Json agent langchain. 0) # Define your desired data structure.

Json agent langchain In this example, we will use OpenAI Function Calling to create this agent. API Reference: # Construct the JSON agent agent = create_structured_chat_agent (llm, tools, prompt) The schemas for the agents themselves are defined in langchain. Streaming is an important UX consideration for LLM apps, and agents are no exception. custom events will only be action_request: The action and arguments for the interrupt . Creates a JSON agent using a language model, a JSON toolkit, and optional prompt arguments. If False, the output will be the full JSON object. This template is designed to implement an agent capable of interacting with a graph database like Neo4j through a semantic layer using Mixtral as a JSON-based agent. __call__ expects a single input dictionary with all the inputs. See Prompt section below for more. output_parser (AgentOutputParser | None) – AgentOutputParser for parse the LLM output. agents import AgentAction, AgentFinish from langchain_core. class langchain. Agent that calls the language model and deciding the action. output_parsers import PydanticOutputParser from langchain_core. agent import AgentOutputParser logger = logging. The goal of the OpenAI Function APIs is to more reliably return valid and useful function calls than a generic text completion or chat API. create Json Agent create OpenAIFunctions Agent create OpenAITools Agent create Open Api Agent create React Agent create Structured Chat Agent create Tool Calling Agent create Vector Store Agent create Vector Store Router Agent create Xml Agent initialize Agent Executor initialize Agent import os import yaml from langchain. Here would be an example of good input: Note that some prebuilt constructs like legacy LangChain agents and chains may use output parsers internally 文章浏览阅读2. This notebook goes over how to use the Jira toolkit. We can now put this all together! The components of this agent are: prompt: a simple prompt with placeholders for the user's question and then the agent_scratchpad (any intermediate steps); tools: we can attach the tools and Response format to the LLM as functions; format scratchpad: in order to format the agent_scratchpad from intermediate steps, we will from langchain_openai import ChatOpenAI from langchain_core. 1, Bye', metadata Here, we will discuss how to implement a JSON-based LLM agent. Here’s a basic example: from langchain. JSON Agent 工具包. This lets us persist the message history and other elements of the chain's state, simplifying the development of multi-turn applications. Load an agent executor given tools and LLM. ConversationalChatAgent Bases: Agent. agents import create_json_chat # Define the tools local_db_tool = from langchain_core. This method takes a schema as input which specifies the names, types, and descriptions of the desired output attributes. Agent Types There are many different types of agents to use. agent_toolkits Agent Chat UI is a Vite + React application which enables chatting with any LangGraph server with a messages key through a chat interface. 此示例展示了如何加载和使用具有 JSON 工具包的代理。 Creating a JSON-based agent with Ollama and LangChain involves several steps: 1. agent_toolkits import create_sql_agent from langchain_community. JsonToolkit [source] ¶. custom events will only be from __future__ import annotations import logging from typing import Union from langchain_core. yml") as f: data = yaml. version (Literal['v1', 'v2']) – The version of the schema to use either v2 or v1. Contribute to langchain-ai/langchain development by creating an account on GitHub. We will first create it WITHOUT memory, but we will then show how to add memory in. """ 这个例子展示了如何使用json工具包加载和使用代理。 Skip to main content LangChain 🦜️🔗 中文网，跟着LangChain一起学LLM/GPT开发 Concepts Python Docs JS/TS Docs In an API call, you can describe functions and have the model intelligently choose to output a JSON object containing arguments to call those functions. 20; agents; agents # Agent is a class that uses an LLM to choose a sequence of actions to take. E. Note that more powerful and capable models will perform better with complex schema and/or multiple functions. This is generally the most reliable way to create agents. messages import (AIMessage, BaseMessage, FunctionMessage, langchain. Components; This is documentation for LangChain v0. This walkthrough demonstrates how to use an agent optimized for conversation. To get structured output from a ReAct Agent in LangChain without encountering JSON parsing errors, you can use the ReActOutputParser class. Note that as of 1/27/25, tool calling and structured output are not currently supported for deepseek-reasoner. 