id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 49 117 |
|---|---|---|
fca5983fa07d-23 | 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 method.
classmethod from_texts(texts: List[s... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-24 | 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://username:password@es_host:9243. For example, to connect to Elastic
Cloud, create th... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-25 | 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 package is not installed.
add_texts(texts: Iterable[str], metadatas: Optional[Lis... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-26 | embeddings,
elasticsearch_url="http://localhost:9200"
)
similarity_search(query: str, k: int = 4, filter: Optional[dict] = None, **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. De... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-27 | Run more texts through the embeddings and add 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.
Returns
List of ids from adding the texts into the vectors... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-28 | 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.vectorstores.faiss.FAISS[source]#
Construct FAISS wrapper from raw... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-29 | 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.
max_mar... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-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 |
fca5983fa07d-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 |
fca5983fa07d-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 |
fca5983fa07d-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 |
fca5983fa07d-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 |
fca5983fa07d-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 |
fca5983fa07d-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 |
fca5983fa07d-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 |
fca5983fa07d-38 | to connect to MyScale.
MyScale can not only search with simple vector indexes,
it also supports complex query with multiple conditions,
constraints and even sub-queries.
For more information, please visit[myscale official site](https://docs.myscale.com/en/overview/)
add_texts(texts: Iterable[str], metadatas: Optional[L... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-39 | Defaults to 32.
metadata (List[dict], optional) – metadata to texts. Defaults to None.
into (Other keyword arguments will pass) – [clickhouse-connect](https://clickhouse.com/docs/en/integrations/python#clickhouse-connect-driver-api)
Returns
MyScale Index
property metadata_column: str#
similarity_search(query: str, k: i... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-40 | Returns
List of (Document, similarity)
Return type
List[Document]
similarity_search_with_relevance_scores(query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any) → List[Tuple[langchain.schema.Document, float]][source]#
Perform a similarity search with MyScale
Parameters
query (str) – query string
k (int... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-41 | must be same size to number of columns. For example:
.. code-block:: python
{
‘id’: ‘text_id’,
‘vector’: ‘text_embedding’,
‘text’: ‘text_plain’,
‘metadata’: ‘metadata_dictionary_in_json’,
}
Defaults to identity map.
Show JSON schema{
"title": "MyScaleSettings",
"description": "MyScale Client Configuration\n\nAttr... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-42 | "type": "object",
"properties": {
"host": {
"title": "Host",
"default": "localhost",
"env_names": "{'myscale_host'}",
"type": "string"
},
"port": {
"title": "Port",
"default": 8443,
"env_names": "{'myscale_port'}",
"type": "int... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-43 | "type": "string"
}
},
"database": {
"title": "Database",
"default": "default",
"env_names": "{'myscale_database'}",
"type": "string"
},
"table": {
"title": "Table",
"default": "langchain",
"env_names": "{'myscale_table'}",
... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-44 | field table: str = 'langchain'#
field username: Optional[str] = None#
class langchain.vectorstores.OpenSearchVectorSearch(opensearch_url: str, index_name: str, embedding_function: langchain.embeddings.base.Embeddings, **kwargs: Any)[source]#
Wrapper around OpenSearch as a vector database.
Example
from langchain import ... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-45 | texts,
embeddings,
opensearch_url="http://localhost:9200"
)
OpenSearch by default supports Approximate Search powered by nmslib, faiss
and lucene engines recommended for large datasets. Also supports brute force
search through Script Scoring and Painless Scripting.
Optional Args:vector_field: Document field emb... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-46 | Returns
List of Documents most similar to the query.
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”.
metadata_field: Document field that metadata is stored in. Defaults to
“metadata”.
C... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-47 | Return docs and it’s scores most similar to query.
By default supports Approximate Search.
