Langchain json agent python example.

Langchain json agent python example Dec 9, 2024 · The schemas for the agents themselves are defined in langchain. A big use case for LangChain is creating agents. ReActJsonSingleInputOutputParser [source] ¶. We'll create a tool_example_to_messages helper function to handle this for us: Sep 21, 2024 · In the context of LangChain, JSON files can serve numerous roles including: I have a super quick tutorial showing you how to create a multi-agent chatbot using LangChain, MCP, RAG, and Ollama from langchain_core. language_models import BaseLanguageModel from langchain_core. from langchain_core. create_json_agent (llm: BaseLanguageModel, toolkit: JsonToolkit, callback_manager: BaseCallbackManager | None = None, prefix: str = 'You are an agent designed to interact with JSON. ReActJsonSingleInputOutputParser# class langchain. outputs import ChatGeneration, Generation class StrInvertCase (BaseGenerationOutputParser [str]): """An example parser that inverts the case of the characters in the message. Apr 7, 2024 · The agent of our example will have the capability to perform searches on Wikipedia from langchain. These are fine for getting started, but past a certain point, you will likely want flexibility and control that they do not offer. The primary Ollama integration now supports tool calling, and should be used instead. For detailed documentation of all AzureChatOpenAI features and configurations head to the API reference. Credentials No credentials are required to use the JSONLoader class. Sep 18, 2024 · from langchain. Bases: AgentOutputParser Enabling a LLM system to query structured data can be qualitatively different from unstructured text data. This tutorial will show how to build a simple Q&A application over a text data source. py", line 636, in plan return self. Apr 24, 2024 · This section will cover building with the legacy LangChain AgentExecutor. 3. This agent works by taking in from langchain_core. Rowling. 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 Observation: the Aug 28, 2024 · Step-by-Step Workflow of How to Build LangChain Agents. \nDo not make up any information that is not contained in the JSON. Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. agents import create_json_chat A Practical Guide with step-by-step Python Code Examples. Apr 2, 2025 · You can expose SQL or Python functions in Unity Catalog as tools for your LangChain agent. example_prompt: converts each example into 1 or more messages through its format_messages method. agent_toolkits import JsonToolkit from langchain. classmethod from_llm (llm: BaseLanguageModel, json_spec: JsonSpec, requests_wrapper: TextRequestsWrapper, allow_dangerous_requests: bool = False, ** kwargs: Any) → OpenAPIToolkit [source] ¶ Create json agent from llm, then initialize. 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. create_json_chat_agent(llm: ~langchain_core. load(yamlFile) as JsonObject; result. K. py", line 1032, in _take_next_step output = self. In this guide, we will delve deep into the world of Langchain and JSON. This does not have access to any tools, or generative UI components. openapi. Sep 9, 2024 · The technical context for this article is Python v3. A ToolCallChunk includes optional string fields for the tool name, args, and id, and includes an optional integer field index that can be used to join chunks together. llms. Skip to main content This is documentation for LangChain v0. JSONAgentOutputParser [source] ¶ Bases: AgentOutputParser. Now we need to update our prompt template and chain so that the examples are included in each prompt. Finally, in this section, we will see how to create LangChain agents step-by-step using the knowledge we have gained in the previous sections. Examples In order to use an example selector, we need to create a list of examples. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. Let’s build a langchain agent that uses a search engine to get information from the web if it doesn’t have specific information. tools import BaseTool, Tool from langchain_core. For an overview of all these types, see the below table. tool import PythonREPLTool from langchain. 0: LangChain agents will continue to be supported, but it is recommended for new use cases to be built with LangGraph. See example usage in LangChain v0. Use Case In this tutorial, we'll configure few-shot examples for self-ask with search. \nYou should only use keys that you know Dec 9, 2024 · class langchain. 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. com/v0. agent_toolkits. Oct 10, 2023 · Agent test example 2. For this example, we'll use the above Pydantic output parser. For working with more advanced agents, we'd recommend checking out LangGraph Agents or the migration guide JSON parser. It can often be useful to have an agent return something with more structure. Tool calling . prompts. This article is Part 4 of a series on building modular AI systems: Part 1: Meet Google A2A: The Protocol That Will Revolutionize Multi-Agent AI Systems Example selectors: Used to select the most relevant examples from a dataset based on a given input. Defaults to None. In this example, we will use OpenAI Tool Calling to create this agent. openai import OpenAI from langchain. agents import initialize_agent, AgentType from langchain_core. requests import TextRequestsWrapper toolkit = RequestsToolkit ( Behind the scenes, this uses Ollama's JSON mode to constrain output to JSON, then passes tools schemas as JSON schema into the prompt. `` ` import os import yaml from langchain. If None and agent_path is also None, will default to AgentType. tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally. We can connect practically any data source (including our own) to a LangChain agent and ask it questions about Dec 9, 2024 · from langchain_core. 