Langchain json agent python example Parameters: *args (Any) – If the chain expects a single input, it can be passed in as the This is the easiest and most reliable way to get structured outputs. Chains . While it is similar in functionality to the PydanticOutputParser, it also supports streaming back partial JSON objects. json_chat. 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 langchain. In real-life langchain. This is useful when you want to answer questions about a JSON blob that’s too large Explore a practical example of using Langchain's JSON agent to streamline data processing and enhance automation. Create an instance of JSONLoader and specify the path to your JSON file. ?” types of questions. BaseMultiActionAgent [source] ¶. agents import (create_json_agent, AgentExecutor) Yes, you can find sample data from the following link: sample data. The tool abstraction in LangChain associates a Python function with a schema that defines the function's name, description and expected arguments. 1 Coinciding with the momentous launch of OpenAI's To create a multi-tool agent, we will utilize the create_json_chat function from LangChain. agents import AgentExecutor, create_json_chat_agent prompt = hub. Next steps . agents. A newer LangChain version is out! JSON Agent Toolkit. partial (bool) – Whether to parse partial JSON objects. Here's an example:. JsonToolkit [source] ¶. Returns:. Here you’ll find answers to “How do I. sql. Pandas DataFrame agent - an agent capable of question-answering over Pandas dataframes, builds on top The agent prompt must have an agent_scratchpad key that is a. input (Any) – The input to the Runnable. custom class langchain. For example: Agents: Agents utilize LLMs to make decisions on actions, execute those actions, observe outcomes, and iterate JSON files. 4. This can be used to guide a model's response, helping it understand the context and generate relevant and coherent language-based output. An agent in LangChain is designed to utilize a language model (LLM) to determine the This example goes over how to load data from JSONLines or JSONL files. This represents a message with role "tool", which contains the result of calling a tool. This agent can make requests to external APIs. Here’s a basic example: from langchain. These guides are goal-oriented and concrete; they're meant to help you complete a specific task. toolkit. output_parsers import JSONAgentOutputParser from langchain. Vectorstore agent - an agent capable of interacting with vector stores. @deprecated ("0. agents import initialize_agent from langchain. agents import (create_json_agent, AgentExecutor) partial (bool) – Whether to parse partial JSON objects. Return type: AgentExecutor. Source code for langchain_community. This is driven by an LLMChain. prompt (BasePromptTemplate) – The prompt to use. I am attempting to write a simple script to provide CSV data analysis to a user. language_models. for example, text Agents: Build an agent that interacts with external tools. json keys pace pyschema python serial text threading trulens feedback feedback dummy dummy endpoint provider embeddings different agents may be needed to retrieve the most useful context. Security Note: This toolkit contains tools that can read and modify. (x_test)\ny_pred. inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects only one param. agent_toolkits import create_python_agent from langchain. language_models import BaseLanguageModel from Convenience method for executing chain. Use with caution, especially when granting access to users. tools import Tool from langchain_openai import OpenAI llm = OpenAI (temperature = 0) search = SearchApiAPIWrapper tools = [Tool (name = "Intermediate Answer", func = search. No JSON pointer example The most simple way of using it, is to specify no JSON pointer. Initialization# import os import yaml from langchain. utilities import SearchApiAPIWrapper from langchain_core. LangChain Agents are fine for getting started, but past a certain point you will likely want flexibility and control that they do not offer. you should be using self_ask_with_search agent type:. 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 I am working on Natural language to query your SQL Database using LangChain powered by ChatGPT. 1. Retrieval Augmented Generation (RAG) Part 1 : Build an application that uses your own documents to inform its responses. It can often be useful to have an agent return something with more structure. 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. chat_models import ChatOpenAI from langchain. agents import AgentAction, AgentFinish from langchain_core. agents import AgentExecutor from langchain. Parameters: result (List) – The result of the LLM call. custom events will only be The agent of our example will have the capability to perform searches on Wikipedia and solve mathematical operations using the Python module from langchain. Parameters:. Now let's try hooking it up to an LLM. prompts import ChatPromptTemplate, MessagesPlaceholder system = '''Assistant is a large language model trained by OpenAI. Only use the information returned by the below tools to construct your final answer. The primary Ollama integration now supports tool calling, and should be used instead. Example JSON Lines File. Prefix to append the llm call with. These are fine for getting started, but past a certain point, you will likely want flexibility and control that they do not offer. ConversationalChatAgent [source] ¶ Bases: Agent Deprecated since version 0. To create a LangChain agent, we start by understanding the core components that make up the agent's functionality. utilities. Prompt templates help to translate user input and parameters into instructions for a language model. While some model providers support built-in ways to return structured output, not all do. llms Setup . """ json_agent: Prompt Templates. You switched accounts on another tab or window. We'll be using the @pinecone-database/pinecone library to interact with Pinecone. Returns: The parsed JSON object. agents import create_json_chat Convenience method for executing chain. Below is an example of a json. render import Open in LangGraph studio. API Reference: It supports Python and 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 Agent with History: Provide the LLM with access to previous steps in the conversation. 