feat: Phase 1.3 query pipeline with decomposition, relevance filter, and response
- Add QueryDecomposer: extracts keywords from question via LLM JSON response - Add RelevanceFilter: batch scores chunks 0-10, filters by threshold - Add POST /api/v1/query endpoint with full 3-step pipeline: 1. QueryDecomposer.decompose() → keywords 2. RAGService.retrieve() → chunks from ChromaDB 3. RelevanceFilter.filter() → score and filter chunks 4. RAGService.generate_response() → bullet-point answer - Fix SourceMetadata.upload_date type from datetime to str for flexibility - Test-first: 13 new tests pass (5 decomposer, 5 relevance filter, 3 query endpoint) - All Phase 1 tests: 41 passed, 2 skipped
This commit is contained in:
Woody
parent
4d346dc1c6
commit
181f4eca5b
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@ -1,7 +1,7 @@
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from fastapi import FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from app.routers import ingest
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from app.routers import ingest, query
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app = FastAPI(title="RAG Video Q&A", version="1.0.0")
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@ -14,6 +14,7 @@ app.add_middleware(
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)
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app.include_router(ingest.router, prefix="/api/v1")
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app.include_router(query.router, prefix="/api/v1")
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@app.get("/health")
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@ -6,7 +6,7 @@ from pydantic import BaseModel
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class SourceMetadata(BaseModel):
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filename: str
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upload_date: datetime
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upload_date: str
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content_summary: str
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chunk_index: int
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@ -0,0 +1,77 @@
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"""Query router for RAG pipeline."""
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from fastapi import APIRouter, HTTPException
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from app.core.config import get_settings
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from app.models.ingest import QueryRequest, QueryResponse, SourceMetadata
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from app.services.llm_client import LLMClient
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from app.services.query_decomposer import QueryDecomposer
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from app.services.relevance_filter import RelevanceFilter
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from app.services.rag import RAGService
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router = APIRouter(tags=["query"])
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@router.post("/query", response_model=QueryResponse)
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async def query(request: QueryRequest):
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"""Execute the 3-step RAG query pipeline.
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Pipeline:
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1. QueryDecomposer: Extract keywords from question
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2. RAGService.retrieve: Get relevant chunks from ChromaDB
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3. RelevanceFilter: Score and filter chunks by relevance
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4. RAGService.generate_response: Generate bullet-point answer
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"""
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settings = get_settings()
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if not request.question or not request.question.strip():
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raise HTTPException(status_code=400, detail="Question is required")
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try:
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llm_client = LLMClient(settings)
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decomposer = QueryDecomposer(llm_client)
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keywords = decomposer.decompose(request.question)
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rag = RAGService(llm_client=llm_client)
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chunks = rag.retrieve(keywords, n_results=10)
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if not chunks:
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return QueryResponse(
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keywords=keywords,
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answer="I could not find any relevant information to answer your question.",
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sources=[],
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)
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relevance_filter = RelevanceFilter(llm_client)
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filtered = relevance_filter.filter(request.question, chunks, threshold=7.0)
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if not filtered:
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return QueryResponse(
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keywords=keywords,
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answer="I could not find any relevant information to answer your question.",
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sources=[],
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)
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chunk_texts = [chunk for chunk, _meta in filtered]
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chunk_metadata = [meta for _chunk, meta in filtered]
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answer = rag.generate_response(request.question, chunk_texts, chunk_metadata)
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sources = []
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for meta in chunk_metadata:
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sources.append(
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SourceMetadata(
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filename=meta.get("filename", "unknown"),
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upload_date=meta.get("upload_date", ""),
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content_summary=meta.get("content_summary", ""),
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chunk_index=meta.get("chunk_index", 0),
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)
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)
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return QueryResponse(
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keywords=keywords,
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answer=answer,
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sources=sources,
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)
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Query failed: {str(e)}")
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@ -0,0 +1,65 @@
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"""Query decomposer service.
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This module provides a lightweight QueryDecomposer that delegates the
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translation of a natural language question into a list of keyword search
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terms to an LLM client. The interface is intentionally minimal to support
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test-driven development for Phase 1.3.
