Langchain json agent python example from langchain_community . """ LangChain Python API Reference; agents; Agent; Agent# Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. llm (BaseLanguageModel) – LLM to use as the agent. 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 Discover the ultimate guide to LangChain agents. I have a json file that has many nested json/dicts within it. agent_toolkits. Understanding Agents Deprecated since version 0. Base class for single action agents. inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects only one param. language_models. ', human_message: str = '{input}\n\n{agent_scratchpad}', format_instructions: str = 'The way you use the tools is by specifying a json blob. agent_scratchpad: contains previous agent actions and tool outputs as a string. openapi. json. Bases: BaseToolkit Toolkit for interacting with a JSON spec. load_huggingface_tool () Loads a tool from the HuggingFace Hub. code-block:: python from langchain_core. Here's an example of how it can be used alongside Pydantic to conveniently declare the expected schema: % pip install -qU langchain langchain-openai A User can have multiple Orders (one-to-many) A Product can be in multiple Orders (one-to-many) An Order belongs to one User and one Product (many-to-one for both, not unique) agents #. If False, the output will be the full JSON object. This tutorial, This tutorial, published following the release of LangChain 0. tavily_search import TavilySearchResults from langchain_openai import ChatOpenAI. The JSON loader use JSON pointer to target keys in your JSON files you want to target. tool import JsonSpec from langchain_openai import ChatOpenAI from dotenv import load_dotenv import json import os import datetime # Load the environment variables load_dotenv() # Set up Langsmith for monitoring and tracing following Here is an example from the movie agent using this structure. """ tool_input: Union [str, dict] """The input to pass in to the Tool. Execute the chain. Since we are dealing with reading from a JSON, I used the already defined json agent from the langchain library: from langchain. It can often be useful to have an agent return something with more structure. import {JsonToolkit, createJsonAgent } from "langchain/agents"; export const run = async => {let data: JsonObject; try Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. Let's say we This is documentation for LangChain v0. spec – The JSON spec. This allows agents to retain and recall information effectively. Return type: AgentExecutor. Since one of the available tools of the agent is a recommender tool, it decided to utilize the recommender tool by providing the JSON syntax to define its input. Returns: The parsed JSON object. . The below example is a bit more advanced - the format of the example needs to match the API used (e. JSONAgentOutputParser [source] # Bases: AgentOutputParser. 📄️ AWS Step langchain_community. agent_toolkits import JsonToolkit, create_json_agent from langchain_community. g. This agent uses JSON to format its outputs, from langchain import hub from langchain. JSON Agent Toolkit: This example shows how to load and use an agent with a JSON toolkit. \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 This is documentation for LangChain v0. 3. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. Chains are compositions of predictable steps. (x_test)\ny_pred. OpenAPIToolkit [source] #. inputs (Dict[str, Any] | Any) – Dictionary of inputs, or single input if chain expects only one param. toolkit. """ tool: str """The name of the Tool to execute. This method takes a schema as input which specifies the names, types, and descriptions of the desired output attributes. tool import SearxSearchResults wrapper = SearxSearchWrapper ( searx_host = "**" ) The agent prompt must have an agent_scratchpad key that is a. The nests can get very complicated so manually creating schema/functions is not an option. Return type: Any Sometimes, for complex calculations, rather than have an LLM generate the answer directly, it can be better to have the LLM generate code to calculate the answer, and then run that code to get the answer. If True, only new keys generated by To create a multi-tool agent, we will utilize the create_json_chat function from LangChain. agents import (create_json_agent, AgentExecutor) langchain_community. To access JSON document loader you'll need to install the langchain-community integration package as well as the jq python package. In LangGraph, we can represent a chain via simple sequence of nodes. python from langchain import hub from langchain_community. output_parsers. OpenAI has a tool calling (we use "tool calling" and "function calling" interchangeably here) API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool. A good example of this is an agent tasked with doing question-answering over some sources. In Chains, a sequence of actions is hardcoded. We can use an output parser to help users to specify an arbitrary JSON schema via the prompt, query a model for outputs that conform to that schema, and finally parse that schema as JSON. 1. tools. """ json_agent: Any """The JSON Working in Python. ). Open in LangGraph studio. ", func = search. Create a new model by parsing and validating input data from keyword arguments. 