0", message = ("Use new agent constructor methods like create_react_agent, create_json_agent, ""create_structured_chat_agent, etc. For comprehensive descriptions of every class and function see the API Reference. It's also unclear what exactly you mean by 'python agent'. 📄️ AWS Step The JsonOutputParser is one built-in option for prompting for and then parsing JSON output. In the below example, we are using the OpenAPI spec for the OpenAI API, which you can find here. Bases: BaseToolkit Toolkit for interacting with an OpenAPI API. Tools are a way to encapsulate a function and its schema langchain. OpenAPIToolkit [source] #. JSON files. Raises: OutputParserException – If the output is not valid JSON. Assuming the bot saved some memories, create a new thread using the + icon. When I use JsonToolkit, how should I perform text splitters and embeddings on the data, and put them into a vector store? json_spec_list = [] for data_dict in json_data: # In my latest article, we introduced the concept of Agents powered by Large Language Models and how they overcome one of the current limitations of our beloved LLMs: the capability of taking action. Simulate, time-travel, and replay your workflows. We'll also be using the danfojs-node library to load the data into an easy to manipulate dataframe. Explore a practical example of using the Langchain JSON loader to streamline data processing and enhance your applications. Here’s an example: ToolMessage . JsonToolkit# class langchain_community. property observation_prefix: str ¶. 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. agents import create_json_chat # Define the You can create two or more agents and use them as tools with initialize_agent(). 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. config (RunnableConfig | None) – The config to use for the Runnable. callbacks. 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 structure includes Parse the result of an LLM call to a JSON object. This will help you getting started with Groq chat models. Agent¶ class langchain. The prompt in the LLMChain MUST include a variable called “agent_scratchpad” where the agent can put its intermediary work. """ input_keys_arg: List [str] = [] return_keys_arg: List [str] = [] stream_runnable: bool = True """Whether to stream from the runnable or not. JSON Toolkit. exceptions import OutputParserException from langchain_core. tip. agent_toolkits. 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). I have a json file that has many nested json/dicts within it. The code JSON Agent# This notebook showcases an agent designed to interact with large JSON/dict objects. load_json# langchain_community. ; an artifact field which can be used to pass along arbitrary artifacts of the tool execution which are useful to track but which should LangChain is essentially a library of abstractions for Python and Javascript, representing common steps and conceptsLaunched by Harrison Chase in October 2022, LangChain enjoyed a meteoric rise to prominence: as of June 2023, it was the single fastest-growing open source project on Github. This agent should Requests Toolkit. agents import AgentType, initialize_agent from langchain_community. __call__ expects a single input dictionary with all the inputs. In addition to role and content, this message has:. For working with more advanced agents, we’d You signed in with another tab or window. Return type. get_all_tool_names Get a list of all possible tool names. If True, only new keys generated by Parameters:. In the custom agent example, it has you managing the chat history manually. In this example, we asked the agent to recommend a good comedy. 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 from __future__ import annotations import logging from typing import Union from langchain_core. For working with more advanced agents, we'd recommend checking out LangGraph Agents or the migration guide Parameters. agent import AgentOutputParser logger = logging. JSON - Advanced Python 11 ; Random Numbers - Advanced Python 12 ; Decorators - Advanced Python 13 Memory is the concept of persisting state between calls of a chain/agent. In this case we’ll use the WebBaseLoader, which uses urllib to load HTML from web URLs and BeautifulSoup to parse it to text. agents import initialize_agent, Tool from langchain. llm (BaseLanguageModel) – LLM to use as the agent. Anthropic is an AI safety and research company, and is the creator of Claude. I can assist in troubleshooting, answering questions, and even guide you to contribute to the repo. Was this helpful? Explore the Langchain create_json_chat agent for building efficient chat applications using JSON data structures. structured_chat. code-block:: python from