langchain_community.vectorstores.pinecone
.Pinecone¶
- class langchain_community.vectorstores.pinecone.Pinecone(index: Any, embedding: Union[Embeddings, Callable], text_key: str, namespace: Optional[str] = None, distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.COSINE)[source]¶
Pinecone vector store.
To use, you should have the
pinecone-client
python package installed.Example
from langchain_community.vectorstores import Pinecone from langchain_community.embeddings.openai import OpenAIEmbeddings import pinecone # The environment should be the one specified next to the API key # in your Pinecone console pinecone.init(api_key="***", environment="...") index = pinecone.Index("langchain-demo") embeddings = OpenAIEmbeddings() vectorstore = Pinecone(index, embeddings.embed_query, "text")
Initialize with Pinecone client.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__
(index, embedding, text_key[, ...])Initialize with Pinecone client.
aadd_documents
(documents, **kwargs)Run more documents through the embeddings and add to the vectorstore.
aadd_texts
(texts[, metadatas])Run more texts through the embeddings and add to the vectorstore.
add_documents
(documents, **kwargs)Run more documents through the embeddings and add to the vectorstore.
add_texts
(texts[, metadatas, ids, ...])Run more texts through the embeddings and add to the vectorstore.
adelete
([ids])Delete by vector ID or other criteria.
afrom_documents
(documents, embedding, **kwargs)Return VectorStore initialized from documents and embeddings.
afrom_texts
(texts, embedding[, metadatas])Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search
(query[, k, ...])Return docs selected using the maximal marginal relevance.
Return docs selected using the maximal marginal relevance.
as_retriever
(**kwargs)Return VectorStoreRetriever initialized from this VectorStore.
asearch
(query, search_type, **kwargs)Return docs most similar to query using specified search type.
asimilarity_search
(query[, k])Return docs most similar to query.
asimilarity_search_by_vector
(embedding[, k])Return docs most similar to embedding vector.
Return docs and relevance scores in the range [0, 1], asynchronously.
asimilarity_search_with_score
(*args, **kwargs)Run similarity search with distance asynchronously.
delete
([ids, delete_all, namespace, filter])Delete by vector IDs or filter.
from_documents
(documents, embedding, **kwargs)Return VectorStore initialized from documents and embeddings.
from_existing_index
(index_name, embedding[, ...])Load pinecone vectorstore from index name.
from_texts
(texts, embedding[, metadatas, ...])Construct Pinecone wrapper from raw documents.
get_pinecone_index
(index_name[, pool_threads])Return a Pinecone Index instance.
max_marginal_relevance_search
(query[, k, ...])Return docs selected using the maximal marginal relevance.
Return docs selected using the maximal marginal relevance.
search
(query, search_type, **kwargs)Return docs most similar to query using specified search type.
similarity_search
(query[, k, filter, namespace])Return pinecone documents most similar to query.
similarity_search_by_vector
(embedding[, k])Return docs most similar to embedding vector.
Return pinecone documents most similar to embedding, along with scores.
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score
(query[, k, ...])Return pinecone documents most similar to query, along with scores.
- __init__(index: Any, embedding: Union[Embeddings, Callable], text_key: str, namespace: Optional[str] = None, distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.COSINE)[source]¶
Initialize with Pinecone client.
- async aadd_documents(documents: List[Document], **kwargs: Any) List[str] ¶
Run more documents through the embeddings and add to the vectorstore.
- Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
- Returns
List of IDs of the added texts.
- Return type
List[str]
- async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) List[str] ¶
Run more texts through the embeddings and add to the vectorstore.
- add_documents(documents: List[Document], **kwargs: Any) List[str] ¶
Run more documents through the embeddings and add to the vectorstore.
- Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
- Returns
List of IDs of the added texts.
- Return type
List[str]
- add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, namespace: Optional[str] = None, batch_size: int = 32, embedding_chunk_size: int = 1000, **kwargs: Any) List[str] [source]¶
Run more texts through the embeddings and add to the vectorstore.
Upsert optimization is done by chunking the embeddings and upserting them. This is done to avoid memory issues and optimize using HTTP based embeddings. For OpenAI embeddings, use pool_threads>4 when constructing the pinecone.Index, embedding_chunk_size>1000 and batch_size~64 for best performance. :param texts: Iterable of strings to add to the vectorstore. :param metadatas: Optional list of metadatas associated with the texts. :param ids: Optional list of ids to associate with the texts. :param namespace: Optional pinecone namespace to add the texts to. :param batch_size: Batch size to use when adding the texts to the vectorstore. :param embedding_chunk_size: Chunk size to use when embedding the texts.
- Returns
List of ids from adding the texts into the vectorstore.
- async adelete(ids: Optional[List[str]] = None, **kwargs: Any) Optional[bool] ¶
Delete by vector ID or other criteria.
- Parameters
ids – List of ids to delete.
**kwargs – Other keyword arguments that subclasses might use.
- Returns
True if deletion is successful, False otherwise, None if not implemented.
- Return type
Optional[bool]
- async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) VST ¶
Return VectorStore initialized from documents and embeddings.
- async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) VST ¶
Return VectorStore initialized from texts and embeddings.
- async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) List[Document] ¶
Return docs selected using the maximal marginal relevance.
- async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) List[Document] ¶
Return docs selected using the maximal marginal relevance.
- as_retriever(**kwargs: Any) VectorStoreRetriever ¶
Return VectorStoreRetriever initialized from this VectorStore.
