from qdrant_client.models import VectorParams, Distance, PointStruct, TextIndexParams, TokenizerType from qdrant_client.http.models import ScoredPoint, Filter, FieldCondition, MatchText from qdrant_client import AsyncQdrantClient from app.settings import logger, settings from app.core.models import GeminiEmbed from app.core.chunks import Chunk from fastapi import HTTPException from uuid import UUID import numpy as np import asyncio import time import re class VectorDatabase: def __init__(self, embedder: GeminiEmbed, host: str = "qdrant", port: int = 6333): self.host: str = host self.client: AsyncQdrantClient = self._initialize_qdrant_client() self.embedder: GeminiEmbed = embedder # embedder is used to convert a user's query async def store(self, collection_name: str, chunks: list[Chunk], batch_size: int = 1000) -> None: points: list[PointStruct] = [] if settings.debug: await logger.info("Start getting text embeddings") start = time.time() vectors = await self.embedder.encode([await chunk.get_raw_text() for chunk in chunks]) if settings.debug: await logger.info(f"Embeddings - {time.time() - start}") for vector, chunk in zip(vectors, chunks): ok = await self.accept_vector(collection_name, vector) if ok: points.append( PointStruct( id=str(chunk.id), vector=vector, payload={ "metadata": await chunk.get_metadata(), "text": await chunk.get_raw_text(), }, ) ) async def _upsert(batch): await self.client.upsert( collection_name=collection_name, points=batch, wait=True, ) batches = [ points[group : group + batch_size] for group in range(0, len(points), batch_size) ] await asyncio.gather(*[_upsert(batch) for batch in batches]) async def cosine_similarity(self, vec1: list[float], vec2: list[float] | list[list[float]]) -> float: loop = asyncio.get_running_loop() def compute_similarity(): if len(vec2) == 0: return 0 vec1_np = np.array(vec1) vec2_np = np.array(vec2) if vec2_np.ndim == 2: vec2_np = vec2_np.T similarities = np.array(vec1_np @ vec2_np / (np.linalg.norm(vec1_np) * np.linalg.norm(vec2_np, axis=0))) return np.max(similarities) return await loop.run_in_executor(None, compute_similarity) async def accept_vector(self, collection_name: str, vector: np.array) -> bool: search = await self.client.query_points( collection_name=collection_name, query=vector, limit=1, with_vectors=True ) most_similar = search.points if not len(most_similar): return True most_similar = most_similar[0] return 1 - await self.cosine_similarity(vector, most_similar.vector) >= settings.max_delta async def construct_keywords_list(self, query: str) -> list[FieldCondition]: loop = asyncio.get_running_loop() def extract_keywords(): keywords = re.findall(r'\b[A-Z]{2,}\b', query) return [ FieldCondition(key="text", match=MatchText(text=word)) for word in keywords if 2 <= len(word) <= 30 ] return await loop.run_in_executor(None, extract_keywords) async def combine_points_without_duplications(self, first: list[ScoredPoint], second: list[ScoredPoint] = None) -> list[ScoredPoint]: combined = [] similarity_vectors = [] to_combine = [first] if second is not None: to_combine.append(second) for group in to_combine: for point in group: similarity = await self.cosine_similarity(point.vector, similarity_vectors) if 1 - similarity > min(settings.max_delta, 0.2): combined.append(point) similarity_vectors.append(point.vector) return combined async def search(self, collection_name: str, query: str, top_k: int = 5) -> list[Chunk]: query_embedded: np.ndarray = await self.embedder.encode(query) if isinstance(query_embedded, list): query_embedded = query_embedded[0] keywords = await self.construct_keywords_list(query) search = await self.client.query_points( collection_name=collection_name, query=query_embedded, limit=top_k + int(top_k * 0.3), query_filter=Filter(should=keywords), with_vectors=True ) mixed_result: list[ScoredPoint] = search.points combined = await self.combine_points_without_duplications(mixed_result) if settings.debug: await logger.info(f"Len of original array -> {len(mixed_result)}") await logger.info(f"Len of combined array -> {len(combined)}") return [ Chunk( id=UUID(point.payload.get("metadata", {}).get("id", "")), filename=point.payload.get("metadata", {}).get("filename", ""), page_number=point.payload.get("metadata", {}).get("page_number", 0), start_index=point.payload.get("metadata", {}).get("start_index", 0), start_line=point.payload.get("metadata", {}).get("start_line", 0), end_line=point.payload.get("metadata", {}).get("end_line", 0), text=point.payload.get("text", ""), ) for point in combined ] def _initialize_qdrant_client(self, max_retries=5, delay=2) -> AsyncQdrantClient: for attempt in range(max_retries): try: client = AsyncQdrantClient(**settings.qdrant.model_dump()) return client except Exception as e: if attempt == max_retries - 1: raise HTTPException( 500, f"Failed to connect to Qdrant server after {max_retries} attempts. " f"Last error: {str(e)}", ) print( f"Connection attempt {attempt + 1} out of {max_retries} failed. " f"Retrying in {delay} seconds..." ) time.sleep(delay) delay *= 2 async def _check_collection_exists(self, collection_name: str) -> bool: try: return await self.client.collection_exists(collection_name) except Exception as e: raise HTTPException( 500, f"Failed to check collection {collection_name} exists. Last error: {str(e)}", ) async def _create_collection(self, collection_name: str) -> None: try: await self.client.create_collection( collection_name=collection_name, vectors_config=VectorParams( size= await self.embedder.get_vector_dimensionality(), distance=Distance.COSINE, ), ) await self.client.create_payload_index( collection_name=collection_name, field_name="text", field_schema=TextIndexParams( type="text", tokenizer=TokenizerType.WORD, min_token_len=2, max_token_len=30, lowercase=True ) ) except Exception as e: raise HTTPException( 500, f"Failed to create collection {self.collection_name}: {str(e)}" ) async def create_collection(self, collection_name: str) -> None: try: if await self._check_collection_exists(collection_name): return await self._create_collection(collection_name) except Exception as e: print(e) raise HTTPException(500, e) async def get_collections(self) -> list[str]: try: return await self.client.get_collections() except Exception as e: print(e) raise HTTPException(500, "Failed to get collection names")