id
stringlengths 14
15
| text
stringlengths 35
2.51k
| source
stringlengths 61
154
|
|---|---|---|
b75952407af0-0
|
langchain.utilities.arxiv.ArxivAPIWrapper¶
class langchain.utilities.arxiv.ArxivAPIWrapper(*, arxiv_search: Any = None, arxiv_exceptions: Any = None, top_k_results: int = 3, load_max_docs: int = 100, load_all_available_meta: bool = False, doc_content_chars_max: Optional[int] = 4000, ARXIV_MAX_QUERY_LENGTH: int = 300)[source]¶
Bases: BaseModel
Wrapper around ArxivAPI.
To use, you should have the arxiv python package installed.
https://lukasschwab.me/arxiv.py/index.html
This wrapper will use the Arxiv API to conduct searches and
fetch document summaries. By default, it will return the document summaries
of the top-k results.
It limits the Document content by doc_content_chars_max.
Set doc_content_chars_max=None if you don’t want to limit the content size.
Parameters
top_k_results – number of the top-scored document used for the arxiv tool
ARXIV_MAX_QUERY_LENGTH – the cut limit on the query used for the arxiv tool.
load_max_docs – a limit to the number of loaded documents
load_all_available_meta –
if True: the metadata of the loaded Documents gets all available meta info(see https://lukasschwab.me/arxiv.py/index.html#Result),
if False: the metadata gets only the most informative fields.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param arxiv_exceptions: Any = None¶
param doc_content_chars_max: Optional[int] = 4000¶
param load_all_available_meta: bool = False¶
param load_max_docs: int = 100¶
param top_k_results: int = 3¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.arxiv.ArxivAPIWrapper.html
|
b75952407af0-1
|
param top_k_results: int = 3¶
load(query: str) → List[Document][source]¶
Run Arxiv search and get the article texts plus the article meta information.
See https://lukasschwab.me/arxiv.py/index.html#Search
Returns: a list of documents with the document.page_content in text format
run(query: str) → str[source]¶
Run Arxiv search and get the article meta information.
See https://lukasschwab.me/arxiv.py/index.html#Search
See https://lukasschwab.me/arxiv.py/index.html#Result
It uses only the most informative fields of article meta information.
validator validate_environment » all fields[source]¶
Validate that the python package exists in environment.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
extra = 'forbid'¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.arxiv.ArxivAPIWrapper.html
|
d4ff1f95cf55-0
|
langchain.utilities.vertexai.init_vertexai¶
langchain.utilities.vertexai.init_vertexai(project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[Credentials] = None) → None[source]¶
Init vertexai.
Parameters
project – The default GCP project to use when making Vertex API calls.
location – The default location to use when making API calls.
credentials – The default custom
credentials to use when making API calls. If not provided credentials
will be ascertained from the environment.
Raises
ImportError – If importing vertexai SDK did not succeed.
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.vertexai.init_vertexai.html
|
fc397df88532-0
|
langchain.utilities.searx_search.SearxSearchWrapper¶
class langchain.utilities.searx_search.SearxSearchWrapper(*, searx_host: str = '', unsecure: bool = False, params: dict = None, headers: Optional[dict] = None, engines: Optional[List[str]] = [], categories: Optional[List[str]] = [], query_suffix: Optional[str] = '', k: int = 10, aiosession: Optional[Any] = None)[source]¶
Bases: BaseModel
Wrapper for Searx API.
To use you need to provide the searx host by passing the named parameter
searx_host or exporting the environment variable SEARX_HOST.
In some situations you might want to disable SSL verification, for example
if you are running searx locally. You can do this by passing the named parameter
unsecure. You can also pass the host url scheme as http to disable SSL.
Example
from langchain.utilities import SearxSearchWrapper
searx = SearxSearchWrapper(searx_host="http://localhost:8888")
Example with SSL disabled:from langchain.utilities import SearxSearchWrapper
# note the unsecure parameter is not needed if you pass the url scheme as
# http
searx = SearxSearchWrapper(searx_host="http://localhost:8888",
unsecure=True)
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param aiosession: Optional[Any] = None¶
param categories: Optional[List[str]] = []¶
param engines: Optional[List[str]] = []¶
param headers: Optional[dict] = None¶
param k: int = 10¶
param params: dict [Optional]¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.searx_search.SearxSearchWrapper.html
|
fc397df88532-1
|
param k: int = 10¶
param params: dict [Optional]¶
param query_suffix: Optional[str] = ''¶
param searx_host: str = ''¶
param 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.
Uses aiohttp. See results for more info.
async arun(query: str, engines: Optional[List[str]] = None, query_suffix: Optional[str] = '', **kwargs: Any) → str[source]¶
Asynchronously version of run.
validator disable_ssl_warnings » unsecure[source]¶
Disable SSL warnings.
results(query: str, num_results: int, engines: Optional[List[str]] = None, categories: Optional[List[str]] = None, query_suffix: Optional[str] = '', **kwargs: Any) → List[Dict][source]¶
Run query through Searx API and returns the results with metadata.
Parameters
query – The query to search for.
query_suffix – Extra suffix appended to the query.
num_results – Limit the number of results to return.
engines – List of engines to use for the query.
categories – List of categories to use for the query.
**kwargs – extra parameters to pass to the searx API.
Returns
{snippet: The description of the result.
title: The title of the result.
link: The link to the result.
engines: The engines used for the result.
category: Searx category of the result.
}
Return type
Dict with the following keys
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.searx_search.SearxSearchWrapper.html
|
fc397df88532-2
|
}
Return type
Dict with the following keys
run(query: str, engines: Optional[List[str]] = None, categories: Optional[List[str]] = None, query_suffix: Optional[str] = '', **kwargs: Any) → str[source]¶
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 parameters to pass to the searx API.
Returns
The result of the query.
Return type
str
Raises
ValueError – If an error occurred with the query.
Example
This will make a query to the qwant engine:
from langchain.utilities import SearxSearchWrapper
searx = SearxSearchWrapper(searx_host="http://my.searx.host")
searx.run("what is the weather in France ?", engine="qwant")
# the same result can be achieved using the `!` syntax of searx
# to select the engine using `query_suffix`
searx.run("what is the weather in France ?", query_suffix="!qwant")
validator validate_params » all fields[source]¶
Validate that custom searx params are merged with default ones.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
extra = 'forbid'¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.searx_search.SearxSearchWrapper.html
|
7bd51cb86842-0
|
langchain.utilities.powerbi.fix_table_name¶
langchain.utilities.powerbi.fix_table_name(table: str) → str[source]¶
Add single quotes around table names that contain spaces.
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.powerbi.fix_table_name.html
|
7e16424fd2f8-0
|
langchain.utilities.zapier.ZapierNLAWrapper¶
class langchain.utilities.zapier.ZapierNLAWrapper(*, zapier_nla_api_key: str, zapier_nla_oauth_access_token: str, zapier_nla_api_base: str = 'https://nla.zapier.com/api/v1/')[source]¶
Bases: BaseModel
Wrapper for Zapier NLA.
Full docs here: https://nla.zapier.com/start/
This wrapper supports both API Key and OAuth Credential auth methods. API Key
is the fastest way to get started using this wrapper.
Call this wrapper with either zapier_nla_api_key or
zapier_nla_oauth_access_token arguments, or set the ZAPIER_NLA_API_KEY
environment variable. If both arguments are set, the Access Token will take
precedence.
For use-cases where LangChain + Zapier NLA is powering a user-facing application,
and LangChain needs access to the end-user’s connected accounts on Zapier.com,
you’ll need to use OAuth. Review the full docs above to learn how to create
your own provider and generate credentials.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param zapier_nla_api_base: str = 'https://nla.zapier.com/api/v1/'¶
param zapier_nla_api_key: str [Required]¶
param zapier_nla_oauth_access_token: str [Required]¶
async alist() → List[Dict][source]¶
Returns a list of all exposed (enabled) actions associated with
current user (associated with the set api_key). Change your exposed
actions here: https://nla.zapier.com/demo/start/
The return list can be empty if no actions exposed. Else will contain
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.zapier.ZapierNLAWrapper.html
|
7e16424fd2f8-1
|
The return list can be empty if no actions exposed. Else will contain
a list of action objects:
[{“id”: str,
“description”: str,
“params”: Dict[str, str]
}]
params will always contain an instructions key, the only required
param. All others optional and if provided will override any AI guesses
(see “understanding the AI guessing flow” here:
https://nla.zapier.com/api/v1/docs)
async alist_as_str() → str[source]¶
Same as list, but returns a stringified version of the JSON for
insertting back into an LLM.
async apreview(action_id: str, instructions: str, params: Optional[Dict] = None) → Dict[source]¶
Same as run, but instead of actually executing the action, will
instead return a preview of params that have been guessed by the AI in
case you need to explicitly review before executing.
async apreview_as_str(*args, **kwargs) → str[source]¶
Same as preview, but returns a stringified version of the JSON for
insertting back into an LLM.
async arun(action_id: str, instructions: str, params: Optional[Dict] = None) → Dict[source]¶
Executes an action that is identified by action_id, must be exposed
(enabled) by the current user (associated with the set api_key). Change
your exposed actions here: https://nla.zapier.com/demo/start/
The return JSON is guaranteed to be less than ~500 words (350
tokens) making it safe to inject into the prompt of another LLM
call.
async arun_as_str(*args, **kwargs) → str[source]¶
Same as run, but returns a stringified version of the JSON for
insertting back into an LLM.
