Apr 29, 2024 · In this example, the create_openai_tools_agent function constructs an agent that can utilize the OpenAI model to intelligently decide when to invoke one or more tools based on the input. ” The Agent Langchain Hub, powered by hwchase17/openai-tools-agent, is a comprehensive platform designed to enhance the capabilities of Large Language Models (LLMs) through the integration of various tools and agents. %pip install --upgrade --quiet slack_sdk > /dev/null. tools (Sequence) – Tools this agent has access to. """ client = client or _get_openai_client() if tool_resources is None: from openai. Create the agent Now that we have defined the tools, we can create the agent. I changed it a bit as I am using Azure OpenAI account referring this. Mar 28, 2024 · ChatGPT Bugs. "return first_int + second_int@tooldefexponentiate(base:int, exponent:int)->int:"Exponentiate the base to the exponent power. openai_tools. agents import create_pandas_dataframe_agent import pandas as pd df = pd. Tools. prompts import PromptTemplate search_tool = DuckDuckGoSearchRun () tools = [search_tool] react_openai_tools = """ Answer the following questions as best you can. Agents let us do just this. 5支持langchain的这两个agent吗？如果支持，该如何使用呢 Apr 16, 2024 · Create a tool to do retrieval of documents. polygon. May 22, 2024 · Create an OpenAI Assistant and instantiate the Runnable. from langchain. agents import AgentExecutor, create_openai_functions_agent from langchain_community. invoke method. Params required to create the agent. base import OpenAIMultiFunctionsAgent from In this guide, we will go over the basic ways to create Chains and Agents that call Tools. from langchain_openai import ChatOpenAI. To use this package, you should first have the LangChain CLI installed: pip install -U langchain-cli. agents import AgentAction from langchain_core. If agent_type is “tool-calling” then llm is expected to support tool calling. Multiply these together. We will start with installing the dependencies: Run below This is a more generalized version of the OpenAI tools agent, which was designed for OpenAI's specific style of tool calling. input_keys. csv For setting up the Gemini environment for LangChain, you can follow the steps provided in the context above. The function to call. format_scratchpad. agents import create_openai_functions_agent. The final thing we will create is an agent - where the LLM decides what steps to take. create_prompt (…) The process of bringing the appropriate information and inserting it into the model prompt is known as Retrieval Augmented Generation (RAG). as_retriever (search_kwargs = {"k": 5}) description = """Use to look up values to 3 days ago · A Runnable sequence representing an agent. LangChain offers a number of tools and functions that allow you to create SQL Agents which can provide a more flexible way of interacting with SQL databases. It's recommended to use the tools agent for OpenAI models. If you are interested for RAG over Apr 11, 2024 · One of the most powerful and obvious uses for LLM tool-calling abilities is to build agents. tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally. OpenAI API has deprecated functions in favor of tools. This is similar to how we pass tools for the agent to use. js. utilities. description ( str) – The description for the tool. This installed some older langchain version and I could not even import the module langchain. 5-turbo" , temperature = 0 ) Agent We'll use an OpenAI chat model and an "openai-tools" agent, which will use OpenAI's function-calling API to drive the agent's tool selection and invocations. I’m creating a langchain agent with an openai model as the LLM. An OpenAI API Key; Getting This is a more generalized version of the OpenAI tools agent, which was designed for OpenAI’s specific style of tool calling. We recommend familiarizing yourself with function calling before reading this guide. instructions = """You are an agent designed to write and execute python code to answer Usage. Attention We're setting streaming=True on the LLM. text_splitter import TokenTextSplitter from langchain. import json from typing import List, Sequence, Tuple from langchain_core. It uses LangChain’s ToolCall interface to support a wider range of provider implementations, such as Anthropic, Google Gemini, and Mistral in addition to OpenAI. retriever ( BaseRetriever) – The retriever to use for the retrieval. Examples: from langchain import hub from langchain_community. Create a new model by parsing and validating input data from keyword arguments. config=config ,) This structure is crucial for the AgentExecutor to correctly identify and use the session_id for managing chat history. pull ( "hwchase17/openai-tools-agent") from langchain import hub from langchain. May 2, 2023 · Knowledge Base: Create a knowledge base of "Stuff You Should Know" podcast episodes, to be accessed through a tool. @propertydefinput_keys ( self) ->List [ str ]: """Return the input keys. :meta private: """returnself. 