Langchain import tool. code-block:: python from langchain_core.
Langchain import tool function_calling import convert_to_openai_tool from How to bind model-specific tools. agents import initialize_agent, AgentType from langchain. _api. In these cases, we want to let the model itself decide how many times to use tools and in what order. config (RunnableConfig | None) – The config to use for the Runnable. convert. 0. agents import create tool calling agent, AgentExecutor prompt = ChatPromptTemplate. retriever (BaseRetriever) – The retriever to use for the retrieval """Different methods for rendering Tools to be passed to LLMs. Create a new model by parsing and validating input data from keyword arguments. function_calling import convert_to_openai_function from langchain_openai import ChatOpenAI Create a BaseTool from a Runnable. pydantic_v1 import BaseModel, Field, create_model from langchain_core. tools import WikipediaQueryRun from langchain_community. """ from typing import Callable, List # For backwards compatibility from langchain_core. Wikipedia tool. name - The name of the tool. By supplying the model with a schema that matches up with a LangChain tool’s signature, along with a name and description of what the tool does, we can get the model to reliably generate valid input. tools import Tool from def create_retriever_tool (retriever: BaseRetriever, name: str, description: str, *, document_prompt: Optional [BasePromptTemplate] = None, document_separator: str = " \n\n ",)-> Tool: """Create a tool to do retrieval of documents. Subsequent invocations of the bound chat model will include tool schemas in every call to the model API. _InfoSQLDatabaseToolInput'> ¶ Pydantic model class to validate and parse the tool’s input arguments. retriever. % % capture --no-stderr from langchain_community. If True, only new keys generated by this chain will be returned. In an API call, you can describe tools and have the model intelligently choose to output a Tool that searches the Wikipedia API. Usage from langchain_community. How to return artifacts from a tool. llms import GradientLLM Serper - Google Search API. Once you've downloaded the credentials. 1, which is no longer actively maintained. This section will cover how to create conversational agents: chatbots that can interact with other systems and APIs using tools. function. runnables. pull ("hwchase17/react") agent = create_react_agent (llm, tools, prompt) agent_executor = AgentExecutor (agent = agent class langchain_core. In order to force our LLM to select a specific tool, we can use the tool_choice parameter to ensure certain behavior. If True, prompts the user for confirmation (y/n) before Create a BaseTool from a Runnable. from functools import partial from typing import Optional from langchain_core. from tempfile import TemporaryDirectory from langchain_community. tools import tool @tool def count_emails (last_n_days: int)-> int: """Dummy function to count number of e-mails. memory import ConversationBufferMemory from langchain_community. Tools can be just about anything — APIs, functions, databases, etc. Key concepts (1) Tool Creation: Use the tool function to create a tool. retrievers import . pydantic_v1 import BaseModel, Field, root_validator from langchain_core. return_only_outputs (bool) – Whether to return only outputs in the response. YouTube Search package searches YouTube videos avoiding using their heavily rate-limited API. Wikipedia. Memory. json . LangChain offers an experimental tool for executing arbitrary Python code. param args_schema: Optional [TypeBaseModel] = None ¶ Pydantic model class to validate and parse the tool’s input arguments. Use with caution. tavily_search import TavilySearchResults from langchain_openai import ChatOpenAI tools = [TavilySearchResults (max_results = 1)] # Choose the LLM that will drive the agent # Only certain models support this model = ChatOpenAI (model = "gpt-4o-mini", temperature = 0) Tool calling allows a model to detect when one or more tools should be called and respond with the inputs that should be passed to those tools. Usage . Args schema should be either: A subclass of Make sure that permissions associated with the tools are narrowly-scoped (e. Tools that contain some level of risk. For example, we can force our tool to call the multiply tool by using the following code: Stream all output from a runnable, as reported to the callback system. Input should be a search query. The LLM can use it to execute any shell commands. Setup . bind_tools method, which receives a list of LangChain tool objects, Pydantic classes, or JSON Schemas and binds them to the chat model in the provider-specific expected format. brave_search. 