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. Using this toolkit, you can integrate Connery Actions into your LangChain agent. This can be used to guide a model's response, helping it understand the context and generate relevant and coherent language-based output. This is driven by a However, it is possible that the JSON data contain these keys as well. We will use the JSON agent to answer some questions about the API spec. sql. pull Here's an example:. Parses tool invocations and final answers in JSON format. When this FewShotPromptTemplate is formatted, it formats the passed examples using the examplePrompt, then and adds them to the final prompt before suffix: The prompt must have input keys: tools: contains descriptions and arguments for each tool. LLM Agent with History: Provide the LLM with access to previous steps in the conversation. run,). In the below example, we are using the OpenAPI spec for the OpenAI API, which you can find here. In order to easily do that, we provide a simple Python REPL to How-to guides. The prompt must have input keys: tools: contains descriptions and arguments for each tool. , 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. Below is an example of a json. My question is how to make sure to import the correct library without problem. from langchain. Building agents with LangChain allows you to leverage the power of language models to perform complex tasks by integrating them with various tools and data sources. return_only_outputs (bool) – Whether to return only outputs in the response. This is useful when you want to answer questions about a JSON blob that's too large to fit in the context window of JSON Chat Agent. base. prompts import ChatPromptTemplate, class langchain. tools (Sequence[]) – Tools this agent has access to. 📄️ OpenAPI Agent Toolkit. JSON Chat Agent. Python agent - an agent capable of producing and executing Python code. LangChain Python API Reference; agents; Agent; Agent# Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. Use this to execute python commands. Here you’ll find answers to “How do I. The other toolkit comprises requests wrappers to send GET and POST requests. Using this toolkit, you can integrate Connery Actions into your LangC JSON Agent Toolkit: This example shows how to load and use an agent with a JSON toolkit. agents import create_json_chat I am working on Natural language to query your SQL Database using LangChain powered by ChatGPT. tools_renderer (Callable[[list[]], str]) – This controls how the tools are class OpenAPIToolkit (BaseToolkit): """Toolkit for interacting with an OpenAPI API. Agent is a class that uses an LLM to choose a sequence of actions to take. , ollama pull llama3 This will download the default tagged version of the from langchain_core. agents import (create_json_agent, AgentExecutor) langchain python agent react differently, for one prompt, it can import scanpy library, but not for the other one. I am attempting to write a simple script to provide CSV data analysis to a user. tool_names: contains all tool names. 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. This is the easiest and most reliable way to get structured outputs. JsonToolkit [source] #. View a list of available models via the model library; e. The user can then exploit the metadata_func to rename the default keys and use the ones from the JSON data. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. If True, the output will be a JSON object containing all the keys that have been returned so far. 0 in January 2024, is your key to creating your first agent with Python. First, follow these instructions to set up and run a local Ollama instance:. tool import PythonREPLTool from langchain. Reminder to always use the exact characters `Final Answer` when responding. agents import create_json_chat # Define the Currently, my approach is to convert the JSON into a CSV file, but this method is not yielding satisfactory results compared to directly uploading the JSON file using relevance. If you want to see the output of a value, you should print it out with `print()`. Security Note: This toolkit contains tools that can read and modify. Complete guide to prompting with LangChain, OpenAI, and Python Creating your chatbot with context takes multiple steps, which is why I wanted to explain a key component to learn how to make an This tutorial, published following the release of LangChain 0. Stay ahead with this up-to-the-minute resource and start your LLM development journey now. Bases: BaseToolkit Toolkit for interacting with an OpenAPI API. agents import AgentExecutor, create_json_chat_agent prompt = hub. Parameters:. Here, the formatted examples will match the format expected for the tool calling API since that's what we're The ToolMessage is required because some of the chat models are hyper-optimized for agents rather than for an extraction use LangGraph docs on common agent architectures; Pre-built agents in LangGraph; Legacy agent concept: AgentExecutor LangChain previously introduced the AgentExecutor as a runtime for agents. Let's create a sequence of steps that, given a This examples showcases a quick way to create multiple tools from the same wrapper. Skip to main content. For example, this toolkit can be used to delete data exposed via an OpenAPI compliant API. Raises: OutputParserException – If the output is not valid JSON. Where possible, schemas are inferred from runnable. This will make it easier for you to manage the webhook URL. , by creating, deleting, or updating, reading underlying data. While it served as an excellent starting point, its limitations became apparent when dealing with more sophisticated and customized agents. The agent of our example will have the capability to perform searches on Wikipedia and solve mathematical operations using the Python module from langchain. Vectorstore agent - an agent capable of interacting with vector stores. This will result in an AgentAction being returned. get_all_tool_names Get a list of all possible tool names. LangChain messages are Python objects that subclass from a BaseMessage. class AgentAction (Serializable): """Represents a request to execute an action by an agent. Example JSON file: JsonToolkit# class langchain_community. No credentials are required to use the JSONLoader class. The log is used to pass along extra information about the action. Expects output to be in one of two formats. , tool calling or JSON mode etc. Chains . This agent can interact with users, process JSON data, and utilize external tools to provide This example shows how to load and use an