Tony20100/langchain_full huggingface dataset:
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.
match_args(query, filter)
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, 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(*args, **kwargs)
Run similarity search with distance.
Parameters
client (supabase.client.Client) – 
embedding (Embeddings) – 
table_name (str) – 
chunk_size (int) –
table_name (str) – 
chunk_size (int) – 
query_name (Union[str, None]) – 
__init__(client: supabase.client.Client, embedding: Embeddings, table_name: str, chunk_size: int = 500, query_name: Union[str, None] = None) → None[source]¶
Initialize with supabase client.
Parameters
client (supabase.client.Client) – 
embedding (Embeddings) – 
table_name (str) – 
chunk_size (int) – 
query_name (Union[str, None]) – 
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]
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
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]¶
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[Any, Any]]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters.
ids (Optional[List[str]]) – 
**kwargs – 
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
add_vectors(vectors: List[List[float]], documents: List[Document], ids: List[str]) → List[str][source]¶
Parameters
vectors (List[List[float]]) – 
documents (List[Document]) – 
ids (List[str]) – 
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.
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
delete(ids: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Delete by vector IDs.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
kwargs (Any) – 
Return type
None
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]] = None, client: Optional[supabase.client.Client] = None, table_name: Optional[str] = 'documents', query_name: Union[str, None] = 'match_documents', chunk_size: int = 500, ids: Optional[List[str]] = None, **kwargs: Any) → SupabaseVectorStore[source]¶
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
client (Optional[supabase.client.Client]) – 
table_name (Optional[str]) – 
query_name (Union[str, None]) – 
chunk_size (int) – 
ids (Optional[List[str]]) – 
kwargs (Any) – 
Return type
SupabaseVectorStore
kwargs (Any) – 
Return type
SupabaseVectorStore
match_args(query: List[float], filter: Optional[Dict[str, Any]]) → Dict[str, Any][source]¶
Parameters
query (List[float]) – 
filter (Optional[Dict[str, Any]]) – 
Return type
Dict[str, Any]
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
max_marginal_relevance_search requires that query_name returns matched
embeddings alongside the match documents. The following function
demonstrates how to do this:
```sql
CREATE FUNCTION match_documents_embeddings(query_embedding vector(1536),
match_count int)
RETURNS TABLE(id uuid,
content text,
metadata jsonb,
embedding vector(1536),
similarity float)
LANGUAGE plpgsql
AS $$
# variable_conflict use_column
BEGINRETURN query
SELECT
id,
content,
metadata,
embedding,
BEGINRETURN query
SELECT
id,
content,
metadata,
embedding,
1 -(docstore.embedding <=> query_embedding) AS similarity
FROMdocstore
ORDER BYdocstore.embedding <=> query_embedding
LIMIT match_count;
END;
$$;
```
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
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, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – 
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, 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]]) – 
kwargs (Any) – 
Returns
List of Documents most similar to the query vector.
Return type
List[Document]
similarity_search_by_vector_returning_embeddings(query: List[float], k: int, filter: Optional[Dict[str, Any]] = None, postgrest_filter: Optional[str] = None) → List[Tuple[Document, float, ndarray[float32, Any]]][source]¶
Parameters
query (List[float]) – 
k (int) – 
filter (Optional[Dict[str, Any]]) – 
postgrest_filter (Optional[str]) – 
Return type
List[Tuple[Document, float, ndarray[float32, Any]]]
Return type
List[Tuple[Document, float, ndarray[float32, Any]]]
similarity_search_by_vector_with_relevance_scores(query: List[float], k: int, filter: Optional[Dict[str, Any]] = None, postgrest_filter: Optional[str] = None, score_threshold: Optional[float] = None) → List[Tuple[Document, float]][source]¶
Parameters
query (List[float]) – 
k (int) – 
filter (Optional[Dict[str, Any]]) – 
postgrest_filter (Optional[str]) – 
score_threshold (Optional[float]) – 
Return type
List[Tuple[Document, float]]
similarity_search_with_relevance_scores(query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
filter (Optional[Dict[str, Any]]) – 
**kwargs – 
Returns
List of Tuples of (doc, similarity_score)
Return type
List[Tuple[Document, float]]
similarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
langchain_elasticsearch.vectorstores.ApproxRetrievalStrategy¶
class langchain_elasticsearch.vectorstores.ApproxRetrievalStrategy(query_model_id: Optional[str] = None, hybrid: Optional[bool] = False, rrf: Optional[Union[dict, bool]] = True)[source]¶
[Deprecated] Approximate retrieval strategy using the HNSW algorithm.
Notes
Deprecated since version 0.2.0: Use DenseVectorStrategy instead.
Methods
__init__([query_model_id, hybrid, rrf])
before_index_setup(client, text_field, ...)
Executes before the index is created.
index(dims_length, vector_query_field, ...)
Create the mapping for the Elasticsearch index.
query(query_vector, query, k, fetch_k, ...)
Executes when a search is performed on the store.
require_inference()
Returns whether or not the strategy requires inference to be performed on the text before it is added to the index.
