mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
ed58eeb9c5
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
420 lines
13 KiB
Python
420 lines
13 KiB
Python
"""Wrapper around Vald vector database."""
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from __future__ import annotations
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from typing import Any, Iterable, List, Optional, Tuple, Type
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import numpy as np
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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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from langchain_community.vectorstores.utils import maximal_marginal_relevance
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class Vald(VectorStore):
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"""Wrapper around Vald vector database.
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To use, you should have the ``vald-client-python`` python package installed.
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Example:
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.. code-block:: python
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from langchain_community.embeddings import HuggingFaceEmbeddings
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from langchain_community.vectorstores import Vald
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texts = ['foo', 'bar', 'baz']
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vald = Vald.from_texts(
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texts=texts,
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embedding=HuggingFaceEmbeddings(),
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host="localhost",
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port=8080,
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skip_strict_exist_check=False,
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)
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"""
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def __init__(
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self,
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embedding: Embeddings,
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host: str = "localhost",
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port: int = 8080,
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grpc_options: Tuple = (
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("grpc.keepalive_time_ms", 1000 * 10),
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("grpc.keepalive_timeout_ms", 1000 * 10),
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),
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grpc_use_secure: bool = False,
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grpc_credentials: Optional[Any] = None,
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):
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self._embedding = embedding
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self.target = host + ":" + str(port)
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self.grpc_options = grpc_options
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self.grpc_use_secure = grpc_use_secure
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self.grpc_credentials = grpc_credentials
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@property
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def embeddings(self) -> Optional[Embeddings]:
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return self._embedding
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def _get_channel(self) -> Any:
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try:
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import grpc
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except ImportError:
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raise ValueError(
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"Could not import grpcio python package. "
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"Please install it with `pip install grpcio`."
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)
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return (
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grpc.secure_channel(
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self.target, self.grpc_credentials, options=self.grpc_options
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)
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if self.grpc_use_secure
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else grpc.insecure_channel(self.target, options=self.grpc_options)
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)
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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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grpc_metadata: Optional[Any] = None,
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skip_strict_exist_check: bool = False,
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**kwargs: Any,
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) -> List[str]:
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"""
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Args:
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skip_strict_exist_check: Deprecated. This is not used basically.
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"""
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try:
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from vald.v1.payload import payload_pb2
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from vald.v1.vald import upsert_pb2_grpc
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except ImportError:
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raise ValueError(
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"Could not import vald-client-python python package. "
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"Please install it with `pip install vald-client-python`."
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)
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channel = self._get_channel()
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# Depending on the network quality,
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# it is necessary to wait for ChannelConnectivity.READY.
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# _ = grpc.channel_ready_future(channel).result(timeout=10)
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stub = upsert_pb2_grpc.UpsertStub(channel)
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cfg = payload_pb2.Upsert.Config(skip_strict_exist_check=skip_strict_exist_check)
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ids = []
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embs = self._embedding.embed_documents(list(texts))
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for text, emb in zip(texts, embs):
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vec = payload_pb2.Object.Vector(id=text, vector=emb)
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res = stub.Upsert(
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payload_pb2.Upsert.Request(vector=vec, config=cfg),
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metadata=grpc_metadata,
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)
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ids.append(res.uuid)
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channel.close()
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return ids
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def delete(
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self,
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ids: Optional[List[str]] = None,
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skip_strict_exist_check: bool = False,
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grpc_metadata: Optional[Any] = None,
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**kwargs: Any,
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) -> Optional[bool]:
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"""
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Args:
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skip_strict_exist_check: Deprecated. This is not used basically.
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"""
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try:
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from vald.v1.payload import payload_pb2
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from vald.v1.vald import remove_pb2_grpc
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except ImportError:
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raise ValueError(
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"Could not import vald-client-python python package. "
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"Please install it with `pip install vald-client-python`."