Also supports Script Scoring and Painless Scripting.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents along with its scores most similar to the q... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-48 | Returns
List of ids from adding the texts into the vectorstore.
classmethod from_existing_index(index_name: str, embedding: langchain.embeddings.base.Embeddings, text_key: str = 'text', namespace: Optional[str] = None) → langchain.vectorstores.pinecone.Pinecone[source]#
Load pinecone vectorstore from index name.
classm... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-49 | k – Number of Documents to return. Defaults to 4.
filter – Dictionary of argument(s) to filter on metadata
namespace – Namespace to search in. Default will search in ‘’ namespace.
Returns
List of Documents most similar to the query and score for each
similarity_search_with_score(query: str, k: int = 4, filter: Optional... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-50 | 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.
Returns
List of ids from adding the texts into the vectorstore.
classmethod from_texts(texts: List[str], embedding: langch... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-51 | port – Port of the REST API interface. Default: 6333
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... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-52 | This is intended to be a quick way to get started.
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... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-53 | Returns
List of Documents most similar to the query.
similarity_search_with_score(query: str, k: int = 4, filter: Optional[Dict[str, Union[str, int, bool, dict, list]]] = None) → List[Tuple[langchain.schema.Document, float]][source]#
Return docs most similar to query.
Parameters
query – Text to look up documents simila... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-54 | Parameters
texts (Iterable[str]) – Iterable of strings/text to add to the vectorstore.
metadatas (Optional[List[dict]], optional) – Optional list of metadatas.
Defaults to None.
embeddings (Optional[List[List[float]]], optional) – Optional pre-generated
embeddings. Defaults to None.
keys (Optional[List[str]], optional)... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-55 | Create a Redis vectorstore from raw documents.
This is a user-friendly interface that:
Embeds documents.
Creates a new index for the embeddings in Redis.
Adds the documents to the newly created Redis index.
This is intended to be a quick way to get started.
.. rubric:: Example
classmethod from_texts_return_keys(texts: ... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-56 | Returns the most similar indexed documents to the query text within the
score_threshold range.
Parameters
query (str) – The query text for which to find similar documents.
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 ... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-57 | Note that the Supabase Python client does not yet support async operations.
If you’d like to use max_marginal_relevance_search, please review the instructions
below on modifying the match_documents function to return matched embeddings.
add_texts(texts: Iterable[str], metadatas: Optional[List[dict[Any, Any]]] = None, *... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-58 | 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.
max_mar... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-59 | to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
query_name: str#
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to query.
similarity_search_by_vector(embeddin... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-60 | Returns
List of Tuples of (doc, similarity_score)
table_name: str#
class langchain.vectorstores.Tair(embedding_function: langchain.embeddings.base.Embeddings, url: str, index_name: str, content_key: str = 'content', metadata_key: str = 'metadata', search_params: Optional[dict] = None, **kwargs: Any)[source]#
add_texts(... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-61 | Connect to an existing Tair index.
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, index_name: str = 'langchain', content_key: str = 'content', metadata_key: str = 'metadata', **kwargs: Any) → langchain.vectorstores.tair.Tair[source]#
Ret... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-62 | "connection_timeout_seconds": 2
}
)
typesense_collection_name = "langchain-memory"
embedding = OpenAIEmbeddings()
vectorstore = Typesense(
typesense_client,
typesense_collection_name,
embedding.embed_query,
"text",
)
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Option... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-63 | protocol="http",
typesense_collection_name="langchain-memory",
)
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, typesense_client: Optional[Client] = None, typesense_client_params: Optional[dict] = None, typesense_collection_na... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-64 | Implementation of Vector Store using Vectara (https://vectara.com).
