Tony20100/langchain_full huggingface dataset:
Returns
The resulting keys for each inserted element.
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
async amax_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
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, expr: Optional[str] = None, **kwargs: str)[source]¶
Delete by vector ID or boolean expression.
Refer to [Milvus documentation](https://milvus.io/docs/delete_data.md)
for notes and examples of expressions.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
expr (Optional[str]) – Boolean expression that specifies the entities to delete.
kwargs (str) – Other parameters in Milvus delete api.
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, collection_name: str = 'LangChainCollection', connection_args: dict[str, Any] = {'host': 'localhost', 'password': '', 'port': '19530', 'secure': False, 'user': ''}, consistency_level: str = 'Session', index_params: Optional[dict] = None, search_params: Optional[dict] = None, drop_old: bool = False, *, ids: Optional[List[str]] = None, **kwargs: Any) → Milvus[source]¶
Create a Milvus collection, indexes it with HNSW, and insert data.
Parameters
texts (List[str]) – Text data.
embedding (Embeddings) – Embedding function.
metadatas (Optional[List[dict]]) – Metadata for each text if it exists.
Defaults to None.
collection_name (str, optional) – Collection name to use. Defaults to
“LangChainCollection”.
connection_args (dict[str, Any], optional) – Connection args to use. Defaults
to DEFAULT_MILVUS_CONNECTION.
consistency_level (str, optional) – Which consistency level to use. Defaults
to “Session”.
index_params (Optional[dict], optional) – Which index_params to use. Defaults
to None.
search_params (Optional[dict], optional) – Which search params to use.
Defaults to None.
drop_old (Optional[bool], optional) – Whether to drop the collection with
that name if it exists. Defaults to False.
ids (Optional[List[str]]) – List of text ids. Defaults to None.
kwargs (Any) – 
Returns
Milvus Vector Store
Return type
Milvus
Returns
Milvus Vector Store
Return type
Milvus
get_pks(expr: str, **kwargs: Any) → Optional[List[int]][source]¶
Get primary keys with expression
Parameters
expr (str) – Expression - E.g: “id in [1, 2]”, or “title LIKE ‘Abc%’”
kwargs (Any) – 
Returns
List of IDs (Primary Keys)
Return type
List[int]
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[float] = None, **kwargs: Any) → List[Document][source]¶
Perform a search and return results that are reordered by MMR.
Parameters
query (str) – The text being searched.
k (int, optional) – How many results to give. Defaults to 4.
fetch_k (int, optional) – Total results to select k from.
Defaults to 20.
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
param (dict, optional) – The search params for the specified index.
Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (float, optional) – How long to wait before timeout error.
Defaults to None.
kwargs (Any) – Collection.search() keyword arguments.
Returns
Document results for search.
Return type
List[Document]
Returns
Document results for search.
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, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[float] = None, **kwargs: Any) → List[Document][source]¶
Perform a search and return results that are reordered by MMR.
Parameters
embedding (str) – The embedding vector being searched.
k (int, optional) – How many results to give. Defaults to 4.
fetch_k (int, optional) – Total results to select k from.
Defaults to 20.
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
param (dict, optional) – The search params for the specified index.
Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (float, optional) – How long to wait before timeout error.
Defaults to None.
kwargs (Any) – Collection.search() keyword arguments.
Returns
Document results for search.
Return type
List[Document]
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
Parameters
query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”,
“mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
Return type
List[Document]
Return type
List[Document]
similarity_search(query: str, k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[float] = None, **kwargs: Any) → List[Document][source]¶
Perform a similarity search against the query string.
Parameters
query (str) – The text to search.
k (int, optional) – How many results to return. Defaults to 4.
param (dict, optional) – The search params for the index type.
Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (int, optional) – How long to wait before timeout error.
Defaults to None.
kwargs (Any) – Collection.search() keyword arguments.
Returns
Document results for search.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[float] = None, **kwargs: Any) → List[Document][source]¶
Perform a similarity search against the query string.
Parameters
embedding (List[float]) – The embedding vector to search.
k (int, optional) – How many results to return. Defaults to 4.
param (dict, optional) – The search params for the index type.
Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (int, optional) – How long to wait before timeout error.
Defaults to None.
kwargs (Any) – Collection.search() keyword arguments.
Returns
Document results for search.
Return type
List[Document]
Returns
Document results for search.
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
List[Tuple[Document, float]]
similarity_search_with_score(query: str, k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[float] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Perform a search on a query string and return results with score.
For more information about the search parameters, take a look at the pymilvus
documentation found here:
https://milvus.io/api-reference/pymilvus/v2.2.6/Collection/search().md
Parameters
query (str) – The text being searched.
k (int, optional) – The amount of results to return. Defaults to 4.
param (dict) – The search params for the specified index.
Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (float, optional) – How long to wait before timeout error.
Defaults to None.
kwargs (Any) – Collection.search() keyword arguments.
Return type
Defaults to None.
kwargs (Any) – Collection.search() keyword arguments.
Return type
List[float], List[Tuple[Document, any, any]]
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[float] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Perform a search on a query string and return results with score.