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.zapier.ZapierNLAWrapper.html
|
7e16424fd2f8-2
|
insertting back into an LLM.
list() → List[Dict][source]¶
Returns a list of all exposed (enabled) actions associated with
current user (associated with the set api_key). Change your exposed
actions here: https://nla.zapier.com/demo/start/
The return list can be empty if no actions exposed. Else will contain
a list of action objects:
[{“id”: str,
“description”: str,
“params”: Dict[str, str]
}]
params will always contain an instructions key, the only required
param. All others optional and if provided will override any AI guesses
(see “understanding the AI guessing flow” here:
https://nla.zapier.com/docs/using-the-api#ai-guessing)
list_as_str() → str[source]¶
Same as list, but returns a stringified version of the JSON for
insertting back into an LLM.
preview(action_id: str, instructions: str, params: Optional[Dict] = None) → Dict[source]¶
Same as run, but instead of actually executing the action, will
instead return a preview of params that have been guessed by the AI in
case you need to explicitly review before executing.
preview_as_str(*args, **kwargs) → str[source]¶
Same as preview, but returns a stringified version of the JSON for
insertting back into an LLM.
run(action_id: str, instructions: str, params: Optional[Dict] = None) → Dict[source]¶
Executes an action that is identified by action_id, must be exposed
(enabled) by the current user (associated with the set api_key). Change
your exposed actions here: https://nla.zapier.com/demo/start/
The return JSON is guaranteed to be less than ~500 words (350
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.zapier.ZapierNLAWrapper.html
|
7e16424fd2f8-3
|
The return JSON is guaranteed to be less than ~500 words (350
tokens) making it safe to inject into the prompt of another LLM
call.
run_as_str(*args, **kwargs) → str[source]¶
Same as run, but returns a stringified version of the JSON for
insertting back into an LLM.
validator validate_environment » all fields[source]¶
Validate that api key exists in environment.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
extra = 'forbid'¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.zapier.ZapierNLAWrapper.html
|
2cb98161dc83-0
|
langchain.utilities.wolfram_alpha.WolframAlphaAPIWrapper¶
class langchain.utilities.wolfram_alpha.WolframAlphaAPIWrapper(*, wolfram_client: Any = None, wolfram_alpha_appid: Optional[str] = None)[source]¶
Bases: BaseModel
Wrapper for Wolfram Alpha.
Docs for using:
Go to wolfram alpha and sign up for a developer account
Create an app and get your APP ID
Save your APP ID into WOLFRAM_ALPHA_APPID env variable
pip install wolframalpha
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param wolfram_alpha_appid: Optional[str] = None¶
run(query: str) → str[source]¶
Run query through WolframAlpha and parse result.
validator validate_environment » all fields[source]¶
Validate that api key and python package exists in environment.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
extra = 'forbid'¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.wolfram_alpha.WolframAlphaAPIWrapper.html
|
baf3ca8a3527-0
|
langchain.utilities.bing_search.BingSearchAPIWrapper¶
class langchain.utilities.bing_search.BingSearchAPIWrapper(*, bing_subscription_key: str, bing_search_url: str, k: int = 10)[source]¶
Bases: BaseModel
Wrapper for Bing Search API.
In order to set this up, follow instructions at:
https://levelup.gitconnected.com/api-tutorial-how-to-use-bing-web-search-api-in-python-4165d5592a7e
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param bing_search_url: str [Required]¶
param bing_subscription_key: str [Required]¶
param k: int = 10¶
results(query: str, num_results: int) → List[Dict][source]¶
Run query through BingSearch and return metadata.
Parameters
query – The query to search for.
num_results – The number of results to return.
Returns
snippet - The description of the result.
title - The title of the result.
link - The link to the result.
Return type
A list of dictionaries with the following keys
run(query: str) → str[source]¶
Run query through BingSearch and parse result.
validator validate_environment » all fields[source]¶
Validate that api key and endpoint exists in environment.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
extra = 'forbid'¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.bing_search.BingSearchAPIWrapper.html
|
dd10ba01e8dc-0
|
langchain.utilities.twilio.TwilioAPIWrapper¶
class langchain.utilities.twilio.TwilioAPIWrapper(*, client: Any = None, account_sid: Optional[str] = None, auth_token: Optional[str] = None, from_number: Optional[str] = None)[source]¶
Bases: BaseModel
Messaging Client using Twilio.
To use, you should have the twilio python package installed,
and the environment variables TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN, and
TWILIO_FROM_NUMBER, or pass account_sid, auth_token, and from_number as
named parameters to the constructor.
Example
from langchain.utilities.twilio import TwilioAPIWrapper
twilio = TwilioAPIWrapper(
account_sid="ACxxx",
auth_token="xxx",
from_number="+10123456789"
)
twilio.run('test', '+12484345508')
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param account_sid: Optional[str] = None¶
Twilio account string identifier.
param auth_token: Optional[str] = None¶
Twilio auth token.
param from_number: Optional[str] = None¶
A Twilio phone number in [E.164](https://www.twilio.com/docs/glossary/what-e164)
format, an
[alphanumeric sender ID](https://www.twilio.com/docs/sms/send-messages#use-an-alphanumeric-sender-id),
or a [Channel Endpoint address](https://www.twilio.com/docs/sms/channels#channel-addresses)
that is enabled for the type of message you want to send. Phone numbers or
[short codes](https://www.twilio.com/docs/sms/api/short-code) purchased from
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.twilio.TwilioAPIWrapper.html
|
dd10ba01e8dc-1
|
Twilio also work here. You cannot, for example, spoof messages from a private
cell phone number. If you are using messaging_service_sid, this parameter
must be empty.
run(body: str, to: str) → str[source]¶
Run body through Twilio and respond with message sid.
Parameters
body – The text of the message you want to send. Can be up to 1,600
characters in length.
to – The destination phone number in
[E.164](https://www.twilio.com/docs/glossary/what-e164) format for
SMS/MMS or
[Channel user address](https://www.twilio.com/docs/sms/channels#channel-addresses)
for other 3rd-party channels.
validator validate_environment » all fields[source]¶
Validate that api key and python package exists in environment.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = False¶
extra = 'forbid'¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.twilio.TwilioAPIWrapper.html
|
cb4a62bcbf99-0
|
langchain.utilities.openapi.OpenAPISpec¶
class langchain.utilities.openapi.OpenAPISpec(*, openapi: str = '3.1.0', info: Info, jsonSchemaDialect: Optional[str] = None, servers: List[Server] = [Server(url='/', description=None, variables=None)], paths: Optional[Dict[str, PathItem]] = None, webhooks: Optional[Dict[str, Union[PathItem, Reference]]] = None, components: Optional[Components] = None, security: Optional[List[Dict[str, List[str]]]] = None, tags: Optional[List[Tag]] = None, externalDocs: Optional[ExternalDocumentation] = None)[source]¶
Bases: OpenAPI
OpenAPI Model that removes misformatted parts of the spec.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param components: Optional[openapi_schema_pydantic.v3.v3_1_0.components.Components] = None¶
An element to hold various schemas for the document.
param externalDocs: Optional[openapi_schema_pydantic.v3.v3_1_0.external_documentation.ExternalDocumentation] = None¶
Additional external documentation.
param info: openapi_schema_pydantic.v3.v3_1_0.info.Info [Required]¶
REQUIRED. Provides metadata about the API. The metadata MAY be used by tooling as required.
param jsonSchemaDialect: Optional[str] = None¶
The default value for the $schema keyword within [Schema Objects](#schemaObject)
contained within this OAS document. This MUST be in the form of a URI.
param openapi: str = '3.1.0'¶
REQUIRED. This string MUST be the [version number](#versions)
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.openapi.OpenAPISpec.html
|
cb4a62bcbf99-1
|
REQUIRED. This string MUST be the [version number](#versions)
of the OpenAPI Specification that the OpenAPI document uses.
The openapi field SHOULD be used by tooling to interpret the OpenAPI document.
This is not related to the API [info.version](#infoVersion) string.
param paths: Optional[Dict[str, openapi_schema_pydantic.v3.v3_1_0.path_item.PathItem]] = None¶
The available paths and operations for the API.
param security: Optional[List[Dict[str, List[str]]]] = None¶
A declaration of which security mechanisms can be used across the API.
The list of values includes alternative security requirement objects that can be used.
Only one of the security requirement objects need to be satisfied to authorize a request.
Individual operations can override this definition.
To make security optional, an empty security requirement ({}) can be included in the array.
param servers: List[openapi_schema_pydantic.v3.v3_1_0.server.Server] = [Server(url='/', description=None, variables=None)]¶
An array of Server Objects, which provide connectivity information to a target server.
If the servers property is not provided, or is an empty array,
the default value would be a [Server Object](#serverObject) with a [url](#serverUrl) value of /.
param tags: Optional[List[openapi_schema_pydantic.v3.v3_1_0.tag.Tag]] = None¶
A list of tags used by the document with additional metadata.
The order of the tags can be used to reflect on their order by the parsing tools.
Not all tags that are used by the [Operation Object](#operationObject) must be declared.
The tags that are not declared MAY be organized randomly or based on the tools’ logic.
Each tag name in the list MUST be unique.
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.openapi.OpenAPISpec.html
|
cb4a62bcbf99-2
|
Each tag name in the list MUST be unique.
param webhooks: Optional[Dict[str, Union[openapi_schema_pydantic.v3.v3_1_0.path_item.PathItem, openapi_schema_pydantic.v3.v3_1_0.reference.Reference]]] = None¶
The incoming webhooks that MAY be received as part of this API and that the API consumer MAY choose to implement.
Closely related to the callbacks feature, this section describes requests initiated other than by an API call,
for example by an out of band registration.