.. rubric:: Example
from langchain.vectorstores import Vectara
vectorstore = Vectara(
vectara_customer_id=vectara_customer_id,
vectara_corpus_id=vectara_corpus_id,
vectara_api_key=vectara_api_key
)
add_texts(texts: Iterable[str], metadatas:... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-65 | 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.
filter – Dictionary of argument(s) to filter on metadata. For example a
filter can be “doc.rating > 3.0 and part.lang = ‘deu’”} see
https://docs.v... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-66 | 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]#
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents: List[langchain.schema.Document], **kwargs: Any) → Li... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-67 | 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[langchain.schema.Document][source]#
Return docs selected using the maximal marginal relevance.
async amax_marginal_relevance_search_by... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-68 | Return VectorStore initialized from documents and embeddings.
abstract classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → langchain.vectorstores.base.VST[source]#
Return VectorStore initialized from texts and embeddings.
max... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-69 | 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[langchain.sch... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-70 | Returns
List of Tuples of (doc, similarity_score)
class langchain.vectorstores.Weaviate(client: typing.Any, index_name: str, text_key: str, embedding: typing.Optional[langchain.embeddings.base.Embeddings] = None, attributes: typing.Optional[typing.List[str]] = None, relevance_score_fn: typing.Optional[typing.Callable[[... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-71 | weaviate = Weaviate.from_texts(
texts,
embeddings,
weaviate_url="http://localhost:8080"
)
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.
Ma... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-72 | Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
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 t... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-73 | 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] = {}, consistency_level: str = 'Session', index_params: Optional[dict] = None, search_params: Optional[dict] = N... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
cba4200d5408-0 | .rst
.pdf
Document Loaders
Document Loaders#
All different types of document loaders.
class langchain.document_loaders.AZLyricsLoader(web_path: Union[str, List[str]], header_template: Optional[dict] = None)[source]#
Loader that loads AZLyrics webpages.
load() → List[langchain.schema.Document][source]#
Load webpage.
cla... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-1 | Loading logic for loading documents from Azure Blob Storage.
load() → List[langchain.schema.Document][source]#
Load documents.
class langchain.document_loaders.AzureBlobStorageFileLoader(conn_str: str, container: str, blob_name: str)[source]#
Loading logic for loading documents from Azure Blob Storage.
load() → List[la... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-2 | load() → List[langchain.schema.Document][source]#
Load bibtex file documents from the given bibtex file path.
See https://bibtexparser.readthedocs.io/en/master/
Parameters
file_path – the path to the bibtex file
Returns
a list of documents with the document.page_content in text format
class langchain.document_loaders.B... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-3 | cookie by logging into the course and then copying the value of the
BbRouter cookie from the browser’s developer tools.
Example
from langchain.document_loaders import BlackboardLoader
loader = BlackboardLoader(
blackboard_course_url="https://blackboard.example.com/webapps/blackboard/execute/announcement?method=sear... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-4 | The Loader uses the Alchemy API to interact with the blockchain.
ALCHEMY_API_KEY environment variable must be set to use this loader.
The API returns 100 NFTs per request and can be paginated using the
startToken parameter.
If get_all_tokens is set to True, the loader will get all tokens
on the contract. Note that for... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-5 | column3: value3
load() → List[langchain.schema.Document][source]#
Load data into document objects.
class langchain.document_loaders.ChatGPTLoader(log_file: str, num_logs: int = - 1)[source]#
Loader that loads conversations from exported ChatGPT data.
load() → List[langchain.schema.Document][source]#
Load data into docu... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-6 | is set to False by default, if set to True all attachments will be downloaded and
ConfluenceReader will extract the text from the attachments and add it to the
Document object. Currently supported attachment types are: PDF, PNG, JPEG/JPG,
SVG, Word and Excel.
Hint: space_key and page_id can both be found in the URL of ... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-7 | Check if a page is publicly accessible.
load(space_key: Optional[str] = None, page_ids: Optional[List[str]] = None, label: Optional[str] = None, cql: Optional[str] = None, include_restricted_content: bool = False, include_archived_content: bool = False, include_attachments: bool = False, include_comments: bool = False,... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-8 | doesn’t match the limit value. If limit is >100 confluence
seems to cap the response to 100. Also, due to the Atlassian Python
package, we don’t get the “next” values from the “_links” key because
they only return the value from the results key. So here, the pagination
starts from 0 and goes until the max_pages, getti... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-9 | Validates proper combinations of init arguments
class langchain.document_loaders.DataFrameLoader(data_frame: Any, page_content_column: str = 'text')[source]#
Load Pandas DataFrames.
load() → List[langchain.schema.Document][source]#
Load from the dataframe.
class langchain.document_loaders.DiffbotLoader(api_token: str, ... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-10 | load() → List[langchain.schema.Document][source]#
Load all chat messages.
pydantic model langchain.document_loaders.DocugamiLoader[source]#
Loader that loads processed docs from Docugami.
