Source code for langchain_community.vectorstores.vearch

from __future__ import annotations

import os
import time
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type

import numpy as np
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore

if TYPE_CHECKING:
    import vearch

DEFAULT_TOPN = 4


[docs]class Vearch(VectorStore): _DEFAULT_TABLE_NAME = "langchain_vearch" _DEFAULT_CLUSTER_DB_NAME = "cluster_client_db" _DEFAULT_VERSION = 1
[docs] def __init__( self, embedding_function: Embeddings, path_or_url: Optional[str] = None, table_name: str = _DEFAULT_TABLE_NAME, db_name: str = _DEFAULT_CLUSTER_DB_NAME, flag: int = _DEFAULT_VERSION, **kwargs: Any, ) -> None: """Initialize vearch vector store flag 1 for cluster,0 for standalone """ try: if flag: import vearch_cluster else: import vearch except ImportError: raise ImportError( "Could not import suitable python package. " "Please install it with `pip install vearch or vearch_cluster`." ) if flag: if path_or_url is None: raise ValueError("Please input url of cluster") if not db_name: db_name = self._DEFAULT_CLUSTER_DB_NAME db_name += "_" db_name += str(uuid.uuid4()).split("-")[-1] self.using_db_name = db_name self.url = path_or_url self.vearch = vearch_cluster.VearchCluster(path_or_url) else: if path_or_url is None: metadata_path = os.getcwd().replace("\\", "/") else: metadata_path = path_or_url if not os.path.isdir(metadata_path): os.makedirs(metadata_path) log_path = os.path.join(metadata_path, "log") if not os.path.isdir(log_path): os.makedirs(log_path) self.vearch = vearch.Engine(metadata_path, log_path) self.using_metapath = metadata_path if not table_name: table_name = self._DEFAULT_TABLE_NAME table_name += "_" table_name += str(uuid.uuid4()).split("-")[-1] self.using_table_name = table_name self.embedding_func = embedding_function self.flag = flag
@property def embeddings(self) -> Optional[Embeddings]: return self.embedding_func
[docs] @classmethod def from_documents( cls: Type[Vearch], documents: List[Document], embedding: Embeddings, path_or_url: Optional[str] = None, table_name: str = _DEFAULT_TABLE_NAME, db_name: str = _DEFAULT_CLUSTER_DB_NAME, flag: int = _DEFAULT_VERSION, **kwargs: Any, ) -> Vearch: """Return Vearch VectorStore""" texts = [d.page_content for d in documents] metadatas = [d.metadata for d in documents] return cls.from_texts( texts=texts, embedding=embedding, metadatas=metadatas, path_or_url=path_or_url, table_name=table_name, db_name=db_name, flag=flag, **kwargs, )
[docs] @classmethod def from_texts( cls: Type[Vearch], texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, path_or_url: Optional[str] = None, table_name: str = _DEFAULT_TABLE_NAME, db_name: str = _DEFAULT_CLUSTER_DB_NAME, flag: int = _DEFAULT_VERSION, **kwargs: Any, ) -> Vearch: """Return Vearch VectorStore""" vearch_db = cls( embedding_function=embedding, embedding=embedding, path_or_url=path_or_url, db_name=db_name, table_name=table_name, flag=flag, ) vearch_db.add_texts(texts=texts, metadatas=metadatas) return vearch_db
def _create_table( self, dim: int = 1024, field_list: List[dict] = [ {"field": "text", "type": "str"}, {"field": "metadata", "type": "str"}, ], ) -> int: """ Create VectorStore Table Args: dim:dimension of vector fields_list: the field you want to store Return: code,0 for success,1 for failed """ type_dict = {"int": vearch.dataType.INT, "str": vearch.dataType.STRING} engine_info = { "index_size": 10000, "retrieval_type": "IVFPQ", "retrieval_param": {"ncentroids": 2048, "nsubvector": 32}, } fields = [ vearch.GammaFieldInfo(fi["field"], type_dict[fi["type"]]) for fi in field_list ] vector_field = vearch.GammaVectorInfo( name="text_embedding", type=vearch.dataType.VECTOR, is_index=True, dimension=dim, model_id="", store_type="MemoryOnly", store_param={"cache_size": 10000}, has_source=False, ) response_code = self.vearch.create_table( engine_info, name=self.using_table_name, fields=fields, vector_field=vector_field, ) return response_code def _create_space( self, dim: int = 1024, ) -> int: """ Create VectorStore space Args: dim:dimension of vector Return: code,0 failed for ,1 for success """ space_config = { "name": self.using_table_name, "partition_num": 1, "replica_num": 1, "engine": { "name": "gamma", "index_size": 1, "retrieval_type": "FLAT", "retrieval_param": { "metric_type": "L2", }, }, "properties": { "text": { "type": "string", }, "metadata": { "type": "string", }, "text_embedding": { "type": "vector", "index": True, "dimension": dim, "store_type": "MemoryOnly", }, }, } response_code = self.vearch.create_space(self.using_db_name, space_config) return response_code
