Tony20100/langchain_full huggingface dataset:
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – 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.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
async amax_marginal_relevance_search_with_score(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, *, filters: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Perform a search and return results that are reordered by MMR.
Parameters
query (str) – Text to look up documents similar to.
k (int, optional) – How many results to give. Defaults to 4.
fetch_k (int, optional) – Total results to select k from.
Defaults to 20.
lambda_mult (float) – 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
filters (str, optional) – Filtering expression. Defaults to None.
kwargs (Any) – 
Returns
List of Documents most similarto the query and score for each
Return type
List[Tuple[Document, float]]
as_retriever(**kwargs: Any) → AzureSearchVectorStoreRetriever[source]¶
Return AzureSearchVectorStoreRetriever initialized from this VectorStore.
Parameters
Return AzureSearchVectorStoreRetriever initialized from this VectorStore.
Parameters
search_type (Optional[str]) – Defines the type of search that
the Retriever should perform.
Can be “similarity” (default), “hybrid”, or
”semantic_hybrid”.
search_kwargs (Optional[Dict]) – Keyword arguments to pass to the
search function. Can include things like:
score_threshold: Minimum relevance thresholdfor 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
kwargs (Any) – 
Returns
Retriever class for VectorStore.
Return type
AzureSearchVectorStoreRetriever
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
Parameters
query (str) – Input text.
search_type (str) – Type of search to perform. Can be “similarity”,
“mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
Return type
List[Document]
async asemantic_hybrid_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Returns the most similar indexed documents to the query text.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
filters – Filtering expression.
kwargs (Any) – 
Returns
A list of documents that are most similar to the query text.
Return type
List[Document]
Return type
List[Document]
async asemantic_hybrid_search_with_score(query: str, k: int = 4, score_type: Literal['score', 'reranker_score'] = 'score', *, score_threshold: Optional[float] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Returns the most similar indexed documents to the query text.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
score_type (Literal['score', 'reranker_score']) – Must either be “score” or “reranker_score”.
Defaulted to “score”.
filters – Filtering expression.
score_threshold (Optional[float]) – 
kwargs (Any) – 
Returns
A list of documents and theircorresponding scores.
Return type
List[Tuple[Document, float]]
async asemantic_hybrid_search_with_score_and_rerank(query: str, k: int = 4, *, filters: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float, float]][source]¶
Return docs most similar to query with a hybrid query.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filters (Optional[str]) – Filtering expression.
kwargs (Any) – 
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[Document, float, float]]
async asimilarity_search(query: str, k: int = 4, *, search_type: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
Return docs most similar to query.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
search_type (Optional[str]) – 
kwargs (Any) – 
Returns
List of Documents most similar to the query.
Return type
List[Document]
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
kwargs (Any) – 
Returns
List of Documents most similar to the query vector.
Return type
List[Document]
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, *, score_threshold: Optional[float] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – 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
score_threshold (Optional[float]) – 
**kwargs – 
Returns
List of Tuples of (doc, similarity_score)
Return type
List[Tuple[Document, float]]
async asimilarity_search_with_score(query: str, *, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
Parameters
query (str) – 
k (int) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
async avector_search(query: str, k: int = 4, *, filters: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Returns the most similar indexed documents to the query text.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
filters (Optional[str]) – 
kwargs (Any) – 
Returns
A list of documents that are most similar to the query text.
Return type
List[Document]
async avector_search_with_score(query: str, k: int = 4, filters: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query.
Parameters
query (str) – Text to look up documents similar to.
k (int, optional) – Number of Documents to return. Defaults to 4.
filters (str, optional) – Filtering expression. Defaults to None.
kwargs (Any) – 
Returns
List of Documents most similarto the query and score for each
Return type
List[Tuple[Document, float]]
delete(ids: Optional[List[str]] = None, **kwargs: Any) → bool[source]¶
Delete by vector ID.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
kwargs (Any) – 
Returns
True if deletion is successful,
False otherwise.
Return type
bool
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
classmethod from_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, *, azure_search_endpoint: str = '', azure_search_key: str = '', index_name: str = 'langchain-index', fields: Optional[List[SearchField]] = None, **kwargs: Any) → AzureSearch[source]¶
Parameters
text_embeddings (Iterable[Tuple[str, List[float]]]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
azure_search_endpoint (str) – 
azure_search_key (str) – 
index_name (str) – 
fields (Optional[List[SearchField]]) – 
kwargs (Any) – 
Return type
AzureSearch
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, azure_search_endpoint: str = '', azure_search_key: str = '', index_name: str = 'langchain-index', fields: Optional[List[SearchField]] = None, **kwargs: Any) → AzureSearch[source]¶
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
azure_search_endpoint (str) – 
azure_search_key (str) – 
index_name (str) – 
fields (Optional[List[SearchField]]) –
index_name (str) – 
fields (Optional[List[SearchField]]) – 
kwargs (Any) – 
Return type
AzureSearch
hybrid_max_marginal_relevance_search_with_score(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, *, filters: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query with a hybrid queryand reorder results by MMR.
Parameters
query (str) – Text to look up documents similar to.
k (int, optional) – Number of Documents to return. Defaults to 4.
fetch_k (int, optional) – Total results to select k from.
Defaults to 20.
lambda_mult (float) – 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
filters (str, optional) – Filtering expression. Defaults to None.
kwargs (Any) – 
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[Document, float]]
hybrid_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Returns the most similar indexed documents to the query text.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
kwargs (Any) – 
Returns
A list of documents that are most similar to the query text.
