id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 49 117 |
|---|---|---|
5641e0c3fcb0-4 | text_embedding_pairs = list(zip(texts, text_embeddings))
db = Annoy.from_embeddings(text_embedding_pairs, embeddings)
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, metric: str = 'angular', trees: int = 100, n_jobs: int = - 1, **kwargs: ... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-5 | embeddings – Embeddings to use when generating queries.
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similar... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-6 | Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
process_index_results(idxs: List[int], dists: List[float]) → List[Tuple[langchain.schema.Document, float]][source]#
Turns annoy results into a list of documents and scores.
Parameters
idxs – List of indices of the documents in the index.... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-7 | to n_trees * n if not provided
Returns
List of Documents most similar to the embedding.
similarity_search_by_vector(embedding: List[float], k: int = 4, search_k: int = - 1, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-8 | Returns
List of Documents most similar to the query and score for each
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, search_k: int = - 1) → List[Tuple[langchain.schema.Document, float]][source]#
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – ... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-9 | ids (Optional[List[str]]) – An optional list of ids.
refresh (bool) – Whether or not to refresh indices with the updated data.
Default True.
Returns
List of IDs of the added texts.
Return type
List[str]
create_index(**kwargs: Any) → Any[source]#
Creates an index in your project.
See
https://docs.nomic.ai/atlas_api.html... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-10 | index_kwargs (Optional[dict]) – Dict of kwargs for index creation.
See https://docs.nomic.ai/atlas_api.html
Returns
Nomic’s neural database and finest rhizomatic instrument
Return type
AtlasDB
classmethod from_texts(texts: List[str], embedding: Optional[langchain.embeddings.base.Embeddings] = None, metadatas: Optional[... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-11 | Returns
Nomic’s neural database and finest rhizomatic instrument
Return type
AtlasDB
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
Run similarity search with AtlasDB
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults t... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-12 | List[str]
delete_collection() → None[source]#
Delete the collection.
classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, ids: Optional[List[str]] = None, collection_name: str = 'langchain', persist_directory: Optional[str] = None, client_settings: Optional[chromadb.config.Setti... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-13 | Otherwise, the data will be ephemeral in-memory.
Parameters
texts (List[str]) – List of texts to add to the collection.
collection_name (str) – Name of the collection to create.
persist_directory (Optional[str]) – Directory to persist the collection.
embedding (Optional[Embeddings]) – Embedding function. Defaults to No... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-14 | Returns
List of Documents selected by maximal marginal relevance.
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[langchain.schema.Document][source]#
Return docs selected using the max... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-15 | List of documents most similar to the query text.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to embedding vector.
:param embedding: Embedding to loo... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-16 | document (Document) – Document to update.
class langchain.vectorstores.DeepLake(dataset_path: str = './deeplake/', token: Optional[str] = None, embedding_function: Optional[langchain.embeddings.base.Embeddings] = None, read_only: Optional[bool] = False, ingestion_batch_size: int = 1024, num_workers: int = 0, verbose: b... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-17 | Returns
List of IDs of the added texts.
Return type
List[str]
delete(ids: Any[List[str], None] = None, filter: Any[Dict[str, str], None] = None, delete_all: Any[bool, None] = None) → bool[source]#
Delete the entities in the dataset
Parameters
ids (Optional[List[str]], optional) – The document_ids to delete.
Defaults to... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-18 | Google Cloud Storage path of the formgcs://bucketname/path/to/dataset Credentials are required
in either the environment
Local file system path of the form ./path/to/dataset or~/path/to/dataset or path/to/dataset.
In-memory path of the form mem://path/to/dataset which doesn’tsave the dataset, but keeps it in memory ins... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-19 | Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs selected using the maximal marginal relevance.... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-20 | Defaults to None.
maximal_marginal_relevance – Whether to use maximal marginal relevance.
Defaults to False.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
return_score – Whether to return the score. Defaults to False.
Returns
List of Documents most similar to the query vector.
similar... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-21 | Wrapper around HnswLib storage.
To use it, you should have the docarray package with version >=0.32.0 installed.