- Parameters
search_type (Optional[str]) – Defines the type of search that the Retriever should perform. Can be “similarity” (default), “mmr”, or “similarity_score_threshold”.
search_kwargs (Optional[Dict]) –
Keyword arguments to pass to the search function. Can include things like:
k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold
for similarity_score_threshold
fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
- Returns
Retriever class for VectorStore.
- Return type
Examples:
# Retrieve more documents with higher diversity # Useful if your dataset has many similar documents docsearch.as_retriever( search_type="mmr", search_kwargs={'k': 6, 'lambda_mult': 0.25} ) # Fetch more documents for the MMR algorithm to consider # But only return the top 5 docsearch.as_retriever( search_type="mmr", search_kwargs={'k': 5, 'fetch_k': 50} ) # Only retrieve documents that have a relevance score # Above a certain threshold docsearch.as_retriever( search_type="similarity_score_threshold", search_kwargs={'score_threshold': 0.8} ) # Only get the single most similar document from the dataset docsearch.as_retriever(search_kwargs={'k': 1}) # Use a filter to only retrieve documents from a specific paper docsearch.as_retriever( search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}} )
- async asearch(query: str, search_type: str, **kwargs: Any) List[Document] ¶
Return docs most similar to query using specified search type.
- async asimilarity_search(query: str, k: int = 4, **kwargs: Any) List[Document] ¶
Return docs most similar to query.
- async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document] ¶
Return docs most similar to embedding vector.
- async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) List[Tuple[Document, float]] ¶
Return docs and relevance scores in the range [0, 1], asynchronously.
0 is dissimilar, 1 is most similar.
- Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs –
kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
- Returns
List of Tuples of (doc, similarity_score)
- async asimilarity_search_with_score(*args: Any, **kwargs: Any) List[Tuple[Document, float]] ¶
Run similarity search with distance asynchronously.
- delete(ids: Optional[List[str]] = None, delete_all: Optional[bool] = None, namespace: Optional[str] = None, filter: Optional[dict] = None, **kwargs: Any) None [source]¶
Delete by vector IDs or filter. :param ids: List of ids to delete. :param filter: Dictionary of conditions to filter vectors to delete.
- classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) VST ¶
Return VectorStore initialized from documents and embeddings.
- classmethod from_existing_index(index_name: str, embedding: Embeddings, text_key: str = 'text', namespace: Optional[str] = None, pool_threads: int = 4) Pinecone [source]¶
Load pinecone vectorstore from index name.
- classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, batch_size: int = 32, text_key: str = 'text', namespace: Optional[str] = None, index_name: Optional[str] = None, upsert_kwargs: Optional[dict] = None, pool_threads: int = 4, embeddings_chunk_size: int = 1000, **kwargs: Any) Pinecone [source]¶
Construct Pinecone wrapper from raw documents.
- This is a user friendly interface that:
Embeds documents.
Adds the documents to a provided Pinecone index
This is intended to be a quick way to get started.
The pool_threads affects the speed of the upsert operations. .. rubric:: Example
from langchain_community.vectorstores import Pinecone from langchain_community.embeddings import OpenAIEmbeddings import pinecone # The environment should be the one specified next to the API key # in your Pinecone console pinecone.init(api_key="***", environment="...") embeddings = OpenAIEmbeddings() pinecone = Pinecone.from_texts( texts, embeddings, index_name="langchain-demo" )
- classmethod get_pinecone_index(index_name: Optional[str], pool_threads: int = 4) Index [source]¶
Return a Pinecone Index instance.
- Parameters
index_name – Name of the index to use.
pool_threads – Number of threads to use for index upsert.
- Returns
Pinecone Index instance.
- max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[dict] = None, namespace: Optional[str] = None, **kwargs: Any) List[Document] [source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
- Parameters
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.
- Returns
List of Documents selected by maximal marginal relevance.
- max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[dict] = None, namespace: Optional[str] = None, **kwargs: Any) List[Document] [source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
- Parameters
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.
- Returns
List of Documents selected by maximal marginal relevance.
- search(query: str, search_type: str, **kwargs: Any) List[Document] ¶
Return docs most similar to query using specified search type.
- similarity_search(query: str, k: int = 4, filter: Optional[dict] = None, namespace: Optional[str] = None, **kwargs: Any) List[Document] [source]¶
Return pinecone documents most similar to query.
- Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter – Dictionary of argument(s) to filter on metadata
namespace – Namespace to search in. Default will search in ‘’ namespace.
- Returns
List of Documents most similar to the query and score for each
- similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document] ¶
Return docs most similar to embedding vector.
- Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
- Returns
List of Documents most similar to the query vector.
- similarity_search_by_vector_with_score(embedding: List[float], *, k: int = 4, filter: Optional[dict] = None, namespace: Optional[str] = None) List[Tuple[Document, float]] [source]¶
Return pinecone documents most similar to embedding, along with scores.
- similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) List[Tuple[Document, float]] ¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
- Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs –
kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
- Returns
List of Tuples of (doc, similarity_score)
- similarity_search_with_score(query: str, k: int = 4, filter: Optional[dict] = None, namespace: Optional[str] = None) List[Tuple[Document, float]] [source]¶
Return pinecone documents most similar to query, along with scores.
- Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter – Dictionary of argument(s) to filter on metadata
namespace – Namespace to search in. Default will search in ‘’ namespace.
- Returns
List of Documents most similar to the query and score for each