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)
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if ids is None:
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raise ValueError("No ids provided to delete")
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channel = self._get_channel()
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# Depending on the network quality,
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# it is necessary to wait for ChannelConnectivity.READY.
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# _ = grpc.channel_ready_future(channel).result(timeout=10)
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stub = remove_pb2_grpc.RemoveStub(channel)
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cfg = payload_pb2.Remove.Config(skip_strict_exist_check=skip_strict_exist_check)
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for _id in ids:
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oid = payload_pb2.Object.ID(id=_id)
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_ = stub.Remove(
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payload_pb2.Remove.Request(id=oid, config=cfg), metadata=grpc_metadata
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)
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channel.close()
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return True
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def similarity_search(
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self,
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query: str,
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k: int = 4,
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radius: float = -1.0,
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epsilon: float = 0.01,
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timeout: int = 3000000000,
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grpc_metadata: Optional[Any] = None,
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**kwargs: Any,
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) -> List[Document]:
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docs_and_scores = self.similarity_search_with_score(
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query, k, radius, epsilon, timeout, grpc_metadata
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)
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docs = []
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for doc, _ in docs_and_scores:
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docs.append(doc)
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return docs
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def similarity_search_with_score(
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self,
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query: str,
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k: int = 4,
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radius: float = -1.0,
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epsilon: float = 0.01,
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timeout: int = 3000000000,
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grpc_metadata: Optional[Any] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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emb = self._embedding.embed_query(query)
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docs_and_scores = self.similarity_search_with_score_by_vector(
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emb, k, radius, epsilon, timeout, grpc_metadata
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)
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return docs_and_scores
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def similarity_search_by_vector(
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self,
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embedding: List[float],
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k: int = 4,
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radius: float = -1.0,
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epsilon: float = 0.01,
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timeout: int = 3000000000,
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grpc_metadata: Optional[Any] = None,
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**kwargs: Any,
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) -> List[Document]:
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docs_and_scores = self.similarity_search_with_score_by_vector(
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embedding, k, radius, epsilon, timeout, grpc_metadata
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)
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docs = []
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for doc, _ in docs_and_scores:
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docs.append(doc)
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return docs
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def similarity_search_with_score_by_vector(
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self,
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embedding: List[float],
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k: int = 4,
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radius: float = -1.0,
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epsilon: float = 0.01,
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timeout: int = 3000000000,
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grpc_metadata: Optional[Any] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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try:
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from vald.v1.payload import payload_pb2
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from vald.v1.vald import search_pb2_grpc
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except ImportError:
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raise ValueError(
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"Could not import vald-client-python python package. "
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"Please install it with `pip install vald-client-python`."
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)
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channel = self._get_channel()
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# Depending on the network quality,
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# it is necessary to wait for ChannelConnectivity.READY.
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# _ = grpc.channel_ready_future(channel).result(timeout=10)
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stub = search_pb2_grpc.SearchStub(channel)
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cfg = payload_pb2.Search.Config(
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num=k, radius=radius, epsilon=epsilon, timeout=timeout
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)
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res = stub.Search(
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payload_pb2.Search.Request(vector=embedding, config=cfg),
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metadata=grpc_metadata,
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)
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docs_and_scores = []
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for result in res.results:
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docs_and_scores.append((Document(page_content=result.id), result.distance))
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channel.close()
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return docs_and_scores
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def max_marginal_relevance_search(
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self,
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query: str,
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k: int = 4,
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fetch_k: int = 20,
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lambda_mult: float = 0.5,
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radius: float = -1.0,
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epsilon: float = 0.01,
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timeout: int = 3000000000,
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grpc_metadata: Optional[Any] = None,
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**kwargs: Any,
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) -> List[Document]:
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emb = self._embedding.embed_query(query)
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docs = self.max_marginal_relevance_search_by_vector(
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emb,
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k=k,
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fetch_k=fetch_k,
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radius=radius,
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epsilon=epsilon,
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timeout=timeout,
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lambda_mult=lambda_mult,
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grpc_metadata=grpc_metadata,
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)
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return docs
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def max_marginal_relevance_search_by_vector(
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self,
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embedding: List[float],
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k: int = 4,
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fetch_k: int = 20,
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lambda_mult: float = 0.5,
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radius: float = -1.0,
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epsilon: float = 0.01,
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timeout: int = 3000000000,
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grpc_metadata: Optional[Any] = None,
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**kwargs: Any,
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) -> List[Document]:
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try:
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from vald.v1.payload import payload_pb2
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from vald.v1.vald import object_pb2_grpc
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except ImportError:
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raise ValueError(
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"Could not import vald-client-python python package. "
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"Please install it with `pip install vald-client-python`."