3 days ago · A Runnable sequence representing an agent. vectorstores import FAISS from langchain_openai import OpenAIEmbeddings vector_db = FAISS. Use LCEL, which simplifies the customization of chains and agents, to build applications; Apply function calling to tasks like tagging and data extraction; Understand tool selection and routing using LangChain tools and LLM function calling – and much more. agents import create_openai_tools_agent agent=create_openai_tools_agent(llm,tools,prompt) Agent Executer. Documentation for LangChain. The factory method for creating an OpenAI tools agent is create_openai_tools_agent(). Jul 4, 2023 · 3. sql import SQLDatabaseChain from langchain. Tools are interfaces that an agent, chain, or LLM can use to interact with the world. tools import WikipediaQueryRun from langchain_community. 2 days ago · Bases: MultiActionAgentOutputParser. LangChain then continue until ‘function_call’ is not returned from the LLM, meaning it’s safe to return to the user! Below is a working code example, notice AgentType. For an easy way to construct this prompt, use OpenAIMultiFunctionsAgent. agents import create_openai_tools_agent from langchain . May 17, 2023 · There are a ton of articles to help you build your first agent with Langchain. llms import AzureOpenAI. In Chains, a sequence of actions is hardcoded. agents import AgentExecutor agent_executor=AgentExecutor(agent=agent,tools=tools,verbose=True) agent_executor Aug 15, 2023 · It allows you to chain together LLM tasks (hence the name) and even allows you to run autonomous agents quickly and easily. LangChain comes with a number of built-in agents that are optimized for different use cases. Can be passed in OpenAI format or as BaseTools. This agent uses a search tool to look up answers to the simpler questions in order to answer the original complex question. For an easy way to construct this prompt, use OpenAIFunctionsAgent. agents Repeated tool use with agents Chains are great when we know the specific sequence of tool usage needed for any user input. Prerequisites Python 3. prompt – The prompt for this agent, should support agent_scratchpad as one of the variables. When any other function is called by OpenAI, we treat that as a tool invocation. Create an agent that uses OpenAI function calling. I just fixed it with a langchain upgrade to the latest version using pip install langchain --upgrade. In order to actually call it, we'll want to create our agent. _types import from langchain_openai import OpenAI llm = OpenAI (temperature = 0) agent = create_react_agent (llm, tools, prompt) agent_executor = AgentExecutor (agent = agent, tools = tools) agent_with_chat_history = RunnableWithMessageHistory (agent_executor, # This is needed because in most real world scenarios, a session id is needed May 8, 2024 · Setting ChatOpenAI and Agent from langchain_openai import ChatOpenAI llm = ChatOpenAI(temperature=0) from langchain. I’m following the ReAct framework for agents using tools. LangChain already has a create_openai_tools_agent() constructor that makes it easy to build an agent with tool-calling models that adhere to the OpenAI tool-calling API, but this won’t work for models like Anthropic and Gemini. And now we can add to it an exponentiate and add tool: @tooldefadd(first_int:int, second_int:int)->int:"Add two integers. instructions (str) – Assistant instructions. 4 days ago · class langchain. tools import DuckDuckGoSearchRun from langchain_openai import ChatOpenAI from langchain. I’m defining a tool for the agent to use to answer a question. Tools in the semantic layer. Agents. pull It can be useful to run the agent as an iterator, to add human-in-the-loop checks as needed. Is there any way that I can use agents and tools to accomplish this? I have created custom tools which run based on specific usecases being triggered. llms import OpenAI from langchain. Describes what the tool does. An zero-shot react agent optimized for chat models. load_tools since it did not exist. 1 day ago · llm (BaseLanguageModel) – LLM to use as the agent. Since the tools in the semantic layer use slightly more complex inputs, I had to dig a little deeper. utilities import WikipediaAPIWrapper from langchain_openai import ChatOpenAI api_wrapper = WikipediaAPIWrapper (top_k_results = 1, doc_content_chars_max = 100) Certain OpenAI models have been finetuned to work with tool calling. 