382e661f1f
Parser Service: - Add /ocr/ingest endpoint (PARSER → RAG in one call) - Add RAG_BASE_URL and RAG_TIMEOUT to config - Add OcrIngestResponse schema - Create file_converter utility for PDF/image → PNG bytes - Endpoint accepts file, dao_id, doc_id, user_id - Automatically parses with dots.ocr and sends to RAG Service Router Integration: - Add _handle_rag_query() method in RouterApp - Combines Memory + RAG → LLM pipeline - Get Memory context (facts, events, summaries) - Query RAG Service for documents - Build prompt with Memory + RAG documents - Call LLM provider with combined context - Return answer with citations Clients: - Create rag_client.py for Router (query RAG Service) - Create memory_client.py for Router (get Memory context) E2E Tests: - Create e2e_rag_pipeline.sh script for full pipeline test - Test ingest → query → router query flow - Add E2E_RAG_README.md with usage examples Docker: - Add RAG_SERVICE_URL and MEMORY_SERVICE_URL to router environment
61 lines
2.2 KiB
Python
61 lines
2.2 KiB
Python
"""
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Configuration for PARSER Service
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"""
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import os
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from typing import Literal, Optional
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from pydantic_settings import BaseSettings
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class Settings(BaseSettings):
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"""Application settings"""
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# Service
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API_HOST: str = "0.0.0.0"
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API_PORT: int = 9400
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# PARSER Model
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PARSER_MODEL_NAME: str = os.getenv("PARSER_MODEL_NAME", os.getenv("DOTS_OCR_MODEL_ID", "rednote-hilab/dots.ocr"))
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PARSER_DEVICE: Literal["cuda", "cpu", "mps"] = os.getenv("PARSER_DEVICE", os.getenv("DEVICE", "cpu"))
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PARSER_MAX_PAGES: int = int(os.getenv("PARSER_MAX_PAGES", "100"))
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PARSER_MAX_RESOLUTION: str = os.getenv("PARSER_MAX_RESOLUTION", "4096x4096")
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PARSER_BATCH_SIZE: int = int(os.getenv("PARSER_BATCH_SIZE", "1"))
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# File handling
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MAX_FILE_SIZE_MB: int = int(os.getenv("MAX_FILE_SIZE_MB", "50"))
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TEMP_DIR: str = os.getenv("TEMP_DIR", "/tmp/parser")
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# PDF processing
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PDF_DPI: int = int(os.getenv("PDF_DPI", "200"))
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PAGE_RANGE: Optional[str] = os.getenv("PAGE_RANGE", None) # e.g., "1-20" for pages 1-20
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# Image processing
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IMAGE_MAX_SIZE: int = int(os.getenv("IMAGE_MAX_SIZE", "2048")) # Max size for longest side
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# Parser mode
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USE_DUMMY_PARSER: bool = os.getenv("USE_DUMMY_PARSER", "false").lower() == "true"
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ALLOW_DUMMY_FALLBACK: bool = os.getenv("ALLOW_DUMMY_FALLBACK", "true").lower() == "true"
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# Runtime
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RUNTIME_TYPE: Literal["local", "remote", "ollama"] = os.getenv("RUNTIME_TYPE", "local")
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RUNTIME_URL: str = os.getenv("RUNTIME_URL", "http://parser-runtime:11435")
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# Ollama configuration (if RUNTIME_TYPE=ollama)
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OLLAMA_BASE_URL: str = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
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# DAGI Router configuration (for qa_pairs 2-stage pipeline)
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ROUTER_BASE_URL: str = os.getenv("ROUTER_BASE_URL", "http://router:9102")
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ROUTER_TIMEOUT: int = int(os.getenv("ROUTER_TIMEOUT", "60"))
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# RAG Service configuration (for ingest pipeline)
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RAG_BASE_URL: str = os.getenv("RAG_BASE_URL", "http://rag-service:9500")
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RAG_TIMEOUT: int = int(os.getenv("RAG_TIMEOUT", "120"))
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class Config:
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env_file = ".env"
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case_sensitive = True
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settings = Settings()
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