viruPVS/langchain-docs huggingface dataset:
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.
strategy – Optional. Retrieval strategy to use when searching the index.
Defaults to ApproxRetrievalStrategy. Can be one of
ExactRetrievalStrategy, ApproxRetrievalStrategy,
or SparseRetrievalStrategy.
distance_strategy – Optional. Distance strategy to use when
searching the index.
Defaults to COSINE. Can be one of COSINE,
EUCLIDEAN_DISTANCE, or DOT_PRODUCT.
If you want to use a cloud hosted Elasticsearch instance, you can pass in the
cloud_id argument instead of the es_url argument.
Example
from langchain_community.vectorstores import ElasticsearchStore
from langchain_community.embeddings.openai import OpenAIEmbeddings
vectorstore = ElasticsearchStore(
    embedding=OpenAIEmbeddings(),
    index_name="langchain-demo",
    es_cloud_id="<cloud_id>"
    es_user="elastic",
    es_password="<password>"
)
You can also connect to an existing Elasticsearch instance by passing in a
pre-existing Elasticsearch connection via the es_connection argument.
Example
from langchain_community.vectorstores import ElasticsearchStore
from langchain_community.embeddings.openai import OpenAIEmbeddings
from elasticsearch import Elasticsearch
es_connection = Elasticsearch("http://localhost:9200")
vectorstore = ElasticsearchStore(
    embedding=OpenAIEmbeddings(),
    index_name="langchain-demo",
    es_connection=es_connection
)
ElasticsearchStore by default uses the ApproxRetrievalStrategy, which uses the
HNSW algorithm to perform approximate nearest neighbor search. This is the
fastest and most memory efficient algorithm.
fastest and most memory efficient algorithm.
If you want to use the Brute force / Exact strategy for searching vectors, you
can pass in the ExactRetrievalStrategy to the ElasticsearchStore constructor.
Example
from langchain_community.vectorstores import ElasticsearchStore
from langchain_community.embeddings.openai import OpenAIEmbeddings
vectorstore = ElasticsearchStore(
    embedding=OpenAIEmbeddings(),
    index_name="langchain-demo",
    es_url="http://localhost:9200",
    strategy=ElasticsearchStore.ExactRetrievalStrategy()
)
Both strategies require that you know the similarity metric you want to use
when creating the index. The default is cosine similarity, but you can also
use dot product or euclidean distance.
Example
from langchain_community.vectorstores import ElasticsearchStore
from langchain_community.embeddings.openai import OpenAIEmbeddings
from langchain_community.vectorstores.utils import DistanceStrategy
vectorstore = ElasticsearchStore(
    embedding=OpenAIEmbeddings(),
    index_name="langchain-demo",
    es_url="http://localhost:9200",
    distance_strategy="DOT_PRODUCT"
)
Attributes
embeddings
Access the query embedding object if available.
Methods
ApproxRetrievalStrategy([query_model_id, ...])
Used to perform approximate nearest neighbor search using the HNSW algorithm.
ExactRetrievalStrategy()
Used to perform brute force / exact nearest neighbor search via script_score.
SparseVectorRetrievalStrategy([model_id])
Used to perform sparse vector search via text_expansion.
__init__(index_name, *[, embedding, ...])
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
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_embeddings(text_embeddings[, metadatas, ...])
Add the given texts and embeddings 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], asynchronously.
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance asynchronously.
connect_to_elasticsearch(*[, es_url, ...])
delete([ids, refresh_indices])
Delete documents from the Elasticsearch index.
from_documents(documents[, embedding, ...])
Delete documents from the Elasticsearch index.
from_documents(documents[, embedding, ...])
Construct ElasticsearchStore wrapper from documents.
from_texts(texts[, embedding, metadatas, ...])
Construct ElasticsearchStore wrapper from raw documents.
get_user_agent()
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, fetch_k, filter])
Return Elasticsearch documents most similar to query.
similarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
similarity_search_by_vector_with_relevance_scores(...)
Return Elasticsearch documents most similar to query, along with 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 Elasticsearch documents most similar to query, along with scores.
static ApproxRetrievalStrategy(query_model_id: Optional[str] = None, hybrid: Optional[bool] = False, rrf: Optional[Union[dict, bool]] = True) → ApproxRetrievalStrategy[source]¶
Used to perform approximate nearest neighbor search
using the HNSW algorithm.
At build index time, this strategy will create a
dense vector field in the index and store the
embedding vectors in the index.
At query time, the text will either be embedded using the
provided embedding function or the query_model_id
will be used to embed the text using the model
deployed to Elasticsearch.
if query_model_id is used, do not provide an embedding function.
if query_model_id is used, do not provide an embedding function.
Parameters
query_model_id – Optional. ID of the model to use to
embed the query text within the stack. Requires
embedding model to be deployed to Elasticsearch.
hybrid – Optional. If True, will perform a hybrid search
using both the knn query and a text query.
Defaults to False.
rrf – Optional. rrf is Reciprocal Rank Fusion.
When hybrid is True,
and rrf is True, then rrf: {}.
and rrf is False, then rrf is omitted.
and isinstance(rrf, dict) is True, then pass in the dict values.
rrf could be passed for adjusting ‘rank_constant’ and ‘window_size’.
static ExactRetrievalStrategy() → ExactRetrievalStrategy[source]¶
Used to perform brute force / exact
nearest neighbor search via script_score.
static SparseVectorRetrievalStrategy(model_id: Optional[str] = None) → SparseRetrievalStrategy[source]¶
Used to perform sparse vector search via text_expansion.
Used for when you want to use ELSER model to perform document search.