agent with a JSON toolkit. After initializing the the LLM and the agent (the csv agent is initialized with a csv file containing data from an online retailer), I run the In this example, the agent will first plan the steps needed to accomplish the objective, then execute the sub-tasks using the tools provided. Then chat with the bot again - if you've completed your setup correctly, the bot should now have access to the LangChain Python API Reference; agent_toolkits; create_sql_agent; create_sql_agent# langchain_community. tools import Tool # You can create the tool to pass to an agent repl_tool = Tool (name = "python_repl", description = "A Python shell. python. create_sql_agent (llm: BaseLanguageModel, toolkit: SQLDatabaseToolkit An AgentExecutor with the specified agent_type agent. You will be able to ask this agent questions, watch it call the search tool, and have conversations with it. llms Parameters:. Pandas DataFrame agent - an agent capable of question-answering over Pandas dataframes, builds on top Pass the examples and formatter to FewShotPromptTemplate Finally, create a FewShotPromptTemplate object. create_json_agent () Construct a json agent from an LLM and tools. Other agent toolkit examples: JSON agent - an agent capable of interacting with a large JSON blob. JsonToolkit¶ class langchain_community. Each json differs drastically. This object takes in the few-shot examples and the formatter for the few-shot examples. If the output signals that an action should be taken, should be in the below format. This section will guide you through the process of creating a LangChain Python agent that can interact with multiple tools, such as databases and search engines. For end-to-end walkthroughs see Tutorials. I am using Langchain's SQL database to chat with my database, it returns answers in the sentence I want the answer in JSON format so I have designed a prompt but sometimes it is not giving the proper format. agent_toolkits agent_toolkits. load_tools (tool_names) Load tools based on How to parse JSON output. the state of a service; e. For conceptual explanations see the Conceptual guide. This function allows us to define the tools the agent will use and how it will interact with them. 0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. The loader will load all strings it finds in the JSON object. A lot of the data is not necessary, and this holds true for other jsons from the same source. Now let's try hooking it up to an LLM. chat_models import ChatOpenAI from langchain. import {JsonToolkit, createJsonAgent } from "langchain/agents"; export const run = async => {let data: JsonObject; try Source code for langchain. This agent can make requests to external APIs. tools . tools. LLM Agent: Build an agent that leverages a modified version of the ReAct framework to do chain-of-thought reasoning. [0mInvalid or incomplete response [32;1m [1;3m In the below example, we are using the OpenAPI spec for the OpenAI API, which you can find here. A newer LangChain version is out! JSON Agent Toolkit. 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. Explore a practical example of using the Langchain JSON loader to streamline data processing and enhance your applications. API It supports Python and Javascript languages and supports various LLM providers Agents and toolkits 📄️ Connery Toolkit. BaseLanguageModel, tools: In this tutorial we will build an agent that can interact with a search engine. This example shows how to load and use an agent with a JSON toolkit. agent_toolkits. agents import By following these steps, you can create a functional JSON chat agent using LangChain. Disclaimer ⚠️. create_json_chat_agent (llm: ~langchain_core. as_tool will instantiate a BaseTool with a name, description, and args_schema from a Runnable. create_json_agent (llm: BaseLanguageModel, toolkit: JsonToolkit, callback_manager: Optional [BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with JSON. 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. agents import create_openai_functions_agent from langchain_openai import ChatOpenAI. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. This agent uses JSON to format its outputs, and is aimed at supporting Chat Models. This example shows how to load and use an agent with a OpenAPI toolkit. However, it is possible that the JSON data contain these keys as well. Simulate, Explore a practical example of using the Langchain JSON loader to streamline data processing and enhance your applications. Explore a practical example of using Langchain's JSON agent to streamline data processing and enhance automation. json_chat. For example, if you're connecting to Spotify, you agents #. class OpenAPIToolkit (BaseToolkit): """Toolkit for interacting with an OpenAPI API. output_parser (AgentOutputParser | None) – AgentOutputParser for parse the LLM output. Input should be a valid python command. Parameters. These guides are goal-oriented and concrete; they're meant to help you complete a specific task. This notebook showcases an agent interacting with large JSON/dict objects. If True, only new keys generated by this chain will be returned. Contains previous agent actions and tool Deprecated since version 0. OpenApi Toolkit: LangChain Messages LangChain provides a unified message format that can be used across all chat models, allowing users to work with different chat models without worrying about the specific details of the message format used by each model provider. By default, most of the agents return a single string. `` ` Here is an example from the movie agent using this structure. tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally. *Security Note*: This toolkit contains tools that can read and modify the state of a service; e. Use with caution, especially when granting access to users. Great! We've got a SQL database that we can query. agents import AgentExecutor, create_json_chat_agent from langchain_community. code-block:: python from langchain import hub from langchain_community. Default is False. Intermediate agent actions and tool output messages will be passed in here. Explore Langchain's JSON mode in Python for efficient data handling and integration in Setup . tip. Simulate, time-travel, and replay your workflows. " Choose an Event Name that is specific to the service you plan to connect to. Should contain all inputs specified in Chain. Example. Some language models are particularly good at writing JSON. """ log: str The agent prompt must have an agent_scratchpad key that is a. 