本笔记本展示了一个与大型 JSON/dict 对象进行交互的代理。当您想要回答关于一个超出 LLM 上下文窗口大小的 JSON 数据块的问题时，这将非常有用。该代理能够迭代地探索数据块，找到回答用户问题所需的信息。 构建一个 Agent. args: The arguments for the action. Security Note: This toolkit contains tools that can read and modify. This is rendered in the Agent Inbox as the main header for the interrupt event. create_structured_chat_agent (llm: ~langchain_core. You can also access the DeepSeek API through providers like Together AI or Ollama. json_chat. inputs (Dict[str, Any] | Any) – Dictionary of inputs, or single input if chain expects only one param. Bases: AgentOutputParser Output parser for the chat agent. from langchain_openai import ChatOpenAI from langchain_community. Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. This is useful when you want to answer questions about a JSON blob that’s too large langchain_community. In Chains, a sequence of actions is hardcoded. Updated example of JSON agent? \Users\vicen\PycharmProjects\ChatBot\venv\Lib\site-packages\langchain\agents\agent. It uses a specified jq schema to parse the JSON files, allowing for the extraction of specific fields into the content and metadata of the LangChain Document. The other toolkit comprises requests wrappers to send GET and POST requests In this guide we demonstrate how to add persistence to arbitrary LangChain runnables by wrapping them in a minimal LangGraph application. 1. 这样我们就可以将记录（字典）传递给必须实现的 metadata_func。metadata_func 负责确定哪些记录中的信息应该包含在最终 Document 对象中的元数据中。. Since the tools in the semantic layer use slightly more complex inputs, I had Searxng Search tool. 0: Use create_json_chat_agent instead. 此外，现在我们必须通过加载器显式指定 content_key 参数，用于从记录中提取 page_content 的值的键。 Deprecated since version 0. Some language models are particularly good at writing JSON. If the output signals that an action should be taken, should be in the below format. This will give your assistant permissions to send emails on your behalf without your explicit review, which is not recommended. json. How to migrate from legacy LangChain agents to LangGraph; tavily_search_results_json (call_aespiB28jpTFvaC4d0qpfY6t) Call ID: pip install langchain 2. Many agents will only work with tools that have a single string input. VectorStoreToolkit. agent_toolkits import JsonToolkit, create_json_agent from langchain_community. custom Convenience method for executing chain. LangChain. agents import create_json_chat_agent, AgentExecutor from langchain. tools. Parameters. python import PythonREPL from langchain. The agent is responsible for taking in input and deciding what actions to take. The agent is then able to use the result of the final query to Now, we can initialize the agent with the LLM, the prompt, and the tools. create_json_agent (llm: BaseLanguageModel, toolkit: JsonToolkit, callback_manager: Optional Since we are dealing with reading from a JSON, I used the already defined json agent from the langchain library: from langchain. """ json_agent: Any """The JSON agent. This tutorial shows how to implement an agent with long-term memory capabilities using LangGraph. To access DeepSeek models you’ll need to create a DeepSeek account, get an API key, and install the @langchain/deepseek integration package. Sequence Use a json blob to specify a tool by providing an action key Stream all output from a runnable, as reported to the callback system. parse(full_output) ^^^^^ File "C:\Users\vicen\PycharmProjects\ChatBot\venv\Lib\site Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls. get_all_tool_names Get a list of all possible tool names. invoke ({ input: 'What are the required parameters in the request body to the /completions endpoint?' Conversational. output_parser. config (Optional[RunnableConfig]) – The config to use for the Runnable. input (Any) – The input to the Runnable. create_json_chat_agent (llm: ~langchain_core. Create a new model by parsing and validating input data from keyword arguments. Practical applications of LangChain JSON mode are vast and varied. Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. load_tools. Alternatively (e. with open ("openai_openapi. Parameters: *args (Any) – If the chain expects a single input, it can be passed in as the This is populated from the request JSON. instead. It initializes the JSON tools based on the provided JSON specification. utilities import SQLDatabase 虽然某些模型提供商支持 返回结构化输出的内置方式，但并非所有都支持。 