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)
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channel = self._get_channel()
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# Depending on the network quality,
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# it is necessary to wait for ChannelConnectivity.READY.
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# _ = grpc.channel_ready_future(channel).result(timeout=10)
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stub = object_pb2_grpc.ObjectStub(channel)
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docs_and_scores = self.similarity_search_with_score_by_vector(
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embedding,
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fetch_k=fetch_k,
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radius=radius,
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epsilon=epsilon,
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timeout=timeout,
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grpc_metadata=grpc_metadata,
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)
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docs = []
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embs = []
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for doc, _ in docs_and_scores:
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vec = stub.GetObject(
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payload_pb2.Object.VectorRequest(
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id=payload_pb2.Object.ID(id=doc.page_content)
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),
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metadata=grpc_metadata,
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)
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embs.append(vec.vector)
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docs.append(doc)
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mmr = maximal_marginal_relevance(
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np.array(embedding),
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embs,
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lambda_mult=lambda_mult,
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k=k,
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)
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channel.close()
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return [docs[i] for i in mmr]
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@classmethod
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def from_texts(
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cls: Type[Vald],
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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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host: str = "localhost",
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port: int = 8080,
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grpc_options: Tuple = (
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("grpc.keepalive_time_ms", 1000 * 10),
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("grpc.keepalive_timeout_ms", 1000 * 10),
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),
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grpc_use_secure: bool = False,
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grpc_credentials: Optional[Any] = None,
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grpc_metadata: Optional[Any] = None,
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skip_strict_exist_check: bool = False,
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**kwargs: Any,
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) -> Vald:
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"""
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Args:
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skip_strict_exist_check: Deprecated. This is not used basically.
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"""
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vald = cls(
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embedding=embedding,
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host=host,
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port=port,
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grpc_options=grpc_options,
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grpc_use_secure=grpc_use_secure,
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grpc_credentials=grpc_credentials,
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**kwargs,
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)
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vald.add_texts(
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texts=texts,
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metadatas=metadatas,
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grpc_metadata=grpc_metadata,
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skip_strict_exist_check=skip_strict_exist_check,
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)
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return vald
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"""We will support if there are any requests."""
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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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# pass
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#
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# def _select_relevance_score_fn(self) -> Callable[[float], float]:
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# pass
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#
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# def _similarity_search_with_relevance_scores(
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# self,
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# query: str,
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# k: int = 4,
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# **kwargs: Any,
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# ) -> List[Tuple[Document, float]]:
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# pass
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#
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# def similarity_search_with_relevance_scores(
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# self,
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# query: str,
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# k: int = 4,
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# **kwargs: Any,
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# ) -> List[Tuple[Document, float]]:
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# pass
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#
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# async def amax_marginal_relevance_search_by_vector(
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# self,
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# embedding: List[float],
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# k: int = 4,
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# fetch_k: int = 20,
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# lambda_mult: float = 0.5,
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# **kwargs: Any,
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# ) -> List[Document]:
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# pass
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#
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# @classmethod
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# async def afrom_texts(
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# cls: Type[VST],
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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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# ) -> VST:
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# pass
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