mirror of
https://github.com/HKUDS/LightRAG.git
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146 lines
4.8 KiB
Python
146 lines
4.8 KiB
Python
import asyncio
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import os
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from tqdm.asyncio import tqdm as tqdm_async
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from dataclasses import dataclass
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import numpy as np
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import hashlib
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import uuid
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from ..utils import logger
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from ..base import BaseVectorStorage
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import pipmaster as pm
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import configparser
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if not pm.is_installed("qdrant_client"):
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pm.install("qdrant_client")
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from qdrant_client import QdrantClient, models
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config = configparser.ConfigParser()
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config.read("config.ini", "utf-8")
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def compute_mdhash_id_for_qdrant(
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content: str, prefix: str = "", style: str = "simple"
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) -> str:
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"""
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Generate a UUID based on the content and support multiple formats.
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:param content: The content used to generate the UUID.
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:param style: The format of the UUID, optional values are "simple", "hyphenated", "urn".
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:return: A UUID that meets the requirements of Qdrant.
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"""
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if not content:
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raise ValueError("Content must not be empty.")
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# Use the hash value of the content to create a UUID.
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hashed_content = hashlib.sha256((prefix + content).encode("utf-8")).digest()
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generated_uuid = uuid.UUID(bytes=hashed_content[:16], version=4)
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# Return the UUID according to the specified format.
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if style == "simple":
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return generated_uuid.hex
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elif style == "hyphenated":
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return str(generated_uuid)
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elif style == "urn":
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return f"urn:uuid:{generated_uuid}"
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else:
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raise ValueError("Invalid style. Choose from 'simple', 'hyphenated', or 'urn'.")
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@dataclass
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class QdrantVectorDBStorage(BaseVectorStorage):
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cosine_better_than_threshold: float = None
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@staticmethod
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def create_collection_if_not_exist(
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client: QdrantClient, collection_name: str, **kwargs
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):
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if client.collection_exists(collection_name):
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return
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client.create_collection(collection_name, **kwargs)
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def __post_init__(self):
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kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {})
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cosine_threshold = kwargs.get("cosine_better_than_threshold")
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if cosine_threshold is None:
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raise ValueError(
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"cosine_better_than_threshold must be specified in vector_db_storage_cls_kwargs"
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)
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self.cosine_better_than_threshold = cosine_threshold
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self._client = QdrantClient(
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url=os.environ.get(
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"QDRANT_URL", config.get("qdrant", "uri", fallback=None)
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),
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api_key=os.environ.get(
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"QDRANT_API_KEY", config.get("qdrant", "apikey", fallback=None)
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),
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)
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self._max_batch_size = self.global_config["embedding_batch_num"]
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QdrantVectorDBStorage.create_collection_if_not_exist(
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self._client,
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self.namespace,
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vectors_config=models.VectorParams(
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size=self.embedding_func.embedding_dim, distance=models.Distance.COSINE
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),
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)
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async def upsert(self, data: dict[str, dict]):
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if not len(data):
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logger.warning("You insert an empty data to vector DB")
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return []
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list_data = [
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{
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"id": k,
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**{k1: v1 for k1, v1 in v.items() if k1 in self.meta_fields},
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}
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for k, v in data.items()
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]
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contents = [v["content"] for v in data.values()]
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batches = [
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contents[i : i + self._max_batch_size]
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for i in range(0, len(contents), self._max_batch_size)
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]
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async def wrapped_task(batch):
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result = await self.embedding_func(batch)
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pbar.update(1)
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return result
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embedding_tasks = [wrapped_task(batch) for batch in batches]
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pbar = tqdm_async(
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total=len(embedding_tasks), desc="Generating embeddings", unit="batch"
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)
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embeddings_list = await asyncio.gather(*embedding_tasks)
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embeddings = np.concatenate(embeddings_list)
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list_points = []
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for i, d in enumerate(list_data):
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list_points.append(
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models.PointStruct(
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id=compute_mdhash_id_for_qdrant(d["id"]),
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vector=embeddings[i],
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payload=d,
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)
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)
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results = self._client.upsert(
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collection_name=self.namespace, points=list_points, wait=True
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)
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return results
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async def query(self, query, top_k=5):
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embedding = await self.embedding_func([query])
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results = self._client.search(
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collection_name=self.namespace,
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query_vector=embedding[0],
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limit=top_k,
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with_payload=True,
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score_threshold=self.cosine_better_than_threshold,
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)
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logger.debug(f"query result: {results}")
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return [{**dp.payload, "id": dp.id, "distance": dp.score} for dp in results]
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