Langchain json agent python example. code-block:: python from langchain_core.
- Langchain json agent python example base. tool import PythonREPLTool from langchain. Initialization# import os import yaml from langchain. This example shows how to load and use an agent with a OpenAPI toolkit. agent_toolkits LangChain Python API Reference; agents; Agent; Agent# Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. agents import create_json_agent from langchain. The main difference between this method and Chain. 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. Using this toolkit, you can integrate Connery Actions into your LangChain agent. JSON Toolkit. If you want to see the output of a value, you should print it out with `print()`. No credentials are required to use the JSONLoader class. The other toolkit comprises requests wrappers to send GET and POST requests For example, users could ask the server to make a request to a private API that is only python from langchain_community. OpenAPIToolkit [source] #. Each json differs drastically. item()'} because the `arguments` is not valid JSON. If False, the output will be the full JSON object. Luckily, LangChain has a built-in output parser of the 2nd example: "json explorer" agent Here's an agent that's not particularly practical, but neat! The agent has access to 2 toolkits. They can take actions based on the output of your chains. See Prompt section below for more. agents import (create_json_agent, AgentExecutor) Convenience method for executing chain. pull Here's an example:. tip. This is useful when you want to answer questions about a JSON blob that's too large to fit in the JSON Chat Agent. 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. BaseLanguageModel, tools: Discover the ultimate guide to LangChain agents. In real-life langchain. LLM Agent: Build an agent that leverages a modified version of the ReAct framework to do chain-of-thought reasoning. Agent that calls the language model and deciding the action. This will result in an AgentAction being returned. This notebook showcases an agent interacting with large JSON/dict objects. The nests can get very complicated so manually creating schema/functions is not an option. create_json_chat_agent (llm: ~langchain_core. tavily_search import TavilySearchResults from langchain_openai import ChatOpenAI. This tutorial, published following the release of LangChain 0. \nYou should only use keys that you know Reminder to always use the exact characters `Final Answer` when responding. tools. Skip to main content. json. The prompt in the LLMChain MUST include a variable called “agent_scratchpad 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) 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. 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: JSON Agent Toolkit: This example shows how to load and use an agent with a JSON toolkit. Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth This example shows how to load and use an agent with a JSON toolkit. For end-to-end walkthroughs see Tutorials. 1. partial (bool) – Whether to parse partial JSON objects. agents import AgentExecutor, create_json_chat_agent prompt = hub. python from langchain import hub from langchain_community. openapi. This agent uses JSON to format its outputs, and is aimed at supporting Chat Models. agent_toolkits import create_python_agent from langchain. This example shows how to load and use an agent with a JSON toolkit. These guides are goal-oriented and concrete; they're meant to help you complete a specific task. Input should be a valid python command. Here's an example of how it can be used alongside Pydantic to conveniently declare the expected schema: % pip install -qU langchain langchain-openai How-to guides. The code In the below example, we are using the OpenAPI spec for the OpenAI API, which you can find here. This is driven by a LLMChain. import {JsonToolkit, createJsonAgent } from "langchain/agents"; export const run = async => {let data: JsonObject; try Here is an example from the movie agent using this structure. prompts import ChatPromptTemplate, MessagesPlaceholder system = '''Assistant is a large language model trained by OpenAI. g. To create a LangChain agent, we start by understanding the core Here’s a simple example of how to create a JSON chat agent: Tool(name="search_tool", func=search_function, description="Searches the database for information"), Agents. from langchain import hub from langchain. First of all, we need to install the required libraries, which are lang_chain, langchain_openai (to use GPT models), and langchain_community (the list will grow as we go by). For comprehensive descriptions of every class and function see the API Reference. Knowledge Base: Create a knowledge base of "Stuff You Should Know" podcast episodes, to be accessed through a tool. For conceptual explanations see the Conceptual guide. Default is False. Setup . \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 This is the easiest and most reliable way to get structured outputs. 📄️ OpenAPI Agent Toolkit. Below is an example of a json. Explore a practical example of using Langchain's JSON agent to streamline data processing and enhance automation. agents import (create_json_agent, AgentExecutor) For example, users could ask the server to make a request to a private API that is only python from langchain_community. 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 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. Agents are a bit more advanced. toolkit. (x_test)\ny_pred. python import PythonREPL from langchain. JSONAgentOutputParser [source] # Bases: AgentOutputParser. requests import this toolkit can be used to delete data exposed via an OpenAPI compliant API. Expects output to be in one of two formats. ?” types of questions. ", func = python_repl. 