2. tools import Tool from langchain_openai import OpenAI llm = OpenAI (temperature = 0) search = GoogleSerperAPIWrapper tools = [Tool (name = "Intermediate Answer", func = search. runnables import Runnable, RunnablePassthrough from langchain_core. param args_schema: Type [BaseModel] = <class 'langchain_community. When you invoke your tool, LangChain will inspect your tool's signature, look for a parameter typed as RunnableConfig, and if it exists, populate that parameter with the correct value. BraveSearch [source] ¶ Bases: BaseTool. This notebook walks through some of them. from langchain_core. Tavily's Search API is a search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed. Note that each ToolMessage must include a tool_call_id that matches an id in the original tool calls that the model generates. class langchain_community. agents import AgentExecutor, create_tool_calling_agent, tool from langchain_anthropic import ChatAnthropic from langchain_core. By including the Ionic Tool in your agent, you are effortlessly providing your users with the ability to shop and transact directly within your agent, and you'll get a cut of the transaction. manager import (AsyncCallbackManagerForToolRun, CallbackManagerForToolRun,) from langchain_core. Users should use v2. """ import ast import re import sys from contextlib import redirect_stdout from io import StringIO from typing import Any, Dict, Optional, Type from langchain_core. Args: retriever: The retriever to use for the retrieval name: The name for the tool. A big use case for LangChain is creating agents. runnables import Runnable from langchain_core. google_drive. Certain OpenAI models have been finetuned to work with tool calling. EdenAiParsingInvoiceTool. How to create tools. Here is the complete code: from dotenv import load_dotenv from langchain import hub from langchain. name: The name of the schema to output. addresses (List[str]): Previous addresses as a list of strings. tools import Tool from langchain_experimental. We can take advantage of this structured output, combined with This page covers how to use the Wolfram Alpha API within LangChain. This is very similar but different from function calling, and thus requires a separate agent type. load_tools (tool_names: List [str], allow_dangerous_tools (bool) – Optional flag to allow dangerous tools. Note: these tools are not recommended for use outside a sandboxed environment! % pip install -qU langchain-community. create_retriever_tool (retriever: BaseRetriever, name: str, description: str, *, document_prompt: Optional [BasePromptTemplate] = None, document_separator: str = '\n\n') → Tool [source] ¶ Create a tool to do retrieval of documents. Tools allow us to extend the capabilities of a model beyond just outputting text/messages. g. In the context of retrieval-augmented generation, summarizing text can help distill the information in a large number of retrieved documents to provide context for a LLM. , containing image data). Bases: BaseTool Tool to run shell commands. Wikipedia is the largest and most-read reference work in history. Here are the names and descriptions for each tool: {rendered_tools} Given the user input, return the name and input of the tool to use. Should contain all inputs specified in Chain. Pydantic model class to validate and parse the tool’s input arguments. Setup You'll need to create an app from the WolframAlpha portal and obtain an appid . getLogger (__name__) SearchApi tool. base. callbacks import (CallbackManagerForToolRun,) from langchain_core. Choosing between multiple tools. ToolNode is a LangChain Runnable that takes graph state (with a list of messages) as input and outputs state update with the result of tool calls. The Dall-E tool allows your agent to create images using OpenAI's Dall-E image generation tool. A wrapper around the Search API. They combine a few things: The name of the tool; A description of what the tool is; JSON schema of what the inputs to the tool are; The function to call; Whether the result of a tool should be returned directly to the user Create a BaseTool from a Runnable. load_tools# langchain_community. First, we'll import the tools. Providers adopt different conventions for formatting tool schemas. Some tools bundled within the PlayWright Browser toolkit include:. Tool that queries the Eden AI Invoice parsing API. property tool_call_schema: type [BaseModel] # Examples using BaseTool. EdenAiTextModerationTool. from __future__ import annotations import textwrap from inspect import signature from typing import Any, Awaitable, Callable, Dict, List, Literal, Optional, Type, Union from