mirror of
https://github.com/DrizzleTime/Foxel.git
synced 2026-09-06 16:17:06 +08:00
Initial commit
This commit is contained in:
shiyu
@@ -0,0 +1,271 @@
|
||||
from typing import Any, Dict, List, Optional, Sequence
|
||||
from uuid import NAMESPACE_URL, uuid5
|
||||
|
||||
from qdrant_client import QdrantClient
|
||||
from qdrant_client.http import models as qmodels
|
||||
|
||||
from .base import BaseVectorProvider
|
||||
|
||||
|
||||
class QdrantProvider(BaseVectorProvider):
|
||||
type = "qdrant"
|
||||
label = "Qdrant"
|
||||
description = "Qdrant vector database (HTTP API)."
|
||||
enabled = True
|
||||
config_schema: List[Dict[str, Any]] = [
|
||||
{
|
||||
"key": "url",
|
||||
"label": "Server URL",
|
||||
"type": "text",
|
||||
"required": True,
|
||||
"placeholder": "http://localhost:6333",
|
||||
},
|
||||
{
|
||||
"key": "api_key",
|
||||
"label": "API Key",
|
||||
"type": "password",
|
||||
"required": False,
|
||||
},
|
||||
]
|
||||
|
||||
def __init__(self, config: Dict[str, Any] | None = None):
|
||||
super().__init__(config)
|
||||
self.client: Optional[QdrantClient] = None
|
||||
|
||||
async def initialize(self) -> None:
|
||||
url = (self.config.get("url") or "").strip()
|
||||
if not url:
|
||||
raise RuntimeError("Qdrant URL is required")
|
||||
|
||||
api_key = (self.config.get("api_key") or None) or None
|
||||
try:
|
||||
client = QdrantClient(url=url, api_key=api_key)
|
||||
client.get_collections()
|
||||
self.client = client
|
||||
except Exception as exc: # pragma: no cover - 依赖外部服务
|
||||
raise RuntimeError(f"Failed to connect to Qdrant at {url}: {exc}") from exc
|
||||
|
||||
def _get_client(self) -> QdrantClient:
|
||||
if not self.client:
|
||||
raise RuntimeError("Qdrant client is not initialized")
|
||||
return self.client
|
||||
|
||||
@staticmethod
|
||||
def _vector_params(vector: bool, dim: int) -> qmodels.VectorParams:
|
||||
size = dim if vector and isinstance(dim, int) and dim > 0 else 1
|
||||
return qmodels.VectorParams(size=size, distance=qmodels.Distance.COSINE)
|
||||
|
||||
def _ensure_payload_indexes(self, client: QdrantClient, collection_name: str) -> None:
|
||||
for field in ("path", "source_path"):
|
||||
try:
|
||||
client.create_payload_index(
|
||||
collection_name=collection_name,
|
||||
field_name=field,
|
||||
field_schema="keyword",
|
||||
)
|
||||
except Exception as exc: # pragma: no cover - 依赖外部服务
|
||||
message = str(exc).lower()
|
||||
if "already exists" in message or "index exists" in message:
|
||||
continue
|
||||
raise
|
||||
|
||||
def ensure_collection(self, collection_name: str, vector: bool, dim: int) -> None:
|
||||
client = self._get_client()
|
||||
try:
|
||||
exists = client.collection_exists(collection_name)
|
||||
except Exception as exc: # pragma: no cover - 依赖外部服务
|
||||
raise RuntimeError(f"Failed to check Qdrant collection '{collection_name}': {exc}") from exc
|
||||
|
||||
if exists:
|
||||
try:
|
||||
self._ensure_payload_indexes(client, collection_name)
|
||||
except Exception:
|
||||
pass
|
||||
return
|
||||
|
||||
vectors_config = self._vector_params(vector, dim)
|
||||
try:
|
||||
client.create_collection(collection_name=collection_name, vectors_config=vectors_config)
|
||||
except Exception as exc: # pragma: no cover
|
||||
if "already exists" in str(exc).lower():
|
||||
try:
|
||||
self._ensure_payload_indexes(client, collection_name)
|
||||
except Exception:
|
||||
pass
|
||||
return
|
||||
raise RuntimeError(f"Failed to create Qdrant collection '{collection_name}': {exc}") from exc
|
||||
|
||||
try:
|
||||
self._ensure_payload_indexes(client, collection_name)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
@staticmethod
|
||||
def _point_id(uid: str) -> str:
|
||||
return str(uuid5(NAMESPACE_URL, uid))
|
||||
|
||||
def _prepare_point(self, data: Dict[str, Any]) -> qmodels.PointStruct:
|
||||
uid = data.get("path")
|
||||
if not uid:
|
||||
raise ValueError("Qdrant upsert requires 'path' in data")
|
||||
|
||||
embedding = data.get("embedding")
|
||||
if embedding is None:
|
||||
vector = [0.0]
|
||||
else:
|
||||
vector = [float(x) for x in embedding]
|
||||
|
||||
payload = {k: v for k, v in data.items() if k != "embedding"}
|
||||
payload.setdefault("vector_id", uid)
|
||||
source_path = payload.get("source_path") or payload.get("path")
|
||||
payload["path"] = source_path
|
||||
return qmodels.PointStruct(id=self._point_id(str(uid)), vector=vector, payload=payload)
|
||||
|
||||
def upsert_vector(self, collection_name: str, data: Dict[str, Any]) -> None:
|
||||
client = self._get_client()
|
||||
point = self._prepare_point(data)
|
||||
client.upsert(collection_name=collection_name, wait=True, points=[point])
|
||||
|
||||
def delete_vector(self, collection_name: str, path: str) -> None:
|
||||
client = self._get_client()
|
||||
condition = qmodels.FieldCondition(
|
||||
key="path",
|
||||
match=qmodels.MatchValue(value=path),
|
||||
)
|
||||
flt = qmodels.Filter(must=[condition])
|
||||
selector = qmodels.FilterSelector(filter=flt)
|
||||
client.delete(collection_name=collection_name, points_selector=selector, wait=True)
|
||||
|
||||
def _format_search_results(self, points: Sequence[qmodels.ScoredPoint]):
|
||||
return [
|
||||
{
|
||||
"id": point.id,
|
||||
"distance": point.score,
|
||||
"entity": point.payload or {},
|
||||
}
|
||||
for point in points
|
||||
]
|
||||
|
||||
def search_vectors(self, collection_name: str, query_embedding, top_k: int):
|
||||
client = self._get_client()
|
||||
vector = [float(x) for x in query_embedding]
|
||||
points = client.search(
|
||||
collection_name=collection_name,
|
||||
query_vector=vector,
|
||||
limit=top_k,
|
||||
with_payload=True,
|
||||
)
|
||||
return [self._format_search_results(points)]
|
||||
|
||||
def search_by_path(self, collection_name: str, query_path: str, top_k: int):
|
||||
client = self._get_client()
|
||||
results: List[Dict[str, Any]] = []
|
||||
offset: Optional[str | int] = None
|
||||
remaining = max(top_k, 1)
|
||||
|
||||
while len(results) < top_k:
|
||||
batch_size = min(max(remaining * 2, 10), 200)
|
||||
records, next_offset = client.scroll(
|
||||
collection_name=collection_name,
|
||||
limit=batch_size,
|
||||
offset=offset,
|
||||
with_payload=True,
|
||||
)
|
||||
if not records:
|
||||
break
|
||||
|
||||
for record in records:
|
||||
payload = record.payload or {}
|
||||
path = payload.get("path")
|
||||
if query_path and path and query_path not in path:
|
||||
continue
|
||||
results.append({"id": record.id, "distance": 1.0, "entity": payload})
|
||||
if len(results) >= top_k:
|
||||
break
|
||||
|
||||
if next_offset is None or len(results) >= top_k:
|
||||
break
|
||||
offset = next_offset
|
||||
remaining = top_k - len(results)
|
||||
|
||||
return [results]
|
||||
|
||||
def _extract_vector_config(self, vectors) -> Optional[qmodels.VectorParams]:
|
||||
if isinstance(vectors, qmodels.VectorParams):
|
||||
return vectors
|
||||
if isinstance(vectors, dict):
|
||||
for value in vectors.values():
|
||||
if isinstance(value, qmodels.VectorParams):
|
||||
return value
|
||||
return None
|
||||
|
||||
def get_all_stats(self) -> Dict[str, Any]:
|
||||
client = self._get_client()
|
||||
try:
|
||||
response = client.get_collections()
|
||||
except Exception as exc: # pragma: no cover
|
||||
raise RuntimeError(f"Failed to list Qdrant collections: {exc}") from exc
|
||||
|
||||
collections: List[Dict[str, Any]] = []
|
||||
total_vectors = 0
|
||||
total_estimated_memory = 0
|
||||
|
||||
for description in response.collections or []:
|
||||
name = description.name
|
||||
try:
|
||||
info = client.get_collection(name)
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
row_count = int(info.points_count or 0)
|
||||
total_vectors += row_count
|
||||
|
||||
vector_params = self._extract_vector_config(info.config.params.vectors if info.config and info.config.params else None)
|
||||
dimension = int(vector_params.size) if vector_params and vector_params.size else None
|
||||
estimated_memory = row_count * dimension * 4 if dimension else 0
|
||||
total_estimated_memory += estimated_memory
|
||||
distance = str(vector_params.distance) if vector_params and vector_params.distance else None
|
||||
|
||||
indexed_rows = int(info.indexed_vectors_count or 0)
|
||||
pending_rows = max(row_count - indexed_rows, 0)
|
||||
|
||||
collections.append(
|
||||
{
|
||||
"name": name,
|
||||
"row_count": row_count,
|
||||
"dimension": dimension,
|
||||
"estimated_memory_bytes": estimated_memory,
|
||||
"is_vector_collection": dimension is not None and dimension > 1,
|
||||
"indexes": [
|
||||
{
|
||||
"index_name": "hnsw",
|
||||
"index_type": "HNSW",
|
||||
"metric_type": distance,
|
||||
"indexed_rows": indexed_rows,
|
||||
"pending_index_rows": pending_rows,
|
||||
"state": info.status,
|
||||
}
|
||||
],
|
||||
}
|
||||
)
|
||||
|
||||
return {
|
||||
"collections": collections,
|
||||
"collection_count": len(collections),
|
||||
"total_vectors": total_vectors,
|
||||
"estimated_total_memory_bytes": total_estimated_memory,
|
||||
"db_file_size_bytes": None,
|
||||
}
|
||||
|
||||
def clear_all_data(self) -> None:
|
||||
client = self._get_client()
|
||||
try:
|
||||
response = client.get_collections()
|
||||
except Exception as exc: # pragma: no cover
|
||||
raise RuntimeError(f"Failed to list Qdrant collections: {exc}") from exc
|
||||
|
||||
for description in response.collections or []:
|
||||
try:
|
||||
client.delete_collection(description.name)
|
||||
except Exception:
|
||||
continue
|
||||
Reference in New Issue
Block a user