Tony20100/langchain_full huggingface dataset:
Return type
List[Document]
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
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][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]
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]][source]¶
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]][source]¶
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 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[source]¶
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
abstract classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST[source]¶
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
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]
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][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.
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][source]¶
Return docs most similar to query using specified search type.
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]
abstract similarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
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]
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]][source]¶
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(*args: Any, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
Examples using VectorStore¶
Fleet AI Context
langchain_community.vectorstores.redis.schema.RedisVectorField¶
class langchain_community.vectorstores.redis.schema.RedisVectorField[source]¶
Bases: RedisField
Base class for Redis vector 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 algorithm: object [Required]¶
param datatype: str = 'FLOAT32'¶
param dims: int [Required]¶
param distance_metric: RedisDistanceMetric = 'COSINE'¶
param initial_cap: Optional[int] = None¶
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
langchain_chroma.vectorstores.Chroma¶
class langchain_chroma.vectorstores.Chroma(collection_name: str = 'langchain', embedding_function: Optional[Embeddings] = None, persist_directory: Optional[str] = None, client_settings: Optional[Settings] = None, collection_metadata: Optional[Dict] = None, client: Optional[ClientAPI] = None, relevance_score_fn: Optional[Callable[[float], float]] = None, create_collection_if_not_exists: Optional[bool] = True)[source]¶
ChromaDB vector store.
To use, you should have the chromadb python package installed.
Example
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = Chroma("langchain_store", embeddings)
Initialize with a Chroma client.
Parameters
collection_name (str) – Name of the collection to create.
embedding_function (Optional[Embeddings]) – Embedding class object. Used to embed texts.
persist_director – Directory to persist the collection.
client_settings (Optional[chromadb.config.Settings]) – Chroma client settings
collection_metadata (Optional[Dict]) – Collection configurations.
client (Optional[chromadb.ClientAPI]) – Chroma client. Documentation:
https://docs.trychroma.com/reference/js-client#class:-chromaclient
relevance_score_fn (Optional[Callable[[float], float]]) – Function to calculate relevance score from distance.
Used only in similarity_search_with_relevance_scores
create_collection_if_not_exists (Optional[bool]) – Whether to create collection
if it doesn’t exist. Defaults to True.
persist_directory (Optional[str]) – 
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__([collection_name, ...])
Initialize with a Chroma client.
__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])
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.
delete([ids])
Run similarity search with distance.
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.
reset_collection()
Resets the collection by deleting the collection and recreating an empty one.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
Run similarity search with Chroma.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
similarity_search_by_vector_with_relevance_scores(...)
Return docs most similar to embedding vector and similarity score.
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 Chroma with distance.
update_document(document_id, document)
Update a document in the collection.
update_documents(ids, documents)
Update a document in the collection.
update_documents(ids, documents)
Update a document in the collection.
__init__(collection_name: str = 'langchain', embedding_function: Optional[Embeddings] = None, persist_directory: Optional[str] = None, client_settings: Optional[Settings] = None, collection_metadata: Optional[Dict] = None, client: Optional[ClientAPI] = None, relevance_score_fn: Optional[Callable[[float], float]] = None, create_collection_if_not_exists: Optional[bool] = True) → None[source]¶
Initialize with a Chroma client.
Parameters
collection_name (str) – Name of the collection to create.
embedding_function (Optional[Embeddings]) – Embedding class object. Used to embed texts.
persist_director – Directory to persist the collection.
client_settings (Optional[Settings]) – Chroma client settings
collection_metadata (Optional[Dict]) – Collection configurations.
client (Optional[ClientAPI]) – Chroma client. Documentation:
https://docs.trychroma.com/reference/js-client#class:-chromaclient
relevance_score_fn (Optional[Callable[[float], float]]) – Function to calculate relevance score from distance.
Used only in similarity_search_with_relevance_scores
create_collection_if_not_exists (Optional[bool]) – Whether to create collection
if it doesn’t exist. Defaults to True.
persist_directory (Optional[str]) – 
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]
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]
add_images(uris: List[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Run more images through the embeddings and add to the vectorstore.
Parameters
uris (List[str]) – File path to the image.
metadatas (Optional[List[dict]]) – Optional list of metadatas.
