Tony20100/langchain_full huggingface dataset:
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 asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
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, **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
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]]
client_search(client: Any, index_name: str, script_query: Dict, size: int) → Any[source]¶
Parameters
client (Any) – 
index_name (str) – 
script_query (Dict) – 
size (int) – 
Return type
Any
create_index(client: Any, index_name: str, mapping: Dict) → None[source]¶
Parameters
client (Any) – 
index_name (str) – 
mapping (Dict) – 
Return type
None
delete(ids: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Delete by vector IDs.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
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
kwargs (Any) – 
Return type
VST
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, index_name: Optional[str] = None, refresh_indices: bool = True, **kwargs: Any) → ElasticVectorSearch[source]¶
Construct ElasticVectorSearch wrapper from raw documents.
This is a user-friendly interface that:
Embeds documents.
Creates a new index for the embeddings in the Elasticsearch instance.
Adds the documents to the newly created Elasticsearch index.
This is intended to be a quick way to get started.
Example
from langchain_community.vectorstores import ElasticVectorSearch
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
elastic_vector_search = ElasticVectorSearch.from_texts(
    texts,
    embeddings,
    elasticsearch_url="http://localhost:9200"
)
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
ids (Optional[List[str]]) – 
index_name (Optional[str]) – 
refresh_indices (bool) – 
kwargs (Any) – 
Return type
ElasticVectorSearch
static get_user_agent() → str[source]¶
Return type
str
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.
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, **kwargs: Any) → List[Document][source]¶
Return docs 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]) – 
kwargs (Any) – 
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, **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
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, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query.
:param query: Text to look up documents similar to.
:param k: Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query.
Parameters
query (str) – 
k (int) – 
filter (Optional[dict]) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
Examples using ElasticVectorSearch¶
Elasticsearch
langchain_community.vectorstores.infinispanvs.InfinispanVS¶
class langchain_community.vectorstores.infinispanvs.InfinispanVS(embedding: Optional[Embeddings] = None, ids: Optional[List[str]] = None, **kwargs: Any)[source]¶
Infinispan VectorStore interface.
This class exposes the method to present Infinispan as a
VectorStore. It relies on the Infinispan class (below) which takes care
of the REST interface with the server.
Example
… code-block:: pythonfrom langchain_community.vectorstores import InfinispanVS
from mymodels import RGBEmbeddings
…
vectorDb = InfinispanVS.from_documents(docs,
embedding=RGBEmbeddings(),
output_fields=[“texture”, “color”],
lambda_key=lambda text,meta: str(meta[“_key”]),
lambda_content=lambda item: item[“color”])
or an empty InfinispanVS instance can be created if preliminary setup
is required before populating the store
… code-block:: pythonfrom langchain_community.vectorstores import InfinispanVS
from mymodels import RGBEmbeddings
…
ispnVS = InfinispanVS()
# configure Infinispan here
# i.e. create cache and schema
# then populate the store
vectorDb = InfinispanVS.from_documents(docs,
embedding=RGBEmbeddings(),
output_fields: [“texture”, “color”],
lambda_key: lambda text,meta: str(meta[“_key”]),
lambda_content: lambda item: item[“color”]})
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__([embedding, ids])
aadd_documents(documents, **kwargs)
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, last_vector])
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.
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.
cache_clear()
Clear the cache for the vector db :returns: An http Response containing the result of the operation
cache_create([config])
Create the cache for the vector db :param config: configuration of the cache.
cache_delete()
cache_delete()
Delete the cache for the vector db :returns: An http Response containing the result of the operation
cache_exists()
Checks if the cache exists :returns: true if exists
cache_index_clear()
Clear the index for the vector db :returns: An http Response containing the result of the operation
cache_index_reindex()
Rebuild the for the vector db :returns: An http Response containing the result of the operation
config_clear()
configure(metadata, dimension)
delete([ids])
Delete by vector ID or other criteria.
from_documents(documents, embedding, **kwargs)
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.
schema_builder(templ, dimension)
schema_create(proto)
Deploy the schema for the vector db :param proto: protobuf schema :type proto: str
schema_delete()
Delete the schema for the vector db :returns: An http Response containing the result of the operation
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])
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])
Perform a search on a query string and return results with score.
similarity_search_with_score_by_vector(embedding)
similarity_search_with_score_by_vector(embedding)
Return docs most similar to embedding vector.