The key name is a unique string to refer to each webhook,
while the (optionally referenced) Path Item Object describes a request
that may be initiated by the API provider and the expected responses.
An [example](../examples/v3.1/webhook-example.yaml) is available.
classmethod from_file(path: Union[str, Path]) → OpenAPISpec[source]¶
Get an OpenAPI spec from a file path.
classmethod from_spec_dict(spec_dict: dict) → OpenAPISpec[source]¶
Get an OpenAPI spec from a dict.
classmethod from_text(text: str) → OpenAPISpec[source]¶
Get an OpenAPI spec from a text.
classmethod from_url(url: str) → OpenAPISpec[source]¶
Get an OpenAPI spec from a URL.
static get_cleaned_operation_id(operation: Operation, path: str, method: str) → str[source]¶
Get a cleaned operation id from an operation id.
get_methods_for_path(path: str) → List[str][source]¶
Return a list of valid methods for the specified path.
get_operation(path: str, method: str) → Operation[source]¶
Get the operation object for a given path and HTTP method.
get_parameters_for_operation(operation: Operation) → List[Parameter][source]¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.openapi.OpenAPISpec.html
|
cb4a62bcbf99-3
|
get_parameters_for_operation(operation: Operation) → List[Parameter][source]¶
Get the components for a given operation.
get_parameters_for_path(path: str) → List[Parameter][source]¶
get_referenced_schema(ref: Reference) → Schema[source]¶
Get a schema (or nested reference) or err.
get_request_body_for_operation(operation: Operation) → Optional[RequestBody][source]¶
Get the request body for a given operation.
get_schema(schema: Union[Reference, Schema]) → Schema[source]¶
classmethod parse_obj(obj: dict) → OpenAPISpec[source]¶
property base_url: str¶
Get the base url.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.openapi.OpenAPISpec.html
|
f040afbb12fe-0
|
langchain.utilities.powerbi.json_to_md¶
langchain.utilities.powerbi.json_to_md(json_contents: List[Dict[str, Union[str, int, float]]], table_name: Optional[str] = None) → str[source]¶
Converts a JSON object to a markdown table.
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.powerbi.json_to_md.html
|
92ebac41992a-0
|
langchain.utilities.google_serper.GoogleSerperAPIWrapper¶
class langchain.utilities.google_serper.GoogleSerperAPIWrapper(*, k: int = 10, gl: str = 'us', hl: str = 'en', type: Literal['news', 'search', 'places', 'images'] = 'search', tbs: Optional[str] = None, serper_api_key: Optional[str] = None, aiosession: Optional[ClientSession] = None, result_key_for_type: dict = {'images': 'images', 'news': 'news', 'places': 'places', 'search': 'organic'})[source]¶
Bases: BaseModel
Wrapper around the Serper.dev Google Search API.
You can create a free API key at https://serper.dev.
To use, you should have the environment variable SERPER_API_KEY
set with your API key, or pass serper_api_key as a named parameter
to the constructor.
Example
from langchain import GoogleSerperAPIWrapper
google_serper = GoogleSerperAPIWrapper()
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param aiosession: Optional[aiohttp.client.ClientSession] = None¶
param gl: str = 'us'¶
param hl: str = 'en'¶
param k: int = 10¶
param serper_api_key: Optional[str] = None¶
param tbs: Optional[str] = None¶
param type: Literal['news', 'search', 'places', 'images'] = 'search'¶
async aresults(query: str, **kwargs: Any) → Dict[source]¶
Run query through GoogleSearch.
async arun(query: str, **kwargs: Any) → str[source]¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.google_serper.GoogleSerperAPIWrapper.html
|
92ebac41992a-1
|
async arun(query: str, **kwargs: Any) → str[source]¶
Run query through GoogleSearch and parse result async.
results(query: str, **kwargs: Any) → Dict[source]¶
Run query through GoogleSearch.
run(query: str, **kwargs: Any) → str[source]¶
Run query through GoogleSearch and parse result.
validator validate_environment » all fields[source]¶
Validate that api key exists in environment.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.google_serper.GoogleSerperAPIWrapper.html
|
afd05bfcdca7-0
|
langchain.utilities.python.PythonREPL¶
class langchain.utilities.python.PythonREPL(*, _globals: Optional[Dict] = None, _locals: Optional[Dict] = None)[source]¶
Bases: BaseModel
Simulates a standalone Python REPL.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param globals: Optional[Dict] [Optional] (alias '_globals')¶
param locals: Optional[Dict] [Optional] (alias '_locals')¶
run(command: str) → str[source]¶
Run command with own globals/locals and returns anything printed.
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.python.PythonREPL.html
|
be80775b4a84-0
|
langchain.utilities.bibtex.BibtexparserWrapper¶
class langchain.utilities.bibtex.BibtexparserWrapper[source]¶
Bases: BaseModel
Wrapper around bibtexparser.
To use, you should have the bibtexparser python package installed.
https://bibtexparser.readthedocs.io/en/master/
This wrapper will use bibtexparser to load a collection of references from
a bibtex file and fetch document summaries.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
get_metadata(entry: Mapping[str, Any], load_extra: bool = False) → Dict[str, Any][source]¶
Get metadata for the given entry.
load_bibtex_entries(path: str) → List[Dict[str, Any]][source]¶
Load bibtex entries from the bibtex file at the given path.
validator validate_environment » all fields[source]¶
Validate that the python package exists in environment.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
extra = 'forbid'¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.bibtex.BibtexparserWrapper.html
|
dbf2a00dd48b-0
|
langchain.utilities.wikipedia.WikipediaAPIWrapper¶
class langchain.utilities.wikipedia.WikipediaAPIWrapper(*, wiki_client: Any = None, top_k_results: int = 3, lang: str = 'en', load_all_available_meta: bool = False, doc_content_chars_max: int = 4000)[source]¶
Bases: BaseModel
Wrapper around WikipediaAPI.
To use, you should have the wikipedia python package installed.
This wrapper will use the Wikipedia API to conduct searches and
fetch page summaries. By default, it will return the page summaries
of the top-k results.
It limits the Document content by doc_content_chars_max.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param doc_content_chars_max: int = 4000¶
param lang: str = 'en'¶
param load_all_available_meta: bool = False¶
param top_k_results: int = 3¶
load(query: str) → List[Document][source]¶
Run Wikipedia search and get the article text plus the meta information.
See
Returns: a list of documents.
run(query: str) → str[source]¶
Run Wikipedia search and get page summaries.
validator validate_environment » all fields[source]¶
Validate that the python package exists in environment.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
extra = 'forbid'¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.wikipedia.WikipediaAPIWrapper.html
|
e974631fce49-0
|
langchain.utilities.awslambda.LambdaWrapper¶
class langchain.utilities.awslambda.LambdaWrapper(*, lambda_client: Any = None, function_name: Optional[str] = None, awslambda_tool_name: Optional[str] = None, awslambda_tool_description: Optional[str] = None)[source]¶
Bases: BaseModel
Wrapper for AWS Lambda SDK.
Docs for using:
pip install boto3
Create a lambda function using the AWS Console or CLI
Run aws configure and enter your AWS credentials
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param awslambda_tool_description: Optional[str] = None¶
param awslambda_tool_name: Optional[str] = None¶
param function_name: Optional[str] = None¶
run(query: str) → str[source]¶
Invoke Lambda function and parse result.
validator validate_environment » all fields[source]¶
Validate that python package exists in environment.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
extra = 'forbid'¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.awslambda.LambdaWrapper.html
|
155df39af40f-0
|
langchain.utilities.scenexplain.SceneXplainAPIWrapper¶
class langchain.utilities.scenexplain.SceneXplainAPIWrapper(_env_file: Optional[Union[str, PathLike, List[Union[str, PathLike]], Tuple[Union[str, PathLike], ...]]] = '<object object>', _env_file_encoding: Optional[str] = None, _env_nested_delimiter: Optional[str] = None, _secrets_dir: Optional[Union[str, PathLike]] = None, *, scenex_api_key: str, scenex_api_url: str = 'https://us-central1-causal-diffusion.cloudfunctions.net/describe')[source]¶
Bases: BaseSettings, BaseModel
Wrapper for SceneXplain API.
In order to set this up, you need API key for the SceneXplain API.
You can obtain a key by following the steps below.
- Sign up for a free account at https://scenex.jina.ai/.
- Navigate to the API Access page (https://scenex.jina.ai/api)
and create a new API key.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param scenex_api_key: str [Required]¶
param scenex_api_url: str = 'https://us-central1-causal-diffusion.cloudfunctions.net/describe'¶
run(image: str) → str[source]¶
Run SceneXplain image explainer.
validator validate_environment » all fields[source]¶
Validate that api key exists in environment.
model Config¶
Bases: BaseConfig
getter_dict¶
alias of GetterDict
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.scenexplain.SceneXplainAPIWrapper.html
|
155df39af40f-1
|
model Config¶
Bases: BaseConfig
getter_dict¶
alias of GetterDict
classmethod customise_sources(init_settings: Callable[[BaseSettings], Dict[str, Any]], env_settings: Callable[[BaseSettings], Dict[str, Any]], file_secret_settings: Callable[[BaseSettings], Dict[str, Any]]) → Tuple[Callable[[BaseSettings], Dict[str, Any]], ...]¶
classmethod get_field_info(name: unicode) → Dict[str, Any]¶
Get properties of FieldInfo from the fields property of the config class.
json_dumps(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw)¶
Serialize obj to a JSON formatted str.
If skipkeys is true then dict keys that are not basic types
(str, int, float, bool, None) will be skipped
instead of raising a TypeError.
If ensure_ascii is false, then the return value can contain non-ASCII
characters if they appear in strings contained in obj. Otherwise, all
such characters are escaped in JSON strings.