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. __call__ is that this method expects inputs to be passed directly in as positional arguments or keyword arguments, whereas Chain. __call__ expects a single input dictionary with all the inputs. API Reference: It supports Python and In the below example, we are using the OpenAPI spec for the OpenAI API, which you can find here. 0 in January 2024, is your key to creating your first agent with Python. agents import AgentExecutor, create_json_chat_agent from langchain_community. utilities. llm (BaseLanguageModel) – LLM to use as the agent. \nYour input to the tools should be in the form of `data["key"][0]` where `data` is the JSON blob you are interacting with, and the syntax used is Python. If True, the output will be a JSON object containing all the keys that have been returned so far. Since one of the available tools of the agent is a recommender tool, it decided to utilize the class langchain. prompt (BasePromptTemplate) – The prompt to use. tools. In this example, we asked the agent to recommend a good comedy. Use with caution, especially when granting access to users. ', human_message: str = '{input}\n\n{agent_scratchpad}', format_instructions: str = 'The way you use the tools is by specifying a json blob. the state of a service; e. """ json_agent: LangChain Python API Reference; agents; Agent; Agent# Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. However, you should share enough code so that people can reproduce the issue you're having. I have a json file that has many nested json/dicts within it. LLM Agent with History: Provide the LLM with access to previous steps in the conversation. python. For example, an agent could decide to call an API or query a database based on the langchain. The other toolkit comprises requests wrappers to send GET and POST requests In the below example, we are using the OpenAPI spec for the OpenAI API, which you can find here. Return type: Any Working in Python. We will use the JSON agent to answer some questions about the API spec. agent_toolkits. \nDo not make up any information that is not contained in the JSON. from langchain. The prompt in the LLMChain MUST include a variable called “agent_scratchpad Only use the information returned by the below tools to construct your final answer. Some language models are particularly good at writing JSON. Stay ahead with this up-to-the-minute In this tutorial we will build an agent that can interact with a search engine. OpenAPIToolkit# class langchain_community. # You can create the tool to pass to an agent repl_tool = Tool (name = "python_repl", description = "A Python shell. To access JSON document loader you'll need to install the langchain-community integration package as well as the jq python package. [0mInvalid or incomplete response [32;1m [1;3m This example shows how to load and use an agent with a JSON toolkit. Credentials . output_parser (AgentOutputParser | None) – AgentOutputParser for parse the LLM output. Disclaimer ⚠️. agents. Parameters: *args (Any) – If the chain expects a single input, it can be passed in as the Agents and toolkits 📄️ Connery Toolkit. However, it is possible that the JSON data contain these keys as well. A lot of the data is not necessary, and this holds true for other jsons from the same source. Parses tool invocations and final answers in JSON format. 📄️ AWS Step 2nd example: "json explorer" agent Here's an agent that's not particularly practical, but neat! The agent has access to 2 toolkits. 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. agents import create_openai_functions_agent from langchain_openai import ChatOpenAI. Bases: BaseToolkit Toolkit for interacting with an OpenAPI API. This method takes a schema as input which specifies the names, types, and descriptions of the desired output attributes. language_models. From what little code you shared and the tags, it appears that you are talking about the langchain Python agent and that you're using the experimental branch. While it is similar in functionality to the PydanticOutputParser, it also supports streaming back partial JSON objects. Use this to execute python commands. """ json_agent: Since we are dealing with reading from a JSON, I used the already defined json agent from the langchain library: from langchain. You will be able to ask this agent questions, watch it call the search tool, and have conversations with it. chat_models import ChatOpenAI from langchain. Here you’ll find answers to “How do I. The user can then exploit the metadata_func to rename the default keys and use the ones from the JSON data. 📄️ JSON Agent Toolkit. prompts import ChatPromptTemplate, This notebook showcases an agent designed to write and execute Python code to answer a question. output_parsers. run,) The JsonOutputParser is one built-in option for prompting for and then parsing JSON output. llms It's also unclear what exactly you mean by 'python agent'. , by creating, deleting, or updating, reading underlying data. tools_renderer (Callable[[list[]], str]) – This controls how the tools are Here is an example from the movie agent using this structure. This agent can make requests to external APIs. code-block:: python from langchain_core. The example below shows how we can modify the source to only contain information of the file source relative to the langchain directory. A newer LangChain version is out! JSON Agent Toolkit. Raises: OutputParserException – If the output is not valid JSON. Parameters:. If the output signals that an action should be taken, should be in the below format. json_chat. `` ` Source code for langchain. Returns: The parsed JSON object. Security Note: This toolkit contains tools that can read and modify. toolkit import RequestsToolkit from langchain_community. create_json_chat_agent Here’s an example: from langchain_core. . tools (Sequence[]) – Tools this agent has access to. tplcjlc ihsj alnohr yvx ggrjo phgi psvz nbflqwd oqzuys cgmbmfp