viruPVS/langchain-docs huggingface dataset:
langchain_community.vectorstores.tencentvectordb.ConnectionParams¶
class langchain_community.vectorstores.tencentvectordb.ConnectionParams(url: str, key: str, username: str = 'root', timeout: int = 10)[source]¶
Tencent vector DB Connection params.
See the following documentation for details:
https://cloud.tencent.com/document/product/1709/95820
Attribute:
url (str)The access address of the vector database serverthat the client needs to connect to.
key (str): API key for client to access the vector database server,which is used for authentication.
username (str) : Account for client to access the vector database server.
timeout (int) : Request Timeout.
Methods
__init__(url, key[, username, timeout])
__init__(url: str, key: str, username: str = 'root', timeout: int = 10)[source]¶
Examples using ConnectionParams¶
Tencent Cloud VectorDB
langchain_community.vectorstores.myscale.MyScaleSettings¶
class langchain_community.vectorstores.myscale.MyScaleSettings[source]¶
Bases: BaseSettings
MyScale client configuration.
Attribute:
myscale_host (str)An URL to connect to MyScale backend.Defaults to ‘localhost’.
myscale_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.
index_type (str): index type string.
index_param (dict): index build parameter.
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 (‘L2’, ‘Cosine’, ‘IP’). Defaults to ‘Cosine’.
column_map (Dict)Column type map to project column name onto langchainsemantics. Must have keys: text, id, vector,
must be same size to number of columns. For example:
.. code-block:: python
{‘id’: ‘text_id’,
‘vector’: ‘text_embedding’,
‘text’: ‘text_plain’,
‘metadata’: ‘metadata_dictionary_in_json’,
}
Defaults to identity map.
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 column_map: Dict[str, str] = {'id': 'id', 'metadata': 'metadata', 'text': 'text', 'vector': 'vector'}¶
param database: str = 'default'¶
param host: str = 'localhost'¶
param index_param: Optional[Dict[str, str]] = None¶
param index_param: Optional[Dict[str, str]] = None¶
param index_type: str = 'MSTG'¶
param metric: str = 'Cosine'¶
param password: Optional[str] = None¶
param port: int = 8443¶
param table: str = 'langchain'¶
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.
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
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
Examples using MyScaleSettings¶
MyScale
langchain_community.vectorstores.redis.schema.read_schema¶
langchain_community.vectorstores.redis.schema.read_schema(index_schema: Optional[Union[Dict[str, List[Any]], str, PathLike]]) → Dict[str, Any][source]¶
Reads in the index schema from a dict or yaml file.
Check if it is a dict and return RedisModel otherwise, check if it’s a path and
read in the file assuming it’s a yaml file and return a RedisModel
langchain_community.vectorstores.zep.ZepVectorStore¶
class langchain_community.vectorstores.zep.ZepVectorStore(collection_name: str, api_url: str, *, api_key: Optional[str] = None, config: Optional[CollectionConfig] = None, embedding: Optional[Embeddings] = None)[source]¶
Zep vector store.
It provides methods for adding texts or documents to the store,
searching for similar documents, and deleting documents.
Search scores are calculated using cosine similarity normalized to [0, 1].
Parameters
api_url (str) – The URL of the Zep API.
collection_name (str) – The name of the collection in the Zep store.
api_key (Optional[str]) – The API key for the Zep API.
config (Optional[CollectionConfig]) – The configuration for the collection.
Required if the collection does not already exist.
embedding (Optional[Embeddings]) – Optional embedding function to use to
embed the texts. Required if the collection is not auto-embedded.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(collection_name, api_url, *[, ...])
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas, document_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_texts(texts[, metadatas, document_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)
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[, metadata, k])
Return docs most similar to query using specified search type.
asimilarity_search(query[, k, metadata])
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 most similar to query.
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance asynchronously.
delete([ids])
Delete by Zep vector UUIDs.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts[, embedding, metadatas, ...])
