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60b97da ee4d71c 60b97da ee4d71c 60b97da | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 | import os
from typing import List, Optional, Tuple
from uuid import uuid4
import numpy as np
from qdrant_client import QdrantClient, models
class QdrantVectorStore:
def __init__(self, embedding_dim: int, index_path: str = None, persist: bool = False):
self.embedding_dim = embedding_dim
self.collection_name = os.getenv("QDRANT_COLLECTION", "repo_qa_chunks")
self.upsert_batch_size = max(1, int(os.getenv("QDRANT_UPSERT_BATCH_SIZE", "64")))
self.client = self._create_client()
self._ensure_collection()
def _create_client(self):
url = self._clean_env("QDRANT_URL")
api_key = self._clean_env("QDRANT_API_KEY")
timeout = int(os.getenv("QDRANT_TIMEOUT_SECONDS", "120"))
if url:
return QdrantClient(
url=url,
api_key=api_key,
timeout=timeout,
check_compatibility=False,
)
return QdrantClient(":memory:")
@staticmethod
def _clean_env(name: str) -> Optional[str]:
value = os.getenv(name)
if value is None:
return None
cleaned = value.strip()
return cleaned or None
def _ensure_collection(self):
if not self.client.collection_exists(self.collection_name):
self.client.create_collection(
collection_name=self.collection_name,
vectors_config=models.VectorParams(
size=self.embedding_dim,
distance=models.Distance.COSINE,
),
)
self._ensure_payload_indexes()
def _ensure_payload_indexes(self):
self.client.create_payload_index(
collection_name=self.collection_name,
field_name="repository_id",
field_schema=models.PayloadSchemaType.INTEGER,
wait=True,
)
def add_embeddings(self, embeddings: np.ndarray, metadata: List[dict]) -> List[int]:
if embeddings.size == 0:
return []
embeddings = embeddings.astype("float32")
if embeddings.ndim == 1:
embeddings = embeddings.reshape(1, -1)
ids = [uuid4().hex for _ in metadata]
points = []
for idx, meta, embedding in zip(ids, metadata, embeddings):
payload = dict(meta)
payload["id"] = idx
points.append(
models.PointStruct(
id=idx,
vector=embedding.tolist(),
payload=payload,
)
)
total_points = len(points)
for start in range(0, total_points, self.upsert_batch_size):
batch = points[start : start + self.upsert_batch_size]
batch_number = (start // self.upsert_batch_size) + 1
total_batches = (total_points + self.upsert_batch_size - 1) // self.upsert_batch_size
print(
f"[qdrant] Upserting batch {batch_number}/{total_batches} "
f"points={len(batch)} progress={start}/{total_points}",
flush=True,
)
self.client.upsert(
collection_name=self.collection_name,
wait=True,
points=batch,
)
return ids
def search(
self,
query_embedding: np.ndarray,
k: int = 10,
repo_filter: Optional[int] = None,
) -> List[Tuple[float, dict]]:
if query_embedding.ndim == 1:
query_embedding = query_embedding.reshape(1, -1)
query_embedding = query_embedding.astype("float32")
query_filter = None
if repo_filter is not None:
query_filter = models.Filter(
must=[
models.FieldCondition(
key="repository_id",
match=models.MatchValue(value=repo_filter),
)
]
)
hits = self.client.search(
collection_name=self.collection_name,
query_vector=query_embedding[0].tolist(),
query_filter=query_filter,
limit=k,
)
return [(float(hit.score), dict(hit.payload or {})) for hit in hits]
def remove_repository(self, repo_id: int):
self.client.delete(
collection_name=self.collection_name,
wait=True,
points_selector=models.FilterSelector(
filter=models.Filter(
must=[
models.FieldCondition(
key="repository_id",
match=models.MatchValue(value=repo_id),
)
]
)
),
)
def clear(self):
if self.client.collection_exists(self.collection_name):
self.client.delete_collection(self.collection_name)
self._ensure_collection()
def save(self):
return None
def load(self):
self._ensure_collection()
def get_stats(self) -> dict:
info = self.client.get_collection(self.collection_name)
return {
"total_vectors": info.points_count or 0,
"embedding_dim": self.embedding_dim,
"collection_name": self.collection_name,
}
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