For more information about the search parameters, take a look at the pymilvus
documentation found here:
https://milvus.io/api-reference/pymilvus/v2.2.6/Collection/search().md
Parameters
embedding (List[float]) – The embedding vector being searched.
k (int, optional) – The amount of results to return. Defaults to 4.
param (dict) – The search params for the specified index.
Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (float, optional) – How long to wait before timeout error.
Defaults to None.
kwargs (Any) – Collection.search() keyword arguments.
Returns
Result doc and score.
Return type
List[Tuple[Document, float]]
upsert(ids: Optional[List[str]] = None, documents: Optional[List[Document]] = None, **kwargs: Any) → Optional[List[str]][source]¶
Update/Insert documents to the vectorstore.
Parameters
ids (Optional[List[str]]) – IDs to update - Let’s call get_pks to get ids with expression
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
Returns
IDs of the added texts.
Return type
List[str]
Examples using Milvus¶
langchain_community.vectorstores.redis.schema.TagFieldSchema¶
class langchain_community.vectorstores.redis.schema.TagFieldSchema[source]¶
Bases: RedisField
Schema for tag 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 case_sensitive: bool = False¶
param name: str [Required]¶
param no_index: bool = False¶
param separator: str = ','¶
param sortable: Optional[bool] = False¶
as_field() → TagField[source]¶
Return type
TagField
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
langchain_community.vectorstores.redis.schema.HNSWVectorField¶
class langchain_community.vectorstores.redis.schema.HNSWVectorField[source]¶
Bases: RedisVectorField
Schema for HNSW vector 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 algorithm: Literal['HNSW'] = 'HNSW'¶
param datatype: str = 'FLOAT32'¶
param dims: int [Required]¶
param distance_metric: RedisDistanceMetric = 'COSINE'¶
param ef_construction: int = 200¶
param ef_runtime: int = 10¶
param epsilon: float = 0.01¶
param initial_cap: Optional[int] = None¶
param m: int = 16¶
param name: str [Required]¶
as_field() → VectorField[source]¶
Return type
VectorField
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
langchain_community.vectorstores.oraclevs.OracleVS¶
class langchain_community.vectorstores.oraclevs.OracleVS(client: Connection, embedding_function: Union[Callable[[str], List[float]], Embeddings], table_name: str, distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE, query: Optional[str] = 'What is a Oracle database', params: Optional[Dict[str, Any]] = None)[source]¶
OracleVS vector store.
To use, you should have both:
- the oracledb python package installed
- a connection string associated with a OracleDBCluster having deployed an
Search index
Example
from langchain.vectorstores import OracleVS
from langchain.embeddings.openai import OpenAIEmbeddings
import oracledb
with oracledb.connect(user = user, passwd = pwd, dsn = dsn) as
connection:
    print ("Database version:", connection.version)
    embeddings = OpenAIEmbeddings()
    query = ""
    vectors = OracleVS(connection, table_name, embeddings, query)
Attributes
embeddings
A property that returns an Embeddings instance embedding_function is an instance of Embeddings, otherwise returns None.
client
Initialize with necessary components.
Methods
__init__(client, embedding_function, table_name)
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, ids])
Add more texts to the vectorstore index.
adelete([ids])
Delete by vector ID or other criteria.
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])
Delete by vector IDs.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas])
Return VectorStore initialized from texts and embeddings.
get_embedding_dimension()
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.
max_marginal_relevance_search_with_score_by_vector(...)
Return docs and their similarity scores selected using the maximal marginal     relevance.
search(query, search_type, **kwargs)
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
Return docs most similar to query.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
similarity_search_by_vector_returning_embeddings(...)
similarity_search_by_vector_with_relevance_scores(...)
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.
Parameters
client (Connection) – 
embedding_function (Union[Callable[[str], List[float]], Embeddings]) – 
table_name (str) – 
distance_strategy (DistanceStrategy) – 
query (Optional[str]) – 
params (Optional[Dict[str, Any]]) – 
__init__(client: Connection, embedding_function: Union[Callable[[str], List[float]], Embeddings], table_name: str, distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE, query: Optional[str] = 'What is a Oracle database', params: Optional[Dict[str, Any]] = None)[source]¶
Parameters
client (Connection) – 
embedding_function (Union[Callable[[str], List[float]], Embeddings]) – 
table_name (str) – 
distance_strategy (DistanceStrategy) – 
query (Optional[str]) – 
params (Optional[Dict[str, 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
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]
add_texts(texts: Iterable[str], metadatas: Optional[List[Dict[Any, Any]]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Add more texts to the vectorstore index.
:param texts: Iterable of strings to add to the vectorstore.
:param metadatas: Optional list of metadatas associated with the texts.
:param ids: Optional list of ids for the texts that are being added to
:param the vector store.:
:param kwargs: vectorstore specific parameters
Parameters
texts (Iterable[str]) – 
metadatas (Optional[List[Dict[Any, Any]]]) – 
ids (Optional[List[str]]) – 
kwargs (Any) – 
Return type
List[str]
kwargs (Any) – 
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
async amax_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.
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]
async amax_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]
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.