langchain_core. It is useful for when you need to interact with a discord channel. Tool calling agents, like those in LangGraph, use this basic flow to answer queries and solve tasks. ocr_invoiceparser. Please use with caution and read the documentation of these tools to understand the risks and how to mitigate them. run,) @tool decorator This @tool decorator is the simplest way to define a custom tool. To use this toolkit, you will need to set up your credentials explained in the Gmail API docs. callbacks import (AsyncCallbackManagerForToolRun, CallbackManager, CallbackManagerForToolRun,) from Tool calling allows a model to respond to a given prompt by generating output that matches a user-defined schema. This includes all inner runs of LLMs, Retrievers, Tools, etc. invoke ("Obama's first name?") API Reference: DuckDuckGoSearchRun "The White House, official residence of the president of the United States, in July 2008. This will be passed to the language. , if the Runnable takes a dict as input and the specific dict keys are not typed), the schema can be specified directly with args_schema. 5-turbo-1106", temperature = 0) Key concepts (1) Tool Creation: Use the @tool decorator to create a tool. A tool is an association between a function and its schema. from langchain_community. custom events will only be Source code for langchain_core. This guide covers how to use LangGraph's prebuilt ToolNode for tool calling. prompts. agents import AgentExecutor, create_openai_functions_agent from langchain_openai import ChatOpenAI llm = ChatOpenAI (temperature = 0, model = "gpt-4o") instructions = """You are an assistant. v1 is for backwards compatibility and will be deprecated in 0. tool_call_chunks attribute. auto import tqdm from typing import List, Union import zipfile # Langchain imports from langchain. To access reference the active config object from your custom tool, you'll need to add a parameter to your tool's signature typed as RunnableConfig. Gmail. Overview Integration details To use Polygon IO tools, you need to install the langchain-community package. First, you need to install wikipedia python package. Agents are systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. inputs (Dict[str, Any] | Any) – Dictionary of inputs, or single input if chain expects only one param. For instance, OpenAI uses a format like this: type: The type of the tool. youtube. utils. Graphs. run,) How to use LangChain tools. tool import GoogleDriveSearchTool from langchain_googledrive. version (Literal['v1', 'v2']) – The version of the schema to use either v2 or v1. The Discord Tool gives your agent the ability to search, read, and write messages to discord channels. What When constructing your own agent, you will need to provide it with a list of Tools that it can In this guide, we will go over the basic ways to create Chains and Agents that call Tools. This will be passed to the language model, so should be Stream all output from a runnable, as reported to the callback system. google_drive import GoogleDriveAPIWrapper # By default, search only in the filename. At the time of writing, this is always "function". To call tools using such models, simply bind tools to them in the usual way, and invoke the model using content blocks of the desired type (e. YouTubeSearchTool [source] ¶ Bases: BaseTool. from langchain . v1. utilities import WikipediaAPIWrapper from pydantic import BaseModel, Field class WikiInputs (BaseModel): """Inputs to the wikipedia tool. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. Tools. """ import ast import re import sys from contextlib import redirect_stdout from io import StringIO from typing import Any, Dict, Optional, Type from langchain. config import run_in from langchain_community. No default will be assigned until the API is stabilized. Parameters. Use from langchain_openai import OpenAIEmbeddings from langchain_community. This represents a message with role "tool", which contains the result of calling a tool. tools import DuckDuckGoSearchRun search = DuckDuckGoSearchRun search. from langchain_googledrive. This toolkit is used to interact with the browser. Tools are interfaces that an agent can use to interact with the world. , for database operations or API requests); from langchain_openai import ChatOpenAI from langgraph. from typing import List from langchain_core. E2BDataAnalysisToolArguments'> ¶. tools. input_keys except for inputs that will be set by the chain’s memory. agents import AgentExecutor, AgentType, initialize_agent, load_tools from langchain. But for certain use cases, how many times we use tools depends on the input. Setup You will need an OpenAI API