Tony20100/langchain_full huggingface dataset:
Create an Astra DB vectorstore from a document list.
Utility method that defers to ‘afrom_texts’ (see that one).
Args: see ‘afrom_texts’, except here you have to supply ‘documents’in place of ‘texts’ and ‘metadatas’.
Returns
an AstraDBVectorStore vectorstore.
Parameters
documents (List[Document]) – 
embedding (Optional[Embeddings]) – 
collection_vector_service_options (Optional[CollectionVectorServiceOptions]) – 
kwargs (Any) – 
Return type
AstraDBVectorStore
async classmethod afrom_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, collection_vector_service_options: Optional[CollectionVectorServiceOptions] = None, **kwargs: Any) → AstraDBVectorStore[source]¶
Create an Astra DB vectorstore from raw texts.
Parameters
texts (List[str]) – the texts to insert.
embedding (Optional[Embeddings]) – the embedding function to use in the store.
metadatas (Optional[List[dict]]) – metadata dicts for the texts.
ids (Optional[List[str]]) – ids to associate to the texts.
collection_vector_service_options (Optional[CollectionVectorServiceOptions]) – specifies the use of server-side
embeddings within Astra DB. Only one of embedding or
collection_vector_service_options can be provided.
**kwargs (Any) – you can pass any argument that you would
to add_texts() and/or to the ‘AstraDBVectorStore’ constructor
(see these methods for details). These arguments will be
routed to the respective methods as they are.
Returns
an AstraDBVectorStore vectorstore.
Return type
AstraDBVectorStore
an AstraDBVectorStore vectorstore.
Return type
AstraDBVectorStore
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: 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
query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return.
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.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
kwargs (Any) – 
Returns
The 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, filter: 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 (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
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.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
kwargs (Any) – 
Returns
The 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.
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
kwargs (Any) – 
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
# 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
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, **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]
async asimilarity_search(query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
kwargs (Any) – 
Returns
The list of Documents most similar to the query.
Return type
List[Document]
The list of Documents most similar to the query.
Return type
List[Document]
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, **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.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
kwargs (Any) – 
Returns
The 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(query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None) → List[Tuple[Document, float]][source]¶
Return docs most similar to query with score.
Parameters
query (str) – Query to look up documents similar to.
Parameters
query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
Returns
The list of (Document, score), the most similar to the query vector.
Return type
List[Tuple[Document, float]]
async asimilarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None) → List[Tuple[Document, float]][source]¶
Return docs most similar to embedding vector with score.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
Returns
The list of (Document, score), the most similar to the query vector.
Return type
List[Tuple[Document, float]]
async asimilarity_search_with_score_id(query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None) → List[Tuple[Document, float, str]][source]¶
Return docs most similar to the query with score and id.
Parameters
query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
Returns
The list of (Document, score, id), the most similar to the query.
Return type
List[Tuple[Document, float, str]]
Return type
List[Tuple[Document, float, str]]
async asimilarity_search_with_score_id_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None) → List[Tuple[Document, float, str]][source]¶
Return docs most similar to embedding vector with score and id.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
Returns
The list of (Document, score, id), the most similar to the query vector.
Return type
List[Tuple[Document, float, str]]
clear() → None[source]¶
Empty the collection of all its stored entries.
Return type
None
delete(ids: Optional[List[str]] = None, concurrency: Optional[int] = None, **kwargs: Any) → Optional[bool][source]¶
Delete by vector ids.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
concurrency (Optional[int]) – max number of threads issuing single-doc delete requests.
Defaults to instance-level setting.
kwargs (Any) – 
Returns
True if deletion is successful, False otherwise.
Return type
Optional[bool]
delete_by_document_id(document_id: str) → bool[source]¶
Remove a single document from the store, given its document ID.
Parameters
document_id (str) – The document ID
Return type
bool
ReturnsTrue if a document has indeed been deleted, False if ID not found.
delete_collection() → None[source]¶
Completely delete the collection from the database (as opposed
to clear(), which empties it only).
to clear(), which empties it only).
Stored data is lost and unrecoverable, resources are freed.
Use with caution.
Return type
None
classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, collection_vector_service_options: Optional[CollectionVectorServiceOptions] = None, **kwargs: Any) → AstraDBVectorStore[source]¶
Create an Astra DB vectorstore from a document list.
Utility method that defers to ‘from_texts’ (see that one).
Args: see ‘from_texts’, except here you have to supply ‘documents’in place of ‘texts’ and ‘metadatas’.
Returns
an AstraDBVectorStore vectorstore.
Parameters
documents (List[Document]) – 
embedding (Optional[Embeddings]) – 
collection_vector_service_options (Optional[CollectionVectorServiceOptions]) – 
kwargs (Any) – 
Return type
AstraDBVectorStore
classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, collection_vector_service_options: Optional[CollectionVectorServiceOptions] = None, **kwargs: Any) → AstraDBVectorStore[source]¶
Create an Astra DB vectorstore from raw texts.
Parameters
texts (List[str]) – the texts to insert.
embedding (Optional[Embeddings]) – the embedding function to use in the store.
This enables client-side embedding functions or calls to external
embedding providers. Only one of embedding or
collection_vector_service_options can be provided.
metadatas (Optional[List[dict]]) – metadata dicts for the texts.
ids (Optional[List[str]]) – ids to associate to the texts.
collection_vector_service_options (Optional[CollectionVectorServiceOptions]) – specifies the use of server-side
collection_vector_service_options (Optional[CollectionVectorServiceOptions]) – specifies the use of server-side
embeddings within Astra DB. Only one of embedding or
collection_vector_service_options can be provided.
