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
88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
"""
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Memory Service Client for Router
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Used to get memory context for RAG queries
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"""
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import os
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import logging
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from typing import Optional, Dict, Any
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import httpx
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logger = logging.getLogger(__name__)
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MEMORY_SERVICE_URL = os.getenv("MEMORY_SERVICE_URL", "http://memory-service:8000")
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class MemoryClient:
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"""Client for Memory Service"""
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def __init__(self, base_url: str = MEMORY_SERVICE_URL):
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self.base_url = base_url.rstrip("/")
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self.timeout = 10.0
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async def get_context(
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self,
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user_id: str,
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agent_id: str,
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team_id: str,
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channel_id: Optional[str] = None,
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limit: int = 10
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) -> Dict[str, Any]:
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"""
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Get memory context for dialogue
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Returns:
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Dictionary with facts, recent_events, dialog_summaries
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"""
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try:
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async with httpx.AsyncClient(timeout=self.timeout) as client:
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# Get user facts
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facts_response = await client.get(
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f"{self.base_url}/facts",
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params={"user_id": user_id, "team_id": team_id, "limit": limit}
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)
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facts = facts_response.json() if facts_response.status_code == 200 else []
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# Get recent memory events
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events_response = await client.get(
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f"{self.base_url}/agents/{agent_id}/memory",
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params={
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"team_id": team_id,
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"channel_id": channel_id,
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"scope": "short_term",
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"kind": "message",
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"limit": limit
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}
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)
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events = events_response.json().get("items", []) if events_response.status_code == 200 else []
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# Get dialog summaries
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summaries_response = await client.get(
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f"{self.base_url}/summaries",
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params={
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"team_id": team_id,
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"channel_id": channel_id,
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"agent_id": agent_id,
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"limit": 5
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}
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)
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summaries = summaries_response.json().get("items", []) if summaries_response.status_code == 200 else []
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return {
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"facts": facts,
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"recent_events": events,
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"dialog_summaries": summaries
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}
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except Exception as e:
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logger.warning(f"Memory context fetch failed: {e}")
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return {
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"facts": [],
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"recent_events": [],
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"dialog_summaries": []
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}
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# Global client instance
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memory_client = MemoryClient()
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