Tony20100/langchain_full huggingface dataset:
es_user – Username to use when connecting to Elasticsearch.
es_password – Password to use when connecting to Elasticsearch.
es_api_key – API key to use when connecting to Elasticsearch.
es_connection – Optional pre-existing Elasticsearch connection.
vector_query_field – Optional. Name of the field
to store the embedding vectors in.
query_field – Optional. Name of the field to store the texts in.
bulk_kwargs (Optional[Dict]) – Optional. Additional arguments to pass to
Elasticsearch bulk.
documents (List[Document]) – 
kwargs (Any) – 
Return type
ElasticsearchStore
classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[Dict[str, Any]]] = None, bulk_kwargs: Optional[Dict] = None, **kwargs: Any) → ElasticsearchStore[source]¶
Construct ElasticsearchStore wrapper from raw documents.
Example
from langchain_community.vectorstores import ElasticsearchStore
from langchain_community.embeddings.openai import OpenAIEmbeddings
db = ElasticsearchStore.from_texts(
    texts,
    // embeddings optional if using
    // a strategy that doesn't require inference
    embeddings,
    index_name="langchain-demo",
    es_url="http://localhost:9200"
)
Parameters
texts (List[str]) – List of texts to add to the Elasticsearch index.
embedding (Optional[Embeddings]) – Embedding function to use to embed the texts.
metadatas (Optional[List[Dict[str, Any]]]) – Optional list of metadatas associated with the texts.
index_name – Name of the Elasticsearch index to create.
es_url – URL of the Elasticsearch instance to connect to.
cloud_id – Cloud ID of the Elasticsearch instance to connect to.
es_user – Username to use when connecting to Elasticsearch.
es_user – Username to use when connecting to Elasticsearch.
es_password – Password to use when connecting to Elasticsearch.
es_api_key – API key to use when connecting to Elasticsearch.
es_connection – Optional pre-existing Elasticsearch connection.
vector_query_field – Optional. Name of the field to
store the embedding vectors in.
query_field – Optional. Name of the field to store the texts in.
distance_strategy – Optional. Name of the distance
strategy to use. Defaults to “COSINE”.
can be one of “COSINE”,
“EUCLIDEAN_DISTANCE”, “DOT_PRODUCT”,
“MAX_INNER_PRODUCT”.
bulk_kwargs (Optional[Dict]) – Optional. Additional arguments to pass to
Elasticsearch bulk.
kwargs (Any) – 
Return type
ElasticsearchStore
static get_user_agent() → str[source]¶
Return type
str
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, fields: Optional[List[str]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversityamong 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.
fields (Optional[List[str]]) – Other fields to get from elasticsearch source. These fields
will be added to the document metadata.
will be added to the document metadata.
kwargs (Any) – 
Returns
A 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]¶
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]¶
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]
similarity_search(query: str, k: int = 4, fetch_k: int = 50, filter: Optional[List[dict]] = None, **kwargs: Any) → List[Document][source]¶
Return Elasticsearch documents most similar to query.
Parameters
Return Elasticsearch documents most similar to query.
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 knn num_candidates.
filter (Optional[List[dict]]) – Array of Elasticsearch filter clauses to apply to the query.
kwargs (Any) – 
Returns
List of Documents most similar to the query,
in descending order of similarity.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
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_by_vector_with_relevance_scores(embedding: List[float], k: int = 4, filter: Optional[List[Dict]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return Elasticsearch documents most similar to query, along with scores.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[List[Dict]]) – Array of Elasticsearch filter clauses to apply to the query.
kwargs (Any) – 
Returns
List of Documents most similar to the embedding and score for each
Return type
List[Tuple[Document, float]]
Return type
List[Tuple[Document, float]]
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, filter: Optional[List[dict]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return Elasticsearch documents most similar to query, along with scores.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[List[dict]]) – Array of Elasticsearch filter clauses to apply to the query.
kwargs (Any) – 
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[Document, float]]
langchain_community.vectorstores.surrealdb.SurrealDBStore¶
class langchain_community.vectorstores.surrealdb.SurrealDBStore(embedding_function: Embeddings, **kwargs: Any)[source]¶
SurrealDB as Vector Store.
To use, you should have the surrealdb python package installed.
Parameters
embedding_function (Embeddings) – Embedding function to use.
dburl – SurrealDB connection url
ns – surrealdb namespace for the vector store. (default: “langchain”)
db – surrealdb database for the vector store. (default: “database”)
collection – surrealdb collection for the vector store.
(default: “documents”)
db_pass ((optional) db_user and) – surrealdb credentials
kwargs (Any) – 
Example
from langchain_community.vectorstores.surrealdb import SurrealDBStore
from langchain_community.embeddings import HuggingFaceEmbeddings
embedding_function = HuggingFaceEmbeddings()
dburl = "ws://localhost:8000/rpc"
ns = "langchain"
db = "docstore"
collection = "documents"
db_user = "root"
db_pass = "root"
sdb = SurrealDBStore.from_texts(
        texts=texts,
        embedding=embedding_function,
        dburl,
        ns, db, collection,
        db_user=db_user, db_pass=db_pass)
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding_function, **kwargs)
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Add list of text along with embeddings to the vector store asynchronously
add_documents(documents, **kwargs)
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas])
Add list of text along with embeddings to the vector store
adelete([ids])
Delete by document ID asynchronously.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])
Create SurrealDBStore from list of text asynchronously
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, filter])
Run similarity search on query asynchronously
asimilarity_search_by_vector(embedding[, k, ...])