**kwargs (Any) – you can pass any argument that you would
to add_texts() and/or to the ‘AstraDBVectorStore’ constructor
(see these methods for details). These arguments will be
routed to the respective methods as they are.
Returns
an AstraDBVectorStore vectorstore.
Return type
AstraDBVectorStore
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: 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
query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return.
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.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
kwargs (Any) – 
Returns
The list of Documents selected by maximal marginal relevance.
Return type
List[Document]
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
Returns
The list of (Document, score, id), the most similar to the query.
Return type
List[Tuple[Document, float, str]]
similarity_search_with_score_id_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None) → List[Tuple[Document, float, str]][source]¶
Return docs most similar to embedding vector with score and id.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter on the metadata to apply.
Returns
The list of (Document, score, id), the most similar to the query vector.
Return type
List[Tuple[Document, float, str]]
langchain_milvus.vectorstores.milvus.cosine_similarity¶
langchain_milvus.vectorstores.milvus.cosine_similarity(X: Union[List[List[float]], List[ndarray], ndarray], Y: Union[List[List[float]], List[ndarray], ndarray]) → ndarray[source]¶
Row-wise cosine similarity between two equal-width matrices.
Parameters
X (Union[List[List[float]], List[ndarray], ndarray]) – 
Y (Union[List[List[float]], List[ndarray], ndarray]) – 
Return type
ndarray
Examples using cosine_similarity¶
How to route between sub-chains
langchain_community.vectorstores.clickhouse.Clickhouse¶
class langchain_community.vectorstores.clickhouse.Clickhouse(embedding: Embeddings, config: Optional[ClickhouseSettings] = None, **kwargs: Any)[source]¶
ClickHouse VectorSearch vector store.
You need a clickhouse-connect python package, and a valid account
to connect to ClickHouse.
ClickHouse can not only search with simple vector indexes,
it also supports complex query with multiple conditions,
constraints and even sub-queries.
For more information, please visit[ClickHouse official site](https://clickhouse.com/clickhouse)
ClickHouse Wrapper to LangChain
embedding_function (Embeddings):
config (ClickHouseSettings): Configuration to ClickHouse Client
Other keyword arguments will pass into
[clickhouse-connect](https://docs.clickhouse.com/)
Attributes
embeddings
Provides access to the embedding mechanism used by the Clickhouse instance.
metadata_column
Methods
__init__(embedding[, config])
ClickHouse Wrapper to LangChain
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, batch_size, ids])
Insert 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, ...])
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 ID or other criteria.
drop()
Helper function: Drop data
escape_str(value)
Escape special characters in a string for Clickhouse SQL queries.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Create ClickHouse wrapper with existing 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, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, where_str])
Perform a similarity search with ClickHouse
similarity_search_by_vector(embedding[, k, ...])
Perform a similarity search with ClickHouse by vectors
similarity_search_with_relevance_scores(query)
Perform a similarity search with ClickHouse by vectors
similarity_search_with_relevance_scores(query)
Perform a similarity search with ClickHouse
similarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
Parameters
embedding (Embeddings) – 
config (Optional[ClickhouseSettings]) – 
kwargs (Any) – 
__init__(embedding: Embeddings, config: Optional[ClickhouseSettings] = None, **kwargs: Any) → None[source]¶
ClickHouse Wrapper to LangChain
embedding_function (Embeddings):
config (ClickHouseSettings): Configuration to ClickHouse Client
Other keyword arguments will pass into
[clickhouse-connect](https://docs.clickhouse.com/)
Parameters
embedding (Embeddings) – 
config (Optional[ClickhouseSettings]) – 
kwargs (Any) – 
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]
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]
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]] = None, batch_size: int = 32, ids: Optional[Iterable[str]] = None, **kwargs: Any) → List[str][source]¶
Insert more texts through the embeddings and add to the VectorStore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the VectorStore.
ids (Optional[Iterable[str]]) – Optional list of ids to associate with the texts.
batch_size (int) – Batch size of insertion
metadata – Optional column data to be inserted
metadatas (Optional[List[dict]]) – 
kwargs (Any) – 
Returns
List of ids from adding the texts into the VectorStore.
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
Parameters
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]¶
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]
drop() → None[source]¶
Helper function: Drop data
Return type
None
escape_str(value: str) → str[source]¶
Escape special characters in a string for Clickhouse SQL queries.
This method is used internally to prepare strings for safe insertion
into SQL queries by escaping special characters that might otherwise
interfere with the query syntax.
Parameters
value (str) – The string to be escaped.
Returns
The escaped string, safe for insertion into SQL queries.
Return type
str
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
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[Dict[Any, Any]]] = None, config: Optional[ClickhouseSettings] = None, text_ids: Optional[Iterable[str]] = None, batch_size: int = 32, **kwargs: Any) → Clickhouse[source]¶
Create ClickHouse wrapper with existing texts
Parameters
embedding_function (Embeddings) – Function to extract text embedding
texts (Iterable[str]) – List or tuple of strings to be added
config (ClickHouseSettings, Optional) – ClickHouse configuration
text_ids (Optional[Iterable], optional) – IDs for the texts.
Defaults to None.
batch_size (int, optional) – Batchsize when transmitting data to ClickHouse.
Defaults to 32.
metadata (List[dict], optional) – metadata to texts. Defaults to None.
into (Other keyword arguments will pass) – [clickhouse-connect](https://clickhouse.com/docs/en/integrations/python#clickhouse-connect-driver-api)
embedding (Embeddings) – 
metadatas (Optional[List[Dict[Any, Any]]]) – 
kwargs (Any) – 
Returns
ClickHouse Index
Return type
Clickhouse
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]