Key which you can get from the OpenAI web site and then set the OPENAI_API_KEY environment variable to the key you just created. If we want our tool to distinguish between message content and other artifacts, we need to specify response_format="content_and_artifact" when defining our tool and make sure that we return a tuple of (content, artifact): % pip install -qU "langchain-core>=0. agents import AgentType , initialize_agent , load_tools from langchain_openai import OpenAI langchain. Agents let us do just this. agents import (AgentExecutor, create_react_agent,) from langchain_core. prompts import ChatPromptTemplate prompt = ChatPromptTemplate. """ query: str = Field (description = "query to look up in Wikipedia, should be 3 or less words") tool = WikipediaQueryRun from langchain_core. as_tool will instantiate a BaseTool with a name, description, and args_schema from a Runnable. NavigateTool (navigate_browser) - navigate to a URL The main difference between using one Tool and many is that we can't be sure which Tool the model will invoke upfront, so we cannot hardcode, like we did in the Quickstart, a specific tool into our chain. Retrieval Augmented Generation (RAG) Part 1 : Build an application that uses your own documents to inform its responses. ValueError: If the tool is a dangerous tool and allow_dangerous_tools is False. When running an LLM in a continuous loop, and providing the capability to browse external data stores and a chat history, context-aware agents can be created. input (Any) – The input to the Runnable. agents import load_tools tools = load_tools (["wolfram-alpha"]) API Reference: load_tools. Please see the how to use a chat model to call tools guide for more information. In addition to role and content, this message has:. Initialize the tool. Additionally, the decorator will use the function's docstring as the tool's description - so a docstring MUST be provided. LLMs are a great tool for this given their proficiency in understanding and synthesizing text. utilities. 4. (2) Tool Binding: The tool needs to be connected to a model that supports tool calling. chains import LLMChain from langchain. Tool that queries the BraveSearch. To use the Google Calendar Tools you need to install the following official peer dependency: Shell (bash) Giving agents access to the shell is powerful (though risky outside a sandboxed environment). tavily_search import TavilySearchResults from langchain import hub Create a BaseTool from a Runnable. messages import HumanMessage from langchain_core. """ return last_n_days * 2 @tool def send_email (message: str, recipient Defining the tool . Once this is done, we'll install the required libraries. tools import MoveFileTool from langchain_core. tools import BaseTool logger = This page covers how to use the SerpAPI search APIs within LangChain. You can do this with: from langchain. Instead we'll add call_tools, a RunnableLambda that takes the output AI message with tools calls and routes to the correct tools. python. runnables import Runnable, RunnablePassthrough from langchain_core. callbacks import Callbacks from langchain_core. configurable_alternatives Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. manager import (Callbacks,) from langchain_core. Parameters:. Serper is a low-cost Google Search API that can be used to add answer box, knowledge graph, and organic results data from Google Search. tools import tool @tool def add (a: int, b: int)-> int: """Adds a and b. Some multimodal models, such as those that can reason over images or audio, support tool calling features as well. We will also use OpenAI for embeddings, but any LangChain embeddings should suffice. text_moderation. tools import HumanInputRun Tools. structured. By themselves, language models can't take actions - they just output text. This module contains various ways to render tools. tools . E2B Data Analysis. """ from typing import Any, Dict, Optional, Sequence, Type, Union from sqlalchemy. We will use a simple LangGraph agent for demonstration purposes. from langchain import hub from langchain. The president of the United States is the head of state and head of Create a BaseTool from a Runnable. In particular, you'll be able to create LLM agents that use custom tools to answer user queries. Now let’s take a look at how we might augment this chain so that it can pick from a number of tools to call. openai_tools. from langchain_openai import ChatOpenAI """A tool for running python code in a REPL. import datetime import json import openai import os import pandas as pd import pinecone import re from tqdm. function: An object containing tool parameters. Where possible, schemas