Class method that returns a ZepVectorStore instance initialized from texts.
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[, metadata, k])
Return docs most similar to query using specified search type.
similarity_search(query[, k, metadata])
Return docs most similar to query.
similarity_search(query[, k, metadata])
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, ...])
Run similarity search with distance.
__init__(collection_name: str, api_url: str, *, api_key: Optional[str] = None, config: Optional[CollectionConfig] = None, embedding: Optional[Embeddings] = 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[str, Any]]] = None, document_ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
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 of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[Dict[str, Any]]] = None, document_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.
document_ids – Optional list of document ids 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¶
Return VectorStore initialized from texts and embeddings.
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
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, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
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
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
)
# 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, metadata: Optional[Dict[str, Any]] = None, k: int = 3, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query.
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Delete by Zep vector UUIDs.
Parameters
ids (Optional[List[str]]) – The UUIDs of the vectors to delete.
Raises
ValueError – If no UUIDs are provided.
Raises
ValueError – If no UUIDs are provided.
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, collection_name: str = '', api_url: str = '', api_key: Optional[str] = None, config: Optional[CollectionConfig] = None, **kwargs: Any) → ZepVectorStore[source]¶
Class method that returns a ZepVectorStore instance initialized from texts.
If the collection does not exist, it will be created.
Parameters
texts (List[str]) – The list of texts to add to the vectorstore.
embedding (Optional[Embeddings]) – Optional embedding function to use to
embed the texts.
metadatas (Optional[List[Dict[str, Any]]]) – Optional list of metadata
associated with the texts.
collection_name (str) – The name of the collection in the Zep store.
api_url (str) – The URL of the Zep API.
api_key (Optional[str]) – The API key for the Zep API.
config (Optional[CollectionConfig]) – The configuration for the collection.
**kwargs – Additional parameters specific to the vectorstore.
Returns
An instance of ZepVectorStore.
Return type
ZepVectorStore
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
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.
Zep determines this automatically and this parameter is
ignored.
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.
metadata – Optional, metadata to filter the resulting set of retrieved docs
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, metadata: Optional[Dict[str, Any]] = 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 – 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.
Zep determines this automatically and this parameter is
ignored.
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.
metadata – Optional, metadata to filter the resulting set of retrieved docs
Returns
List of Documents selected by maximal marginal relevance.
Returns
List of Documents selected by maximal marginal relevance.
search(query: str, search_type: str, metadata: Optional[Dict[str, Any]] = None, k: int = 3, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
similarity_search_by_vector(embedding: List[float], k: int = 4, metadata: Optional[Dict[str, Any]] = 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.
metadata – Optional, metadata filter
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, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
langchain_community.vectorstores.chroma.Chroma¶
class langchain_community.vectorstores.chroma.Chroma(collection_name: str = 'langchain', embedding_function: Optional[Embeddings] = None, persist_directory: Optional[str] = None, client_settings: Optional[chromadb.config.Settings] = None, collection_metadata: Optional[Dict] = None, client: Optional[chromadb.Client] = None, relevance_score_fn: Optional[Callable[[float], float]] = None)[source]¶
ChromaDB vector store.
To use, you should have the chromadb python package installed.
Example
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = Chroma("langchain_store", embeddings)
Initialize with a Chroma client.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__([collection_name, ...])
Initialize with a Chroma client.
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_images(uris[, metadatas, ids])
Run more images through the embeddings and add 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_texts(texts, embedding[, metadatas])
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.
delete([ids])
Delete by vector IDs.
delete_collection()
Delete the collection.
encode_image(uri)
Get base64 string from image URI.
from_documents(documents[, embedding, ids, ...])
Create a Chroma vectorstore from a list of documents.
from_texts(texts[, embedding, metadatas, ...])
Create a Chroma vectorstore from a raw documents.
get([ids, where, limit, offset, ...])
Gets 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.
persist()
Persist the collection.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.