To use, you should have the lxml python package installed.
field access_token: Optional[str] = None#
field api: str = 'https://api.d... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-11 | are written into the page_content and none into the metadata.
load() → List[langchain.schema.Document][source]#
Load data into document objects.
class langchain.document_loaders.EverNoteLoader(file_path: str, load_single_document: bool = True)[source]#
EverNote Loader.
Loads an EverNote notebook export file e.g. my_not... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-12 | load() → List[langchain.schema.Document][source]#
Load documents.
class langchain.document_loaders.GCSFileLoader(project_name: str, bucket: str, blob: str)[source]#
Loading logic for loading documents from GCS.
load() → List[langchain.schema.Document][source]#
Load documents.
class langchain.document_loaders.GitLoader(... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-13 | A Generic Google Api Client.
To use, you should have the google_auth_oauthlib,youtube_transcript_api,google
python package installed.
As the google api expects credentials you need to set up a google account and
register your Service. “https://developers.google.com/docs/api/quickstart/python”
Example
from langchain.doc... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-14 | from langchain.document_loaders import GoogleApiYoutubeLoader
google_api_client = GoogleApiClient(
service_account_path=Path("path_to_your_sec_file.json")
)
loader = GoogleApiYoutubeLoader(
google_api_client=google_api_client,
channel_name = "CodeAesthetic"
)
load.load()
add_video_info: bool = True#
caption... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-15 | class langchain.document_loaders.GutenbergLoader(file_path: str)[source]#
Loader that uses urllib to load .txt web files.
load() → List[langchain.schema.Document][source]#
Load file.
class langchain.document_loaders.HNLoader(web_path: Union[str, List[str]], header_template: Optional[dict] = None)[source]#
Load Hacker N... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-16 | class langchain.document_loaders.IFixitLoader(web_path: str)[source]#
Load iFixit repair guides, device wikis and answers.
iFixit is the largest, open repair community on the web. The site contains nearly
100k repair manuals, 200k Questions & Answers on 42k devices, and all the data is
licensed under CC-BY.
This loader... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-17 | Load from a list of image files
class langchain.document_loaders.JSONLoader(file_path: Union[str, pathlib.Path], jq_schema: str, content_key: Optional[str] = None, metadata_func: Optional[Callable[[Dict, Dict], Dict]] = None, text_content: bool = True)[source]#
Loads a JSON file and references a jq schema provided to l... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-18 | Load MediaWiki dump from XML file
.. rubric:: Example
from langchain.document_loaders import MWDumpLoader
loader = MWDumpLoader(
file_path="myWiki.xml",
encoding="utf8"
)
docs = loader.load()
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
c... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-19 | property url: str#
wait_for_processing(pdf_id: str) → None[source]#
class langchain.document_loaders.ModernTreasuryLoader(resource: str, organization_id: Optional[str] = None, api_key: Optional[str] = None)[source]#
load() → List[langchain.schema.Document][source]#
Load data into document objects.
class langchain.docum... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-20 | Loader that loads Obsidian files from disk.
