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author | Gustaf Rydholm <gustaf.rydholm@gmail.com> | 2024-04-06 00:18:57 +0200 |
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committer | Gustaf Rydholm <gustaf.rydholm@gmail.com> | 2024-04-06 00:18:57 +0200 |
commit | a1603d4c6c29f414304fc379074eb81b5b00c5d0 (patch) | |
tree | 2ebad5348fe62148db405a4637eb49274f7c9766 /rag/db/vectors.py | |
parent | 093553777355e6d1d6c2dc9b0326909bf9859cba (diff) |
Add logging in dbs
Diffstat (limited to 'rag/db/vectors.py')
-rw-r--r-- | rag/db/vectors.py | 53 |
1 files changed, 53 insertions, 0 deletions
diff --git a/rag/db/vectors.py b/rag/db/vectors.py new file mode 100644 index 0000000..9e8becb --- /dev/null +++ b/rag/db/vectors.py @@ -0,0 +1,53 @@ +import os +from dataclasses import dataclass +from typing import Dict, List + +from qdrant_client import QdrantClient +from qdrant_client.http.models import StrictFloat +from qdrant_client.models import Distance, ScoredPoint, VectorParams, PointStruct +from loguru import logger as log + + +@dataclass +class Point: + id: str + vector: List[StrictFloat] + payload: Dict[str, str] + + +class Vectors: + def __init__(self): + self.dim = int(os.environ["EMBEDDING_DIM"]) + self.collection_name = os.environ["QDRANT_COLLECTION_NAME"] + self.client = QdrantClient(url=os.environ["QDRANT_URL"]) + self.__configure() + + def __configure(self): + collections = list(map(lambda col: col.name, self.client.get_collections())) + if self.collection_name not in collections: + log.debug(f"Configuring collection {self.collection_name}...") + self.client.create_collection( + collection_name=self.collection_name, + vectors_config=VectorParams(size=self.dim, distance=Distance.COSINE), + ) + else: + log.debug(f"Collection {self.collection_name} already exists...") + + def add(self, points: List[Point]): + log.debug(f"Inserting {len(points)} vectors into the vector db...") + self.client.upload_points( + collection_name=self.collection_name, + points=[ + PointStruct(id=point.id, vector=point.vector, payload=point.payload) + for point in points + ], + parallel=4, + max_retries=3, + ) + + def search(self, query: List[float], limit: int = 4) -> List[ScoredPoint]: + log.debug("Searching for vectors...") + hits = self.client.search( + collection_name=self.collection_name, query_vector=query, limit=limit + ) + return hits |