output = 10.0 } ["gemini-2.5-flash"] -model_id = "gemini/gemini-2.5-flash" +model_id = "pipelex/gemini-2.5-flash" inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 0.30, output = 2.50 } ["gemini-2.5-flash-lite"] -model_id = "gemini/gemini-2.5-flash-lite" +model_id = "pipelex/gemini-2.5-flash-lite" inputs = ["text", "images"] outputs = ["text", "structured"] costs = { input = 0.10, output = 0.40 } +["gemini-3.0-pro"] +model_id = "pipelex/gemini-3.0-pro" +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 2, output = 12.0 } + # --- XAI LLMs -------------------------------------------------------------- [grok-3] diff --git a/.pipelex/inference/backends/portkey.toml b/.pipelex/inference/backends/portkey.toml new file mode 100644 index 0000000..9a7ede7 --- /dev/null +++ b/.pipelex/inference/backends/portkey.toml @@ -0,0 +1,263 @@ +################################################################################ +# Portkey Configuration +################################################################################ +# +# This file defines the model specifications for the Portkey backend. +# It contains model definitions for various AI models. +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots must be quoted (e.g., ["gpt-4.1"]) +# - Model costs are in USD per million tokens (input/output) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "llm" +sdk = "portkey_completions" +structure_method = "instructor/openai_tools" +prompting_target = "anthropic" + +################################################################################ +# LANGUAGE MODELS +################################################################################ + +# --- OpenAI LLMs -------------------------------------------------------------- +[gpt-4o-mini] +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 0.15, output = 0.6 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +[gpt-4o] +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 2.5, output = 10.0 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +["gpt-4.1-nano"] +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 0.1, output = 0.4 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +["gpt-4.1-mini"] +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 0.4, output = 1.6 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +["gpt-4.1"] +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 2, output = 8 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +[o1] +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 15.0, output = 60.0 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +[o3-mini] +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 1.1, output = 4.4 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +[o3] +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 2, output = 8 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +[o4-mini] +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 1.1, output = 4.4 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +[gpt-5-nano] +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 0.05, output = 0.4 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +[gpt-5-mini] +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 0.25, output = 2.0 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +[gpt-5] +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +["gpt-5.1"] +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +["gpt-5.1-codex"] +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +# --- Claude LLMs -------------------------------------------------------------- +[claude-3-haiku] +model_id = "claude-3-haiku-20240307" +max_tokens = 4096 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 0.25, output = 1.25 } +x-portkey-provider = "@anthropic" + +["claude-3.7-sonnet"] +model_id = "claude-3-7-sonnet-20250219" +max_tokens = 8192 +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } +x-portkey-provider = "@anthropic" + +[claude-4-sonnet] +model_id = "claude-sonnet-4-20250514" +max_tokens = 64000 +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } +x-portkey-provider = "@anthropic" + +[claude-4-opus] +model_id = "claude-opus-4-20250514" +max_tokens = 32000 +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } +x-portkey-provider = "@anthropic" + +["claude-4.1-opus"] +model_id = "claude-opus-4-1-20250805" +max_tokens = 32000 +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } +x-portkey-provider = "@anthropic" + +["claude-4.5-sonnet"] +model_id = "claude-sonnet-4-5-20250929" +max_tokens = 64000 +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } +x-portkey-provider = "@anthropic" + +["claude-4.5-haiku"] +model_id = "claude-haiku-4-5-20251001" +max_tokens = 64000 +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 1.0, output = 5.0 } +x-portkey-provider = "@anthropic" + +["claude-4.5-opus"] +model_id = "claude-opus-4-5-20251101" +max_tokens = 64000 +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 5.0, output = 25.0 } +x-portkey-provider = "@anthropic" + +# --- Gemini LLMs -------------------------------------------------------------- +["gemini-2.0-flash"] +model_id = "gemini-2.0-flash" +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 0.10, output = 0.40 } +x-portkey-provider = "@google" + +["gemini-2.5-pro"] +model_id = "gemini-2.5-pro" +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 1.25, output = 10.0 } +x-portkey-provider = "@google" + +["gemini-2.5-flash"] +model_id = "gemini-2.5-flash" +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 0.30, output = 2.50 } +x-portkey-provider = "@google" + +["gemini-2.5-flash-lite"] +model_id = "gemini-2.5-flash-lite" +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +costs = { input = 0.10, output = 0.40 } +x-portkey-provider = "@google" + +["gemini-3.0-pro"] +model_id = "gemini-3-pro-preview" +inputs = ["text", "images", "pdf"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 2, output = 12.0 } +x-portkey-provider = "@google" diff --git a/.pipelex/inference/backends/scaleway.toml b/.pipelex/inference/backends/scaleway.toml new file mode 100644 index 0000000..20fe792 --- /dev/null +++ b/.pipelex/inference/backends/scaleway.toml @@ -0,0 +1,67 @@ +################################################################################ +# Groq Backend Configuration +################################################################################ +# +# This file defines the model specifications for Scaleway models. +# It contains model definitions for various LLM models accessible through +# the Groq API, including text-only and vision-capable models. +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots or slashes must be quoted (e.g., ["meta-llama/llama-4-scout"]) +# - Model costs are in USD per million tokens (input/output) +# - Vision models support max 5 images per request, 33MP max resolution +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "llm" +sdk = "openai" +structure_method = "instructor/json" + +# --- DeepSeek Models ---------------------------------------------------------- +[deepseek-r1-distill-llama-70b] +max_tokens = 32768 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.90, output = 0.90 } + +# --- Meta Llama 3.x Series ---------------------------------------------------- +["llama-3.1-8b-instruct"] +max_tokens = 131072 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.2, output = 0.2 } + +["llama-3.3-70b-instruct"] +max_tokens = 32768 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.90, output = 0.90 } + +# --- OpenAI GPT-OSS Models ---------------------------------------------------- +[gpt-oss-120b] +max_tokens = 65536 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.15, output = 0.60 } + +# --- Qwen 3 ------------------------------------------------------------------- +[qwen3-235b-a22b-instruct-2507] +max_tokens = 40960 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.75, output = 2.25 } + +[qwen3-coder-30b-a3b-instruct] +max_tokens = 40960 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.20, output = 0.80 } diff --git a/.pipelex/inference/deck/base_deck.toml b/.pipelex/inference/deck/base_deck.toml index 954a65d..63ab39e 100644 --- a/.pipelex/inference/deck/base_deck.toml +++ b/.pipelex/inference/deck/base_deck.toml @@ -22,20 +22,20 @@ base-gpt = "gpt-4o" base-gemini = "gemini-2.5-flash" base-mistral = "mistral-medium" base-groq = "llama-3.3-70b-versatile" +base-grok = "grok-4-fast-non-reasoning" -best-gpt = "gpt-4o" -best-claude = "claude-4.1-opus" -best-gemini = "gemini-2.5-pro" +best-gpt = "gpt-5.1" +best-claude = "claude-4.5-opus" +best-gemini = "gemini-3.0-pro" best-mistral = "mistral-medium" -best-grok = "grok-3" # Groq-specific aliases fast-groq = "llama-3.1-8b-instant" -vision-groq = "meta-llama/llama-4-scout-17b-16e-instruct" +vision-groq = "llama-4-scout-17b-16e-instruct" # Image generation aliases base-img-gen = "flux-pro/v1.1" -best-img-gen = "flux-pro/v1.1-ultra" +best-img-gen = "flux-2" fast-img-gen = "fast-lightning-sdxl" #################################################################################################### @@ -43,35 +43,49 @@ fast-img-gen = "fast-lightning-sdxl" #################################################################################################### [waterfalls] + +# --- Waterfalls for LLMs --------------------------------------------------------------------- smart_llm = [ + "claude-4.5-opus", "claude-4.5-sonnet", + "gemini-3.0-pro", + "gpt-5.1", "claude-4.1-opus", - "claude-4-sonnet", "gemini-2.5-pro", - "gpt-4o", # we're using gpt-4o here rather than gpt-5 until we'll handle reasoning parameters - "grok-3", + "claude-4-sonnet", + "grok-4-fast-non-reasoning", ] smart_llm_with_vision = [ + "claude-4.5-opus", "claude-4.5-sonnet", + "gemini-3.0-pro", + "gpt-5.1", "claude-4.1-opus", - "claude-4-sonnet", "gemini-2.5-pro", - "gpt-4o", # we use gpt-4o here rather than gpt-5 until we handle reasoning parameters - "grok-3", + "claude-4-sonnet", + "grok-4-fast-non-reasoning", ] smart_llm_for_structured = [ + "claude-4.5-opus", "claude-4.5-sonnet", + "gemini-3.0-pro", + "gpt-5.1", "claude-4.1-opus", "claude-4-sonnet", - "gpt-4o", # we use gpt-4o here rather than gpt-5 until we handle reasoning parameters ] -llm_for_creativity = ["claude-4.1-opus", "gemini-2.5-pro", "gpt-4o"] +llm_for_creativity = [ + "claude-4.5-opus", + "claude-4.1-opus", + "gemini-2.5-pro", + "gpt-5.1", +] llm_for_large_codebase = [ "gemini-2.5-pro", "claude-4.5-sonnet", - "gpt-4o", # we use gpt-4o here rather than gpt-5 until we handle reasoning parameters + "gemini-3.0-pro", + "gpt-5.1", "gemini-2.5-flash", - "grok-3", + "grok-4-fast-non-reasoning", ] cheap_llm = [ "gpt-4o-mini", @@ -81,8 +95,8 @@ cheap_llm = [ "grok-3-mini", ] cheap_llm_for_vision = [ - "gpt-4o-mini", "gemini-2.5-flash-lite", + "gpt-4o-mini", "claude-3-haiku", ] cheap_llm_for_structured = ["gpt-4o-mini", "mistral-small", "claude-3-haiku"] @@ -93,6 +107,14 @@ cheap_llm_for_creativity = [ "claude-4.5-haiku", ] +# --- Waterfalls for Extracts --------------------------------------------------------------------- +pdf_text_extractor = [ + "azure-document-intelligence", + "mistral-ocr", + "pypdfium2-extract-pdf", +] +image_text_extractor = ["mistral-ocr"] + #################################################################################################### # LLM Presets #################################################################################################### @@ -119,7 +141,7 @@ llm_to_analyze_large_codebase = { model = "base-claude", temperature = 0.1 } # Vision skills llm_for_img_to_text_cheap = { model = "gpt-4o-mini", temperature = 0.1 } llm_for_img_to_text = { model = "base-claude", temperature = 0.1 } -llm_for_diagram_to_text = { model = "base-claude", temperature = 0.3 } +llm_for_diagram_to_text = { model = "best-claude", temperature = 0.3 } llm_for_table_to_text = { model = "base-claude", temperature = 0.3 } # Image generation prompting skills @@ -151,11 +173,11 @@ for_object = "cheap_llm_for_structured" #################################################################################################### [extract] -choice_default = "extract_text_from_visuals" +choice_default = "extract_ocr_from_document" [extract.presets] -extract_text_from_visuals = { model = "mistral-ocr", max_nb_images = 100, image_min_size = 50 } -extract_text_from_pdf = { model = "pypdfium2-extract-text", max_nb_images = 100, image_min_size = 50 } +extract_ocr_from_document = { model = "azure-document-intelligence", max_nb_images = 100, image_min_size = 50 } +extract_basic_from_pdf = { model = "pypdfium2-extract-pdf", max_nb_images = 100, image_min_size = 50 } #################################################################################################### # Image Generation Presets @@ -170,6 +192,7 @@ choice_default = "gen_image_basic" gen_image_basic = { model = "base-img-gen", quality = "medium", guidance_scale = 7.5, is_moderated = true, safety_tolerance = 3 } gen_image_fast = { model = "fast-img-gen", nb_steps = 4, guidance_scale = 5.0, is_moderated = true, safety_tolerance = 3 } gen_image_high_quality = { model = "best-img-gen", quality = "high", guidance_scale = 8.0, is_moderated = true, safety_tolerance = 3 } +gen_image_openai_low_quality = { model = "gpt-image-1", quality = "low" } # Specific skills img_gen_for_art = { model = "best-img-gen", quality = "high", guidance_scale = 9.0, is_moderated = false, safety_tolerance = 5 } diff --git a/.pipelex/inference/routing_profiles.toml b/.pipelex/inference/routing_profiles.toml index fa3f1d4..bf40281 100644 --- a/.pipelex/inference/routing_profiles.toml +++ b/.pipelex/inference/routing_profiles.toml @@ -9,47 +9,53 @@ # ========================================================================================= # Which profile to use (change this to switch routing) -active = "pipelex_first" +active = "pipelex_gateway_first" -# We recommend using the "pipelex_first" profile to get a head start with all models. -# The Pipelex Inference backend is currently not recommended for production use, -# but it's great for development and testing. -# To use the Pipelex Inference backend (pipelex_first profile): +# We recommend using the "pipelex_gateway_first" profile to get a head start with all models. +# To use the Pipelex Gateway backend: # 1. Join our Discord community to get your free API key (no credit card required): # Visit https://go.pipelex.com/discord and request your key in the appropriate channel -# 2. Set the environment variable: export PIPELEX_INFERENCE_API_KEY="your-api-key" -# 3. The .pipelex/inference/backends.toml is already configured with api_key = "${PIPELEX_INFERENCE_API_KEY}" -# which will get the key from the environment variable. +# 2. Set the environment variable (or add it to your .env file): +# - Linux/macOS: export PIPELEX_GATEWAY_API_KEY="your-api-key" +# - Windows CMD: set PIPELEX_GATEWAY_API_KEY=your-api-key +# - Windows PowerShell: $env:PIPELEX_GATEWAY_API_KEY="your-api-key" +# 3. The .pipelex/inference/backends.toml is already configured with api_key = "${PIPELEX_GATEWAY_API_KEY}" +# which will get the key from the environment variable. # ========================================================================================= # Routing Profiles # ========================================================================================= -[profiles.pipelex_first] -description = "Use Pipelex Inference backend for all its supported models" -default = "pipelex_inference" +[profiles.pipelex_gateway_first] +description = "Use Pipelex Gateway backend for all its supported models" +default = "pipelex_gateway" fallback_order = [ - "pipelex_inference", + "pipelex_gateway", "azure_openai", "bedrock", + "google", "blackboxai", "mistral", "fal", ] -[profiles.pipelex_first.routes] +[profiles.pipelex_gateway_first.routes] # Pattern matching: "model-pattern" = "backend-name" -"gpt-*" = "pipelex_inference" -"claude-*" = "pipelex_inference" -"grok-*" = "pipelex_inference" -"gemini-*" = "pipelex_inference" -[profiles.pipelex_first.optional_routes] # Each optional route is considered only if its backend is available +[profiles.pipelex_gateway_first.optional_routes] # Each optional route is considered only if its backend is available +"gpt-*" = "pipelex_gateway" +"gpt-image-1" = "openai" +"claude-*" = "pipelex_gateway" +"grok-*" = "pipelex_gateway" +"gemini-*" = "pipelex_gateway" "*-sdxl" = "fal" "flux-*" = "fal" -"gpt-image-1" = "openai" "mistral-ocr" = "mistral" +[profiles.all_pipelex_gateway] +description = "Use Pipelex Gateway for all its supported models" +default = "pipelex_gateway" + [profiles.all_anthropic] description = "Use Anthropic backend for all its supported models" default = "anthropic" @@ -78,6 +84,10 @@ default = "google" description = "Use groq backend for all its supported models" default = "groq" +[profiles.all_huggingface] +description = "Use HuggingFace backend for all its supported models" +default = "huggingface" + [profiles.all_mistral] description = "Use Mistral backend for all its supported models" default = "mistral" @@ -90,18 +100,22 @@ default = "ollama" description = "Use OpenAI backend for all its supported models" default = "openai" -[profiles.all_pipelex_inference] -description = "Use Pipelex Inference backend for all its supported models" -default = "pipelex_inference" +[profiles.all_portkey] +description = "Use Portkey backend for all its supported models" +default = "portkey" -[profiles.all_vertexai] -description = "Use Vertex AI backend for all its supported models" -default = "vertexai" +[profiles.all_scaleway] +description = "Use Scaleway backend for all its supported models" +default = "scaleway" [profiles.all_xai] description = "Use xAI backend for all its supported models" default = "xai" +[profiles.all_internal] +description = "Use internal backend for all its supported models" +default = "internal" + # ========================================================================================= # Custom Profiles # ========================================================================================= @@ -123,7 +137,7 @@ default = "xai" # ========================================================================================= [profiles.example_routing_using_patterns] description = "Example routing profile using patterns" -default = "pipelex_inference" +default = "pipelex_gateway" [profiles.example_routing_using_patterns.routes] # Pattern matching: "model-pattern" = "backend-name" @@ -143,17 +157,17 @@ default = "pipelex_inference" description = "Example routing profile using specific models" [profiles.example_routing_using_specific_models.routes] -"gpt-5-nano" = "pipelex_inference" +"gpt-5-nano" = "pipelex_gateway" "gpt-4o-mini" = "blackboxai" "gpt-5-mini" = "openai" "gpt-5-chat" = "azure_openai" -"claude-4-sonnet" = "pipelex_inference" +"claude-4-sonnet" = "pipelex_gateway" "claude-3.7-sonnet" = "blackboxai" -"gemini-2.5-flash-lite" = "pipelex_inference" +"gemini-2.5-flash-lite" = "pipelex_gateway" "gemini-2.5-flash" = "blackboxai" "gemini-2.5-pro" = "vertexai" -"grok-3" = "pipelex_inference" +"grok-3" = "pipelex_gateway" "grok-3-mini" = "xai" diff --git a/.pipelex/pipelex.toml b/.pipelex/pipelex.toml index b0d36c0..7a7ddaf 100644 --- a/.pipelex/pipelex.toml +++ b/.pipelex/pipelex.toml @@ -2,11 +2,11 @@ # Pipelex Configuration File #################################################################################################### # -# This configuration file is copied to your project's .pipelex/ directory when you run: -# pipelex init config +# This configuration file is copied to client projects' .pipelex/ directory when running: +# `pipelex init config` # # Purpose: -# - This file allows you to override Pipelex's default settings for your specific project +# - This file allows to override Pipelex's default settings for specific projects # - Feel free to modify any settings below to suit your needs # - You can add any configuration sections that exist in the main pipelex.toml # @@ -14,11 +14,11 @@ # - See the full default configuration in: pipelex/pipelex.toml (in the Pipelex package) # - See the configuration structure classes in: pipelex/config.py and pipelex/cogt/config_cogt.py # -# Common Customizations: +# Common customizations are proposed below, such as: # - Logging levels and behavior # - Excluded directories for scanning # - LLM prompt dumping for debugging -# - Feature flags for tracking and reporting +# - Feature flags # - Observer and reporting output directories # # Documentation: https://docs.pipelex.com @@ -26,64 +26,136 @@ # #################################################################################################### +[pipelex.pipeline_execution_config] +# Uncomment to disable conversion of data URLs to pipelex-storage:// URIs +# is_normalize_data_urls_to_storage = false + +[pipelex.pipeline_execution_config.graph_config.data_inclusion] +# Uncomment to include stuff data in graph outputs: +stuff_json_content = true +stuff_text_content = true +stuff_html_content = true +error_stack_traces = true + +[pipelex.pipeline_execution_config.graph_config.graphs_inclusion] +# Uncomment to customize which graph outputs are generated (all enabled by default): +# graphspec_json = false +# mermaidflow_mmd = false +# mermaidflow_html = false +# reactflow_viewspec = false +# reactflow_html = false + +[pipelex.pipeline_execution_config.graph_config.reactflow_config] +# Uncomment to customize ReactFlow graph rendering: +# edge_type = "bezier" # Options: "bezier", "smoothstep", "step", "straight" +# nodesep = 50 # Horizontal spacing between nodes +# ranksep = 80 # Vertical spacing between ranks/levels +# initial_zoom = 1.0 # Initial zoom level (1.0 = 100%) +# pan_to_top = true # Pan to show top of graph on load + +[pipelex.storage_config] +# Storage method: "local" (default), "in_memory", "s3", or "gcp" +# method = "local" + +# Whether to fetch remote HTTP URLs and store them locally +# is_fetch_remote_content_enabled = true + +# --- Local Storage Configuration --- +# Uncomment to customize local storage settings: +[pipelex.storage_config.local] +# uri_format = "{primary_id}/{secondary_id}/{hash}.{extension}" +# local_storage_path = ".pipelex/storage" + +# --- AWS S3 Storage Configuration --- +# Uncomment to use S3 storage (requires boto3: `pip install pipelex[s3]`): +[pipelex.storage_config.s3] +# uri_format = "{primary_id}/{secondary_id}/{hash}.{extension}" +# bucket_name = "your-bucket-name" +# region = "us-east-1" +# signed_urls_lifespan_seconds = 3600 # Set to "disabled" for public URLs + +# --- Google Cloud Storage Configuration --- +# Uncomment to use GCP storage (requires google-cloud-storage: `pip install pipelex[gcp-storage]`): +[pipelex.storage_config.gcp] +# uri_format = "{primary_id}/{secondary_id}/{hash}.{extension}" +# bucket_name = "your-bucket-name" +# project_id = "your-project-id" +# signed_urls_lifespan_seconds = 3600 # Set to "disabled" for public URLs + [pipelex.scan_config] -excluded_dirs = [ - "env", - ".env", - ".git", - "__pycache__", - ".pytest_cache", - ".mypy_cache", - ".ruff_cache", - "results", -] +# Uncomment to customize the excluded directories for scanning +# excluded_dirs = [ +# ".venv", +# "venv", +# "env", +# ".env", +# "virtualenv", +# ".virtualenv", +# ".git", +# "__pycache__", +# ".pytest_cache", +# ".mypy_cache", +# ".ruff_cache", +# "node_modules", +# "results", +# ] [pipelex.builder_config] -# Uncomment to change where the generated pipelines are saved +# Uncomment to change where the generated pipelines are saved: # default_output_dir = "." # default_bundle_file_name = "bundle" # default_directory_base_name = "pipeline" [pipelex.log_config] -default_log_level = "INFO" +# Uncomment to change the default log level: +# default_log_level = "INFO" # Uncomment to log to stderr instead of stdout # console_log_target = "stderr" # console_print_target = "stderr" [pipelex.log_config.package_log_levels] -pipelex = "INFO" +# Uncomment to change the log level for specific packages: +# pipelex = "INFO" + +[pipelex.observer_config] +# Uncomment to change the directory where the observer will save its results: +# observer_dir = "results/observer" + +[pipelex.feature_config] +# WIP/Experimental feature flags: +# is_reporting_enabled = true + +[pipelex.reporting_config] +# Uncomment to customize the reporting configuration: +# is_log_costs_to_console = false +# is_generate_cost_report_file_enabled = false +# cost_report_dir_path = "reports" +# cost_report_base_name = "cost_report" +# cost_report_extension = "csv" +# cost_report_unit_scale = 1.0 [cogt] [cogt.model_deck_config] -# Uncomment to disable model fallback: it will raise errors instead of using secondary model options +# Uncomment to disable model fallback: it will raise errors instead of using secondary model options: # is_model_fallback_enabled = false # Uncomment to change the reaction to missing presets: "raise" (default), "log" or "none" # missing_presets_reaction = "raise" +[cogt.tenacity_config] +# Uncomment to change those values as needed: +# max_retries = 50 # Maximum number of retry attempts before giving up +# wait_multiplier = 0.2 # Multiplier applied to the wait time between retries (in seconds) +# wait_max = 20 # Maximum wait time between retries (in seconds) +# wait_exp_base = 1.3 # Base for exponential backoff calculation + [cogt.llm_config] -# Uncomment any of these to enable dumping the inputs or outputs of text-genration with an LLM +# Uncomment any of these to enable dumping the inputs or outputs of text-generation with an LLM: # is_dump_text_prompts_enabled = true # is_dump_response_text_enabled = true [cogt.llm_config.instructor_config] -# Uncomment any of these to enable dumping the kwargs, response or errors of the instructor +# Uncomment any of these to enable dumping the kwargs, response or errors when generating structured content: # is_dump_kwargs_enabled = true # is_dump_response_enabled = true # is_dump_error_enabled = true - -[pipelex.observer_config] -observer_dir = "results/observer" - -[pipelex.feature_config] -# WIP/Experimental feature flags -is_pipeline_tracking_enabled = false -is_reporting_enabled = true - -[pipelex.reporting_config] -is_log_costs_to_console = false -is_generate_cost_report_file_enabled = true -cost_report_dir_path = "reports" -cost_report_base_name = "cost_report" -cost_report_extension = "csv" -cost_report_unit_scale = 1.0 diff --git a/.pipelex/pipelex_service.toml b/.pipelex/pipelex_service.toml new file mode 100644 index 0000000..afe39a2 --- /dev/null +++ b/.pipelex/pipelex_service.toml @@ -0,0 +1,19 @@ +#################################################################################################### +# Pipelex Service Configuration +#################################################################################################### +# +# This file stores settings related to Pipelex managed services. +# Currently used for Pipelex Gateway terms acceptance. +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +#################################################################################################### + +[agreement] +# Set to true after accepting Pipelex terms of service. +terms_accepted = true + +# Note: when using pipelex_gateway, telemetry is enabled to monitor service usage. +# We collect technical data (model, pipe type...) and quantitative data (token counts...) +# but NOT your content, pipe codes, or output class names. diff --git a/.pipelex/telemetry.toml b/.pipelex/telemetry.toml index 4640a40..eb2c537 100644 --- a/.pipelex/telemetry.toml +++ b/.pipelex/telemetry.toml @@ -1,21 +1,92 @@ #################################################################################################### -# Pipelex Telemetry Configuration +# Custom Telemetry Configuration #################################################################################################### # -# This file controls telemetry settings for Pipelex usage analytics. -# Telemetry helps us improve Pipelex by understanding how it's used. +# This file controls YOUR custom telemetry settings for observability and analytics. +# Configure your own PostHog, Langfuse, or OTLP-compatible backends here. +# +# NOTE: When using Pipelex Gateway, identified telemetry is automatically enabled +# (tied to your Gateway API key, hashed for security). This allows us to monitor +# service quality, enforce fair usage, and provide you with better support. +# Gateway telemetry operates independently from your settings below - you can have both! +# +# To disable all telemetry, set the DO_NOT_TRACK=1 environment variable. # # Documentation: https://docs.pipelex.com # Support: https://go.pipelex.com/discord # #################################################################################################### -telemetry_mode = "off" # Values: "off" | "anonymous" | "identified" -host = "https://eu.i.posthog.com" -project_api_key = "phc_HPJnNKpIXh0SxNDYyTAyUtnq9KxNNZJWQszynsWVx4Y" -respect_dnt = true -redact = ["prompt", "system_prompt", "response", "file_path", "url"] -geoip_enabled = true -dry_mode_enabled = false -verbose_enabled = false -user_id = "" +# ────────────────────────────────────────────────────────────────────────────── +# PostHog Configuration (Event tracking + AI span