id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 59 127 |
|---|---|---|
fe18070e4f36-22 | load_questions_and_answers(url_override: Optional[str] = None) → List[langchain.schema.Document][source]#
static load_suggestions(query: str = '', doc_type: str = 'all') → List[langchain.schema.Document][source]#
class langchain.document_loaders.IMSDbLoader(web_path: Union[str, List[str]], header_template: Optional[dic... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-23 | [“”, “”, “”] -> schema = .[]
load() → List[langchain.schema.Document][source]#
Load and return documents from the JSON file.
class langchain.document_loaders.JoplinLoader(access_token: Optional[str] = None, port: int = 41184, host: str = 'localhost')[source]#
Loader that fetches notes from Joplin.
In order to use this ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-24 | encoding (str, optional) – Charset encoding, defaults to “utf8”
load() → List[langchain.schema.Document][source]#
Load from file path.
class langchain.document_loaders.MastodonTootsLoader(mastodon_accounts: Sequence[str], number_toots: Optional[int] = 100, exclude_replies: bool = False, access_token: Optional[str] = No... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-25 | Convenience constructor that builds the MaxCompute API wrapper fromgiven parameters.
Parameters
query – SQL query to execute.
endpoint – MaxCompute endpoint.
project – A project is a basic organizational unit of MaxCompute, which is
similar to a database.
access_id – MaxCompute access ID. Should be passed in directly o... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-26 | :type request_timeout_sec: int
load() → List[langchain.schema.Document][source]#
Load documents from the Notion database.
:returns: List of documents.
:rtype: List[Document]
load_page(page_id: str) → langchain.schema.Document[source]#
Read a page.
class langchain.document_loaders.NotionDirectoryLoader(path: str)[source... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-27 | Return type
List[Document]
Raises
ValueError – If the specified drive ID
does not correspond to a drive in the OneDrive storage. –
class langchain.document_loaders.OnlinePDFLoader(file_path: str)[source]#
Loader that loads online PDFs.
load() → List[langchain.schema.Document][source]#
Load documents.
class langchain.d... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-28 | alias of langchain.document_loaders.pdf.PyPDFLoader
class langchain.document_loaders.PlaywrightURLLoader(urls: List[str], continue_on_failure: bool = True, headless: bool = True, remove_selectors: Optional[List[str]] = None)[source]#
Loader that uses Playwright and to load a page and unstructured to load the html.
This... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-29 | load() → List[langchain.schema.Document][source]#
Load data into document objects.
class langchain.document_loaders.PyPDFLoader(file_path: str)[source]#
Loads a PDF with pypdf and chunks at character level.
Loader also stores page numbers in metadatas.
lazy_load() → Iterator[langchain.schema.Document][source]#
Lazy loa... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-30 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-31 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-32 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-33 | load() → List[langchain.schema.Document][source]#
Load data into document objects.
class langchain.document_loaders.TelegramChatApiLoader(chat_entity: Optional[EntityLike] = None, api_id: Optional[int] = None, api_hash: Optional[str] = None, username: Optional[str] = None, file_path: str = 'telegram_data.json')[source]... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-34 | Lazily load the file.
load() → List[langchain.schema.Document][source]#
Load file.
class langchain.document_loaders.TomlLoader(source: Union[str, pathlib.Path])[source]#
A TOML document loader that inherits from the BaseLoader class.