You can install it with pip install “langchain[docarray]”.
classmethod from_params(embedding: langchain.embeddings.base.Embeddings, work_dir: str, n_dim: int, dist_metric: Literal['cosine', 'ip', 'l2'] = 'co... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-22 | with new added ones. Defaults to True.
num_threads (int) – Sets the number of cpu threads to use. Defaults to 1.
**kwargs – Other keyword arguments to be passed to the get_doc_cls method.
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, wo... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-23 | Initialize DocArrayInMemorySearch store.
Parameters
embedding (Embeddings) – Embedding function.
metric (str) – metric for exact nearest-neighbor search.
Can be one of: “cosine_sim”, “euclidean_dist” and “sqeuclidean_dist”.
Defaults to “cosine_sim”.
**kwargs – Other keyword arguments to be passed to the get_doc_cls met... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-24 | embedding = OpenAIEmbeddings()
elastic_vector_search = ElasticVectorSearch(
elasticsearch_url="http://localhost:9200",
index_name="test_index",
embedding=embedding
)
To connect to an Elasticsearch instance that requires login credentials,
including Elastic Cloud, use the Elasticsearch URL format
https://use... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-25 | index_name (str) – The name of the Elasticsearch index for the embeddings.
embedding (Embeddings) – An object that provides the ability to embed text.
It should be an instance of a class that subclasses the Embeddings
abstract base class, such as OpenAIEmbeddings()
Raises
ValueError – If the elasticsearch python packag... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-26 | from langchain import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
elastic_vector_search = ElasticVectorSearch.from_texts(
texts,
embeddings,
elasticsearch_url="http://localhost:9200"
)
similarity_search(query: str, k: int = 4, filter: Optional[dict] ... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-27 | add_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]#
Run more texts through the embeddings and add to the vectorstore.
Parameters
text_embeddings – Iterable pairs of string and embedding to
add to ... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-28 | text_embedding_pairs = list(zip(texts, text_embeddings))
faiss = FAISS.from_embeddings(text_embedding_pairs, embeddings)
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → langchain.vectorsto... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-29 | 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 Documen... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-30 | and index_to_docstore_id to.
index_name – for saving with a specific index file name
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-31 | Returns
List of Documents most similar to the query and score for each
class langchain.vectorstores.LanceDB(connection: Any, embedding: langchain.embeddings.base.Embeddings, vector_key: Optional[str] = 'vector', id_key: Optional[str] = 'id', text_key: Optional[str] = 'text')[source]#
Wrapper around LanceDB vector datab... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-32 | Return documents most similar to the query
Parameters
query – String to query the vectorstore with.
k – Number of documents to return.
Returns
List of documents most similar to the query.
class langchain.vectorstores.Milvus(embedding_function: langchain.embeddings.base.Embeddings, collection_name: str = 'LangChainColle... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-33 | Returns
The resulting keys for each inserted element.
Return type
List[str]
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'LangChainCollection', connection_args: dict[str, Any] = {'host': 'localhost', 'password': ... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-34 | Returns
Milvus Vector Store
Return type
Milvus
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[langchain.schema.Document][source]#
Perform a search and return... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-35 | Parameters
embedding (str) – The embedding vector being searched.
k (int, optional) – How many results to give. Defaults to 4.
fetch_k (int, optional) – Total results to select k from.
Defaults to 20.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to ... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-36 | Returns
Document results for search.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[langchain.schema.Document][source]#
Perform a similarity search against the query... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-37 | Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (int, optional) – How long to wait before timeout error.
Defaults to None.
kwargs – Collection.search() keyword arguments.
Return type
List[float], List[Tuple[Document, any, any]]
similarity_search_with_score_by_vector(embedding: L... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-38 | To use, you should have both:
- the pymongo python package installed
- a connection string associated with a MongoDB Atlas Cluster having deployed an
Atlas Search index
Example
from langchain.vectorstores import MongoDBAtlasVectorSearch
from langchain.embeddings.openai import OpenAIEmbeddings
from pymongo import MongoC... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-39 | This is intended to be a quick way to get started.
Example
similarity_search(query: str, k: int = 4, pre_filter: Optional[dict] = None, post_filter_pipeline: Optional[List[Dict]] = None, **kwargs: Any) → List[langchain.schema.Document][source]#
Return MongoDB documents most similar to query.
