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d98e3cc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 | from __future__ import annotations
import argparse
import json
import os
import sys
from pathlib import Path
from typing import Any
from qdrant_client import QdrantClient
from qdrant_client.http.exceptions import ResponseHandlingException, UnexpectedResponse
from qdrant_client.models import FieldCondition, Filter, MatchValue
try:
from src.retrieval.env_utils import load_env_file
except ModuleNotFoundError:
from env_utils import load_env_file
load_env_file()
try:
from src.retrieval.embedding_model import E5Embedder, MODEL_NAME
except ModuleNotFoundError:
from embedding_model import E5Embedder, MODEL_NAME
PROJECT_ROOT = Path(__file__).resolve().parents[2]
DEFAULT_COLLECTION_NAME = "mental_health_rag"
SOURCE_OPTIONS = {"both", "cci", "amod"}
class RetrievalEngine:
def __init__(
self,
model_name: str = MODEL_NAME,
collection_name: str | None = None,
) -> None:
url = os.getenv("QDRANT_URL")
api_key = os.getenv("QDRANT_API_KEY")
if not url or not api_key:
raise EnvironmentError("Set QDRANT_URL and QDRANT_API_KEY before searching.")
self.collection_name = collection_name or os.getenv("QDRANT_COLLECTION", DEFAULT_COLLECTION_NAME)
self.client = QdrantClient(url=url, api_key=api_key, timeout=60, check_compatibility=False)
self.embedder = E5Embedder(model_name)
def search(self, query: str, source: str = "both", top_k: int = 5) -> list[dict[str, Any]]:
source = source.lower()
if source not in SOURCE_OPTIONS:
raise ValueError("source must be one of: both, cci, amod.")
query_vector = self.embedder.encode([f"query: {query}"])[0].tolist()
points = self._query_points(query_vector, source, top_k)
return [self._format_result(point, rank + 1) for rank, point in enumerate(points)]
def _query_points(self, query_vector: list[float], source: str, limit: int) -> list[Any]:
response = self.client.query_points(
collection_name=self.collection_name,
query=query_vector,
query_filter=self._source_filter(source),
limit=limit,
with_payload=True,
)
return list(response.points)
def _source_filter(self, source: str) -> Filter | None:
if source == "both":
return None
return Filter(must=[FieldCondition(key="source_type", match=MatchValue(value=source))])
def _format_result(self, point: Any, rank: int) -> dict[str, Any]:
payload = point.payload or {}
metadata = payload.get("metadata", {})
display_text = payload.get("display_text")
if payload.get("source_type") == "amod" and metadata.get("question"):
display_text = f"Question: {metadata['question']}\n\nAnswer: {display_text}"
return {
"rank": rank,
"score": round(float(point.score), 4),
"id": payload.get("record_id"),
"source_type": payload.get("source_type"),
"source": payload.get("source"),
"title": payload.get("title"),
"topic": payload.get("topic"),
"text": display_text,
"metadata": metadata,
}
def main() -> None:
parser = argparse.ArgumentParser(description="Search the Qdrant mental-health retrieval index.")
parser.add_argument("query", help="User question to search for.")
parser.add_argument("--source", choices=sorted(SOURCE_OPTIONS), default="both")
parser.add_argument("--top-k", type=int, default=5)
parser.add_argument("--collection", default=os.getenv("QDRANT_COLLECTION", DEFAULT_COLLECTION_NAME))
args = parser.parse_args()
try:
engine = RetrievalEngine(collection_name=args.collection)
except EnvironmentError as error:
print(f"Configuration error: {error}", file=sys.stderr)
raise SystemExit(1) from error
try:
results = engine.search(args.query, source=args.source, top_k=args.top_k)
except (ResponseHandlingException, UnexpectedResponse) as error:
print(f"Qdrant search error: {error}", file=sys.stderr)
raise SystemExit(1) from error
print(json.dumps(results, indent=2, ensure_ascii=False))
if __name__ == "__main__":
main()
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