are inferred from runnable. json file, you can start using the Gmail API. Parameters: llm (BaseLanguageModel) – LLM to use as the agent. InvoiceParsingInput. pydantic_v1 import BaseModel, Field from langchain_core. In the tools Quickstart we went over how to build a Chain that calls a single multiply tool. """ tools = [] callbacks = _handle_callbacks (callback_manager = kwargs. ShellTool [source] ¶. prompts import (BasePromptTemplate, PromptTemplate, aformat_document, format_document,) from langchain_core. tools import BaseTool from pydantic import BaseModel, Field, model_validator logger = logging. tools = load_tools (["human", "ddg-search"], llm = math_llm, input_func = get_input) # Or you can directly instantiate the tool from langchain_community . This notebook walks through connecting a LangChain email to the Gmail API. tools import Tool from langchain_google_community import GoogleSearchAPIWrapper search = GoogleSearchAPIWrapper tool = Tool (name = "google_search", description = "Search Google for recent results. Tools can be passed to chat models that support tool calling allowing the model to request the execution of a specific function with specific inputs. This page covers how to use the Serper Google Search API within LangChain. prebuilt import create_react_agent llm = ChatOpenAI (model = "gpt-4o-mini") system_message = """ You have access to an API to help answer user queries. deprecation import deprecated from langchain_core. agents import create_tool_calling_agent agent = create_tool_calling_agent (model, tools, prompt) API Reference: create_tool_calling_agent. Pydantic model Tools are interfaces that an agent, chain, or LLM can use to interact with the world. Some models, like the OpenAI models released in Fall 2023, also support parallel function calling, which allows you to invoke multiple functions (or the same function multiple times) in a single model call. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. E2B's cloud environments are great runtime sandboxes for LLMs. This is ideal for building tools such as code interpreters, or Advanced Data Analysis like in ChatGPT. Args schema should be either: A description: The description for the tool. language_models import BaseLanguageModel from langchain_core. """ from langchain_community. Tool calling Structured output JSON mode Image input Audio input Video input Token-level streaming Native async Token usage Logprobs; from langchain_groq import ChatGroq llm = ChatGroq (model = "mixtral-8x7b-32768", temperature = 0, max_tokens = None, timeout = None, max_retries = 2, Generally, such models are better at tool calling than non-fine-tuned models, and are recommended for use cases that require tool calling. param ask_human_input: bool = False ¶. First, let’s define our model and tools: First, let’s define our model and tools: How to stream events from a tool; How to stream tool calls; How to use LangChain tools; How to handle tool errors; How to use few-shot prompting with tool calling; How to trim messages; How use a vector store to retrieve data; How to create and query vector stores Source code for langchain_community. A ToolCallChunk includes optional string fields for the tool name, args, and id, and includes an optional integer field index that can be used to join chunks together. Tools are a way to encapsulate a function and its schema in a way that from langchain_community. Related You’ve now seen how to pass tool calls back to a Discord Tool. When tools are called in a streaming context, message chunks will be populated with tool call chunk objects in a list via the . load_tools. utilities import GoogleSerperAPIWrapper from langchain_core. tools import HumanInputRun Whether the tool only accepts a single input. Setup Here we demonstrate how to call tools with multimodal data, such as images. messages import ToolMessage ToolMessage(content='42', tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL') Example: A ToolMessage where only part of the tool output is sent to the model and the full output is passed in to artifact # flake8: noqa """Tools for interacting with a SQL database. For this guide, we’ll be using an tool calling agent with a single tool for searching the web. vectorstores import FAISS from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain. sql_database. Fields are optional because portions of a tool from langchain_community. function_calling import convert_to_openai_function from langchain_openai import ChatOpenAI from typing import List from langchain_core. agents import AgentExecutor, create_react_agent, load_tools from langchain_openai import ChatOpenAI llm = ChatOpenAI (temperature = 0. It is designed to work well out-of-box with LangGraph's prebuilt ReAct agent, but can also work with any StateGraph def create_retriever_tool (retriever: BaseRetriever, name: str, description: str, *, document_prompt: Optional [BasePromptTemplate] = None, document_separator: str = " \n\n ",)-> Tool: """Create a tool to do retrieval of documents. The SearchApi tool connects your agents and chains to the internet. Key concepts . The tool abstraction in LangChain associates a TypeScript function with a schema that defines the function's name, description and input. agent_toolkits import JsonToolkit, create_json_agent from langchain_community . callbacks. utils. 