2 days ago · Agent is a class that uses an LLM to choose a sequence of actions to take. As we can see, the agent will first choose which tables are relevant and then add the schema for those tables and a few sample rows to the prompt. name ( str) – The name for the tool. To demonstrate the AgentExecutorIterator functionality, we will set up a problem where an Agent must: Retrieve three prime numbers from a Tool. prompt (ChatPromptTemplate) – The prompt to use. NOTE: for this example we will only show how to create an agent using OpenAI models, as local models runnable on consumer hardware are not reliable enough yet. For this example, let’s try out the OpenAI tools agent, which makes use of the new OpenAI tool-calling API (this is only available in the latest OpenAI models, and differs from function-calling in that Jun 9, 2024 · LangChain Agents are a revolutionary concept that combines the capabilities of large language models (like those developed by OpenAI) with specific APIs to perform sophisticated tasks. langchain==0. Returns: OpenAIAssistantRunnable configured to run using the created assistant. 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. Since create_openai_tools_agent returns a RunnableSequence and not a BaseSingleActionAgent, the property input_keys of the AgentExecutor doesn't work for this agent anymore. Use LangGraph to build stateful agents with May 22, 2023 · import os import platform import openai import gradio as gr import chromadb import langchain from langchain. agents import Tool, AgentType from langchain. chat_message_histories import ChatMessageHistory. prompt: The prompt to use. Reload to refresh your session. vectorstores import Chroma from langchain. The main difference between using one Tool and many is that we can't You signed in with another tab or window. llm ( BaseLanguageModel) – LLM to use as the agent. messages import ( AIMessage, BaseMessage, ToolMessage, ) from langchain. tool_resources (Optional[Union[AssistantToolResources, dict, NotGiven]]) – Assistant Mar 19, 2024 · Description. agent. And it requires passing in the llm, tools and prompt we setup above. Bases: MultiActionAgentOutputParser. While the goal of more reliably returning valid and useful function calls is the same as the functions agent, the ability to return multiple tools at once results in both fewer These output parsers extract tool calls from OpenAI's function calling API responses. You switched accounts on another tab or window. {'input': 'what is LangChain?', 'output': 'LangChain is an open source framework for building applications based on large language models (LLMs). Setup The integration lives in the langchain-community package. In these cases, we want to let the model itself decide how many times to use tools and in what order. Setup Most models that support tool calling can be Knowledge Base: Create a knowledge base of "Stuff You Should Know" podcast episodes, to be accessed through a tool. Includes an LLM, tools, and prompt. LangChain has a number of components designed to help build Q&A applications, and RAG applications more generally. 5-14b-chat进行测试，发现用langchain的这两个agent，均不能调用工具，所以请问一下qwen1. OpenAIToolsAgentOutputParser [source] ¶. 0. The key to using models with tools is correctly prompting a model and parsing its response so that it chooses the right tools and provides the 3 days ago · Returns: An AgentExecutor with the specified agent_type agent and access to a PythonAstREPLTool with the DataFrame(s) and any user-provided extra_tools. from langchain import hub from langchain. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source building blocks, components, and third-party integrations . import os. agents import AgentExecutor, create_react_agent prompt = hub. We will be using LangGraph to construct the agent. Apr 9, 2024 · classlangchain. This isn't calling that tool yet - it's just telling us to. There are many possible use-cases for this – here are just a few off the top of my head: Personal AI Email Assistant Tavily's Search API is a search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed. Jan 18, 2024 · Here, we define the parts used in the agent and create the agent and the agent executor. . Those have shown good performance with OpenAI API, which is a powerful model. 