mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
fa5d49f2c1
ran ```bash g grep -l "langchain.vectorstores" | xargs -L 1 sed -i '' "s/langchain\.vectorstores/langchain_community.vectorstores/g" g grep -l "langchain.document_loaders" | xargs -L 1 sed -i '' "s/langchain\.document_loaders/langchain_community.document_loaders/g" g grep -l "langchain.chat_loaders" | xargs -L 1 sed -i '' "s/langchain\.chat_loaders/langchain_community.chat_loaders/g" g grep -l "langchain.document_transformers" | xargs -L 1 sed -i '' "s/langchain\.document_transformers/langchain_community.document_transformers/g" g grep -l "langchain\.graphs" | xargs -L 1 sed -i '' "s/langchain\.graphs/langchain_community.graphs/g" g grep -l "langchain\.memory\.chat_message_histories" | xargs -L 1 sed -i '' "s/langchain\.memory\.chat_message_histories/langchain_community.chat_message_histories/g" gco master libs/langchain/tests/unit_tests/*/test_imports.py gco master libs/langchain/tests/unit_tests/**/test_public_api.py ```
450 lines
14 KiB
Python
450 lines
14 KiB
Python
import asyncio
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from typing import (
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Any,
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Iterable,
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List,
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Optional,
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Tuple,
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)
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_core.vectorstores import VectorStore
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class SurrealDBStore(VectorStore):
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"""
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SurrealDB as Vector Store.
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To use, you should have the ``surrealdb`` python package installed.
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Args:
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embedding_function: Embedding function to use.
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dburl: SurrealDB connection url
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ns: surrealdb namespace for the vector store. (default: "langchain")
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db: surrealdb database for the vector store. (default: "database")
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collection: surrealdb collection for the vector store.
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(default: "documents")
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(optional) db_user and db_pass: surrealdb credentials
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Example:
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.. code-block:: python
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from langchain_community.vectorstores.surrealdb import SurrealDBStore
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from langchain_community.embeddings import HuggingFaceEmbeddings
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embedding_function = HuggingFaceEmbeddings()
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dburl = "ws://localhost:8000/rpc"
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ns = "langchain"
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db = "docstore"
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collection = "documents"
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db_user = "root"
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db_pass = "root"
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sdb = SurrealDBStore.from_texts(
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texts=texts,
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embedding=embedding_function,
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dburl,
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ns, db, collection,
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db_user=db_user, db_pass=db_pass)
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"""
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def __init__(
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self,
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embedding_function: Embeddings,
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**kwargs: Any,
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) -> None:
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from surrealdb import Surreal
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self.collection = kwargs.pop("collection", "documents")
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self.ns = kwargs.pop("ns", "langchain")
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self.db = kwargs.pop("db", "database")
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self.dburl = kwargs.pop("dburl", "ws://localhost:8000/rpc")
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self.embedding_function = embedding_function
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self.sdb = Surreal(self.dburl)
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self.kwargs = kwargs
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async def initialize(self) -> None:
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"""
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Initialize connection to surrealdb database
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and authenticate if credentials are provided
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"""
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await self.sdb.connect(self.dburl)
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if "db_user" in self.kwargs and "db_pass" in self.kwargs:
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user = self.kwargs.get("db_user")
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password = self.kwargs.get("db_pass")
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await self.sdb.signin({"user": user, "pass": password})
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await self.sdb.use(self.ns, self.db)
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@property
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def embeddings(self) -> Optional[Embeddings]:
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return (
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self.embedding_function
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if isinstance(self.embedding_function, Embeddings)
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else None
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)
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async def aadd_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Add list of text along with embeddings to the vector store asynchronously
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Args:
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texts (Iterable[str]): collection of text to add to the database
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Returns:
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List of ids for the newly inserted documents
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"""
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embeddings = self.embedding_function.embed_documents(list(texts))
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ids = []
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for idx, text in enumerate(texts):
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data = {"text": text, "embedding": embeddings[idx]}
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if metadatas is not None and idx < len(metadatas):
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data["metadata"] = metadatas[idx]
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record = await self.sdb.create(
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self.collection,
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data,
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)
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ids.append(record[0]["id"])
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return ids
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Add list of text along with embeddings to the vector store
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Args:
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texts (Iterable[str]): collection of text to add to the database
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Returns:
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List of ids for the newly inserted documents
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"""
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async def _add_texts(
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> List[str]:
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await self.initialize()
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return await self.aadd_texts(texts, metadatas, **kwargs)
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return asyncio.run(_add_texts(texts, metadatas, **kwargs))
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async def adelete(
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self,
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ids: Optional[List[str]] = None,
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**kwargs: Any,
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) -> Optional[bool]:
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"""Delete by document ID asynchronously.