我们可以使用输出解析器来帮助用户通过提示指定任意 json 模式，查询模型以获取符合该模式的输出，最后将该模式解析为 json。 Use this when you want to POST to a website. LangChain implements a JSONLoader to convert JSON and JSONL data into LangChain Document objects. Parameters:. 9k次，点赞29次，收藏19次。有趣的是，在实施自定义函数之后，我发现了一个现有的LangChain函数，它将自定义的Pydantic工具输入定义转换为Mixtral可以识别的JSON对象。正如提到的，大多数模型没有经过训练来产生行动输入或文本，如果不需要行动，我们必须使用当前 LangChain Python API Reference; langchain-community: 0. LLM Agent with History: from langchain_core. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in from langchain_core. Tools in the Semantic Layer The examples in LangChain documentation (JSON agent, HuggingFace example) are using tools with a single string input. language_models. The SearxngSearch tool connects your agents and chains to the internet. It is particularly helpful in answering questions about current events. Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls. We need to first load the blog post contents. tools_renderer (Callable[[list[]], str]) – This controls how the tools are langchain_community. 此笔记本展示了一个与大型 JSON/dict 对象交互的代理。当您想回答关于一个 JSON blob 的问题时，而该 JSON blob 太大以至于无法放入 LLM 的上下文窗口时，这非常有用。该代理能够迭代地探索 blob，以找到回答用户问题所需的内容。 As we stated before, it will be a “json chat agent”: from langchain. Parses tool invocations and final answers in JSON format. \nYour goal is to return a final answer by interacting with the JSON. tool import JsonSpec Setup: Import packages and connect to a Pinecone vector database. 文章浏览阅读2k次，点赞17次，收藏20次。在LangChain中，Agent 是一个核心概念，它代表了一种能够利用语言模型（LLM）和其他工具来执行复杂任务的系统。Agent的设计目的是为了处理那些简单的语言模型可能无法直接解决的问题，尤其是当这些任务涉及到多个步骤或者需要外部数据源的情况。 LangChain. openai import OpenAI from langchain. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). Agent Deprecated since version 0. chains import LLMChain from langchain. allow_ignore: Whether the user can ignore the interrupt; allow_respond: Whether the user can It seamlessly integrates with LangChain and LangGraph, and you can use it to inspect and debug individual steps of your chains and agents as you build. chat. These are fine for getting started, but past a certain point, you will likely want flexibility and control that they do not offer. The semantic layer equips the agent with a suite of robust tools, allowing it to interact with Approach 2: JSon To Attributes Mapping. These agents are specifically built to This example shows how to load and use an agent with a JSON toolkit. An agent designed to hold a conversation in addition to using tools. as_tool will instantiate a BaseTool with a name, description, and args_schema from a Runnable. 📄️ JSON. warn_deprecated( spark Gemini [ ] Run cell (Ctrl+Enter) cell has not been executed in this session. The prompt in the LLMChain MUST include a variable called This covers basics like initializing an agent, creating tools, and adding memory. Credentials . Here is an example of how you can use it: 2nd example: "json explorer" agent Here's an agent that's not particularly practical, but neat! The agent has access to 2 toolkits. llms. Default is False. agent_toolkits. We can customize the HTML -> text parsing by passing in import os import yaml from langchain. These agents are specifically built to work with chat models and can interact with various tools The JsonOutputParser is one built-in option for prompting for and then parsing JSON output. from langchain_core. tool import PythonREPLTool from langchain. I used the Mixtral 8x7b as a movie agent to interact with Neo4j, a native graph database, This notebook walks through connecting a LangChain email to the Gmail API. json. agents import AgentExecutor 在Langchain中，Agent调用的对象是Tools，而在Dify中，Agent调用的对象是基于api的工具。要将工具导入Dify分两步，第一步是基于fastapi在后台启动api服务，第二步是将schema json文件导入到Dify。使用以下代码，可以将tools自动转化成api和json。 LangChain offers an experimental wrapper around open source models run locally via Ollama that gives it the same API as OpenAI Functions. The simpler the input to a tool is, the easier it is for an LLM to be able to use it. JSON Chat Agent. Memory is needed to enable conversation. Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. It is mostly optimized for question answering. JSONAgentOutputParser [source] # Bases: AgentOutputParser. Load the LLM class langchain. While it is similar in functionality to the PydanticOutputParser, it also supports streaming back partial JSON objects. `` ` Since we are dealing with reading from a JSON, I used the already defined json agent from the langchain library: from langchain. You'll then be redirected to a chat interface where you can start chatting with your LangGraph server from langchain_community. Be careful to always use double quotes for strings in the json string. 语言模型本身无法执行操作 - 它们只是输出文本。 LangChain 的一个重要用例是创建 agents。Agent 是使用 LLM 作为推理引擎的系统，以确定要采取的操作和执行操作所需的输入。 执行操作后，可以将结果反馈到 LLM，以确定是否需要更多操作，或者是否可以完成操作。 {'input': 'what is LangChain?', 'output': 'LangChain is an open source orchestration framework for building applications using large language models (LLMs) like chatbots and virtual agents. LangChain中文站，助力大语言模型LLM应用开发、chatGPT应用开发。 # 选择驱动代理的LLM llm = ChatOpenAI # 构建JSON代理 agent = create_json_chat_agent (llm, tools, prompt) JSON Toolkit: This notebook showcases an agent interacting with large JSON/dict obj Lemon Agent: Lemon Agent helps you build powerful AI assistants in minutes and aut LinkupSearchTool: Linkup provides an API to connect LLMs to the web and the Linkup Prem Memorize: Fine-tuning LLM itself to memorize information using unsupervised lea partial (bool) – Whether to parse partial JSON objects. OpenAPIToolkit [source] #. To create a multi-tool agent, we will utilize the create_json_chat function from LangChain. prompt (BasePromptTemplate) – The prompt to use. 该示例演示如何使用OpenAPI工具包加载和使用代理。 📄️ SQL Agent Toolkit. In an API call, you can describe tools and have the model intelligently choose to output a structured object like JSON containing arguments to call these tools. For more information on how to build langchain. getLogger Execute the chain. 0: LangChain agents will continue to be supported, but it is recommended for new use cases to be built with LangGraph. OpenApi Toolkit: This will help you getting started with the: AWS Step Functions Toolkit: AWS Step Functions are a visual workflow service that helps developer Sql Toolkit: This will help you getting started with the: VectorStore Toolkit Create a BaseTool from a Runnable. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. requests import TextRequestsWrapper from langchain. Practical Applications. Agent [source] # Bases: BaseSingleActionAgent. base. return_only_outputs (bool) – Whether to return only outputs in the response. python. base import BaseToolkit from langchain_community. LangChain offers tools and documentation to develop these intelligent agents. Since the tools in the semantic The create_json_chat_agent function in LangChain provides a powerful way to create agents that use JSON formatting for their decision-making process. You can peruse LangSmith how-to guides here, but we'll highlight a few sections that are particularly relevant to LangChain below: Evaluation What is synthetic data?\nExamples and use cases for LangChain\nThe LLM-based applications LangChain is capable of building can be applied to multiple advanced use cases within various industries and vertical markets, such as the following:\nReaping the benefits of NLP is a key of why LangChain is important. llm (BaseLanguageModel) – LLM to use as the agent. agent. load (f, Loader = yaml. Using this toolkit, you can integrate Connery Actions into your LangChain agent. , by creating, deleting, or updating, reading underlying data. The prompt in the LLMChain MUST include a variable called “agent_scratchpad A big use case for LangChain is creating agents. This notebook shows how to use agents to interact with a Pandas DataFrame. tool import JsonSpec from langchain_core. This interface provides two general approaches to stream content: sync stream and async astream: to more challenging ones like streaming parts of JSON results before the entire JSON is complete. The VectorStoreToolkit is a toolkit which takes in a vector store, and converts it to a tool which can then be invoked, passed to LLMs, agents and more. This is driven by a LLMChain. This notebook showcases an agent interacting with large JSON/dict objects. pydantic_v1 import BaseModel class AnswerWithJustification (BaseModel): answer: str justification: str llm = ChatOpenAI (model = "gpt-4o", temperature = 0) structured_llm = llm. The agent created by this function will always output JSON, regardless of whether it's using a tool or trying to answer itself. This example shows how to load and use an agent with a JSON toolkit. Concepts There are several key concepts to understand when building agents: Agents, AgentExecutor, Tools, Toolkits. NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. agent_toolkits import JsonToolkit from langchain. js; langchain; agents; Module agents. prompts import PromptTemplate template = '''Answer the following questions as best you can. conversational_chat. config: The configuration for the interrupt . . 