3. Specifically, I will examine the utilization of the open-source library Langchain, combined with OpenAI and AWS, to create an AI agent embodying “AI Bad Bunny. Jun 29, 2023 · LangChain has introduced a new type of message, “FunctionMessage” to pass the result of calling the tool, back to the LLM. LangChain Agents #2: OpenAI Functions Agent Apr 24, 2024 · This isn't calling that tool yet - it's just telling us to. To create a new LangChain project and install this as the only package, you can do: langchain app new my-app --package openai-functions-agent. Creating a chat They accept a config with a key ( "session_id" by default) that specifies what conversation history to fetch and prepend to the input, and append the output to the same conversation history. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. json . Here is an example input for a How this works is that we will pass the Response schema to the OpenAI LLM via their functions parameter. llms import OpenAI from langchain. chat_models import ChatOpenAI from langchain. OPENAI_FUNCTIONS . toolkit import PolygonToolkit from langchain_community. JSON schema of what the inputs to the tool are. tools: Tools this agent has access to. Then, I tried many of them and I realize that it does not actually work well with local LLMs like Vicuna or Alpaca. So let’s initialise our agent. They combine a few things: The name of the tool. agent_toolkits import SQLDatabaseToolkit. agents . This log can be used in a few ways. llms import HuggingFaceEndpoint. 1 and langchain 0. Parses a message into agent actions/finish. openai_functions_multi_agent. Feb 24, 2023 · 4. Name: langchain Version: 0. llm ( BaseLanguageModel) – Language model to use for the agent. model = ChatOpenAI(temperature=0, streaming=True) Oct 4, 2023 · from langchain. name (str) – Assistant name. tavily_search import TavilySearchResults. The examples in LangChain documentation (JSON agent, HuggingFace example) use tools with a single string input. Read about all the available agent types here. agents import create_pandas_dataframe_agent. openai import OpenAIEmbeddings from langchain. Deprecated since version 0. client: OpenAI or AzureOpenAI client. # Only certain models support this. A zero shot agent that does a reasoning step before acting. agents import AgentExecutor, create_tool_calling_agent, tool from langchain_anthropic import ChatAnthropic from langchain_core. agent_toolkits import OpenAPIToolkit, create_openapi_agent from langchain_community . It uses LangChain's ToolCall interface to support a wider range of provider implementations, such as Anthropic, Google Gemini, and Mistral in addition to OpenAI. Currently, when invoked the agent returns: The input; The output; The chat history; How can I retrieve the documents used to create the output? Thanks. tools (Sequence[Union[BaseTool, dict]]) – Assistant tools. tools. Below is an example: from langchain_community. Check out AgentGPT, a great example of this. Bases: AgentActionMessageLog. This notebook walks through connecting LangChain to your Slack account. Apr 11, 2024 · Create the agent Now that we have defined the tools, we can create the agent. paramlog:str[Required] ¶. 150. polygon import PolygonAPIWrapper from langchain_openai import ChatOpenAI llm = ChatOpenAI (temperature = 0) instructions from langchain. langgraph is an extension of langchain aimed at building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. from_texts (artists + albums, OpenAIEmbeddings ()) retriever = vector_db. initialize_agent(): This method is typically used to set up or initialize an agent with necessary configurations, models, or Apr 12, 2024 · It is relatively easy to create and use the langchain frameworks to create a robust agent leveraging state of an art LLM models and tools. The Tavily Search tool is used here to demonstrate web search capabilities. LangGraph exposes high level interfaces for creating common types of agents, as well as a low-level API for composing custom flows. Once you've received a SLACK_USER_TOKEN, you can input it as an environmental variable below. Override init to support instantiation by position for backward compat. read_csv("titanic. create_prompt (…) Notes. We will be using a tool calling agent - for more information on this type of agent, as well as other options, see this guide. In this simple problem we can demonstrate adding some logic to verify intermediate LangChain provides integrations for over 25 different embedding methods, as well as for over 50 different vector storesLangChain is a tool for building applications using large language models (LLMs) like chatbots and virtual agents. chains import LLMMathChain from langchain. tools = [TavilySearchResults(max_results=1)] # Choose the LLM that will drive the agent. . Class hierarchy: Jun 1, 2023 · How LangChain Works With OpenAI's LLMs. Must provide exactly one of ‘toolkit First, let's initialize Tavily and an OpenAI chat model capable of tool calling: from langchain_community. tool import JsonSpec from langchain_openai import OpenAI Jan 24, 