Parameters
embedding (Optional[Embeddings]) – 
ids (Optional[List[str]]) – 
kwargs (Any) – 
__init__(embedding: Optional[Embeddings] = None, ids: Optional[List[str]] = None, **kwargs: Any)[source]¶
Parameters
embedding (Optional[Embeddings]) – 
ids (Optional[List[str]]) – 
kwargs (Any) – 
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
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
Returns
List of IDs of the added texts.
Return type
List[str]
Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, last_vector: Optional[List[float]] = None, **kwargs: Any) → List[str][source]¶
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.
last_vector (Optional[List[float]]) – 
**kwargs – 
Returns
List of ids from adding the texts into the vectorstore.
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
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]]
cache_clear() → Response[source]¶
Clear the cache for the vector db
:returns: An http Response containing the result of the operation
Return type
Response
cache_create(config: str = '') → Response[source]¶
Create the cache for the vector db
:param config: configuration of the cache.
:type config: str
Returns
An http Response containing the result of the operation
Parameters
config (str) –
An http Response containing the result of the operation
Parameters
config (str) – 
Return type
Response
cache_delete() → Response[source]¶
Delete the cache for the vector db
:returns: An http Response containing the result of the operation
Return type
Response
cache_exists() → bool[source]¶
Checks if the cache exists
:returns: true if exists
Return type
bool
cache_index_clear() → Response[source]¶
Clear the index for the vector db
:returns: An http Response containing the result of the operation
Return type
Response
cache_index_reindex() → Response[source]¶
Rebuild the for the vector db
:returns: An http Response containing the result of the operation
Return type
Response
config_clear() → None[source]¶
Return type
None
configure(metadata: dict, dimension: int) → None[source]¶
Parameters
metadata (dict) – 
dimension (int) – 
Return type
None
delete(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]
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
kwargs (Any) – 
Return type
VST
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, clear_old: Optional[bool] = True, auto_config: Optional[bool] = True, **kwargs: Any) → InfinispanVS[source]¶
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
ids (Optional[List[str]]) – 
clear_old (Optional[bool]) – 
auto_config (Optional[bool]) – 
kwargs (Any) – 
Return type
InfinispanVS
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]
schema_builder(templ: dict, dimension: int) → str[source]¶
Parameters
templ (dict) – 
dimension (int) – 
Return type
str
schema_create(proto: str) → Response[source]¶
Deploy the schema for the vector db
:param proto: protobuf schema
:type proto: str
Returns
An http Response containing the result of the operation
Parameters
proto (str) – 
Return type
Response
schema_delete() → Response[source]¶
Delete the schema for the vector db
:returns: An http Response containing the result of the operation
Return type
Response
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
Parameters
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]
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document][source]¶
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, **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
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]¶
Perform a search on a query string and return results with score.
Parameters
query (str) – The text being searched.
k (int, optional) – The amount of results to return. Defaults to 4.
kwargs (Any) – 
Returns
List[Tuple[Document, float]]
Return type
List[Tuple[Document, float]]
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4) → List[Tuple[Document, float]][source]¶
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.
Returns
List of pair (Documents, score) most similar to the query vector.
Return type
List[Tuple[Document, float]]
Examples using InfinispanVS¶
Infinispan
Infinispan VS
langchain_community.vectorstores.kinetica.KineticaSettings¶
class langchain_community.vectorstores.kinetica.KineticaSettings[source]¶
Bases: BaseSettings
Kinetica client configuration.
Attribute:
host (str)An URL to connect to MyScale backend.Defaults to ‘localhost’.
port (int) : URL port to connect with HTTP. Defaults to 8443.
username (str) : Username to login. Defaults to None.
password (str) : Password to login. Defaults to None.
database (str) : Database name to find the table. Defaults to ‘default’.
table (str) : Table name to operate on.
Defaults to ‘vector_table’.
metric (str)Metric to compute distance,supported are (‘angular’, ‘euclidean’, ‘manhattan’, ‘hamming’,
‘dot’). Defaults to ‘angular’.
https://github.com/spotify/annoy/blob/main/src/annoymodule.cc#L149-L169
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 database: str = 'langchain'¶
param host: str = 'http://127.0.0.1'¶
param metric: str = 'l2'¶
param password: Optional[str] = None¶
param port: int = 9191¶
param table: str = 'langchain_kinetica_embeddings'¶
param username: Optional[str] = None¶
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.