[docs] def add_texts( self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any, ) -> List[str]: """ Returns: List of ids from adding the texts into the vectorstore. """ embeddings = None if self.embedding_func is not None: embeddings = self.embedding_func.embed_documents(list(texts)) if embeddings is None: raise ValueError("embeddings is None") if self.flag: dbs_list = self.vearch.list_dbs() if self.using_db_name not in dbs_list: create_db_code = self.vearch.create_db(self.using_db_name) if not create_db_code: raise ValueError("create db failed!!!") space_list = self.vearch.list_spaces(self.using_db_name) if self.using_table_name not in space_list: create_space_code = self._create_space(len(embeddings[0])) if not create_space_code: raise ValueError("create space failed!!!") docid = [] if embeddings is not None and metadatas is not None: for text, metadata, embed in zip(texts, metadatas, embeddings): profiles: dict[str, Any] = {} profiles["text"] = text profiles["metadata"] = metadata["source"] embed_np = np.array(embed) profiles["text_embedding"] = { "feature": (embed_np / np.linalg.norm(embed_np)).tolist() } insert_res = self.vearch.insert_one( self.using_db_name, self.using_table_name, profiles ) if insert_res["status"] == 200: docid.append(insert_res["_id"]) continue else: retry_insert = self.vearch.insert_one( self.using_db_name, self.using_table_name, profiles ) docid.append(retry_insert["_id"]) continue else: table_path = os.path.join( self.using_metapath, self.using_table_name + ".schema" ) if not os.path.exists(table_path): dim = len(embeddings[0]) response_code = self._create_table(dim) if response_code: raise ValueError("create table failed!!!") if embeddings is not None and metadatas is not None: doc_items = [] for text, metadata, embed in zip(texts, metadatas, embeddings): profiles_v: dict[str, Any] = {} profiles_v["text"] = text profiles_v["metadata"] = metadata["source"] embed_np = np.array(embed) profiles_v["text_embedding"] = embed_np / np.linalg.norm(embed_np) doc_items.append(profiles_v) docid = self.vearch.add(doc_items) t_time = 0 while len(docid) != len(embeddings): time.sleep(0.5) if t_time > 6: break t_time += 1 self.vearch.dump() return docid
def _load(self) -> None: """ load vearch engine for standalone vearch """ self.vearch.load()
[docs] @classmethod def load_local( cls, embedding: Embeddings, path_or_url: Optional[str] = None, table_name: str = _DEFAULT_TABLE_NAME, db_name: str = _DEFAULT_CLUSTER_DB_NAME, flag: int = _DEFAULT_VERSION, **kwargs: Any, ) -> Vearch: """Load the local specified table of standalone vearch. Returns: Success or failure of loading the local specified table """ if not path_or_url: raise ValueError("No metadata path!!!") if not table_name: raise ValueError("No table name!!!") table_path = os.path.join(path_or_url, table_name + ".schema") if not os.path.exists(table_path): raise ValueError("vearch vectorbase table not exist!!!") vearch_db = cls( embedding_function=embedding, path_or_url=path_or_url, table_name=table_name, db_name=db_name, flag=flag, ) vearch_db._load() return vearch_db
[docs] def similarity_search_by_vector( self, embedding: List[float], k: int = DEFAULT_TOPN, **kwargs: Any, ) -> List[Document]: """The most k similar documents and scores of the specified query. Args: embeddings: embedding vector of the query. k: The k most similar documents to the text query. min_score: the score of similar documents to the text query Returns: The k most similar documents to the specified text query. 0 is dissimilar, 1 is the most similar. """ embed = np.array(embedding) if self.flag: query_data = { "query": { "sum": [ { "field": "text_embedding", "feature": (embed / np.linalg.norm(embed)).tolist(), } ], }, "size": k, "fields": ["text", "metadata"], } query_result = self.vearch.search( self.using_db_name, self.using_table_name, query_data ) res = query_result["hits"]["hits"] else: query_data = { "vector": [ { "field": "text_embedding", "feature": embed / np.linalg.norm(embed), } ], "fields": [], "is_brute_search": 1, "retrieval_param": {"metric_type": "InnerProduct", "nprobe": 20}, "topn": k, } query_result = self.vearch.search(query_data) res = query_result[0]["result_items"] docs = [] for item in res: content = "" meta_data = {} if self.flag: item = item["_source"] for item_key in item: if item_key == "text": content = item[item_key] continue if item_key == "metadata": meta_data["source"] = item[item_key] continue docs.append(Document(page_content=content, metadata=meta_data)) return docs