langchain_core. Using this toolkit, you can integrate Connery Actions into your LangChain agent. Example:- Consider CSV file having data:-question, answer 2+3, 22/7, 9+2, _python_agent from langchain. from langchain_core. create_json_chat_agent (llm: ~langchain_core. The goal of tools APIs is to more reliably return valid and useful tool calls than This tutorial demonstrates text summarization using built-in chains and LangGraph. messages import (AIMessage, BaseMessage, FunctionMessage, class RunnableAgent (BaseSingleActionAgent): """Agent powered by Runnables. Here's an example of how it can be used alongside Pydantic to conveniently declare the expected schema: % pip install -qU langchain langchain-openai For example, users could ask the server to make a request to a private API that is only python from langchain_community. output_parser (AgentOutputParser | None) – AgentOutputParser for parse the LLM output. 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_sql_agent (llm: BaseLanguageModel, toolkit: SQLDatabaseToolkit An AgentExecutor with the specified agent_type agent. If you want to see the output of a value, you should print it out with `print()`. JSONAgentOutputParser [source] # Bases: AgentOutputParser. If the output signals that an action should be taken, should be in the below format. 0: Use create_json_chat_agent instead. 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. The second argument is a JSONPointer to the property to extract from each JSON object in the file. This page covers how to use the SearxNG search API within LangChain. I updated my ResponseSchema by specifying JSON format in description and it gives me expected result. prompts import ChatPromptTemplate, This example shows how to load and use an agent with a JSON toolkit. \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. Parameters: *args (Any) – If the chain expects a single input, it can be passed in as the I noticed that in the langchain documentation there was no happy medium where it's explained how to add a memory to both the AgentExecutor and the chat itself. All functionality related to Anthropic models. This example shows how to load and use an agent with a JSON toolkit. Parameters: *args (Any) – If the chain expects a single input, it can be passed in as the However, it is possible that the JSON data contain these keys as well. \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 Deprecated since version 0. Context. The nests can get very complicated so manually creating schema/functions is not an option. Bases: AgentOutputParser Output parser for the chat agent. If True then underlying LLM is invoked in a . custom events will only be 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. Understanding Agents This repository contains an example weather query application based on the IBM Developer Blog Post "Create a LangChain AI Agent in Python using watsonx" - thomassuedbroecker/agent Skip to content from langchain_core. create_json_chat_agent Here’s an example: from langchain_core. I am getting flat dictionary from parser. 0: LangChain agents will continue to be supported, but it is recommended for new use cases to be built with LangGraph. Reload to refresh your session. 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. property llm_prefix: str ¶. JsonToolkit [source] #. We'll use the Document type from Langchain to keep the data structure consistent across the indexing process and retrieval agent. Hello @naarkhoo!I'm Dosu, an AI bot that's here to help you out. langchain. By quickly identifying this gap, we can quickly 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 Anthropic. a tool_call_id field which conveys the id of the call to the tool that was called to produce this result. It is broken into two parts: installation and setup, and then references to the specific SearxNG API wrapper. tools_renderer (Callable[[list[]], str]) – This controls how the tools are Discover the ultimate guide to LangChain agents. In this example you will create a langchain agent and use TruLens to identify gaps in tool coverage. Users should use v2. A lot of the data is not necessary, and this holds true for other jsons from the same source. This is useful when you want to answer questions about a JSON blob that’s too large 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"), Contains previous agent actions and tool outputs as messages. utils. Example JSON file: 🤖. The examples in LangChain documentation (JSON agent, HuggingFace example) are using tools with a single string input. Below is the code snippet that is working. pull Here's an example:. Great! We've got a SQL database that we can query. tools. agents import (create_json_agent, AgentExecutor) This agent uses JSON to format its outputs, and is aimed at supporting Chat Models. You will be able to ask this agent questions, watch it call the search tool, and have conversations with it. Python agent - an agent capable of producing and executing Python code. 