from __future__ import annotations
import logging
import uuid
from typing import (
Any,
Iterable,
List,
Optional,
Tuple,
)
import numpy as np
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.utils import get_from_env
from langchain_core.vectorstores import VectorStore
from langchain_community.vectorstores.utils import maximal_marginal_relevance
logger = logging.getLogger(__name__)
[docs]class DashVector(VectorStore):
"""`DashVector` vector store.
To use, you should have the ``dashvector`` python package installed.
Example:
.. code-block:: python
from langchain_community.vectorstores import DashVector
from langchain_community.embeddings.openai import OpenAIEmbeddings
import dashvector
client = dashvector.Client(api_key="***")
client.create("langchain", dimension=1024)
collection = client.get("langchain")
embeddings = OpenAIEmbeddings()
vectorstore = DashVector(collection, embeddings.embed_query, "text")
"""
[docs] def __init__(
self,
collection: Any,
embedding: Embeddings,
text_field: str,
):
"""Initialize with DashVector collection."""
try:
import dashvector
except ImportError:
raise ValueError(
"Could not import dashvector python package. "
"Please install it with `pip install dashvector`."
)
if not isinstance(collection, dashvector.Collection):
raise ValueError(
f"collection should be an instance of dashvector.Collection, "
f"bug got {type(collection)}"
)
self._collection = collection
self._embedding = embedding
self._text_field = text_field
def _similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query vector, along with scores"""
# query by vector
ret = self._collection.query(embedding, topk=k, filter=filter)
if not ret:
raise ValueError(
f"Fail to query docs by vector, error {self._collection.message}"
)
docs = []
for doc in ret:
metadata = doc.fields
text = metadata.pop(self._text_field)
score = doc.score
docs.append((Document(page_content=text, metadata=metadata), score))
return docs
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
batch_size: int = 25,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of ids associated with the texts.
batch_size: Optional batch size to upsert docs.
kwargs: vectorstore specific parameters
Returns:
List of ids from adding the texts into the vectorstore.
"""
ids = ids or [str(uuid.uuid4().hex) for _ in texts]
text_list = list(texts)
for i in range(0, len(text_list), batch_size):
# batch end
end = min(i + batch_size, len(text_list))
batch_texts = text_list[i:end]
batch_ids = ids[i:end]
batch_embeddings = self._embedding.embed_documents(list(batch_texts))
# batch metadatas
if metadatas:
batch_metadatas = metadatas[i:end]
else:
batch_metadatas = [{} for _ in range(i, end)]
for metadata, text in zip(batch_metadatas, batch_texts):
metadata[self._text_field] = text
# batch upsert to collection
docs = list(zip(batch_ids, batch_embeddings, batch_metadatas))
ret = self._collection.upsert(docs)
if not ret:
raise ValueError(
f"Fail to upsert docs to dashvector vector database,"
f"Error: {ret.message}"
)
return ids
[docs] def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> bool:
"""Delete by vector ID.
Args:
ids: List of ids to delete.
Returns:
True if deletion is successful,
False otherwise.
"""
return bool(self._collection.delete(ids))
[docs] def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to search documents similar to.
k: Number of documents to return. Default to 4.
filter: Doc fields filter conditions that meet the SQL where clause
specification.
Returns:
List of Documents most similar to the query text.
"""
docs_and_scores = self.similarity_search_with_relevance_scores(query, k, filter)
return [doc for doc, _ in docs_and_scores]
[docs] def similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
filter: Optional[str] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query text , alone with relevance scores.
Less is more similar, more is more dissimilar.
Args:
query: input text
k: Number of Documents to return. Defaults to 4.
filter: Doc fields filter conditions that meet the SQL where clause
specification.
Returns:
List of Tuples of (doc, similarity_score)
"""
embedding = self._embedding.embed_query(query)
return self._similarity_search_with_score_by_vector(
embedding, k=k, filter=filter
)
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Doc fields filter conditions that meet the SQL where clause
specification.
Returns:
List of Documents most similar to the query vector.
"""
docs_and_scores = self._similarity_search_with_score_by_vector(
embedding, k, filter
)
return [doc for doc, _ in docs_and_scores]
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[dict] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Doc fields filter conditions that meet the SQL where clause
specification.
Returns:
List of Documents selected by maximal marginal relevance.
"""
embedding = self._embedding.embed_query(query)
return self.max_marginal_relevance_search_by_vector(
embedding, k, fetch_k, lambda_mult, filter
)
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[dict] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Doc fields filter conditions that meet the SQL where clause
specification.
Returns:
List of Documents selected by maximal marginal relevance.
"""
# query by vector
ret = self._collection.query(
embedding, topk=fetch_k, filter=filter, include_vector=True
)
if not ret:
raise ValueError(
f"Fail to query docs by vector, error {self._collection.message}"
)
candidate_embeddings = [doc.vector for doc in ret]
mmr_selected = maximal_marginal_relevance(
np.array(embedding), candidate_embeddings, lambda_mult, k
)
metadatas = [ret.output[i].fields for i in mmr_selected]
return [
Document(page_content=metadata.pop(self._text_field), metadata=metadata)
for metadata in metadatas
]
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
dashvector_api_key: Optional[str] = None,
collection_name: str = "langchain",
text_field: str = "text",
batch_size: int = 25,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> DashVector:
"""Return DashVector VectorStore initialized from texts and embeddings.
This is the quick way to get started with dashvector vector store.
Example:
.. code-block:: python
from langchain_community.vectorstores import DashVector
from langchain_community.embeddings import OpenAIEmbeddings
import dashvector
embeddings = OpenAIEmbeddings()
dashvector = DashVector.from_documents(
docs,
embeddings,
dashvector_api_key="{DASHVECTOR_API_KEY}"
)
"""
try:
import dashvector
except ImportError:
raise ValueError(
"Could not import dashvector python package. "
"Please install it with `pip install dashvector`."
)
dashvector_api_key = dashvector_api_key or get_from_env(
"dashvector_api_key", "DASHVECTOR_API_KEY"
)
dashvector_client = dashvector.Client(api_key=dashvector_api_key)
dashvector_client.delete(collection_name)
collection = dashvector_client.get(collection_name)
if not collection:
dim = len(embedding.embed_query(texts[0]))
# create collection if not existed
resp = dashvector_client.create(collection_name, dimension=dim)
if resp:
collection = dashvector_client.get(collection_name)
else:
raise ValueError(
"Fail to create collection. " f"Error: {resp.message}."
)
dashvector_vector_db = cls(collection, embedding, text_field)
dashvector_vector_db.add_texts(texts, metadatas, ids, batch_size)
return dashvector_vector_db