tracing) +# ────────────────────────────────────────────────────────────────────────────── + +[custom_posthog] +mode = "off" # Values: "off" | "anonymous" | "identified" +# user_id = "your_user_id" # Required when mode = "identified" +endpoint = "${POSTHOG_ENDPOINT}" # Default: https://us.i.posthog.com (or https://eu.i.posthog.com for EU) +api_key = "${POSTHOG_API_KEY}" # Get from PostHog Project Settings +geoip = true # Enable GeoIP lookup +debug = false # Enable PostHog debug mode +redact_properties = [ + "prompt", + "system_prompt", + "response", + "file_path", + "url", +] # Event properties to redact + +# AI span tracing to YOUR PostHog (does NOT affect Langfuse/OTLP - they receive full data) +[custom_posthog.tracing] +enabled = false # Send AI spans to your PostHog + +# Privacy controls for data sent to YOUR PostHog only +[custom_posthog.tracing.capture] +content = false # Capture prompt/completion content +# content_max_length = 1000 # Max length for captured content (omit for unlimited) +pipe_codes = false # Include pipe codes in span names/attributes +output_class_names = false # Include output class names in span names/attributes + +# ────────────────────────────────────────────────────────────────────────────── +# Portkey SDK Configuration +# ────────────────────────────────────────────────────────────────────────────── + +[custom_portkey] +force_debug_enabled = false +force_tracing_enabled = false + +# ────────────────────────────────────────────────────────────────────────────── +# Langfuse Integration +# Note: Langfuse receives FULL span data (no redaction) +# ────────────────────────────────────────────────────────────────────────────── + +[langfuse] +enabled = false +# endpoint = "https://cloud.langfuse.com" # Override for self-hosted Langfuse +# public_key = "${LANGFUSE_PUBLIC_KEY}" # Langfuse public key +# secret_key = "${LANGFUSE_SECRET_KEY}" # Langfuse secret key + +# ────────────────────────────────────────────────────────────────────────────── +# Additional OTLP Exporters (array for multiple) +# Note: OTLP exporters receive FULL span data (no redaction) +# ────────────────────────────────────────────────────────────────────────────── + +# [[otlp]] +# name = "my-collector" # Identifier for logging +# endpoint = "https://..." # OTLP endpoint URL +# headers = { Authorization = "Bearer ${OTLP_AUTH_TOKEN}" } # Headers for OTLP export + +# ────────────────────────────────────────────────────────────────────────────── +# Custom Telemetry Allowed Modes +# Controls which integration modes can use custom telemetry settings above. +# ────────────────────────────────────────────────────────────────────────────── + +[telemetry_allowed_modes] +ci = false # CI environments don't use custom telemetry +cli = true # CLI usage allows custom telemetry +docker = true # Docker deployments allow custom telemetry +fastapi = true # FastAPI integrations allow custom telemetry +mcp = true # MCP integrations allow custom telemetry +n8n = true # n8n integrations allow custom telemetry +pytest = false # Tests don't use custom telemetry +python = false # Direct Python SDK usage doesn't use custom telemetry by default diff --git a/CHANGELOG.md b/CHANGELOG.md index cc76214..d07e2be 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,11 @@ # Changelog +## [v0.0.12] - 2026-01-15 + +### Changed + +- Bump `pipelex` to `v0.18.0`, the `Chicago` release: See changelog [here](https://docs.pipelex.com/changelog/) + ## [v0.0.11] - 2025-12-01 ### Added diff --git a/Makefiles/Makefile.local.mk b/Makefiles/Makefile.local.mk index af1c750..1e2643d 100644 --- a/Makefiles/Makefile.local.mk +++ b/Makefiles/Makefile.local.mk @@ -30,5 +30,5 @@ local-run-api: docker run -p 8081:8081 \ -e ENV=$(ENV) \ -e API_KEY="${API_KEY}" \ - -e PIPELEX_INFERENCE_API_KEY="${PIPELEX_INFERENCE_API_KEY}" \ + -e PIPELEX_GATEWAY_API_KEY="${PIPELEX_GATEWAY_API_KEY}" \ $(LOCAL_DOCKER_IMAGE_NAME) diff --git a/README.md b/README.md index 49dd048..81d23a1 100644 --- a/README.md +++ b/README.md @@ -39,41 +39,37 @@ The **Pipelex API Server** is a FastAPI-based REST API that allows you to execut ### 1. Configure Environment -Create a `.env` file with your API key and LLM provider configuration: +Create a `.env` file with your API keys: ```bash -# Required: Your API authentication key. This is the API key that will be required to access the API. +# Required: API key to authenticate requests to this API server API_KEY=your-api-key-here -# AI inference provider API key -PIPELEX_INFERENCE_API_KEY=your-pipelex-inference-api-key - -# TEMPORARY: Required for image generation (will be integrated into unified inference system) -FAL_API_KEY=your-fal-api-key +# Required: Pipelex Gateway API key to access all LLMs +PIPELEX_GATEWAY_API_KEY=your-pipelex-gateway-api-key ``` -You can get a free Pipelex Inference API key ($20 of free credits) by joining our [Discord community](https://go.pipelex.com/discord) and requesting it in the appropriate channel. +You can get a free Pipelex Gateway API key ($20 of free credits) here https://app.pipelex.com > **For complete API key configuration**, see the [API Key Configuration section](https://github.com/Pipelex/pipelex#api-key-configuration) in the main Pipelex repository. ### 2. Run with Docker -**Option A: Using Docker Compose (Recommended)** - -See [`docker-compose.yml`](docker-compose.yml) for reference. +**Option A: Using Docker Run (Recommended)** ```bash -docker-compose up +docker run --name pipelex-api -p 8081:8081 \ + -e API_KEY=your-api-key-here \ + -e PIPELEX_GATEWAY_API_KEY=your-pipelex-gateway-api-key \ + pipelex/pipelex-api:latest ``` -**Option B: Using Docker Run** +**Option B: Using Docker Compose** + +See [`docker-compose.yml`](docker-compose.yml) for reference. ```bash -docker run --name pipelex-api -p 8081:8081 \ - -e API_KEY=your-api-key-here \ - -e PIPELEX_INFERENCE_API_KEY=your-pipelex-inference-api-key \ - -e FAL_API_KEY=your-fal-api-key \ - pipelex/pipelex-api:latest +docker-compose up ``` **Option C: Build Locally** @@ -82,8 +78,7 @@ docker run --name pipelex-api -p 8081:8081 \ docker build -t pipelex-api . docker run --name pipelex-api -p 8081:8081 \ -e API_KEY=your-api-key-here \ - -e PIPELEX_INFERENCE_API_KEY=your-pipelex-inference-api-key \ - -e FAL_API_KEY=your-fal-api-key \ + -e PIPELEX_GATEWAY_API_KEY=your-pipelex-gateway-api-key \ pipelex-api ``` diff --git a/api/routes/helpers.py b/api/routes/helpers.py index 919f996..73bdff1 100644 --- a/api/routes/helpers.py +++ b/api/routes/helpers.py @@ -70,7 +70,7 @@ def extract_pipe_structures(pipes: list[PipeAbstract]) -> dict[str, dict[str, An inputs_specs[input_name] = get_concept_structure(concept) # Process output - extract concept structure - output_spec = get_concept_structure(pipe.output) + output_spec = get_concept_structure(pipe.output.concept) # Store structure info for this pipe pipe_structures[pipe.code] = { diff --git a/api/routes/pipelex/pipe_builder.py b/api/routes/pipelex/pipe_builder.py index 615e14c..a9e41df 100644 --- a/api/routes/pipelex/pipe_builder.py +++ b/api/routes/pipelex/pipe_builder.py @@ -41,7 +41,7 @@ async def build_pipe(request_data: PipeBuilderRequest): # Execute the pipe_builder pipeline library_manager = get_library_manager() builder_loop = BuilderLoop() - pipelex_bundle_spec = await builder_loop.build_and_fix(inputs={"brief": request_data.brief}, builder_pipe="pipe_builder") + pipelex_bundle_spec, _ = await builder_loop.build_and_fix(inputs={"brief": request_data.brief}, builder_pipe="pipe_builder") blueprint = pipelex_bundle_spec.to_blueprint() library_id, _ = library_manager.open_library() diff --git a/api/security.py b/api/security.py index f285bd8..d679358 100644 --- a/api/security.py +++ b/api/security.py @@ -1,4 +1,4 @@ -"""Authentication - Pluggable JWT or API Key validation.""" +"""Authentication - API Key (default) or JWT validation.""" from typing import Annotated, Any, cast @@ -10,8 +10,7 @@ security = HTTPBearer() -# JWT Configuration -JWT_SECRET = get_optional_env("JWT_SECRET_KEY") +# JWT Configuration (only used when USE_JWT=true) JWT_ALGORITHM = "HS256" @@ -21,6 +20,8 @@ async def verify_jwt(credentials: Annotated[HTTPAuthorizationCredentials, Depend The JWT token is extracted from the Authorization header (Bearer token). It is verified using the JWT_SECRET_KEY from environment variables. + This method is only used when USE_JWT=true is set. + Args: credentials: The credentials extracted from the Authorization header @@ -30,7 +31,8 @@ async def verify_jwt(credentials: Annotated[HTTPAuthorizationCredentials, Depend Raises: HTTPException: If token is invalid or expired """ - if not JWT_SECRET: + jwt_secret = get_optional_env("JWT_SECRET_KEY") + if not jwt_secret: log.error("JWT_SECRET_KEY environment variable is not set") msg = "Server configuration error: JWT_SECRET_KEY not configured" raise HTTPException( @@ -44,7 +46,7 @@ async def verify_jwt(credentials: Annotated[HTTPAuthorizationCredentials, Depend # Decode and verify the JWT token from the request payload = jwt.decode( token, - JWT_SECRET, + jwt_secret, algorithms=[JWT_ALGORITHM], ) @@ -88,7 +90,10 @@ async def verify_jwt(credentials: Annotated[HTTPAuthorizationCredentials, Depend async def verify_api_key(credentials: Annotated[HTTPAuthorizationCredentials, Depends(security)]) -> str: - """Validate static API key (local/development mode). + """Validate static API key (default authentication method). + + This is the default authentication method. It validates the provided + Bearer token against the API_KEY environment variable. Args: credentials: The credentials extracted from the Authorization header @@ -97,7 +102,7 @@ async def verify_api_key(credentials: Annotated[HTTPAuthorizationCredentials, De str: The validated API key Raises: - HTTPException: If API key is invalid + HTTPException: If API key is invalid or not configured """ try: api_key = get_optional_env("API_KEY") @@ -119,7 +124,7 @@ async def verify_api_key(credentials: Annotated[HTTPAuthorizationCredentials, De headers={"WWW-Authenticate": "Bearer"}, ) - log.info("✅ API key validated (local mode)") + log.info("✅ API key validated") return credentials.credentials except HTTPException: @@ -134,12 +139,13 @@ async def verify_api_key(credentials: Annotated[HTTPAuthorizationCredentials, De ) from exc -# Auto-select authentication method based on environment def get_auth_dependency(): - """Select authentication dependency based on USE_JWT environment variable. + """Select authentication method based on environment. + + By default, uses API key authentication. Set USE_JWT=true to enable JWT authentication. Returns: - The appropriate authentication function (verify_jwt or verify_api_key) + The appropriate authentication function (verify_api_key by default, or verify_jwt if enabled) """ use_jwt = get_optional_env("USE_JWT") == "true" return verify_jwt if use_jwt else verify_api_key diff --git a/docker-compose.yml b/docker-compose.yml index 12aa8e6..c98c086 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -5,5 +5,4 @@ services: - "8081:8081" environment: - API_KEY=${API_KEY} - - PIPELEX_INFERENCE_API_KEY=${PIPELEX_INFERENCE_API_KEY} - - FAL_API_KEY=${FAL_API_KEY} + - PIPELEX_GATEWAY_API_KEY=${PIPELEX_GATEWAY_API_KEY} diff --git a/docs/index.md b/docs/index.md index 856c954..f1bff51 100644 --- a/docs/index.md +++ b/docs/index.md @@ -16,39 +16,35 @@ The Pipelex API is currently available for local deployment only. You can deploy ### 1. Configure Environment -Create a `.env` file with your API key and LLM provider configuration: +Create a `.env` file with your API keys: ```bash -# Required: Your API authentication key +# Required: API key to authenticate requests to this API server API_KEY=your-api-key-here -# AI inference provider API key -PIPELEX_INFERENCE_API_KEY=your-pipelex-inference-api-key - -# TEMPORARY: Required for image generation (will be integrated into unified inference system) -FAL_API_KEY=your-fal-api-key +# Required: Pipelex Gateway API key to access all LLMs +PIPELEX_GATEWAY_API_KEY=your-pipelex-gateway-api-key ``` -You can get a free Pipelex Inference API key ($20 of free credits) by joining our [Discord community](https://go.pipelex.com/discord). +You can get a free Pipelex Gateway API key ($20 of free credits) here https://app.pipelex.com > For complete API key configuration, see the [API Key Configuration section](https://github.com/Pipelex/pipelex#api-key-configuration) in the main Pipelex repository. ### 2. Run with Docker -**Option A: Using Docker Compose (Recommended)** +**Option A: Using Docker Run (Recommended)** ```bash -docker-compose up +docker run --name pipelex-api -p 8081:8081 \ + -e API_KEY=your-api-key-here \ + -e PIPELEX_GATEWAY_API_KEY=your-pipelex-gateway-api-key \ + pipelex/pipelex-api:latest ``` -**Option B: Using Docker Run** +**Option B: Using Docker Compose** ```bash -docker run --name pipelex-api -p 8081:8081 \ - -e API_KEY=your-api-key-here \ - -e PIPELEX_INFERENCE_API_KEY=your-pipelex-inference-api-key \ - -e FAL_API_KEY=your-fal-api-key \ - pipelex/pipelex-api:latest +docker-compose up ``` **Option C: Build Locally** @@ -57,8 +53,7 @@ docker run --name pipelex-api -p 8081:8081 \ docker build -t pipelex-api . docker run --name pipelex-api -p 8081:8081 \ -e API_KEY=your-api-key-here \ - -e PIPELEX_INFERENCE_API_KEY=your-pipelex-inference-api-key \ - -e FAL_API_KEY=your-fal-api-key \ + -e PIPELEX_GATEWAY_API_KEY=your-pipelex-gateway-api-key \ pipelex-api ``` @@ -78,12 +73,41 @@ http://localhost:8081/api/v1 ## Authentication -Include your API key in the Authorization header: +The API supports two authentication methods: + +### API Key Authentication (Default) + +By default, the API uses static API key authentication. Include your API key in the Authorization header: ``` Authorization: Bearer YOUR_API_KEY ``` +The API key is configured via the `API_KEY` environment variable when deploying the server. + +### JWT Authentication (Optional) + +For production deployments requiring user-specific tokens, JWT authentication is available. + +**To enable JWT authentication:** + +1. Set the `USE_JWT=true` environment variable +2. Set the `JWT_SECRET_KEY` environment variable with your secret key + +```bash +docker run --name pipelex-api -p 8081:8081 \ + -e USE_JWT=true \ + -e JWT_SECRET_KEY=your-jwt-secret-key \ + -e PIPELEX_GATEWAY_API_KEY=your-pipelex-gateway-api-key \ + pipelex/pipelex-api:latest +``` + +**JWT Requirements:** + +- Tokens must be signed with the HS256 algorithm +- Tokens must contain an `email` field in the payload +- Pass the JWT in the Authorization header: `Authorization: Bearer YOUR_JWT_TOKEN` + ## API Endpoints The Pipelex API provides three main capabilities: diff --git a/pyproject.toml b/pyproject.toml index 52d5ca2..87db9b9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "pipelex-api" -version = "0.0.11" +version = "0.0.12" description = "Pipelex API" authors = [{ name = "Evotis S.A.S.", email = "oss@pipelex.com" }] maintainers = [{ name = "Pipelex staff", email = "oss@pipelex.com" }] @@ -16,12 +16,16 @@ classifiers = [ ] dependencies = [ - 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ignore[reportUnknownMemberType] token, jwt_secret, algorithms=[JWT_ALGORITHM], From b3fd4f8f69c1b8fab84fcf9152114a64859663db Mon Sep 17 00:00:00 2001 From: Thomas Hebrard Date: Thu, 26 Feb 2026 00:35:04 +0100 Subject: [PATCH 3/3] Chicago (#28) (#32) --- .blackboxrules | 329 ++- .cursor/rules/run_pipelex.mdc | 21 +- .cursor/rules/write_pipelex.mdc | 228 +- .github/copilot-instructions.md | 329 ++- .gitignore | 1 - .pipelex/inference/deck/1_llm_deck.toml | 79 + .pipelex/inference/deck/2_img_gen_deck.toml | 49 + .pipelex/inference/deck/3_extract_deck.toml | 42 + .pipelex/inference/deck/base_deck.toml | 202 -- .pipelex/inference/deck/overrides.toml | 19 - .../inference/deck/x_custom_extract_deck.toml | 39 + .../inference/deck/x_custom_llm_deck.toml | 71 + .pipelex/inference/routing_profiles.toml | 45 +- .pipelex/pipelex.toml | 222 +- .windsurfrules.md | 329 ++- AGENTS.md | 329 ++- CLAUDE.md | 329 ++- api/main.py | 2 +- api/routes/__init__.py | 4 +- api/routes/{pipelex => }/health.py | 7 +- api/routes/helpers.py | 81 - api/routes/pipelex/__init__.py | 8 +- api/routes/pipelex/build/__init__.py | 13 + api/routes/pipelex/build/inputs.py | 41 + api/routes/pipelex/build/output.py | 43 + api/routes/pipelex/build/pipe.py | 42 + api/routes/pipelex/build/runner.py | 75 + api/routes/pipelex/hello_world.plx | 13 - api/routes/pipelex/pipe_builder.py | 125 -- api/routes/pipelex/pipeline.py | 146 +- .../pipelex/{plx_validator.py => validate.py} | 32 +- api/routes/{main.py => version.py} | 2 +- api/security.py | 5 +- docs/pipe-builder.md | 8 +- docs/pipe-run.md | 24 +- docs/pipe-validate.md | 10 +- pyproject.toml | 6 +- uv.lock | 1945 +++++++++-------- 38 files changed, 3095 insertions(+), 2200 deletions(-) create mode 100644 .pipelex/inference/deck/1_llm_deck.toml create mode 100644 .pipelex/inference/deck/2_img_gen_deck.toml create mode 100644 .pipelex/inference/deck/3_extract_deck.toml delete mode 100644 .pipelex/inference/deck/base_deck.toml delete mode 100644 .pipelex/inference/deck/overrides.toml create mode 100644 .pipelex/inference/deck/x_custom_extract_deck.toml create mode 100644 .pipelex/inference/deck/x_custom_llm_deck.toml rename api/routes/{pipelex => }/health.py (51%) delete mode 100644 api/routes/helpers.py create mode 100644 api/routes/pipelex/build/__init__.py create mode 100644 api/routes/pipelex/build/inputs.py create mode 100644 api/routes/pipelex/build/output.py create mode 100644 api/routes/pipelex/build/pipe.py create mode 100644 api/routes/pipelex/build/runner.py delete mode 100644 api/routes/pipelex/hello_world.plx delete mode 100644 api/routes/pipelex/pipe_builder.py rename api/routes/pipelex/{plx_validator.py => validate.py} (52%) rename api/routes/{main.py => version.py} (89%) diff --git a/.blackboxrules b/.blackboxrules index 9f0a612..792cfa5 100644 --- a/.blackboxrules +++ b/.blackboxrules @@ -1,12 +1,12 @@ -# Pipelex Rules +# Pipelex Coding Rules ## Guide to write or edit pipelines using the Pipelex language in .plx files - Always first write your "plan" in natural language, then transcribe it in pipelex. - You should ALWAYS RUN validation when you are writing or editing a `.plx` file. It will ensure the pipe is runnable. If not, iterate. - For a specific file: `pipelex validate path_to_file.plx` - - For all pipelines: `pipelex validate all` + - For all pipelines: `pipelex validate --all` - **IMPORTANT**: Ensure the Python virtual environment is activated before running `pipelex` commands. For standard installations, the venv is named `.venv` - always check that first. The commands will not work without proper venv activation. - Please use POSIX standard for files. (empty lines, no trailing whitespaces, etc.) @@ -23,10 +23,10 @@ A pipeline file has three main sections: #### Domain Statement ```plx -domain = "domain_name" +domain = "domain_code" description = "Description of the domain" # Optional ``` -Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. +Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. #### Concept Definitions @@ -41,20 +41,28 @@ ConceptName = "Description of the concept" - Use PascalCase for concept names - Never use plurals (no "Stories", use "Story") - lists are handled implicitly by Pipelex - Avoid circumstantial adjectives (no "LargeText", use "Text") - focus on the essence of what the concept represents -- Don't redefine native concepts (Text, Image, PDF, TextAndImages, Number, Page) +- Don't redefine native concepts (Text, Image, PDF, TextAndImages, Number, Page, JSON) **Native Concepts:** -Pipelex provides built-in native concepts: `Text`, `Image`, `PDF`, `TextAndImages`, `Number`, `Page`. Use these directly or refine them when appropriate. +Pipelex provides built-in native concepts: `Text`, `Image`, `PDF`, `TextAndImages`, `Number`, `Page`, `JSON`. Use these directly or refine them when appropriate. -**Refining Native Concepts:** -To create a concept that specializes a native concept without adding fields: +**Refining Concepts:** +To create a concept that specializes another concept without adding fields, use `refines`: ```plx +## Refining a native concept [concept.Landscape] description = "A scenic outdoor photograph" refines = "Image" + +## Refining a custom concept (must be in domain.ConceptCode format) +[concept.PremiumCustomer] +description = "A premium customer with special benefits" +refines = "myapp.Customer" ``` +Note: When refining a custom (non-native) concept, you must use the fully qualified concept ref in `domain.ConceptCode` format. Pipelex automatically handles the dependency order to ensure referenced concepts are loaded first. + For details on how to structure concepts with fields, see the "Structuring Models" section below. #### Pipe Definitions @@ -62,7 +70,7 @@ For details on how to structure concepts with fields, see the "Structuring Model ### Pipe Base Definition ```plx -[pipe.your_pipe_name] +[pipe.your_pipe_code] type = "PipeLLM" description = "A description of what your pipe does" inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" } @@ -72,7 +80,7 @@ output = "ConceptName" The pipes will all have at least this base definition. - `inputs`: Dictionary of key being the variable used in the prompts, and the value being the ConceptName. It should ALSO LIST THE INPUTS OF THE INTERMEDIATE STEPS (if PipeSequence) or of the conditional pipes (if PipeCondition). So If you have this error: -`StaticValidationError: missing_input_variable • domain='expense_validator' • pipe='validate_expense' • +`PipeValidationError: missing_input_variable • domain='expense_validator' • pipe='validate_expense' • variable='['invoice']'`` That means that the pipe validate_expense is missing the input `invoice` because one of the subpipe is needing it. @@ -127,16 +135,16 @@ For concepts with structured fields, define them inline using TOML syntax: description = "A commercial document issued by a seller to a buyer" [concept.Invoice.structure] -invoice_number = "The unique invoice identifier" +invoice_number = "The unique invoice identifier" # This will be optional by default issue_date = { type = "date", description = "The date the invoice was issued", required = true } total_amount = { type = "number", description = "The total invoice amount", required = true } -vendor_name = "The name of the vendor" -line_items = { type = "list", item_type = "text", description = "List of items", required = false } +vendor_name = "The name of the vendor" # This will be optional by default +line_items = { type = "list", item_type = "text", description = "List of items" } ``` -**Supported inline field types:** `text`, `integer`, `boolean`, `number`, `date`, `list`, `dict` +**Supported inline field types:** `text`, `integer`, `boolean`, `number`, `date`, `list`, `dict`, `concept` -**Field properties:** `type`, `description`, `required` (default: true), `default_value`, `choices`, `item_type` (for lists), `key_type` and `value_type` (for dicts) +**Field properties:** `type`, `description`, `required` (default: false), `default_value`, `choices`, `item_type` (for lists), `key_type` and `value_type` (for dicts), `concept_ref` (for concept references), `item_concept_ref` (for lists of concepts) **Simple syntax** (creates required text field): ```plx @@ -145,9 +153,46 @@ field_name = "Field description" **Detailed syntax** (with explicit properties): ```plx -field_name = { type = "text", description = "Field description", required = false, default_value = "default" } +field_name = { type = "text", description = "Field description", default_value = "default" } +``` + +**Concept reference syntax** (referencing another concept): +```plx +## Single concept reference +customer = { type = "concept", concept_ref = "myapp.Customer", description = "The customer" } + +## List of concepts +line_items = { type = "list", item_type = "concept", item_concept_ref = "myapp.LineItem", description = "Line items" } ``` +Example with concept references: +```plx +[concept.Customer] +description = "A customer entity" + +[concept.Customer.structure] +name = { type = "text", description = "Customer name" } +email = { type = "text", description = "Customer email" } + +[concept.LineItem] +description = "A line item in an invoice" + +[concept.LineItem.structure] +product = { type = "text", description = "Product name" } +quantity = { type = "integer", description = "Quantity ordered" } +unit_price = { type = "number", description = "Price per unit" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +customer = { type = "concept", concept_ref = "myapp.Customer", description = "The customer" } +items = { type = "list", item_type = "concept", item_concept_ref = "myapp.LineItem", description = "Line items" } +total = { type = "number", description = "Invoice total" } +``` + +Note: Pipelex automatically determines the correct loading order for concepts based on their dependencies (topological sort), so concepts can reference each other across domains as long as there are no circular dependencies. + **3. Python StructuredContent Class (For Advanced Features)** Create a Python class when you need: @@ -200,12 +245,14 @@ class Invoice(StructuredContent): #### Inline Structure Limitations Inline structures: -- ✅ Support all common field types (text, number, date, list, dict, etc.) +- ✅ Support all common field types (text, number, date, list, dict, concept, etc.) - ✅ Support required/optional fields, defaults, choices +- ✅ Support concept-to-concept references (type = "concept" with concept_ref) +- ✅ Support lists of concepts (type = "list" with item_type = "concept") +- ✅ Support refining both native and custom concepts - ✅ Generate full Pydantic models with validation - ❌ Cannot have custom validators or complex validation logic - ❌ Cannot have computed properties or custom methods -- ❌ Cannot refine custom (non-native) concepts - ❌ Limited IDE autocomplete compared to explicit Python classes @@ -471,7 +518,7 @@ The PipeExtract operator is used to extract text and images from an image or a P [pipe.extract_info] type = "PipeExtract" description = "extract the information" -inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input. +inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input. output = "Page" ``` @@ -480,7 +527,7 @@ Using Extract Model Settings: [pipe.extract_with_model] type = "PipeExtract" description = "Extract with specific model" -inputs = { document = "PDF" } +inputs = { document = "Document" } output = "Page" model = "base_extract_mistral" # Use predefined extract preset or model alias ``` @@ -588,15 +635,16 @@ $sales_rep.phone | $sales_rep.email """ ``` -#### Key Parameters +#### Key Parameters (Template Mode) -- `template`: Inline template string (mutually exclusive with template_name) +- `template`: Inline template string (mutually exclusive with template_name and construct) - `template_name`: Name of a predefined template (mutually exclusive with template) - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.) - `templating_style`: Styling options for template rendering - `extra_context`: Additional context variables for template For more control, you can use a nested `template` section instead of the `template` field: + - `template.template`: The template string - `template.category`: Template type - `template.templating_style`: Styling options @@ -604,9 +652,143 @@ For more control, you can use a nested `template` section instead of the `templa #### Template Variables Use the same variable insertion rules as PipeLLM: + - `@variable` for block insertion (multi-line content) - `$variable` for inline insertion (short text) +#### Construct Mode (for StructuredContent Output) + +PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures. + +**When to use construct mode:** + +- You need to output a structured object (not just Text) +- You want to deterministically compose fields from existing data +- No LLM is needed - just data composition and templating + +##### Basic Construct Usage + +```plx +[concept.SalesSummary] +description = "A structured sales summary" + +[concept.SalesSummary.structure] +report_title = { type = "text", description = "Title of the report" } +customer_name = { type = "text", description = "Customer name" } +deal_value = { type = "number", description = "Deal value" } +summary_text = { type = "text", description = "Generated summary text" } + +[pipe.compose_summary] +type = "PipeCompose" +description = "Compose a sales summary from deal data" +inputs = { deal = "Deal" } +output = "SalesSummary" + +[pipe.compose_summary.construct] +report_title = "Monthly Sales Report" +customer_name = { from = "deal.customer_name" } +deal_value = { from = "deal.amount" } +summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" } +``` + +##### Field Composition Methods + +There are four ways to define field values in a construct: + +**1. Fixed Value (literal)** + +Use a literal value directly: + +```plx +[pipe.compose_report.construct] +report_title = "Annual Report" +report_year = 2024 +is_draft = false +``` + +**2. Variable Reference (`from`)** + +Get a value from working memory using a dotted path: + +```plx +[pipe.compose_report.construct] +customer_name = { from = "deal.customer_name" } +total_amount = { from = "order.total" } +street_address = { from = "customer.address.street" } +``` + +**3. Template (`template`)** + +Render a Jinja2 template with variable substitution: + +```plx +[pipe.compose_report.construct] +invoice_number = { template = "INV-$order.id" } +summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" } +``` + +**4. Nested Construct** + +For nested structures, use a TOML subsection: + +```plx +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Complete Construct Example + +```plx +domain = "invoicing" + +[concept.Address] +description = "A postal address" + +[concept.Address.structure] +street = { type = "text", description = "Street address" } +city = { type = "text", description = "City name" } +country = { type = "text", description = "Country name" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +invoice_number = { type = "text", description = "Invoice number" } +total = { type = "number", description = "Total amount" } + +[pipe.compose_invoice] +type = "PipeCompose" +description = "Compose an invoice from order and customer data" +inputs = { order = "Order", customer = "Customer" } +output = "Invoice" + +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Key Parameters (Construct Mode) + +- `construct`: Dictionary mapping field names to their composition rules +- Each field can be: + - A literal value (string, number, boolean) + - A dict with `from` key for variable reference + - A dict with `template` key for template rendering + - A nested dict for nested structures + +**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive. + ### PipeImgGen operator The PipeImgGen operator is used to generate images using AI image generation models. @@ -820,7 +1002,7 @@ Presets are meant to record the choice of an llm with its hyper parameters (temp Examples: ```toml -llm_for_complex_reasoning = { model = "base-claude", temperature = 1 } +llm_to_engineer = { model = "base-claude", temperature = 1 } llm_to_extract_invoice = { model = "claude-3-7-sonnet", temperature = 0.1, max_tokens = "auto" } ``` @@ -851,7 +1033,7 @@ You can override the predefined llm presets by setting them in `.pipelex/inferen ALWAYS RUN validation when you are finished writing pipelines: This checks for errors. If there are errors, iterate until it works. - For a specific bundle/file: `pipelex validate path_to_file.plx` -- For all pipelines: `pipelex validate all` +- For all pipelines: `pipelex validate --all` - Remember: Ensure your Python virtual environment is activated (typically `.venv` for standard installations) before running `pipelex` commands. Then, create an example file to run the pipeline in the `examples` folder. @@ -859,21 +1041,6 @@ But don't write documentation unless asked explicitly to. ## Guide to execute a pipeline and write example code -### Prerequisites: Virtual Environment - -**CRITICAL**: Before running any `pipelex` commands or `pytest`, you MUST activate the appropriate Python virtual environment. Without proper venv activation, these commands will not work. - -For standard installations, the virtual environment is named `.venv`. Always check this first: - -```bash -## Activate the virtual environment (standard installation) -source .venv/bin/activate # On macOS/Linux -## or -.venv\Scripts\activate # On Windows -``` - -If your installation uses a different venv name or location, activate that one instead. All subsequent `pipelex` and `pytest` commands assume the venv is active. - ### Example to execute a pipeline with text output ```python @@ -967,13 +1134,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa }, ) -## Here we have a single input and it's a PDF. -## Because PDFContent is a native concept, we can use it directly as a value, +## Here we have a single input and it's a document. +## Because DocumentContent is a native concept, we can use it directly as a value, ## the system knows what content it corresponds to: pipe_output = await execute_pipeline( pipe_code="power_extractor_dpe", inputs={ - "document": PDFContent(url=pdf_url), + "document": DocumentContent(url=pdf_url), }, ) @@ -1096,82 +1263,4 @@ result_list = pipe_output.main_stuff_as_items(item_type=GanttChart) ``` --- - -## Rules to choose LLM models used in PipeLLMs. - -### LLM Configuration System - -In order to use it in a pipe, an LLM is referenced by its llm_handle (alias) and possibly by an llm_preset. -LLM configurations are managed through the new inference backend system with files located in `.pipelex/inference/`: - -- **Model Deck**: `.pipelex/inference/deck/base_deck.toml` and `.pipelex/inference/deck/overrides.toml` -- **Backends**: `.pipelex/inference/backends.toml` and `.pipelex/inference/backends/*.toml` -- **Routing**: `.pipelex/inference/routing_profiles.toml` - -### LLM Handles - -An llm_handle can be either: -1. **A direct model name** (like "gpt-4o-mini", "claude-3-sonnet") - automatically available for all models loaded by the inference backend system -2. **An alias** - user-defined shortcuts that map to model names, defined in the `[aliases]` section: - -```toml -[aliases] -base-claude = "claude-4.5-sonnet" -base-gpt = "gpt-5" -base-gemini = "gemini-2.5-flash" -base-mistral = "mistral-medium" -``` - -The system first looks for direct model names, then checks aliases if no direct match is found. The system handles model routing through backends automatically. - -### Using an LLM Handle in a PipeLLM - -Here is an example of using an llm_handle to specify which LLM to use in a PipeLLM: - -```plx -[pipe.hello_world] -type = "PipeLLM" -description = "Write text about Hello World." -output = "Text" -model = { model = "gpt-5", temperature = 0.9 } -prompt = """ -Write a haiku about Hello World. -""" -``` - -As you can see, to use the LLM, you must also indicate the temperature (float between 0 and 1) and max_tokens (either an int or the string "auto"). - -### LLM Presets - -Presets are meant to record the choice of an llm with its hyper parameters (temperature and max_tokens) if it's good for a particular task. LLM Presets are skill-oriented. - -Examples: -```toml -llm_for_complex_reasoning = { model = "base-claude", temperature = 1 } -llm_to_extract_invoice = { model = "claude-3-7-sonnet", temperature = 0.1, max_tokens = "auto" } -``` - -The interest is that these presets can be used to set the LLM choice in a PipeLLM, like this: - -```plx -[pipe.extract_invoice] -type = "PipeLLM" -description = "Extract invoice information from an invoice text transcript" -inputs = { invoice_text = "InvoiceText" } -output = "Invoice" -model = "llm_to_extract_invoice" -prompt = """ -Extract invoice information from this invoice: - -The category of this invoice is: $invoice_details.category. - -@invoice_text -""" -``` - -The setting here `model = "llm_to_extract_invoice"` works because "llm_to_extract_invoice" has been declared as an llm_preset in the deck. -You must not use an LLM preset in a PipeLLM that does not exist in the deck. If needed, you can add llm presets. - - -You can override the predefined llm presets by setting them in `.pipelex/inference/deck/overrides.toml`. diff --git a/.cursor/rules/run_pipelex.mdc b/.cursor/rules/run_pipelex.mdc index 78aed74..7650051 100644 --- a/.cursor/rules/run_pipelex.mdc +++ b/.cursor/rules/run_pipelex.mdc @@ -6,21 +6,6 @@ globs: --- # Guide to execute a pipeline and write example code -## Prerequisites: Virtual Environment - -**CRITICAL**: Before running any `pipelex` commands or `pytest`, you MUST activate the appropriate Python virtual environment. Without proper venv activation, these commands will not work. - -For standard installations, the virtual environment is named `.venv`. Always check this first: - -```bash -# Activate the virtual environment (standard installation) -source .venv/bin/activate # On macOS/Linux -# or -.venv\Scripts\activate # On Windows -``` - -If your installation uses a different venv name or location, activate that one instead. All subsequent `pipelex` and `pytest` commands assume the venv is active. - ## Example to execute a pipeline with text output ```python @@ -114,13 +99,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa }, ) -# Here we have a single input and it's a PDF. -# Because PDFContent is a native concept, we can use it directly as a value, +# Here we have a single input and it's a document. +# Because DocumentContent is a native concept, we can use it directly as a value, # the system knows what content it corresponds to: pipe_output = await execute_pipeline( pipe_code="power_extractor_dpe", inputs={ - "document": PDFContent(url=pdf_url), + "document": DocumentContent(url=pdf_url), }, ) diff --git a/.cursor/rules/write_pipelex.mdc b/.cursor/rules/write_pipelex.mdc index 2d977d3..781789e 100644 --- a/.cursor/rules/write_pipelex.mdc +++ b/.cursor/rules/write_pipelex.mdc @@ -10,7 +10,7 @@ globs: - Always first write your "plan" in natural language, then transcribe it in pipelex. - You should ALWAYS RUN validation when you are writing or editing a `.plx` file. It will ensure the pipe is runnable. If not, iterate. - For a specific file: `pipelex validate path_to_file.plx` - - For all pipelines: `pipelex validate all` + - For all pipelines: `pipelex validate --all` - **IMPORTANT**: Ensure the Python virtual environment is activated before running `pipelex` commands. For standard installations, the venv is named `.venv` - always check that first. The commands will not work without proper venv activation. - Please use POSIX standard for files. (empty lines, no trailing whitespaces, etc.) @@ -27,10 +27,10 @@ A pipeline file has three main sections: ### Domain Statement ```plx -domain = "domain_name" +domain = "domain_code" description = "Description of the domain" # Optional ``` -Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. +Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. ### Concept Definitions @@ -45,20 +45,28 @@ ConceptName = "Description of the concept" - Use PascalCase for concept names - Never use plurals (no "Stories", use "Story") - lists are handled implicitly by Pipelex - Avoid circumstantial adjectives (no "LargeText", use "Text") - focus on the essence of what the concept represents -- Don't redefine native concepts (Text, Image, PDF, TextAndImages, Number, Page) +- Don't redefine native concepts (Text, Image, PDF, TextAndImages, Number, Page, JSON) **Native Concepts:** -Pipelex provides built-in native concepts: `Text`, `Image`, `PDF`, `TextAndImages`, `Number`, `Page`. Use these directly or refine them when appropriate. +Pipelex provides built-in native concepts: `Text`, `Image`, `PDF`, `TextAndImages`, `Number`, `Page`, `JSON`. Use these directly or refine them when appropriate. -**Refining Native Concepts:** -To create a concept that specializes a native concept without adding fields: +**Refining Concepts:** +To create a concept that specializes another concept without adding fields, use `refines`: ```plx +# Refining a native concept [concept.Landscape] description = "A scenic outdoor photograph" refines = "Image" + +# Refining a custom concept (must be in domain.ConceptCode format) +[concept.PremiumCustomer] +description = "A premium customer with special benefits" +refines = "myapp.Customer" ``` +Note: When refining a custom (non-native) concept, you must use the fully qualified concept ref in `domain.ConceptCode` format. Pipelex automatically handles the dependency order to ensure referenced concepts are loaded first. + For details on how to structure concepts with fields, see the "Structuring Models" section below. ### Pipe Definitions @@ -66,7 +74,7 @@ For details on how to structure concepts with fields, see the "Structuring Model ## Pipe Base Definition ```plx -[pipe.your_pipe_name] +[pipe.your_pipe_code] type = "PipeLLM" description = "A description of what your pipe does" inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" } @@ -76,7 +84,7 @@ output = "ConceptName" The pipes will all have at least this base definition. - `inputs`: Dictionary of key being the variable used in the prompts, and the value being the ConceptName. It should ALSO LIST THE INPUTS OF THE INTERMEDIATE STEPS (if PipeSequence) or of the conditional pipes (if PipeCondition). So If you have this error: -`StaticValidationError: missing_input_variable • domain='expense_validator' • pipe='validate_expense' • +`PipeValidationError: missing_input_variable • domain='expense_validator' • pipe='validate_expense' • variable='['invoice']'`` That means that the pipe validate_expense is missing the input `invoice` because one of the subpipe is needing it. @@ -131,16 +139,16 @@ For concepts with structured fields, define them inline using TOML syntax: description = "A commercial document issued by a seller to a buyer" [concept.Invoice.structure] -invoice_number = "The unique invoice identifier" +invoice_number = "The unique invoice identifier" # This will be optional by default issue_date = { type = "date", description = "The date the invoice was issued", required = true } total_amount = { type = "number", description = "The total invoice amount", required = true } -vendor_name = "The name of the vendor" -line_items = { type = "list", item_type = "text", description = "List of items", required = false } +vendor_name = "The name of the vendor" # This will be optional by default +line_items = { type = "list", item_type = "text", description = "List of items" } ``` -**Supported inline field types:** `text`, `integer`, `boolean`, `number`, `date`, `list`, `dict` +**Supported inline field types:** `text`, `integer`, `boolean`, `number`, `date`, `list`, `dict`, `concept` -**Field properties:** `type`, `description`, `required` (default: true), `default_value`, `choices`, `item_type` (for lists), `key_type` and `value_type` (for dicts) +**Field properties:** `type`, `description`, `required` (default: false), `default_value`, `choices`, `item_type` (for lists), `key_type` and `value_type` (for dicts), `concept_ref` (for concept references), `item_concept_ref` (for lists of concepts) **Simple syntax** (creates required text field): ```plx @@ -149,9 +157,46 @@ field_name = "Field description" **Detailed syntax** (with explicit properties): ```plx -field_name = { type = "text", description = "Field description", required = false, default_value = "default" } +field_name = { type = "text", description = "Field description", default_value = "default" } +``` + +**Concept reference syntax** (referencing another concept): +```plx +# Single concept reference +customer = { type = "concept", concept_ref = "myapp.Customer", description = "The customer" } + +# List of concepts +line_items = { type = "list", item_type = "concept", item_concept_ref = "myapp.LineItem", description = "Line items" } +``` + +Example with concept references: +```plx +[concept.Customer] +description = "A customer entity" + +[concept.Customer.structure] +name = { type = "text", description = "Customer name" } +email = { type = "text", description = "Customer email" } + +[concept.LineItem] +description = "A line item in an invoice" + +[concept.LineItem.structure] +product = { type = "text", description = "Product name" } +quantity = { type = "integer", description = "Quantity ordered" } +unit_price = { type = "number", description = "Price per unit" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +customer = { type = "concept", concept_ref = "myapp.Customer", description = "The customer" } +items = { type = "list", item_type = "concept", item_concept_ref = "myapp.LineItem", description = "Line items" } +total = { type = "number", description = "Invoice total" } ``` +Note: Pipelex automatically determines the correct loading order for concepts based on their dependencies (topological sort), so concepts can reference each other across domains as long as there are no circular dependencies. + **3. Python StructuredContent Class (For Advanced Features)** Create a Python class when you need: @@ -204,12 +249,14 @@ class Invoice(StructuredContent): ### Inline Structure Limitations Inline structures: -- ✅ Support all common field types (text, number, date, list, dict, etc.) +- ✅ Support all common field types (text, number, date, list, dict, concept, etc.) - ✅ Support required/optional fields, defaults, choices +- ✅ Support concept-to-concept references (type = "concept" with concept_ref) +- ✅ Support lists of concepts (type = "list" with item_type = "concept") +- ✅ Support refining both native and custom concepts - ✅ Generate full Pydantic models with validation - ❌ Cannot have custom validators or complex validation logic - ❌ Cannot have computed properties or custom methods -- ❌ Cannot refine custom (non-native) concepts - ❌ Limited IDE autocomplete compared to explicit Python classes @@ -475,7 +522,7 @@ The PipeExtract operator is used to extract text and images from an image or a P [pipe.extract_info] type = "PipeExtract" description = "extract the information" -inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input. +inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input. output = "Page" ``` @@ -484,7 +531,7 @@ Using Extract Model Settings: [pipe.extract_with_model] type = "PipeExtract" description = "Extract with specific model" -inputs = { document = "PDF" } +inputs = { document = "Document" } output = "Page" model = "base_extract_mistral" # Use predefined extract preset or model alias ``` @@ -592,15 +639,16 @@ $sales_rep.phone | $sales_rep.email """ ``` -### Key Parameters +### Key Parameters (Template Mode) -- `template`: Inline template string (mutually exclusive with template_name) +- `template`: Inline template string (mutually exclusive with template_name and construct) - `template_name`: Name of a predefined template (mutually exclusive with template) - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.) - `templating_style`: Styling options for template rendering - `extra_context`: Additional context variables for template For more control, you can use a nested `template` section instead of the `template` field: + - `template.template`: The template string - `template.category`: Template type - `template.templating_style`: Styling options @@ -608,9 +656,143 @@ For more control, you can use a nested `template` section instead of the `templa ### Template Variables Use the same variable insertion rules as PipeLLM: + - `@variable` for block insertion (multi-line content) - `$variable` for inline insertion (short text) +### Construct Mode (for StructuredContent Output) + +PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures. + +**When to use construct mode:** + +- You need to output a structured object (not just Text) +- You want to deterministically compose fields from existing data +- No LLM is needed - just data composition and templating + +#### Basic Construct Usage + +```plx +[concept.SalesSummary] +description = "A structured sales summary" + +[concept.SalesSummary.structure] +report_title = { type = "text", description = "Title of the report" } +customer_name = { type = "text", description = "Customer name" } +deal_value = { type = "number", description = "Deal value" } +summary_text = { type = "text", description = "Generated summary text" } + +[pipe.compose_summary] +type = "PipeCompose" +description = "Compose a sales summary from deal data" +inputs = { deal = "Deal" } +output = "SalesSummary" + +[pipe.compose_summary.construct] +report_title = "Monthly Sales Report" +customer_name = { from = "deal.customer_name" } +deal_value = { from = "deal.amount" } +summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" } +``` + +#### Field Composition Methods + +There are four ways to define field values in a construct: + +**1. Fixed Value (literal)** + +Use a literal value directly: + +```plx +[pipe.compose_report.construct] +report_title = "Annual Report" +report_year = 2024 +is_draft = false +``` + +**2. Variable Reference (`from`)** + +Get a value from working memory using a dotted path: + +```plx +[pipe.compose_report.construct] +customer_name = { from = "deal.customer_name" } +total_amount = { from = "order.total" } +street_address = { from = "customer.address.street" } +``` + +**3. Template (`template`)** + +Render a Jinja2 template with variable substitution: + +```plx +[pipe.compose_report.construct] +invoice_number = { template = "INV-$order.id" } +summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" } +``` + +**4. Nested Construct** + +For nested structures, use a TOML subsection: + +```plx +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +#### Complete Construct Example + +```plx +domain = "invoicing" + +[concept.Address] +description = "A postal address" + +[concept.Address.structure] +street = { type = "text", description = "Street address" } +city = { type = "text", description = "City name" } +country = { type = "text", description = "Country name" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +invoice_number = { type = "text", description = "Invoice number" } +total = { type = "number", description = "Total amount" } + +[pipe.compose_invoice] +type = "PipeCompose" +description = "Compose an invoice from order and customer data" +inputs = { order = "Order", customer = "Customer" } +output = "Invoice" + +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +#### Key Parameters (Construct Mode) + +- `construct`: Dictionary mapping field names to their composition rules +- Each field can be: + - A literal value (string, number, boolean) + - A dict with `from` key for variable reference + - A dict with `template` key for template rendering + - A nested dict for nested structures + +**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive. + ## PipeImgGen operator The PipeImgGen operator is used to generate images using AI image generation models. @@ -824,7 +1006,7 @@ Presets are meant to record the choice of an llm with its hyper parameters (temp Examples: ```toml -llm_for_complex_reasoning = { model = "base-claude", temperature = 1 } +llm_to_engineer = { model = "base-claude", temperature = 1 } llm_to_extract_invoice = { model = "claude-3-7-sonnet", temperature = 0.1, max_tokens = "auto" } ``` @@ -855,7 +1037,7 @@ You can override the predefined llm presets by setting them in `.pipelex/inferen ALWAYS RUN validation when you are finished writing pipelines: This checks for errors. If there are errors, iterate until it works. - For a specific bundle/file: `pipelex validate path_to_file.plx` -- For all pipelines: `pipelex validate all` +- For all pipelines: `pipelex validate --all` - Remember: Ensure your Python virtual environment is activated (typically `.venv` for standard installations) before running `pipelex` commands. Then, create an example file to run the pipeline in the `examples` folder. diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md index 9f0a612..792cfa5 100644 --- a/.github/copilot-instructions.md +++ b/.github/copilot-instructions.md @@ -1,12 +1,12 @@ -# Pipelex Rules +# Pipelex Coding Rules ## Guide to write or edit pipelines using the Pipelex language in .plx files - Always first write your "plan" in natural language, then transcribe it in pipelex. - You should ALWAYS RUN validation when you are writing or editing a `.plx` file. It will ensure the pipe is runnable. If not, iterate. - For a specific file: `pipelex validate path_to_file.plx` - - For all pipelines: `pipelex validate all` + - For all pipelines: `pipelex validate --all` - **IMPORTANT**: Ensure the Python virtual environment is activated before running `pipelex` commands. For standard installations, the venv is named `.venv` - always check that first. The commands will not work without proper venv activation. - Please use POSIX standard for files. (empty lines, no trailing whitespaces, etc.) @@ -23,10 +23,10 @@ A pipeline file has three main sections: #### Domain Statement ```plx -domain = "domain_name" +domain = "domain_code" description = "Description of the domain" # Optional ``` -Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. +Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. #### Concept Definitions @@ -41,20 +41,28 @@ ConceptName = "Description of the concept" - Use PascalCase for concept names - Never use plurals (no "Stories", use "Story") - lists are handled implicitly by Pipelex - Avoid circumstantial adjectives (no "LargeText", use "Text") - focus on the essence of what the concept represents -- Don't redefine native concepts (Text, Image, PDF, TextAndImages, Number, Page) +- Don't redefine native concepts (Text, Image, PDF, TextAndImages, Number, Page, JSON) **Native Concepts:** -Pipelex provides built-in native concepts: `Text`, `Image`, `PDF`, `TextAndImages`, `Number`, `Page`. Use these directly or refine them when appropriate. +Pipelex provides built-in native concepts: `Text`, `Image`, `PDF`, `TextAndImages`, `Number`, `Page`, `JSON`. Use these directly or refine them when appropriate. -**Refining Native Concepts:** -To create a concept that specializes a native concept without adding fields: +**Refining Concepts:** +To create a concept that specializes another concept without adding fields, use `refines`: ```plx +## Refining a native concept [concept.Landscape] description = "A scenic outdoor photograph" refines = "Image" + +## Refining a custom concept (must be in domain.ConceptCode format) +[concept.PremiumCustomer] +description = "A premium customer with special benefits" +refines = "myapp.Customer" ``` +Note: When refining a custom (non-native) concept, you must use the fully qualified concept ref in `domain.ConceptCode` format. Pipelex automatically handles the dependency order to ensure referenced concepts are loaded first. + For details on how to structure concepts with fields, see the "Structuring Models" section below. #### Pipe Definitions @@ -62,7 +70,7 @@ For details on how to structure concepts with fields, see the "Structuring Model ### Pipe Base Definition ```plx -[pipe.your_pipe_name] +[pipe.your_pipe_code] type = "PipeLLM" description = "A description of what your pipe does" inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" } @@ -72,7 +80,7 @@ output = "ConceptName" The pipes will all have at least this base definition. - `inputs`: Dictionary of key being the variable used in the prompts, and the value being the ConceptName. It should ALSO LIST THE INPUTS OF THE INTERMEDIATE STEPS (if PipeSequence) or of the conditional pipes (if PipeCondition). So If you have this error: -`StaticValidationError: missing_input_variable • domain='expense_validator' • pipe='validate_expense' • +`PipeValidationError: missing_input_variable • domain='expense_validator' • pipe='validate_expense' • variable='['invoice']'`` That means that the pipe validate_expense is missing the input `invoice` because one of the subpipe is needing it. @@ -127,16 +135,16 @@ For concepts with structured fields, define them inline using TOML syntax: description = "A commercial document issued by a seller to a buyer" [concept.Invoice.structure] -invoice_number = "The unique invoice identifier" +invoice_number = "The unique invoice identifier" # This will be optional by default issue_date = { type = "date", description = "The date the invoice was issued", required = true } total_amount = { type = "number", description = "The total invoice amount", required = true } -vendor_name = "The name of the vendor" -line_items = { type = "list", item_type = "text", description = "List of items", required = false } +vendor_name = "The name of the vendor" # This will be optional by default +line_items = { type = "list", item_type = "text", description = "List of items" } ``` -**Supported inline field types:** `text`, `integer`, `boolean`, `number`, `date`, `list`, `dict` +**Supported inline field types:** `text`, `integer`, `boolean`, `number`, `date`, `list`, `dict`, `concept` -**Field properties:** `type`, `description`, `required` (default: true), `default_value`, `choices`, `item_type` (for lists), `key_type` and `value_type` (for dicts) +**Field properties:** `type`, `description`, `required` (default: false), `default_value`, `choices`, `item_type` (for lists), `key_type` and `value_type` (for dicts), `concept_ref` (for concept references), `item_concept_ref` (for lists of concepts) **Simple syntax** (creates required text field): ```plx @@ -145,9 +153,46 @@ field_name = "Field description" **Detailed syntax** (with explicit properties): ```plx -field_name = { type = "text", description = "Field description", required = false, default_value = "default" } +field_name = { type = "text", description = "Field description", default_value = "default" } +``` + +**Concept reference syntax** (referencing another concept): +```plx +## Single concept reference +customer = { type = "concept", concept_ref = "myapp.Customer", description = "The customer" } + +## List of concepts +line_items = { type = "list", item_type = "concept", item_concept_ref = "myapp.LineItem", description = "Line items" } ``` +Example with concept references: +```plx +[concept.Customer] +description = "A customer entity" + +[concept.Customer.structure] +name = { type = "text", description = "Customer name" } +email = { type = "text", description = "Customer email" } + +[concept.LineItem] +description = "A line item in an invoice" + +[concept.LineItem.structure] +product = { type = "text", description = "Product