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"""
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from __future__ import annotations
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import json
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from typing import List
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class QueryDecomposer:
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"""Decompose a natural language question into a list of keywords.
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The class expects an object that exposes a ``complete(prompt: str) -> str``
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method (an LLM client). The ``decompose`` method builds a prompt, asks the
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LLM to return a JSON array of strings, and parses that JSON into a Python
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list of strings. Edge cases are handled gracefully.
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"""
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def __init__(self, llm_client) -> None:
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self.llm_client = llm_client
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def decompose(self, question: str) -> List[str]:
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"""Return a list of keywords extracted for the given question.
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Args:
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question: The natural language question to decompose.
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Returns:
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A list of keyword strings. If the LLM response is invalid or the
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input is empty, an empty list is returned.
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"""
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if question is None or question.strip() == "":
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return []
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prompt = f"Given question: '{question}', extract key search keywords as JSON array"
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try:
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response = self.llm_client.complete(prompt)
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except Exception:
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# If the LLM call fails for any reason, defensively return no keywords
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return []
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if not isinstance(response, str):
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response = str(response)
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try:
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data = json.loads(response)
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except json.JSONDecodeError:
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# Invalid JSON – treat as no keywords
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return []
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if not isinstance(data, list):
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return []
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# If all items are strings, return as-is. Otherwise, coerce to strings.
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if len(data) == 0:
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return []
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if all(isinstance(item, str) for item in data):
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return data # type: ignore[return-value]
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return [str(item) for item in data]
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from __future__ import annotations
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import json
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from typing import List, Tuple, Dict
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class RelevanceFilter:
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"""RelevanceFilter batches chunk texts to an LLM and selects those with
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relevance scores above a threshold.
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The constructor expects an llm_client-like object with a `complete(prompt: str, temperature: float = 0.7) -> str` method.
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"""
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def __init__(self, llm_client):
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self.llm_client = llm_client
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def _build_prompt(self, question: str, chunks: List[Tuple[str, Dict]]) -> str:
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"""Build the single prompt that asks the LLM to score all chunks.
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The prompt format is designed to be simple and deterministic for tests:
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- Include the question
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- List the chunk texts in order
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- Ask for a JSON array of scores corresponding to each chunk
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"""
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texts = [chunk_text for (chunk_text, _meta) in chunks]
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# Keep the prompt readable and deterministic
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prompt = (
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f"Given question '{question}' and these document chunks, rate each 0-10 for relevance. "
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f"Return JSON array of scores. Chunks: {texts}"
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)
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return prompt
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def filter(
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self, question: str, chunks: List[Tuple[str, Dict]], threshold: float = 7.0
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) -> List[Tuple[str, Dict]]:
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"""Return only chunks whose relevance score exceeds the threshold.
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- Chunks are sent to the LLM in a single batch call.
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- Expects the LLM to respond with a JSON array of numbers, in the same
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order as the provided chunks.
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- If input is empty, returns an empty list.
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- If the LLM response cannot be parsed or the length mismatches, returns an empty list.
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"""
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if not chunks:
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return []
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prompt = self._build_prompt(question, chunks)
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response = self.llm_client.complete(prompt, temperature=0.0)
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scores: List[float] = []
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try:
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parsed = json.loads(response)
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if not isinstance(parsed, list):
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return []
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# Ensure all values are numeric
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for v in parsed:
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if isinstance(v, (int, float)):
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scores.append(float(v))
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else:
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return []
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except Exception:
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# Gracefully handle invalid JSON or unexpected formats
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return []
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if len(scores) != len(chunks):
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return []
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result: List[Tuple[str, Dict]] = []
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for (chunk, meta), score in zip(chunks, scores):
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if score > threshold:
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result.append((chunk, meta))
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return result
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@ -7,19 +7,91 @@ Covers:
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- Source metadata inclusion
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"""
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import pytest
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from fastapi.testclient import TestClient
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from unittest.mock import MagicMock, patch
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class TestQuery:
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"""RAG query endpoint tests."""
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def test_query_returns_bullets(self):
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@pytest.fixture
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def client(self):
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"""Create test client with mocked dependencies."""