Use the knnBeta Operator av... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-40 | Parameters
query – Text to look up documents similar to.
k – Optional Number of Documents to return. Defaults to 4.
pre_filter – Optional Dictionary of argument(s) to prefilter on document
fields.
post_filter_pipeline – Optional Pipeline of MongoDB aggregation stages
following the knnBeta search.
Returns
List of Docume... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-41 | Helper function: Drop data
escape_str(value: str) → str[source]#
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[Dict[Any, Any]]] = None, config: Optional[langchain.vectorstores.myscale.MyScaleSettings] = None, text_ids: Optional[Iterable[str]] = None, ... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-42 | of SQL injection. When dealing with metadatas, remember to
use {self.metadata_column}.attribute instead of attribute
alone. The default name for it is metadata.
Returns
List of Documents
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, where_str: Optional[str] = None, **kwargs:... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-43 | Returns
List of documents
Return type
List[Document]
pydantic settings langchain.vectorstores.MyScaleSettings[source]#
MyScale Client Configuration
Attribute:
myscale_host (str)An URL to connect to MyScale backend.Defaults to ‘localhost’.
myscale_port (int) : URL port to connect with HTTP. Defaults to 8443.
username (s... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-44 | Show JSON schema{
"title": "MyScaleSettings",
"description": "MyScale Client Configuration\n\nAttribute:\n myscale_host (str) : An URL to connect to MyScale backend.\n Defaults to 'localhost'.\n myscale_port (int) : URL port to connect with HTTP. Defaults to 8443.\n username (str)... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-45 | },
"port": {
"title": "Port",
"default": 8443,
"env_names": "{'myscale_port'}",
"type": "integer"
},
"username": {
"title": "Username",
"env_names": "{'myscale_username'}",
"type": "string"
},
"password": {
"title": "P... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-46 | },
"table": {
"title": "Table",
"default": "langchain",
"env_names": "{'myscale_table'}",
"type": "string"
},
"metric": {
"title": "Metric",
"default": "cosine",
"env_names": "{'myscale_metric'}",
"type": "string"
}
},
... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-47 | Example
from langchain import OpenSearchVectorSearch
opensearch_vector_search = OpenSearchVectorSearch(
"http://localhost:9200",
"embeddings",
embedding_function
)
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, bulk_size: int = 500, **kwargs: Any) → List[str][source]#
Run more texts... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-48 | search through Script Scoring and Painless Scripting.
Optional Args:vector_field: Document field embeddings are stored in. Defaults to
“vector_field”.
text_field: Document field the text of the document is stored in. Defaults
to “text”.
Optional Keyword Args for Approximate Search:engine: “nmslib”, “faiss”, “lucene”; d... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-49 | metadata_field: Document field that metadata is stored in. Defaults to
“metadata”.
Can be set to a special value “*” to include the entire document.
Optional Args for Approximate Search:search_type: “approximate_search”; default: “approximate_search”
boolean_filter: A Boolean filter consists of a Boolean query that
con... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-50 | k – Number of Documents to return. Defaults to 4.
Returns
List of Documents along with its scores most similar to the query.
Optional Args:same as similarity_search
class langchain.vectorstores.Pinecone(index: Any, embedding_function: Callable, text_key: str, namespace: Optional[str] = None)[source]#
Wrapper around Pin... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-51 | Load pinecone vectorstore from index name.
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, batch_size: int = 32, text_key: str = 'text', index_name: Optional[str] = None, namespace: Optional[str] = None, *... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-52 | Returns
List of Documents most similar to the query and score for each
similarity_search_with_score(query: str, k: int = 4, filter: Optional[dict] = None, namespace: Optional[str] = None) → List[Tuple[langchain.schema.Document, float]][source]#
Return pinecone documents most similar to query, along with scores.
Paramet... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-53 | metadatas – Optional list of metadatas associated with the texts.
Returns
List of ids from adding the texts into the vectorstore.
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, location: Optional[str] = None, url: Optional[str] = None, p... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-54 | grpc_port – Port of the gRPC interface. Default: 6334
prefer_grpc – If true - use gPRC interface whenever possible in custom methods.