13. How to pass run time values to tools. Execute the chain. Returns 2 * last_n_days. chat import ChatPromptTemplate from langchain_core. Return your response as a JSON blob with 'name' and 'arguments Source code for langchain_core. Tools are utilities designed to be called by a model: their inputs are designed to be generated by models, and their outputs are designed to be passed back to models. Class property must be public! from typing import Type from langchain. from_messages ([("system", "You are a helpful assistant"), For convenience, Runnables that accept a string or object input can be converted to tools using the asTool method, which allows for the specification of names, descriptions, and additional schema information for arguments. tools import BaseTool from langchain. utilities import GoogleSearchAPIWrapper from langchain_core. tool import SemanticScholarQueryRun tools = [ SemanticScholarQueryRun ( ) ] API Reference: SemanticScholarQueryRun class langchain_community. The rest of this section will assume you’re using Tavily. E2B's Data Analysis sandbox allows for safe code execution in a sandboxed environment. Dependencies . . create_openai_tools_agent (llm: BaseLanguageModel, tools: Sequence [BaseTool], prompt: ChatPromptTemplate, strict: bool | None = None) → Runnable [source] # Create an agent that uses OpenAI tools. agent_toolkits. 19" I was able to solve this problem for myself! You need to define a custom class property and give it a value in __init__. a tool_call_id field which conveys the id of the call to the tool that was called to produce this result. Ionic is a plug and play ecommerce marketplace for AI Assistants. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in Tools. Adapters. This will be passed to the language model, so should be useful for when you need to find something on or summarize a webpage. '2\n' # You can create the tool to pass to an agent repl_tool = Tool (name = "python_repl", description = "A Python shell. _QuerySQLCheckerToolInput'> ¶ Pydantic model class to validate and parse the tool’s input arguments. e2b_data_analysis. """ return a + b @tool def multiply (a: int, b: int)-> int: """Multiplies a and b. json. Tool that queries YouTube. They combine a few things: The name of the tool; A description of what the tool is; Schema of what the inputs to the tool are; The function to call; Whether the result of a tool should be returned directly to the user Interface LangChain tools must implement. callbacks import (AsyncCallbackManagerForToolRun, CallbackManagerForToolRun,) from langchain. manager import CallbackManagerForToolRun, AsyncCallbackManagerForToolRun from typing import Optional, Type, Callable from pydantic import Field import requests import json # APIキーをセット (変数名はLangChain側で決められ Here we will demonstrate how to convert a LangChain Runnable into a tool that can be used by agents, chains, or chat models. ToolMessage . This gives the model awareness of the tool and the associated input schema required by the tool. semanticscholar. % % capture --no-stderr from typing import Optional, Sequence from langchain_core. Google Calendar Tool. The key to using models with tools is correctly prompting a model and parsing its response so that it chooses the right tools and Overview . StructuredTool [source] # Bases: BaseTool. Skip to main content. user LangChain ChatModels supporting tool calling features implement a . Args: pets: List of favorite pets to set. tools (Sequence) – Tools this agent has The Gmail Tool allows your agent to create and view messages from a linked email account. agents import AgentType, initialize_agent from langchain_community. document_prompt: The prompt to use for the document. input should be a comma separated list of "valid URL including protocol","what you want to find on the page or empty string for a summary". Depending on the LLM you are using and the prompting strategy you are using, you may want Tools to be rendered in a different way. ", func = search. Related LangGraph quickstart; Few shot prompting with tools class langchain_community. param api_wrapper: WikipediaAPIWrapper [Required] ¶ param args_schema: Type [BaseModel] = <class 'langchain_community. Here we will demonstrate how to use this method to convert a LangChain Runnable into a tool that can be used by agents, chains, or chat models. They BaseTool implements the standard Runnable Interface. The Runnable Interface has This notebook takes you through how to use LangChain to augment an OpenAI model with access to external tools. tools import render_text_description rendered_tools = render_text_description (tools) print Create a BaseTool from a Runnable. 