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Args:
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ids: List of ids to delete.
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**kwargs: Other keyword arguments that subclasses might use.
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Returns:
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Optional[bool]: True if deletion is successful,
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False otherwise.
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"""
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if ids is None:
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await self.sdb.delete(self.collection)
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return True
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else:
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if isinstance(ids, str):
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await self.sdb.delete(ids)
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return True
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else:
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if isinstance(ids, list) and len(ids) > 0:
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_ = [await self.sdb.delete(id) for id in ids]
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return True
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return False
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def delete(
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self,
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ids: Optional[List[str]] = None,
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**kwargs: Any,
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) -> Optional[bool]:
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"""Delete by document ID.
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Args:
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ids: List of ids to delete.
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**kwargs: Other keyword arguments that subclasses might use.
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Returns:
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Optional[bool]: True if deletion is successful,
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False otherwise.
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"""
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async def _delete(ids: Optional[List[str]], **kwargs: Any) -> Optional[bool]:
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await self.initialize()
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return await self.adelete(ids=ids, **kwargs)
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return asyncio.run(_delete(ids, **kwargs))
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async def _asimilarity_search_by_vector_with_score(
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self, embedding: List[float], k: int = 4, **kwargs: Any
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) -> List[Tuple[Document, float]]:
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"""Run similarity search for query embedding asynchronously
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and return documents and scores
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Args:
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embedding (List[float]): Query embedding.
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k (int): Number of results to return. Defaults to 4.
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Returns:
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List of Documents most similar along with scores
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"""
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args = {
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"collection": self.collection,
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"embedding": embedding,
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"k": k,
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"score_threshold": kwargs.get("score_threshold", 0),
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}
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query = """select id, text, metadata,
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vector::similarity::cosine(embedding,{embedding}) as similarity
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from {collection}
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where vector::similarity::cosine(embedding,{embedding}) >= {score_threshold}
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order by similarity desc LIMIT {k}
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""".format(**args)
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results = await self.sdb.query(query)
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if len(results) == 0:
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return []
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return [
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(
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Document(
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page_content=result["text"],
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metadata={"id": result["id"], **result["metadata"]},
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),
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result["similarity"],
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)
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for result in results[0]["result"]
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]
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async def asimilarity_search_with_relevance_scores(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Tuple[Document, float]]:
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"""Run similarity search asynchronously and return relevance scores
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Args:
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query (str): Query
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k (int): Number of results to return. Defaults to 4.
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Returns:
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List of Documents most similar along with relevance scores
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"""
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query_embedding = self.embedding_function.embed_query(query)
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return [
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(document, similarity)
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for document, similarity in (
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await self._asimilarity_search_by_vector_with_score(
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query_embedding, k, **kwargs
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)
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)
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]
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def similarity_search_with_relevance_scores(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Tuple[Document, float]]:
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"""Run similarity search synchronously and return relevance scores
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Args:
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query (str): Query
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k (int): Number of results to return. Defaults to 4.
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Returns:
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List of Documents most similar along with relevance scores
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"""
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async def _similarity_search_with_relevance_scores() -> (
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List[Tuple[Document, float]]
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):
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await self.initialize()
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return await self.asimilarity_search_with_relevance_scores(
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query, k, **kwargs
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)
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return asyncio.run(_similarity_search_with_relevance_scores())
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async def asimilarity_search_with_score(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Tuple[Document, float]]:
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"""Run similarity search asynchronously and return distance scores
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Args:
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query (str): Query
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k (int): Number of results to return. Defaults to 4.