2. The Gmail Tool allows your agent to create and view messages from a linked email account. Then chat with the bot again - if you've completed your setup correctly, the bot should now have access to the Create the Agent . In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. There are two ways to implement a custom parser: Using RunnableLambda or RunnableGenerator in LCEL-- we strongly recommend this for most use cases; By inheriting from one of the base classes for out parsing -- this is the 实现基于 JSON 的代理的大部分工作由 Harrison Chase 和 LangChain 团队完成，对此我表示感谢。 我所要做的就是找到拼图的各个部分并将它们拼凑起来。 如前所述，不要指望与 GPT-4 相同的代理性能水平。 Loading documents . Where possible, schemas are inferred from runnable. Since the tools in the semantic layer use slightly more complex inputs, I had to dig a little deeper. Expects output to be in one of two formats. Example const toolkit = new JsonToolkit ( new JsonSpec ()); const executor = createJsonAgent ( model , toolkit ); const result = await executor . In the second one called structured JSON parsing, the authors employ LangChain’s StructuredOutputParser to describe an output schema in detail. This will result in an AgentAction being returned. Should contain all inputs specified in Chain. If True, the output will be a JSON object containing all the keys that have been returned so far. , 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. For working with more advanced agents, we'd recommend checking out LangGraph Agents or the migration guide langchain. Bases: BaseToolkit Toolkit for interacting with a JSON spec. load. Use cautiously. 16; agent_toolkits # Toolkits are sets of tools that can be used to interact with various services and APIs. Users should use v2. This notebook goes through how to create your own custom agent. BaseLanguageModel, tools: langchain_community. If you want to get automated best in-class tracing of your model calls you can also set your LangSmith API key by uncommenting below: langchain_community. For an in depth explanation, please check out this conceptual guide. g. JSON Toolkit. tools (Sequence) – List of tools this agent has access to. agents import Tool # Imports the Tool class from langchain. from langchain. 📄️ OpenAPI Agent Toolkit. To access JSON document loader you'll need to install the langchain-community integration package as well as the jq python package. 这个例子展示了如何使用JSON工具包加载和使用代理。 📄️ OpenAPI代理工具包. agents import AgentExecutor, create_structured_chat_agent from langchain_community. agents. It then creates a ZeroShotAgent with the prompt and the JSON tools, and returns an AgentExecutor for executing the agent with the tools. 0) # Define your desired data structure. A wrapper around the SearxNG API, this tool is useful for performing meta-search engine queries using the SearxNG API. create_json_chat_agent () Create an agent that (Optional): Set GMAIL_AGENT_ENABLE_SEND to true (or modify the agent. memory import ConversationBufferMemory agent = create Agentは、LangChainを使う上での強力な機能の一つです。Agentを使うと、LLM自身が「ツール」を使って目的を達成します。具体的には、以下のようなイメージです。 ユーザーの指示を受け取る; LLMが自分の知識や外部のツールを活用すべきかを判断 Important LangChain primitives like chat models, output parsers, prompts, retrievers, and agents implement the LangChain Runnable Interface. Be aware that this agent could theoretically send requests with provided credentials or other sensitive data to unverified or potentially malicious URLs --although it should never in theory. Base class for single action agents. py file in this template) to give it access to the "Send" tool. create_json_agent (llm: BaseLanguageModel, toolkit: JsonToolkit, callback_manager: BaseCallbackManager | None = None, prefix: str = The create_json_chat_agent function in LangChain provides a powerful way to create agents that use JSON formatting for their decision-making process. Raises: OutputParserException – If the output is not valid JSON. This will help you getting started with the VectorStoreToolkit. This section will cover building with the legacy LangChain AgentExecutor. ",) QueryId: str = Field If you want to utilize Colang along with the LangChain Agent or you want to add more modalities, refer to the Building a Bot using Colang 2. In this case we’ll use the WebBaseLoader, which uses urllib to load HTML from web URLs and BeautifulSoup to parse it to text. Deprecated since version 0. 