Default: False
https – If true - use HTTPS(SSL) protocol. Default: None
api_key – API key for authentication in Qdrant Cloud. Default: None
prefix – If not None - add prefix to the REST ... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-55 | Example
from langchain import Qdrant
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
qdrant = Qdrant.from_texts(texts, embeddings, "localhost")
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[langchain.schema.Docu... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-56 | Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query and score for each.
class langchain.vectorstores.Redis(redis_url: str, index_name: str, embedding_function: typing... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-57 | Defaults to None.
batch_size (int, optional) – Batch size to use for writes. Defaults to 1000.
Returns
List of ids added to the vectorstore
Return type
List[str]
as_retriever(**kwargs: Any) → langchain.vectorstores.redis.RedisVectorStoreRetriever[source]#
static drop_index(index_name: str, delete_documents: bool, **kwa... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-58 | This is intended to be a quick way to get started.
.. rubric:: Example
classmethod from_texts_return_keys(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, index_name: Optional[str] = None, content_key: str = 'content', metadata_key: str = 'metadata', vector_key:... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-59 | k (int) – The number of documents to return. Default is 4.
score_threshold (float) – The minimum matching score required for a document
0.2. (to be considered a match. Defaults to) –
similarity (Because the similarity calculation algorithm is based on cosine) –
:param :
:param the smaller the angle:
:param the higher... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-60 | kwargs – vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, persist_path: Optional[str] = None, **kwargs: Any) →... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-61 | :param k: Number of Documents to return. Defaults to 4.
:param fetch_k: Number of Documents to fetch to pass to MMR algorithm.
:param 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.
Retur... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-62 | 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.
kwargs – vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
add_vectors(vecto... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-63 | demonstrates how to do this:
```sql
CREATE FUNCTION match_documents_embeddings(query_embedding vector(1536),
match_count int)
RETURNS TABLE(id bigint,
content text,
metadata jsonb,
embedding vector(1536),
similarity float)
LANGUAGE plpgsql
AS $$
# variable_conflict use_column
BEGINRETURN query
SELECT
id,
content,
metad... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-64 | 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_returning_embeddings(query: List[float], k: int) → List[Tuple[langchai... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-65 | Add texts data to an existing index.
create_index_if_not_exist(dim: int, distance_type: str, index_type: str, data_type: str, **kwargs: Any) → bool[source]#
static drop_index(index_name: str = 'langchain', **kwargs: Any) → bool[source]#
Drop an existing index.
Parameters
index_name (str) – Name of the index to drop.
Re... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-66 | Returns the most similar indexed documents to the query text.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
Returns
A list of documents that are most similar to the query text.
Return type
List[Document]
class langchain.vectorstor... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-67 | 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.
Returns
List of ids from adding the texts into the vectorstore.
classmethod from_client_params(embedding: langchain.embeddings.base.... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-68 | Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter – typesense filter_by expression to filter documents on
Returns
List of Documents most similar to the query and score for each
similarity_search_with_score(query: str, k: int = 4, filter: Optional[str] = ''... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-69 | Returns
List of ids from adding the texts into the vectorstore.
as_retriever(**kwargs: Any) → langchain.vectorstores.vectara.VectaraRetriever[source]#
classmethod from_texts(texts: List[str], embedding: Optional[langchain.embeddings.base.Embeddings] = None, metadatas: Optional[List[dict]] = None, **kwargs: Any) → langc... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-70 | Return Vectara 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 5.
alpha – parameter for hybrid search (called “lambda” in Vectara
documentation).
filter – Dictionary of argument(s) to filter on metadata. For exam... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-71 | 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.
kwargs – vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
async classmethod... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-72 | Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[langchain.schema.Docum... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-73 | 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.
max_marginal_relevance_search_by_vector(embedding: List[float], k: int =... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-74 | k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query vector.
similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[langchain.schema.Document, float]][source]#
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 i... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-75 | Upload texts with metadata (properties) to Weaviate.
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → langchain.vectorstores.weaviate.Weaviate[source]#
Construct Weaviate wrapper from raw documents.