🏃. ; an artifact field which can be used to pass along arbitrary artifacts of the tool execution which are useful to track but which should Tavily Search. pydantic_v1 import BaseModel, Field from langchain. tools import Tool search = GoogleSearchAPIWrapper tool = Tool (name = "google_search", description = "Search Google for recent results. Args: user_id (int): the user ID. This notebook shows off usage of various search tools. tool import JsonSpec from langchain_openai import OpenAI API Reference: OpenAPIToolkit | create_openapi_agent | JsonSpec | OpenAI from langchain_core. The decorator uses the function name as the tool name by default, but this can be overridden by passing a string as the first argument. The WikipediaQueryRun tool connects your agents and chains to Wikipedia. tools import BaseTool from from langchain_core. """ return a * b tools = [add, multiply] API Reference: tool. We’ll focus on Chains since Agents can route between multiple tools by default. tools import InjectedToolArg, tool from typing_extensions import Annotated user_to_pets = {} @tool (parse_docstring = True) def update_favorite_pets (pets: List [str], user_id: Annotated [str, InjectedToolArg])-> None: """Add the list of favorite pets. Note: this guide requires langchain-core >= 0. tool import JsonSpec from langchain_openai import OpenAI Example: A ToolMessage representing a result of 42 from a tool call with id. runnables import ConfigurableField from langchain_openai import ChatOpenAI model = ChatOpenAI (max_tokens = 20). 0) tools = load_tools (["arxiv"],) prompt = hub. or - A subclass of pydantic. prompts import ChatPromptTemplate prompt = ChatPromptTemplate. wikipedia. utils import ConfigurableField from langchain_openai import ChatOpenAI model = ChatAnthropic (model_name = "claude-3-sonnet-20240229"). Args schema should be either: A subclass of pydantic. Initialize tool. Was The WolframAlpha tool connects your agents and chains to WolframAlpha's state-of-the-art computational intelligence engine. WikipediaQueryInput'> ¶ Pydantic model class to validate and parse the tool’s input arguments. Tools are interfaces that an agent, chain, or LLM can use to interact with the world. Tools. import inspect from typing import Any, Callable, Dict, Literal, Optional, Type, Union, get_type_hints from langchain_core. API Reference: Tool | PythonREPL. import logging import platform import warnings from typing import Any, List, Optional, Type, Union from langchain_core. tool = GoogleDriveSearchTool (api_wrapper = GoogleDriveAPIWrapper (folder_id = folder_id, num_results = 2, Create a BaseTool from a Runnable. Finally, we combine the agent (the brains) with the tools inside the AgentExecutor (which will repeatedly call the agent and execute tools). The default will be powered by Tavily, but you can switch it out for any similar tool. schema - The schema of the tool, defined with a Zod object. agents import load_tools tools = load_tools (["serpapi"]) API import os from langchain. Our previous chain from the multiple tools guides Tool for getting metadata about a SQL database. tools import tool @tool def add(a: int, b: int) -> int: “””Adds two numbers together””” # this docstring gets used as the description return a + b # the actions our tool performs. prompts import ChatPromptTemplate system_prompt = f"""You are an assistant that has access to the following set of tools. search. E2BDataAnalysisTool [source] ¶. This can be useful in combination with an LLM that can generate code to perform more powerful computations. retriever import create_retriever_tool from langchain_community. The Google Calendar Tools allow your agent to create and view Google Calendar events from a linked calendar. How to call tools using ToolNode¶. This tool is handy when you need to answer questions about current events. This helps the model match tool responses with tool calls. Agents: Build an agent that interacts with external tools. How to add a semantic layer over graph