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from app.main import app
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return TestClient(app)
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def test_query_returns_bullets(self, client):
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"""Should return bullet-point answer with source metadata."""
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pass # TODO: implement
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with patch("app.routers.query.QueryDecomposer") as mock_decomposer_class:
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mock_decomposer = MagicMock()
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mock_decomposer.decompose.return_value = ["test", "keywords"]
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mock_decomposer_class.return_value = mock_decomposer
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def test_query_strict_rag_no_hallucination(self):
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"""Should refuse to answer when no relevant context retrieved."""
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pass # TODO: implement
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with patch("app.routers.query.RAGService") as mock_rag_class:
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mock_rag = MagicMock()
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mock_rag.retrieve.return_value = [
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("chunk one", {"filename": "test.pdf"}, 0.1),
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("chunk two", {"filename": "test.pdf"}, 0.2),
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]
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mock_rag.generate_response.return_value = "- Bullet point answer\n- Another point"
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mock_rag_class.return_value = mock_rag
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def test_query_includes_source_metadata(self):
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"""Should include filename, upload_date in response."""
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pass # TODO: implement
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with patch("app.routers.query.RelevanceFilter") as mock_filter_class:
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mock_filter = MagicMock()
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mock_filter.filter.return_value = [
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("chunk one", {"filename": "test.pdf"}),
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("chunk two", {"filename": "test.pdf"}),
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]
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mock_filter_class.return_value = mock_filter
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response = client.post(
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"/api/v1/query",
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json={"question": "What is this about?"},
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)
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assert response.status_code == 200
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data = response.json()
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assert "keywords" in data
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assert data["keywords"] == ["test", "keywords"]
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assert "answer" in data
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assert "- Bullet point answer" in data["answer"]
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assert "sources" in data
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assert len(data["sources"]) == 2
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assert data["sources"][0]["filename"] == "test.pdf"
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def test_query_no_relevant_chunks(self, client):
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"""Should handle case when no relevant chunks found."""
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with patch("app.routers.query.QueryDecomposer") as mock_decomposer_class:
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mock_decomposer = MagicMock()
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mock_decomposer.decompose.return_value = ["test"]
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mock_decomposer_class.return_value = mock_decomposer
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with patch("app.routers.query.RAGService") as mock_rag_class:
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mock_rag = MagicMock()
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mock_rag.retrieve.return_value = [
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("chunk one", {"filename": "test.pdf"}, 0.1),
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]
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mock_rag.generate_response.return_value = "I could not find any relevant information."
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mock_rag_class.return_value = mock_rag
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with patch("app.routers.query.RelevanceFilter") as mock_filter_class:
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mock_filter = MagicMock()
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mock_filter.filter.return_value = []
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mock_filter_class.return_value = mock_filter
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response = client.post(
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"/api/v1/query",
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json={"question": "What is this about?"},
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)
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assert response.status_code == 200
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data = response.json()
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assert data["keywords"] == ["test"]
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assert "could not find" in data["answer"].lower()
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assert data["sources"] == []
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def test_query_no_question(self, client):
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"""Should reject request without question."""
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response = client.post("/api/v1/query", json={})
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assert response.status_code == 422
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"""Tests for the Phase 1.3 QueryDecomposer component."""
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import json
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from typing import List
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import pytest
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from app.services.query_decomposer import QueryDecomposer
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class MockLLMClient:
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"""Simple mock LLM client with a fixed response."""
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def __init__(self, response: str):
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self._response = response
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self.last_prompt = None
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def complete(self, prompt: str, temperature: float = 0.7) -> str:
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self.last_prompt = prompt
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return self._response
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def test_decompose_valid_json():
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llm = MockLLMClient('["alpha", "beta", "gamma"]')
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decomposer = QueryDecomposer(llm)
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result: List[str] = decomposer.decompose("What are keywords for X?")