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. See full docs here. , by creating, deleting, or updating, reading underlying data. \nDo not make up any information that is not contained in the JSON. ', human_message: str = '{input}\n\n{agent_scratchpad}', format_instructions: str = 'The way you use the tools is by specifying a json blob. \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 Disclaimer ⚠️. We can use DocumentLoaders for this, which are objects that load in data from a source and return a list of Document objects. This notebook showcases an agent interacting with large JSON/dict objects. JSONAgentOutputParser [source] ¶ Bases: AgentOutputParser. chat. from langchain. "), removal = "1. The example below shows how we can modify the source to only contain information of the file source relative to the langchain directory. For end-to-end walkthroughs see Tutorials. agent_toolkits langchain_community. No default will be assigned until the API is stabilized. The main difference between this method and Chain. agents import (create_json_agent, AgentExecutor) tool_run_logging_kwargs → Dict ¶. json_path (str) – The path to the json file. This code is an adapter that converts our example to a list of messages Explore a practical example of using Langchain's JSON agent to streamline data processing and enhance automation. You signed out in another tab or window. Intermediate agent actions and tool output messages will be passed in here. agents import create_json_agent from langchain. Each json differs drastically. callbacks import BaseCallbackManager from langchain_core. tavily_search import TavilySearchResults from langchain_openai import ChatOpenAI. The string representation of the json file. tool import PythonREPLTool Step 1 Explore Langchain's JSON mode in Python for efficient data handling and integration in your applications. Each line in the JSONL file corresponds to a separate document in LangChain. item()'} because the `arguments` is not valid JSON. load_huggingface_tool () Loads a tool from the HuggingFace Hub. Prefix to append the observation with. This method takes a schema as input which specifies the names, types, and descriptions of the desired output attributes. BaseLanguageModel, tools: In this tutorial we will build an agent that can interact with a search engine. tools. See here for information on using those abstractions and a comparison with the methods demonstrated in this tutorial. One document will be created for each JSON object in the file. See example usage in LangChain v0. For detailed documentation of all ChatGroq features and configurations head to the API reference. agents import AgentType # you can define a different llm llm = OpenAI(temperature=0) search = langchain. 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 Execute the chain. agent_toolkits import create_csv_agent from langchain. create_json_agent () Construct a json agent from an LLM and tools. input_keys except for inputs that will be set by the chain’s memory. Explore a technical example of JSON output related to Langchain, showcasing its structure and usage. Returns: 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. Use this to execute python commands. Input should be a valid python command. tool_names: contains all tool names. base. 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. . Lemon Agent. Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth Source code for langchain. 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 Other agent toolkit examples: JSON agent - an agent capable of interacting with a large JSON blob. agent_scratchpad: contains previous agent actions and tool outputs as a string. Contact. Lemon Agent helps you build powerful AI assistants in minutes and automate workflows by allowing for accurate and reliable read and write operations in tools like Airtable, Hubspot, Discord, Notion, Slack and Github. create_structured_chat_agent Here’s an example: 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). from langchain import OpenAI, SerpAPIWrapper from langchain. from langchain import hub from langchain. We need to first load the blog post contents. """ from __future__ import annotations from typing import TYPE_CHECKING, Any, Dict, List, Optional from langchain_core. 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. __call__ is that this method expects inputs to be passed directly in as positional arguments or keyword arguments, whereas Chain. A good example of this is an agent tasked with doing question-answering over some sources. OpenAPIToolkit# class langchain_community. Example JSONLines file: Design intelligent agents that execute multi-step processes autonomously. Bases: BaseToolkit Toolkit for interacting with a JSON spec. Return type: class langchain. 0. openapi. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory. agent_toolkits import JsonToolkit, create_json_agent from langchain_community. prompts import PromptTemplate template = '''Answer the following questions as best you can. Parameters. 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. conversational_chat. The other toolkit comprises requests wrappers to send GET and POST requests Here is an example from the movie agent using this structure. Explore Langchain's JSON mode in Python for efficient data handling and integration in I am using StructuredParser of Langchain library. 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. custom Overview . JsonToolkit¶ class langchain_community. SearxNG Search API. load_json (json_path: str | Path) → str [source] # Load json file to a string. However, you should share enough code so that people can reproduce the issue you're having. 