FRONT_MATTER_REGEX = re.compile('^---\\n(.*?)\\n---\\n', re.MULTILINE|re.DOTALL)#
load() → List[langchain.schema.Document][source]#
Load documents.
pydantic model langchain.document_loaders.OneDriveLoader[source]#
field auth_with_token: bool = False#
field drive_id: str [Requ... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-21 | Eagerly load the content.
class langchain.document_loaders.PDFMinerPDFasHTMLLoader(file_path: str)[source]#
Loader that uses PDFMiner to load PDF files as HTML content.
load() → List[langchain.schema.Document][source]#
Load file.
class langchain.document_loaders.PDFPlumberLoader(file_path: str, text_kwargs: Optional[Ma... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-22 | load() → List[langchain.schema.Document][source]#
Load documents.
class langchain.document_loaders.PyMuPDFLoader(file_path: str)[source]#
Loader that uses PyMuPDF to load PDF files.
load(**kwargs: Optional[Any]) → List[langchain.schema.Document][source]#
Load file.
class langchain.document_loaders.PyPDFDirectoryLoader(... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-23 | Load Python files, respecting any non-default encoding if specified.
class langchain.document_loaders.ReadTheDocsLoader(path: Union[str, pathlib.Path], encoding: Optional[str] = None, errors: Optional[str] = None, custom_html_tag: Optional[Tuple[str, dict]] = None, **kwargs: Optional[Any])[source]#
Loader that loads Re... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-24 | Loader for .srt (subtitle) files.
load() → List[langchain.schema.Document][source]#
Load using pysrt file.
class langchain.document_loaders.SeleniumURLLoader(urls: List[str], continue_on_failure: bool = True, browser: Literal['chrome', 'firefox'] = 'chrome', binary_location: Optional[str] = None, executable_path: Optio... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-25 | Loader that fetches a sitemap and loads those URLs.
load() → List[langchain.schema.Document][source]#
Load sitemap.
parse_sitemap(soup: Any) → List[dict][source]#
Parse sitemap xml and load into a list of dicts.
class langchain.document_loaders.SlackDirectoryLoader(zip_path: str, workspace_url: Optional[str] = None)[so... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-26 | Load documents.
langchain.document_loaders.TelegramChatLoader#
alias of langchain.document_loaders.telegram.TelegramChatFileLoader
class langchain.document_loaders.TextLoader(file_path: str, encoding: Optional[str] = None, autodetect_encoding: bool = False)[source]#
Load text files.
Parameters
file_path – Path to the f... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-27 | Twitter tweets loader.
Read tweets of user twitter handle.
First you need to go to
https://developer.twitter.com/en/docs/twitter-api
/getting-started/getting-access-to-the-twitter-api
to get your token. And create a v2 version of the app.
classmethod from_bearer_token(oauth2_bearer_token: str, twitter_users: Sequence[s... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-28 | Loader that uses unstructured to load epub files.
class langchain.document_loaders.UnstructuredEmailLoader(file_path: Union[str, List[str]], mode: str = 'single', **unstructured_kwargs: Any)[source]#
Loader that uses unstructured to load email files.
class langchain.document_loaders.UnstructuredFileIOLoader(file: Union... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-29 | Loader that uses unstructured to load PDF files.
class langchain.document_loaders.UnstructuredPowerPointLoader(file_path: Union[str, List[str]], mode: str = 'single', **unstructured_kwargs: Any)[source]#
Loader that uses unstructured to load powerpoint files.
class langchain.document_loaders.UnstructuredRTFLoader(file_... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-30 | Load weather data for the given locations.
class langchain.document_loaders.WebBaseLoader(web_path: Union[str, List[str]], header_template: Optional[dict] = None)[source]#
Loader that uses urllib and beautiful soup to load webpages.
aload() → List[langchain.schema.Document][source]#
Load text from the urls in web_path ... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
cba4200d5408-31 | load() → List[langchain.schema.Document][source]#
Load data into document objects.
class langchain.document_loaders.YoutubeLoader(video_id: str, add_video_info: bool = False, language: str = 'en', continue_on_failure: bool = False)[source]#
Loader that loads Youtube transcripts.
static extract_video_id(youtube_url: str... | https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
1ef9da7d89db-0 | .rst
.pdf
Experimental Modules
Contents
Autonomous Agents
Generative Agents
Experimental Modules#
This module contains experimental modules and reproductions of existing work using LangChain primitives.
