viruPVS/langchain-docs huggingface dataset:
Attributes
DEFAULT_INDEX_TYPE
embeddings
Access the query embedding object if available.
Methods
__init__(service_url, embedding[, ...])
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_embeddings(texts, embeddings[, ...])
Add embeddings to the vectorstore.
aadd_texts(texts[, metadatas, ids])
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_embeddings(texts, embeddings[, ...])
Add embeddings 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_embeddings(text_embeddings, embedding)
Construct TimescaleVector wrapper from raw documents and pre- generated 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, filter, ...])
Run similarity search with TimescaleVector with distance.
Run similarity search with TimescaleVector with distance.
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], asynchronously.
asimilarity_search_with_score(query[, k, ...])
Return docs most similar to query.
asimilarity_search_with_score_by_vector(...)
create_index([index_type])
date_to_range_filter(**kwargs)
delete([ids])
Delete by vector ID or other criteria.
delete_by_metadata(filter, **kwargs)
Delete by vector ID or other criteria.
drop_index()
drop_tables()
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding)
Construct TimescaleVector wrapper from raw documents and pre- generated embeddings.
from_existing_index(embedding[, ...])
Get instance of an existing TimescaleVector store.This method will return the instance of the store without inserting any new embeddings
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
get_service_url(kwargs)
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.
service_url_from_db_params(host, port, ...)
Return connection string from database parameters.
similarity_search(query[, k, filter, predicates])
Run similarity search with TimescaleVector with distance.
Run similarity search with TimescaleVector with distance.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Return docs most similar to query.
similarity_search_with_score_by_vector(embedding)
__init__(service_url: str, embedding: Embeddings, collection_name: str = 'langchain_store', num_dimensions: int = 1536, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, pre_delete_collection: bool = False, logger: Optional[Logger] = None, relevance_score_fn: Optional[Callable[[float], float]] = None, time_partition_interval: Optional[timedelta] = None, **kwargs: Any) → None[source]¶
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_embeddings(texts: Iterable[str], embeddings: List[List[float]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Add embeddings to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
embeddings – List of list of embedding vectors.
metadatas – List of metadatas associated with the texts.
kwargs – vectorstore specific parameters
kwargs – vectorstore specific parameters
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
kwargs – vectorstore specific parameters
Returns
List of ids from adding the texts into 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_embeddings(texts: Iterable[str], embeddings: List[List[float]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Add embeddings to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
embeddings – List of list of embedding vectors.
metadatas – List of metadatas associated with the texts.
kwargs – vectorstore specific parameters
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
kwargs – vectorstore specific parameters
Returns
kwargs – vectorstore specific parameters
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_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain_store', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → TimescaleVector[source]¶
Construct TimescaleVector wrapper from raw documents and pre-
generated embeddings.
Return VectorStore initialized from documents and embeddings.
Postgres connection string is required
“Either pass it as a parameter
or set the TIMESCALE_SERVICE_URL environment variable.
Example
from langchain_community.vectorstores import TimescaleVector
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
tvs = TimescaleVector.from_embeddings(text_embedding_pairs, embeddings)
tvs = TimescaleVector.from_embeddings(text_embedding_pairs, embeddings)
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain_store', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → TimescaleVector[source]¶
Return VectorStore initialized from texts and embeddings.
Postgres connection string is required
“Either pass it as a parameter
or set the TIMESCALE_SERVICE_URL environment variable.
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
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
VectorStoreRetriever
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.
Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, filter: Optional[Union[dict, list]] = None, predicates: Optional[Predicates] = None, **kwargs: Any) → List[Document][source]¶
Run similarity search with TimescaleVector with distance.