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. To create a LangChain agent, we start by understanding the core JSON Agent# This notebook showcases an agent designed to interact with large JSON/dict objects. If True, the output will be a JSON object containing all the keys that have been returned so far. v1 is for backwards compatibility and will be deprecated in 0. This function allows us to define the tools the agent will use and how it will interact with them. This tutorial, published following the release of LangChain 0. Some language models are particularly good at writing JSON. Example JSON file: input: str # This is the example text tool_calls: List [BaseModel] # Instances of pydantic model that should be extracted def tool_example_to_messages (example: Example)-> List [BaseMessage]: """Convert an example into a list of messages that can be fed into an LLM. In LangGraph, we can represent a chain via simple sequence of nodes. Base class for single action agents. See Prompt section below for more. """Json agent. Skip to main content. We'll start by importing the necessary libraries. agents import AgentExecutor, create_json_chat_agent from langchain_community. Luckily, LangChain has a built-in output parser of the This notebook showcases an agent designed to write and execute Python code to answer a question. Creating a LangChain Agent. Default is False. For conceptual explanations see the Conceptual guide. If False, the output will be the full JSON object. 0",) class Agent (BaseSingleActionAgent): """Agent that calls the language model and deciding the action. The user can then exploit the metadata_func to rename the default keys and use the ones from the JSON data. I am using the CSV agent which is essentially a wrapper for the Pandas Dataframe agent, both of which are included in langchain-experimental. \nYou should only use keys that you know In the below example, we are using the OpenAPI spec for the OpenAI API, which you can find here. Retrieval Augmented Generation (RAG) Part 2 : Build a RAG application that incorporates a memory of its user interactions and multi-step retrieval. json. 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. This section will cover building with the legacy LangChain AgentExecutor. ChatOutputParser [source] ¶. Knowledge Base: Create a knowledge base of "Stuff You Should Know" podcast episodes, to be accessed through a tool. We will use the JSON agent to answer some questions about the API spec. agent_toolkits. The loader will load all strings it finds in the JSON object. format_scratchpad import format_log_to_str from langchain. json import parse_json_markdown from langchain. getLogger By default, most of the agents return a single string. While we're waiting for a human maintainer, feel free to Parameters:. this agent="zero-shot-react-description" is not the right agent type for the search engine. tool import PythonREPLTool from langchain. Should contain all inputs specified in Chain. Prompting Best Practices Deprecated since version 0. Here’s an example: 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) For example, users could ask the server to make a request to a private API that is only python from langchain_community. This example shows how to load and use an agent with a OpenAPI toolkit. We can customize the HTML -> text parsing by passing in This was an experimental wrapper that bolted-on tool calling support to models that do not natively support it. agents import create_openai_functions_agent from langchain_openai import ChatOpenAI. [0mInvalid or incomplete response [32;1m [1;3m Convenience method for executing chain. Bases: BaseModel Base Multi Action Agent class. config (Optional[RunnableConfig]) – The config to use for the Runnable. Agent that calls the language model and deciding the action. Company. 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. Bases: BaseSingleActionAgent [Deprecated] Agent that calls the language model and deciding the action. output_parser. The prompt in the LLMChain MUST include a variable This section covered building with LangChain Agents. Restack. Dict. In this story we are going to focus on how you can build an ElasticSearch agent in Python using the infrastructure provided by LangChain. For detailed documentation of all API toolkit features and configurations head to the API reference for RequestsToolkit. An Agent can be seen as a kind of wrapper that uses an LLM as a reasoning engine, plus it has the capability of interacting with tools that we can provide and @deprecated ("0. Reminder to always use the exact characters `Final Answer` when responding. 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. Example. The JSON loader use JSON pointer to target keys in your JSON files you want to target. This is useful when you want to answer questions about a JSON blob that's too large to fit in the JSON Chat Agent. Here is an example from the movie agent using this structure. The prompt in the LLMChain MUST include a variable called Design intelligent agents that execute multi-step processes autonomously. 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: Working in Python. 📄️ JSON Agent Toolkit. While it served as an excellent starting Since we are dealing with reading from a JSON, I used the already defined json agent from the langchain library: from langchain. agent_types import AgentType from langchain_experimental. run, description = "useful for The model then uses this single example to extrapolate and generate text accordingly. LangChain Python API Reference; agents; Agent; Agent# Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. load_tools. json_structure: Defines the expected JSON structure with placeholders for actual data. Let's create a sequence of steps that, given a How to parse JSON output. """ # noqa: E501 from __future__ import annotations import json from typing import Any, List, Literal, Sequence, Union from langchain_core. Key concepts . 