This is a user-friendly... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-76 | Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs selected using the maximal marginal relevance.... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-77 | Look up similar documents by embedding vector in Weaviate.
similarity_search_with_score(query: str, k: int = 4, **kwargs: Any) → List[Tuple[langchain.schema.Document, float]][source]#
class langchain.vectorstores.Zilliz(embedding_function: langchain.embeddings.base.Embeddings, collection_name: str = 'LangChainCollectio... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
5641e0c3fcb0-78 | Defaults to None.
search_params (Optional[dict], optional) – Which search params to use.
Defaults to None.
drop_old (Optional[bool], optional) – Whether to drop the collection with
that name if it exists. Defaults to False.
Returns
Zilliz Vector Store
Return type
Zilliz
previous
Document Loaders
next
Retrievers
By Harr... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
91755aa8b5b4-0 | .rst
.pdf
SearxNG Search
Contents
Quick Start
Searching
Engine Parameters
Search Tips
SearxNG Search#
Utility for using SearxNG meta search API.
SearxNG is a privacy-friendly free metasearch engine that aggregates results from
multiple search engines and databases and
supports the OpenSearch
specification.
More detai... | https://python.langchain.com/en/latest/reference/modules/searx_search.html |
91755aa8b5b4-1 | # assuming the searx host is set as above or exported as an env variable
s = SearxSearchWrapper(engines=['google', 'bing'],
language='es')
Search Tips#
Searx offers a special
search syntax
that can also be used instead of passing engine parameters.
For example the following query:
s = SearxSearchWra... | https://python.langchain.com/en/latest/reference/modules/searx_search.html |
91755aa8b5b4-2 | use a self hosted instance and disable the rate limiter.
If you are self-hosting an instance you can customize the rate limiter for your
own network as described here.
For a list of public SearxNG instances see https://searx.space/
class langchain.utilities.searx_search.SearxResults(data: str)[source]#
Dict like wrappe... | https://python.langchain.com/en/latest/reference/modules/searx_search.html |
91755aa8b5b4-3 | field params: dict [Optional]#
field query_suffix: Optional[str] = ''#
field searx_host: str = ''#
field unsecure: bool = False#
async aresults(query: str, num_results: int, engines: Optional[List[str]] = None, query_suffix: Optional[str] = '', **kwargs: Any) → List[Dict][source]#
Asynchronously query with json results... | https://python.langchain.com/en/latest/reference/modules/searx_search.html |
91755aa8b5b4-4 | Run query through Searx API and parse results.
You can pass any other params to the searx query API.
Parameters
query – The query to search for.
query_suffix – Extra suffix appended to the query.
engines – List of engines to use for the query.
categories – List of categories to use for the query.
**kwargs – extra param... | https://python.langchain.com/en/latest/reference/modules/searx_search.html |
1c9bc9e0ad7d-0 | .rst
.pdf
SerpAPI
SerpAPI#
For backwards compatiblity.
pydantic model langchain.serpapi.SerpAPIWrapper[source]#
Wrapper around SerpAPI.
To use, you should have the google-search-results python package installed,
and the environment variable SERPAPI_API_KEY set with your API key, or pass
serpapi_api_key as a named param... | https://python.langchain.com/en/latest/reference/modules/serpapi.html |
58b3cb6ae83d-0 | .rst
.pdf
Agents
Agents#
Interface for agents.
pydantic model langchain.agents.Agent[source]#
Class responsible for calling the language model and deciding the action.
This is driven by an LLMChain. The prompt in the LLMChain MUST include
a variable called “agent_scratchpad” where the agent can put its
intermediary wor... | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-1 | Construct an agent from an LLM and tools.
get_allowed_tools() → Optional[List[str]][source]#
get_full_inputs(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Dict[str, Any][source]#
Create the full inputs for the LLMChain from intermediate steps.
plan(intermediate_steps: List[Tuple[l... | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-2 | field early_stopping_method: str = 'force'#
field handle_parsing_errors: Union[bool, str, Callable[[OutputParserException], str]] = False#
field max_execution_time: Optional[float] = None#
field max_iterations: Optional[int] = 15#
field return_intermediate_steps: bool = False#
field tools: Sequence[BaseTool] [Required]... | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-3 | SELF_ASK_WITH_SEARCH = 'self-ask-with-search'#
STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION = 'structured-chat-zero-shot-react-description'#
ZERO_SHOT_REACT_DESCRIPTION = 'zero-shot-react-description'#
pydantic model langchain.agents.BaseMultiActionAgent[source]#
Base Agent class.
abstract async aplan(intermediate_steps... | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-4 | Return response when agent has been stopped due to max iterations.
save(file_path: Union[pathlib.Path, str]) → None[source]#
Save the agent.