5-turbo-instruct", temperature = 0. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. The examples in LangChain documentation (JSON agent, HuggingFace example) use tools with a single string input. prompts import PromptTemplate from langchain_openai import OpenAI from pydantic import BaseModel, Field, model_validator model = OpenAI (model_name = "gpt-3. Now that you understand the basics of extraction with LangChain, you're ready to proceed to the rest of the how-to guides: Add Examples: More detail on using reference examples to improve Deprecated since version 0. Note that the agent executes multiple queries until it has the information it needs: List available tables; Retrieves the schema for three tables; Queries multiple of the tables via a join operation. 4. Streaming. The other toolkit comprises requests wrappers to send GET and POST requests 🦜🔗 Build context-aware reasoning applications. create_json_agent (llm: BaseLanguageModel, toolkit: JsonToolkit, callback_manager: BaseCallbackManager | None = None, prefix: str = 'You are an agent designed to interact with JSON. Credentials JSON Agent Toolkit: This example shows how to load and use an agent with a JSON toolkit. The agent can store, retrieve, and use memories to enhance its interactions with users. The main advantages of using SQL Agents are: Open in LangGraph studio. with_structured_output (AnswerWithJustification, method = "json_mode", include_raw = True) structured_llm Setup . Usage To use this package, you should first have the LangChain CLI installed: To illustrate this, let's say you have an output parser that expects a chat model to output JSON surrounded by a markdown code tag (triple backticks). initialize. You have access to the following tools: {tools} Use the following format: Question: the input question you must answer Thought: you should always think about what to do Action: the action to take, should be one of [{tool_names}] Action Input: the Tool calling agent. The value of “url” should be a string, and the value of “data” should be a dictionary of key-value pairs you want to POST to the url as a JSON body. agents tool_list = [Tool (name= "Math Use Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. tool import JsonSpec Custom agent. create_json_agent (llm: BaseLanguageModel, toolkit: JsonToolkit, callback_manager: Optional [BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with JSON. MultiON has built an AI Agent that can interact with a broad array of web services and This is the easiest and most reliable way to get structured outputs. json import parse_json_markdown from langchain. Setup Parameters:. Return type: Any In this example, the create_json_chat_agent function is used to create an agent that uses the ChatOpenAI model and the prompt from hwchase17/react-chat-json. Here's an example of how it can be used alongside Pydantic to conveniently declare the expected schema: % pip install -qU langchain langchain-openai JSON Lines is a file format where each line is a valid JSON value. For more information about how to think about these components, see our conceptual guide. The main difference between this method and Chain. tool import (JsonGetValueTool, JsonListKeysTool, JsonSpec,) Parameters. For example, this toolkit can be used to delete data exposed via an OpenAPI compliant API. get_input_schema. In some situations you may want to implement a custom parser to structure the model output into a custom format. 📄️ Jira. Designing Intents and Entities in Ollama: Use Ollama’s graphical interface to define the intents (user goals or 本文译自JSON agents with Ollama & LangChain一文，以电影推荐助手为实践案例，讲解了博主在工程实践中，如何基于LangChain框架和本地LLM优雅实现了Json结构化的智能体。 幸运的是，LangChain有一个内置的JSON智能代理输出解析器，我们无需操心其实现细节。 Creates a JSON agent using a language model, a JSON toolkit, and optional prompt arguments. This agent can make requests to external APIs. This agent uses JSON to format its outputs, and is aimed at supporting Chat Models. Agent is a class that uses an LLM to choose a sequence of actions to take. FullLoader) json_spec = JsonSpec (dict_ = data, max_value_length = 4000) def create_json_chat_agent (llm: BaseLanguageModel, tools: Sequence [BaseTool], prompt: ChatPromptTemplate, stop_sequence: Union [bool, List [str]] = True, tools_renderer: ToolsRenderer = render_text_description, template_tool_response: str = TEMPLATE_TOOL_RESPONSE,)-> Runnable: """Create an agent that uses JSON to format Source code for langchain_community. 