Return type
List[Document]
Return type
List[Document]
hybrid_search_with_relevance_scores(query: str, k: int = 4, *, score_threshold: Optional[float] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Parameters
query (str) – 
k (int) – 
score_threshold (Optional[float]) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
hybrid_search_with_score(query: str, k: int = 4, filters: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query with a hybrid query.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filters (Optional[str]) – 
kwargs (Any) – 
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[Document, float]]
max_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.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – 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.
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
max_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.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – 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.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
max_marginal_relevance_search_with_score(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, *, filters: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Perform a search and return results that are reordered by MMR.
Parameters
query (str) – Text to look up documents similar to.
k (int, optional) – How many results to give. Defaults to 4.
fetch_k (int, optional) – Total results to select k from.
Defaults to 20.
Defaults to 20.
lambda_mult (float) – 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
filters (str, optional) – Filtering expression. Defaults to None.
kwargs (Any) – 
Returns
List of Documents most similarto the query and score for each
Return type
List[Tuple[Document, float]]
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
Parameters
query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”,
“mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
Return type
List[Document]
semantic_hybrid_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Returns the most similar indexed documents to the query text.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
filters – Filtering expression.
kwargs (Any) – 
Returns
A list of documents that are most similar to the query text.
Return type
List[Document]
semantic_hybrid_search_with_score(query: str, k: int = 4, score_type: Literal['score', 'reranker_score'] = 'score', *, score_threshold: Optional[float] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Returns the most similar indexed documents to the query text.
Parameters
Returns the most similar indexed documents to the query text.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
score_type (Literal['score', 'reranker_score']) – Must either be “score” or “reranker_score”.
Defaulted to “score”.
filters – Filtering expression.
score_threshold (Optional[float]) – 
kwargs (Any) – 
Returns
A list of documents and theircorresponding scores.
Return type
List[Tuple[Document, float]]
semantic_hybrid_search_with_score_and_rerank(query: str, k: int = 4, *, filters: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float, float]][source]¶
Return docs most similar to query with a hybrid query.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filters (Optional[str]) – Filtering expression.
kwargs (Any) – 
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[Document, float, float]]
similarity_search(query: str, k: int = 4, *, search_type: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
search_type (Optional[str]) – 
kwargs (Any) – 
Returns
List of Documents most similar to the query.
Return type
List[Document]
Returns
List of Documents most similar to the query.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
kwargs (Any) – 
Returns
List of Documents most similar to the query vector.
Return type
List[Document]
similarity_search_with_relevance_scores(query: str, k: int = 4, *, score_threshold: Optional[float] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – 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
score_threshold (Optional[float]) – 
**kwargs – 
Returns
List of Tuples of (doc, similarity_score)
Return type
List[Tuple[Document, float]]
similarity_search_with_score(query: str, *, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
Parameters
query (str) – 
k (int) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
vector_search(query: str, k: int = 4, *, filters: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Returns the most similar indexed documents to the query text.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
filters (Optional[str]) – 
kwargs (Any) – 
Returns
A list of documents that are most similar to the query text.
Return type
List[Document]
vector_search_with_score(query: str, k: int = 4, filters: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query.
Parameters
query (str) – Text to look up documents similar to.
k (int, optional) – Number of Documents to return. Defaults to 4.
filters (str, optional) – Filtering expression. Defaults to None.
kwargs (Any) – 
Returns
List of Documents most similarto the query and score for each
Return type
List[Tuple[Document, float]]
Examples using AzureSearch¶
Azure AI Search
langchain_community.vectorstores.manticore_search.ManticoreSearch¶
class langchain_community.vectorstores.manticore_search.ManticoreSearch(embedding: Embeddings, *, config: Optional[ManticoreSearchSettings] = None, **kwargs: Any)[source]¶
ManticoreSearch Engine vector store.
To use, you should have the manticoresearch python package installed.
Example
from langchain_community.vectorstores import Manticore
from langchain_community.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = ManticoreSearch(embeddings)
ManticoreSearch Wrapper to LangChain
Parameters
embedding (Embeddings) – Text embedding model.
config (ManticoreSearchSettings) – Configuration of ManticoreSearch Client
**kwargs – Other keyword arguments will pass into Configuration of API client
manticoresearch-python. See
https://github.com/manticoresoftware/manticoresearch-python for more.
Attributes
embeddings
Access the query embedding object if available.
metadata_column
Methods
__init__(embedding, *[, config])
ManticoreSearch Wrapper to LangChain
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, batch_size, ...])
Insert 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.
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.
amax_marginal_relevance_search_by_vector(...)
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.
asimilarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
delete([ids])
Delete by vector ID or other criteria.
drop()
Helper function: Drop data
from_documents(documents, embedding, *[, ...])
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
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])
Perform a similarity search with ManticoreSearch
similarity_search_by_vector(embedding[, k])
similarity_search_by_vector(embedding[, k])
Perform a similarity search with ManticoreSearch by vectors
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
__init__(embedding: Embeddings, *, config: Optional[ManticoreSearchSettings] = None, **kwargs: Any) → None[source]¶
ManticoreSearch Wrapper to LangChain
Parameters
embedding (Embeddings) – Text embedding model.
config (ManticoreSearchSettings) – Configuration of ManticoreSearch Client
**kwargs – Other keyword arguments will pass into Configuration of API client
manticoresearch-python. See
https://github.com/manticoresoftware/manticoresearch-python for more.
Return type
None
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
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.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters.
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]