viruPVS/langchain-docs huggingface dataset:
langchain_community.vectorstores.redis.schema.NumericFieldSchema¶
class langchain_community.vectorstores.redis.schema.NumericFieldSchema[source]¶
Bases: RedisField
Schema for numeric fields in Redis.
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]¶
param no_index: bool = False¶
param sortable: Optional[bool] = False¶
as_field() → NumericField[source]¶
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
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 – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – 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 – set to True to make a deep copy of the model
Returns
new model instance
langchain_community.vectorstores.myscale.has_mul_sub_str¶
langchain_community.vectorstores.myscale.has_mul_sub_str(s: str, *args: Any) → bool[source]¶
Check if a string contains multiple substrings.
:param s: string to check.
:param *args: substrings to check.
Returns
True if all substrings are in the string, False otherwise.
langchain_community.vectorstores.sklearn.BaseSerializer¶
class langchain_community.vectorstores.sklearn.BaseSerializer(persist_path: str)[source]¶
Base class for serializing data.
Methods
__init__(persist_path)
extension()
The file extension suggested by this serializer (without dot).
load()
Loads the data from the persist_path
save(data)
Saves the data to the persist_path
__init__(persist_path: str) → None[source]¶
abstract classmethod extension() → str[source]¶
The file extension suggested by this serializer (without dot).
abstract load() → Any[source]¶
Loads the data from the persist_path
abstract save(data: Any) → None[source]¶
Saves the data to the persist_path
langchain_community.vectorstores.elasticsearch.ExactRetrievalStrategy¶
class langchain_community.vectorstores.elasticsearch.ExactRetrievalStrategy[source]¶
Exact retrieval strategy using the script_score query.
Methods
__init__()
before_index_setup(client, text_field, ...)
Executes before the index is created.
index(dims_length, vector_query_field, ...)
Create the mapping for the Elasticsearch index.
query(query_vector, query, k, fetch_k, ...)
Executes when a search is performed on the store.
require_inference()
Returns whether or not the strategy requires inference to be performed on the text before it is added to the index.
__init__()¶
before_index_setup(client: Elasticsearch, text_field: str, vector_query_field: str) → None¶
Executes before the index is created. Used for setting up
any required Elasticsearch resources like a pipeline.
Parameters
client – The Elasticsearch client.
text_field – The field containing the text data in the index.
vector_query_field – The field containing the vector
representations in the index.
index(dims_length: Optional[int], vector_query_field: str, similarity: Optional[DistanceStrategy]) → Dict[source]¶
Create the mapping for the Elasticsearch index.
query(query_vector: Optional[List[float]], query: Optional[str], k: int, fetch_k: int, vector_query_field: str, text_field: str, filter: Optional[List[dict]], similarity: Optional[DistanceStrategy]) → Dict[source]¶
Executes when a search is performed on the store.
Parameters
query_vector – The query vector,
or None if not using vector-based query.
query – The text query, or None if not using text-based query.
k – The total number of results to retrieve.
fetch_k – The number of results to fetch initially.
fetch_k – The number of results to fetch initially.
vector_query_field – The field containing the vector
representations in the index.
text_field – The field containing the text data in the index.
filter – List of filter clauses to apply to the query.
similarity – The similarity strategy to use, or None if not using one.
Returns
The Elasticsearch query body.
Return type
Dict
require_inference() → bool¶
Returns whether or not the strategy requires inference
to be performed on the text before it is added to the index.
Returns
Whether or not the strategy requires inference
to be performed on the text before it is added to the index.
Return type
bool
langchain_community.vectorstores.hologres.Hologres¶
class langchain_community.vectorstores.hologres.Hologres(connection_string: str, embedding_function: Embeddings, ndims: int = 1536, table_name: str = 'langchain_pg_embedding', pre_delete_table: bool = False, logger: Optional[Logger] = None)[source]¶
Hologres API vector store.
connection_string is a hologres connection string.
embedding_function any embedding function implementinglangchain.embeddings.base.Embeddings interface.
ndims is the number of dimensions of the embedding output.
table_name is the name of the table to store embeddings and data.(default: langchain_pg_embedding)
- NOTE: The table will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
pre_delete_table if True, will delete the table if it exists.(default: False)
- Useful for testing.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(connection_string, embedding_function)
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_embeddings(texts, embeddings, metadatas, ...)
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.
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], asynchronously.
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance asynchronously.
connection_string_from_db_params(host, port, ...)
Return connection string from database parameters.
delete([ids])
Delete by vector ID or other criteria.
from_documents(documents, embedding[, ...])
Return VectorStore initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding)
Construct Hologres wrapper from raw documents and pre- generated embeddings.
from_existing_index(embedding[, ndims, ...])