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. JsonOutputParser [source] #. tools. This will result in an AgentAction being returned. In the coming examples, we will build an agent capable of explaining any topic via three mediums: text, image, or video. chat_models import ChatOpenAI Docling parses PDF, DOCX, PPTX, HTML, and other formats into a rich unified representation including document layout, tables etc. llm (BaseLanguageModel) – Language model to use as the agent. 1 Coinciding with the momentous launch of OpenAI's Nov 26, 2023 · Thought:Traceback (most recent call last): File "C:\Users\vicen\PycharmProjects\ChatBot\venv\Lib\site-packages\langchain\agents\agent. ). Dec 9, 2024 · Deprecated since version 0. Bases: BaseCumulativeTransformOutputParser[Any] Parse the output of an LLM The chain then answers the user query using the Cypher query results. Kor is another library for extraction where schema and examples can be provided to the LLM. This is an example parse shown just for demonstration purposes and to keep The below example is a bit more advanced - the format of the example needs to match the API used (e. 5. g. 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. prompts import ChatPromptTemplate, MessagesPlaceholder system = '''Assistant is a large language model trained by OpenAI. agent_toolkits. For detailed documentation of all GmailToolkit features and configurations head to the API reference. May 30, 2023 · Since we are dealing with reading from a JSON, I used the already defined json agent from the langchain library: from langchain. Be careful to always use double quotes for strings in the json string. AzureChatOpenAI. LLM interference is only one functionality provided. base import create_json_agent from langchain_community In agents, a language model is used as a reasoning engine to determine which actions to take and in which order. tool import JsonSpec Only use the information returned by the below tools to construct your final answer. Since the tools in the semantic layer use slightly more complex inputs, I had to dig a little deeper. agents. Stay ahead with this up-to-the-minute resource and start your LLM development journey now. """Requests toolkit. ChatPromptTemplate, stop_sequence: bool | ~typing. "Action", "Adventure", This example shows how to load and use an agent with a JSON toolkit. Dec 9, 2024 · """Chain that takes in an input and produces an action and action input. In an API call, you can describe functions and have the model intelligently choose to output a JSON object containing arguments to call these functions. This tutorial, published following the release of LangChain 0. This examples showcases a quick way to create multiple tools from the same wrapper. Use Pydantic to declare your data model. tool import SearxSearchResults wrapper = SearxSearchWrapper ( searx_host = "**" ) from langchain_core. agents import AgentExecutor, create_json_chat_agent from langchain_community . Then, set OPENAI_API_TYPE to azure_ad . base import create_json_agent from langchain_community In this tutorial, we'll learn how to create a prompt template that uses few-shot examples. Let’s now explore how to build a langchain agent in Python. Here's an example of how it can be used alongside Pydantic to conveniently declare the expected schema: However, it is possible that the JSON data contain these keys as well. 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. Agent is a class that uses an LLM to choose a sequence of actions to take. chains import LLMChain from langchain. 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 method takes a schema as input which specifies the names, types, and descriptions of the desired output attributes. Advanced LangChain Features. Create a new model by parsing and validating input data from keyword arguments. By themselves, language models can't take actions - they just output text. chat. For full guidance on creating Unity Catalog functions and using them in LangChain, see the Databricks UC Toolkit documentation . This notebook goes through how to create your own custom agent. tools import Tool from langchain. Agent that calls the language model and deciding the action. """ from __future__ import annotations import asyncio import json import logging import time from abc import abstractmethod from pathlib import Path from typing import (Any, AsyncIterator, Callable, Dict, Iterator, List, Optional, Sequence, Tuple, Union, cast,) import yaml 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. Callable [ [~typing. Read about all the agent types here. By default, most of the agents return a single string. Custom agent. Since we're working with OpenAI function-calling, we'll need to do a bit of extra structuring to send example inputs and outputs to the model. agents. ChatOllama. PythonTypeScriptpip install -U langsmithyarn add langchain langsmithCreate an API key To create an API key head to the setting pages. Deprecated since version 0. tool import JsonSpec Important LangChain primitives like chat