Parameters
query_model_id (Optional[str]) – 
hybrid (Optional[bool]) – 
rrf (Optional[Union[dict, bool]]) – 
__init__(query_model_id: Optional[str] = None, hybrid: Optional[bool] = False, rrf: Optional[Union[dict, bool]] = True)[source]¶
Parameters
query_model_id (Optional[str]) – 
hybrid (Optional[bool]) – 
rrf (Optional[Union[dict, bool]]) – 
before_index_setup(client: Elasticsearch, text_field: str, vector_query_field: str) → None[source]¶
Executes before the index is created. Used for setting up
any required Elasticsearch resources like a pipeline.
Parameters
client (Elasticsearch) – The Elasticsearch client.
Parameters
client (Elasticsearch) – The Elasticsearch client.
text_field (str) – The field containing the text data in the index.
vector_query_field (str) – The field containing the vector
representations in the index.
Return type
None
index(dims_length: Optional[int], vector_query_field: str, text_field: str, similarity: Optional[DistanceStrategy]) → Dict[source]¶
Create the mapping for the Elasticsearch index.
Parameters
dims_length (Optional[int]) – 
vector_query_field (str) – 
text_field (str) – 
similarity (Optional[DistanceStrategy]) – 
Return type
Dict
query(query_vector: Optional[List[float]], query: Optional[str], k: int, fetch_k: int, vector_query_field: str, text_field: str, filter: List[dict], similarity: Optional[DistanceStrategy]) → Dict[source]¶
Executes when a search is performed on the store.
Parameters
query_vector (Optional[List[float]]) – The query vector,
or None if not using vector-based query.
query (Optional[str]) – The text query, or None if not using text-based query.
k (int) – The total number of results to retrieve.
fetch_k (int) – The number of results to fetch initially.
vector_query_field (str) – The field containing the vector
representations in the index.
text_field (str) – The field containing the text data in the index.
filter (List[dict]) – List of filter clauses to apply to the query.
similarity (Optional[DistanceStrategy]) – The similarity strategy to use, or None if not using one.
Returns
The Elasticsearch query body.
Return type
Dict
require_inference() → bool¶
Returns whether or not the strategy requires inference
langchain_community.vectorstores.meilisearch.Meilisearch¶
class langchain_community.vectorstores.meilisearch.Meilisearch(embedding: Embeddings, client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, index_name: str = 'langchain-demo', text_key: str = 'text', metadata_key: str = 'metadata', *, embedders: Optional[Dict[str, Any]] = None)[source]¶
Meilisearch vector store.
To use this, you need to have meilisearch python package installed,
and a running Meilisearch instance.
To learn more about Meilisearch Python, refer to the in-depth
Meilisearch Python documentation: https://meilisearch.github.io/meilisearch-python/.
See the following documentation for how to run a Meilisearch instance:
https://www.meilisearch.com/docs/learn/getting_started/quick_start.
Example
from langchain_community.vectorstores import Meilisearch
from langchain_community.embeddings.openai import OpenAIEmbeddings
import meilisearch
# api_key is optional; provide it if your meilisearch instance requires it
client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***')
embeddings = OpenAIEmbeddings()
embedders = {
    "theEmbedderName": {
        "source": "userProvided",
        "dimensions": "1536"
    }
}
vectorstore = Meilisearch(
    embedding=embeddings,
    embedders=embedders,
    client=client,
    index_name='langchain_demo',
    text_key='text')
Initialize with Meilisearch client.
Attributes
embeddings
Initialize with Meilisearch client.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding[, client, url, api_key, ...])
Initialize with Meilisearch client.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids, embedder_name])
Run more texts through the embedding and add them to the vector store.
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)
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
delete([ids])
Delete by vector ID or other criteria.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Construct Meilisearch wrapper from raw documents.
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, filter, ...])
Return meilisearch documents most similar to the query.
similarity_search_by_vector(embedding[, k, ...])
Return meilisearch documents most similar to embedding vector.
similarity_search_by_vector_with_scores(...)
Return meilisearch documents most similar to embedding vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Return meilisearch documents most similar to the query, along with scores.
Parameters
embedding (Embeddings) – 
client (Optional[Client]) – 
url (Optional[str]) – 
api_key (Optional[str]) – 
index_name (str) – 
text_key (str) – 
metadata_key (str) – 
embedders (Optional[Dict[str, Any]]) –
embedders (Optional[Dict[str, Any]]) – 
__init__(embedding: Embeddings, client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, index_name: str = 'langchain-demo', text_key: str = 'text', metadata_key: str = 'metadata', *, embedders: Optional[Dict[str, Any]] = None)[source]¶
Initialize with Meilisearch client.
Parameters
embedding (Embeddings) – 
client (Optional[Client]) – 
url (Optional[str]) – 
api_key (Optional[str]) – 
index_name (str) – 
text_key (str) – 
metadata_key (str) – 
embedders (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
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]¶
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, ids: Optional[List[str]] = None, embedder_name: Optional[str] = 'default', **kwargs: Any) → List[str][source]¶
Run more texts through the embedding and add them to the vector store.
Parameters
texts (Iterable[str]) – Iterable of strings/text to add to the vectorstore.
embedder_name (Optional[str]) – Name of the embedder. Defaults to “default”.
metadatas (Optional[List[dict]]) – Optional list of metadata.
Defaults to None.
Optional[List[str]] (ids) – Optional list of IDs.
Defaults to None.
ids (Optional[List[str]]) – 
kwargs (Any) – 
Returns
List of IDs of the texts added to 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
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.
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]