name" } +quantity = { type = "integer", description = "Quantity ordered" } +unit_price = { type = "number", description = "Price per unit" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +customer = { type = "concept", concept_ref = "myapp.Customer", description = "The customer" } +items = { type = "list", item_type = "concept", item_concept_ref = "myapp.LineItem", description = "Line items" } +total = { type = "number", description = "Invoice total" } +``` + +Note: Pipelex automatically determines the correct loading order for concepts based on their dependencies (topological sort), so concepts can reference each other across domains as long as there are no circular dependencies. + **3. Python StructuredContent Class (For Advanced Features)** Create a Python class when you need: @@ -200,12 +245,14 @@ class Invoice(StructuredContent): #### Inline Structure Limitations Inline structures: -- ✅ Support all common field types (text, number, date, list, dict, etc.) +- ✅ Support all common field types (text, number, date, list, dict, concept, etc.) - ✅ Support required/optional fields, defaults, choices +- ✅ Support concept-to-concept references (type = "concept" with concept_ref) +- ✅ Support lists of concepts (type = "list" with item_type = "concept") +- ✅ Support refining both native and custom concepts - ✅ Generate full Pydantic models with validation - ❌ Cannot have custom validators or complex validation logic - ❌ Cannot have computed properties or custom methods -- ❌ Cannot refine custom (non-native) concepts - ❌ Limited IDE autocomplete compared to explicit Python classes @@ -471,7 +518,7 @@ The PipeExtract operator is used to extract text and images from an image or a P [pipe.extract_info] type = "PipeExtract" description = "extract the information" -inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input. +inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input. output = "Page" ``` @@ -480,7 +527,7 @@ Using Extract Model Settings: [pipe.extract_with_model] type = "PipeExtract" description = "Extract with specific model" -inputs = { document = "PDF" } +inputs = { document = "Document" } output = "Page" model = "base_extract_mistral" # Use predefined extract preset or model alias ``` @@ -588,15 +635,16 @@ $sales_rep.phone | $sales_rep.email """ ``` -#### Key Parameters +#### Key Parameters (Template Mode) -- `template`: Inline template string (mutually exclusive with template_name) +- `template`: Inline template string (mutually exclusive with template_name and construct) - `template_name`: Name of a predefined template (mutually exclusive with template) - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.) - `templating_style`: Styling options for template rendering - `extra_context`: Additional context variables for template For more control, you can use a nested `template` section instead of the `template` field: + - `template.template`: The template string - `template.category`: Template type - `template.templating_style`: Styling options @@ -604,9 +652,143 @@ For more control, you can use a nested `template` section instead of the `templa #### Template Variables Use the same variable insertion rules as PipeLLM: + - `@variable` for block insertion (multi-line content) - `$variable` for inline insertion (short text) +#### Construct Mode (for StructuredContent Output) + +PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures. + +**When to use construct mode:** + +- You need to output a structured object (not just Text) +- You want to deterministically compose fields from existing data +- No LLM is needed - just data composition and templating + +##### Basic Construct Usage + +```plx +[concept.SalesSummary] +description = "A structured sales summary" + +[concept.SalesSummary.structure] +report_title = { type = "text", description = "Title of the report" } +customer_name = { type = "text", description = "Customer name" } +deal_value = { type = "number", description = "Deal value" } +summary_text = { type = "text", description = "Generated summary text" } + +[pipe.compose_summary] +type = "PipeCompose" +description = "Compose a sales summary from deal data" +inputs = { deal = "Deal" } +output = "SalesSummary" + +[pipe.compose_summary.construct] +report_title = "Monthly Sales Report" +customer_name = { from = "deal.customer_name" } +deal_value = { from = "deal.amount" } +summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" } +``` + +##### Field Composition Methods + +There are four ways to define field values in a construct: + +**1. Fixed Value (literal)** + +Use a literal value directly: + +```plx +[pipe.compose_report.construct] +report_title = "Annual Report" +report_year = 2024 +is_draft = false +``` + +**2. Variable Reference (`from`)** + +Get a value from working memory using a dotted path: + +```plx +[pipe.compose_report.construct] +customer_name = { from = "deal.customer_name" } +total_amount = { from = "order.total" } +street_address = { from = "customer.address.street" } +``` + +**3. Template (`template`)** + +Render a Jinja2 template with variable substitution: + +```plx +[pipe.compose_report.construct] +invoice_number = { template = "INV-$order.id" } +summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" } +``` + +**4. Nested Construct** + +For nested structures, use a TOML subsection: + +```plx +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Complete Construct Example + +```plx +domain = "invoicing" + +[concept.Address] +description = "A postal address" + +[concept.Address.structure] +street = { type = "text", description = "Street address" } +city = { type = "text", description = "City name" } +country = { type = "text", description = "Country name" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +invoice_number = { type = "text", description = "Invoice number" } +total = { type = "number", description = "Total amount" } + +[pipe.compose_invoice] +type = "PipeCompose" +description = "Compose an invoice from order and customer data" +inputs = { order = "Order", customer = "Customer" } +output = "Invoice" + +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Key Parameters (Construct Mode) + +- `construct`: Dictionary mapping field names to their composition rules +- Each field can be: + - A literal value (string, number, boolean) + - A dict with `from` key for variable reference + - A dict with `template` key for template rendering + - A nested dict for nested structures + +**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive. + ### PipeImgGen operator The PipeImgGen operator is used to generate images using AI image generation models. @@ -820,7 +1002,7 @@ Presets are meant to record the choice of an llm with its hyper parameters (temp Examples: ```toml -llm_for_complex_reasoning = { model = "base-claude", temperature = 1 } +llm_to_engineer = { model = "base-claude", temperature = 1 } llm_to_extract_invoice = { model = "claude-3-7-sonnet", temperature = 0.1, max_tokens = "auto" } ``` @@ -851,7 +1033,7 @@ You can override the predefined llm presets by setting them in `.pipelex/inferen ALWAYS RUN validation when you are finished writing pipelines: This checks for errors. If there are errors, iterate until it works. - For a specific bundle/file: `pipelex validate path_to_file.plx` -- For all pipelines: `pipelex validate all` +- For all pipelines: `pipelex validate --all` - Remember: Ensure your Python virtual environment is activated (typically `.venv` for standard installations) before running `pipelex` commands. Then, create an example file to run the pipeline in the `examples` folder. @@ -859,21 +1041,6 @@ But don't write documentation unless asked explicitly to. ## Guide to execute a pipeline and write example code -### Prerequisites: Virtual Environment - -**CRITICAL**: Before running any `pipelex` commands or `pytest`, you MUST activate the appropriate Python virtual environment. Without proper venv activation, these commands will not work. - -For standard installations, the virtual environment is named `.venv`. Always check this first: - -```bash -## Activate the virtual environment (standard installation) -source .venv/bin/activate # On macOS/Linux -## or -.venv\Scripts\activate # On Windows -``` - -If your installation uses a different venv name or location, activate that one instead. All subsequent `pipelex` and `pytest` commands assume the venv is active. - ### Example to execute a pipeline with text output ```python @@ -967,13 +1134,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa }, ) -## Here we have a single input and it's a PDF. -## Because PDFContent is a native concept, we can use it directly as a value, +## Here we have a single input and it's a document. +## Because DocumentContent is a native concept, we can use it directly as a value, ## the system knows what content it corresponds to: pipe_output = await execute_pipeline( pipe_code="power_extractor_dpe", inputs={ - "document": PDFContent(url=pdf_url), + "document": DocumentContent(url=pdf_url), }, ) @@ -1096,82 +1263,4 @@ result_list = pipe_output.main_stuff_as_items(item_type=GanttChart) ``` --- - -## Rules to choose LLM models used in PipeLLMs. - -### LLM Configuration System - -In order to use it in a pipe, an LLM is referenced by its llm_handle (alias) and possibly by an llm_preset. -LLM configurations are managed through the new inference backend system with files located in `.pipelex/inference/`: - -- **Model Deck**: `.pipelex/inference/deck/base_deck.toml` and `.pipelex/inference/deck/overrides.toml` -- **Backends**: `.pipelex/inference/backends.toml` and `.pipelex/inference/backends/*.toml` -- **Routing**: `.pipelex/inference/routing_profiles.toml` - -### LLM Handles - -An llm_handle can be either: -1. **A direct model name** (like "gpt-4o-mini", "claude-3-sonnet") - automatically available for all models loaded by the inference backend system -2. **An alias** - user-defined shortcuts that map to model names, defined in the `[aliases]` section: - -```toml -[aliases] -base-claude = "claude-4.5-sonnet" -base-gpt = "gpt-5" -base-gemini = "gemini-2.5-flash" -base-mistral = "mistral-medium" -``` - -The system first looks for direct model names, then checks aliases if no direct match is found. The system handles model routing through backends automatically. - -### Using an LLM Handle in a PipeLLM - -Here is an example of using an llm_handle to specify which LLM to use in a PipeLLM: - -```plx -[pipe.hello_world] -type = "PipeLLM" -description = "Write text about Hello World." -output = "Text" -model = { model = "gpt-5", temperature = 0.9 } -prompt = """ -Write a haiku about Hello World. -""" -``` - -As you can see, to use the LLM, you must also indicate the temperature (float between 0 and 1) and max_tokens (either an int or the string "auto"). - -### LLM Presets - -Presets are meant to record the choice of an llm with its hyper parameters (temperature and max_tokens) if it's good for a particular task. LLM Presets are skill-oriented. - -Examples: -```toml -llm_for_complex_reasoning = { model = "base-claude", temperature = 1 } -llm_to_extract_invoice = { model = "claude-3-7-sonnet", temperature = 0.1, max_tokens = "auto" } -``` - -The interest is that these presets can be used to set the LLM choice in a PipeLLM, like this: - -```plx -[pipe.extract_invoice] -type = "PipeLLM" -description = "Extract invoice information from an invoice text transcript" -inputs = { invoice_text = "InvoiceText" } -output = "Invoice" -model = "llm_to_extract_invoice" -prompt = """ -Extract invoice information from this invoice: - -The category of this invoice is: $invoice_details.category. - -@invoice_text -""" -``` - -The setting here `model = "llm_to_extract_invoice"` works because "llm_to_extract_invoice" has been declared as an llm_preset in the deck. -You must not use an LLM preset in a PipeLLM that does not exist in the deck. If needed, you can add llm presets. - - -You can override the predefined llm presets by setting them in `.pipelex/inference/deck/overrides.toml`. diff --git a/.gitignore b/.gitignore index 3dd0d7c..a4c50c3 100644 --- a/.gitignore +++ b/.gitignore @@ -7,7 +7,6 @@ __pycache__/ # Distribution / packaging dist/ -build/ *.egg-info/ # mkdocs diff --git a/.pipelex/inference/deck/1_llm_deck.toml b/.pipelex/inference/deck/1_llm_deck.toml new file mode 100644 index 0000000..6b9d8c3 --- /dev/null +++ b/.pipelex/inference/deck/1_llm_deck.toml @@ -0,0 +1,79 @@ +#################################################################################################### +# Pipelex Model Deck - LLM Configuration +#################################################################################################### +# +# This file defines model defaults, aliases, and presets for LLMs +# +# Model Reference Syntax: +# - Preset: $preset_name or preset:preset_name +# - Alias: @alias_name or alias:alias_name +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +#################################################################################################### + +#################################################################################################### +# LLM Default Choices +#################################################################################################### + +[llm.choice_defaults] +default_temperature = 0.5 +for_text = "@default-general" +for_object = "@default-general" + +#################################################################################################### +# LLM Aliases +#################################################################################################### + +[llm.aliases] +best-gpt = "gpt-5.2" +best-claude = "claude-4.5-opus" +best-gemini = "gemini-3.0-pro" +best-mistral = "mistral-large-3" + +# Default aliases (first choice from waterfalls) +default-general = "claude-4.5-sonnet" +default-premium = "claude-4.5-opus" +default-premium-vision = "claude-4.5-opus" +default-premium-structured = "claude-4.5-opus" +default-large-context-code = "gemini-3.0-pro" +default-large-context-text = "gemini-2.5-flash" +default-small = "gemini-2.5-flash-lite" +default-small-structured = "gemini-2.5-flash-lite" +default-small-vision = "gemini-2.5-flash-lite" +default-small-creative = "gemini-2.5-flash-lite" + +#################################################################################################### +# LLM Presets +#################################################################################################### + +[llm.presets] + +# Writing +writing-factual = { model = "@default-premium", temperature = 0.1 } +writing-creative = { model = "@default-premium", temperature = 0.9 } + +# Retrieval +retrieval = { model = "@default-large-context-text", temperature = 0.1 } + +# Engineering +engineering-structured = { model = "@default-premium-structured", temperature = 0.2 } +engineering-code = { model = "@default-premium", temperature = 0.1 } +engineering-codebase-analysis = { model = "@best-gemini", temperature = 0.1 } + +# Vision +vision = { model = "@default-premium-vision", temperature = 0.5 } +vision-cheap = { model = "@default-small-vision", temperature = 0.5 } +vision-diagram = { model = "@default-premium-vision", temperature = 0.3 } +vision-table = { model = "@default-premium-vision", temperature = 0.3 } + +# Image generation prompting +img-gen-prompting = { model = "@default-premium", temperature = 0.5 } +img-gen-prompting-cheap = { model = "@default-small", temperature = 0.5 } + +# Testing +testing-text = { model = "@default-small", temperature = 0.5 } +testing-structured = { model = "@default-small-structured", temperature = 0.1 } +testing-vision = { model = "@default-small-vision", temperature = 0.5 } +testing-vision-structured = { model = "@default-small-vision", temperature = 0.5 } diff --git a/.pipelex/inference/deck/2_img_gen_deck.toml b/.pipelex/inference/deck/2_img_gen_deck.toml new file mode 100644 index 0000000..8b1725c --- /dev/null +++ b/.pipelex/inference/deck/2_img_gen_deck.toml @@ -0,0 +1,49 @@ +#################################################################################################### +# Pipelex Model Deck - Image Generation Configuration +#################################################################################################### +# +# This file defines model aliases and presets for image generation models +# +# Model Reference Syntax: +# - Preset: $preset_name or preset:preset_name +# - Alias: @alias_name or alias:alias_name +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +#################################################################################################### + +#################################################################################################### +# Image Generation Default Choices +#################################################################################################### + +[img_gen] +default_quality = "medium" +choice_default = "$gen-image" + +#################################################################################################### +# Image Generation Aliases +#################################################################################################### + +[img_gen.aliases] +best-gpt = "gpt-image-1.5" +best-gemini = "nano-banana-pro" +best-blackforestlabs = "flux-2-pro" + +default-general = "flux-2-pro" +default-premium = "nano-banana-pro" +default-small = "gpt-image-1-mini" + +#################################################################################################### +# Image Generation Presets +#################################################################################################### + +[img_gen.presets] + +# General purpose +gen-image = { model = "@default-general", quality = "medium" } +gen-image-fast = { model = "@default-small", quality = "low" } +gen-image-high-quality = { model = "@default-premium", quality = "high" } + +# Testing +gen-image-testing = { model = "@default-small", quality = "low" } diff --git a/.pipelex/inference/deck/3_extract_deck.toml b/.pipelex/inference/deck/3_extract_deck.toml new file mode 100644 index 0000000..1092489 --- /dev/null +++ b/.pipelex/inference/deck/3_extract_deck.toml @@ -0,0 +1,42 @@ +#################################################################################################### +# Pipelex Model Deck - Base Configuration +#################################################################################################### +# +# This file defines model aliases and presets for Document extraction models, including +# extraction of text and images from documents and OCR and text extraction from images. +# +# Model Reference Syntax: +# - Preset: $preset_name or preset:preset_name +# - Alias: @alias_name or alias:alias_name +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +#################################################################################################### + +#################################################################################################### +# Document Extraction Default Choices +#################################################################################################### + +[extract] +choice_default = "$extract-all-from-document" + +#################################################################################################### +# Aliases +#################################################################################################### + +[extract.aliases] +default-extract-document = "azure-document-intelligence" +default-extract-text-from-pdf = "pypdfium2-extract-pdf" +default-software-extract-no-inference = "pypdfium2-extract-pdf" + +#################################################################################################### +# Extract Presets +#################################################################################################### + +[extract.presets] +extract-all-from-document = { model = "@default-extract-document", max_nb_images = 100, image_min_size = 50 } +extract-text-from-pdf = { model = "@default-extract-text-from-pdf", max_nb_images = 100, image_min_size = 50 } + +# Testing +extract-testing = { model = "@default-software-extract-no-inference", max_nb_images = 100, image_min_size = 50 } diff --git a/.pipelex/inference/deck/base_deck.toml b/.pipelex/inference/deck/base_deck.toml deleted file mode 100644 index 63ab39e..0000000 --- a/.pipelex/inference/deck/base_deck.toml +++ /dev/null @@ -1,202 +0,0 @@ -#################################################################################################### -# Pipelex Model Deck - Base Configuration -#################################################################################################### -# -# This file defines model aliases and presets for: -# - LLMs (language models for text generation and structured output) -# - Image generation models (for creating images from text prompts) -# - Document extraction models (OCR and text extraction from PDFs/images) -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -#################################################################################################### - -#################################################################################################### -# Aliases -#################################################################################################### - -[aliases] -base-claude = "claude-4.5-sonnet" -base-gpt = "gpt-4o" -base-gemini = "gemini-2.5-flash" -base-mistral = "mistral-medium" -base-groq = "llama-3.3-70b-versatile" -base-grok = "grok-4-fast-non-reasoning" - -best-gpt = "gpt-5.1" -best-claude = "claude-4.5-opus" -best-gemini = "gemini-3.0-pro" -best-mistral = "mistral-medium" - -# Groq-specific aliases -fast-groq = "llama-3.1-8b-instant" -vision-groq = "llama-4-scout-17b-16e-instruct" - -# Image generation aliases -base-img-gen = "flux-pro/v1.1" -best-img-gen = "flux-2" -fast-img-gen = "fast-lightning-sdxl" - -#################################################################################################### -# Waterfalls -#################################################################################################### - -[waterfalls] - -# --- Waterfalls for LLMs --------------------------------------------------------------------- -smart_llm = [ - "claude-4.5-opus", - "claude-4.5-sonnet", - "gemini-3.0-pro", - "gpt-5.1", - "claude-4.1-opus", - "gemini-2.5-pro", - "claude-4-sonnet", - "grok-4-fast-non-reasoning", -] -smart_llm_with_vision = [ - "claude-4.5-opus", - "claude-4.5-sonnet", - "gemini-3.0-pro", - "gpt-5.1", - "claude-4.1-opus", - "gemini-2.5-pro", - "claude-4-sonnet", - "grok-4-fast-non-reasoning", -] -smart_llm_for_structured = [ - "claude-4.5-opus", - "claude-4.5-sonnet", - "gemini-3.0-pro", - "gpt-5.1", - "claude-4.1-opus", - "claude-4-sonnet", -] -llm_for_creativity = [ - "claude-4.5-opus", - "claude-4.1-opus", - "gemini-2.5-pro", - "gpt-5.1", -] -llm_for_large_codebase = [ - "gemini-2.5-pro", - "claude-4.5-sonnet", - "gemini-3.0-pro", - "gpt-5.1", - "gemini-2.5-flash", - "grok-4-fast-non-reasoning", -] -cheap_llm = [ - "gpt-4o-mini", - "gemini-2.5-flash-lite", - "mistral-small", - "claude-3-haiku", - "grok-3-mini", -] -cheap_llm_for_vision = [ - "gemini-2.5-flash-lite", - "gpt-4o-mini", - "claude-3-haiku", -] -cheap_llm_for_structured = ["gpt-4o-mini", "mistral-small", "claude-3-haiku"] -cheap_llm_for_creativity = [ - "gemini-2.5-flash", - "grok-3-mini", - "gpt-4o-mini", - "claude-4.5-haiku", -] - -# --- Waterfalls for Extracts --------------------------------------------------------------------- -pdf_text_extractor = [ - "azure-document-intelligence", - "mistral-ocr", - "pypdfium2-extract-pdf", -] -image_text_extractor = ["mistral-ocr"] - -#################################################################################################### -# LLM Presets -#################################################################################################### - -[llm.presets] - -# LLM Presets — Specific skills ------------------------------------------------------------- - -# Generation skills -llm_for_factual_writing = { model = "base-gpt", temperature = 0.1 } -llm_for_creative_writing = { model = "base-gpt", temperature = 0.9 } -llm_for_writing_cheap = { model = "gpt-4o-mini", temperature = 0.3 } - -# Retrieve and answer questions skills -llm_to_answer_questions_cheap = { model = "gpt-4o-mini", temperature = 0.3 } -llm_to_answer_questions = { model = "base-claude", temperature = 0.3 } -llm_to_retrieve = { model = "base-claude", temperature = 0.1 } - -# Engineering skills -llm_to_engineer = { model = "smart_llm_for_structured", temperature = 0.2 } -llm_to_code = { model = "base-claude", temperature = 0.1 } -llm_to_analyze_large_codebase = { model = "base-claude", temperature = 0.1 } - -# Vision skills -llm_for_img_to_text_cheap = { model = "gpt-4o-mini", temperature = 0.1 } -llm_for_img_to_text = { model = "base-claude", temperature = 0.1 } -llm_for_diagram_to_text = { model = "best-claude", temperature = 0.3 } -llm_for_table_to_text = { model = "base-claude", temperature = 0.3 } - -# Image generation prompting skills -llm_to_prompt_img_gen = { model = "base-claude", temperature = 0.2 } -llm_to_prompt_img_gen_cheap = { model = "gpt-4o-mini", temperature = 0.5 } - -# Groq-specific presets (fast inference, low cost) -llm_groq_fast_text = { model = "fast-groq", temperature = 0.7 } -llm_groq_balanced = { model = "base-groq", temperature = 0.5 } -llm_groq_vision = { model = "vision-groq", temperature = 0.3 } - -# LLM Presets — For Testing --------------------------------------------------------------------- - -llm_for_testing_gen_text = { model = "cheap_llm", temperature = 0.5 } -llm_for_testing_gen_object = { model = "cheap_llm_for_structured", temperature = 0.1 } -llm_for_testing_vision = { model = "cheap_llm_for_vision", temperature = 0.5 } -llm_for_testing_vision_structured = { model = "cheap_llm_for_vision", temperature = 0.5 } - -#################################################################################################### -# LLM Choices -#################################################################################################### - -[llm.choice_defaults] -for_text = "cheap_llm" -for_object = "cheap_llm_for_structured" - -#################################################################################################### -# Extract Presets -#################################################################################################### - -[extract] -choice_default = "extract_ocr_from_document" - -[extract.presets] -extract_ocr_from_document = { model = "azure-document-intelligence", max_nb_images = 100, image_min_size = 50 } -extract_basic_from_pdf = { model = "pypdfium2-extract-pdf", max_nb_images = 100, image_min_size = 50 } - -#################################################################################################### -# Image Generation Presets -#################################################################################################### - -[img_gen] -choice_default = "gen_image_basic" - -[img_gen.presets] - -# General purpose -gen_image_basic = { model = "base-img-gen", quality = "medium", guidance_scale = 7.5, is_moderated = true, safety_tolerance = 3 } -gen_image_fast = { model = "fast-img-gen", nb_steps = 4, guidance_scale = 5.0, is_moderated = true, safety_tolerance = 3 } -gen_image_high_quality = { model = "best-img-gen", quality = "high", guidance_scale = 8.0, is_moderated = true, safety_tolerance = 3 } -gen_image_openai_low_quality = { model = "gpt-image-1", quality = "low" } - -# Specific skills -img_gen_for_art = { model = "best-img-gen", quality = "high", guidance_scale = 9.0, is_moderated = false, safety_tolerance = 5 } -img_gen_for_diagram = { model = "base-img-gen", quality = "medium", guidance_scale = 7.0, is_moderated = true, safety_tolerance = 2 } -img_gen_for_mockup = { model = "base-img-gen", quality = "medium", guidance_scale = 6.5, is_moderated = true, safety_tolerance = 3 } -img_gen_for_product = { model = "best-img-gen", quality = "high", guidance_scale = 8.5, is_moderated = true, safety_tolerance = 2 } -img_gen_for_testing = { model = "fast-img-gen", nb_steps = 4, guidance_scale = 4.0, is_moderated = true, safety_tolerance = 4 } diff --git a/.pipelex/inference/deck/overrides.toml b/.pipelex/inference/deck/overrides.toml deleted file mode 100644 index 08814db..0000000 --- a/.pipelex/inference/deck/overrides.toml +++ /dev/null @@ -1,19 +0,0 @@ -#################################################################################################### -# Pipelex Model Deck - Overrides -#################################################################################################### -# -# This file allows you to override the default model choices defined in base_deck.toml. -# You can customize presets for LLMs, image generation, and document extraction models. -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -#################################################################################################### - -#################################################################################################### -# LLM Deck overrides -#################################################################################################### - -[llm.choice_overrides] -for_text = "disabled" -for_object = "disabled" diff --git