At build index time, this strategy will create a pipeline that
will embed the text using the ELSER model and store the
resulting tokens in the index.
At query time, the text will be embedded using the ELSER
model and the resulting tokens will be used to
perform a text_expansion query.
Parameters
model_id – Optional. Default is “.elser_model_1”.
ID of the model to use to embed the query text
within the stack. Requires embedding model to be
deployed to Elasticsearch.
within the stack. Requires embedding model to be
deployed to Elasticsearch.
__init__(index_name: str, *, embedding: ~typing.Optional[~langchain_core.embeddings.Embeddings] = None, es_connection: ~typing.Optional[Elasticsearch] = None, es_url: ~typing.Optional[str] = None, es_cloud_id: ~typing.Optional[str] = None, es_user: ~typing.Optional[str] = None, es_api_key: ~typing.Optional[str] = None, es_password: ~typing.Optional[str] = None, vector_query_field: str = 'vector', query_field: str = 'text', distance_strategy: ~typing.Optional[~typing.Literal[<DistanceStrategy.COSINE: 'COSINE'>, <DistanceStrategy.DOT_PRODUCT: 'DOT_PRODUCT'>, <DistanceStrategy.EUCLIDEAN_DISTANCE: 'EUCLIDEAN_DISTANCE'>]] = None, strategy: ~langchain_community.vectorstores.elasticsearch.BaseRetrievalStrategy = <langchain_community.vectorstores.elasticsearch.ApproxRetrievalStrategy object>)[source]¶
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
add_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, refresh_indices: bool = True, create_index_if_not_exists: bool = True, bulk_kwargs: Optional[Dict] = None, **kwargs: Any) → List[str][source]¶
Add the given texts and embeddings to the vectorstore.
Parameters
text_embeddings – Iterable pairs of string and embedding to
add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
ids – Optional list of unique IDs.
refresh_indices – Whether to refresh the Elasticsearch indices
after adding the texts.
create_index_if_not_exists – Whether to create the Elasticsearch
index if it doesn’t already exist.
*bulk_kwargs – Additional arguments to pass to Elasticsearch bulk.
- chunk_size: Optional. Number of texts to add to the
index at a time. Defaults to 500.
Returns
List of ids from adding the texts into the vectorstore.
add_texts(texts: Iterable[str], metadatas: Optional[List[Dict[Any, Any]]] = None, ids: Optional[List[str]] = None, refresh_indices: bool = True, create_index_if_not_exists: bool = True, bulk_kwargs: Optional[Dict] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
ids – Optional list of ids to associate with the texts.
refresh_indices – Whether to refresh the Elasticsearch indices
refresh_indices – Whether to refresh the Elasticsearch indices
after adding the texts.
create_index_if_not_exists – Whether to create the Elasticsearch
index if it doesn’t already exist.
*bulk_kwargs – Additional arguments to pass to Elasticsearch bulk.
- chunk_size: Optional. Number of texts to add to the
index at a time. Defaults to 500.
Returns
List of ids from adding the texts into the vectorstore.
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
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.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
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], asynchronously.
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
Run similarity search with distance asynchronously.
static connect_to_elasticsearch(*, es_url: Optional[str] = None, cloud_id: Optional[str] = None, api_key: Optional[str] = None, username: Optional[str] = None, password: Optional[str] = None) → Elasticsearch[source]¶
delete(ids: Optional[List[str]] = None, refresh_indices: Optional[bool] = True, **kwargs: Any) → Optional[bool][source]¶
Delete documents from the Elasticsearch index.
Parameters
ids – List of ids of documents to delete.
refresh_indices – Whether to refresh the index
after deleting documents. Defaults to True.
classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, bulk_kwargs: Optional[Dict] = None, **kwargs: Any) → ElasticsearchStore[source]¶
Construct ElasticsearchStore wrapper from documents.
Example
from langchain_community.vectorstores import ElasticsearchStore
from langchain_community.embeddings.openai import OpenAIEmbeddings
db = ElasticsearchStore.from_documents(
    texts,
    embeddings,
    index_name="langchain-demo",
    es_url="http://localhost:9200"
)
Parameters
texts – List of texts to add to the Elasticsearch index.
embedding – Embedding function to use to embed the texts.
Do not provide if using a strategy
that doesn’t require inference.
metadatas – 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_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.
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. Additional arguments to pass to
Elasticsearch bulk.
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 of texts to add to the Elasticsearch index.
embedding – Embedding function to use to embed the texts.
metadatas – 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_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”.
strategy to use. Defaults to “COSINE”.
can be one of “COSINE”,
“EUCLIDEAN_DISTANCE”, “DOT_PRODUCT”.
bulk_kwargs – Optional. Additional arguments to pass to
Elasticsearch bulk.
static get_user_agent() → str[source]¶
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 – Other fields to get from elasticsearch source. These fields
will be added to the document metadata.
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 – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
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
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to knn num_candidates.
filter – Array of Elasticsearch filter clauses to apply to the query.
Returns
List of Documents most similar to the query,
in descending order of similarity.
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query vector.
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
Return Elasticsearch documents most similar to query, along with scores.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter – Array of Elasticsearch filter clauses to apply to the query.
Returns
List of Documents most similar to the embedding and score for each
similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(query: str, k: int = 4, filter: Optional[List[dict]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return Elasticsearch documents most similar to query, along with scores.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter – Array of Elasticsearch filter clauses to apply to the query.
Returns
List of Documents most similar to the query and score for each
Examples using ElasticsearchStore¶
Elasticsearch
Indexing
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 – 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
Example
from langchain.vectorstores.surrealdb import SurrealDBStore
from langchain.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)
Run more documents through the embeddings and add to the vectorstore.