Run similarity search on query embedding asynchronously
asimilarity_search_with_relevance_scores(query)
Run similarity search asynchronously and return relevance scores
asimilarity_search_with_score(query[, k, filter])
Run similarity search asynchronously and return distance scores
delete([ids])
Delete by document ID.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas])
Create SurrealDBStore from list of text
initialize()
Initialize connection to surrealdb database and authenticate if credentials are provided
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
Run similarity search on query
similarity_search_by_vector(embedding[, k, ...])
Run similarity search on query embedding
similarity_search_with_relevance_scores(query)
Run similarity search synchronously and return relevance scores
similarity_search_with_score(query[, k, filter])
Run similarity search synchronously and return distance scores
__init__(embedding_function: Embeddings, **kwargs: Any) → None[source]¶
Parameters
embedding_function (Embeddings) – 
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][source]¶
Add list of text along with embeddings to the vector store asynchronously
Parameters
texts (Iterable[str]) – collection of text to add to the database
metadatas (Optional[List[dict]]) – 
kwargs (Any) – 
Returns
List of ids for the newly inserted documents
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
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, **kwargs: Any) → List[str][source]¶
Add list of text along with embeddings to the vector store
Parameters
texts (Iterable[str]) – collection of text to add to the database
metadatas (Optional[List[dict]]) – 
kwargs (Any) – 
Returns
List of ids for the newly inserted documents
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
Delete by document ID asynchronously.
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.
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) → SurrealDBStore[source]¶
Create SurrealDBStore from list of text asynchronously
Parameters
texts (List[str]) – list of text to vectorize and store
Parameters
texts (List[str]) – list of text to vectorize and store
embedding (Optional[Embeddings]) – Embedding function.
dburl (str) – SurrealDB connection url
(default: “ws://localhost:8000/rpc”)
ns (str) – surrealdb namespace for the vector store.
(default: “langchain”)
db (str) – surrealdb database for the vector store.
(default: “database”)
collection (str) – surrealdb collection for the vector store.
(default: “documents”)
db_pass ((optional) db_user and) – surrealdb credentials
metadatas (Optional[List[dict]]) – 
kwargs (Any) – 
Returns
SurrealDBStore object initialized and ready for use.
Return type
SurrealDBStore
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, *, filter: 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.
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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) – 
Returns
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, *, filter: 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
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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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.
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
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, str]] = None, **kwargs: Any) → List[Document][source]¶
Run similarity search on query asynchronously
Parameters
query (str) – Query
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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, *, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document][source]¶
Run similarity search on query embedding asynchronously
Parameters
embedding (List[float]) – Query embedding
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) – 
Returns
List of Documents most similar to the query
Return type
List[Document]
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, *, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search asynchronously and return relevance scores
Parameters
query (str) – Query
k (int) – Number of results to return. Defaults to 4.
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) – 
Returns
List of Documents most similar along with relevance scores
Return type
List[Tuple[Document, float]]
async asimilarity_search_with_score(query: str, k: int = 4, *, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search asynchronously and return distance scores
Parameters
query (str) – Query
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) – 
Returns
List of Documents most similar along with relevance distance scores
Return type
List[Tuple[Document, float]]
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
Delete by document ID.
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.
Return type
Optional[bool]
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, **kwargs: Any) → SurrealDBStore[source]¶
Create SurrealDBStore from list of text
Parameters
texts (List[str]) – list of text to vectorize and store
embedding (Optional[Embeddings]) – Embedding function.
dburl (str) – SurrealDB connection url
ns (str) – surrealdb namespace for the vector store.
(default: “langchain”)
db (str) – surrealdb database for the vector store.
(default: “database”)
collection (str) – surrealdb collection for the vector store.
(default: “documents”)
db_pass ((optional) db_user and) – surrealdb credentials
metadatas (Optional[List[dict]]) – 
kwargs (Any) – 
Returns
SurrealDBStore object initialized and ready for use.
Return type
SurrealDBStore
async initialize() → None[source]¶
Initialize connection to surrealdb database
and authenticate if credentials are provided
Return type
None
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, *, filter: 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.
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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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, *, filter: 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
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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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]¶
Return docs most similar to query using specified search type.
Parameters
query (str) – Input text
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]
similarity_search(query: str, k: int = 4, *, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document][source]¶
Run similarity search on query
Parameters
query (str) – Query
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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, *, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document][source]¶
Run similarity search on query embedding
Parameters
embedding (List[float]) – Query embedding
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) – 
Returns
List of Documents most similar to the query
Return type
List[Document]
similarity_search_with_relevance_scores(query: str, k: int = 4, *, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search synchronously and return relevance scores
Parameters
query (str) – Query
k (int) – Number of results to return. Defaults to 4.