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Add dashvector vectorstore (#9163)
## Description Add `Dashvector` vectorstore for langchain - [dashvector quick start](https://help.aliyun.com/document_detail/2510223.html) - [dashvector package description](https://pypi.org/project/dashvector/) ## How to use ```python from langchain.vectorstores.dashvector import DashVector dashvector = DashVector.from_documents(docs, embeddings) ``` --------- Co-authored-by: smallrain.xuxy <smallrain.xuxy@alibaba-inc.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
Xiaoyu Xee
GitHub
@@ -33,6 +33,7 @@ from langchain.vectorstores.cassandra import Cassandra
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from langchain.vectorstores.chroma import Chroma
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from langchain.vectorstores.clarifai import Clarifai
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from langchain.vectorstores.clickhouse import Clickhouse, ClickhouseSettings
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from langchain.vectorstores.dashvector import DashVector
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from langchain.vectorstores.deeplake import DeepLake
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from langchain.vectorstores.dingo import Dingo
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from langchain.vectorstores.docarray import DocArrayHnswSearch, DocArrayInMemorySearch
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@@ -83,6 +84,7 @@ __all__ = [
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"Chroma",
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"Clickhouse",
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"ClickhouseSettings",
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"DashVector",
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"DeepLake",
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"Dingo",
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"DocArrayHnswSearch",
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365
libs/langchain/langchain/vectorstores/dashvector.py
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365
libs/langchain/langchain/vectorstores/dashvector.py
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@@ -0,0 +1,365 @@
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"""Wrapper around DashVector vector database."""
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from __future__ import annotations
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import logging
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import uuid
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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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import numpy as np
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from langchain.docstore.document import Document
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from langchain.embeddings.base import Embeddings
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from langchain.utils import get_from_env
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from langchain.vectorstores.base import VectorStore
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from langchain.vectorstores.utils import maximal_marginal_relevance
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logger = logging.getLogger(__name__)
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class DashVector(VectorStore):
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"""Wrapper around DashVector vector database.
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To use, you should have the ``dashvector`` python package installed.
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Example:
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.. code-block:: python
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from langchain.vectorstores import dashvector
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from langchain.embeddings.openai import OpenAIEmbeddings
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import dashvector
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client = dashvector.Client.init(api_key="***")
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client.create("langchain")
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collection = client.get("langchain")
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embeddings = OpenAIEmbeddings()
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vectorstore = Dashvector(collection, embeddings.embed_query, "text")
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"""
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def __init__(
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self,
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collection: Any,
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embedding: Embeddings,
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text_field: str,
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):
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"""Initialize with DashVector collection."""
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try:
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import dashvector
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except ImportError:
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raise ValueError(
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"Could not import dashvector python package. "
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"Please install it with `pip install dashvector`."
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)
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if not isinstance(collection, dashvector.Collection):
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raise ValueError(
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f"collection should be an instance of dashvector.Collection, "
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f"bug got {type(collection)}"
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)
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self._collection = collection
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self._embedding = embedding
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self._text_field = text_field
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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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filter: Optional[str] = None,
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) -> List[Tuple[Document, float]]:
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"""Return docs most similar to query vector, along with scores"""
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# query by vector
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ret = self._collection.query(embedding, topk=k, filter=filter)
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if not ret:
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raise ValueError(
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f"Fail to query docs by vector, error {self._collection.message}"
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)
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docs = []
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for doc in ret:
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metadata = doc.fields
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text = metadata.pop(self._text_field)
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score = doc.score
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docs.append((Document(page_content=text, metadata=metadata), score))
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return docs
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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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ids: Optional[List[str]] = None,
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batch_size: int = 25,
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**kwargs: Any,
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) -> List[str]:
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"""Run more texts through the embeddings and add to the vectorstore.
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Args:
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texts: Iterable of strings to add to the vectorstore.
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metadatas: Optional list of metadatas associated with the texts.
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ids: Optional list of ids associated with the texts.
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batch_size: Optional batch size to upsert docs.
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kwargs: vectorstore specific parameters
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Returns:
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List of ids from adding the texts into the vectorstore.