If check_circular is false, then the circular reference check
for container types will be skipped and a circular reference will
result in an RecursionError (or worse).
If allow_nan is false, then it will be a ValueError to
serialize out of range float values (nan, inf, -inf) in
strict compliance of the JSON specification, instead of using the
JavaScript equivalents (NaN, Infinity, -Infinity).
If indent is a non-negative integer, then JSON array elements and
object members will be pretty-printed with that indent level. An indent
level of 0 will only insert newlines. None is the most compact
representation.
If specified, separators should be an (item_separator, key_separator)
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.scenexplain.SceneXplainAPIWrapper.html
|
155df39af40f-2
|
representation.
If specified, separators should be an (item_separator, key_separator)
tuple. The default is (', ', ': ') if indent is None and
(',', ': ') otherwise. To get the most compact JSON representation,
you should specify (',', ':') to eliminate whitespace.
default(obj) is a function that should return a serializable version
of obj or raise TypeError. The default simply raises TypeError.
If sort_keys is true (default: False), then the output of
dictionaries will be sorted by key.
To use a custom JSONEncoder subclass (e.g. one that overrides the
.default() method to serialize additional types), specify it with
the cls kwarg; otherwise JSONEncoder is used.
json_loads(*, cls=None, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw)¶
Deserialize s (a str, bytes or bytearray instance
containing a JSON document) to a Python object.
object_hook is an optional function that will be called with the
result of any object literal decode (a dict). The return value of
object_hook will be used instead of the dict. This feature
can be used to implement custom decoders (e.g. JSON-RPC class hinting).
object_pairs_hook is an optional function that will be called with the
result of any object literal decoded with an ordered list of pairs. The
return value of object_pairs_hook will be used instead of the dict.
This feature can be used to implement custom decoders. If object_hook
is also defined, the object_pairs_hook takes priority.
parse_float, if specified, will be called with the string
of every JSON float to be decoded. By default this is equivalent to
float(num_str). This can be used to use another datatype or parser
for JSON floats (e.g. decimal.Decimal).
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.scenexplain.SceneXplainAPIWrapper.html
|
155df39af40f-3
|
for JSON floats (e.g. decimal.Decimal).
parse_int, if specified, will be called with the string
of every JSON int to be decoded. By default this is equivalent to
int(num_str). This can be used to use another datatype or parser
for JSON integers (e.g. float).
parse_constant, if specified, will be called with one of the
following strings: -Infinity, Infinity, NaN.
This can be used to raise an exception if invalid JSON numbers
are encountered.
To use a custom JSONDecoder subclass, specify it with the cls
kwarg; otherwise JSONDecoder is used.
classmethod parse_env_var(field_name: unicode, raw_val: unicode) → Any¶
classmethod prepare_field(field: ModelField) → None¶
Optional hook to check or modify fields during model creation.
alias_generator = None¶
allow_inf_nan = True¶
allow_mutation = True¶
allow_population_by_field_name = False¶
anystr_lower = False¶
anystr_strip_whitespace = False¶
anystr_upper = False¶
arbitrary_types_allowed = True¶
case_sensitive = False¶
copy_on_model_validation = 'shallow'¶
env_file = None¶
env_file_encoding = None¶
env_nested_delimiter = None¶
env_prefix = ''¶
error_msg_templates = {}¶
extra = 'forbid'¶
fields = {}¶
frozen = False¶
json_encoders = {}¶
keep_untouched = ()¶
max_anystr_length = None¶
min_anystr_length = 0¶
orm_mode = False¶
post_init_call = 'before_validation'¶
schema_extra = {}¶
secrets_dir = None¶
smart_union = False¶
title = None¶
underscore_attrs_are_private = False¶
use_enum_values = False¶
validate_all = True¶
validate_assignment = False¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.scenexplain.SceneXplainAPIWrapper.html
|
2c4100e21f1c-0
|
langchain.output_parsers.boolean.BooleanOutputParser¶
class langchain.output_parsers.boolean.BooleanOutputParser(*, true_val: str = 'YES', false_val: str = 'NO')[source]¶
Bases: BaseOutputParser[bool]
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param false_val: str = 'NO'¶
param true_val: str = 'YES'¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str¶
Instructions on how the LLM output should be formatted.
parse(text: str) → bool[source]¶
Parse the output of an LLM call to a boolean.
Parameters
text – output of language model
Returns
boolean
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.boolean.BooleanOutputParser.html
|
2c4100e21f1c-1
|
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.boolean.BooleanOutputParser.html
|
303a5f7006d9-0
|
langchain.output_parsers.fix.OutputFixingParser¶
class langchain.output_parsers.fix.OutputFixingParser(*, parser: BaseOutputParser[T], retry_chain: LLMChain)[source]¶
Bases: BaseOutputParser[T]
Wraps a parser and tries to fix parsing errors.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param parser: langchain.schema.BaseOutputParser[langchain.output_parsers.fix.T] [Required]¶
param retry_chain: langchain.chains.llm.LLMChain [Required]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_llm(llm: BaseLanguageModel, parser: BaseOutputParser[T], prompt: BasePromptTemplate = PromptTemplate(input_variables=['completion', 'error', 'instructions'], output_parser=None, partial_variables={}, template='Instructions:\n--------------\n{instructions}\n--------------\nCompletion:\n--------------\n{completion}\n--------------\n\nAbove, the Completion did not satisfy the constraints given in the Instructions.\nError:\n--------------\n{error}\n--------------\n\nPlease try again. Please only respond with an answer that satisfies the constraints laid out in the Instructions:', template_format='f-string', validate_template=True)) → OutputFixingParser[T][source]¶
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(completion: str) → T[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.fix.OutputFixingParser.html
|
303a5f7006d9-1
|
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.fix.OutputFixingParser.html
|
ad350233fbc9-0
|
langchain.output_parsers.list.CommaSeparatedListOutputParser¶
class langchain.output_parsers.list.CommaSeparatedListOutputParser[source]¶
Bases: ListOutputParser
Parse out comma separated lists.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(text: str) → List[str][source]¶
Parse the output of an LLM call.
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.list.CommaSeparatedListOutputParser.html
|
ad350233fbc9-1
|
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.list.CommaSeparatedListOutputParser.html
|
e10d746acbc6-0
|
langchain.output_parsers.list.ListOutputParser¶
class langchain.output_parsers.list.ListOutputParser[source]¶
Bases: BaseOutputParser
Class to parse the output of an LLM call to a list.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str¶
Instructions on how the LLM output should be formatted.
abstract parse(text: str) → List[str][source]¶
Parse the output of an LLM call.
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.list.ListOutputParser.html
|
e10d746acbc6-1
|
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.list.ListOutputParser.html
|
1528f54609be-0
|
langchain.output_parsers.combining.CombiningOutputParser¶
class langchain.output_parsers.combining.CombiningOutputParser(*, parsers: List[BaseOutputParser])[source]¶
Bases: BaseOutputParser
Class to combine multiple output parsers into one.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param parsers: List[langchain.schema.BaseOutputParser] [Required]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(text: str) → Dict[str, Any][source]¶
Parse the output of an LLM call.
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
validator validate_parsers » all fields[source]¶
Validate the parsers.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.combining.CombiningOutputParser.html
|
1528f54609be-1
|
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.combining.CombiningOutputParser.html
|
3435fb553124-0
|
langchain.output_parsers.openai_functions.PydanticAttrOutputFunctionsParser¶
class langchain.output_parsers.openai_functions.PydanticAttrOutputFunctionsParser(*, args_only: bool = True, pydantic_schema: Any = None, attr_name: str)[source]¶
Bases: PydanticOutputFunctionsParser
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_only: bool = True¶
param attr_name: str [Required]¶
param pydantic_schema: Any = None¶
parse_result(result: List[Generation]) → Any[source]¶
Parse LLM Result.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.openai_functions.PydanticAttrOutputFunctionsParser.html
|
724e0cc9ff6d-0
|
langchain.output_parsers.regex_dict.RegexDictParser¶
class langchain.output_parsers.regex_dict.RegexDictParser(*, regex_pattern: str = "{}:\\s?([^.'\\n']*)\\.?", output_key_to_format: Dict[str, str], no_update_value: Optional[str] = None)[source]¶
Bases: BaseOutputParser
Class to parse the output into a dictionary.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param no_update_value: Optional[str] = None¶
param output_key_to_format: Dict[str, str] [Required]¶
param regex_pattern: str = "{}:\\s?([^.'\\n']*)\\.?"¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str¶
Instructions on how the LLM output should be formatted.
parse(text: str) → Dict[str, str][source]¶
Parse the output of an LLM call.
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.regex_dict.RegexDictParser.html
|
724e0cc9ff6d-1
|
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.regex_dict.RegexDictParser.html
|
940675cc3cec-0
|
langchain.output_parsers.json.parse_and_check_json_markdown¶
langchain.output_parsers.json.parse_and_check_json_markdown(text: str, expected_keys: List[str]) → dict[source]¶
Parse a JSON string from a Markdown string and check that it
contains the expected keys.
Parameters
text – The Markdown string.
expected_keys – The expected keys in the JSON string.
Returns
The parsed JSON object as a Python dictionary.
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.json.parse_and_check_json_markdown.html
|
d69240d0110d-0
|
langchain.output_parsers.loading.load_output_parser¶
langchain.output_parsers.loading.load_output_parser(config: dict) → dict[source]¶
Load output parser.
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.loading.load_output_parser.html
|
5a1489c7615d-0
|
langchain.output_parsers.retry.RetryWithErrorOutputParser¶
class langchain.output_parsers.retry.RetryWithErrorOutputParser(*, parser: BaseOutputParser[T], retry_chain: LLMChain)[source]¶
Bases: BaseOutputParser[T]
Wraps a parser and tries to fix parsing errors.
Does this by passing the original prompt, the completion, AND the error
that was raised to another language model and telling it that the completion
did not work, and raised the given error. Differs from RetryOutputParser
in that this implementation provides the error that was raised back to the
LLM, which in theory should give it more information on how to fix it.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param parser: langchain.schema.BaseOutputParser[langchain.output_parsers.retry.T] [Required]¶
param retry_chain: langchain.chains.llm.LLMChain [Required]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_llm(llm: BaseLanguageModel, parser: BaseOutputParser[T], prompt: BasePromptTemplate = PromptTemplate(input_variables=['completion', 'error', 'prompt'], output_parser=None, partial_variables={}, template='Prompt:\n{prompt}\nCompletion:\n{completion}\n\nAbove, the Completion did not satisfy the constraints given in the Prompt.\nDetails: {error}\nPlease try again:', template_format='f-string', validate_template=True)) → RetryWithErrorOutputParser[T][source]¶
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(completion: str) → T[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.retry.RetryWithErrorOutputParser.html
|
5a1489c7615d-1
|
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt_value: PromptValue) → T[source]¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.retry.RetryWithErrorOutputParser.html
|
45e4e1863287-0
|
langchain.output_parsers.structured.ResponseSchema¶
class langchain.output_parsers.structured.ResponseSchema(*, name: str, description: str, type: str = 'string')[source]¶
Bases: BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param description: str [Required]¶
param name: str [Required]¶
param type: str = 'string'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.structured.ResponseSchema.html
|
bebfa17e0150-0
|
langchain.output_parsers.retry.RetryOutputParser¶
class langchain.output_parsers.retry.RetryOutputParser(*, parser: BaseOutputParser[T], retry_chain: LLMChain)[source]¶
Bases: BaseOutputParser[T]
Wraps a parser and tries to fix parsing errors.
Does this by passing the original prompt and the completion to another
LLM, and telling it the completion did not satisfy criteria in the prompt.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param parser: langchain.schema.BaseOutputParser[langchain.output_parsers.retry.T] [Required]¶
param retry_chain: langchain.chains.llm.LLMChain [Required]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_llm(llm: BaseLanguageModel, parser: BaseOutputParser[T], prompt: BasePromptTemplate = PromptTemplate(input_variables=['completion', 'prompt'], output_parser=None, partial_variables={}, template='Prompt:\n{prompt}\nCompletion:\n{completion}\n\nAbove, the Completion did not satisfy the constraints given in the Prompt.\nPlease try again:', template_format='f-string', validate_template=True)) → RetryOutputParser[T][source]¶
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(completion: str) → T[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt_value: PromptValue) → T[source]¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.retry.RetryOutputParser.html
|
bebfa17e0150-1
|
parse_with_prompt(completion: str, prompt_value: PromptValue) → T[source]¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.retry.RetryOutputParser.html
|
bf739c9dd3c8-0
|
langchain.output_parsers.enum.EnumOutputParser¶
class langchain.output_parsers.enum.EnumOutputParser(*, enum: Type[Enum])[source]¶
Bases: BaseOutputParser
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param enum: Type[enum.Enum] [Required]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(response: str) → Any[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
validator raise_deprecation » all fields[source]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.enum.EnumOutputParser.html
|
bf739c9dd3c8-1
|
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.enum.EnumOutputParser.html
|
1ddf25188369-0
|
langchain.output_parsers.openai_functions.OutputFunctionsParser¶
class langchain.output_parsers.openai_functions.OutputFunctionsParser(*, args_only: bool = True)[source]¶
Bases: BaseLLMOutputParser[Any]
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_only: bool = True¶
parse_result(result: List[Generation]) → Any[source]¶
Parse LLM Result.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.openai_functions.OutputFunctionsParser.html
|
146b7b425ba6-0
|
langchain.output_parsers.structured.StructuredOutputParser¶
class langchain.output_parsers.structured.StructuredOutputParser(*, response_schemas: List[ResponseSchema])[source]¶
Bases: BaseOutputParser
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param response_schemas: List[langchain.output_parsers.structured.ResponseSchema] [Required]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_response_schemas(response_schemas: List[ResponseSchema]) → StructuredOutputParser[source]¶
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(text: str) → Any[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.structured.StructuredOutputParser.html
|
146b7b425ba6-1
|
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.structured.StructuredOutputParser.html
|
c9fe79a9d9e2-0
|
langchain.output_parsers.pydantic.PydanticOutputParser¶
class langchain.output_parsers.pydantic.PydanticOutputParser(*, pydantic_object: Type[T])[source]¶
Bases: BaseOutputParser[T]
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param pydantic_object: Type[langchain.output_parsers.pydantic.T] [Required]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(text: str) → T[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.pydantic.PydanticOutputParser.html
|
c9fe79a9d9e2-1
|
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.pydantic.PydanticOutputParser.html
|
1d67d87b8789-0
|
langchain.output_parsers.datetime.DatetimeOutputParser¶
class langchain.output_parsers.datetime.DatetimeOutputParser(*, format: str = '%Y-%m-%dT%H:%M:%S.%fZ')[source]¶
Bases: BaseOutputParser[datetime]
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param format: str = '%Y-%m-%dT%H:%M:%S.%fZ'¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(response: str) → datetime[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.datetime.DatetimeOutputParser.html
|
1d67d87b8789-1
|
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.datetime.DatetimeOutputParser.html
|
cf1a2ff8fa9d-0
|
langchain.output_parsers.openai_functions.JsonOutputFunctionsParser¶
class langchain.output_parsers.openai_functions.JsonOutputFunctionsParser(*, args_only: bool = True)[source]¶
Bases: OutputFunctionsParser
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_only: bool = True¶
parse_result(result: List[Generation]) → Any[source]¶
Parse LLM Result.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.openai_functions.JsonOutputFunctionsParser.html
|
9bfef693fef6-0
|
langchain.output_parsers.rail_parser.GuardrailsOutputParser¶
class langchain.output_parsers.rail_parser.GuardrailsOutputParser(*, guard: Any = None, api: Optional[Callable] = None, args: Any = None, kwargs: Any = None)[source]¶
Bases: BaseOutputParser
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api: Optional[Callable] = None¶
param args: Any = None¶
param guard: Any = None¶
param kwargs: Any = None¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_pydantic(output_class: Any, num_reasks: int = 1, api: Optional[Callable] = None, *args: Any, **kwargs: Any) → GuardrailsOutputParser[source]¶
classmethod from_rail(rail_file: str, num_reasks: int = 1, api: Optional[Callable] = None, *args: Any, **kwargs: Any) → GuardrailsOutputParser[source]¶
classmethod from_rail_string(rail_str: str, num_reasks: int = 1, api: Optional[Callable] = None, *args: Any, **kwargs: Any) → GuardrailsOutputParser[source]¶
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
parse(text: str) → Dict[source]¶
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.rail_parser.GuardrailsOutputParser.html
|
9bfef693fef6-1
|
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.rail_parser.GuardrailsOutputParser.html
|
709811056098-0
|
langchain.output_parsers.openai_functions.JsonKeyOutputFunctionsParser¶
class langchain.output_parsers.openai_functions.JsonKeyOutputFunctionsParser(*, args_only: bool = True, key_name: str)[source]¶
Bases: JsonOutputFunctionsParser
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_only: bool = True¶
param key_name: str [Required]¶
parse_result(result: List[Generation]) → Any[source]¶
Parse LLM Result.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.openai_functions.JsonKeyOutputFunctionsParser.html
|
145f0035cd01-0
|
langchain.output_parsers.regex.RegexParser¶
class langchain.output_parsers.regex.RegexParser(*, regex: str, output_keys: List[str], default_output_key: Optional[str] = None)[source]¶
Bases: BaseOutputParser
Class to parse the output into a dictionary.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param default_output_key: Optional[str] = None¶
param output_keys: List[str] [Required]¶
param regex: str [Required]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
get_format_instructions() → str¶
Instructions on how the LLM output should be formatted.
parse(text: str) → Dict[str, str][source]¶
Parse the output of an LLM call.
parse_result(result: List[Generation]) → T¶
Parse LLM Result.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.regex.RegexParser.html
|
145f0035cd01-1
|
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.regex.RegexParser.html
|
9ea2907a5c89-0
|
langchain.output_parsers.json.parse_json_markdown¶
langchain.output_parsers.json.parse_json_markdown(json_string: str) → dict[source]¶
Parse a JSON string from a Markdown string.
Parameters
json_string – The Markdown string.
Returns
The parsed JSON object as a Python dictionary.
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.json.parse_json_markdown.html
|
08e6f88856d5-0
|
langchain.output_parsers.openai_functions.PydanticOutputFunctionsParser¶
class langchain.output_parsers.openai_functions.PydanticOutputFunctionsParser(*, args_only: bool = True, pydantic_schema: Any = None)[source]¶
Bases: OutputFunctionsParser
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_only: bool = True¶
param pydantic_schema: Any = None¶
parse_result(result: List[Generation]) → Any[source]¶
Parse LLM Result.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.openai_functions.PydanticOutputFunctionsParser.html
|
8eaf1795a9f2-0
|
langchain.base_language.BaseLanguageModel¶
class langchain.base_language.BaseLanguageModel[source]¶
Bases: Serializable, ABC
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
abstract async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → LLMResult[source]¶
Take in a list of prompt values and return an LLMResult.
classmethod all_required_field_names() → Set[source]¶
abstract async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str[source]¶
Predict text from text.
abstract async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage[source]¶
Predict message from messages.
abstract generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → LLMResult[source]¶
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) → int[source]¶
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[BaseMessage]) → int[source]¶
Get the number of tokens in the message.
get_token_ids(text: str) → List[int][source]¶
Get the token present in the text.
abstract predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str[source]¶
Predict text from text.
|
https://api.python.langchain.com/en/latest/base_language/langchain.base_language.BaseLanguageModel.html
|
8eaf1795a9f2-1
|
Predict text from text.
abstract predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage[source]¶
Predict message from messages.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
model Config¶
Bases: object
extra = 'ignore'¶
|
https://api.python.langchain.com/en/latest/base_language/langchain.base_language.BaseLanguageModel.html
|
2b37f8cb857b-0
|
langchain.document_loaders.url_selenium.SeleniumURLLoader¶
class langchain.document_loaders.url_selenium.SeleniumURLLoader(urls: List[str], continue_on_failure: bool = True, browser: Literal['chrome', 'firefox'] = 'chrome', binary_location: Optional[str] = None, executable_path: Optional[str] = None, headless: bool = True, arguments: List[str] = [])[source]¶
Bases: BaseLoader
Loader that uses Selenium and to load a page and unstructured to load the html.
This is useful for loading pages that require javascript to render.
urls¶
List of URLs to load.
Type
List[str]
continue_on_failure¶
If True, continue loading other URLs on failure.
Type
bool
browser¶
The browser to use, either ‘chrome’ or ‘firefox’.
Type
str
binary_location¶
The location of the browser binary.
Type
Optional[str]
executable_path¶
The path to the browser executable.
Type
Optional[str]
headless¶
If True, the browser will run in headless mode.
Type
bool
arguments [List[str]]
List of arguments to pass to the browser.
Load a list of URLs using Selenium and unstructured.
Methods
__init__(urls[, continue_on_failure, ...])
Load a list of URLs using Selenium and unstructured.
lazy_load()
A lazy loader for document content.
load()
Load the specified URLs using Selenium and create Document instances.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load the specified URLs using Selenium and create Document instances.
Returns
A list of Document instances with loaded content.
Return type
List[Document]
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.url_selenium.SeleniumURLLoader.html
|
2b37f8cb857b-1
|
Returns
A list of Document instances with loaded content.
Return type
List[Document]
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.url_selenium.SeleniumURLLoader.html
|
f66e3c816526-0
|
langchain.document_loaders.epub.UnstructuredEPubLoader¶
class langchain.document_loaders.epub.UnstructuredEPubLoader(file_path: Union[str, List[str]], mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileLoader
Loader that uses unstructured to load epub files.
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.epub.UnstructuredEPubLoader.html
|
d7f9814b1574-0
|
langchain.document_loaders.url_playwright.PlaywrightURLLoader¶
class langchain.document_loaders.url_playwright.PlaywrightURLLoader(urls: List[str], continue_on_failure: bool = True, headless: bool = True, remove_selectors: Optional[List[str]] = None)[source]¶
Bases: BaseLoader
Loader that uses Playwright and to load a page and unstructured to load the html.
This is useful for loading pages that require javascript to render.
urls¶
List of URLs to load.
Type
List[str]
continue_on_failure¶
If True, continue loading other URLs on failure.
Type
bool
headless¶
If True, the browser will run in headless mode.
Type
bool
Load a list of URLs using Playwright and unstructured.
Methods
__init__(urls[, continue_on_failure, ...])
Load a list of URLs using Playwright and unstructured.
lazy_load()
A lazy loader for document content.
load()
Load the specified URLs using Playwright and create Document instances.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load the specified URLs using Playwright and create Document instances.
Returns
A list of Document instances with loaded content.
Return type
List[Document]
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.url_playwright.PlaywrightURLLoader.html
|
4f11a5b378bf-0
|
langchain.document_loaders.parsers.language.language_parser.LanguageParser¶
class langchain.document_loaders.parsers.language.language_parser.LanguageParser(language: Optional[Language] = None, parser_threshold: int = 0)[source]¶
Bases: BaseBlobParser
Language parser that split code using the respective language syntax.
Each top-level function and class in the code is loaded into separate documents.
Furthermore, an extra document is generated, containing the remaining top-level code
that excludes the already segmented functions and classes.
This approach can potentially improve the accuracy of QA models over source code.
Currently, the supported languages for code parsing are Python and JavaScript.
The language used for parsing can be configured, along with the minimum number of
lines required to activate the splitting based on syntax.
Examples
from langchain.text_splitter.Language
from langchain.document_loaders.generic import GenericLoader
from langchain.document_loaders.parsers import LanguageParser
loader = GenericLoader.from_filesystem(
"./code",
glob="**/*",
suffixes=[".py", ".js"],
parser=LanguageParser()
)
docs = loader.load()
Example instantiations to manually select the language:
… code-block:: python
from langchain.text_splitter import Language
loader = GenericLoader.from_filesystem(“./code”,
glob=”**/*”,
suffixes=[“.py”],
parser=LanguageParser(language=Language.PYTHON)
)
Example instantiations to set number of lines threshold:
… code-block:: python
loader = GenericLoader.from_filesystem(“./code”,
glob=”**/*”,
suffixes=[“.py”],
parser=LanguageParser(parser_threshold=200)
)
Language parser that split code using the respective language syntax.
Parameters
language – If None (default), it will try to infer language from source.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.parsers.language.language_parser.LanguageParser.html
|
4f11a5b378bf-1
|
Parameters
language – If None (default), it will try to infer language from source.
parser_threshold – Minimum lines needed to activate parsing (0 by default).
Methods
__init__([language, parser_threshold])
Language parser that split code using the respective language syntax.
lazy_parse(blob)
Lazy parsing interface.
parse(blob)
Eagerly parse the blob into a document or documents.
lazy_parse(blob: Blob) → Iterator[Document][source]¶
Lazy parsing interface.
Subclasses are required to implement this method.
Parameters
blob – Blob instance
Returns
Generator of documents
parse(blob: Blob) → List[Document]¶
Eagerly parse the blob into a document or documents.
This is a convenience method for interactive development environment.
Production applications should favor the lazy_parse method instead.
Subclasses should generally not over-ride this parse method.
Parameters
blob – Blob instance
Returns
List of documents
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.parsers.language.language_parser.LanguageParser.html
|
0ef314eca46c-0
|
langchain.document_loaders.git.GitLoader¶
class langchain.document_loaders.git.GitLoader(repo_path: str, clone_url: Optional[str] = None, branch: Optional[str] = 'main', file_filter: Optional[Callable[[str], bool]] = None)[source]¶
Bases: BaseLoader
Loads files from a Git repository into a list of documents.
Repository can be local on disk available at repo_path,
or remote at clone_url that will be cloned to repo_path.
Currently supports only text files.
Each document represents one file in the repository. The path points to
the local Git repository, and the branch specifies the branch to load
files from. By default, it loads from the main branch.
Methods
__init__(repo_path[, clone_url, branch, ...])
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.git.GitLoader.html
|
3c71f26f4151-0
|
langchain.document_loaders.parsers.pdf.PDFPlumberParser¶
class langchain.document_loaders.parsers.pdf.PDFPlumberParser(text_kwargs: Optional[Mapping[str, Any]] = None)[source]¶
Bases: BaseBlobParser
Parse PDFs with PDFPlumber.
Initialize the parser.
Parameters
text_kwargs – Keyword arguments to pass to pdfplumber.Page.extract_text()
Methods
__init__([text_kwargs])
Initialize the parser.
lazy_parse(blob)
Lazily parse the blob.
parse(blob)
Eagerly parse the blob into a document or documents.
lazy_parse(blob: Blob) → Iterator[Document][source]¶
Lazily parse the blob.
parse(blob: Blob) → List[Document]¶
Eagerly parse the blob into a document or documents.
This is a convenience method for interactive development environment.
Production applications should favor the lazy_parse method instead.
Subclasses should generally not over-ride this parse method.
Parameters
blob – Blob instance
Returns
List of documents
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.parsers.pdf.PDFPlumberParser.html
|
1d488b3c4c03-0
|
langchain.document_loaders.blob_loaders.schema.BlobLoader¶
class langchain.document_loaders.blob_loaders.schema.BlobLoader[source]¶
Bases: ABC
Abstract interface for blob loaders implementation.
Implementer should be able to load raw content from a storage system according
to some criteria and return the raw content lazily as a stream of blobs.
Methods
__init__()
yield_blobs()
A lazy loader for raw data represented by LangChain's Blob object.
abstract yield_blobs() → Iterable[Blob][source]¶
A lazy loader for raw data represented by LangChain’s Blob object.
Returns
A generator over blobs
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.blob_loaders.schema.BlobLoader.html
|
5ec76c272c4e-0
|
langchain.document_loaders.onedrive.OneDriveLoader¶
class langchain.document_loaders.onedrive.OneDriveLoader(*, settings: _OneDriveSettings = None, drive_id: str, folder_path: Optional[str] = None, object_ids: Optional[List[str]] = None, auth_with_token: bool = False)[source]¶
Bases: BaseLoader, BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param auth_with_token: bool = False¶
param drive_id: str [Required]¶
param folder_path: Optional[str] = None¶
param object_ids: Optional[List[str]] = None¶
param settings: langchain.document_loaders.onedrive._OneDriveSettings [Optional]¶
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Loads all supported document files from the specified OneDrive drive a
nd returns a list of Document objects.
Returns
A list of Document objects
representing the loaded documents.