Parameters
file_path – Path to file to save the agent to.
Example:
.. code-block:: python
# If working with agent executor
agent.agent.save(file_path=”path/agent.yaml”)
tool_run_l... | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-5 | get_allowed_tools() → Optional[List[str]][source]#
abstract plan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, **kwargs: Any) → Union[langchain.schema.AgentAction, l... | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-6 | classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool], prefix: str = 'Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-7 | say to the Human, or if you do not need to use a tool, you MUST use the format:\n\n```\nThought: Do I need to use a tool? No\n{ai_prefix}: [your response here]\n```', ai_prefix: str = 'AI', human_prefix: str = 'Human', input_variables: Optional[List[str]] = None) → langchain.prompts.prompt.PromptTemplate[source]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-8 | Create prompt in the style of the zero shot agent.
Parameters
tools – List of tools the agent will have access to, used to format the
prompt.
prefix – String to put before the list of tools.
suffix – String to put after the list of tools.
ai_prefix – String to use before AI output.
human_prefix – String to use before h... | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-9 | classmethod from_llm_and_tools(llm: langchain.base_language.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.agent.AgentOutputParser] = None, prefix: str = 'Assistant is a large la... | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-10 | the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n```\n\nWhen you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:\n\n```\nThought: Do I need to use a tool? No\n{ai_prefix}: [your response ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-11 | Construct an agent from an LLM and tools.
property llm_prefix: str#
Prefix to append the llm call with.
property observation_prefix: str#
Prefix to append the observation with.
pydantic model langchain.agents.ConversationalChatAgent[source]#
An agent designed to hold a conversation in addition to using tools.
field out... | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-12 | classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool], system_message: str = 'Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide r... | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-13 | classmethod from_llm_and_tools(llm: langchain.base_language.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.agent.AgentOutputParser] = None, system_message: str = 'Assistant is a ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-14 | with a single action, and NOTHING else):\n\n{{{{input}}}}", input_variables: Optional[List[str]] = None, **kwargs: Any) → langchain.agents.agent.Agent[source]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-15 | Construct an agent from an LLM and tools.
property llm_prefix: str#
Prefix to append the llm call with.
property observation_prefix: str#
Prefix to append the observation with.
pydantic model langchain.agents.LLMSingleActionAgent[source]#
field llm_chain: langchain.chains.llm.LLMChain [Required]#
field output_parser: l... | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-16 | pydantic model langchain.agents.MRKLChain[source]#
Chain that implements the MRKL system.
Example
from langchain import OpenAI, MRKLChain
from langchain.chains.mrkl.base import ChainConfig
llm = OpenAI(temperature=0)
prompt = PromptTemplate(...)
chains = [...]
mrkl = MRKLChain.from_chains(llm=llm, prompt=prompt)
Valida... | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-17 | action_description="useful for doing math"
)
]
mrkl = MRKLChain.from_chains(llm, chains)
pydantic model langchain.agents.ReActChain[source]#
Chain that implements the ReAct paper.
Example
from langchain import ReActChain, OpenAI
react = ReAct(llm=OpenAI())
Validators
raise_deprecation » all fields
set_verbose » ver... | https://python.langchain.com/en/latest/reference/modules/agents.html |
58b3cb6ae83d-18 | field output_parser: langchain.agents.agent.AgentOutputParser [Optional]#
classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool], prefix: str = 'Respond to the human as helpfully and accurately as possible. You have access to the following tools:', suffix: str = 'Begin! Reminder to ALWAYS respond with... | https://python.langchain.com/en/latest/reference/modules/agents.html |
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