This class can be initialized with either a single source file or a source
directory c... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-35 | include_comments – Whether to include the comments on the card in the
document.
include_checklist – Whether to include the checklist on the card in the
document.
card_filter – Filter on card status. Valid values are “closed”, “open”,
“all”.
extra_metadata – List of additional metadata fields to include as document
meta... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-36 | load() → List[langchain.schema.Document][source]#
Load tweets.
class langchain.document_loaders.UnstructuredAPIFileIOLoader(file: Union[IO, Sequence[IO]], mode: str = 'single', url: str = 'https://api.unstructured.io/general/v0/general', api_key: str = '', **unstructured_kwargs: Any)[source]#
Loader that uses the unstr... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-37 | Loader that uses unstructured to load file IO objects.
class langchain.document_loaders.UnstructuredFileLoader(file_path: Union[str, List[str]], mode: str = 'single', **unstructured_kwargs: Any)[source]#
Loader that uses unstructured to load files.
class langchain.document_loaders.UnstructuredHTMLLoader(file_path: Unio... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-38 | Loader that uses unstructured to load rtf files.
class langchain.document_loaders.UnstructuredURLLoader(urls: List[str], continue_on_failure: bool = True, mode: str = 'single', **unstructured_kwargs: Any)[source]#
Loader that uses unstructured to load HTML files.
load() → List[langchain.schema.Document][source]#
Load f... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-39 | aload() → List[langchain.schema.Document][source]#
Load text from the urls in web_path async into Documents.
default_parser: str = 'html.parser'#
Default parser to use for BeautifulSoup.
async fetch_all(urls: List[str]) → Any[source]#
Fetch all urls concurrently with rate limiting.
load() → List[langchain.schema.Docume... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
fe18070e4f36-40 | Load data into document objects.
class langchain.document_loaders.YoutubeLoader(video_id: str, add_video_info: bool = False, language: Union[str, Sequence[str]] = 'en', translation: str = 'en', continue_on_failure: bool = False)[source]#
Loader that loads Youtube transcripts.
static extract_video_id(youtube_url: str) →... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_loaders.html |
0efe78fd32e2-0 | .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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/serpapi.html |
6d5c37c1dbcb-0 | .rst
.pdf
Vector Stores
Vector Stores#
Wrappers on top of vector stores.
class langchain.vectorstores.AnalyticDB(connection_string: str, embedding_function: langchain.embeddings.base.Embeddings, collection_name: str = 'langchain', collection_metadata: Optional[dict] = None, pre_delete_collection: bool = False, logger: ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-1 | Return connection string from database parameters.
create_collection() → None[source]#
create_tables_if_not_exists() → None[source]#
delete_collection() → None[source]#
drop_tables() → None[source]#
classmethod from_documents(documents: List[langchain.schema.Document], embedding: langchain.embeddings.base.Embeddings, c... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-2 | k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query.
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None, **kwargs: Any) → List[langchain.schema.Docum... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-3 | Example
from langchain import Annoy
db = Annoy(embedding_function, index, docstore, index_to_docstore_id)
add_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.
Parameters
texts – Iterable of strings t... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-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: ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-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.... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-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 – ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-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[... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-12 | Parameters
documents (List[Document]) – List of documents to add to the vectorstore.
embedding (Optional[Embeddings]) – Embedding function. Defaults to None.
table_name (str) – Name of the collection to create.
logging_and_data_dir (Optional[str]) – Directory to persist the table.
client (Optional[awadb.Client]) – AwaD... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-13 | Return docs most similar to query.
similarity_search_by_vector(embedding: Optional[List[float]] = None, k: int = 4, scores: Optional[list] = None, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up documents similar to.
k –... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-14 | Add texts data to an existing index.
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, azure_search_endpoint: str = '', azure_search_key: str = '', index_name: str = 'langchain-index', **kwargs: Any) → langchain.vectorstores.azuresearch.Azu... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-15 | Return type
List[Document]
semantic_hybrid_search_with_score(query: str, k: int = 4, filters: Optional[str] = None) → List[Tuple[langchain.schema.Document, float]][source]#
Return docs most similar to query with an hybrid query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return.... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-16 | Returns
List of Documents most similar to the query and score for each
class langchain.vectorstores.Chroma(collection_name: str = 'langchain', embedding_function: Optional[Embeddings] = None, persist_directory: Optional[str] = None, client_settings: Optional[chromadb.config.Settings] = None, collection_metadata: Option... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-17 | Create a Chroma vectorstore from a list of documents.
If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephemeral in-memory.
Parameters
collection_name (str) – Name of the collection to create.
persist_directory (Optional[str]) – Directory to persist the collection... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-18 | ids (Optional[List[str]]) – List of document IDs. Defaults to None.
client_settings (Optional[chromadb.config.Settings]) – Chroma client settings
Returns
Chroma vectorstore.
Return type
Chroma
get(include: Optional[List[str]] = None) → Dict[str, Any][source]#
Gets the collection.
Parameters
include (Optional[List[str]]... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-19 | Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 t... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-20 | :param k: Number of Documents to return. Defaults to 4.
:type k: int
:param filter: Filter by metadata. Defaults to None.
:type filter: Optional[Dict[str, str]]
Returns
List of Documents most similar to the query vector.
similarity_search_with_score(query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, **kwa... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-21 | add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, batch_size: int = 32, ids: Optional[Iterable[str]] = None, **kwargs: Any) → List[str][source]#
Insert more texts through the embeddings and add to the VectorStore.
Parameters
texts – Iterable of strings to add to the VectorStore.
ids – Optional lis... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-22 | Returns
ClickHouse Index
property metadata_column: str#
similarity_search(query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any) → List[langchain.schema.Document][source]#
Perform a similarity search with ClickHouse
Parameters
query (str) – query string
k (int, optional) – Top K neighbors to retrieve. ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-23 | Perform a similarity search with ClickHouse
Parameters
query (str) – query string
k (int, optional) – Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional) – where condition string.
Defaults to None.
NOTE – Please do not let end-user to fill this and always be aware
of SQL injection. When deal... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-24 | ‘uuid’: ‘global_unique_id’
‘embedding’: ‘text_embedding’,
‘document’: ‘text_plain’,
‘metadata’: ‘metadata_dictionary_in_json’,
}
Defaults to identity map.
Show JSON schema{
"title": "ClickhouseSettings", | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-25 | Show JSON schema{
"title": "ClickhouseSettings",
"description": "ClickHouse Client Configuration\n\nAttribute:\n clickhouse_host (str) : An URL to connect to MyScale backend.\n Defaults to 'localhost'.\n clickhouse_port (int) : URL port to connect with HTTP. Defaults to 8443.\n us... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-26 | "type": "object",
"properties": {
"host": {
"title": "Host",
"default": "localhost",
"env_names": "{'clickhouse_host'}",
"type": "string"
},
"port": {
"title": "Port",
"default": 8123,
"env_names": "{'clickhouse_port'}",
"type"... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-27 | "type": "string"
}
},
"column_map": {
"title": "Column Map",
"default": {
"id": "id",
"uuid": "uuid",
"document": "document",
"embedding": "embedding",
"metadata": "metadata"
},
"env_names": "{'clickhous... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-28 | port (int)
table (str)
username (Optional[str])
field column_map: Dict[str, str] = {'document': 'document', 'embedding': 'embedding', 'id': 'id', 'metadata': 'metadata', 'uuid': 'uuid'}#
field database: str = 'default'#
field host: str = 'localhost'#
field index_param: Optional[Union[List, Dict]] = [100, "'L2Distance'"... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-29 | To use, you should have the deeplake python package installed.
Example
from langchain.vectorstores import DeepLake
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = DeepLake("langchain_store", embeddings.embed_query)
add_texts(texts: Iterable[str], metadatas: Optiona... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-30 | Force delete dataset by path
classmethod from_texts(texts: List[str], embedding: Optional[langchain.embeddings.base.Embeddings] = None, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, dataset_path: str = './deeplake/', **kwargs: Any) → langchain.vectorstores.deeplake.DeepLake[source]#
Create a ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-31 | Returns
Deep Lake dataset.
Return type
DeepLake
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 similarity to q... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-32 | persist() → None[source]#
Persist the collection.
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to query.