Get instance of an existing Hologres 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_connection_string(kwargs)
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
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, filter])
Run similarity search with Hologres 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, filter])
Return docs most similar to query.
similarity_search_with_score_by_vector(embedding)
__init__(connection_string: str, embedding_function: Embeddings, ndims: int = 1536, table_name: str = 'langchain_pg_embedding', pre_delete_table: bool = False, logger: Optional[Logger] = None) → 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_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
(List[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: List[dict], ids: List[str], **kwargs: Any) → None[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
List of ids from adding the texts into the vectorstore.
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
classmethod connection_string_from_db_params(host: str, port: int, database: str, user: str, password: str) → str[source]¶
Return connection string from database parameters.
delete(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]
classmethod from_documents(documents: List[Document], embedding: Embeddings, ndims: int = 1536, table_name: str = 'langchain_pg_embedding', ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → Hologres[source]¶
Return VectorStore initialized from documents and embeddings.
Hologres connection string is required
“Either pass it as a parameter
or set the HOLOGRES_CONNECTION_STRING environment variable.
Create the connection string by calling
HologresVector.connection_string_from_db_params
Create the connection string by calling
HologresVector.connection_string_from_db_params
classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ndims: int = 1536, table_name: str = 'langchain_pg_embedding', ids: Optional[List[str]] = None, pre_delete_table: bool = False, **kwargs: Any) → Hologres[source]¶
Construct Hologres wrapper from raw documents and pre-
generated embeddings.
Return VectorStore initialized from documents and embeddings.
Hologres connection string is required
“Either pass it as a parameter
or set the HOLOGRES_CONNECTION_STRING environment variable.
Create the connection string by calling
HologresVector.connection_string_from_db_params
Example
from langchain_community.vectorstores import Hologres
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
faiss = Hologres.from_embeddings(text_embedding_pairs, embeddings)
classmethod from_existing_index(embedding: Embeddings, ndims: int = 1536, table_name: str = 'langchain_pg_embedding', pre_delete_table: bool = False, **kwargs: Any) → Hologres[source]¶
Get instance of an existing Hologres store.This method will
return the instance of the store without inserting any new
embeddings
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ndims: int = 1536, table_name: str = 'langchain_pg_embedding', ids: Optional[List[str]] = None, pre_delete_table: bool = False, **kwargs: Any) → Hologres[source]¶
Return VectorStore initialized from texts and embeddings.
Hologres connection string is required
“Either pass it as a parameter
or set the HOLOGRES_CONNECTION_STRING environment variable.
Create the connection string by calling
HologresVector.connection_string_from_db_params
classmethod get_connection_string(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.
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
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any) → List[Document][source]¶
Run similarity search with Hologres 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: List[float], k: int = 4, filter: Optional[dict] = 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.
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:
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(query: str, k: int = 4, filter: Optional[dict] = None) → 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: List[float], k: int = 4, filter: Optional[dict] = None) → List[Tuple[Document, float]][source]¶
Examples using Hologres¶
Hologres
langchain_community.vectorstores.epsilla.Epsilla¶
class langchain_community.vectorstores.epsilla.Epsilla(client: Any, embeddings: Embeddings, db_path: Optional[str] = '/tmp/langchain-epsilla', db_name: Optional[str] = 'langchain_store')[source]¶
Wrapper around Epsilla vector database.
As a prerequisite, you need to install pyepsilla package
and have a running Epsilla vector database (for example, through our docker image)
See the following documentation for how to run an Epsilla vector database:
https://epsilla-inc.gitbook.io/epsilladb/quick-start
Parameters
client (Any) – Epsilla client to connect to.
embeddings (Embeddings) – Function used to embed the texts.
db_path (Optional[str]) – The path where the database will be persisted.
Defaults to “/tmp/langchain-epsilla”.
db_name (Optional[str]) – Give a name to the loaded database.
Defaults to “langchain_store”.
Example
from langchain_community.vectorstores import Epsilla
from pyepsilla import vectordb
client = vectordb.Client()
embeddings = OpenAIEmbeddings()
db_path = "/tmp/vectorstore"
db_name = "langchain_store"
epsilla = Epsilla(client, embeddings, db_path, db_name)
Initialize with necessary components.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(client, embeddings[, db_path, db_name])
Initialize with necessary components.
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)
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ...])
Embed texts and add them to the database.
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], asynchronously.
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance asynchronously.
clear_data([collection_name])
Clear data in a collection.
delete([ids])
Delete by vector ID or other criteria.
from_documents(documents, embedding[, ...])
Create an Epsilla vectorstore from a list of documents.
from_texts(texts, embedding[, metadatas, ...])
Create an Epsilla vectorstore from raw documents.
get([collection_name, response_fields])
Get the collection.
get([collection_name, response_fields])
Get the collection.
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, collection_name])
Return the documents that are semantically most relevant to the 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(*args, **kwargs)
Run similarity search with distance.
use_collection(collection_name)
Set default collection to use.
__init__(client: Any, embeddings: Embeddings, db_path: Optional[str] = '/tmp/langchain-epsilla', db_name: Optional[str] = 'langchain_store')[source]¶
Initialize with necessary components.
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters