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# add client_name also for arg in kwargs: if arg.startswith("ssl") or arg == "client_name": sentinel_args[arg] = kwargs[arg] # sentinel user/pass is part of sentinel_kwargs, user/pass for redis server # connection as direct parameter in kwargs sentinel_client = redis.sentinel.Sentine...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/redis.html
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Source code for langchain.utilities.github """Util that calls GitHub.""" from __future__ import annotations import json from typing import TYPE_CHECKING, Any, Dict, List, Optional from langchain.pydantic_v1 import BaseModel, Extra, root_validator from langchain.utils import get_from_dict_or_env if TYPE_CHECKING: fr...
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) try: from github import Auth, GithubIntegration except ImportError: raise ImportError( "PyGithub is not installed. " "Please install it with `pip install PyGithub`" ) with open(github_app_private_key, "r") as f: pr...
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and each issue's title and number. """ issues = self.github_repo_instance.get_issues(state="open") if issues.totalCount > 0: parsed_issues = self.parse_issues(issues) parsed_issues_str = ( "Found " + str(len(parsed_issues)) + " issues:\n" + str(parsed_issu...
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in the string, and the body are the rest of the string. For example, "Updated README\nmade changes to add info" Returns: str: A success or failure message """ if self.github_base_branch == self.github_branch: return """Cannot make a pull request because ...
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return "Unable to make comment due to error:\n" + str(e) [docs] def create_file(self, file_query: str) -> str: """ Creates a new file on the Github repo Parameters: file_query(str): a string which contains the file path and the file contents. The file path is the first...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/github.html
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""" Updates a file with new content. Parameters: file_query(str): Contains the file path and the file contents. The old file contents is wrapped in OLD <<<< and >>>> OLD The new file contents is wrapped in NEW <<<< and >>>> NEW For example: ...
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""" Deletes a file from the repo Parameters: file_path(str): Where the file is Returns: str: Success or failure message """ try: file = self.github_repo_instance.get_contents(file_path) self.github_repo_instance.delete_file( ...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/github.html
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Source code for langchain.utilities.sql_database """SQLAlchemy wrapper around a database.""" from __future__ import annotations import warnings from typing import Any, Iterable, List, Optional, Sequence import sqlalchemy from sqlalchemy import MetaData, Table, create_engine, inspect, select, text from sqlalchemy.engine...
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view_support: bool = False, max_string_length: int = 300, ): """Create engine from database URI.""" self._engine = engine self._schema = schema if include_tables and ignore_tables: raise ValueError("Cannot specify both include_tables and ignore_tables") se...
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self._custom_table_info = custom_table_info if self._custom_table_info: if not isinstance(self._custom_table_info, dict): raise TypeError( "table_info must be a dictionary with table names as keys and the " "desired table info as values" ...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/sql_database.html
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**kwargs: Any, ) -> SQLDatabase: """ Class method to create an SQLDatabase instance from a Databricks connection. This method requires the 'databricks-sql-connector' package. If not installed, it can be added using `pip install databricks-sql-connector`. Args: cat...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/sql_database.html
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cluster the notebook is attached to. Defaults to None. engine_args (Optional[dict]): The arguments to be used when connecting Databricks. Defaults to None. **kwargs (Any): Additional keyword arguments for the `from_uri` method. Returns: SQLDatabase: An instanc...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/sql_database.html
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"Need to provide either 'warehouse_id' or 'cluster_id'." ) if warehouse_id and cluster_id: raise ValueError("Can't have both 'warehouse_id' or 'cluster_id'.") if warehouse_id: http_path = f"/sql/1.0/warehouses/{warehouse_id}" else: http_path = ...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/sql_database.html
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with a default value of "". tenant (str): The name of the tenant used to connect to the CnosDB service, with a default value of "cnosdb". database (str): The name of the database in the CnosDB tenant. Returns: SQLDatabase: An instance of SQLDatabase configured...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/sql_database.html
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"""Get information about specified tables. Follows best practices as specified in: Rajkumar et al, 2022 (https://arxiv.org/abs/2204.00498) If `sample_rows_in_table_info`, the specified number of sample rows will be appended to each table description. This can increase performance as ...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/sql_database.html
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table_info += f"\n{self._get_table_indexes(table)}\n" if self._sample_rows_in_table_info: table_info += f"\n{self._get_sample_rows(table)}\n" if has_extra_info: table_info += "*/" tables.append(table_info) tables.sort() final_str = "\n\...
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f"{columns_str}\n" f"{sample_rows_str}" ) def _execute(self, command: str, fetch: Optional[str] = "all") -> Sequence: """ Executes SQL command through underlying engine. If the statement returns no rows, an empty list is returned. """ with self._engine.beg...
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""" result = self._execute(command, fetch) # Convert columns values to string to avoid issues with sqlalchemy # truncating text if not result: return "" elif isinstance(result, list): res: Sequence = [ tuple(truncate_word(c, length=self._ma...
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except SQLAlchemyError as e: """Format the error message""" return f"Error: {e}"
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Source code for langchain.utilities.openapi """Utility functions for parsing an OpenAPI spec.""" from __future__ import annotations import copy import json import logging import re from enum import Enum from pathlib import Path from typing import TYPE_CHECKING, Dict, List, Optional, Union import requests import yaml fr...
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raise ValueError("No paths found in spec") return self.paths def _get_path_strict(self, path: str) -> PathItem: path_item = self._paths_strict.get(path) if not path_item: raise ValueError(f"No path found for {path}") return path_item @prope...
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parameters = self._parameters_strict if ref_name not in parameters: raise ValueError(f"No parameter found for {ref_name}") return parameters[ref_name] def _get_root_referenced_parameter(self, ref: Reference) -> Parameter: """Get the root reference or err.""" ...
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request_bodies = self._request_bodies_strict if ref_name not in request_bodies: raise ValueError(f"No request body found for {ref_name}") return request_bodies[ref_name] def _get_root_referenced_request_body( self, ref: Reference ) -> Optional[RequestB...
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def parse_obj(cls, obj: dict) -> OpenAPISpec: try: cls._alert_unsupported_spec(obj) return super().parse_obj(obj) except ValidationError as e: # We are handling possibly misconfigured specs and # want to do a best-effort job to get ...
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def from_url(cls, url: str) -> OpenAPISpec: """Get an OpenAPI spec from a URL.""" response = requests.get(url) return cls.from_text(response.text) @property def base_url(self) -> str: """Get the base url.""" return self.servers[0].url [docs] ...
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raise ValueError(f"No {method} method found for {path}") return operation_obj [docs] def get_parameters_for_operation(self, operation: Operation) -> List[Parameter]: """Get the components for a given operation.""" from openapi_schema_pydantic import Reference param...
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Source code for langchain.utilities.bibtex """Util that calls bibtexparser.""" import logging from typing import Any, Dict, List, Mapping from langchain.pydantic_v1 import BaseModel, Extra, root_validator logger = logging.getLogger(__name__) OPTIONAL_FIELDS = [ "annotate", "booktitle", "editor", "howpub...
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import bibtexparser with open(path) as file: entries = bibtexparser.load(file).entries return entries [docs] def get_metadata( self, entry: Mapping[str, Any], load_extra: bool = False ) -> Dict[str, Any]: """Get metadata for the given entry.""" publication = en...
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Source code for langchain.utilities.python import functools import logging import multiprocessing import sys from io import StringIO from typing import Dict, Optional from langchain.pydantic_v1 import BaseModel, Field logger = logging.getLogger(__name__) @functools.lru_cache(maxsize=None) def warn_once() -> None: "...
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# create a Process p = multiprocessing.Process( target=self.worker, args=(command, self.globals, self.locals, queue) ) # start it p.start() # wait for the process to finish or kill it after timeout seconds p.join(timeout) ...
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Source code for langchain.utilities.tensorflow_datasets import logging from typing import Any, Callable, Dict, Iterator, List, Optional from langchain.pydantic_v1 import BaseModel, root_validator from langchain.schema import Document logger = logging.getLogger(__name__) [docs]class TensorflowDatasets(BaseModel): ""...
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}, ) tsds_client = TensorflowDatasets( dataset_name="mlqa/en", split_name="train", load_max_docs=MAX_DOCS, sample_to_document_function=mlqaen_example_to_document, ) """ dataset_name: str =...
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for s in self.dataset.take(self.load_max_docs) if self.sample_to_document_function is not None ) [docs] def load(self) -> List[Document]: """Download a selected dataset. Returns: a list of Documents. """ return list(self.lazy_load())
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Source code for langchain.utilities.dataforseo_api_search import base64 from typing import Dict, Optional from urllib.parse import quote import aiohttp import requests from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator from langchain.utils import get_from_dict_or_env [docs]class DataForSeoAPIWrap...
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@root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that login and password exists in environment.""" login = get_from_dict_or_env(values, "api_login", "DATAFORSEO_LOGIN") password = get_from_dict_or_env(values, "api_password", "DATAFORSEO_PASSWORD") va...
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obj = {"keyword": quote(keyword)} obj = {**obj, **self.default_params, **self.params} data = [obj] _url = ( f"https://api.dataforseo.com/v3/serp/{obj['se_name']}" f"/{obj['se_type']}/live/advanced" ) return { "url": _url, "headers":...
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) as response: res = await response.json() else: async with self.aiosession.post( request_details["url"], headers=request_details["headers"], json=request_details["data"], ) as response: res = await respo...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/dataforseo_api_search.html
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if isinstance(v, dict): self._cleanup_unnecessary_items(v) return d def _process_response(self, res: dict) -> str: """Process response from DataForSEO SERP API.""" toret = "No good search result found" for task in res.get("tasks", []): for result in task.g...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/dataforseo_api_search.html
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Source code for langchain.utilities.metaphor_search """Util that calls Metaphor Search API. In order to set this up, follow instructions at: """ import json from typing import Dict, List, Optional import aiohttp import requests from langchain.pydantic_v1 import BaseModel, Extra, root_validator from langchain.utils impo...
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"endPublishedDate": end_published_date, "useAutoprompt": use_autoprompt, } response = requests.post( # type: ignore f"{METAPHOR_API_URL}/search", headers=headers, json=params, ) response.raise_for_status() search_results...
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start_crawl_date: If specified, only pages we crawled after start_crawl_date will be returned. end_crawl_date: If specified, only pages we crawled before end_crawl_date will be returned. start_published_date: If specified, only pages published after start_published_date will be returned. ...
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end_published_date: Optional[str] = None, use_autoprompt: Optional[bool] = None, ) -> List[Dict]: """Get results from the Metaphor Search API asynchronously.""" # Function to perform the API call async def fetch() -> str: headers = {"X-Api-Key": self.metaphor_api_key} ...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/metaphor_search.html
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"author": result.get("author", "Unknown Author"), "published_date": result.get("publishedDate", "Unknown Date"), } ) return cleaned_results
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/metaphor_search.html
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Source code for langchain.utilities.arxiv """Util that calls Arxiv.""" import logging import os import re from typing import Any, Dict, List, Optional from langchain.pydantic_v1 import BaseModel, root_validator from langchain.schema import Document logger = logging.getLogger(__name__) [docs]class ArxivAPIWrapper(BaseMo...
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content Example: .. code-block:: python from langchain.utilities.arxiv import ArxivAPIWrapper arxiv = ArxivAPIWrapper( top_k_results = 3, ARXIV_MAX_QUERY_LENGTH = 300, load_max_docs = 3, load_all_available_meta = False, ...
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import arxiv values["arxiv_search"] = arxiv.Search values["arxiv_exceptions"] = ( arxiv.ArxivError, arxiv.UnexpectedEmptyPageError, arxiv.HTTPError, ) values["arxiv_result"] = arxiv.Result except ImportError: ...
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f"Summary: {result.summary}" for result in results ] if docs: return "\n\n".join(docs)[: self.doc_content_chars_max] else: return "No good Arxiv Result was found" [docs] def load(self, query: str) -> List[Document]: """ Run Arxiv search and ...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/arxiv.html
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docs: List[Document] = [] for result in results: try: doc_file_name: str = result.download_pdf() with fitz.open(doc_file_name) as doc_file: text: str = "".join(page.get_text() for page in doc_file) except (FileNotFoundError, fitz.fitz.F...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/arxiv.html
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Source code for langchain.utilities.gitlab """Util that calls gitlab.""" from __future__ import annotations import json from typing import TYPE_CHECKING, Any, Dict, List, Optional from langchain.pydantic_v1 import BaseModel, Extra, root_validator from langchain.utils import get_from_dict_or_env if TYPE_CHECKING: fr...
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values, "gitlab_personal_access_token", "GITLAB_PERSONAL_ACCESS_TOKEN" ) gitlab_branch = get_from_dict_or_env( values, "gitlab_branch", "GITLAB_BRANCH", default="main" ) gitlab_base_branch = get_from_dict_or_env( values, "gitlab_base_branch", "GITLAB_BASE_BRANCH",...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/gitlab.html
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""" Fetches all open issues from the repo Returns: str: A plaintext report containing the number of issues and each issue's title and number. """ issues = self.gitlab_repo_instance.issues.list(state="opened") if len(issues) > 0: parsed_issues =...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/gitlab.html
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""" Makes a pull request from the bot's branch to the base branch Parameters: pr_query(str): a string which contains the PR title and the PR body. The title is the first line in the string, and the body are the rest of the string. For example, "Updated REA...
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""" issue_number = int(comment_query.split("\n\n")[0]) comment = comment_query[len(str(issue_number)) + 2 :] try: issue = self.gitlab_repo_instance.issues.get(issue_number) issue.notes.create({"body": comment}) return "Commented on issue " + str(issue_number) ...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/gitlab.html
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Parameters: file_path(str): the file path Returns: str: The file decoded as a string """ file = self.gitlab_repo_instance.files.get(file_path, self.gitlab_branch) return file.decode().decode("utf-8") [docs] def update_file(self, file_query: str) -> str: ...
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"commit_message": "Create " + file_path, "actions": [ { "action": "update", "file_path": file_path, "content": updated_file_content, } ], } self.gitlab_...
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return self.delete_file(query) else: raise ValueError("Invalid mode" + mode)
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Source code for langchain.utilities.vertexai """Utilities to init Vertex AI.""" from typing import TYPE_CHECKING, Optional if TYPE_CHECKING: from google.auth.credentials import Credentials [docs]def raise_vertex_import_error(minimum_expected_version: str = "1.33.0") -> None: """Raise ImportError related to Vert...
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Source code for langchain.utilities.max_compute from __future__ import annotations from typing import TYPE_CHECKING, Iterator, List, Optional from langchain.utils import get_from_env if TYPE_CHECKING: from odps import ODPS [docs]class MaxComputeAPIWrapper: """Interface for querying Alibaba Cloud MaxCompute tabl...
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"https://pyodps.readthedocs.io/." ) from ex access_id = access_id or get_from_env("access_id", "MAX_COMPUTE_ACCESS_ID") secret_access_key = secret_access_key or get_from_env( "secret_access_key", "MAX_COMPUTE_SECRET_ACCESS_KEY" ) client = ODPS( access_...
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Source code for langchain.utilities.apify from typing import TYPE_CHECKING, Any, Callable, Dict, Optional from langchain.pydantic_v1 import BaseModel, root_validator from langchain.schema.document import Document from langchain.utils import get_from_dict_or_env if TYPE_CHECKING: from langchain.document_loaders impo...
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dataset_mapping_function: Callable[[Dict], Document], *, build: Optional[str] = None, memory_mbytes: Optional[int] = None, timeout_secs: Optional[int] = None, ) -> "ApifyDatasetLoader": """Run an Actor on the Apify platform and wait for results to be ready. Args: ...
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dataset_mapping_function: Callable[[Dict], Document], *, build: Optional[str] = None, memory_mbytes: Optional[int] = None, timeout_secs: Optional[int] = None, ) -> "ApifyDatasetLoader": """Run an Actor on the Apify platform and wait for results to be ready. Args: ...
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dataset_mapping_function: Callable[[Dict], Document], *, build: Optional[str] = None, memory_mbytes: Optional[int] = None, timeout_secs: Optional[int] = None, ) -> "ApifyDatasetLoader": """Run a saved Actor task on Apify and wait for results to be ready. Args: ...
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self, task_id: str, task_input: Dict, dataset_mapping_function: Callable[[Dict], Document], *, build: Optional[str] = None, memory_mbytes: Optional[int] = None, timeout_secs: Optional[int] = None, ) -> "ApifyDatasetLoader": """Run a saved Actor task on...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/apify.html
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Source code for langchain.utilities.awslambda """Util that calls Lambda.""" import json from typing import Any, Dict, Optional from langchain.pydantic_v1 import BaseModel, Extra, root_validator [docs]class LambdaWrapper(BaseModel): """Wrapper for AWS Lambda SDK. To use, you should have the ``boto3`` package ins...
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Invokes the lambda function and returns the result. Args: query: an input to passed to the lambda function as the ``body`` of a JSON object. """ # noqa: E501 res = self.lambda_client.invoke( FunctionName=self.function_name, ...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/awslambda.html
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Source code for langchain.utilities.wikipedia """Util that calls Wikipedia.""" import logging from typing import Any, Dict, List, Optional from langchain.pydantic_v1 import BaseModel, root_validator from langchain.schema import Document logger = logging.getLogger(__name__) WIKIPEDIA_MAX_QUERY_LENGTH = 300 [docs]class W...
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if wiki_page := self._fetch_page(page_title): if summary := self._formatted_page_summary(page_title, wiki_page): summaries.append(summary) if not summaries: return "No good Wikipedia Search Result was found" return "\n\n".join(summaries)[: self.doc_content...
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self.wiki_client.exceptions.DisambiguationError, ): return None [docs] def load(self, query: str) -> List[Document]: """ Run Wikipedia search and get the article text plus the meta information. See Returns: a list of documents. """ page_titles = sel...
https://api.python.langchain.com/en/latest/_modules/langchain/utilities/wikipedia.html
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Source code for langchain.docstore.arbitrary_fn from typing import Callable, Union from langchain.docstore.base import Docstore from langchain.schema import Document [docs]class DocstoreFn(Docstore): """Langchain Docstore via arbitrary lookup function. This is useful when: * it's expensive to construct an ...
https://api.python.langchain.com/en/latest/_modules/langchain/docstore/arbitrary_fn.html
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Source code for langchain.docstore.in_memory """Simple in memory docstore in the form of a dict.""" from typing import Dict, List, Optional, Union from langchain.docstore.base import AddableMixin, Docstore from langchain.docstore.document import Document [docs]class InMemoryDocstore(Docstore, AddableMixin): """Simp...
https://api.python.langchain.com/en/latest/_modules/langchain/docstore/in_memory.html
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""" if search not in self._dict: return f"ID {search} not found." else: return self._dict[search]
https://api.python.langchain.com/en/latest/_modules/langchain/docstore/in_memory.html
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Source code for langchain.docstore.base """Interface to access to place that stores documents.""" from abc import ABC, abstractmethod from typing import Dict, List, Union from langchain.docstore.document import Document [docs]class Docstore(ABC): """Interface to access to place that stores documents.""" [docs] @...
https://api.python.langchain.com/en/latest/_modules/langchain/docstore/base.html
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Source code for langchain.docstore.wikipedia """Wrapper around wikipedia API.""" from typing import Union from langchain.docstore.base import Docstore from langchain.docstore.document import Document [docs]class Wikipedia(Docstore): """Wrapper around wikipedia API.""" [docs] def __init__(self) -> None: "...
https://api.python.langchain.com/en/latest/_modules/langchain/docstore/wikipedia.html
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Source code for langchain.agents.utils from typing import Sequence from langchain.tools.base import BaseTool [docs]def validate_tools_single_input(class_name: str, tools: Sequence[BaseTool]) -> None: """Validate tools for single input.""" for tool in tools: if not tool.is_single_input: raise...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/utils.html
28f83a08f340-0
Source code for langchain.agents.tools """Interface for tools.""" from typing import List, Optional from langchain.callbacks.manager import ( AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) from langchain.tools.base import BaseTool, Tool, tool [docs]class InvalidTool(BaseTool): """Tool that is ...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/tools.html
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Source code for langchain.agents.load_tools # flake8: noqa """Load tools.""" import warnings from typing import Any, Dict, List, Optional, Callable, Tuple from mypy_extensions import Arg, KwArg from langchain.agents.tools import Tool from langchain.schema.language_model import BaseLanguageModel from langchain.callbacks...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html
f36440b0ee99-1
RequestsPatchTool, RequestsPostTool, RequestsPutTool, ) from langchain.tools.eleven_labs.text2speech import ElevenLabsText2SpeechTool from langchain.tools.scenexplain.tool import SceneXplainTool from langchain.tools.searx_search.tool import SearxSearchResults, SearxSearchRun from langchain.tools.shell.tool impo...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html
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from langchain.utilities.openweathermap import OpenWeatherMapAPIWrapper from langchain.utilities.dataforseo_api_search import DataForSeoAPIWrapper def _get_python_repl() -> BaseTool: return PythonREPLTool() def _get_tools_requests_get() -> BaseTool: return RequestsGetTool(requests_wrapper=TextRequestsWrapper())...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html
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description="Useful for when you need to answer questions about math.", func=LLMMathChain.from_llm(llm=llm).run, coroutine=LLMMathChain.from_llm(llm=llm).arun, ) def _get_open_meteo_api(llm: BaseLanguageModel) -> BaseTool: chain = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DO...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html
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tmdb_bearer_token = kwargs["tmdb_bearer_token"] chain = APIChain.from_llm_and_api_docs( llm, tmdb_docs.TMDB_DOCS, headers={"Authorization": f"Bearer {tmdb_bearer_token}"}, ) return Tool( name="TMDB API", description="Useful for when you want to get information from Th...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html
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def _get_wikipedia(**kwargs: Any) -> BaseTool: return WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper(**kwargs)) def _get_arxiv(**kwargs: Any) -> BaseTool: return ArxivQueryRun(api_wrapper=ArxivAPIWrapper(**kwargs)) def _get_golden_query(**kwargs: Any) -> BaseTool: return GoldenQueryRun(api_wrapper=Golden...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html
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return Tool( "Dall-E Image Generator", DallEAPIWrapper(**kwargs).run, "A wrapper around OpenAI DALL-E API. Useful for when you need to generate images from a text description. Input should be an image description.", ) def _get_twilio(**kwargs: Any) -> BaseTool: return Tool( name=...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html
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def _get_graphql_tool(**kwargs: Any) -> BaseTool: graphql_endpoint = kwargs["graphql_endpoint"] wrapper = GraphQLAPIWrapper(graphql_endpoint=graphql_endpoint) return BaseGraphQLTool(graphql_wrapper=wrapper) def _get_openweathermap(**kwargs: Any) -> BaseTool: return OpenWeatherMapQueryRun(api_wrapper=Ope...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html
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"google-search-results-json": ( _get_google_search_results_json, ["google_api_key", "google_cse_id", "num_results"], ), "searx-search-results-json": ( _get_searx_search_results_json, ["searx_host", "engines", "num_results", "aiosession"], ), "bing-search": (_get_bing_sear...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html
f36440b0ee99-9
"arxiv": ( _get_arxiv, ["top_k_results", "load_max_docs", "load_all_available_meta"], ), "golden-query": (_get_golden_query, ["golden_api_key"]), "pubmed": (_get_pubmed, ["top_k_results"]), "human": (_get_human_tool, ["prompt_func", "input_func"]), "awslambda": ( _get_lambda_...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html
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[docs]def load_huggingface_tool( task_or_repo_id: str, model_repo_id: Optional[str] = None, token: Optional[str] = None, remote: bool = False, **kwargs: Any, ) -> BaseTool: """Loads a tool from the HuggingFace Hub. Args: task_or_repo_id: Task or model repo id. model_repo_id: ...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html
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callbacks: Callbacks = None, **kwargs: Any, ) -> List[BaseTool]: """Load tools based on their name. Args: tool_names: name of tools to load. llm: An optional language model, may be needed to initialize certain tools. callbacks: Optional callback manager or list of callback handlers. ...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html
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missing_keys = set(extra_keys).difference(kwargs) if missing_keys: raise ValueError( f"Tool {name} requires some parameters that were not " f"provided: {missing_keys}" ) sub_kwargs = {k: kwargs[k] for k in extra_keys} ...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html
e6d9fca7cb29-0
Source code for langchain.agents.agent_iterator from __future__ import annotations import logging import time from abc import ABC, abstractmethod from asyncio import CancelledError from functools import wraps from typing import ( TYPE_CHECKING, Any, Callable, Dict, List, NoReturn, Optional, ...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html
e6d9fca7cb29-1
self, agent_executor: AgentExecutor, inputs: Any, callbacks: Callbacks = None, *, tags: Optional[list[str]] = None, include_run_info: bool = False, async_: bool = False, ): """ Initialize the AgentExecutorIterator with the given AgentExecutor, ...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html
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return self._tags @tags.setter @rebuild_callback_manager_on_set def tags(self, tags: Optional[List[str]]) -> None: """When tags are changed after __init__, rebuild callback mgr""" self._tags = tags @property def agent_executor(self) -> AgentExecutor: return self._agent_execut...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html
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) [docs] def reset(self) -> None: """ Reset the iterator to its initial state, clearing intermediate steps, iterations, and time elapsed. """ logger.debug("(Re)setting AgentExecutorIterator to fresh state") self.intermediate_steps: list[tuple[AgentAction, str]] = [] ...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html
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return self._final_outputs @final_outputs.setter def final_outputs(self, outputs: Optional[Dict[str, Any]]) -> None: # have access to intermediate steps by design in iterator, # so return only outputs may as well always be true. self._final_outputs = None if outputs: ...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html
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""" pass async def _on_first_async_step(self) -> None: """ Perform any necessary setup for the first step of the asynchronous iterator. """ # on first step, need to await callback manager and start async timeout ctxmgr if self.iterations == 0: assert isins...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html
e6d9fca7cb29-6
return await self._acall_next() except StopAsyncIteration: raise except (TimeoutError, CancelledError): await self.timeout_manager.__aexit__(None, None, None) self.timeout_manager = None return await self._astop() except BaseException as e: ...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html
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run_manager: Optional[CallbackManagerForChainRun], ) -> Dict[str, Union[str, List[Tuple[AgentAction, str]]]]: """ Process the output of the next step, handling AgentFinish and tool return cases. """ logger.debug("Processing output of Agent loop step") if isinstance(ne...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html
e6d9fca7cb29-8
""" Process the output of the next async step, handling AgentFinish and tool return cases. """ logger.debug("Processing output of async Agent loop step") if isinstance(next_step_output, AgentFinish): logger.debug( "Hit AgentFinish: _areturn -> on_chain...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html
e6d9fca7cb29-9
self.intermediate_steps, **self.inputs, ) assert ( isinstance(self.run_manager, CallbackManagerForChainRun) or self.run_manager is None ) returned_output = self.agent_executor._return( output, self.intermediate_steps, run_manager=self.run_m...
https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html