a/.pipelex/inference/deck/x_custom_extract_deck.toml b/.pipelex/inference/deck/x_custom_extract_deck.toml new file mode 100644 index 0000000..c3c3b9f --- /dev/null +++ b/.pipelex/inference/deck/x_custom_extract_deck.toml @@ -0,0 +1,39 @@ +#################################################################################################### +# Pipelex Model Deck - Custom Configurations for Document Extraction Models +#################################################################################################### +# +# This file allows you to override or complete the base model decks. +# +# ADVANCED USERS ONLY: This file is for users who bring their own API keys and connect directly +# to AI providers (Azure, Mistral, etc.) without using the Pipelex Gateway. +# +# If you're using the standard Pipelex Gateway setup, you don't need to modify this file. +# The Gateway handles model routing automatically and supports all available models. +# +# Waterfalls are useful when using multiple backends directly - they define ordered lists +# of models that are resolved at configuration time based on which backends are available. +# This enables defining pipelines that work across different environments with varying +# backend configurations. +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +#################################################################################################### + + +#################################################################################################### +# Waterfalls — ordered lists of models resolved at configuration time by backend availability +# +# Example (uncomment to use): +# [extract.waterfalls] +# document_extractor = ["azure-document-intelligence", "mistral-document-ai-2505"] +# pdf_text_extractor = [ +# "azure-document-intelligence", +# "mistral-document-ai-2505", +# "pypdfium2-extract-pdf", +# ] +# image_text_extractor = [ +# "azure-document-intelligence", +# "mistral-document-ai-2505", +# ] +#################################################################################################### diff --git a/.pipelex/inference/deck/x_custom_llm_deck.toml b/.pipelex/inference/deck/x_custom_llm_deck.toml new file mode 100644 index 0000000..eb47480 --- /dev/null +++ b/.pipelex/inference/deck/x_custom_llm_deck.toml @@ -0,0 +1,71 @@ +#################################################################################################### +# Pipelex Model Deck - Custom Configurations for LLMs +#################################################################################################### +# +# This file allows you to override or complete the base model decks. +# +# ADVANCED USERS ONLY: This file is for users who bring their own API keys and connect directly +# to AI providers (OpenAI, Anthropic, Google, etc.) without using the Pipelex Gateway. +# +# If you're using the standard Pipelex Gateway setup, you don't need to modify this file. +# The Gateway handles model routing automatically and supports all available models. +# +# Waterfalls are useful when using multiple backends directly - they define ordered lists +# of models that are resolved at configuration time based on which backends are available. +# This enables defining pipelines that work across different environments with varying +# backend configurations. +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +#################################################################################################### + +#################################################################################################### +# LLM Deck overrides +#################################################################################################### + +[llm.choice_overrides] +for_text = "disabled" +for_object = "disabled" + + +#################################################################################################### +# Waterfalls — ordered lists of models resolved at configuration time by backend availability +# +# Example (uncomment to use): +# [llm.waterfalls] +# premium-llm = ["claude-4.5-opus", "gemini-3.0-pro", "gpt-5.2", "grok-4"] +# premium-llm-vision = [ +# "claude-4.5-opus", +# "gemini-3.0-pro", +# "gpt-5.2", +# "grok-4-fast-reasoning", +# ] +# premium-llm-structured = [ +# "claude-4.5-opus", +# "gemini-3.0-pro", +# "gpt-5.2", +# "grok-4", +# ] +# large-context-llm-code = [ +# "gemini-3.0-pro", +# "claude-4.5-opus", +# "gpt-5.2", +# "grok-4-fast-reasoning", +# ] +# large-context-llm-text = ["gemini-2.5-flash", "claude-4.5-sonnet"] +# small-llm = [ +# "gemini-2.5-flash-lite", +# "gpt-4o-mini", +# "claude-3-haiku", +# "phi-4", +# "grok-3-mini", +# ] +# small-llm-structured = [ +# "gemini-2.5-flash-lite", +# "gpt-4o-mini", +# "claude-3-haiku", +# ] +# small-llm-vision = ["gemini-2.5-flash-lite", "gpt-4o-mini", "claude-3-haiku"] +# small-llm-creative = ["gemini-2.5-flash-lite", "gpt-4o-mini", "claude-3-haiku"] +#################################################################################################### diff --git a/.pipelex/inference/routing_profiles.toml b/.pipelex/inference/routing_profiles.toml index bf40281..59a77db 100644 --- a/.pipelex/inference/routing_profiles.toml +++ b/.pipelex/inference/routing_profiles.toml @@ -9,49 +9,18 @@ # ========================================================================================= # Which profile to use (change this to switch routing) -active = "pipelex_gateway_first" +active = "all_pipelex_gateway" -# We recommend using the "pipelex_gateway_first" profile to get a head start with all models. +# We recommend using the "all_pipelex_gateway" profile to get a head start with all models. # To use the Pipelex Gateway backend: -# 1. Join our Discord community to get your free API key (no credit card required): -# Visit https://go.pipelex.com/discord and request your key in the appropriate channel -# 2. Set the environment variable (or add it to your .env file): -# - Linux/macOS: export PIPELEX_GATEWAY_API_KEY="your-api-key" -# - Windows CMD: set PIPELEX_GATEWAY_API_KEY=your-api-key -# - Windows PowerShell: $env:PIPELEX_GATEWAY_API_KEY="your-api-key" -# 3. The .pipelex/inference/backends.toml is already configured with api_key = "${PIPELEX_GATEWAY_API_KEY}" -# which will get the key from the environment variable. +# 1. Get your API key at https://app.pipelex.com (free credits included) +# 2. Add it to your .env file: PIPELEX_GATEWAY_API_KEY=your-key-here +# 3. Run `pipelex init` and accept the Gateway terms of service # ========================================================================================= # Routing Profiles # ========================================================================================= -[profiles.pipelex_gateway_first] -description = "Use Pipelex Gateway backend for all its supported models" -default = "pipelex_gateway" -fallback_order = [ - "pipelex_gateway", - "azure_openai", - "bedrock", - "google", - "blackboxai", - "mistral", - "fal", -] - -[profiles.pipelex_gateway_first.routes] -# Pattern matching: "model-pattern" = "backend-name" - -[profiles.pipelex_gateway_first.optional_routes] # Each optional route is considered only if its backend is available -"gpt-*" = "pipelex_gateway" -"gpt-image-1" = "openai" -"claude-*" = "pipelex_gateway" -"grok-*" = "pipelex_gateway" -"gemini-*" = "pipelex_gateway" -"*-sdxl" = "fal" -"flux-*" = "fal" -"mistral-ocr" = "mistral" - [profiles.all_pipelex_gateway] description = "Use Pipelex Gateway for all its supported models" default = "pipelex_gateway" @@ -108,6 +77,10 @@ default = "portkey" description = "Use Scaleway backend for all its supported models" default = "scaleway" +[profiles.all_vertexai] +description = "Use Vertex AI backend for all its supported models" +default = "vertexai" + [profiles.all_xai] description = "Use xAI backend for all its supported models" default = "xai" diff --git a/.pipelex/pipelex.toml b/.pipelex/pipelex.toml index 7a7ddaf..df029c2 100644 --- a/.pipelex/pipelex.toml +++ b/.pipelex/pipelex.toml @@ -6,15 +6,15 @@ # `pipelex init config` # # Purpose: -# - This file allows to override Pipelex's default settings for specific projects -# - Feel free to modify any settings below to suit your needs -# - You can add any configuration sections that exist in the main pipelex.toml +# - This file allows you to override Pipelex's default settings for specific projects +# - All values below are set to their defaults - modify them as needed +# - The values here will override the defaults from the Pipelex package # # Finding Available Settings: # - See the full default configuration in: pipelex/pipelex.toml (in the Pipelex package) # - See the configuration structure classes in: pipelex/config.py and pipelex/cogt/config_cogt.py # -# Common customizations are proposed below, such as: +# Common customizations include: # - Logging levels and behavior # - Excluded directories for scanning # - LLM prompt dumping for debugging @@ -26,136 +26,164 @@ # #################################################################################################### +#################################################################################################### +# Pipeline Execution Config +#################################################################################################### + [pipelex.pipeline_execution_config] -# Uncomment to disable conversion of data URLs to pipelex-storage:// URIs -# is_normalize_data_urls_to_storage = false +# Set to false to disable conversion of incoming data URLs to pipelex-storage:// URIs +is_normalize_data_urls_to_storage = false +# Set to false to disable generation of execution graphs +is_generate_graph = true [pipelex.pipeline_execution_config.graph_config.data_inclusion] -# Uncomment to include stuff data in graph outputs: +# Control what data is included in graph outputs stuff_json_content = true stuff_text_content = true stuff_html_content = true error_stack_traces = true [pipelex.pipeline_execution_config.graph_config.graphs_inclusion] -# Uncomment to customize which graph outputs are generated (all enabled by default): -# graphspec_json = false -# mermaidflow_mmd = false -# mermaidflow_html = false -# reactflow_viewspec = false -# reactflow_html = false +# Control which graph outputs are generated +graphspec_json = true +mermaidflow_mmd = true +mermaidflow_html = true +reactflow_viewspec = true +reactflow_html = true [pipelex.pipeline_execution_config.graph_config.reactflow_config] -# Uncomment to customize ReactFlow graph rendering: -# edge_type = "bezier" # Options: "bezier", "smoothstep", "step", "straight" -# nodesep = 50 # Horizontal spacing between nodes -# ranksep = 80 # Vertical spacing between ranks/levels -# initial_zoom = 1.0 # Initial zoom level (1.0 = 100%) -# pan_to_top = true # Pan to show top of graph on load +# Customize ReactFlow graph rendering +edge_type = "bezier" # Options: "bezier", "smoothstep", "step", "straight" +nodesep = 50 # Horizontal spacing between nodes +ranksep = 30 # Vertical spacing between ranks/levels +initial_zoom = 1.0 # Initial zoom level (1.0 = 100%) +pan_to_top = true # Pan to show top of graph on load -[pipelex.storage_config] -# Storage method: "local" (default), "in_memory", "s3", or "gcp" -# method = "local" +#################################################################################################### +# Storage Config +#################################################################################################### +[pipelex.storage_config] +# Storage method: "local", "in_memory" (default), "s3", or "gcp" +method = "s3" # Whether to fetch remote HTTP URLs and store them locally -# is_fetch_remote_content_enabled = true +is_fetch_remote_content_enabled = true -# --- Local Storage Configuration --- -# Uncomment to customize local storage settings: [pipelex.storage_config.local] -# uri_format = "{primary_id}/{secondary_id}/{hash}.{extension}" -# local_storage_path = ".pipelex/storage" +# Local storage settings +uri_format = "{primary_id}/{secondary_id}/{hash}.{extension}" +local_storage_path = ".pipelex/storage" + +[pipelex.storage_config.in_memory] +# In-memory storage settings +uri_format = "{primary_id}/{secondary_id}/{hash}.{extension}" -# --- AWS S3 Storage Configuration --- -# Uncomment to use S3 storage (requires boto3: `pip install pipelex[s3]`): [pipelex.storage_config.s3] -# uri_format = "{primary_id}/{secondary_id}/{hash}.{extension}" -# bucket_name = "your-bucket-name" -# region = "us-east-1" -# signed_urls_lifespan_seconds = 3600 # Set to "disabled" for public URLs +# AWS S3 storage settings (requires boto3: `pip install pipelex[s3]`) +uri_format = "{primary_id}/{secondary_id}/{hash}.{extension}" +bucket_name = "pipelex-assets" +region = "eu-west-3" +signed_urls_lifespan_seconds = 3600 # Set to "disabled" for public URLs -# --- Google Cloud Storage Configuration --- -# Uncomment to use GCP storage (requires google-cloud-storage: `pip install pipelex[gcp-storage]`): [pipelex.storage_config.gcp] -# uri_format = "{primary_id}/{secondary_id}/{hash}.{extension}" -# bucket_name = "your-bucket-name" -# project_id = "your-project-id" -# signed_urls_lifespan_seconds = 3600 # Set to "disabled" for public URLs +# Google Cloud Storage settings (requires google-cloud-storage: `pip install pipelex[gcp-storage]`) +uri_format = "{primary_id}/{secondary_id}/{hash}.{extension}" +bucket_name = "" +project_id = "" +signed_urls_lifespan_seconds = 3600 # Set to "disabled" for public URLs + +#################################################################################################### +# Scan Config +#################################################################################################### [pipelex.scan_config] -# Uncomment to customize the excluded directories for scanning -# excluded_dirs = [ -# ".venv", -# "venv", -# "env", -# ".env", -# "virtualenv", -# ".virtualenv", -# ".git", -# "__pycache__", -# ".pytest_cache", -# ".mypy_cache", -# ".ruff_cache", -# "node_modules", -# "results", -# ] +# Directories to exclude when scanning for pipeline files +excluded_dirs = [ + ".venv", + "venv", + "env", + ".env", + "virtualenv", + ".virtualenv", + ".git", + "__pycache__", + ".pytest_cache", + ".mypy_cache", + ".ruff_cache", + "node_modules", + "results", +] + +#################################################################################################### +# Builder Config +#################################################################################################### [pipelex.builder_config] -# Uncomment to change where the generated pipelines are saved: -# default_output_dir = "." -# default_bundle_file_name = "bundle" -# default_directory_base_name = "pipeline" +# Settings for generated pipelines +default_output_dir = "." +default_bundle_file_name = "bundle" +default_directory_base_name = "pipeline" -[pipelex.log_config] -# Uncomment to change the default log level: -# default_log_level = "INFO" +#################################################################################################### +# Log Config +#################################################################################################### -# Uncomment to log to stderr instead of stdout -# console_log_target = "stderr" -# console_print_target = "stderr" +[pipelex.log_config] +# Default logging level: "DEBUG", "INFO", "WARNING", "ERROR" +default_log_level = "INFO" +# Log output target: "stdout" or "stderr" +console_log_target = "stdout" +console_print_target = "stdout" [pipelex.log_config.package_log_levels] -# Uncomment to change the log level for specific packages: -# pipelex = "INFO" +# Log levels for specific packages (use "-" instead of "." in package names) +pipelex = "INFO" -[pipelex.observer_config] -# Uncomment to change the directory where the observer will save its results: -# observer_dir = "results/observer" +#################################################################################################### +# Feature Config +#################################################################################################### [pipelex.feature_config] -# WIP/Experimental feature flags: -# is_reporting_enabled = true +# WIP/Experimental feature flags +is_reporting_enabled = true + +#################################################################################################### +# Reporting Config +#################################################################################################### [pipelex.reporting_config] -# Uncomment to customize the reporting configuration: -# is_log_costs_to_console = false -# is_generate_cost_report_file_enabled = false -# cost_report_dir_path = "reports" -# cost_report_base_name = "cost_report" -# cost_report_extension = "csv" -# cost_report_unit_scale = 1.0 - -[cogt] +# Cost reporting settings +is_log_costs_to_console = false +is_generate_cost_report_file_enabled = false +cost_report_dir_path = "reports" +cost_report_base_name = "cost_report" +cost_report_extension = "csv" +cost_report_unit_scale = 1.0 + +#################################################################################################### +# Cogt (Cognitive Tools) Config +#################################################################################################### + [cogt.model_deck_config] -# Uncomment to disable model fallback: it will raise errors instead of using secondary model options: -# is_model_fallback_enabled = false -# Uncomment to change the reaction to missing presets: "raise" (default), "log" or "none" -# missing_presets_reaction = "raise" +# Model fallback behavior: if true, uses secondary model options when primary fails +is_model_fallback_enabled = true +# Reaction to missing presets: "raise", "log", or "none" +missing_presets_reaction = "log" [cogt.tenacity_config] -# Uncomment to change those values as needed: -# max_retries = 50 # Maximum number of retry attempts before giving up -# wait_multiplier = 0.2 # Multiplier applied to the wait time between retries (in seconds) -# wait_max = 20 # Maximum wait time between retries (in seconds) -# wait_exp_base = 1.3 # Base for exponential backoff calculation +# Retry behavior for API calls +max_retries = 50 # Maximum number of retry attempts before giving up +wait_multiplier = 0.2 # Multiplier applied to the wait time between retries (in seconds) +wait_max = 20 # Maximum wait time between retries (in seconds) +wait_exp_base = 1.3 # Base for exponential backoff calculation [cogt.llm_config] -# Uncomment any of these to enable dumping the inputs or outputs of text-generation with an LLM: -# is_dump_text_prompts_enabled = true -# is_dump_response_text_enabled = true +# Enable dumping of LLM inputs/outputs for debugging +is_dump_text_prompts_enabled = false +is_dump_response_text_enabled = false [cogt.llm_config.instructor_config] -# Uncomment any of these to enable dumping the kwargs, response or errors when generating structured content: -# is_dump_kwargs_enabled = true -# is_dump_response_enabled = true -# is_dump_error_enabled = true +# Enable dumping of structured content generation details for debugging +is_dump_kwargs_enabled = false +is_dump_response_enabled = false +is_dump_error_enabled = false diff --git a/.windsurfrules.md b/.windsurfrules.md index 9f0a612..792cfa5 100644 --- a/.windsurfrules.md +++ b/.windsurfrules.md @@ -1,12 +1,12 @@ -# Pipelex Rules +# Pipelex Coding Rules ## Guide to write or edit pipelines using the Pipelex language in .plx files - Always first write your "plan" in natural language, then transcribe it in pipelex. - You should ALWAYS RUN validation when you are writing or editing a `.plx` file. It will ensure the pipe is runnable. If not, iterate. - For a specific file: `pipelex validate path_to_file.plx` - - For all pipelines: `pipelex validate all` + - For all pipelines: `pipelex validate --all` - **IMPORTANT**: Ensure the Python virtual environment is activated before running `pipelex` commands. For standard installations, the venv is named `.venv` - always check that first. The commands will not work without proper venv activation. - Please use POSIX standard for files. (empty lines, no trailing whitespaces, etc.) @@ -23,10 +23,10 @@ A pipeline file has three main sections: #### Domain Statement ```plx -domain = "domain_name" +domain = "domain_code" description = "Description of the domain" # Optional ``` -Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. +Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. #### Concept Definitions @@ -41,20 +41,28 @@ ConceptName = "Description of the concept" - Use PascalCase for concept names - Never use plurals (no "Stories", use "Story") - lists are handled implicitly by Pipelex - Avoid circumstantial adjectives (no "LargeText", use "Text") - focus on the essence of what the concept represents -- Don't redefine native concepts (Text, Image, PDF, TextAndImages, Number, Page) +- Don't redefine native concepts (Text, Image, PDF, TextAndImages, Number, Page, JSON) **Native Concepts:** -Pipelex provides built-in native concepts: `Text`, `Image`, `PDF`, `TextAndImages`, `Number`, `Page`. Use these directly or refine them when appropriate. +Pipelex provides built-in native concepts: `Text`, `Image`, `PDF`, `TextAndImages`, `Number`, `Page`, `JSON`. Use these directly or refine them when appropriate. -**Refining Native Concepts:** -To create a concept that specializes a native concept without adding fields: +**Refining Concepts:** +To create a concept that specializes another concept without adding fields, use `refines`: ```plx +## Refining a native concept [concept.Landscape] description = "A scenic outdoor photograph" refines = "Image" + +## Refining a custom concept (must be in domain.ConceptCode format) +[concept.PremiumCustomer] +description = "A premium customer with special benefits" +refines = "myapp.Customer" ``` +Note: When refining a custom (non-native) concept, you must use the fully qualified concept ref in `domain.ConceptCode` format. Pipelex automatically handles the dependency order to ensure referenced concepts are loaded first. + For details on how to structure concepts with fields, see the "Structuring Models" section below. #### Pipe Definitions @@ -62,7 +70,7 @@ For details on how to structure concepts with fields, see the "Structuring Model ### Pipe Base Definition ```plx -[pipe.your_pipe_name] +[pipe.your_pipe_code] type = "PipeLLM" description = "A description of what your pipe does" inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" } @@ -72,7 +80,7 @@ output = "ConceptName" The pipes will all have at least this base definition. - `inputs`: Dictionary of key being the variable used in the prompts, and the value being the ConceptName. It should ALSO LIST THE INPUTS OF THE INTERMEDIATE STEPS (if PipeSequence) or of the conditional pipes (if PipeCondition). So If you have this error: -`StaticValidationError: missing_input_variable • domain='expense_validator' • pipe='validate_expense' • +`PipeValidationError: missing_input_variable • domain='expense_validator' • pipe='validate_expense' • variable='['invoice']'`` That means that the pipe validate_expense is missing the input `invoice` because one of the subpipe is needing it. @@ -127,16 +135,16 @@ For concepts with structured fields, define them inline using TOML syntax: description = "A commercial document issued by a seller to a buyer" [concept.Invoice.structure] -invoice_number = "The unique invoice identifier" +invoice_number = "The unique invoice identifier" # This will be optional by default issue_date = { type = "date", description = "The date the invoice was issued", required = true } total_amount = { type = "number", description = "The total invoice amount", required = true } -vendor_name = "The name of the vendor" -line_items = { type = "list", item_type = "text", description = "List of items", required = false } +vendor_name = "The name of the vendor" # This will be optional by default +line_items = { type = "list", item_type = "text", description = "List of items" } ``` -**Supported inline field types:** `text`, `integer`, `boolean`, `number`, `date`, `list`, `dict` +**Supported inline field types:** `text`, `integer`, `boolean`, `number`, `date`, `list`, `dict`, `concept` -**Field properties:** `type`, `description`, `required` (default: true), `default_value`, `choices`, `item_type` (for lists), `key_type` and `value_type` (for dicts) +**Field properties:** `type`, `description`, `required` (default: false), `default_value`, `choices`, `item_type` (for lists), `key_type` and `value_type` (for dicts), `concept_ref` (for concept references), `item_concept_ref` (for lists of concepts) **Simple syntax** (creates required text field): ```plx @@ -145,9 +153,46 @@ field_name = "Field description" **Detailed syntax** (with explicit properties): ```plx -field_name = { type = "text", description = "Field description", required = false, default_value = "default" } +field_name = { type = "text", description = "Field description", default_value = "default" } +``` + +**Concept reference syntax** (referencing another concept): +```plx +## Single concept reference +customer = { type = "concept", concept_ref = "myapp.Customer", description = "The customer" } + +## List of concepts +line_items = { type = "list", item_type = "concept", item_concept_ref = "myapp.LineItem", description = "Line items" } ``` +Example with concept references: +```plx +[concept.Customer] +description = "A customer entity" + +[concept.Customer.structure] +name = { type = "text", description = "Customer name" } +email = { type = "text", description = "Customer email" } + +[concept.LineItem] +description = "A line item in an invoice" + +[concept.LineItem.structure] +product = { type = "text", description = "Product name" } +quantity = { type = "integer", description = "Quantity ordered" } +unit_price = { type = "number", description = "Price per unit" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +customer = { type = "concept", concept_ref = "myapp.Customer", description = "The customer" } +items = { type = "list", item_type = "concept", item_concept_ref = "myapp.LineItem", description = "Line items" } +total = { type = "number", description = "Invoice total" } +``` + +Note: Pipelex automatically determines the correct loading order for concepts based on their dependencies (topological sort), so concepts can reference each other across domains as long as there are no circular dependencies. + **3. Python StructuredContent Class (For Advanced Features)** Create a Python class when you need: @@ -200,12 +245,14 @@ class Invoice(StructuredContent): #### Inline Structure Limitations Inline structures: -- ✅ Support all common field types (text, number, date, list, dict, etc.) +- ✅ Support all common field types (text, number, date, list, dict, concept, etc.) - ✅ Support required/optional fields, defaults, choices +- ✅ Support concept-to-concept references (type = "concept" with concept_ref) +- ✅ Support lists of concepts (type = "list" with item_type = "concept") +- ✅ Support refining both native and custom concepts - ✅ Generate full Pydantic models with validation - ❌ Cannot have custom validators or complex validation logic - ❌ Cannot have computed properties or custom methods -- ❌ Cannot refine custom (non-native) concepts - ❌ Limited IDE autocomplete compared to explicit Python classes @@ -471,7 +518,7 @@ The PipeExtract operator is used to extract text and images from an image or a P [pipe.extract_info] type = "PipeExtract" description = "extract the information" -inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input. +inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input. output = "Page" ``` @@ -480,7 +527,7 @@ Using Extract Model Settings: [pipe.extract_with_model] type = "PipeExtract" description = "Extract with specific model" -inputs = { document = "PDF" } +inputs = { document = "Document" } output = "Page" model = "base_extract_mistral" # Use predefined extract preset or model alias ``` @@ -588,15 +635,16 @@ $sales_rep.phone | $sales_rep.email """ ``` -#### Key Parameters +#### Key Parameters (Template Mode) -- `template`: Inline template string (mutually exclusive with template_name) +- `template`: Inline template string (mutually exclusive with template_name and construct) - `template_name`: Name of a predefined template (mutually exclusive with template) - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.) - `templating_style`: Styling options for template rendering - `extra_context`: Additional context variables for template For more control, you can use a nested `template` section instead of the `template` field: + - `template.template`: The template string - `template.category`: Template type - `template.templating_style`: Styling options @@ -604,9 +652,143 @@ For more control, you can use a nested `template` section instead of the `templa #### Template Variables Use the same variable insertion rules as PipeLLM: + - `@variable` for block insertion (multi-line content) - `$variable` for inline insertion (short text) +#### Construct Mode (for StructuredContent Output) + +PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures. + +**When to use construct mode:** + +- You need to output a structured object (not just Text) +- You want to deterministically compose fields from existing data +- No LLM is needed - just