database. Alternatively (e. engine import Result from pydantic import BaseModel, Field, root_validator, model_validator, ConfigDict from langchain_core. A toolkit is a collection of tools meant to be used together. """ return True llm Ionic Shopping Tool. shell. tavily_search import TavilySearchResults from langchain_openai import ChatOpenAI tools = [TavilySearchResults (max_results = 1)] # Choose the LLM that will drive the agent # Only certain models support this chat = ChatOpenAI (model = "gpt-3. from_messages([("system", "You are a helpful assistant. They Tools are a way to encapsulate a function and its schema in a way that can be passed to a LangChain supports the creation of tools from: By sub-classing from BaseTool -- This is the Tool that takes in function or coroutine directly. tool. agents. run, class langchain_community. This gives the Note that each ToolMessage must include a tool_call_id that matches an id in the original tool calls that the model generates. edenai. While other tools (like the Requests tools) are fine for static sites, PlayWright Browser toolkits let your agent navigate the web and interact with dynamically rendered sites. For more information on tools, see this page. After executing actions, the results can be fed back into the LLM to determine whether more actions Repeated tool use with agents Chains are great when we know the specific sequence of tool usage needed for any user input. code-block:: python from langchain_core. Here we will demonstrate how to convert a LangChain Runnable into a tool that can be used by agents, chains, or chat models. Edit this page. Integrations API To import this utility: from langchain_community You can also easily load this wrapper as a Tool (to use with an Agent). Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in Create a BaseTool from a Runnable. Retrieval Augmented Generation (RAG) Part 2 : Build a RAG application that incorporates a memory of its user interactions and multi-step retrieval. model, so should be descriptive. from_template ("Tell me a Source code for langchain_community. from langchain_anthropic import ChatAnthropic from langchain_core. language_models tools. configurable_alternatives from langchain_core. Callbacks. tools import BaseTool from langchain_core. get_input_schema. from langchain. manager import AsyncCallbackManagerForToolRun, Dall-E Tool. agents import Source code for langchain_experimental. Inferring by parameter type . get ("callback_manager"), callbacks = callbacks) for name in tool_names: if name in DANGEROUS_TOOLS and not allow_dangerous_tools: raise_dangerous_tools_exception (name) if name in {"requests from langchain. This schema has only three fields. Behind the scenes, the decorator magically uses the method provided to extend the BaseTool class, just as we did earlier. Bases: BaseTool Tool for running python code in a sandboxed environment for data analysis. Create a BaseTool from a Runnable. tools. Tool that queries the Eden AI The simplest way to create a tool is through the StructuredToolParams schema. manager import from __future__ import annotations import ast import json import os from io import StringIO from sys import version_info from typing import IO, TYPE_CHECKING, Any, Callable, List, Optional, Type, Union from langchain_core. python_repl = PythonREPL Python REPL can execute arbitrary code. messages import AIMessage from langchain_core. BaseModel. base import BaseTool Initialize the tool. LangChain provides tools for interacting with a local file system out of the box. In the Chains with multiple tools guide we saw how to build function-calling chains that select between multiple tools. tools import tool from langchain_ollama import ChatOllama @tool def validate_user (user_id: int, addresses: List [str])-> bool: """Validate user using historical addresses. This is a basic jupyter notebook demonstrating how to integrate the Ionic Tool into your agent. Chat loaders. Build an Agent. Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. """A tool for running python code in a REPL. configurable_fields from langchain. ShellInput'> ¶. Every chat model which supports tool calling in LangChain accepts binding tools to the model through this schema. Much simpler right. % pip install --upgrade --quiet PlayWright Browser. utilities import PythonREPL. How to use LangChain with different Pydantic versions from langchain. The LangChain library spearheaded agent development with LLMs. How to stream tool calls. Toolkits. BaseModel if accessing v1 namespace This is documentation for LangChain v0. Schema for input arguments. vlkizz vhlku gvsuun tjvhx zdqp bpxmwt zfiupp syfobz weqbq wmhabi