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query.
async asimilarity_search_by_vector(embedding: Optional[List[float]], k: int = 4, filter: Optional[Union[dict, list]] = None, predicates: Optional[Predicates] = None, **kwargs: Any) → List[Document][source]¶
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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query 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
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
async asimilarity_search_with_score(query: str, k: int = 4, filter: Optional[Union[dict, list]] = None, predicates: Optional[Predicates] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query and score for each
async asimilarity_search_with_score_by_vector(embedding: Optional[List[float]], k: int = 4, filter: Optional[Union[dict, list]] = None, predicates: Optional[Predicates] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
create_index(index_type: Union[IndexType, str] = IndexType.TIMESCALE_VECTOR, **kwargs: Any) → None[source]¶
date_to_range_filter(**kwargs: Any) → Any[source]¶
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
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]
delete_by_metadata(filter: Union[Dict[str, str], List[Dict[str, str]]], **kwargs: Any) → Optional[bool][source]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
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]
drop_index() → None[source]¶
drop_tables() → None[source]¶
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain_store', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → TimescaleVector[source]¶
Construct TimescaleVector wrapper from raw documents and pre-
generated embeddings.
Return VectorStore initialized from documents and embeddings.
Postgres connection string is required
“Either pass it as a parameter
or set the TIMESCALE_SERVICE_URL environment variable.
Example
from langchain_community.vectorstores import TimescaleVector
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
tvs = TimescaleVector.from_embeddings(text_embedding_pairs, embeddings)
classmethod from_existing_index(embedding: Embeddings, collection_name: str = 'langchain_store', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, pre_delete_collection: bool = False, **kwargs: Any) → TimescaleVector[source]¶
Get instance of an existing TimescaleVector store.This method will
return the instance of the store without inserting any new
embeddings
return the instance of the store without inserting any new
embeddings
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain_store', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → TimescaleVector[source]¶
Return VectorStore initialized from texts and embeddings.
Postgres connection string is required
“Either pass it as a parameter
or set the TIMESCALE_SERVICE_URL environment variable.
classmethod get_service_url(kwargs: Dict[str, Any]) → str[source]¶
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 – 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, **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.
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.
classmethod service_url_from_db_params(host: str, port: int, database: str, user: str, password: str) → str[source]¶
Return connection string from database parameters.
similarity_search(query: str, k: int = 4, filter: Optional[Union[dict, list]] = None, predicates: Optional[Predicates] = None, **kwargs: Any) → List[Document][source]¶
Run similarity search with TimescaleVector with distance.
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query.
similarity_search_by_vector(embedding: Optional[List[float]], k: int = 4, filter: Optional[Union[dict, list]] = None, predicates: Optional[Predicates] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up documents similar to.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query vector.
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[Union[dict, list]] = None, predicates: Optional[Predicates] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query and score for each
similarity_search_with_score_by_vector(embedding: Optional[List[float]], k: int = 4, filter: Optional[Union[dict, list]] = None, predicates: Optional[Predicates] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
langchain_community.vectorstores.azuresearch.AzureSearchVectorStoreRetriever¶
class langchain_community.vectorstores.azuresearch.AzureSearchVectorStoreRetriever[source]¶
Bases: BaseRetriever
Retriever that uses Azure Cognitive Search.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param k: int = 4¶
Number of documents to return.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the retriever. Defaults to None
This metadata will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a retriever with its
use case.
param search_type: str = 'hybrid'¶
Type of search to perform. Options are “similarity”, “hybrid”,
“semantic_hybrid”.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the retriever. Defaults to None
These tags will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a retriever with its
use case.
param vectorstore: langchain_community.vectorstores.azuresearch.AzureSearch [Required]¶
Azure Search instance used to find similar documents.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
langchain_community.vectorstores.llm_rails.LLMRails¶
class langchain_community.vectorstores.llm_rails.LLMRails(datastore_id: Optional[str] = None, api_key: Optional[str] = None)[source]¶
Implementation of Vector Store using LLMRails.
See https://llmrails.com/
Example
from langchain_community.vectorstores import LLMRails
vectorstore = LLMRails(
    api_key=llm_rails_api_key,
    datastore_id=datastore_id
)
Initialize with LLMRails API.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__([datastore_id, api_key])
Initialize with LLMRails API.
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_files(files_list[, metadatas])
LLMRails provides a way to add documents directly via our API where pre-processing and chunking occurs internally in an optimal way This method provides a way to use that API in LangChain
add_texts(texts[, metadatas])
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.