Return type
List[Document]
Raises
ValueError – If the specified drive ID
does not correspond to a drive in the OneDrive storage. –
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.onedrive.OneDriveLoader.html
|
d6de7f649aa4-0
|
langchain.document_loaders.telegram.TelegramChatFileLoader¶
class langchain.document_loaders.telegram.TelegramChatFileLoader(path: str)[source]¶
Bases: BaseLoader
Loader that loads Telegram chat json directory dump.
Initialize with path.
Methods
__init__(path)
Initialize with path.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.telegram.TelegramChatFileLoader.html
|
bb6520b19ac0-0
|
langchain.document_loaders.merge.MergedDataLoader¶
class langchain.document_loaders.merge.MergedDataLoader(loaders: List)[source]¶
Bases: BaseLoader
Merge documents from a list of loaders
Initialize with a list of loaders
Methods
__init__(loaders)
Initialize with a list of loaders
lazy_load()
Lazy load docs from each individual loader.
load()
Load docs.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document][source]¶
Lazy load docs from each individual loader.
load() → List[Document][source]¶
Load docs.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.merge.MergedDataLoader.html
|
6c93e3e23e87-0
|
langchain.document_loaders.blackboard.BlackboardLoader¶
class langchain.document_loaders.blackboard.BlackboardLoader(blackboard_course_url: str, bbrouter: str, load_all_recursively: bool = True, basic_auth: Optional[Tuple[str, str]] = None, cookies: Optional[dict] = None)[source]¶
Bases: WebBaseLoader
Loader that loads all documents from a Blackboard course.
This loader is not compatible with all Blackboard courses. It is only
compatible with courses that use the new Blackboard interface.
To use this loader, you must have the BbRouter cookie. You can get this
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=search&context=course_entry&course_id=_123456_1",
bbrouter="expires:12345...",
)
documents = loader.load()
Initialize with blackboard course url.
The BbRouter cookie is required for most blackboard courses.
Parameters
blackboard_course_url – Blackboard course url.
bbrouter – BbRouter cookie.
load_all_recursively – If True, load all documents recursively.
basic_auth – Basic auth credentials.
cookies – Cookies.
Raises
ValueError – If blackboard course url is invalid.
Methods
__init__(blackboard_course_url, bbrouter[, ...])
Initialize with blackboard course url.
aload()
Load text from the urls in web_path async into Documents.
check_bs4()
Check if BeautifulSoup4 is installed.
download(path)
Download a file from a url.
fetch_all(urls)
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.blackboard.BlackboardLoader.html
|
6c93e3e23e87-1
|
download(path)
Download a file from a url.
fetch_all(urls)
Fetch all urls concurrently with rate limiting.
lazy_load()
Lazy load text from the url(s) in web_path.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
parse_filename(url)
Parse the filename from a url.
scrape([parser])
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls[, parser])
Fetch all urls, then return soups for all results.
Attributes
bs_get_text_kwargs
kwargs for beatifulsoup4 get_text
default_parser
Default parser to use for BeautifulSoup.
raise_for_status
Raise an exception if http status code denotes an error.
requests_kwargs
kwargs for requests
requests_per_second
Max number of concurrent requests to make.
web_path
base_url
folder_path
load_all_recursively
aload() → List[Document]¶
Load text from the urls in web_path async into Documents.
check_bs4() → None[source]¶
Check if BeautifulSoup4 is installed.
Raises
ImportError – If BeautifulSoup4 is not installed.
download(path: str) → None[source]¶
Download a file from a url.
Parameters
path – Path to the file.
async fetch_all(urls: List[str]) → Any¶
Fetch all urls concurrently with rate limiting.
lazy_load() → Iterator[Document]¶
Lazy load text from the url(s) in web_path.
load() → List[Document][source]¶
Load data into document objects.
Returns
List of documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
parse_filename(url: str) → str[source]¶
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.blackboard.BlackboardLoader.html
|
6c93e3e23e87-2
|
Load documents and split into chunks.
parse_filename(url: str) → str[source]¶
Parse the filename from a url.
Parameters
url – Url to parse the filename from.
Returns
The filename.
scrape(parser: Optional[str] = None) → Any¶
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls: List[str], parser: Optional[str] = None) → List[Any]¶
Fetch all urls, then return soups for all results.
base_url: str¶
bs_get_text_kwargs: Dict[str, Any] = {}¶
kwargs for beatifulsoup4 get_text
default_parser: str = 'html.parser'¶
Default parser to use for BeautifulSoup.
folder_path: str¶
load_all_recursively: bool¶
raise_for_status: bool = False¶
Raise an exception if http status code denotes an error.
requests_kwargs: Dict[str, Any] = {}¶
kwargs for requests
requests_per_second: int = 2¶
Max number of concurrent requests to make.
property web_path: str¶
web_paths: List[str]¶
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.blackboard.BlackboardLoader.html
|
6609da19cbb8-0
|
langchain.document_loaders.org_mode.UnstructuredOrgModeLoader¶
class langchain.document_loaders.org_mode.UnstructuredOrgModeLoader(file_path: str, mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileLoader
Loader that uses unstructured to load Org-Mode files.
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.org_mode.UnstructuredOrgModeLoader.html
|
35ae80bdb879-0
|
langchain.document_loaders.azure_blob_storage_container.AzureBlobStorageContainerLoader¶
class langchain.document_loaders.azure_blob_storage_container.AzureBlobStorageContainerLoader(conn_str: str, container: str, prefix: str = '')[source]¶
Bases: BaseLoader
Loading logic for loading documents from Azure Blob Storage.
Initialize with connection string, container and blob prefix.
Methods
__init__(conn_str, container[, prefix])
Initialize with connection string, container and blob prefix.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.azure_blob_storage_container.AzureBlobStorageContainerLoader.html
|
d774ce3a3001-0
|
langchain.document_loaders.bigquery.BigQueryLoader¶
class langchain.document_loaders.bigquery.BigQueryLoader(query: str, project: Optional[str] = None, page_content_columns: Optional[List[str]] = None, metadata_columns: Optional[List[str]] = None, credentials: Optional[Credentials] = None)[source]¶
Bases: BaseLoader
Loads a query result from BigQuery into a list of documents.
Each document represents one row of the result. The page_content_columns
are written into the page_content of the document. The metadata_columns
are written into the metadata of the document. By default, all columns
are written into the page_content and none into the metadata.
Initialize BigQuery document loader.
Parameters
query – The query to run in BigQuery.
project – Optional. The project to run the query in.
page_content_columns – Optional. The columns to write into the page_content
of the document.
metadata_columns – Optional. The columns to write into the metadata of the
document.
credentials – google.auth.credentials.Credentials, optional
override (Credentials for accessing Google APIs. Use this parameter to) – default credentials, such as to use Compute Engine
(google.auth.compute_engine.Credentials) or Service Account
(google.oauth2.service_account.Credentials) credentials directly.
Methods
__init__(query[, project, ...])
Initialize BigQuery document loader.
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.bigquery.BigQueryLoader.html
|
d774ce3a3001-1
|
Load documents and split into chunks.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.bigquery.BigQueryLoader.html
|
2db69461ec52-0
|
langchain.document_loaders.azure_blob_storage_file.AzureBlobStorageFileLoader¶
class langchain.document_loaders.azure_blob_storage_file.AzureBlobStorageFileLoader(conn_str: str, container: str, blob_name: str)[source]¶
Bases: BaseLoader
Loading logic for loading documents from Azure Blob Storage.
Initialize with connection string, container and blob name.
Methods
__init__(conn_str, container, blob_name)
Initialize with connection string, container and blob name.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.azure_blob_storage_file.AzureBlobStorageFileLoader.html
|
51d264b70545-0
|
langchain.document_loaders.parsers.audio.OpenAIWhisperParser¶
class langchain.document_loaders.parsers.audio.OpenAIWhisperParser[source]¶
Bases: BaseBlobParser
Transcribe and parse audio files.
Audio transcription is with OpenAI Whisper model.
Methods
__init__()
lazy_parse(blob)
Lazily parse the blob.
parse(blob)
Eagerly parse the blob into a document or documents.
lazy_parse(blob: Blob) → Iterator[Document][source]¶
Lazily parse the blob.
parse(blob: Blob) → List[Document]¶
Eagerly parse the blob into a document or documents.
This is a convenience method for interactive development environment.
Production applications should favor the lazy_parse method instead.
Subclasses should generally not over-ride this parse method.
Parameters
blob – Blob instance
Returns
List of documents
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.parsers.audio.OpenAIWhisperParser.html
|
1577e4ba45fe-0
|
langchain.document_loaders.confluence.ConfluenceLoader¶
class langchain.document_loaders.confluence.ConfluenceLoader(url: str, api_key: Optional[str] = None, username: Optional[str] = None, oauth2: Optional[dict] = None, token: Optional[str] = None, cloud: Optional[bool] = True, number_of_retries: Optional[int] = 3, min_retry_seconds: Optional[int] = 2, max_retry_seconds: Optional[int] = 10, confluence_kwargs: Optional[dict] = None)[source]¶
Bases: BaseLoader
Load Confluence pages. Port of https://llamahub.ai/l/confluence
This currently supports username/api_key, Oauth2 login or personal access token
authentication.
Specify a list page_ids and/or space_key to load in the corresponding pages into
Document objects, if both are specified the union of both sets will be returned.
You can also specify a boolean include_attachments to include attachments, this
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.
Confluence API supports difference format of page content. The storage format is the
raw XML representation for storage. The view format is the HTML representation for
viewing with macros are rendered as though it is viewed by users. You can pass
a enum content_format argument to load() to specify the content format, this is
set to ContentFormat.STORAGE by default.