models, output parsers, prompts, retrievers, and agents implement the LangChain Runnable Interface. JSONFormer offers another way for structured decoding of a subset of the JSON Schema. Part 2 extends the implementation to accommodate conversation-style interactions and multi-step retrieval processes. `` ` How to: use legacy LangChain Agents (AgentExecutor) How to: migrate from legacy LangChain agents to LangGraph; Callbacks Callbacks allow you to hook into the various stages of your LLM application's execution. prompts impor Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. `` ` Apr 11, 2024 · Use of LangChain is not necessary - LangSmith works on its own!Install LangSmith We offer Python and Typescript SDKs for all your LangSmith needs. This is generally the most reliable way to create agents. tool_call_chunks attribute. In this quickstart we'll show you how to build a simple LLM application with LangChain. Presidential Speeches RAG with Pinecone An application that allows users to ask questions about US presidental speeches by applying Retrieval-Augmented Generation (RAG) over a Pinecone vector database. langchain. This notebook showcases an agent designed to write and execute Python code to answer a question. In Chains, a sequence of actions is hardcoded. For complete control over the path of the agent, we need to ensure firstly that it’s finding the right student ID. Feature Description; 🔄 Ease of use: Create your first MCP capable agent you need only 6 lines of code: 🤖 LLM Flexibility: Works with any langchain supported LLM that supports tool calling (OpenAI, Anthropic, Groq, LLama etc. Here, the formatted examples will match the format expected for the OpenAI tool calling API since that’s what we’re using. LangChain agents aren’t limited to searching the Internet. First we pull a relevant prompt and populate it with its required parameters: Load an agent executor given tools and LLM. ) 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。 Deprecated since version 0. List [str] = True, tools_renderer: ~typing. So, let's get started! How to Load a JSON File in Langchain in Python? Loading a JSON file into Langchain using Python is a straightforward process. The user can then exploit the metadata_func to rename the default keys and use the ones from the JSON data. Next, use the DefaultAzureCredential class to get a token from AAD by calling get_token as shown below. prompts import PromptTemplate template = '''Answer the following questions as best you can. \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 here). This toolkit interacts with the GMail API to read messages, draft and send messages, and more. load(f, Loader=yaml. Newer OpenAI models have been fine-tuned to detect when one or more function(s) should be called and respond with the inputs that should be passed to the function(s). agent. The chain searches for relevant reviews based The below example is a bit more advanced - the format of the example needs to match the API used (e. agents module. See this section for general instructions on installing integration packages. This application will translate text from English into another language. output_parser. agent_toolkits import create_python_agent from langchain. base. There are several key components here: Schema LangChain has several abstractions to make working with agents easy. Dec 13, 2023 · I would like to have a few shot learning (few example) on top of my json_agent meaning my json agent already has seen some examples this is the way I hve done it so far from langchain. Agents: Build an agent that interacts with external tools. agent (AgentType | None) – Agent type to use. In the below example, we are using the OpenAPI spec for the OpenAI API, which you can find here. LangChain has a few different types of example selectors. base import BaseToolkit from langchain_community. Here is an example input for a recommender tool. classmethod from_llm (llm: BaseLanguageModel, json_spec: JsonSpec, requests_wrapper: TextRequestsWrapper, allow_dangerous_requests: bool = False, ** kwargs: Any,) → OpenAPIToolkit [source] # Create json agent from llm, then initialize. BaseLanguageModel, tools: ~typing. After executing actions, the results can be fed back into the LLM to The agent is able to iteratively explore the blob to find what it needs to answer the user's question. A common example would be to convert each example into one human message and one AI message response, or a human message followed by a function call message. parse(full_output) ^^^^^ File "C:\Users\vicen Dec 9, 2024 · def create_json_chat_agent (llm: BaseLanguageModel, tools: Sequence [BaseTool], prompt: ChatPromptTemplate, stop_sequence: Union [bool, List [str]] = True, tools 2nd example: "json explorer" agent Here's an agent that's not particularly practical, but neat! The agent has access to 2 toolkits. agents import AgentExecutor, create_structured_chat_agent from langchain_community . 1. Because different models have different strengths, it may be helpful to pass in your own system prompt. You'll have to use an LLM with sufficient capacity to generate well-formed JSON. LangChain provides several abstractions and wrapper to build complex LLM apps. Mar 1, 2023 · Other agent toolkit examples: JSON agent - an agent capable of interacting with a large JSON blob. Here's an example: Use this when you want to POST to a website. react_json_single_input. Use within an agent Following the SQL Q&A Tutorial, below we equip a simple question-answering agent with the tools in our toolkit. Pydantic's BaseModel is like a Python dataclass, but with actual type checking + coercion. Load the LLM Dec 9, 2024 · class langchain. 