2024 · Running agents with LangChain. This means they are only usable with models that support function calling, and specifically the latest tools and toolchoice parameters. It simplifies the process of programming and integration with external data sources and software workflows. Tool(. Sep 12, 2023 · Initializing the LangChain Agent. This is needed for older versions of LangChain. Tools allow us to extend the capabilities of a model beyond just outputting text/messages. Setup Any models that support tool calling can be used in Mar 3, 2024 · I want to trigger this chain for a particular usecase. I’m using openai version 1. If a tool_calls parameter is passed, then that is used to get the tool names and tool inputs. code-block:: python from langchain_openai import ChatOpenAI from langchain_experimental. embeddings. 3 days ago · Construct a SQL agent from an LLM and toolkit or database. LLM Agent with Tools: Extend the agent with access to multiple tools and test that it uses them to answer questions. OpenAIToolAgentAction[source] ¶. Below is the snippet of my code -. In this blog post, we'll explore how to create agents and define custom tools that those agents can use. agent import AgentExecutor llm = ChatOpenAI ( model = "gpt-3. agent_toolkits. API Reference: create_openai_functions_agent | ChatOpenAI. LangChain is a framework for developing applications powered by large language models (LLMs). 2. Agents select and use Tools and Toolkits for actions. If one is not passed, then the AIMessage is assumed to be the final output. prompts import Differences between load_tools, initialize_agent, and create_openai_tools_agent Methods: These methods are used for different purposes: load_tools(): This function is used to load the default tools provided by LangChain. output_parsers. If you want to add this to an existing project, you can just run: langchain app add openai Sep 24, 2023 · yes i want the same so in the place of this code i want to use gpt4all not openai. Jul 13, 2024 · prompt – The prompt for this agent, should support agent_scratchpad as one of the variables. agent_toolkits import create_retriever_tool from langchain_community. 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. How to create custom tools. Also, ensure that there are no typos or 4 days ago · llm – This should be an instance of ChatOpenAI, specifically a model that supports using functions. We will be using an OpenAI Functions agent - for more information on this type of agent, as well as other options, see this guide. Apr 4, 2023 · This time, I will venture into the realm of AI agents, which can intelligently employ a variety of tools based on user input. To use this toolkit, you will need to get a token explained in the Slack API docs. I had a similar issue installing langchain with all integrations via pip install langchain[all]. agents. The code is below. 10 and up have some issues with some of LangChain’s modules. utilities import SerpAPIWrapper, SQLDatabase from langchain_experimental. Create the model. I’m running the python 3 code below. This will be passed to the language model, so should be unique and somewhat descriptive. 4 days ago · Args: llm: LLM to use as the agent. When the Response function is called by OpenAI, we want to use that as a signal to return to the user. toolkit ( Optional[SQLDatabaseToolkit]) – SQLDatabaseToolkit for the agent to use. But for certain use cases, how many times we use tools depends on the input. prompt = hub. We pass OpenAI API key here as a tool. Besides the actual function that is called, the Tool consists of several components: Must be unique within a set of tools provided to an LLM or agent. Whether the result of a tool should be returned directly to the user. langchain : Chains, agents, and retrieval strategies that make up an application's cognitive architecture. First, we choose the LLM we want to be guiding the agent. Start applying these new capabilities to build and improve your applications today. openai_tools import OpenAIToolAgentAction def _create_tool_message 2 days ago · An agent that breaks down a complex question into a series of simpler questions. 8 Summary: Building applications with LLMs through composability Tool calling . tools . Example: . We have just integrated a ChatHuggingFace wrapper that lets you create agents based on open-source models in 🦜🔗LangChain. This will be passed to the language This is probably the most reliable type of agent, but is only compatible with function calling. Parameters. May 22, 2024 · Can be passed in OpenAI format model: Assistant model to use. tools – The tools this agent has access to. See Prompt section below for more. First, we create the LLM object from ChatOpenAI class for OpeAI API. document_loaders import PyPDFLoader from Jan 16, 2024 · One of the approaches to building an RAG model with Langchian in Python needs to use the following steps: Importing the necessary modules from LangChain and the standard library. LangChain also allows you to create apps that can take actions – such as surf the web, send emails, and complete other API-related tasks. 