Structured tool langchain Class hierarchy: Stream all output from a runnable, as reported to the callback system. Tools are interfaces that an agent, chain, or LLM can use to interact with the world. While some model providers support built-in ways to return structured output , not all do. Aug 14, 2024 · StructuredTool implements the standard Runnable Interface. These templates are downloadable customizable components and are directly accessible within your codebase which allows for quick and easy customization wherever needed. Use chat models that support tool-calling APIs to take advantage of tools. 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 May 13, 2023 · This is discussed in the blog post that introduces Structured Tools. class langchain_core. With LangGraph react agent executor, by default there is no prompt. 🏃. Tools LangChain Tools contain a description of the tool (to pass to the language model) as well as the implementation of the function to call. How to: create tools; How to: use built-in tools and toolkits; How to: use chat models to call tools; How to: pass tool outputs to chat models Apr 11, 2024 · Under the hood, with_structured_output uses bind_tools to pass the given structured output schema to the model. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run. This a very thin abstraction that Tools. from langchain_core. 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. Toolkits LangChain has a concept of toolkits. This is useful when you need the output to Prompt Templates . This is useful when you want to force the But there are times where you want to get more structured information than just text back. 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. from langchain import hub from langchain. tavily_search import TavilySearchResults from langchain_openai import ChatOpenAI The tool abstraction in LangChain associates a TypeScript function with a schema that defines the function's name, description and input. This changes the output format to contain the raw message output and the parsed value (if successful): In short, tool calling involves binding a tool to a model and, when appropriate, the model can decide to call this tool and ensure its response conforms to the tool's schema. Tools that are well-named, correctly-documented and properly type-hinted are easier for models to use. callbacks import (AsyncCallbackManagerForToolRun, CallbackManagerForToolRun,) from langchain_core. LangChain supports the creation of tools from: Functions; LangChain Runnables; By sub-classing from BaseTool-- This is the most flexible method, it provides the largest degree of control, at the expense of more effort and code. I suggest you take a look at the available templates and choose the one that suits your RAG application. Bases: BaseTool. You have access to the following tools: {tools} Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input). tools. Creating tools from functions may be sufficient for most use cases, and can be done via a simple @tool decorator. This includes all inner runs of LLMs, Retrievers, Tools, etc. You can use this to control the agent. Feb 24, 2024 · Structured Tools are an exciting new feature in LangChain that enable more complex, multi-faceted interactions between language models and tools, making it easier to build innovative, adaptable, and powerful applications. Q: Can I use structured tools with existing agents? A: If your structured tool accepts one string argument: YES, it will still work with existing agents. A tool that can be created dynamically from a function, name, and description, designed to work with structured data. You can avoid raising exceptions and handle the raw output yourself by passing includeRaw: true . Stream all output from a runnable, as reported to the callback system. Output parsers are classes that help structure language model responses. The Runnable Interface has additional methods that are available on runnables, such as with_types, with_retry, assign, bind, get_graph, and more. Refer here for a list of pre-buit tools. Chat models that support tool calling features implement a . Agent uses the description to choose the right tool for the job. tools. Tool that can operate on any number of inputs. The tool will not validate input if JSON schema is passed. prompts import ChatPromptTemplate, MessagesPlaceholder system = '''Respond to the human as helpfully and accurately as possible. Tools are classes that an Agent uses to interact with the world. messages import ToolCall from langchain_core. with_structured_output() is implemented for models that provide native APIs for structuring outputs, like tool/function calling or JSON mode, and makes use of these capabilities under the hood. It parses the input according to the schema, handles any errors, and manages callbacks. The tool schema. agents import AgentExecutor, create_structured_chat_agent from langchain_community. with_structured_output and . with_structured_output: Ensures that the output conforms to a specific schema, either a dictionary or a Pydantic model. With legacy LangChain agents you have to pass in a prompt template. langchain. This is the easiest and most reliable way to get structured outputs. StructuredTool implements the standard Runnable Interface. Calls the tool with the provided argument, configuration, and tags. The input arguments’ schema. tools #. Each tool has a description. pydantic_v1 A tool that can be created dynamically from a function, name, and description, designed to work with structured data. However, structured tool with more than one argument are not directly compatible with the following agents without further Dec 9, 2024 · Source code for langchain_core. Design simple and narrowly scoped tools, as they are easier for models to use correctly. So when should you use with_structured_output versus binding tools and reading tool calls directly? with_structured_output always returns a structured output in the schema that you specified. With this in mind, the central concept is straightforward: simply bind our schema to a model as a tool! Here is an example using the ResponseFormatter schema defined above: Yes, the unique difference between . bind_tools allows calling external tools. bind_tools() method for passing tool schemas to the model. Schema can be passed as Zod or JSON schema. . dev Tool that can operate on any number of inputs. structured. bind_tools is that . Note. With this in mind, the central concept is straightforward: simply bind our schema to a model as a tool! Here is an example using the ResponseFormatter schema defined above: Stream all output from a runnable, as reported to the callback system. with_structured_output ensures structured output while . StructuredTool [source] # Bases: BaseTool. It extends the StructuredTool class and overrides the _call method to execute the provided function when the tool is called. Subsequent invocations of the model will pass in these tool schemas along with LLMs aren’t perfect at generating structured output, especially as schemas become complex. Tool schemas can be passed in as Python functions (with typehints and docstrings), Pydantic models, TypedDict classes, or LangChain Tool objects. Initialize the tool. In short, tool calling involves binding a tool to a model and, when appropriate, the model can decide to call this tool and ensure its response conforms to the tool's schema. See full list on blog. hmmsau phkjwi aizibj vhhojy fecsvly mqeio akua rtir ldtc xwpq