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. Expects output to be in one of two formats. If the output signals that an action should be taken, should be in the below format. \nYou should only use keys that you know agents #. run("What are the required parameters in the request body to the /completions endpoint?") > Entering new AgentExecutor chain Jul 1, 2024 · Upon investigation of the latest docs, I found that LangChain provides JsonToolkit, specifically designed to handle JSON https://python. How to build a langchain agent in Python. prompts import ChatPromptTemplate , MessagesPlaceholder Pass the examples and formatter to FewShotPromptTemplate Finally, create a FewShotPromptTemplate object. This allows agents to retain and recall information effectively. tavily_search import TavilySearchResults from langchain_openai import ChatOpenAI A big use case for LangChain is creating agents. 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). A few-shot prompt template can be constructed from either a set of examples, or from an Example Selector object. """ from __future__ import annotations from typing import Any, List from langchain_core. language_models. ConversationalChatAgent [source] ¶ Bases: Agent Deprecated since version 0. The other toolkit comprises requests wrappers to send GET and POST requests Oct 13, 2023 · Let’s see an example where we will create an agent that accesses Arxiv, a famous portal for pre-publishing research papers. , tool calling or JSON mode etc. Since the tools in the semantic layer use slightly more complex inputs, I had The JsonOutputParser is one built-in option for prompting for and then parsing JSON output. This interface provides two general approaches to stream content: sync stream and async astream: a default implementation of streaming that streams the final output from the chain. 2 documentation here. requests import RequestsWrapper from langchain. Luckily, LangChain has a built-in output parser of the Feb 19, 2025 · Build an Agent. Bases: AgentOutputParser Output parser for the chat 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. Async programming: The basics that one should know to use LangChain in an asynchronous context. toolkit import RequestsToolkit from langchain_community . FullLoader) llm=OpenAI(temperature=0), toolkit=json_toolkit, verbose=True. Parameters To use AAD in Python with LangChain, install the azure-identity package. 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). Pandas DataFrame agent - an agent capable of question-answering over Pandas dataframes, builds on top To access JSON document loader you'll need to install the langchain-community integration package as well as the jq python package. 0. conversational_chat. Important LangChain primitives like LLMs, parsers, prompts, retrievers, and agents implement the LangChain Runnable Interface. 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. Initialization Dec 9, 2024 · from langchain_core. intermediateSteps, Was this page helpful? You can also leave detailed feedback on GitHub. ZERO_SHOT_REACT_DESCRIPTION. We will use the JSON agent to answer some questions about the API spec. Parameters Apr 25, 2025 · python_a2a + mcp + langchain. python. In this guide, we will walk through creating a custom example selector. While some model providers support built-in ways to return structured output, not all do. Here's a quick step-by-step guide with sample code: from langchain. Ollama allows you to run open-source large language models, such as Llama 2, locally. 0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. json_agent_executor. It simplifies the generation of structured few-shot examples by just requiring Pydantic representations of the corresponding tool calls. Figma is a collaborative web application for interface design. output_parsers import BaseGenerationOutputParser from langchain_core. From the basics to practical examples, we've got you covered. 0: Use create_json_chat_agent instead. from langchain. List [~langchain_c Jan 11, 2024 · Discover the ultimate guide to LangChain agents. 0 in January 2024, is your key to creating your first agent with Python. param requests_wrapper: TextRequestsWrapper [Required] ¶ The requests wrapper. Agents are systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. Examples include MRKL systems and frameworks like HuggingGPT, which facilitate task planning and execution. def create_json_chat_agent (llm: BaseLanguageModel, tools: Sequence [BaseTool], prompt: ChatPromptTemplate, stop_sequence: Union [bool, List [str]] = True, tools class langchain. Feb 20, 2024 · Here, we will discuss how to implement a JSON-based LLM agent. This is a plain chat agent, which simply passes the conversation to an LLM and generates a text response. Base class for single action agents. BaseTool], prompt: ~langchain_core. This is a relatively simple LLM application - it's just a single LLM call plus some prompting. Learn more with Twilio. examples: A list of dictionary examples to include in the final prompt. 