Parameters
query – text to embed and run the query on.
k – Number of Documents to return.
Defaults to 4.
query – Text to look up docume... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-33 | Run similarity search with Deep Lake with distance returned.
Parameters
query (str) – Query text to search for.
distance_metric – L2 for Euclidean, L1 for Nuclear, max L-infinity
distance, cos for cosine similarity, ‘dot’ for dot product.
Defaults to L2.
k (int) – Number of results to return. Defaults to 4.
filter (Opt... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-34 | n_dim (int) – dimension of an embedding.
dist_metric (str) – Distance metric for DocArrayHnswSearch can be one of:
“cosine”, “ip”, and “l2”. Defaults to “cosine”.
max_elements (int) – Maximum number of vectors that can be stored.
Defaults to 1024.
index (bool) – Whether an index should be built for this field.
Defaults... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-35 | n_dim (int) – dimension of an embedding.
**kwargs – Other keyword arguments to be passed to the __init__ method.
Returns
DocArrayHnswSearch Vector Store
class langchain.vectorstores.DocArrayInMemorySearch(doc_index: BaseDocIndex, embedding: langchain.embeddings.base.Embeddings)[source]#
Wrapper around in-memory storage... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-36 | if it exists. Defaults to None.
metric (str) – metric for exact nearest-neighbor search.
Can be one of: “cosine_sim”, “euclidean_dist” and “sqeuclidean_dist”.
Defaults to “cosine_sim”.
Returns
DocArrayInMemorySearch Vector Store
class langchain.vectorstores.ElasticVectorSearch(elasticsearch_url: str, index_name: str, e... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-37 | Locate the “elastic” user and click “Edit”
Click “Reset password”
Follow the prompts to reset the password
The format for Elastic Cloud URLs is
https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
Example
from langchain import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embeddi... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-38 | create_index(client: Any, index_name: str, mapping: Dict) → None[source]#
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, elasticsearch_url: Optional[str] = None, index_name: Optional[str] = None, refresh_indices: bool = True, **kwargs: A... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-39 | :param k: Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query.
class langchain.vectorstores.FAISS(embedding_function: typing.Callable, index: typing.Any, docstore: langchain.docstore.base.Docstore, index_to_docstore_id: typing.Dict[int, str], relevance_score_fn: typing.Opti... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-40 | Returns
List of ids from adding the texts into the vectorstore.
classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → langchain.vectorstores.faiss.FAISS[source... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-41 | Load FAISS index, docstore, and index_to_docstore_id from disk.
Parameters
folder_path – folder path to load index, docstore,
and index_to_docstore_id from.
embeddings – Embeddings to use when generating queries
index_name – for saving with a specific index file name
max_marginal_relevance_search(query: str, k: int = 4... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-42 | k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch before filtering 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.
Return... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-43 | Defaults to 20.
Returns
List of Documents most similar to the query.
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, fetch_k: int = 20, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to embedding vector.
Parameters
embedding – ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-44 | Return docs most similar to query.
Parameters
embedding – Embedding vector to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
fetch_k – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-45 | kwargs – vectorstore specific parameters
add_texts(texts: Iterable[str], 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
texts – Iterable of strings to add to the vectorstore.
metada... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-46 | Construct Hologres wrapper from raw documents and pre-
generated embeddings.
Return VectorStore initialized from documents and embeddings.
Postgres connection string is required
“Either pass it as a parameter
or set the HOLOGRES_CONNECTION_STRING environment variable.
Example
from langchain import Hologres
from langcha... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-47 | Run similarity search with Hologres with distance.
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query.
similarity_search_by_vector(embedding... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-48 | Wrapper around LanceDB vector database.
To use, you should have lancedb python package installed.