data composition and templating + +##### Basic Construct Usage + +```plx +[concept.SalesSummary] +description = "A structured sales summary" + +[concept.SalesSummary.structure] +report_title = { type = "text", description = "Title of the report" } +customer_name = { type = "text", description = "Customer name" } +deal_value = { type = "number", description = "Deal value" } +summary_text = { type = "text", description = "Generated summary text" } + +[pipe.compose_summary] +type = "PipeCompose" +description = "Compose a sales summary from deal data" +inputs = { deal = "Deal" } +output = "SalesSummary" + +[pipe.compose_summary.construct] +report_title = "Monthly Sales Report" +customer_name = { from = "deal.customer_name" } +deal_value = { from = "deal.amount" } +summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" } +``` + +##### Field Composition Methods + +There are four ways to define field values in a construct: + +**1. Fixed Value (literal)** + +Use a literal value directly: + +```plx +[pipe.compose_report.construct] +report_title = "Annual Report" +report_year = 2024 +is_draft = false +``` + +**2. Variable Reference (`from`)** + +Get a value from working memory using a dotted path: + +```plx +[pipe.compose_report.construct] +customer_name = { from = "deal.customer_name" } +total_amount = { from = "order.total" } +street_address = { from = "customer.address.street" } +``` + +**3. Template (`template`)** + +Render a Jinja2 template with variable substitution: + +```plx +[pipe.compose_report.construct] +invoice_number = { template = "INV-$order.id" } +summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" } +``` + +**4. Nested Construct** + +For nested structures, use a TOML subsection: + +```plx +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Complete Construct Example + +```plx +domain = "invoicing" + +[concept.Address] +description = "A postal address" + +[concept.Address.structure] +street = { type = "text", description = "Street address" } +city = { type = "text", description = "City name" } +country = { type = "text", description = "Country name" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +invoice_number = { type = "text", description = "Invoice number" } +total = { type = "number", description = "Total amount" } + +[pipe.compose_invoice] +type = "PipeCompose" +description = "Compose an invoice from order and customer data" +inputs = { order = "Order", customer = "Customer" } +output = "Invoice" + +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Key Parameters (Construct Mode) + +- `construct`: Dictionary mapping field names to their composition rules +- Each field can be: + - A literal value (string, number, boolean) + - A dict with `from` key for variable reference + - A dict with `template` key for template rendering + - A nested dict for nested structures + +**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive. + ### PipeImgGen operator The PipeImgGen operator is used to generate images using AI image generation models. @@ -820,7 +1002,7 @@ Presets are meant to record the choice of an llm with its hyper parameters (temp Examples: ```toml -llm_for_complex_reasoning = { model = "base-claude", temperature = 1 } +llm_to_engineer = { model = "base-claude", temperature = 1 } llm_to_extract_invoice = { model = "claude-3-7-sonnet", temperature = 0.1, max_tokens = "auto" } ``` @@ -851,7 +1033,7 @@ You can override the predefined llm presets by setting them in `.pipelex/inferen ALWAYS RUN validation when you are finished writing pipelines: This checks for errors. If there are errors, iterate until it works. - For a specific bundle/file: `pipelex validate path_to_file.plx` -- For all pipelines: `pipelex validate all` +- For all pipelines: `pipelex validate --all` - Remember: Ensure your Python virtual environment is activated (typically `.venv` for standard installations) before running `pipelex` commands. Then, create an example file to run the pipeline in the `examples` folder. @@ -859,21 +1041,6 @@ But don't write documentation unless asked explicitly to. ## Guide to execute a pipeline and write example code -### Prerequisites: Virtual Environment - -**CRITICAL**: Before running any `pipelex` commands or `pytest`, you MUST activate the appropriate Python virtual environment. Without proper venv activation, these commands will not work. - -For standard installations, the virtual environment is named `.venv`. Always check this first: - -```bash -## Activate the virtual environment (standard installation) -source .venv/bin/activate # On macOS/Linux -## or -.venv\Scripts\activate # On Windows -``` - -If your installation uses a different venv name or location, activate that one instead. All subsequent `pipelex` and `pytest` commands assume the venv is active. - ### Example to execute a pipeline with text output ```python @@ -967,13 +1134,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa }, ) -## Here we have a single input and it's a PDF. -## Because PDFContent is a native concept, we can use it directly as a value, +## Here we have a single input and it's a document. +## Because DocumentContent is a native concept, we can use it directly as a value, ## the system knows what content it corresponds to: pipe_output = await execute_pipeline( pipe_code="power_extractor_dpe", inputs={ - "document": PDFContent(url=pdf_url), + "document": DocumentContent(url=pdf_url), }, ) @@ -1096,82 +1263,4 @@ result_list = pipe_output.main_stuff_as_items(item_type=GanttChart) ``` --- - -## Rules to choose LLM models used in PipeLLMs. - -### LLM Configuration System - -In order to use it in a pipe, an LLM is referenced by its llm_handle (alias) and possibly by an llm_preset. -LLM configurations are managed through the new inference backend system with files located in `.pipelex/inference/`: - -- **Model Deck**: `.pipelex/inference/deck/base_deck.toml` and `.pipelex/inference/deck/overrides.toml` -- **Backends**: `.pipelex/inference/backends.toml` and `.pipelex/inference/backends/*.toml` -- **Routing**: `.pipelex/inference/routing_profiles.toml` - -### LLM Handles - -An llm_handle can be either: -1. **A direct model name** (like "gpt-4o-mini", "claude-3-sonnet") - automatically available for all models loaded by the inference backend system -2. **An alias** - user-defined shortcuts that map to model names, defined in the `[aliases]` section: - -```toml -[aliases] -base-claude = "claude-4.5-sonnet" -base-gpt = "gpt-5" -base-gemini = "gemini-2.5-flash" -base-mistral = "mistral-medium" -``` - -The system first looks for direct model names, then checks aliases if no direct match is found. The system handles model routing through backends automatically. - -### Using an LLM Handle in a PipeLLM - -Here is an example of using an llm_handle to specify which LLM to use in a PipeLLM: - -```plx -[pipe.hello_world] -type = "PipeLLM" -description = "Write text about Hello World." -output = "Text" -model = { model = "gpt-5", temperature = 0.9 } -prompt = """ -Write a haiku about Hello World. -""" -``` - -As you can see, to use the LLM, you must also indicate the temperature (float between 0 and 1) and max_tokens (either an int or the string "auto"). - -### LLM Presets - -Presets are meant to record the choice of an llm with its hyper parameters (temperature and max_tokens) if it's good for a particular task. LLM Presets are skill-oriented. - -Examples: -```toml -llm_for_complex_reasoning = { model = "base-claude", temperature = 1 } -llm_to_extract_invoice = { model = "claude-3-7-sonnet", temperature = 0.1, max_tokens = "auto" } -``` - -The interest is that these presets can be used to set the LLM choice in a PipeLLM, like this: - -```plx -[pipe.extract_invoice] -type = "PipeLLM" -description = "Extract invoice information from an invoice text transcript" -inputs = { invoice_text = "InvoiceText" } -output = "Invoice" -model = "llm_to_extract_invoice" -prompt = """ -Extract invoice information from this invoice: - -The category of this invoice is: $invoice_details.category. - -@invoice_text -""" -``` - -The setting here `model = "llm_to_extract_invoice"` works because "llm_to_extract_invoice" has been declared as an llm_preset in the deck. -You must not use an LLM preset in a PipeLLM that does not exist in the deck. If needed, you can add llm presets. - - -You can override the predefined llm presets by setting them in `.pipelex/inference/deck/overrides.toml`. diff --git a/AGENTS.md b/AGENTS.md index 9f0a612..792cfa5 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -1,12 +1,12 @@ -# Pipelex Rules +# Pipelex Coding Rules ## Guide to write or edit pipelines using the Pipelex language in .plx files - Always first write your "plan" in natural language, then transcribe it in pipelex. - You should ALWAYS RUN validation when you are writing or editing a `.plx` file. It will ensure the pipe is runnable. If not, iterate. - For a specific file: `pipelex validate path_to_file.plx` - - For all pipelines: `pipelex validate all` + - For all pipelines: `pipelex validate --all` - **IMPORTANT**: Ensure the Python virtual environment is activated before running `pipelex` commands. For standard installations, the venv is named `.venv` - always check that first. The commands will not work without proper venv activation. - Please use POSIX standard for files. (empty lines, no trailing whitespaces, etc.) @@ -23,10 +23,10 @@ A pipeline file has three main sections: #### Domain Statement ```plx -domain = "domain_name" +domain = "domain_code" description = "Description of the domain" # Optional ``` -Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. +Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. #### Concept Definitions @@ -41,20 +41,28 @@ ConceptName = "Description of the concept" - Use PascalCase for concept names - Never use plurals (no "Stories", use "Story") - lists are handled implicitly by Pipelex - Avoid circumstantial adjectives (no "LargeText", use "Text") - focus on the essence of what the concept represents -- Don't redefine native concepts (Text, Image, PDF, TextAndImages, Number, Page) +- Don't redefine native concepts (Text, Image, PDF, TextAndImages, Number, Page, JSON) **Native Concepts:** -Pipelex provides built-in native concepts: `Text`, `Image`, `PDF`, `TextAndImages`, `Number`, `Page`. Use these directly or refine them when appropriate. +Pipelex provides built-in native concepts: `Text`, `Image`, `PDF`, `TextAndImages`, `Number`, `Page`, `JSON`. Use these directly or refine them when appropriate. -**Refining Native Concepts:** -To create a concept that specializes a native concept without adding fields: +**Refining Concepts:** +To create a concept that specializes another concept without adding fields, use `refines`: ```plx +## Refining a native concept [concept.Landscape] description = "A scenic outdoor photograph" refines = "Image" + +## Refining a custom concept (must be in domain.ConceptCode format) +[concept.PremiumCustomer] +description = "A premium customer with special benefits" +refines = "myapp.Customer" ``` +Note: When refining a custom (non-native) concept, you must use the fully qualified concept ref in `domain.ConceptCode` format. Pipelex automatically handles the dependency order to ensure referenced concepts are loaded first. + For details on how to structure concepts with fields, see the "Structuring Models" section below. #### Pipe Definitions @@ -62,7 +70,7 @@ For details on how to structure concepts with fields, see the "Structuring Model ### Pipe Base Definition ```plx -[pipe.your_pipe_name] +[pipe.your_pipe_code] type = "PipeLLM" description = "A description of what your pipe does" inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" } @@ -72,7 +80,7 @@ output = "ConceptName" The pipes will all have at least this base definition. - `inputs`: Dictionary of key being the variable used in the prompts, and the value being the ConceptName. It should ALSO LIST THE INPUTS OF THE INTERMEDIATE STEPS (if PipeSequence) or of the conditional pipes (if PipeCondition). So If you have this error: -`StaticValidationError: missing_input_variable • domain='expense_validator' • pipe='validate_expense' • +`PipeValidationError: missing_input_variable • domain='expense_validator' • pipe='validate_expense' • variable='['invoice']'`` That means that the pipe validate_expense is missing the input `invoice` because one of the subpipe is needing it. @@ -127,16 +135,16 @@ For concepts with structured fields, define them inline using TOML syntax: description = "A commercial document issued by a seller to a buyer" [concept.Invoice.structure] -invoice_number = "The unique invoice identifier" +invoice_number = "The unique invoice identifier" # This will be optional by default issue_date = { type = "date", description = "The date the invoice was issued", required = true } total_amount = { type = "number", description = "The total invoice amount", required = true } -vendor_name = "The name of the vendor" -line_items = { type = "list", item_type = "text", description = "List of items", required = false } +vendor_name = "The name of the vendor" # This will be optional by default +line_items = { type = "list", item_type = "text", description = "List of items" } ``` -**Supported inline field types:** `text`, `integer`, `boolean`, `number`, `date`, `list`, `dict` +**Supported inline field types:** `text`, `integer`, `boolean`, `number`, `date`, `list`, `dict`, `concept` -**Field properties:** `type`, `description`, `required` (default: true), `default_value`, `choices`, `item_type` (for lists), `key_type` and `value_type` (for dicts) +**Field properties:** `type`, `description`, `required` (default: false), `default_value`, `choices`, `item_type` (for lists), `key_type` and `value_type` (for dicts), `concept_ref` (for concept references), `item_concept_ref` (for lists of concepts) **Simple syntax** (creates required text field): ```plx @@ -145,9 +153,46 @@ field_name = "Field description" **Detailed syntax** (with explicit properties): ```plx -field_name = { type = "text", description = "Field description", required = false, default_value = "default" } +field_name = { type = "text", description = "Field description", default_value = "default" } +``` + +**Concept reference syntax** (referencing another concept): +```plx +## Single concept reference +customer = { type = "concept", concept_ref = "myapp.Customer", description = "The customer" } + +## List of concepts +line_items = { type = "list", item_type = "concept", item_concept_ref = "myapp.LineItem", description = "Line items" } ``` +Example with concept references: +```plx +[concept.Customer] +description = "A customer entity" + +[concept.Customer.structure] +name = { type = "text", description = "Customer name" } +email = { type = "text", description = "Customer email" } + +[concept.LineItem] +description = "A line item in an invoice" + +[concept.LineItem.structure] +product = { type = "text", description = "Product name" } +quantity = { type = "integer", description = "Quantity ordered" } +unit_price = { type = "number", description = "Price per unit" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +customer = { type = "concept", concept_ref = "myapp.Customer", description = "The customer" } +items = { type = "list", item_type = "concept", item_concept_ref = "myapp.LineItem", description = "Line items" } +total = { type = "number", description = "Invoice total" } +``` + +Note: Pipelex automatically determines the correct loading order for concepts based on their dependencies (topological sort), so concepts can reference each other across domains as long as there are no circular dependencies. + **3. Python StructuredContent Class (For Advanced Features)** Create a Python class when you need: @@ -200,12 +245,14 @@ class Invoice(StructuredContent): #### Inline Structure Limitations Inline structures: -- ✅ Support all common field types (text, number, date, list, dict, etc.) +- ✅ Support all common field types (text, number, date, list, dict, concept, etc.) - ✅ Support required/optional fields, defaults, choices +- ✅ Support concept-to-concept references (type = "concept" with concept_ref) +- ✅ Support lists of concepts (type = "list" with item_type = "concept") +- ✅ Support refining both native and custom concepts - ✅ Generate full Pydantic models with validation - ❌ Cannot have custom validators or complex validation logic - ❌ Cannot have computed properties or custom methods -- ❌ Cannot refine custom (non-native) concepts - ❌ Limited IDE autocomplete compared to explicit Python classes @@ -471,7 +518,7 @@ The PipeExtract operator is used to extract text and images from an image or a P [pipe.extract_info] type = "PipeExtract" description = "extract the information" -inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input. +inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input. output = "Page" ``` @@ -480,7 +527,7 @@ Using Extract Model Settings: [pipe.extract_with_model] type = "PipeExtract" description = "Extract with specific model" -inputs = { document = "PDF" } +inputs = { document = "Document" } output = "Page" model = "base_extract_mistral" # Use predefined extract preset or model alias ``` @@ -588,15 +635,16 @@ $sales_rep.phone | $sales_rep.email """ ``` -#### Key Parameters +#### Key Parameters (Template Mode) -- `template`: Inline template string (mutually exclusive with template_name) +- `template`: Inline template string (mutually exclusive with template_name and construct) - `template_name`: Name of a predefined template (mutually exclusive with template) - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.) - `templating_style`: Styling options for template rendering - `extra_context`: Additional context variables for template For more control, you can use a nested `template` section instead of the `template` field: + - `template.template`: The template string - `template.category`: Template type - `template.templating_style`: Styling options @@ -604,9 +652,143 @@ For more control, you can use a nested `template` section instead of the `templa #### Template Variables Use the same variable insertion rules as PipeLLM: + - `@variable` for block insertion (multi-line content) - `$variable` for inline insertion (short text) +#### Construct Mode (for StructuredContent Output) + +PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures. + +**When to use construct mode:** + +- You need to output a structured object (not just Text) +- You want to deterministically compose fields from existing data +- No LLM is needed - just data composition and templating + +##### Basic Construct Usage + +```plx +[concept.SalesSummary] +description = "A structured sales summary" + +[concept.SalesSummary.structure] +report_title = { type = "text", description = "Title of the report" } +customer_name = { type = "text", description = "Customer name" } +deal_value = { type = "number", description = "Deal value" } +summary_text = { type = "text", description = "Generated summary text" } + +[pipe.compose_summary] +type = "PipeCompose" +description = "Compose a sales summary from deal data" +inputs = { deal = "Deal" } +output = "SalesSummary" + +[pipe.compose_summary.construct] +report_title = "Monthly Sales Report" +customer_name = { from = "deal.customer_name" } +deal_value = { from = "deal.amount" } +summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" } +``` + +##### Field Composition Methods + +There are four ways to define field values in a construct: + +**1. Fixed Value (literal)** + +Use a literal value directly: + +```plx +[pipe.compose_report.construct] +report_title = "Annual Report" +report_year = 2024 +is_draft = false +``` + +**2. Variable Reference (`from`)** + +Get a value from working memory using a dotted path: + +```plx +[pipe.compose_report.construct] +customer_name = { from = "deal.customer_name" } +total_amount = { from = "order.total" } +street_address = { from = "customer.address.street" } +``` + +**3. Template (`template`)** + +Render a Jinja2 template with variable substitution: + +```plx +[pipe.compose_report.construct] +invoice_number = { template = "INV-$order.id" } +summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" } +``` + +**4. Nested Construct** + +For nested structures, use a TOML subsection: + +```plx +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Complete Construct Example + +```plx +domain = "invoicing" + +[concept.Address] +description = "A postal address" + +[concept.Address.structure] +street = { type = "text", description = "Street address" } +city = { type = "text", description = "City name" } +country = { type = "text", description = "Country name" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +invoice_number = { type = "text", description = "Invoice number" } +total = { type = "number", description = "Total amount" } + +[pipe.compose_invoice] +type = "PipeCompose" +description = "Compose an invoice from order and customer data" +inputs = { order = "Order", customer = "Customer" } +output = "Invoice" + +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Key Parameters (Construct Mode) + +- `construct`: Dictionary mapping field names to their composition rules +- Each field can be: + - A literal value (string, number, boolean) + - A dict with `from` key for variable reference + - A dict with `template` key for template rendering + - A nested dict for nested structures + +**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive. + ### PipeImgGen operator The PipeImgGen operator is used to generate images using AI image generation models. @@ -820,7 +1002,7 @@ Presets are meant to record the choice of an llm with its hyper parameters (temp Examples: ```toml -llm_for_complex_reasoning = { model = "base-claude", temperature = 1 } +llm_to_engineer = { model = "base-claude", temperature = 1 } llm_to_extract_invoice = { model = "claude-3-7-sonnet", temperature = 0.1, max_tokens = "auto" } ``` @@ -851,7 +1033,7 @@ You can override the predefined llm presets by setting them in `.pipelex/inferen ALWAYS RUN validation when you are finished writing pipelines: This checks for errors. If there are errors, iterate until it works. - For a specific bundle/file: `pipelex validate path_to_file.plx` -- For all pipelines: `pipelex validate all` +- For all pipelines: `pipelex validate --all` - Remember: Ensure your Python virtual environment is activated (typically `.venv` for standard installations) before running `pipelex` commands. Then, create an example file to run the pipeline in the `examples` folder. @@ -859,21 +1041,6 @@ But don't write documentation unless asked explicitly to. ## Guide to execute a pipeline and write example code -### Prerequisites: Virtual Environment - -**CRITICAL**: Before running any `pipelex` commands or `pytest`, you MUST activate the appropriate Python virtual environment. Without proper venv activation, these commands will not work. - -For standard installations, the virtual environment is named `.venv`. Always check this first: - -```bash -## Activate the virtual environment (standard installation) -source .venv/bin/activate # On macOS/Linux -## or -.venv\Scripts\activate # On Windows -``` - -If your installation uses a different venv name or location, activate that one instead. All subsequent `pipelex` and `pytest` commands assume the venv is active. - ### Example to execute a pipeline with text output ```python @@ -967,13 +1134,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa }, ) -## Here we have a single input and it's a PDF. -## Because PDFContent is a native concept, we can use it directly as a value, +## Here we have a single input and it's a document. +## Because DocumentContent is a native concept, we can use it directly as a value, ## the system knows what content it corresponds to: pipe_output = await execute_pipeline( pipe_code="power_extractor_dpe", inputs={ - "document": PDFContent(url=pdf_url), + "document": DocumentContent(url=pdf_url), }, ) @@ -1096,82 +1263,4 @@ result_list = pipe_output.main_stuff_as_items(item_type=GanttChart) ``` --- - -## Rules to choose LLM models used in PipeLLMs. - -### LLM Configuration System - -In order to use it in a pipe, an LLM is referenced by its llm_handle (alias) and possibly by an llm_preset. -LLM configurations are managed through the new inference backend system with files located in `.pipelex/inference/`: - -- **Model Deck**: `.pipelex/inference/deck/base_deck.toml` and `.pipelex/inference/deck/overrides.toml` -- **Backends**: `.pipelex/inference/backends.toml` and `.pipelex/inference/backends/*.toml` -- **Routing**: `.pipelex/inference/routing_profiles.toml` - -### LLM Handles - -An llm_handle can be either: -1. **A direct model name** (like "gpt-4o-mini", "claude-3-sonnet") - automatically available for all models loaded by the inference backend system -2. **An alias** - user-defined shortcuts that map to model names, defined in the `[aliases]` section: - -```toml -[aliases] -base-claude = "claude-4.5-sonnet" -base-gpt = "gpt-5" -base-gemini = "gemini-2.5-flash" -base-mistral = "mistral-medium" -``` - -The system first looks for direct model names, then checks aliases if no direct match is found. The system handles model routing through backends automatically. - -### Using an LLM Handle in a PipeLLM - -Here is an example of using an llm_handle to specify which LLM to use in a PipeLLM: - -```plx -[pipe.hello_world] -type = "PipeLLM" -description = "Write text about Hello World." -output = "Text" -model = { model = "gpt-5", temperature = 0.9 } -prompt = """ -Write a haiku about Hello World. -""" -``` - -As you can see, to use the LLM, you must also indicate the temperature (float between 0 and 1) and max_tokens (either an int or the string "auto"). - -### LLM Presets - -Presets are meant to record the choice of an llm with its hyper parameters (temperature and max_tokens) if it's good for a particular task. LLM Presets are skill-oriented. - -Examples: -```toml -llm_for_complex_reasoning = { model = "base-claude", temperature = 1 } -llm_to_extract_invoice = { model = "claude-3-7-sonnet", temperature = 0.1, max_tokens = "auto" } -``` - -The interest is that these presets can be used to set the LLM choice in a PipeLLM, like this: - -```plx -[pipe.extract_invoice] -type = "PipeLLM" -description = "Extract invoice information from an invoice text transcript" -inputs = { invoice_text = "InvoiceText" } -output = "Invoice" -model = "llm_to_extract_invoice" -prompt = """ -Extract invoice information from this invoice: - -The category of this invoice is: $invoice_details.category. - -@invoice_text -""" -``` - -The setting here `model = "llm_to_extract_invoice"` works because "llm_to_extract_invoice" has been declared as an llm_preset in the deck. -You must not use an LLM preset in a PipeLLM that does not exist in the deck. If needed, you can add llm presets. - - -You can override the predefined llm presets by setting them in `.pipelex/inference/deck/overrides.toml`. diff --git a/CLAUDE.md b/CLAUDE.md index 9f0a612..792cfa5 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -1,12 +1,12 @@ -# Pipelex Rules +# Pipelex Coding Rules ## Guide to write or edit pipelines using the Pipelex language in .plx files - Always first write your "plan" in natural language, then transcribe it in pipelex. - You should ALWAYS RUN validation when you are writing or editing a `.plx` file. It will ensure the pipe is runnable. If not, iterate. - For a specific file: `pipelex validate path_to_file.plx` - - For all pipelines: `pipelex validate all` + - For all pipelines: `pipelex validate --all` - **IMPORTANT**: Ensure the Python virtual environment is activated before running `pipelex` commands. For standard installations, the venv is named `.venv` - always check that first. The commands will not work without proper venv activation. - Please use POSIX standard for files. (empty lines, no trailing whitespaces, etc.) @@ -23,10 +23,10 @@ A pipeline file has three main sections: #### Domain Statement ```plx -domain = "domain_name" +domain = "domain_code" description = "Description of the domain" # Optional ``` -Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. +Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. #### Concept Definitions @@ -41,20 +41,28 @@ ConceptName = "Description of the concept" - Use PascalCase for concept names - Never use plurals (no "Stories", use "Story") - lists are handled implicitly by Pipelex - Avoid circumstantial adjectives (no "LargeText", use "Text") - focus on the essence of what the concept represents -- Don't redefine native concepts (Text, Image, PDF, TextAndImages, Number, Page) +- Don't redefine native concepts (Text, Image, PDF, TextAndImages, Number, Page, JSON) **Native Concepts:** -Pipelex provides built-in native concepts: `Text`, `Image`, `PDF`, `TextAndImages`, `Number`, `Page`. Use these directly or refine them when appropriate. +Pipelex provides built-in native concepts: `Text`, `Image`, `PDF`, `TextAndImages`, `Number`, `Page`, `JSON`. Use these directly or refine them when appropriate. -**Refining Native Concepts:** -To create a concept that specializes a native concept without adding fields: +**Refining Concepts:** +To create a concept that specializes another concept without adding fields, use `refines`: ```plx +## Refining a native concept [concept.Landscape] description = "A scenic outdoor photograph" refines = "Image" + +## Refining a custom concept (must be in domain.ConceptCode format) +[concept.PremiumCustomer] +description = "A premium customer with special benefits" +refines = "myapp.Customer" ``` +Note: When refining a custom (non-native) concept, you must use the fully qualified concept ref in `domain.ConceptCode` format. Pipelex automatically handles the dependency order to ensure referenced concepts are loaded first. + For details on how to structure concepts with fields, see the "Structuring Models" section below. #### Pipe Definitions @@ -62,7 +70,7 @@ For details on how to structure concepts with fields, see the "Structuring Model ### Pipe Base Definition ```plx -[pipe.your_pipe_name] +[pipe.your_pipe_code] type = "PipeLLM" description = "A description of what your pipe does" inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" } @@ -72,7 +80,7 @@ output = "ConceptName" The pipes will all have at least this base definition. - `inputs`: Dictionary of key being the variable used in the prompts, and the value being the ConceptName. It should ALSO LIST THE INPUTS OF THE INTERMEDIATE STEPS (if PipeSequence) or of the conditional pipes (if PipeCondition). So If you have this error: -`StaticValidationError: missing_input_variable • domain='expense_validator' • pipe='validate_expense' • +`PipeValidationError: missing_input_variable • domain='expense_validator' • pipe='validate_expense' • variable='['invoice']'`` That means that the pipe validate_expense is missing the input `invoice` because one of the subpipe is needing it. @@ -127,16 +135,16 @@ For concepts with structured fields, define them inline using TOML syntax: description = "A commercial document issued by a seller to a buyer" [concept.Invoice.structure] -invoice_number = "The unique invoice identifier" +invoice_number = "The unique invoice identifier" # This will be optional by default issue_date = { type = "date", description = "The date the invoice was issued", required = true } total_amount = { type = "number", description = "The total invoice amount", required = true } -vendor_name = "The name of the vendor" -line_items = { type = "list", item_type = "text", description = "List of items", required = false } +vendor_name = "The name of the vendor" # This will be optional by default +line_items = { type = "list", item_type = "text", description = "List of items" } ``` -**Supported inline field types:** `text`, `integer`, `boolean`, `number`, `date`, `list`, `dict` +**Supported inline field types:** `text`, `integer`, `boolean`, `number`, `date`, `list`, `dict`, `concept` -**Field properties:** `type`, `description`, `required` (default: true), `default_value`, `choices`, `item_type` (for lists), `key_type` and `value_type` (for dicts) +**Field properties:** `type`, `description`, `required` (default: false), `default_value`, `choices`, `item_type` (for lists), `key_type` and `value_type` (for dicts), `concept_ref` (for concept references), `item_concept_ref` (for lists of concepts) **Simple syntax** (creates required text field): ```plx @@ -145,9 +153,46 @@ field_name = "Field description" **Detailed syntax** (with explicit properties): ```plx -field_name = { type = "text", description = "Field description", required = false, default_value = "default" } +field_name = { type = "text", description = "Field description", default_value = "default" } +``` + +**Concept reference syntax** (referencing another concept): +```plx +## Single concept reference +customer = { type = "concept", concept_ref = "myapp.Customer", description = "The customer" } + +## List of concepts +line_items = { type = "list", item_type = "concept", item_concept_ref = "myapp.LineItem", description = "Line items" } ``` +Example with concept references: +```plx +[concept.Customer] +description = "A customer entity" + +[concept.Customer.structure] +name = { type = "text", description = "Customer name" } +email = { type = "text", description = "Customer email" } + +[concept.LineItem] +description = "A line item in an invoice" + +[concept.LineItem.structure] +product = { type = "text", description = "Product name" } +quantity = { type = "integer", description = "Quantity ordered" } +unit_price = { type = "number", description = "Price per unit" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +customer = { type = "concept", concept_ref = "myapp.Customer", description = "The customer" } +items = { type = "list", item_type = "concept", item_concept_ref = "myapp.LineItem", description = "Line items" } +total = { type = "number", description = "Invoice total" } +``` + +Note: Pipelex automatically determines the correct loading order for concepts based on their dependencies (topological sort), so concepts can reference each other across domains as long as there are no circular dependencies. + **3. Python StructuredContent Class (For Advanced Features)** Create a Python class when you need: @@ -200,12 +245,14 @@ class Invoice(StructuredContent): #### Inline Structure Limitations Inline structures: -- ✅ Support all common field types (text, number, date, list, dict, etc.) +- ✅ Support all common field types (text, number, date, list, dict, concept, etc.) - ✅ Support required/optional fields, defaults, choices +- ✅ Support concept-to-concept references (type = "concept" with concept_ref) +- ✅ Support lists of concepts (type = "list" with item_type = "concept") +- ✅ Support refining both native and custom concepts - ✅ Generate full Pydantic models with validation - ❌ Cannot have custom validators or complex validation logic - ❌ Cannot have computed properties or custom methods -- ❌ Cannot refine custom (non-native) concepts - ❌ Limited IDE autocomplete compared to explicit Python classes @@ -471,7 +518,7 @@ The PipeExtract operator is used to extract text and images from an image or a P [pipe.extract_info] type = "PipeExtract" description = "extract the information" -inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input. +inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input. output = "Page" ``` @@ -480,7 +527,7 @@ Using Extract Model Settings: [pipe.extract_with_model] type = "PipeExtract" description = "Extract with specific model" -inputs = { document = "PDF" } +inputs = { document = "Document" } output = "Page" model = "base_extract_mistral" # Use predefined extract preset or model alias ``` @@ -588,15 +635,16 @@ $sales_rep.phone | $sales_rep.email """ ``` -#### Key Parameters +#### Key Parameters (Template Mode) -- `template`: Inline template string (mutually exclusive with template_name) +- `template`: Inline template string (mutually exclusive with template_name and construct) - `template_name`: Name of a predefined template (mutually exclusive with template) - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.) - `templating_style`: Styling options for template rendering - `extra_context`: Additional context variables for template For more control, you can use a nested `template` section instead of the `template` field: + - `template.template`: The template string - `template.category`: Template type - `template.templating_style`: Styling options @@ -604,9 +652,143 @@ For more control, you can use a nested `template` section instead of the `templa #### Template Variables Use the same variable insertion rules as PipeLLM: + - `@variable` for block insertion (multi-line content) - `$variable` for inline insertion (short text) +#### Construct Mode (for StructuredContent Output) + +PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures. + +**When to use construct mode:** + +- You need to output a structured object (not just Text) +- You want to deterministically compose fields from existing data +- No LLM is needed - just data composition and templating + +##### Basic Construct Usage + +```plx +[concept.SalesSummary] +description = "A structured sales summary" + +[concept.SalesSummary.structure] +report_title = { type = "text", description = "Title of the report" } +customer_name = { type = "text", description = "Customer name" } +deal_value = { type = "number", description = "Deal value" } +summary_text = { type = "text", description = "Generated summary text" } + +[pipe.compose_summary] +type = "PipeCompose" +description = "Compose a sales summary from deal data" +inputs = { deal = "Deal" } +output = "SalesSummary" + +[pipe.compose_summary.construct] +report_title = "Monthly Sales Report" +customer_name = { from = "deal.customer_name" } +deal_value = { from = "deal.amount" } +summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" } +``` + +##### Field Composition Methods + +There are four ways to define field values in a construct: + +**1. Fixed Value (literal)** + +Use a literal value directly: + +```plx +[pipe.compose_report.construct] +report_title = "Annual Report" +report_year = 2024 +is_draft = false +``` + +**2. Variable Reference (`from`)** + +Get a value from working memory using a dotted path: + +```plx +[pipe.compose_report.construct] +customer_name = { from = "deal.customer_name" } +total_amount = { from = "order.total" } +street_address = { from = "customer.address.street" } +``` + +**3. Template (`template`)** + +Render a Jinja2 template with variable substitution: + +```plx +[pipe.compose_report.construct] +invoice_number = { template = "INV-$order.id" } +summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" } +``` + +**4. Nested Construct** + +For nested structures, use a TOML subsection: + +```plx +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Complete Construct Example + +```plx +domain = "invoicing" + +[concept.Address] +description = "A postal address" + +[concept.Address.structure] +street = { type = "text", description = "Street address" } +city = { type = "text", description = "City name" } +country = { type = "text", description = "Country name" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +invoice_number = { type = "text", description = "Invoice number" } +total = { type = "number", description = "Total amount" } + +[pipe.compose_invoice] +type = "PipeCompose" +description = "Compose an invoice from order and customer data" +inputs = { order = "Order", customer = "Customer" } +output = "Invoice" + +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Key Parameters (Construct Mode) + +- `construct`: Dictionary mapping field names to their composition rules +- Each field can be: + - A literal value (string, number, boolean) + - A dict with `from` key for variable reference + - A dict with `template` key for template rendering + - A nested dict for nested structures + +**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive. + ### PipeImgGen operator The PipeImgGen operator is used to generate images using AI image generation models. @@ -820,7 +1002,7 @@ Presets are meant to record the choice of an llm with its hyper parameters (temp Examples: ```toml -llm_for_complex_reasoning = { model = "base-claude", temperature = 1 } +llm_to_engineer = { model = "base-claude", temperature = 1 } llm_to_extract_invoice = { model = "claude-3-7-sonnet", temperature = 0.1, max_tokens = "auto" } ``` @@ -851,7 +1033,7 @@ You can override the predefined llm presets by setting them in `.pipelex/inferen ALWAYS RUN validation when you are finished writing pipelines: This checks for errors. If there are errors, iterate until it works. - For a specific bundle/file: `pipelex validate path_to_file.plx` -- For all pipelines: `pipelex validate all` +- For all pipelines: `pipelex validate --all` - Remember: Ensure your Python virtual environment is activated (typically `.venv` for standard installations) before running `pipelex` commands. Then, create an example file to run the pipeline in the `examples` folder. @@ -859,21 +1041,6 @@ But don't write documentation unless asked explicitly to. ## Guide to execute a pipeline and write example code -### Prerequisites: Virtual Environment - -**CRITICAL**: Before running any `pipelex` commands or `pytest`, you MUST activate the appropriate Python virtual environment. Without proper venv activation, these commands will not work. - -For standard installations, the virtual environment is named `.venv`. Always check this first: - -```bash -## Activate the virtual environment (standard installation) -source .venv/bin/activate # On macOS/Linux -## or -.venv\Scripts\activate # On Windows -``` - -If your installation uses a different venv name or location, activate that one instead. All subsequent `pipelex` and `pytest` commands assume the venv is active. - ### Example to execute a pipeline with text output ```python @@ -967,13 +1134,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa }, ) -## Here we have a single input and it's a PDF. -## Because PDFContent is a native concept, we can use it directly as a value, +## Here we have a single input and it's a document. +## Because DocumentContent is a native concept, we can use it directly as a value, ## the system knows what content it corresponds to: pipe_output = await execute_pipeline( pipe_code="power_extractor_dpe", inputs={ - "document": PDFContent(url=pdf_url), + "document": DocumentContent(url=pdf_url), }, ) @@ -1096,82 +1263,4 @@ result_list = pipe_output.main_stuff_as_items(item_type=GanttChart) ``` --- - -## Rules to choose LLM models used in PipeLLMs. - -### LLM Configuration System - -In order to use it in a pipe, an LLM is referenced by its llm_handle (alias) and possibly by an llm_preset. -LLM configurations are managed through the new inference backend system with files located in `.pipelex/inference/`: - -- **Model Deck**: `.pipelex/inference/deck/base_deck.toml` and `.pipelex/inference/deck/overrides.toml` -- **Backends**: `.pipelex/inference/backends.toml` and `.pipelex/inference/backends/*.toml` -- **Routing**: `.pipelex/inference/routing_profiles.toml` - -### LLM Handles - -An llm_handle can be either: -1. **A direct model name** (like "gpt-4o-mini", "claude-3-sonnet") - automatically available for all models loaded by the inference backend system -2. **An alias** - user-defined shortcuts that map to model names, defined in the `[aliases]` section: - -```toml -[aliases] -base-claude = "claude-4.5-sonnet" -base-gpt = "gpt-5" -base-gemini = "gemini-2.5-flash" -base-mistral = "mistral-medium" -``` - -The system first looks for direct model names, then checks aliases if no direct match is found. The system handles model routing through backends automatically. - -### Using an LLM Handle in a PipeLLM - -Here is an example of using an llm_handle to specify which LLM to use in a PipeLLM: - -```plx -[pipe.hello_world] -type = "PipeLLM" -description = "Write text about Hello World." -output = "Text" -model = { model = "gpt-5", temperature = 0.9 } -prompt = """ -Write a haiku about Hello World. -""" -``` - -As you can see, to use the LLM, you must also indicate the temperature (float between 0 and 1) and max_tokens (either an int or the string "auto"). - -### LLM Presets - -Presets are meant to record the choice of an llm with its hyper parameters (temperature and max_tokens) if it's good for a particular task. LLM Presets are skill-oriented. - -Examples: -```toml -llm_for_complex_reasoning = { model = "base-claude", temperature = 1 } -llm_to_extract_invoice = { model = "claude-3-7-sonnet", temperature = 0.1, max_tokens = "auto" } -``` - -The interest is that these presets can be used to set the LLM choice in a PipeLLM, like this: - -```plx -[pipe.extract_invoice] -type = "PipeLLM" -description = "Extract invoice information from an invoice text transcript" -inputs = { invoice_text = "InvoiceText" } -output = "Invoice" -model = "llm_to_extract_invoice" -prompt = """ -Extract invoice information from this invoice: - -The category of this invoice is: $invoice_details.category. - -@invoice_text -""" -``` - -The setting here `model = "llm_to_extract_invoice"` works because "llm_to_extract_invoice" has been declared as an llm_preset in the deck. -You must not use an LLM preset in a PipeLLM that does not exist in the deck. If needed, you can add llm presets. - - -You can override the predefined llm presets by setting them in `.pipelex/inference/deck/overrides.toml`. diff --git a/api/main.py b/api/main.py index 5a3144b..bb4d8bd 100644 --- a/api/main.py +++ b/api/main.py @@ -4,7 +4,7 @@ from pipelex.system.runtime import IntegrationMode from api.routes import router as api_router -from api.routes.pipelex.health import router as health_router +from api.routes.health import router as health_router from api.security import get_auth_dependency Pipelex.make(IntegrationMode.FASTAPI) diff --git a/api/routes/__init__.py b/api/routes/__init__.py index 0dcfa49..1a05791 100644 --- a/api/routes/__init__.py +++ b/api/routes/__init__.py @@ -1,12 +1,12 @@ from fastapi import APIRouter -from api.routes.main import router as main_router +from api.routes.version import router as version_router from .pipelex import router as pipelex_router from .uploader import router as uploader_router router = APIRouter() -router.include_router(main_router) +router.include_router(version_router) router.include_router(pipelex_router) router.include_router(uploader_router) diff --git a/api/routes/pipelex/health.py b/api/routes/health.py similarity index 51% rename from api/routes/pipelex/health.py rename to api/routes/health.py index fa1414d..a608ac4 100644 --- a/api/routes/pipelex/health.py +++ b/api/routes/health.py @@ -1,17 +1,14 @@ from fastapi import APIRouter from fastapi.responses import JSONResponse -router = APIRouter(tags=["pipeline"]) +router = APIRouter(tags=["health"]) @router.get("/health", response_model=dict) async def get_health(): - """Health check endpoint to verify the API is running. - Returns a simple JSON response indicating the service is healthy. - """ return JSONResponse( content={ "status": "ok", - "message": "API is running", + "message": "Pipelex API is running", } ) diff --git a/api/routes/helpers.py b/api/routes/helpers.py deleted file mode 100644 index 73bdff1..0000000 --- a/api/routes/helpers.py +++ /dev/null @@ -1,81 +0,0 @@ -from typing import Any - -from pipelex.core.concepts.concept import Concept -from pipelex.core.pipes.pipe_abstract import PipeAbstract -from pipelex.hub import get_class_registry - - -def get_concept_structure(concept: Concept) -> dict[str, Any]: - """Extract structure information for a concept. - - Args: - concept: Concept object to extract structure from - - Returns: - Dictionary containing: - { - "concept_code": str, - "structure_class_name": str, - "class_structure": {schema from Pydantic model_json_schema} - } - """ - class_registry = get_class_registry() - - try: - structure_class = class_registry.get_required_class(concept.structure_class_name) - class_structure = structure_class.model_json_schema() - - return { - "concept_code": concept.code, - "structure_class_name": concept.structure_class_name, - "class_structure": class_structure, - } - except Exception as e: - return { - "concept_code": concept.code, - "structure_class_name": concept.structure_class_name, - "error": str(e), - } - - -def extract_pipe_structures(pipes: list[PipeAbstract]) -> dict[str, dict[str, Any]]: - """Extract structure information for a list of pipes. - - For each pipe, extracts input and output concept structures. - - Args: - pipes: List of PipeAbstract objects to extract structures from - - Returns: - Dictionary mapping pipe_code to structure information: - { - "pipe_code": { - "inputs": { - "input_name": {concept_structure}, - ... - }, - "output": {concept_structure} - }, - ... - } - """ - pipe_structures: dict[str, dict[str, Any]] = {} - - for pipe in pipes: - inputs_specs: dict[str, Any] = {} - - # Process inputs - extract concept structures - for input_name, input_spec in pipe.inputs.root.items(): - concept = input_spec.concept - inputs_specs[input_name] = get_concept_structure(concept) - - # Process output - extract concept structure - output_spec = get_concept_structure(pipe.output.concept) - - # Store structure info for this pipe - pipe_structures[pipe.code] = { - "inputs": inputs_specs, - "output": output_spec, - } - - return pipe_structures diff --git a/api/routes/pipelex/__init__.py b/api/routes/pipelex/__init__.py index bad27d8..d682583 100644 --- a/api/routes/pipelex/__init__.py +++ b/api/routes/pipelex/__init__.py @@ -1,11 +1,11 @@ from fastapi import APIRouter -from .pipe_builder import router as pipe_builder_router +from .build import router as build_router from .pipeline import router as pipeline_router -from .plx_validator import router as plx_validator_router +from .validate import router as validate_router router = APIRouter() +router.include_router(build_router) router.include_router(pipeline_router) -router.include_router(pipe_builder_router) -router.include_router(plx_validator_router) +router.include_router(validate_router) diff --git a/api/routes/pipelex/build/__init__.py b/api/routes/pipelex/build/__init__.py new file mode 100644 index 0000000..3e7e8d3 --- /dev/null +++ b/api/routes/pipelex/build/__init__.py @@ -0,0 +1,13 @@ +from fastapi import APIRouter + +from .inputs import router as inputs_router +from .output import router as output_router +from .pipe import router as pipe_router +from .runner import router as runner_router + +router = APIRouter() + +router.include_router(inputs_router) +router.include_router(output_router) +router.include_router(pipe_router) +router.include_router(runner_router) diff --git a/api/routes/pipelex/build/inputs.py b/api/routes/pipelex/build/inputs.py new file mode 100644 index 0000000..a0ddab4 --- /dev/null +++ b/api/routes/pipelex/build/inputs.py @@ -0,0 +1,41 @@ +import json +import traceback +from typing import Any + +from fastapi import APIRouter, HTTPException +from pipelex import log +from pipelex.hub import get_library_manager, get_required_pipe, set_current_library +from pipelex.pipeline.validate_bundle import validate_bundle +from pydantic import BaseModel, Field + +router = APIRouter(tags=["build"]) + + +class BuildInputsRequest(BaseModel): + mthds_content: str = Field(..., description="PLX content to load pipes from") + pipe_code: str = Field(..., description="Pipe code to generate inputs JSON for") + + +@router.post("/build/inputs") +async def build_inputs(request_data: BuildInputsRequest) -> Any: + """Generate example input JSON for a pipe.""" + library_manager = get_library_manager() + + try: + validate_bundle_result = await validate_bundle(mthds_content=request_data.mthds_content) + blueprint = validate_bundle_result.blueprints[0] + + library_id, _ = library_manager.open_library() + set_current_library(library_id) + library_manager.load_from_blueprints(library_id=library_id, blueprints=[blueprint]) + + the_pipe = get_required_pipe(pipe_code=request_data.pipe_code) + inputs_json_str = the_pipe.inputs.render_inputs(indent=2) + + return json.loads(inputs_json_str) + + except Exception as exc: + log.error(f"Error generating inputs JSON for pipe '{request_data.pipe_code}':") + traceback.print_exc() + + raise HTTPException(status_code=500, detail=str(exc)) from exc diff --git a/api/routes/pipelex/build/output.py b/api/routes/pipelex/build/output.py new file mode 100644 index 0000000..31b4148 --- /dev/null +++ b/api/routes/pipelex/build/output.py @@ -0,0 +1,43 @@ +import json +import traceback +from typing import Any + +from fastapi import APIRouter, HTTPException +from pipelex import log +from pipelex.core.concepts.concept_representation_generator import ConceptRepresentationFormat +from pipelex.core.pipes.output.output_renderer import render_output +from pipelex.hub import get_library_manager, get_required_pipe, set_current_library +from pipelex.pipeline.validate_bundle import validate_bundle +from pydantic import BaseModel, Field + +router = APIRouter(tags=["build"]) + + +class BuildOutputRequest(BaseModel): + mthds_content: str = Field(..., description="PLX content to load pipes from") + pipe_code: str = Field(..., description="Pipe code to generate output JSON for") + format: ConceptRepresentationFormat = Field(default=ConceptRepresentationFormat.SCHEMA, description="Format to generate output in") + + +@router.post("/build/output") +async def build_output(request_data: BuildOutputRequest) -> Any: + library_manager = get_library_manager() + + try: + validate_bundle_result = await validate_bundle(mthds_content=request_data.mthds_content) + blueprint = validate_bundle_result.blueprints[0] + + library_id, _ = library_manager.open_library() + set_current_library(library_id) + library_manager.load_from_blueprints(library_id=library_id, blueprints=[blueprint]) + + the_pipe = get_required_pipe(pipe_code=request_data.pipe_code) + output_json_str = render_output(the_pipe, output_format=request_data.format) + + return json.loads(output_json_str) + + except Exception as exc: + log.error(f"Error generating output JSON for pipe '{request_data.pipe_code}':") + traceback.print_exc() + + raise HTTPException(status_code=500, detail=str(exc)) from exc diff --git a/api/routes/pipelex/build/pipe.py b/api/routes/pipelex/build/pipe.py new file mode 100644 index 0000000..22ff0f5 --- /dev/null +++ b/api/routes/pipelex/build/pipe.py @@ -0,0 +1,42 @@ +from typing import Any + +from fastapi import APIRouter +from fastapi.responses import JSONResponse +from pipelex.builder.builder_loop import BuilderLoop +from pipelex.language.mthds_factory import MthdsFactory +from pydantic import BaseModel, Field + +router = APIRouter(tags=["build"]) + + +class BuildPipeRequest(BaseModel): + brief: str = Field(..., description="Brief description of the pipeline to build") + + +class BuildPipeResponse(BaseModel): + mthds_content: str = Field(..., description="Generated PLX content as string") + pipelex_bundle_blueprint: dict[str, Any] = Field(..., description="Generated pipelex bundle blueprint") + success: bool = Field(default=True, description="Whether the operation was successful") + message: str = Field(default="Pipeline generated successfully", description="Status message") + + +@router.post("/build/pipe", response_model=BuildPipeResponse) +async def build_pipe(request_data: BuildPipeRequest): + """Build a pipeline from a brief description. + + This endpoint takes a brief description and generates both PLX content + and the corresponding pipelex bundle blueprint. + """ + builder_loop = BuilderLoop() + pipelex_bundle_spec, _ = await builder_loop.build_and_fix(inputs={"brief": request_data.brief}, builder_pipe="pipe_builder") + blueprint = pipelex_bundle_spec.to_blueprint() + + mthds_content = MthdsFactory.make_mthds_content(blueprint=blueprint) + response_data = BuildPipeResponse( + mthds_content=mthds_content, + pipelex_bundle_blueprint=blueprint.model_dump(serialize_as_any=True), + success=True, + message="Pipeline generated successfully", + ) + + return JSONResponse(content=response_data.model_dump(serialize_as_any=True)) diff --git a/api/routes/pipelex/build/runner.py b/api/routes/pipelex/build/runner.py new file mode 100644 index 0000000..17b8079 --- /dev/null +++ b/api/routes/pipelex/build/runner.py @@ -0,0 +1,75 @@ +import traceback + +from fastapi import APIRouter, HTTPException +from fastapi.responses import JSONResponse +from pipelex import log +from pipelex.builder.runner_code import generate_runner_code +from pipelex.core.interpreter.interpreter import PipelexInterpreter +from pipelex.hub import get_library_manager, get_required_pipe, set_current_library +from pipelex.pipe_run.dry_run import dry_run_pipes +from pydantic import BaseModel, Field + +router = APIRouter(tags=["build"]) + + +class BuildRunnerRequest(BaseModel): + mthds_content: str = Field(..., description="PLX content to load and generate runner code for") + pipe_code: str = Field(..., description="Pipe code to generate runner code for") + + +class BuildRunnerResponse(BaseModel): + python_code: str = Field(..., description="Generated Python code for running the workflow") + pipe_code: str = Field(..., description="Pipe code that was used") + success: bool = Field(default=True, description="Whether the operation was successful") + message: str = Field(default="Runner code generated successfully", description="Status message") + + +@router.post("/build/runner", response_model=BuildRunnerResponse) +async def build_runner(request_data: BuildRunnerRequest): + """Generate Python runner code for a pipe from PLX content. + + This endpoint: + 1. Parses and validates PLX content + 2. Loads pipes from the blueprint + 3. Validates and dry-runs all pipes + 4. Generates runner code for the specified pipe + 5. Cleans up loaded pipes + 6. Returns the generated Python code + """ + library_manager = get_library_manager() + blueprint = None + + library_id, _ = library_manager.open_library() + set_current_library(library_id) + try: + # Parse PLX content into a bundle blueprint + converter = PipelexInterpreter() + blueprint = converter.make_pipelex_bundle_blueprint(mthds_content=request_data.mthds_content) + + # Load pipes from the blueprint + pipes = library_manager.load_from_blueprints(library_id=library_id, blueprints=[blueprint]) + + # Validate all pipes + for pipe in pipes: + pipe.validate_with_libraries() + await dry_run_pipes(pipes=[pipe], raise_on_failure=True) + + # Get the required pipe