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"""
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ids = ids or [str(uuid.uuid4().hex) for _ in texts]
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text_list = list(texts)
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for i in range(0, len(text_list), batch_size):
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# batch end
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end = min(i + batch_size, len(text_list))
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batch_texts = text_list[i:end]
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batch_ids = ids[i:end]
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batch_embeddings = self._embedding.embed_documents(list(batch_texts))
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# batch metadatas
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if metadatas:
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batch_metadatas = metadatas[i:end]
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else:
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batch_metadatas = [{} for _ in range(i, end)]
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for metadata, text in zip(batch_metadatas, batch_texts):
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metadata[self._text_field] = text
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# batch upsert to collection
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docs = list(zip(batch_ids, batch_embeddings, batch_metadatas))
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ret = self._collection.upsert(docs)
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if not ret:
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raise ValueError(
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f"Fail to upsert docs to dashvector vector database,"
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f"Error: {ret.message}"
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)
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return ids
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def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> bool:
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"""Delete by vector ID.
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Args:
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ids: List of ids to delete.
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Returns:
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True if deletion is successful,
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False otherwise.
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"""
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return bool(self._collection.delete(ids))
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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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filter: Optional[str] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs most similar to query.
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Args:
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query: Text to search documents similar to.
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k: Number of documents to return. Default to 4.
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filter: Doc fields filter conditions that meet the SQL where clause
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specification.
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Returns:
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List of Documents most similar to the query text.
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"""
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docs_and_scores = self.similarity_search_with_relevance_scores(query, k, filter)
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return [doc for doc, _ in docs_and_scores]
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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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filter: Optional[str] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""Return docs most similar to query text , alone with relevance scores.
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Less is more similar, more is more dissimilar.
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Args:
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query: input text
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k: Number of Documents to return. Defaults to 4.
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filter: Doc fields filter conditions that meet the SQL where clause
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specification.
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Returns:
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List of Tuples of (doc, similarity_score)
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"""
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embedding = self._embedding.embed_query(query)
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return self._similarity_search_with_score_by_vector(
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embedding, k=k, filter=filter
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)
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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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filter: Optional[str] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs most similar to embedding vector.
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Args:
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embedding: Embedding to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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filter: Doc fields filter conditions that meet the SQL where clause
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specification.
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Returns:
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List of Documents most similar to the query vector.
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"""
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docs_and_scores = self._similarity_search_with_score_by_vector(
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embedding, k, filter
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)
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return [doc for doc, _ in 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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filter: Optional[dict] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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among selected documents.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
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lambda_mult: Number between 0 and 1 that determines the degree
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of diversity among the results with 0 corresponding
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to maximum diversity and 1 to minimum diversity.
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Defaults to 0.5.
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filter: Doc fields filter conditions that meet the SQL where clause
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specification.
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Returns:
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List of Documents selected by maximal marginal relevance.
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"""
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embedding = self._embedding.embed_query(query)
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return self.max_marginal_relevance_search_by_vector(
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embedding, k, fetch_k, lambda_mult, filter
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)
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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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filter: Optional[dict] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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among selected documents.
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Args:
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embedding: Embedding to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
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lambda_mult: Number between 0 and 1 that determines the degree
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of diversity among the results with 0 corresponding
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to maximum diversity and 1 to minimum diversity.
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Defaults to 0.5.
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filter: Doc fields filter conditions that meet the SQL where clause
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specification.
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Returns:
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List of Documents selected by maximal marginal relevance.
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"""
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# query by vector
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ret = self._collection.query(
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embedding, topk=fetch_k, filter=filter, include_vector=True
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)
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if not ret:
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raise ValueError(
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f"Fail to query docs by vector, error {self._collection.message}"
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)
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candidate_embeddings = [doc.vector for doc in ret]
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mmr_selected = maximal_marginal_relevance(
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np.array(embedding), candidate_embeddings, lambda_mult, k
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)
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metadatas = [ret.output[i].fields for i in mmr_selected]
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return [
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Document(page_content=metadata.pop(self._text_field), metadata=metadata)
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for metadata in metadatas
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]
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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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dashvector_api_key: Optional[str] = None,
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collection_name: str = "langchain",
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text_field: str = "text",
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batch_size: int = 25,
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ids: Optional[List[str]] = None,
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**kwargs: Any,
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) -> DashVector:
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"""Return DashVector VectorStore initialized from texts and embeddings.