Example
db = lancedb.connect('./lancedb')
table = db.open_table('my_table')
vectorstore = LanceDB(table, embedding_function)
vectorstore.add_texts(['text1', 'text2'])
result = vectorstore.similarity_search('text1')
add_tex... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-49 | Returns
List of documents most similar to the query.
class langchain.vectorstores.MatchingEngine(project_id: str, index: MatchingEngineIndex, endpoint: MatchingEngineIndexEndpoint, embedding: Embeddings, gcs_client: storage.Client, gcs_bucket_name: str, credentials: Optional[Credentials] = None)[source]#
Vertex Matchin... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-50 | region – The default location making the API calls. It must have
regional. (the same location as the GCS bucket and must be) –
gcs_bucket_name – The location where the vectors will be stored in
created. (order for the index to be) –
index_id – The id of the created index.
endpoint_id – The id of the created endpoint.... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-51 | Wrapper around the Milvus vector database.
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, timeout: Optional[int] = None, batch_size: int = 1000, **kwargs: Any) → List[str][source]#
Insert text data into Milvus.
Inserting data when the collection has not be made yet will result
in creating a new... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-52 | 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': ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-53 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-54 | 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 ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-55 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-56 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-57 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-58 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-59 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-60 | 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, ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-61 | 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:... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-62 | List of documents most similar to the query text
and cosine distance in float for each.
Lower score represents more similarity.
Return type
List[Document]
pydantic settings langchain.vectorstores.MyScaleSettings[source]#
MyScale Client Configuration
Attribute:
myscale_host (str)An URL to connect to MyScale backend.Defa... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-63 | 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)... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-64 | },
"port": {
"title": "Port",
"default": 8443,
"env_names": "{'myscale_port'}",
"type": "integer"
},
"username": {
"title": "Username",
"env_names": "{'myscale_username'}",
"type": "string"
},
"password": {
"title": "P... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-65 | },
"table": {
"title": "Table",
"default": "langchain",
"env_names": "{'myscale_table'}",
"type": "string"
},
"metric": {
"title": "Metric",
"default": "cosine",
"env_names": "{'myscale_metric'}",
"type": "string"
}
},
... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-66 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-67 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-68 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-69 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-70 | 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, *... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-71 | 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 =... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-72 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-73 | 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. Ids have to be
uuid-like strings.
batch_size – How many vectors upload per-request.
Default: 64
Returns
List of ids from adding the ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-74 | Construct Qdrant wrapper from a list of texts.
Parameters
texts – A list of texts to be indexed in Qdrant.
embedding – A subclass of Embeddings, responsible for text vectorization.
metadatas – An optional list of metadata. If provided it has to be of the same
length as a list of texts.
ids – Optional list of ids to ass... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-75 | collection_name – Name of the Qdrant collection to be used. If not provided,
it will be created randomly. Default: None
distance_func – Distance function. One of: “Cosine” / “Euclid” / “Dot”.
Default: “Cosine”
content_payload_key – A payload key used to store the content of the document.
Default: “page_content”
metadat... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-76 | init_from – Use data stored in another collection to initialize this collection
**kwargs – Additional arguments passed directly into REST client initialization
This is a user-friendly interface that:
1. Creates embeddings, one for each text
2. Initializes the Qdrant database as an in-memory docstore by default
(and ove... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-77 | Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
similarity_search(query: str, k: int = 4, filter: Optional[MetadataFilter] = None, search_params: Optional[common_types.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, consistency: Optional[common_types.Re... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-78 | Returns
List of Documents most similar to the query.
similarity_search_with_score(query: str, k: int = 4, filter: Optional[MetadataFilter] = None, search_params: Optional[common_types.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, consistency: Optional[common_types.ReadConsistency] = No... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
6d5c37c1dbcb-79 | distance in float for each.
Lower score represents more similarity.
class langchain.vectorstores.Redis(redis_url: str, index_name: str, embedding_function: typing.Callable, content_key: str = 'content', metadata_key: str = 'metadata', vector_key: str = 'content_vector', relevance_score_fn: typing.Optional[typing.Callab... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html |
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