0 in January 2024, is your key to creating your first agent with Python. version (Literal['v1', 'v2']) – The version of the schema to use either v2 or v1. Agents, on the other hand, Dall-E — futuristic humanoid robot. Here class langchain. tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally. 📄️ OpenAPI Agent Toolkit. python. If the output signals that an action should be taken, should be in the The agent prompt must have an agent_scratchpad key that is a. Let's say we want the agent to respond not only with the answer, but also a list of the sources used. If you really need the nested format, you can convert it easily in Python: Tool calling . Create a new model by parsing and validating input data from keyword arguments. requests import this toolkit can be used to delete data exposed via an OpenAPI compliant API. Agent [source] ¶. The prompt in the LLMChain MUST include a variable called “agent_scratchpad 2nd example: "json explorer" agent Here's an agent that's not particularly practical, but neat! The agent has access to 2 toolkits. output_parsers. custom from langchain_core. Chains are compositions of predictable steps. Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth from langchain. python import PythonREPL from langchain. To access JSON document loader you'll need to install the langchain-community integration package as well as the jq python package. This section will guide you through the process of setting up your development environment, creating a simple agent, and exploring the capabilities of We recommend that you use LangGraph for building agents. 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. load_tools (tool_names) Load tools based on JSON Agent Toolkit: This example shows how to load and use an agent with a JSON toolkit. Return logging kwargs for tool run. g. 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. JSON Agent Toolkit: This example shows how to load and use an agent with a JSON toolkit. The prompt in the LLMChain MUST include a variable called “agent_scratchpad Agents and toolkits 📄️ Connery Toolkit. agent. load. serializable import Serializable from langchain_core. MessagesPlaceholder. 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 input to the action import contextlib from tempfile import TemporaryFile from dotenv import load_dotenv from langchain. This agent uses JSON to format its outputs, and is aimed at supporting Chat Models. The prompt must have input keys: tools: contains descriptions and arguments for each tool. 2 documentation here. run,) LLM Agent: Build an agent that leverages a modified version of the ReAct framework to do chain-of-thought reasoning. python from langchain import hub from langchain_community. spec – The JSON spec. # You can create the tool to pass to an agent repl_tool = Tool (name = "python_repl", description = "A Python shell. This is driven by a LLMChain. """ json_agent: 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. This page covers all integrations between Anthropic models and LangChain. This will result in an AgentAction being returned. return_only_outputs (bool) – Whether to return only outputs in the response. Explore Langchain's integration with OpenAI's JSON mode for enhanced data handling and processing capabilities. ⚠️ Security note ⚠️ Loading documents . If you don't have it in the AgentExecutor, it doesn't see previous steps. Parses tool invocations and final answers in JSON format. A previous version of this page showcased the legacy chains StuffDocumentsChain, MapReduceDocumentsChain, and RefineDocumentsChain. toolkit import RequestsToolkit from langchain_community. Since the tools in the semantic layer use slightly more complex inputs, I had In the below example, we are using the OpenAPI spec for the OpenAI API, which you can find here. We can use the Requests toolkit to construct agents that generate HTTP requests. Credentials . tools (Sequence[]) – Tools this agent has access to. Expects output to be in one of two formats. from dotenv import load_dotenv, find_dotenv import openai import os from langchain. import {JsonToolkit, createJsonAgent } from "langchain/agents"; export const run = async => {let data: JsonObject; try langchain. How-to guides. Most connectors available today are focused on read-only operations, limiting the potential of LLMs. prompts import ChatPromptTemplate, class langchain. from langchain_community. """ runnable: Runnable [dict, Union [AgentAction, AgentFinish]] """Runnable to call to get agent action. JSONAgentOutputParser¶ class langchain. 0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. `` ` This example shows how to load and use an agent with a JSON toolkit. Please see the following resources for more information: 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. `` ` The schemas for the agents themselves are defined in langchain. Return type: Any However, it is possible that the JSON data contain these keys as well. BaseMultiActionAgent¶ class langchain. memory import ConversationBufferMemory from langchain. param format_instructions: str = 'The way you use the tools is by specifying a json blob. ", func = python_repl. The other toolkit comprises requests wrappers to send GET and POST requests 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. the state of a service; e. Explore Langchain's JSON mode in Python for efficient data handling and integration in your applications. No credentials are required to use the JSONLoader class. For a list of all Groq models, visit this link.
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