Tony20100/langchain_full huggingface dataset:
kwargs (Any) – 
Return type
None
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_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.
Parameters
index_name (str) – 
embedding (Embeddings) – 
text_key (str) – 
namespace (Optional[str]) – 
pool_threads (int) – 
Return type
Pinecone
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]¶
DEPRECATED: use langchain_pinecone.PineconeVectorStore.from_texts instead:
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.
Example
from langchain_pinecone import PineconeVectorStore
Example
from langchain_pinecone import PineconeVectorStore
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
index_name = "my-index"
namespace = "my-namespace"
vectorstore = Pinecone(
    index_name=index_name,
    embedding=embedding,
    namespace=namespace,
)
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
ids (Optional[List[str]]) – 
batch_size (int) – 
text_key (str) – 
namespace (Optional[str]) – 
index_name (Optional[str]) – 
upsert_kwargs (Optional[dict]) – 
pool_threads (int) – 
embeddings_chunk_size (int) – 
kwargs (Any) – 
Return type
Pinecone
classmethod get_pinecone_index(index_name: Optional[str], pool_threads: int = 4) → Index[source]¶
Return a Pinecone Index instance.
Parameters
index_name (Optional[str]) – Name of the index to use.
pool_threads (int) – Number of threads to use for index upsert.
Returns
Pinecone Index instance.
Return type
Index
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 (str) – Text to look up documents similar to.
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.
Defaults to 0.5.
filter (Optional[dict]) – 
namespace (Optional[str]) – 
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, 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 (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.
filter (Optional[dict]) – 
namespace (Optional[str]) – 
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
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]
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 (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[dict]) – Dictionary of argument(s) to filter on metadata
namespace (Optional[str]) – Namespace to search in. Default will search in ‘’ namespace.
kwargs (Any) – 
Returns
List of Documents most similar to the query and score for each
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]
List of Documents most similar to the query vector.
Return type
List[Document]
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.
Parameters
embedding (List[float]) – 
k (int) – 
filter (Optional[dict]) – 
namespace (Optional[str]) – 
Return type
List[Tuple[Document, float]]
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 (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
Returns
List of Tuples of (doc, similarity_score)
Return type
List[Tuple[Document, float]]
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 (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[dict]) – Dictionary of argument(s) to filter on metadata
langchain_chroma.vectorstores.maximal_marginal_relevance¶
langchain_chroma.vectorstores.maximal_marginal_relevance(query_embedding: ndarray, embedding_list: list, lambda_mult: float = 0.5, k: int = 4) → List[int][source]¶
Calculate maximal marginal relevance.
Parameters
query_embedding (ndarray) – Query embedding.
embedding_list (list) – List of embeddings to select from.
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.
k (int) – Number of Documents to return. Defaults to 4.
Returns
List of indices of embeddings selected by maximal marginal relevance.
Return type
List[int]
langchain_community.vectorstores.pgembedding.BaseModel¶
class langchain_community.vectorstores.pgembedding.BaseModel(**kwargs: Any)[source]¶
Base model for all SQL stores.
A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and
values in kwargs.
Only keys that are present as
attributes of the instance’s class are allowed. These could be,
for example, any mapped columns or relationships.
Attributes
metadata
registry
uuid
Methods
__init__(**kwargs)
A simple constructor that allows initialization from kwargs.
Parameters
kwargs (Any) – 
__init__(**kwargs: Any) → None¶
A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and
values in kwargs.
Only keys that are present as
attributes of the instance’s class are allowed. These could be,
for example, any mapped columns or relationships.
Parameters
self (Any) – 
kwargs (Any) – 
Return type
None
langchain_community.vectorstores.tencentvectordb.MetaField¶
class langchain_community.vectorstores.tencentvectordb.MetaField[source]¶
Bases: BaseModel
MetaData Field for Tencent vector DB.
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 data_type: Union[str, Enum] [Required]¶
param description: Optional[str] = None¶
param index: bool = False¶
param name: str [Required]¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
Parameters
by_alias (bool) – 
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
Model
Examples using MetaField¶
Tencent Cloud VectorDB
langchain_community.vectorstores.azure_cosmos_db.CosmosDBVectorSearchType¶
class langchain_community.vectorstores.azure_cosmos_db.CosmosDBVectorSearchType(value)[source]¶
Cosmos DB Vector Search Type as enumerator.