and generate runner code + pipe = get_required_pipe(request_data.pipe_code) + python_code = generate_runner_code(pipe=pipe) + + # Create the response + response_data = BuildRunnerResponse( + python_code=python_code, + pipe_code=request_data.pipe_code, + success=True, + message="Runner code generated successfully", + ) + + return JSONResponse(content=response_data.model_dump(serialize_as_any=True)) + + except Exception as exc: + log.error(f"Error generating runner code for pipe '{request_data.pipe_code}':") + traceback.print_exc() + + raise HTTPException(status_code=500, detail=str(exc)) from exc diff --git a/api/routes/pipelex/hello_world.plx b/api/routes/pipelex/hello_world.plx deleted file mode 100644 index db3f488..0000000 --- a/api/routes/pipelex/hello_world.plx +++ /dev/null @@ -1,13 +0,0 @@ -domain = "quick_start" -description = "Discovering Pipelex" - -[pipe] -[pipe.hello_world] -type = "PipeLLM" -description = "Write text about Hello World." -output = "Text" -model = { model = "gpt-4o-mini", temperature = 0.9, max_tokens = "auto" } -prompt = """ -Write a haiku about Hello World. -""" - diff --git a/api/routes/pipelex/pipe_builder.py b/api/routes/pipelex/pipe_builder.py deleted file mode 100644 index a9e41df..0000000 --- a/api/routes/pipelex/pipe_builder.py +++ /dev/null @@ -1,125 +0,0 @@ -import traceback -from typing import Any - -from fastapi import APIRouter, HTTPException -from fastapi.responses import JSONResponse -from pipelex import log -from pipelex.builder.builder_loop import BuilderLoop -from pipelex.builder.runner_code import generate_runner_code -from pipelex.core.interpreter.interpreter import PipelexInterpreter -from pipelex.hub import get_library_manager, get_required_pipe, set_current_library -from pipelex.language.plx_factory import PlxFactory -from pipelex.pipe_run.dry_run import dry_run_pipes -from pydantic import BaseModel, Field - -from api.routes.helpers import extract_pipe_structures - -router = APIRouter(tags=["pipe-builder"]) - - -class PipeBuilderRequest(BaseModel): - brief: str = Field(..., description="Brief description of the pipeline to build") - - -class PipeBuilderResponse(BaseModel): - plx_content: str = Field(..., description="Generated PLX content as string") - pipelex_bundle_blueprint: dict[str, Any] = Field(..., description="Generated pipelex bundle blueprint") - pipe_structures: dict[str, dict[str, Any]] = Field( - default_factory=dict, description="Structure class information for each pipe's inputs and output" - ) - success: bool = Field(default=True, description="Whether the operation was successful") - message: str = Field(default="Pipeline generated successfully", description="Status message") - - -@router.post("/pipe-builder/build", response_model=PipeBuilderResponse) -async def build_pipe(request_data: PipeBuilderRequest): - """Build a pipeline from a brief description. - - This endpoint takes a brief description and generates both PLX content - and the corresponding pipelex bundle blueprint, along with pipe structures. - """ - # Execute the pipe_builder pipeline - library_manager = get_library_manager() - builder_loop = BuilderLoop() - pipelex_bundle_spec, _ = await builder_loop.build_and_fix(inputs={"brief": request_data.brief}, builder_pipe="pipe_builder") - blueprint = pipelex_bundle_spec.to_blueprint() - - library_id, _ = library_manager.open_library() - set_current_library(library_id) - # Load pipes temporarily to extract structures - pipes = library_manager.load_from_blueprints(library_id=library_id, blueprints=[blueprint]) - pipe_structures = extract_pipe_structures(pipes) - - plx_content = PlxFactory.make_plx_content(blueprint=blueprint) - response_data = PipeBuilderResponse( - plx_content=plx_content, - pipelex_bundle_blueprint=blueprint.model_dump(serialize_as_any=True), - pipe_structures=pipe_structures, - success=True, - message="Pipeline generated successfully", - ) - - return JSONResponse(content=response_data.model_dump(serialize_as_any=True)) - - -class RunnerCodeRequest(BaseModel): - plx_content: str = Field(..., description="PLX content to load and generate runner code for") - pipe_code: str = Field(..., description="Pipe code to generate runner code for") - - -class RunnerCodeResponse(BaseModel): - python_code: str = Field(..., description="Generated Python code for running the workflow") - pipe_code: str = Field(..., description="Pipe code that was used") - success: bool = Field(default=True, description="Whether the operation was successful") - message: str = Field(default="Runner code generated successfully", description="Status message") - - -@router.post("/pipe-builder/generate-runner", response_model=RunnerCodeResponse) -async def generate_runner(request_data: RunnerCodeRequest): - """Generate Python runner code for a pipe from PLX content. - - This endpoint: - 1. Parses and validates PLX content - 2. Loads pipes from the blueprint - 3. Validates and dry-runs all pipes - 4. Generates runner code for the specified pipe - 5. Cleans up loaded pipes - 6. Returns the generated Python code - """ - library_manager = get_library_manager() - blueprint = None - - library_id, _ = library_manager.open_library() - set_current_library(library_id) - try: - # Parse PLX content into a bundle blueprint - converter = PipelexInterpreter() - blueprint = converter.make_pipelex_bundle_blueprint(plx_content=request_data.plx_content) - - # Load pipes from the blueprint - pipes = library_manager.load_from_blueprints(library_id=library_id, blueprints=[blueprint]) - - # Validate all pipes - for pipe in pipes: - pipe.validate_with_libraries() - await dry_run_pipes(pipes=[pipe], raise_on_failure=True) - - # Get the required pipe and generate runner code - pipe = get_required_pipe(request_data.pipe_code) - python_code = generate_runner_code(pipe=pipe) - - # Create the response - response_data = RunnerCodeResponse( - python_code=python_code, - pipe_code=request_data.pipe_code, - success=True, - message="Runner code generated successfully", - ) - - return JSONResponse(content=response_data.model_dump(serialize_as_any=True)) - - except Exception as exc: - log.error(f"Error generating runner code for pipe '{request_data.pipe_code}':") - traceback.print_exc() - - raise HTTPException(status_code=500, detail=str(exc)) from exc diff --git a/api/routes/pipelex/pipeline.py b/api/routes/pipelex/pipeline.py index 299cc05..bae4eee 100644 --- a/api/routes/pipelex/pipeline.py +++ b/api/routes/pipelex/pipeline.py @@ -1,65 +1,108 @@ +from __future__ import annotations + +import asyncio import traceback -from typing import Annotated +from typing import TYPE_CHECKING, Annotated, Any, cast from fastapi import APIRouter, Depends, HTTPException, Request -from fastapi.responses import JSONResponse from kajson import kajson +from mthds.client.pipeline import PipelineRequest, PipelineState from pipelex import log -from pipelex.client.pipeline_request_factory import PipelineRequestFactory -from pipelex.client.pipeline_response_factory import PipelineResponseFactory -from pipelex.client.protocol import PipelineRequest, PipelineResponse, PipelineState -from pipelex.pipeline.execute import execute_pipeline -from pipelex.pipeline.start import start_pipeline +from pipelex.config import get_config +from pipelex.hub import get_pipe_router +from pipelex.pipeline.pipeline_response import PipelexPipelineExecuteResponse, PipelexPipelineStartResponse +from pipelex.pipeline.pipeline_run_setup import pipeline_run_setup +from pipelex.pipeline.runner import PipelexRunner +from typing_extensions import override from api.routes.pipelex.utils import get_current_iso_timestamp +if TYPE_CHECKING: + from mthds.client.models.pipe_output import VariableMultiplicity + from mthds.client.models.pipeline_inputs import PipelineInputs + from mthds.client.models.working_memory import WorkingMemoryAbstract + from pipelex.core.memory.working_memory import WorkingMemory + from pipelex.core.pipes.pipe_output import PipeOutput + + router = APIRouter(tags=["pipeline"]) +class ApiRunner(PipelexRunner): + """API runner that extends PipelexRunner with start_pipeline support.""" + + @override + async def start_pipeline( + self, + pipe_code: str | None = None, + mthds_content: str | None = None, + inputs: PipelineInputs | WorkingMemoryAbstract[Any] | None = None, + output_name: str | None = None, + output_multiplicity: VariableMultiplicity | None = None, + dynamic_output_concept_code: str | None = None, + ) -> PipelexPipelineStartResponse: + """Start a pipeline execution asynchronously without waiting for completion. + + Setup (validation, library loading) runs synchronously so errors are + returned to the caller immediately. Only the pipe execution itself + runs in the background. + """ + created_at = get_current_iso_timestamp() + pipelex_inputs: PipelineInputs | WorkingMemory | None = cast("PipelineInputs | WorkingMemory | None", inputs) + + execution_config = self.execution_config or get_config().pipelex.pipeline_execution_config + pipe_job, pipeline_run_id, _ = await pipeline_run_setup( + execution_config=execution_config, + library_id=self.library_id, + library_dirs=self.library_dirs, + pipe_code=pipe_code, + mthds_content=mthds_content, + bundle_uri=self.bundle_uri, + inputs=pipelex_inputs, + output_name=output_name, + output_multiplicity=output_multiplicity, + dynamic_output_concept_code=dynamic_output_concept_code, + pipe_run_mode=self.pipe_run_mode, + search_domain_codes=self.search_domain_codes, + user_id=self.user_id, + ) + + task: asyncio.Task[PipeOutput] = asyncio.create_task(get_pipe_router().run(pipe_job)) + self._running_tasks[pipeline_run_id] = task + + return PipelexPipelineStartResponse( + pipeline_run_id=pipeline_run_id, + created_at=created_at, + pipeline_state=PipelineState.STARTED, + ) + + async def request_deserialization(request: Request) -> PipelineRequest: - """Dependency that deserializes the request body using kajson""" + """Dependency that deserializes the request body using kajson.""" body = await request.body() body_str = body.decode("utf-8") request_data = kajson.loads(body_str) - return PipelineRequestFactory.make_from_body(request_data) + return PipelineRequest.from_body(request_data) -@router.post("/pipeline/execute", response_model=PipelineResponse) +@router.post("/pipeline/execute", response_model=PipelexPipelineExecuteResponse) async def execute( pipeline_request: Annotated[PipelineRequest, Depends(request_deserialization)], -): - """Executes a pipe with the given memory and waits for completion. - - This endpoint can operate in two modes: - 1. If 'plx_content' is provided: validates, loads, and executes pipes from the PLX content - 2. If 'plx_content' is not provided: executes an already-loaded pipe - - This is a blocking operation that doesn't return until the pipe execution is complete. - """ +) -> PipelexPipelineExecuteResponse: + """Execute a pipeline and wait for completion.""" try: - created_at = get_current_iso_timestamp() - pipe_output = await execute_pipeline( + runner = ApiRunner() + return await runner.execute_pipeline( pipe_code=pipeline_request.pipe_code, - plx_content=pipeline_request.plx_content, + mthds_content=pipeline_request.mthds_content, inputs=pipeline_request.inputs, output_name=pipeline_request.output_name, output_multiplicity=pipeline_request.output_multiplicity, dynamic_output_concept_code=pipeline_request.dynamic_output_concept_code, ) - - return PipelineResponseFactory.make_from_pipe_output( - status="success", - pipeline_run_id=pipe_output.pipeline_run_id, - pipeline_state=PipelineState.COMPLETED, - created_at=created_at, - finished_at=get_current_iso_timestamp(), - pipe_output=pipe_output, - ) - except Exception as exc: log.error("Pipeline execution error details:") traceback.print_exc() - raise HTTPException( status_code=500, detail={ @@ -69,49 +112,24 @@ async def execute( ) from exc -@router.post("/pipeline/start", response_model=PipelineResponse) +@router.post("/pipeline/start", response_model=PipelexPipelineStartResponse) async def start( pipeline_request: Annotated[PipelineRequest, Depends(request_deserialization)], -): - """Starts a pipe execution with the given memory but does not wait for completion. - - This endpoint can operate in two modes: - 1. If 'plx_content' is provided: validates, loads pipes from the PLX content, then starts execution - 2. If 'plx_content' is not provided: starts execution of an already-loaded pipe - - This is a non-blocking operation that returns immediately with a workflow ID. - - Note: If plx_content is provided, pipes remain loaded after this call returns. - """ +) -> PipelexPipelineStartResponse: + """Start a pipeline execution asynchronously without waiting for completion.""" try: - if pipeline_request.plx_content: - raise HTTPException(status_code=400, detail="PLX content is not supported when using the route 'start'") - if not pipeline_request.pipe_code: - raise HTTPException(status_code=400, detail="Pipe code is required when using the route 'start'") - - created_at = get_current_iso_timestamp() - pipeline_run_id, _ = await start_pipeline( + runner = ApiRunner() + return await runner.start_pipeline( pipe_code=pipeline_request.pipe_code, + mthds_content=pipeline_request.mthds_content, inputs=pipeline_request.inputs, output_name=pipeline_request.output_name, output_multiplicity=pipeline_request.output_multiplicity, dynamic_output_concept_code=pipeline_request.dynamic_output_concept_code, ) - - response_data = PipelineResponse( - pipeline_run_id=pipeline_run_id, - pipeline_state=PipelineState.STARTED, - created_at=created_at, - pipe_output=None, - main_stuff_name=None, - status="success", - ) - return JSONResponse(content=response_data.model_dump(serialize_as_any=True)) - except Exception as exc: log.error("Pipeline start error details:") traceback.print_exc() - raise HTTPException( status_code=500, detail={ diff --git a/api/routes/pipelex/plx_validator.py b/api/routes/pipelex/validate.py similarity index 52% rename from api/routes/pipelex/plx_validator.py rename to api/routes/pipelex/validate.py index 51e6a10..19e5b40 100644 --- a/api/routes/pipelex/plx_validator.py +++ b/api/routes/pipelex/validate.py @@ -1,32 +1,25 @@ -from typing import Any - from fastapi import APIRouter from fastapi.responses import JSONResponse from pipelex.core.bundles.pipelex_bundle_blueprint import PipelexBundleBlueprint from pipelex.pipeline.validate_bundle import validate_bundle from pydantic import BaseModel, Field -from api.routes.helpers import extract_pipe_structures - -router = APIRouter() +router = APIRouter(tags=["validate"]) -class PlxValidatorRequest(BaseModel): - plx_content: str = Field(..., description="PLX content to validate") +class ValidateRequest(BaseModel): + mthds_content: str = Field(..., description="PLX content to validate") -class PlxValidatorResponse(BaseModel): - plx_content: str = Field(..., description="The PLX content that was validated") +class ValidateResponse(BaseModel): + mthds_content: str = Field(..., description="The PLX content that was validated") pipelex_bundle_blueprint: PipelexBundleBlueprint = Field(..., description="Generated pipelex bundle blueprint") - pipe_structures: dict[str, dict[str, Any]] = Field( - default_factory=dict, description="Structure class information for each pipe's inputs and output" - ) success: bool = Field(default=True, description="Whether the validation was successful") message: str = Field(default="PLX content validated successfully", description="Status message") -@router.post("/validate", response_model=PlxValidatorResponse) -async def validate_pipes(request_data: PlxValidatorRequest): +@router.post("/validate", response_model=ValidateResponse) +async def validate_plx(request_data: ValidateRequest): """Validate PLX content by parsing, loading, and dry-running pipes. This endpoint takes PLX content and validates it by: @@ -34,17 +27,14 @@ async def validate_pipes(request_data: PlxValidatorRequest): 2. Loading pipes into the library 3. Running static validation and dry runs - Returns validation results with blueprint and pipe structures. + Returns validation results with blueprint. """ - validate_bundle_result = await validate_bundle(plx_content=request_data.plx_content) + validate_bundle_result = await validate_bundle(mthds_content=request_data.mthds_content) blueprint = validate_bundle_result.blueprints - pipes = validate_bundle_result.pipes - pipe_structures = extract_pipe_structures(pipes) - response_data = PlxValidatorResponse( - plx_content=request_data.plx_content, + response_data = ValidateResponse( + mthds_content=request_data.mthds_content, pipelex_bundle_blueprint=blueprint[0], - pipe_structures=pipe_structures, success=True, message="PLX content validated successfully", ) diff --git a/api/routes/main.py b/api/routes/version.py similarity index 89% rename from api/routes/main.py rename to api/routes/version.py index ec4708b..5c122ec 100644 --- a/api/routes/main.py +++ b/api/routes/version.py @@ -2,7 +2,7 @@ from fastapi import APIRouter -router = APIRouter() +router = APIRouter(tags=["version"]) @router.get("/pipelex_version") diff --git a/api/security.py b/api/security.py index 1159373..0868e80 100644 --- a/api/security.py +++ b/api/security.py @@ -1,6 +1,6 @@ """Authentication - API Key (default) or JWT validation.""" -from typing import Annotated, Any, cast +from typing import Annotated, Any import jwt from fastapi import Depends, HTTPException, status @@ -62,7 +62,7 @@ async def verify_jwt(credentials: Annotated[HTTPAuthorizationCredentials, Depend ) log.info(f"✅ JWT validated for {email}") - return cast("dict[str, Any]", payload) # Contains: sub, email, provider, iat, exp + return payload # Contains: sub, email, provider, iat, exp except jwt.ExpiredSignatureError as exc: log.warning("JWT token has expired") @@ -124,7 +124,6 @@ async def verify_api_key(credentials: Annotated[HTTPAuthorizationCredentials, De headers={"WWW-Authenticate": "Bearer"}, ) - log.info("✅ API key validated") return credentials.credentials except HTTPException: diff --git a/docs/pipe-builder.md b/docs/pipe-builder.md index b6ce75b..bf7f59b 100644 --- a/docs/pipe-builder.md +++ b/docs/pipe-builder.md @@ -22,7 +22,7 @@ Generate a pipeline from a brief description using AI, then optionally generate ```json { - "plx_content": "domain = \"invoice_extraction\"\n\n[concept]\nInvoice = \"An invoice document\"\n...", + "mthds_content": "domain = \"invoice_extraction\"\n\n[concept]\nInvoice = \"An invoice document\"\n...", "pipelex_bundle_blueprint": { "domain": "invoice_extraction", "concepts": { ... }, @@ -51,7 +51,7 @@ Request: Response: ```json { - "plx_content": "domain = \"invoice_extraction\"\ndescription = \"Extract invoice information from PDF documents\"\n\n[concept]\nInvoice = \"An invoice document with structured information\"\n\n[concept.Invoice.structure]\ninvoice_number = { type = \"text\", description = \"The invoice number\", required = true }\ndate = { type = \"date\", description = \"Invoice date\", required = true }\ntotal_amount = { type = \"number\", description = \"Total amount\", required = true }\nvendor_name = { type = \"text\", description = \"Vendor name\", required = true }\n\n[pipe.extract_invoice]\ntype = \"PipeSequence\"\ndescription = \"Extract invoice information from PDF\"\ninputs = { document = \"PDF\" }\noutput = \"Invoice\"\nsteps = [\n { pipe = \"extract_text\", result = \"text\" },\n { pipe = \"parse_invoice\", result = \"invoice\" }\n]\n\n[pipe.extract_text]\ntype = \"PipeExtract\"\ndescription = \"Extract text from PDF\"\ninputs = { document = \"PDF\" }\noutput = \"Page\"\n\n[pipe.parse_invoice]\ntype = \"PipeLLM\"\ndescription = \"Parse invoice information\"\ninputs = { pages = \"Page[]\" }\noutput = \"Invoice\"\nmodel = { model = \"gpt-4o\", temperature = 0.1 }\nprompt = \"\"\"\nExtract the invoice information from the following pages:\n\n@pages\n\"\"\"", + "mthds_content": "domain = \"invoice_extraction\"\ndescription = \"Extract invoice information from PDF documents\"\n\n[concept]\nInvoice = \"An invoice document with structured information\"\n\n[concept.Invoice.structure]\ninvoice_number = { type = \"text\", description = \"The invoice number\", required = true }\ndate = { type = \"date\", description = \"Invoice date\", required = true }\ntotal_amount = { type = \"number\", description = \"Total amount\", required = true }\nvendor_name = { type = \"text\", description = \"Vendor name\", required = true }\n\n[pipe.extract_invoice]\ntype = \"PipeSequence\"\ndescription = \"Extract invoice information from PDF\"\ninputs = { document = \"PDF\" }\noutput = \"Invoice\"\nsteps = [\n { pipe = \"extract_text\", result = \"text\" },\n { pipe = \"parse_invoice\", result = \"invoice\" }\n]\n\n[pipe.extract_text]\ntype = \"PipeExtract\"\ndescription = \"Extract text from PDF\"\ninputs = { document = \"PDF\" }\noutput = \"Page\"\n\n[pipe.parse_invoice]\ntype = \"PipeLLM\"\ndescription = \"Parse invoice information\"\ninputs = { pages = \"Page[]\" }\noutput = \"Invoice\"\nmodel = { model = \"gpt-4o\", temperature = 0.1 }\nprompt = \"\"\"\nExtract the invoice information from the following pages:\n\n@pages\n\"\"\"", "pipelex_bundle_blueprint": { "domain": "invoice_extraction", "description": "Extract invoice information from PDF documents", @@ -120,14 +120,14 @@ Generate Python runner code for executing a pipeline. ```json { - "plx_content": "domain = \"my_domain\"\n\n[concept]\nMyResult = \"A result\"\n\n[pipe.my_pipe]\ntype = \"PipeLLM\"\ndescription = \"Process input\"\noutput = \"MyResult\"\nprompt = \"Generate output\"", + "mthds_content": "domain = \"my_domain\"\n\n[concept]\nMyResult = \"A result\"\n\n[pipe.my_pipe]\ntype = \"PipeLLM\"\ndescription = \"Process input\"\noutput = \"MyResult\"\nprompt = \"Generate output\"", "pipe_code": "my_pipe" } ``` **Request Fields:** -- `plx_content` (string, required): PLX content to load and generate runner code for +- `mthds_content` (string, required): PLX content to load and generate runner code for - `pipe_code` (string, required): Pipe code to generate runner code for **Response:** diff --git a/docs/pipe-run.md b/docs/pipe-run.md index c0a963a..8e94fca 100644 --- a/docs/pipe-run.md +++ b/docs/pipe-run.md @@ -13,7 +13,7 @@ Execute a Pipelex pipeline with flexible inputs and wait for completion. ```json { "pipe_code": "your_pipeline_code", - "plx_content": null, + "mthds_content": null, "inputs": { "input_name": "simple text or object" }, @@ -25,8 +25,8 @@ Execute a Pipelex pipeline with flexible inputs and wait for completion. **Request Fields:** -- `pipe_code` (string, optional): The code identifying the pipeline to execute. Required if no `plx_content` is provided, or if `plx_content` doesn't have a `main_pipe` property. Can be combined with `plx_content` to specify which pipe to execute from the provided PLX definition. -- `plx_content` (string, optional): Inline pipeline definition. If provided without `pipe_code`, must have a `main_pipe` property. +- `pipe_code` (string, optional): The code identifying the pipeline to execute. Required if no `mthds_content` is provided, or if `mthds_content` doesn't have a `main_pipe` property. Can be combined with `mthds_content` to specify which pipe to execute from the provided PLX definition. +- `mthds_content` (string, optional): Inline pipeline definition. If provided without `pipe_code`, must have a `main_pipe` property. - `inputs` (PipelineInputs, optional): Flexible input format - see [Input Format: PipelineInputs](#input-format-pipelineinputs) below - `output_name` (string, optional): Name for the output slot - `output_multiplicity` (string, optional): Output multiplicity setting (`"single"`, `"variable"`, or a specific number) @@ -34,10 +34,10 @@ Execute a Pipelex pipeline with flexible inputs and wait for completion. **Validation Rules:** -- At least one of `pipe_code` or `plx_content` must be provided +- At least one of `pipe_code` or `mthds_content` must be provided - If only `pipe_code`: Must reference a pipe already registered in the library -- If only `plx_content`: Must contain a `main_pipe` property -- If both: `pipe_code` specifies which pipe to execute from the `plx_content` +- If only `mthds_content`: Must contain a `main_pipe` property +- If both: `pipe_code` specifies which pipe to execute from the `mthds_content` **Response:** @@ -101,7 +101,7 @@ Start a pipeline execution without waiting for completion (non-blocking). ```json { "pipe_code": "your_pipeline_code", - "plx_content": null, + "mthds_content": null, "inputs": { "input_name": "simple text or object" }, @@ -113,8 +113,8 @@ Start a pipeline execution without waiting for completion (non-blocking). **Request Fields:** -- `pipe_code` (string, optional): The code identifying the pipeline to execute. Required if no `plx_content` is provided, or if `plx_content` doesn't have a `main_pipe` property. -- `plx_content` (string, optional): Inline pipeline definition. If provided without `pipe_code`, must have a `main_pipe` property. +- `pipe_code` (string, optional): The code identifying the pipeline to execute. Required if no `mthds_content` is provided, or if `mthds_content` doesn't have a `main_pipe` property. +- `mthds_content` (string, optional): Inline pipeline definition. If provided without `pipe_code`, must have a `main_pipe` property. - `inputs` (PipelineInputs, optional): Flexible input format - see [Input Format: PipelineInputs](#input-format-pipelineinputs) below - `output_name` (string, optional): Name for the output slot - `output_multiplicity` (string, optional): Output multiplicity setting (`"single"`, `"variable"`, or a specific number) @@ -122,10 +122,10 @@ Start a pipeline execution without waiting for completion (non-blocking). **Validation Rules:** -- At least one of `pipe_code` or `plx_content` must be provided +- At least one of `pipe_code` or `mthds_content` must be provided - If only `pipe_code`: Must reference a pipe already registered in the library -- If only `plx_content`: Must contain a `main_pipe` property -- If both: `pipe_code` specifies which pipe to execute from the `plx_content` +- If only `mthds_content`: Must contain a `main_pipe` property +- If both: `pipe_code` specifies which pipe to execute from the `mthds_content` **Important Notes:** diff --git a/docs/pipe-validate.md b/docs/pipe-validate.md index f2cd37b..bc0e68c 100644 --- a/docs/pipe-validate.md +++ b/docs/pipe-validate.md @@ -8,19 +8,19 @@ Validate PLX content by parsing, loading, and dry-running pipes without executin ```json { - "plx_content": "domain = \"my_domain\"\n\n[concept]\nMyResult = \"A result\"\n\n[pipe.my_pipe]\ntype = \"PipeLLM\"\ndescription = \"Process input\"\noutput = \"MyResult\"\nprompt = \"Generate output\"" + "mthds_content": "domain = \"my_domain\"\n\n[concept]\nMyResult = \"A result\"\n\n[pipe.my_pipe]\ntype = \"PipeLLM\"\ndescription = \"Process input\"\noutput = \"MyResult\"\nprompt = \"Generate output\"" } ``` **Request Fields:** -- `plx_content` (string, required): PLX content to validate +- `mthds_content` (string, required): PLX content to validate **Response:** ```json { - "plx_content": "domain = \"my_domain\"...", + "mthds_content": "domain = \"my_domain\"...", "pipelex_bundle_blueprint": { "domain": "my_domain", "concepts": { ... }, @@ -50,14 +50,14 @@ Validate PLX content by parsing, loading, and dry-running pipes without executin Request: ```json { - "plx_content": "domain = \"greeting\"\n\n[concept]\nGreeting = \"A friendly greeting\"\n\n[pipe.hello]\ntype = \"PipeLLM\"\ndescription = \"Generate a greeting\"\noutput = \"Greeting\"\nmodel = { model = \"gpt-4o-mini\", temperature = 0.7 }\nprompt = \"Generate a friendly greeting\"" + "mthds_content": "domain = \"greeting\"\n\n[concept]\nGreeting = \"A friendly greeting\"\n\n[pipe.hello]\ntype = \"PipeLLM\"\ndescription = \"Generate a greeting\"\noutput = \"Greeting\"\nmodel = { model = \"gpt-4o-mini\", temperature = 0.7 }\nprompt = \"Generate a friendly greeting\"" } ``` Response: ```json { - "plx_content": "domain = \"greeting\"\n\n[concept]\nGreeting = \"A friendly greeting\"\n\n[pipe.hello]\ntype = \"PipeLLM\"\ndescription = \"Generate a greeting\"\noutput = \"Greeting\"\nmodel = { model = \"gpt-4o-mini\", temperature = 0.7 }\nprompt = \"Generate a friendly greeting\"", + "mthds_content": "domain = \"greeting\"\n\n[concept]\nGreeting = \"A friendly greeting\"\n\n[pipe.hello]\ntype = \"PipeLLM\"\ndescription = \"Generate a greeting\"\noutput = \"Greeting\"\nmodel = { model = \"gpt-4o-mini\", temperature = 0.7 }\nprompt = \"Generate a friendly greeting\"", "pipelex_bundle_blueprint": { "domain": "greeting", "description": null, diff --git a/pyproject.toml b/pyproject.toml index 87db9b9..e3bd81e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,16 +16,12 @@ classifiers = [ ] dependencies = [ - "pipelex[mistralai,anthropic,google,google-genai,bedrock,fal]", + "pipelex[mistralai,anthropic,google,google-genai,bedrock,fal]==0.18.0", "fastapi>=0.118.0", "pyjwt>=2.10.1", "uvicorn>=0.37.0", ] -[tool.uv.sources] -pipelex = { git = "https://github.com/Pipelex/pipelex.git", branch = "feature/Chicago" } - - [build-system] requires = ["hatchling"] build-backend = "hatchling.build" diff --git a/uv.lock b/uv.lock 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