Autonomous Agents#
Here, we document the BabyAGI and AutoGPT classes from the langchain.experimental module.
class ... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
1ef9da7d89db-1 | Get the next task.
property input_keys: List[str]#
Input keys this chain expects.
property output_keys: List[str]#
Output keys this chain expects.
prioritize_tasks(this_task_id: int, objective: str) → List[Dict][source]#
Prioritize tasks.
class langchain.experimental.AutoGPT(ai_name: str, memory: langchain.vectorstores... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
1ef9da7d89db-2 | Summary of the events in the plan that the agent took.
generate_dialogue_response(observation: str, now: Optional[datetime.datetime] = None) → Tuple[bool, str][source]#
React to a given observation.
generate_reaction(observation: str, now: Optional[datetime.datetime] = None) → Tuple[bool, str][source]#
React to a given... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
1ef9da7d89db-3 | field traits: str = 'N/A'#
Permanent traits to ascribe to the character.
class langchain.experimental.GenerativeAgentMemory(*, llm: langchain.base_language.BaseLanguageModel, memory_retriever: langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever, verbose: bool = False, reflection_threshold: Opt... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
1ef9da7d89db-4 | The core language model.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str][source]#
Return key-value pairs given the text input to the chain.
field memory_retriever: langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever [Required]#
The retriever to fetch related memories.
property m... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
1d31b68ba334-0 | .rst
.pdf
LLMs
LLMs#
Wrappers on top of large language models APIs.
pydantic model langchain.llms.AI21[source]#
Wrapper around AI21 large language models.
To use, you should have the environment variable AI21_API_KEY
set with your API key.
Example
from langchain.llms import AI21
ai21 = AI21(model="j2-jumbo-instruct")
V... | https://python.langchain.com/en/latest/reference/modules/llms.html |
1d31b68ba334-1 | field numResults: int = 1#
How many completions to generate for each prompt.
field presencePenalty: langchain.llms.ai21.AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True)#
Penalizes repeated tokens.
field temperat... | https://python.langchain.com/en/latest/reference/modules/llms.html |
1d31b68ba334-2 | Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#
Creates a new model setting __dict__ a... | https://python.langchain.com/en/latest/reference/modules/llms.html |
1d31b68ba334-3 | Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Take in a list of p... | https://python.langchain.com/en/latest/reference/modules/llms.html |
1d31b68ba334-4 | Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.AlephAlpha[source... | https://python.langchain.com/en/latest/reference/modules/llms.html |
1d31b68ba334-5 | True: apply control by adding the log(control_factor) to attention scores.
False: (attention_scores - - attention_scores.min(-1)) * control_factor
field echo: bool = False#
Echo the prompt in the completion.
field frequency_penalty: float = 0.0#
Penalizes repeated tokens according to frequency.
field log_probs: Optiona... | https://python.langchain.com/en/latest/reference/modules/llms.html |
1d31b68ba334-6 | field stop_sequences: Optional[List[str]] = None#
Stop sequences to use.
field temperature: float = 0.0#
A non-negative float that tunes the degree of randomness in generation.
field tokens: Optional[bool] = False#
return tokens of completion.
field top_k: int = 0#
Number of most likely tokens to consider at each step.... | https://python.langchain.com/en/latest/reference/modules/llms.html |
1d31b68ba334-7 | Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#
Creates a new model setting __dict__ a... | https://python.langchain.com/en/latest/reference/modules/llms.html |
1d31b68ba334-8 | Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Take in a list of p... | https://python.langchain.com/en/latest/reference/modules/llms.html |
1d31b68ba334-9 | Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.Anthropic[source]... | https://python.langchain.com/en/latest/reference/modules/llms.html |
1d31b68ba334-10 | Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Run the LLM on the given pro... | https://python.langchain.com/en/latest/reference/modules/llms.html |
1d31b68ba334-11 | Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep co... | https://python.langchain.com/en/latest/reference/modules/llms.html |
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