Hint: space_key and page_id can both be found in the URL of a page in Confluence
- https://yoursite.atlassian.com/wiki/spaces/<space_key>/pages/<page_id>
Example
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.confluence.ConfluenceLoader.html
|
1577e4ba45fe-1
|
Example
from langchain.document_loaders import ConfluenceLoader
loader = ConfluenceLoader(
url="https://yoursite.atlassian.com/wiki",
username="me",
api_key="12345"
)
documents = loader.load(space_key="SPACE",limit=50)
Parameters
url (str) – _description_
api_key (str, optional) – _description_, defaults to None
username (str, optional) – _description_, defaults to None
oauth2 (dict, optional) – _description_, defaults to {}
token (str, optional) – _description_, defaults to None
cloud (bool, optional) – _description_, defaults to True
number_of_retries (Optional[int], optional) – How many times to retry, defaults to 3
min_retry_seconds (Optional[int], optional) – defaults to 2
max_retry_seconds (Optional[int], optional) – defaults to 10
confluence_kwargs (dict, optional) – additional kwargs to initialize confluence with
Raises
ValueError – Errors while validating input
ImportError – Required dependencies not installed.
Methods
__init__(url[, api_key, username, oauth2, ...])
is_public_page(page)
Check if a page is publicly accessible.
lazy_load()
A lazy loader for document content.
load([space_key, page_ids, label, cql, ...])
param space_key
Space key retrieved from a confluence URL, defaults to None
load_and_split([text_splitter])
Load documents and split into chunks.
paginate_request(retrieval_method, **kwargs)
Paginate the various methods to retrieve groups of pages.
process_attachment(page_id[, ocr_languages])
process_doc(link)
process_image(link[, ocr_languages])
process_page(page, include_attachments, ...)
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.confluence.ConfluenceLoader.html
|
1577e4ba45fe-2
|
process_image(link[, ocr_languages])
process_page(page, include_attachments, ...)
process_pages(pages, ...[, ocr_languages])
Process a list of pages into a list of documents.
process_pdf(link[, ocr_languages])
process_svg(link[, ocr_languages])
process_xls(link)
validate_init_args([url, api_key, username, ...])
Validates proper combinations of init arguments
is_public_page(page: dict) → bool[source]¶
Check if a page is publicly accessible.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
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, content_format: ContentFormat = ContentFormat.STORAGE, limit: Optional[int] = 50, max_pages: Optional[int] = 1000, ocr_languages: Optional[str] = None) → List[Document][source]¶
Parameters
space_key (Optional[str], optional) – Space key retrieved from a confluence URL, defaults to None
page_ids (Optional[List[str]], optional) – List of specific page IDs to load, defaults to None
label (Optional[str], optional) – Get all pages with this label, defaults to None
cql (Optional[str], optional) – CQL Expression, defaults to None
include_restricted_content (bool, optional) – defaults to False
include_archived_content (bool, optional) – Whether to include archived content,
defaults to False
include_attachments (bool, optional) – defaults to False
include_comments (bool, optional) – defaults to False
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.confluence.ConfluenceLoader.html
|
1577e4ba45fe-3
|
include_comments (bool, optional) – defaults to False
content_format (ContentFormat) – Specify content format, defaults to ContentFormat.STORAGE
limit (int, optional) – Maximum number of pages to retrieve per request, defaults to 50
max_pages (int, optional) – Maximum number of pages to retrieve in total, defaults 1000
ocr_languages (str, optional) – The languages to use for the Tesseract agent. To use a
language, you’ll first need to install the appropriate
Tesseract language pack.
Raises
ValueError – _description_
ImportError – _description_
Returns
_description_
Return type
List[Document]
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
paginate_request(retrieval_method: Callable, **kwargs: Any) → List[source]¶
Paginate the various methods to retrieve groups of pages.
Unfortunately, due to page size, sometimes the Confluence API
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, getting the limit number
of pages with each request. We have to manually check if there
are more docs based on the length of the returned list of pages, rather than
just checking for the presence of a next key in the response like this page
would have you do:
https://developer.atlassian.com/server/confluence/pagination-in-the-rest-api/
Parameters
retrieval_method (callable) – Function used to retrieve docs
Returns
List of documents
Return type
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.confluence.ConfluenceLoader.html
|
1577e4ba45fe-4
|
Returns
List of documents
Return type
List
process_attachment(page_id: str, ocr_languages: Optional[str] = None) → List[str][source]¶
process_doc(link: str) → str[source]¶
process_image(link: str, ocr_languages: Optional[str] = None) → str[source]¶
process_page(page: dict, include_attachments: bool, include_comments: bool, content_format: ContentFormat, ocr_languages: Optional[str] = None) → Document[source]¶
process_pages(pages: List[dict], include_restricted_content: bool, include_attachments: bool, include_comments: bool, content_format: ContentFormat, ocr_languages: Optional[str] = None) → List[Document][source]¶
Process a list of pages into a list of documents.
process_pdf(link: str, ocr_languages: Optional[str] = None) → str[source]¶
process_svg(link: str, ocr_languages: Optional[str] = None) → str[source]¶
process_xls(link: str) → str[source]¶
static validate_init_args(url: Optional[str] = None, api_key: Optional[str] = None, username: Optional[str] = None, oauth2: Optional[dict] = None, token: Optional[str] = None) → Optional[List][source]¶
Validates proper combinations of init arguments
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.confluence.ConfluenceLoader.html
|
6d820b2f8c6d-0
|
langchain.document_loaders.embaas.EmbaasDocumentExtractionParameters¶
class langchain.document_loaders.embaas.EmbaasDocumentExtractionParameters[source]¶
Bases: TypedDict
Parameters for the embaas document extraction API.
Methods
__init__(*args, **kwargs)
clear()
copy()
fromkeys([value])
Create a new dictionary with keys from iterable and values set to value.
get(key[, default])
Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(k[,d])
If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem()
Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default])
Insert key with a value of default if key is not in the dictionary.
update([E, ]**F)
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()
Attributes
mime_type
The mime type of the document.
file_extension
The file extension of the document.
file_name
The file name of the document.
should_chunk
Whether to chunk the document into pages.
chunk_size
The maximum size of the text chunks.
chunk_overlap
The maximum overlap allowed between chunks.
chunk_splitter
The text splitter class name for creating chunks.
separators
The separators for chunks.
should_embed
Whether to create embeddings for the document in the response.
model
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.embaas.EmbaasDocumentExtractionParameters.html
|
6d820b2f8c6d-1
|
should_embed
Whether to create embeddings for the document in the response.
model
The model to pass to the Embaas document extraction API.
instruction
The instruction to pass to the Embaas document extraction API.
clear() → None. Remove all items from D.¶
copy() → a shallow copy of D¶
fromkeys(value=None, /)¶
Create a new dictionary with keys from iterable and values set to value.
get(key, default=None, /)¶
Return the value for key if key is in the dictionary, else default.
items() → a set-like object providing a view on D's items¶
keys() → a set-like object providing a view on D's keys¶
pop(k[, d]) → v, remove specified key and return the corresponding value.¶
If the key is not found, return the default if given; otherwise,
raise a KeyError.
popitem()¶
Remove and return a (key, value) pair as a 2-tuple.
Pairs are returned in LIFO (last-in, first-out) order.
Raises KeyError if the dict is empty.
setdefault(key, default=None, /)¶
Insert key with a value of default if key is not in the dictionary.
Return the value for key if key is in the dictionary, else default.
update([E, ]**F) → None. Update D from dict/iterable E and F.¶
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k]
If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v
In either case, this is followed by: for k in F: D[k] = F[k]
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.embaas.EmbaasDocumentExtractionParameters.html
|
6d820b2f8c6d-2
|
values() → an object providing a view on D's values¶
chunk_overlap: int¶
The maximum overlap allowed between chunks.
chunk_size: int¶
The maximum size of the text chunks.
chunk_splitter: str¶
The text splitter class name for creating chunks.
file_extension: str¶
The file extension of the document.
file_name: str¶
The file name of the document.
instruction: str¶
The instruction to pass to the Embaas document extraction API.
mime_type: str¶
The mime type of the document.
model: str¶
The model to pass to the Embaas document extraction API.
separators: List[str]¶
The separators for chunks.
should_chunk: bool¶
Whether to chunk the document into pages.
should_embed: bool¶
Whether to create embeddings for the document in the response.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.embaas.EmbaasDocumentExtractionParameters.html
|
8cd85f08e19f-0
|
langchain.document_loaders.notebook.concatenate_cells¶
langchain.document_loaders.notebook.concatenate_cells(cell: dict, include_outputs: bool, max_output_length: int, traceback: bool) → str[source]¶
Combine cells information in a readable format ready to be used.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.notebook.concatenate_cells.html
|
67c1622da840-0
|
langchain.document_loaders.embaas.BaseEmbaasLoader¶
class langchain.document_loaders.embaas.BaseEmbaasLoader(*, embaas_api_key: Optional[str] = None, api_url: str = 'https://api.embaas.io/v1/document/extract-text/bytes/', params: EmbaasDocumentExtractionParameters = {})[source]¶
Bases: BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_url: str = 'https://api.embaas.io/v1/document/extract-text/bytes/'¶
The URL of the embaas document extraction API.
param embaas_api_key: Optional[str] = None¶
param params: langchain.document_loaders.embaas.EmbaasDocumentExtractionParameters = {}¶
Additional parameters to pass to the embaas document extraction API.
validator validate_environment » all fields[source]¶
Validate that api key and python package exists in environment.
|
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.embaas.BaseEmbaasLoader.html
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.