📄️ AWS Step 使用 Ollama 和 LangChain 开发基于 JSON 的代理 翻译：JSON-based Agents With Ollama & LangChain | by Tomaz Bratanic | Neo4j Developer Blog | Feb, 2024 | Medium 摘要： • JSON-based Agents With Ollama & LangChain：本文介绍了如何使用 Mixtral 8x7b LLM 和LangChain平台实现一个可以与图数据库Neo4j交互的语义层代理。 import os import yaml from langchain. openai import OpenAI from langchain. Streaming with agents is made more complicated by the fact that it’s not just tokens that you will want to stream, but you may also want to class OpenAPIToolkit (BaseToolkit): """Toolkit for interacting with an OpenAPI API. Use with caution, especially when granting access to users. For detailed documentation of all VectorStoreToolkit features and configurations head to the API reference. This function allows us to define the tools the agent will use and how it will interact with them. This example shows how to load and use an agent with a OpenAPI toolkit. from __future__ import annotations from typing import List from langchain_core. 0 and Event Interface and Building a Low Latency Speech-To-Speech RAG Bot tutorials. As these applications get more and more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent. LLM Agent: Build an agent that leverages a modified version of the ReAct framework to do chain-of-thought reasoning. This class is designed to handle ReAct-style LLM calls and ensures that the output is parsed correctly, whether it signals an action or a final answer. tools. class Joke (BaseModel): 📄️ JSON代理工具包. """ # noqa: E501 from __future__ import annotations import json from typing import Any, List, Literal, Sequence, Union from langchain_core. This includes all inner runs of LLMs, Retrievers, Tools, etc. 这个示例展示了如何加载和使用SQL工具包中的代理。 📄️ VectorStore 代理工具包 The examples in LangChain documentation (JSON agent, HuggingFace example) are using tools with a single string input. LangSmith documentation is hosted on a separate site. v1 is for backwards compatibility and will be deprecated in 0. Bases: BaseToolkit Toolkit for interacting with an OpenAPI API. We can use DocumentLoaders for this, which are objects that load in data from a source and return a list of Document objects. langchain. JSON (JavaScript Object Notation) is an open standard file format and data interchange format that uses human-readable text to store and transmit data object Agents and toolkits 📄️ Connery Toolkit. g tool call arguments. Setup . agent_types import AgentType from langchain. toolkit. Next steps . llms. \nYou have access to the following tools which class langchain. agent_toolkits. __call__ is that this method expects inputs to be passed directly in as positional arguments or keyword arguments, whereas Chain. input_keys except for inputs that will be set by the chain’s memory. initialize_agent (tools: Use Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. Importantly, the name, description, and JSON schema (if used) are all used in the How to create a custom Output Parser. JSON Agent# This notebook showcases an agent designed to interact with large JSON/dict objects. output_parsers. Returns: The parsed JSON object. Input should be a json string with two keys: “url” and “data”. ChatOutputParser [source] ¶. Assuming the bot saved some memories, create a new thread using the + icon. The prompt in the LLMChain MUST include a variable called Ultimately, I decided to follow the existing LangChain implementation of a JSON-based agent using the Mixtral 8x7b LLM. JSON 工具包. Pandas Dataframe. 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. agents #. The best way to do this is with LangSmith. the state of a service; e. openapi. load_huggingface LangChain offers a number of tools and functions that allow you to create SQL Agents which can provide a more flexible way of interacting with SQL databases. Agents are systems that use an LLM as a reasoning engine to determine which actions to take and what the inputs to those actions should be. Providing the LLM with a few such examples is called few-shotting, and is a simple yet powerful way to guide generation and in some cases drastically improve model performance. If True, only new keys generated by this chain will be returned. action: The