VECTOR_IVF = 'vector-ivf'¶
IVF vector index
VECTOR_HNSW = 'vector-hnsw'¶
HNSW vector index
Examples using CosmosDBVectorSearchType¶
Azure Cosmos DB
Model caches
langchain_community.vectorstores.redis.schema.RedisField¶
class langchain_community.vectorstores.redis.schema.RedisField[source]¶
Bases: BaseModel
Base class for Redis fields.
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 name: str [Required]¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
langchain_community.vectorstores.tiledb.get_documents_array_uri_from_group¶
langchain_community.vectorstores.tiledb.get_documents_array_uri_from_group(group: Any) → str[source]¶
Get the URI of the documents array from group.
Parameters
group (Any) – TileDB group object.
Returns
URI of the documents array.
Return type
str
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 the documents which have the specified ids.
from_documents(documents, embedding[, ...])
Return Vearch VectorStore
from_texts(texts, embedding[, metadatas, ...])
Return Vearch VectorStore
get([ids])
Return docs according ids.
load_local(embedding[, path_or_url, ...])
Load the local specified table of standalone vearch.
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])
Return docs most similar to query.
similarity_search_by_vector(embedding[, k])
The most k similar documents and scores of the specified query.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k])
The most k similar documents and scores of the specified query.
Parameters
embedding_function (Embeddings) – 
path_or_url (Optional[str]) – 
table_name (str) – 
db_name (str) – 
flag (int) –
db_name (str) – 
flag (int) – 
kwargs (Any) – 
__init__(embedding_function: Embeddings, path_or_url: Optional[str] = None, table_name: str = 'langchain_vearch', db_name: str = 'cluster_client_db', flag: int = 1, **kwargs: Any) → None[source]¶
Initialize vearch vector store
flag 1 for cluster,0 for standalone
Parameters
embedding_function (Embeddings) – 
path_or_url (Optional[str]) – 
table_name (str) – 
db_name (str) – 
flag (int) – 
kwargs (Any) – 
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]
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
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]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]¶
Returns
List of ids from adding the texts into the vectorstore.
Parameters
texts (Iterable[str]) – 
metadatas (Optional[List[dict]]) – 
kwargs (Any) – 
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – 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.
Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
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, **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 (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
Returns
List of Tuples of (doc, similarity_score)
Return type
List[Tuple[Document, float]]
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
Delete the documents which have the specified ids.
Parameters
ids (Optional[List[str]]) – The ids of the embedding vectors.
**kwargs (Any) – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful.
False otherwise, None if not implemented.
Return type
True if deletion is successful.
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Embeddings, path_or_url: Optional[str] = None, table_name: str = 'langchain_vearch', db_name: str = 'cluster_client_db', flag: int = 1, **kwargs: Any) → Vearch[source]¶
Return Vearch VectorStore
Parameters
documents (List[Document]) – 
embedding (Embeddings) – 
path_or_url (Optional[str]) – 
table_name (str) – 
db_name (str) – 
flag (int) – 
kwargs (Any) – 
Return type
Vearch
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, path_or_url: Optional[str] = None, table_name: str = 'langchain_vearch', db_name: str = 'cluster_client_db', flag: int = 1, **kwargs: Any) → Vearch[source]¶
Return Vearch VectorStore
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
path_or_url (Optional[str]) – 
table_name (str) – 
db_name (str) – 
flag (int) – 
kwargs (Any) – 
Return type
Vearch
get(ids: Optional[List[str]] = None, **kwargs: Any) → Dict[str, Document][source]¶
Return docs according ids.
Parameters
ids (Optional[List[str]]) – The ids of the embedding vectors.
kwargs (Any) – 
Returns
Documents which satisfy the input conditions.
Return type
Dict[str, Document]
Returns
Documents which satisfy the input conditions.
Return type
Dict[str, Document]
classmethod load_local(embedding: Embeddings, path_or_url: Optional[str] = None, table_name: str = 'langchain_vearch', db_name: str = 'cluster_client_db', flag: int = 1, **kwargs: Any) → Vearch[source]¶
Load the local specified table of standalone vearch.
:returns: Success or failure of loading the local specified table
Parameters
embedding (Embeddings) – 
path_or_url (Optional[str]) – 
table_name (str) – 
db_name (str) – 
flag (int) – 
kwargs (Any) – 
Return type
Vearch
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.
Defaults to 0.5.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
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]
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]
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query (str) – 
k (int) – 
kwargs (Any) – 
Return type
List[Document]