Langchain agent prompt python json. This is driven by a LLMChain.

• Langchain agent prompt python json This PromptValue can be passed to an LLM or a ChatModel, and can also be cast to a string or a list of messages. Agent that calls the language model and deciding the action. The schema you pass to with_structured_output will only be used for parsing the model outputs, it will not be passed to the model the way it is with tool calling. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. prompt (ChatPromptTemplate) – The prompt to use. Dec 9, 2024 · parse_with_prompt (completion: str, prompt: PromptValue) → Any ¶ Parse the output of an LLM call with the input prompt for context. prompts import ChatPromptTemplate, MessagesPlaceholder system = '''Assistant is a large language model trained by OpenAI. completion (str) – String output of a Feb 28, 2024 · However, those models have a custom prompt engineering schema for function-calling they follow, which is not well documented, or they can’t be used for anything other than function-calling. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). LangChain Python API Reference; agent_toolkits; create_json_agent; create_json_agent# langchain_community. This is useful when you want to answer questions about a JSON blob that's too large to fit in the context window of an LLM. This notebook showcases an agent designed to write and execute Python code to answer a question. LangChain implements a JSONLoader to convert JSON and JSONL data into LangChain Document objects. Dec 13, 2023 · The create_json_agent function you're using to create your JSON agent takes a verbose parameter. This can be useful for debugging, but you might want to set it to False in a production environment to reduce the amount of logging. from langchain import hub from langchain . Create a new model by parsing and validating input data from keyword arguments. base. The create_json_chat_agent function in LangChain provides a powerful way to create agents that use JSON formatting for their decision-making process. 1 Coinciding with the momentous launch of OpenAI's This example shows how to load and use an agent with a JSON toolkit. By themselves, language models can't take actions - they just output text. In this guide, we'll learn how to create a simple prompt template that provides the model with example inputs and outputs when generating. This output parser allows users to specify an arbitrary JSON schema and query LLMs for outputs that conform to that schema. Do NOT respond with anything except a JSON snippet no matter what!") → Runnable [source] # Create an agent that uses JSON to format its logic, build for Chat Models. For more information about how to think about these components, see our conceptual guide. Returns: OpenAI API has deprecated functions in favor of tools. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. . json. \nYou have Mar 4, 2025 · Using these components, we can create langchain agents that extend an LLM’s capabilities. class Joke (BaseModel): setup: str = Field (description = "question to set up a joke") JSON Toolkit. output_parsers import JsonOutputParser from langchain_core. The prompt in the LLMChain MUST include a variable called “agent_scratchpad” where the agent can put its intermediary work. In my implementation, I took heavy inspiration from the existing hwchase17/react-json prompt available in LangChain hub. It's recommended to use the tools agent for OpenAI models. agent_toolkits. Skip to main content This is documentation for LangChain v0. 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. Prompt Templates output a PromptValue. Let’s build a langchain agent that uses a search engine to get information from the web if it doesn’t have specific information. Sep 11, 2023 · LangChain is a framework designed to speed up the development of AI-driven applications. To see if the model you're using supports JSON mode, check its entry in the API reference. Parameters. You can peruse LangSmith how-to guides here, but we'll highlight a few sections that are particularly relevant to LangChain below: Evaluation JSON. completion (str) – String output of a JSON Lines is a file format where each line is a valid JSON value. prompt: The prompt to use. 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. Dec 9, 2024 · Agent that calls the language model and deciding the action. JSON parser. Parameters: llm (BaseLanguageModel) – LLM to use as the agent. A big use case for LangChain is creating agents. See Prompt section below for more. tools: Tools this agent has access to. Feb 19, 2025 · Build an Agent. LangSmith documentation is hosted on a separate site. Agents are systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. If True, adds a stop token of "Observation:" to avoid hallucinates. The agent is able to iteratively explore the blob to find what it needs to answer the user's question. Apr 11, 2024 · Now, we can initialize the agent with the LLM, the prompt, and the tools. 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). This agent uses JSON to format its outputs, and is aimed at supporting Chat Models. See Dec 9, 2024 · Args: llm: LLM to use as the agent. prompts import PromptTemplate from langchain_openai import ChatOpenAI from pydantic import BaseModel, Field model = ChatOpenAI (temperature = 0) # Define your desired data structure. Let’s now explore how to build a langchain agent in Python. Keep in mind that large language models are leaky abstractions! You'll have to use an LLM with sufficient capacity to generate well-formed JSON. create_json_agent (llm: BaseLanguageModel, toolkit: JsonToolkit, callback_manager: BaseCallbackManager | None = None, prefix: str = 'You are an agent designed to interact with JSON. The difference between the two is that the tools API allows the model to request that multiple functions be invoked at once, which can reduce response times in some architectures. The prompt is largely provided in the event the OutputParser wants to retry or fix the output in some way, and needs information from the prompt to do so. If using JSON mode you'll have to still specify the desired schema in the model prompt. 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. Dec 9, 2024 · from langchain_core. This is driven by a LLMChain. 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. It provides a suite of components for crafting prompt templates, connecting to diverse data sources, and interacting seamlessly with various tools. These agents are specifically built to work with chat models and can interact with various tools while maintaining a structured conversation flow. Parameters: completion (str) – String output of a language model. tools (Sequence) – Tools this agent has access to. Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. It simplifies prompt engineering, data input and output, and tool interaction, so we can focus on core logic. The prompt uses the following system Prompt Templates take as input a dictionary, where each key represents a variable in the prompt template to fill in. \nYour goal is to return a final answer by interacting with the JSON. stop_sequence: bool or list of str. If this parameter is set to True , the agent will print detailed information about its operation. prompt (PromptValue) – Input PromptValue. 1, which is no longer actively maintained. Feb 28, 2024 · JSON-based Prompt for an LLM Agent. Ultimately, I decided to follow the existing LangChain implementation of a JSON-based agent using the Mixtral 8x7b LLM. How to build a langchain agent in Python. 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. Mar 9, 2025 · Prompt Engineering（提示工程）是与大模型高效沟通的关键技能。通过精心设计的Prompt，可以让模型生成更准确、更有用的结果。本文将从基础知识到高级策略，全面解析Prompt Engineering的核心技巧，并通过实战案例帮助你掌握这一新兴技能。 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 notebook showcases an agent interacting with large JSON/dict objects. The agent is responsible for taking in input and deciding what actions to take. After executing actions, the results can be fed back into the LLM to Parse the output of an LLM call with the input prompt for context. agents import AgentExecutor , create_json_chat_agent from langchain_core. urh tcnfls noau mxyci yvx zbnki qly flho hmgsx kgl sdzg evapf ywlykd oxvh azji