2/docs/integrations/toolkits/json/ so I set about utilising this tool for the job. llms. param format_instructions: str = 'The way you use the tools is by specifying a json blob. Input should be a json string with two keys: “url” and “data”. It leverages a team of AI agents to guide you through the initial steps of defining, assessing, and solving machine learning problems. Jul 11, 2023 · In this tutorial, you will learn how to query LangChain Agents in Python with an OpenAPI Agent, CSV Agent, and Pandas Dataframe Agent. """ from __future__ import annotations import asyncio import json import logging import time from abc import abstractmethod from pathlib import Path from typing import (Any, AsyncIterator, Callable, Dict, Iterator, List, Optional, Sequence, Tuple, Union, cast,) import yaml Figma. from langchain_core . output_parsers. param requests_wrapper: TextRequestsWrapper [Required] # The requests wrapper. This was an experimental wrapper that bolted-on tool calling support to models that do not natively support it. JsonOutputParser# class langchain_core. agent_toolkits Figma. json_chat. Whereas in the latter it is common to generate text that can be searched against a vector database, the approach for structured data is often for the LLM to write and execute queries in a DSL, such as SQL. AgentAction This is a dataclass that represents the action an agent should take. What is LangChain agent? LangChain Python API Reference; agent_toolkits; create_json_agent; create_json_agent# langchain_community. We will request the agent to return some information about a research paper. This object takes in the few-shot examples and the formatter for the few-shot examples. data = yaml. Feb 20, 2025 · from bs4 import BeautifulSoup from langchain. \n\nGPT-3 can translate language, write essays, generate computer code, and more — all with limited to no supervision. \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. Agents let us do just this. Bases: AgentOutputParser Parses ReAct-style LLM calls 'Large language models (LLMs) represent a major advancement in AI, with the promise of transforming domains through learned knowledge. Using an example set Create the example set Aug 13, 2024 · To get structured output from a ReAct Agent in LangChain without encountering JSON parsing errors, you can use the ReActOutputParser class. Still, this is a great way to get started with LangChain - a lot of features can be built with just some prompting and an LLM call! from langchain_community. plan( ^^^^^ File "C:\Users\vicen\PycharmProjects\ChatBot\venv\Lib\site-packages\langchain\agents\agent. How to: pass in callbacks at runtime; How to: attach callbacks to a module; How to: pass callbacks into a module constructor This example shows how to load and use an agent with a JSON toolkit. LLM sizes have been increasing 10X every year for the last few years, and as these models grow in complexity and size, so do their capabilities. This interface provides two general approaches to stream content: sync stream and async astream : a default implementation of streaming that streams the final output from the chain. When tools are called in a streaming context, message chunks will be populated with tool call chunk objects in a list via the . langchain. prompts import ChatPromptTemplate, MessagesPlaceholder system = '''Respond to the human as helpfully and accurately as possible. The JSON agent. Example selectors are used in few-shot prompting to select examples for a prompt. agents import (create_json_agent, AgentExecutor) from langchain. from langchain_community . Kor is optimized to work for a parsing approach. \nYou have from langchain. Dec 9, 2024 · Only use the information returned by the below tools to construct your final answer. Streaming . \nYour goal is to return a final answer by interacting with the JSON. load. requests import TextRequestsWrapper from langchain. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. We will first create it WITHOUT memory, but we will then show how to add memory in. python import PythonREPL from langchain. Vectorstore agent - an agent capable of interacting with vector stores. LangChain comes with a number of built-in agents that are optimized for different use cases. 11 and langchain v. How to parse JSON output. This output parser allows users to specify an arbitrary JSON schema and query LLMs for outputs that conform to that schema. import os import yaml from langchain. To build reference examples for data extraction, we build a chat history containing a sequence of: HumanMessage containing example inputs; AIMessage containing example tool calls; ToolMessage containing example tool outputs. We'll use the tool calling agent, which is generally the most reliable kind and the recommended one for most use cases. 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. LangChain Neo4j Reviews Vector Chain: This is very similar to the chain you built in Step 1, except now patient review embeddings are stored in Neo4j. searx_search . When this FewShotPromptTemplate is formatted, it formats the passed examples using the example_prompt, then and adds them to the final prompt before suffix: 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. Retrieval Augmented Generation (RAG) Part 2 : Build a RAG application that incorporates a memory of its user interactions and multi-step retrieval. Parses tool invocations and final answers in JSON format. Dec 9, 2024 · class langchain. 