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This is the quick way to get started with dashvector vector store.
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Example:
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.. code-block:: python
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from langchain.vectorstores import DashVector
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from langchain.embeddings import OpenAIEmbeddings
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import dashvector
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embeddings = OpenAIEmbeddings()
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dashvector = DashVector.from_documents(
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docs,
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embeddings,
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dashvector_api_key="{DASHVECTOR_API_KEY}"
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)
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"""
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try:
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import dashvector
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except ImportError:
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raise ValueError(
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"Could not import dashvector python package. "
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"Please install it with `pip install dashvector`."
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)
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dashvector_api_key = dashvector_api_key or get_from_env(
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"dashvector_api_key", "DASHVECTOR_API_KEY"
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)
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dashvector_client = dashvector.Client(api_key=dashvector_api_key)
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dashvector_client.delete(collection_name)
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collection = dashvector_client.get(collection_name)
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if not collection:
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dim = len(embedding.embed_query(texts[0]))
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# create collection if not existed
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resp = dashvector_client.create(collection_name, dimension=dim)
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if resp:
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collection = dashvector_client.get(collection_name)
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else:
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raise ValueError(
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"Fail to create collection. " f"Error: {resp.message}."
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)
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dashvector_vector_db = cls(collection, embedding, text_field)
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dashvector_vector_db.add_texts(texts, metadatas, ids, batch_size)
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return dashvector_vector_db
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@@ -0,0 +1,75 @@
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from time import sleep
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from langchain.schema import Document
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from langchain.vectorstores import DashVector
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from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
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texts = ["foo", "bar", "baz"]
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ids = ["1", "2", "3"]
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def test_dashvector_from_texts() -> None:
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dashvector = DashVector.from_texts(
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texts=texts,
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embedding=FakeEmbeddings(),
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ids=ids,
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)
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# the vector insert operation is async by design, we wait here a bit for the
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# insertion to complete.
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sleep(0.5)
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output = dashvector.similarity_search("foo", k=1)
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assert output == [Document(page_content="foo")]
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def test_dashvector_with_text_with_metadatas() -> None:
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metadatas = [{"meta": i} for i in range(len(texts))]
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dashvector = DashVector.from_texts(
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texts=texts,
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embedding=FakeEmbeddings(),
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metadatas=metadatas,
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ids=ids,
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)
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# the vector insert operation is async by design, we wait here a bit for the
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# insertion to complete.
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sleep(0.5)
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output = dashvector.similarity_search("foo", k=1)
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assert output == [Document(page_content="foo", metadata={"meta": 0})]
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def test_dashvector_search_with_filter() -> None:
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metadatas = [{"meta": i} for i in range(len(texts))]
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dashvector = DashVector.from_texts(
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texts=texts,
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embedding=FakeEmbeddings(),
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metadatas=metadatas,
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ids=ids,
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)
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# the vector insert operation is async by design, we wait here a bit for the
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# insertion to complete.
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sleep(0.5)
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output = dashvector.similarity_search("foo", filter="meta=2")
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assert output == [Document(page_content="baz", metadata={"meta": 2})]
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def test_dashvector_search_with_scores() -> None:
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dashvector = DashVector.from_texts(
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texts=texts,
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embedding=FakeEmbeddings(),
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ids=ids,
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)
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# the vector insert operation is async by design, we wait here a bit for the
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# insertion to complete.
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sleep(0.5)
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output = dashvector.similarity_search_with_relevance_scores("foo")
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docs, scores = zip(*output)
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assert scores[0] < scores[1] < scores[2]
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assert list(docs) == [
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Document(page_content="foo"),
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Document(page_content="bar"),
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Document(page_content="baz"),
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]
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