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Returns:
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List of Documents most similar along with relevance distance scores
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"""
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query_embedding = self.embedding_function.embed_query(query)
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return [
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(document, similarity)
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for document, similarity in (
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await self._asimilarity_search_by_vector_with_score(
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query_embedding, k, **kwargs
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)
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)
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]
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def similarity_search_with_score(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Tuple[Document, float]]:
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"""Run similarity search synchronously and return distance scores
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Args:
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query (str): Query
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k (int): Number of results to return. Defaults to 4.
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Returns:
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List of Documents most similar along with relevance distance scores
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"""
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async def _similarity_search_with_score() -> List[Tuple[Document, float]]:
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await self.initialize()
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return await self.asimilarity_search_with_score(query, k, **kwargs)
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return asyncio.run(_similarity_search_with_score())
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async def asimilarity_search_by_vector(
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self, embedding: List[float], k: int = 4, **kwargs: Any
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) -> List[Document]:
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"""Run similarity search on query embedding asynchronously
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Args:
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embedding (List[float]): Query embedding
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k (int): Number of results to return. Defaults to 4.
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Returns:
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List of Documents most similar to the query
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"""
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return [
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document
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for document, _ in await self._asimilarity_search_by_vector_with_score(
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embedding, k, **kwargs
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)
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]
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def similarity_search_by_vector(
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self, embedding: List[float], k: int = 4, **kwargs: Any
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) -> List[Document]:
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"""Run similarity search on query embedding
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Args:
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embedding (List[float]): Query embedding
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k (int): Number of results to return. Defaults to 4.
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Returns:
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List of Documents most similar to the query
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"""
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async def _similarity_search_by_vector() -> List[Document]:
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await self.initialize()
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return await self.asimilarity_search_by_vector(embedding, k, **kwargs)
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return asyncio.run(_similarity_search_by_vector())
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async def asimilarity_search(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Document]:
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"""Run similarity search on query asynchronously
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Args:
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query (str): Query
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k (int): Number of results to return. Defaults to 4.
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Returns:
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List of Documents most similar to the query
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"""
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query_embedding = self.embedding_function.embed_query(query)
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return await self.asimilarity_search_by_vector(query_embedding, k, **kwargs)
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def similarity_search(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Document]:
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"""Run similarity search on query
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Args:
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query (str): Query
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k (int): Number of results to return. Defaults to 4.
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Returns:
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List of Documents most similar to the query
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"""
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async def _similarity_search() -> List[Document]:
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await self.initialize()
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return await self.asimilarity_search(query, k, **kwargs)
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return asyncio.run(_similarity_search())
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@classmethod
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async def afrom_texts(
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cls,
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texts: List[str],
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embedding: Embeddings,
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> "SurrealDBStore":
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"""Create SurrealDBStore from list of text asynchronously
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Args:
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texts (List[str]): list of text to vectorize and store
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embedding (Optional[Embeddings]): Embedding function.
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dburl (str): SurrealDB connection url
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(default: "ws://localhost:8000/rpc")
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ns (str): surrealdb namespace for the vector store.
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(default: "langchain")
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db (str): surrealdb database for the vector store.
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(default: "database")
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collection (str): surrealdb collection for the vector store.
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(default: "documents")
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(optional) db_user and db_pass: surrealdb credentials
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Returns:
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SurrealDBStore object initialized and ready for use."""
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sdb = cls(embedding, **kwargs)
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await sdb.initialize()
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await sdb.aadd_texts(texts, metadatas, **kwargs)
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return sdb
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@classmethod
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def from_texts(
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cls,
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texts: List[str],
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embedding: Embeddings,
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> "SurrealDBStore":
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"""Create SurrealDBStore from list of text
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Args:
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texts (List[str]): list of text to vectorize and store
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embedding (Optional[Embeddings]): Embedding function.
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dburl (str): SurrealDB connection url
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ns (str): surrealdb namespace for the vector store.
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(default: "langchain")
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db (str): surrealdb database for the vector store.
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(default: "database")
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collection (str): surrealdb collection for the vector store.
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(default: "documents")
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(optional) db_user and db_pass: surrealdb credentials
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Returns:
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SurrealDBStore object initialized and ready for use."""
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sdb = asyncio.run(cls.afrom_texts(texts, embedding, metadatas, **kwargs))
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return sdb
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