[docs] def similarity_search_with_score( self, query: str, k: int = DEFAULT_TOPN, **kwargs: Any, ) -> List[Tuple[Document, float]]: """The most k similar documents and scores of the specified query. Args: embeddings: embedding vector of the query. k: The k most similar documents to the text query. min_score: the score of similar documents to the text query Returns: The k most similar documents to the specified text query. 0 is dissimilar, 1 is the most similar. """ if self.embedding_func is None: raise ValueError("embedding_func is None!!!") embeddings = self.embedding_func.embed_query(query) embed = np.array(embeddings) if self.flag: query_data = { "query": { "sum": [ { "field": "text_embedding", "feature": (embed / np.linalg.norm(embed)).tolist(), } ], }, "size": k, "fields": ["text_embedding", "text", "metadata"], } query_result = self.vearch.search( self.using_db_name, self.using_table_name, query_data ) res = query_result["hits"]["hits"] else: query_data = { "vector": [ { "field": "text_embedding", "feature": embed / np.linalg.norm(embed), } ], "fields": [], "is_brute_search": 1, "retrieval_param": {"metric_type": "InnerProduct", "nprobe": 20}, "topn": k, } query_result = self.vearch.search(query_data) res = query_result[0]["result_items"] results: List[Tuple[Document, float]] = [] for item in res: content = "" meta_data = {} if self.flag: score = item["_score"] item = item["_source"] for item_key in item: if item_key == "text": content = item[item_key] continue if item_key == "metadata": meta_data["source"] = item[item_key] continue if self.flag != 1 and item_key == "score": score = item[item_key] continue tmp_res = (Document(page_content=content, metadata=meta_data), score) results.append(tmp_res) return results
def _similarity_search_with_relevance_scores( self, query: str, k: int = 4, **kwargs: Any, ) -> List[Tuple[Document, float]]: return self.similarity_search_with_score(query, k, **kwargs)
[docs] def delete( self, ids: Optional[List[str]] = None, **kwargs: Any, ) -> Optional[bool]: """Delete the documents which have the specified ids. Args: ids: The ids of the embedding vectors. **kwargs: Other keyword arguments that subclasses might use. Returns: Optional[bool]: True if deletion is successful. False otherwise, None if not implemented. """ ret: Optional[bool] = None tmp_res = [] if ids is None or ids.__len__() == 0: return ret for _id in ids: if self.flag: ret = self.vearch.delete(self.using_db_name, self.using_table_name, _id) else: ret = self.vearch.del_doc(_id) tmp_res.append(ret) ret = all(i == 0 for i in tmp_res) return ret
[docs] def get( self, ids: Optional[List[str]] = None, **kwargs: Any, ) -> Dict[str, Document]: """Return docs according ids. Args: ids: The ids of the embedding vectors. Returns: Documents which satisfy the input conditions. """ results: Dict[str, Document] = {} if ids is None or ids.__len__() == 0: return results if self.flag: query_data = {"query": {"ids": ids}} docs_detail = self.vearch.mget_by_ids( self.using_db_name, self.using_table_name, query_data ) for record in docs_detail: if record["found"] is False: continue content = "" meta_info = {} for field in record["_source"]: if field == "text": content = record["_source"][field] continue elif field == "metadata": meta_info["source"] = record["_source"][field] continue results[record["_id"]] = Document( page_content=content, metadata=meta_info ) else: for id in ids: docs_detail = self.vearch.get_doc_by_id(id) if docs_detail == {}: continue content = "" meta_info = {} for field in docs_detail: if field == "text": content = docs_detail[field] continue elif field == "metadata": meta_info["source"] = docs_detail[field] continue results[docs_detail["_id"]] = Document( page_content=content, metadata=meta_info ) return results