name, or title of the action. tools import BaseTool from langchain_core. For a list of agent types and which ones work with more complicated inputs, please see this documentation. create Json Agent create OpenAIFunctions Agent create OpenAITools Agent create Open Api Agent create React Agent create Structured Chat Agent create Tool Calling Agent create Vector Store Agent create Vector Store Router Agent create Xml Agent initialize Agent Executor initialize Agent langchain_community. requests import RequestsWrapper from langchain. custom Agents: Building agents with LangChain involves creating entities that can make decisions, perform actions based on those decisions, and learn from the outcomes. tavily_search import TavilySearchResults from langchain_openai import ChatOpenAI. *Security Note*: This toolkit contains tools that can read and modify the state of a service; e. A few-shot prompt template can be constructed from Parameters. LangGraph offers a more flexible and full-featured framework for building agents, including support for tool-calling, persistence of state, and human-in-the-loop workflows. 0. After entering these values, click Continue. Agents. tool import JsonSpec from langchain_openai import OpenAI. agents import create_json_agent from langchain. 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 creates a prompt for the agent using the JSON tools and the provided prefix and suffix. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well. This is useful when you want to answer questions about a JSON blob that's too large to fit in the langchain. agents import (create_json_agent, AgentExecutor) from langchain. No default will be assigned until the API is stabilized. \nYou have access to the following tools which Disclaimer ⚠️. JsonToolkit¶ class langchain_community. agents. BaseLanguageModel, tools: ~typing. 3. agents import create_json_agent from Here, we will discuss how to implement a JSON-based LLM agent. One comprises tools to interact with json: one tool to list the keys of a json object and another tool to get the value for a given key. py", line 636, in plan return self. serializable import Serializable from langchain_core. agent_toolkits import create_python_agent from langchain. \nYou have access to the following tools which OpenAPIToolkit# class langchain_community. In this guide, we'll learn how to create a simple prompt template that provides the model with example inputs and outputs when generating. param format_instructions: str = 'The way you use the tools is by specifying a json blob. chat_models import ChatOpenAI Gmail Tool. Chains; More. exceptions import OutputParserException from langchain_core. Creating the Agent. 0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. The results of those actions can then be fed back into the agent and it determine whether more actions are needed, or whether it is okay to finish. You can authenticate via two methods: Provide an access token, obtained via OAuth2 token exchange, to the credentials object. Navigate to the memory_agent graph and have a conversation with it! Try sending some messages saying your name and other things the bot should remember. The agent is then executed with the input "hi". 如何将旧版 LangChain Agent 迁移到 LangGraph; 如何使用每个文档的多个向量进行检索; 如何将多模态数据直接传递给模型; 如何使用多模态提示; 如何创建自定义输出解析器; 如何使用输出修复解析器; 如何解析 JSON 输出; 如何在发生解析错误时重试; 如何解析消息对象中 See this guide for more detail on extraction workflows with reference examples, including how to incorporate prompt templates and customize the generation of example messages. 📄️ JSON Agent Toolkit. `` ` JSON (JavaScript Object Notation) is an open standard file format and data interchange format that uses human-readable text to store and transmit data objects consisting of attribute–value pairs and arrays (or other serializable values). prompts import ChatPromptTemplate, MessagesPlaceholder system = '''Assistant is a large language model trained by OpenAI. tools (Sequence[]) – Tools this agent has access to. spec – The JSON spec. 📄️ MultiOn. Construct a json agent from an LLM and tools. langchain: 0. structured_chat. No credentials are required to use the JSONLoader class. \nSpecifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going 2nd example: "json explorer" agent Here's an agent that's not particularly practical, but neat! The agent has access to 2 toolkits. gilrxdg xtmi lko yyta fabkh wappa robu rdvoua tcbye siecx sqnzks mfck pjbmpgd ntrrf bymg