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. OpenAI's function and tool calling; For example, see OpenAI's JSON mode. ChatOutputParser [source] ¶. Python agent - an agent capable of producing and executing Python code. Here's what happens if we pass it a result that does not comply with the schema: from typing import List Mar 3, 2025 · #output for the above code Page: Harry Potter and the Philosopher's Stone (film) Summary: Harry Potter and the Philosopher's Stone (also known as Harry Potter and the Sorcerer's Stone in the United States) is a 2001 fantasy film directed by Chris Columbus and produced by David Heyman, from a screenplay by Steve Kloves, based on the 1997 novel of the same name by J. Let's say we want the agent to respond not only with the answer, but also a list of the sources used. All examples should work with a newer library version as well. While this tutorial focuses how to use examples with a tool calling model, this technique is generally applicable, and will work also with JSON more or prompt based techniques. . , making them ready for generative AI workflows like RAG. tools import tool from langchain_openai import This is a multi-part tutorial: Part 1 (this guide) introduces RAG and walks through a minimal implementation. 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. tools. This guide will help you get started with AzureOpenAI chat models. API Reference: JsonToolkit | create_json_agent | JsonSpec | OpenAI. In this example, we asked the agent to recommend a good comedy. The chain’s response is fed back to the LangChain agent and sent to the user. 1, which is no longer actively maintained. openai import OpenAI from langchain. tools . To create an agent that accesses tools, import the load_tools, initialize_agent methods, and AgentType object from the langchain. \n\nThe only values Deprecated since version 0. agents #. The other toolkit comprises requests wrappers to send GET and POST requests Dec 9, 2024 · """Requests toolkit. """ # noqa: E501 from __future__ import annotations import json from typing import Any, List, Literal, Sequence, Union from langchain_core. agents import Agent # Create an agent with a specific task agent = Agent(task="Classify the sentiment of the following text: '{input}'", model=model) # Evaluate the agent's decision It is up to each specific implementation as to how those examples are selected. agents import create_json_agent from langchain. messages import (AIMessage, BaseMessage, FunctionMessage, HumanMessage,) 2nd example: "json explorer" agent Here's an agent that's not particularly practical, but neat! The agent has access to 2 toolkits. 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. ReActJsonSingleInputOutputParser [source] #. This will help you getting started with the GMail toolkit. "Tool calling" in this case refers to a specific type of model API Using these components, we can create langchain agents that extend an LLM’s capabilities. The example below shows how we can modify the source to only contain information of the file source relative to the langchain directory. A good example of this is an agent tasked with doing question-answering over some sources. Let's see what individual tools are inside the Jira toolkit. - LangGraph - For building complex agents with customizable architecture - LangGraph Platform - For deployment and scaling of agents The README also mentions installation instructions (`pip install -U langchain`) and links to various resources including tutorials, how-to guides, conceptual guides, and API references. While it is similar in functionality to the PydanticOutputParser, it also supports streaming back partial JSON objects. 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. Parameters: tools (Sequence) – List of tools this agent has access to. Retrieval Augmented Generation (RAG) Part 1 : Build an application that uses your own documents to inform its responses. serializable import Serializable from langchain_core. The examples in LangChain documentation (JSON agent, HuggingFace example) use tools with a single string input. json. This notebook covers how to load data from the Figma REST API into a format that can be ingested into LangChain, along with example usage for code generation. Memory is needed to enable conversation. This repository contains a series of agents intended to be used with the Agent Chat UI (repo). **Tool Use** enables agents to interact with external APIs and tools, enhancing their capabilities beyond the limitations of their training data. agent_types import AgentType from langchain. The goal of the OpenAI tools APIs is to more reliably return valid and Dec 9, 2024 · The JSON agent. LangChain adopts this convention for structuring tool calls into conversation across LLM model providers. In the OpenAI family, DaVinci can do reliably but Curie's ability already drops off dramatically. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. LangChain includes a utility function tool_example_to_messages that will generate a valid sequence for most model providers. Sequence [~langchain_core. utilities . Feb 28, 2024 · The examples in LangChain documentation (JSON agent, HuggingFace example) are using tools with a single string input. JSONAgentOutputParser [source] # Bases: AgentOutputParser. tavily_search import TavilySearchResults from langchain_openai import ChatOpenAI Feb 28, 2024 · Here is an example from the movie agent using this structure. Here is an example of how you can use it: class langchain. gfsud msnhkf zgzfc cwb sqvt yuultxb okh gnnmebs talks hsdqtkqe