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. Create a BaseTool from a Runnable. In my own setup, I am using Openai's GPT3. The action consists of the name of the tool to execute and the input to pass to the tool. agent_scratchpad: must be a MessagesPlaceholder. JsonToolkit [source] ¶. agents. get_input_schema. The code In the below example, we are using the OpenAPI spec for the OpenAI API, which you can find here. Contains previous agent actions and tool I am working on Natural language to query your SQL Database using LangChain powered by ChatGPT. agent_toolkits import create_python_agent from langchain. 📄️ JSON Agent Toolkit. While it is similar in functionality to the PydanticOutputParser, it also supports streaming back partial JSON objects. prompt (BasePromptTemplate) – The prompt to use. Next steps . Context. While some model providers support built-in ways to return structured output, not all do. Choose the first option for "Receive a web request with a JSON payload. python import PythonREPL from langchain. langchain. Initialization# import os import yaml from langchain. No JSON pointer example The most simple way of using it, is to specify no JSON pointer. 2, which is no longer actively maintained. Assuming the bot saved some memories, create a new thread using the + icon. For comprehensive descriptions of every class and function see the API Reference. load_tools. Building a Langchain agent in Python involves leveraging the Langchain framework to create applications that integrate large language models (LLMs) with external sources of data and computation. 1, which is no longer tool calling or JSON mode etc. Design intelligent agents that execute multi-step processes autonomously. OpenAPIToolkit# class langchain_community. Here’s an example: partial (bool) – Whether to parse partial JSON objects. searx_search . \nYour goal is to return a final answer by interacting with the JSON. To create a LangChain agent, we start by understanding the core Example: . Alternatively (e. MessagesPlaceholder. Examples include MRKL systems and frameworks like HuggingGPT, which facilitate task planning and execution. 5 along with Pinecone and from langchain_community. Here, the formatted examples will match the format expected for the OpenAI tool calling API since that’s what we’re using. item()'} because the `arguments` is not valid JSON. Luckily, LangChain has a built-in output parser of the Tool calling . Luckily, LangChain has a built-in output parser of the Execute the chain. input_keys except for inputs that will be set by the chain’s memory. """ json_agent: Any """The JSON agent. 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. This section will guide you through the process of setting up your development environment, creating a simple agent, and exploring the capabilities of Langchain for building Prompt Templates. Here’s a basic example: from langchain. Prompt templates help to translate user input and parameters into instructions for a language model. Python Agent; Fibonacci Example; Training neural net; Python Agent# This notebook showcases an agent designed to write and execute python code to answer a question. I am using the CSV agent which is essentially a wrapper for the Pandas Dataframe agent, both of which are included in langchain-experimental. **Tool Use** enables agents to interact with external APIs and tools, enhancing their capabilities beyond the limitations of their training data. See Prompt section below for more. 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 Setup . The agent will search the web for information about the requests library in Python, fetch the content of the relevant results, and then summarize them into a guide. This notebook showcases an agent designed to write and execute Python code to answer a question. Explore Langchain's JSON mode in Python for efficient data handling and integration in your applications. The example below shows how we can modify the source to only contain information of the file source relative to the langchain directory. agents import create_json_agent from langchain. 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: 2nd example: "json explorer" agent Here's an agent that's not particularly practical, but neat! The agent has access to 2 toolkits. Credentials . I am using Langchain's SQL database to chat with my database, it llm_1=OpenAI(openai_api_key=openai_api_key,temperature=0) db_agent=SQLDatabaseChain(llm = llm_1,database = input_db Try to setup your prompt JSON files. Download and install Ollama onto the available supported platforms (including Windows Subsystem for Linux); Fetch available LLM model via ollama pull <name-of-model>. Agent that calls the language model and deciding the action. 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. \nYou have access to the following tools which help you 2nd example: "json explorer" agent Here's an agent that's not particularly practical, but neat! The agent has access to 2 toolkits. This is driven by a The JsonOutputParser is one built-in option for prompting for and then parsing JSON output. 0: LangChain agents will continue to be supported, but it is recommended for new use cases to be built with LangGraph. In this example, we asked the agent to recommend a good comedy. ?” types of questions. JSON Agent Toolkit. yrpze sbfd vpdcqfo invkhd ggkxej ghuoqdcy phecb nia qyr boss