6 langchain-community Feb 20, 2024 · Here, we will discuss how to implement a JSON-based LLM agent. 0: Use create_openai_tools_agent instead. This is very similar but different from function calling, and thus requires a separate agent type. I hope this helps! Source code for langchain. agents import AgentExecutor, create_openai_tools_agent I'd like to extract the documents retrieved by create_retriever_tool when this is used to create an OpenAI agent with create_openai_tools_agent. If you're still facing the error, double-check that the config dictionary is consistently structured and passed in every invocation of the agent_executor. Introduction. 9 3. Note: Here we focus on Q&A for unstructured data. It returns as output either an AgentAction or AgentFinish. This platform stands out for its ability to streamline complex workflows and provide developers with the tools necessary to create LangChain provides integrations for over 25 different embedding methods, as well as for over 50 different vector storesLangChain is a tool for building applications using large language models (LLMs) like chatbots and virtual agents. tools ( Sequence[BaseTool]) – Tools this agent has access to. I am trying to use Langchain for structured data using these steps from the official document. Additional information to log about the action. agents import AgentExecutor, create_openai_tools_agent from langchain. The autoreload extension is already loaded. 1. System Info. Apr 10, 2024 · In order to setup an agent in LangChain, we need to use one of the factory methods provided for creating the agent of our choice. We also need to install the tavily-python package itself. Is meant to be used with OpenAI models, as it relies on the specific tool_calls parameter from OpenAI to convey what tools to use. The main advantages of using SQL Agents are: It can answer questions based on the databases schema as well as on the databases content (like describing a specific table). Tools can be just about anything — APIs, functions, databases, etc. Currently we are using a high level interface to construct the agent, but the nice Feb 7, 2024 · 用qwen1. # set the LANGCHAIN_API_KEY environment variable (create key in settings) from langchain import hub. When constructing an agent, you will need to provide it with a list of Tool s that it can use. A description of what the tool is. It allows developers to leverage the power of LLMs to create applications that can generate responses to user queries, such as answering questions or creating images from text prompts. You signed out in another tab or window. So, I decide to modify and optimize the Langchain agent with local LLMs. langgraph. %load_ext autoreload %autoreload 2. These steps involve setting up the OpenAI API key, configuring Astra DB, optionally configuring a Cassandra cluster, saving and applying the configuration, and verifying the environment variables. "return base**exponent. Will create default OpenAI client (Assistant v2) if not specified. agents import AgentExecutor, create_sql_agent. Should work with OpenAI function calling, so either be an OpenAI model that supports that or a wrapper of a different model that adds in equivalent support. agents. Note: Please use your OpenAI key for this, which should be kept private. from langchain_community. Here's the code to initialize the LangChain Agent and connect it to your SQL database. This will allow us to stream tokens from the agent using the astream_events API. ›. Create the agent Now that we have defined the tools and the LLM, we can create the agent. One of the first things to do when building an agent is to decide what tools it should have access to. In order to actually calll it, we'll want to create our agent. The code to create the ChatModel and give it tools is really simple, you can check it all in the Langchain doc. NOTE: for this example we will only show how to create an agent using OpenAI models, as local models are not reliable enough yet. If a tool_calls parameter is passed, then 2. It takes as input all the same input variables as the prompt passed in does. Examples include langchain_openai and langchain_anthropic. LangGraph : A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. ke sd vm oe gc wv cv xc to oi