When querying, you can filter on this metadata.
ids (Optional[List[str]]) – Optional list of IDs.
kwargs (Any) – 
Returns
List of IDs of the added images.
Raises
ValueError – When metadata is incorrect.
Return type
List[str]
Raises
ValueError – When metadata is incorrect.
Return type
List[str]
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[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas.
When querying, you can filter on this metadata.
ids (Optional[List[str]]) – Optional list of IDs.
kwargs (Any) – 
Returns
List of IDs of the added texts.
Raises
ValueError – When metadata is incorrect.
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]]
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
delete_collection() → None[source]¶
Delete the collection.
Return type
None
encode_image(uri: str) → str[source]¶
Return type
None
encode_image(uri: str) → str[source]¶
Get base64 string from image URI.
Parameters
uri (str) – 
Return type
str
classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, ids: Optional[List[str]] = None, collection_name: str = 'langchain', persist_directory: Optional[str] = None, client_settings: Optional[Settings] = None, client: Optional[ClientAPI] = None, collection_metadata: Optional[Dict] = None, **kwargs: Any) → Chroma[source]¶
Create a Chroma vectorstore from a list of documents.
If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephemeral in-memory.
Parameters
collection_name (str) – Name of the collection to create.
persist_directory (Optional[str]) – Directory to persist the collection.
ids (Optional[List[str]]) – List of document IDs. Defaults to None.
documents (List[Document]) – List of documents to add to the vectorstore.
embedding (Optional[Embeddings]) – Embedding function. Defaults to None.
client_settings (Optional[Settings]) – Chroma client settings
collection_metadata (Optional[Dict]) – Collection configurations.
Defaults to None.
client (Optional[ClientAPI]) – 
kwargs (Any) – 
Returns
Chroma vectorstore.
Return type
Chroma
Returns
Chroma vectorstore.
Return type
Chroma
classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, collection_name: str = 'langchain', persist_directory: Optional[str] = None, client_settings: Optional[Settings] = None, client: Optional[ClientAPI] = None, collection_metadata: Optional[Dict] = None, **kwargs: Any) → Chroma[source]¶
Create a Chroma vectorstore from a raw documents.
If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephemeral in-memory.
Parameters
texts (List[str]) – List of texts to add to the collection.
collection_name (str) – Name of the collection to create.
persist_directory (Optional[str]) – Directory to persist the collection.
embedding (Optional[Embeddings]) – Embedding function. Defaults to None.
metadatas (Optional[List[dict]]) – List of metadatas. Defaults to None.
ids (Optional[List[str]]) – List of document IDs. Defaults to None.
client_settings (Optional[Settings]) – Chroma client settings
collection_metadata (Optional[Dict]) – Collection configurations.
Defaults to None.
client (Optional[ClientAPI]) – 
kwargs (Any) – 
Returns
Chroma vectorstore.
Return type
Chroma
get(ids: Optional[OneOrMany[ID]] = None, where: Optional[Where] = None, limit: Optional[int] = None, offset: Optional[int] = None, where_document: Optional[WhereDocument] = None, include: Optional[List[str]] = None) → Dict[str, Any][source]¶
Gets the collection.
Parameters
Gets the collection.
Parameters
ids (Optional[OneOrMany[ID]]) – The ids of the embeddings to get. Optional.
where (Optional[Where]) – A Where type dict used to filter results by.
E.g. {“color” : “red”, “price”: 4.20}. Optional.
limit (Optional[int]) – The number of documents to return. Optional.
offset (Optional[int]) – The offset to start returning results from.
Useful for paging results with limit. Optional.
where_document (Optional[WhereDocument]) – A WhereDocument type dict used to filter by the documents.
E.g. {$contains: “hello”}. Optional.
include (Optional[List[str]]) – A list of what to include in the results.
Can contain “embeddings”, “metadatas”, “documents”.
Ids are always included.
Defaults to [“metadatas”, “documents”]. Optional.
Returns
A dict with the keys “ids”, “embeddings”, “metadatas”, “documents”.
Return type
Dict[str, Any]
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, where_document: Optional[Dict[str, 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.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.