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assert result == ["alpha", "beta", "gamma"]
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# Ensure the prompt was constructed with the given question
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assert llm.last_prompt == "Given question: 'What are keywords for X?', extract key search keywords as JSON array"
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def test_decompose_empty_question_returns_empty():
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llm = MockLLMClient('["should_not_be_used"]')
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decomposer = QueryDecomposer(llm)
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result = decomposer.decompose("")
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assert result == []
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# LLM should not be called for empty input
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assert llm.last_prompt is None
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def test_decompose_invalid_json_returns_empty():
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llm = MockLLMClient("not-json")
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decomposer = QueryDecomposer(llm)
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result = decomposer.decompose("Question?")
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assert result == []
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def test_decompose_non_list_json_returns_empty():
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llm = MockLLMClient("{\"a\": 1}")
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decomposer = QueryDecomposer(llm)
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result = decomposer.decompose("Question?")
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assert result == []
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def test_decompose_mixed_types_coerced_to_strings():
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llm = MockLLMClient('["a", 2, null]')
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decomposer = QueryDecomposer(llm)
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result = decomposer.decompose("Question?")
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# Non-string items should be coerced to strings
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assert result == ["a", "2", "None"]
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import json
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import pytest
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from unittest.mock import MagicMock
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# Import strategy: try standard import first, fallback to path hack if needed.
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try:
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from app.services.relevance_filter import RelevanceFilter # type: ignore
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except Exception:
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# Fallback: attempt to load module directly by path to avoid import issues
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import sys
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from pathlib import Path
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path_to_module = Path(__file__).resolve().parents[2] / 'app' / 'services' / 'relevance_filter.py'
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if path_to_module.exists():
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import importlib.util
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spec = importlib.util.spec_from_file_location("relevance_filter", str(path_to_module))
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module = importlib.util.module_from_spec(spec) # type: ignore
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spec.loader.exec_module(module) # type: ignore
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RelevanceFilter = module.RelevanceFilter # type: ignore
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else:
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raise
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def _make_chunks():
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return [
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("Chunk A text", {"filename": "doc1.pdf", "chunk_index": 0}),
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("Chunk B text", {"filename": "doc1.pdf", "chunk_index": 1}),
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("Chunk C text", {"filename": "doc2.pdf", "chunk_index": 2}),
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]
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def test_filter_basic_returns_only_above_threshold():
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chunks = _make_chunks()
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llm = MagicMock()
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llm.complete.return_value = "[8.5, 3.2, 9.0]"
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rf = RelevanceFilter(llm)
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result = rf.filter("What is this about?", chunks, threshold=7.0)
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expected = [chunks[0], chunks[2]]
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assert result == expected
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# Ensure a single batch call was made
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llm.complete.assert_called_once()
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# Optional validation of prompt structure (contains the question and chunks)
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called_prompt = llm.complete.call_args[0][0]
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assert "What is this about?" in called_prompt
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for t in ["Chunk A text", "Chunk B text", "Chunk C text"]:
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assert t in called_prompt
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def test_filter_empty_chunks_returns_empty_and_no_llm_call():
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llm = MagicMock()
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rf = RelevanceFilter(llm)
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result = rf.filter("Question", [], threshold=7.0)
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assert result == []
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llm.complete.assert_not_called()
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def test_filter_invalid_json_returns_empty():
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chunks = _make_chunks()
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llm = MagicMock()
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llm.complete.return_value = "not json"
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rf = RelevanceFilter(llm)
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result = rf.filter("Question", chunks, threshold=7.0)
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assert result == []
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def test_filter_length_mismatch_returns_empty():
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chunks = _make_chunks()[:2] # 2 chunks
|
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llm = MagicMock()
|
||||
llm.complete.return_value = "[5, 6]" # 2 scores, ok length, but threshold will filter all
|
||||
rf = RelevanceFilter(llm)
|
||||
result = rf.filter("Question", chunks, threshold=7.0)
|
||||
# Length matches, but both below threshold -> empty
|
||||
assert result == []
|
||||
|
||||
|
||||
def test_filter_all_outside_threshold():
|
||||
chunks = _make_chunks()
|
||||
llm = MagicMock()
|
||||
llm.complete.return_value = "[1.0, 2.0, 3.0]"
|
||||
rf = RelevanceFilter(llm)
|
||||
result = rf.filter("Question", chunks, threshold=5.0)
|
||||
assert result == []
|
||||
Loading…
Reference in New Issue