Langchain agentexecutor example

Langchain agentexecutor example. tip. Agents select and use Tools and Toolkits for actions. LLMs are very general in nature, which means that while they can LangChain is a framework for developing applications powered by language models. Nov 27, 2023 · langchain python agent react differently, for one prompt, it can import scanpy library, but not for the other one. You signed out in another tab or window. invoke() instead. エージェントの機能 「エージェント」はLLMを使用して、実行するアクションとその順序を決定します。アクションは、「ツールを実行してその出力を観察」「ユーザーに戻る」のいずれかになり Cap the max number of iterations. Now, let's go over a more general cyclic example. These agents promise a number of improvements over traditional Reasoning and Action (ReAct)-style agents. retriever import create_retriever_tool from langchain_community. chat_message_histories import ChatMessageHistory from langchain_core. read_csv ( "titanic. 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. Country, SUM(i. The jsonpatch ops can be applied in order to construct state. -t langchain-streamlit-agent:latest. Only use the output of your code to answer the question. There are multiple ways to define a tool. llm ( BaseLanguageModel) – Language model to use for the agent. Run the docker container directly; docker run -d --name langchain-streamlit-agent -p 8051:8051 langchain-streamlit-agent:latest . Apr 21, 2023 · Even though PalChain requires an LLM (and a corresponding prompt) to parse the user’s question written in natural language, there are some chains in LangChain that don’t need one. agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser from langchain. inputs ( Union[Dict[str, Any], Any]) – Dictionary of raw inputs, or single input if chain expects only one param. Apr 19, 2023 · I have made a conversational agent and am trying to stream its responses to the Gradio chatbot interface. 2) AIMessage: contains the extracted information from the model. May 17, 2023 · LangChain License: MIT License. , few-shot examples) or validation for expected parameters. 5 days ago · The prompt in the LLMChain MUST include a variable called “agent_scratchpad” where the agent can put its intermediary work. Here are some parts of my code: # Loading the LLM. , the client side looks like this: from langchain. runnables. base import StructuredChatAgent from langchain. structured_chat. It takes an agent and a set of tools as input, along with other optional parameters such as a callback manager, verbosity level, whether to return intermediate steps This comes in the form of an extra key in the return value, which is a list of (action, observation) tuples. 1. For a overview of the different types and when to use them, please check out this section. run but I would like to get the various observations and graph the results. graph = Neo4jGraph() # Import movie information. Utilize the ChatHuggingFace class to enable any of these LLMs to interface with LangChain's Chat Messages abstraction. LangChain provides: A standard interface for agents. Examples: . LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. If the Agent returns an AgentFinish, then return that directly to the user. agents import AgentExecutor from langchain import hub # retrieval tool (retrieve from the vector database), where retriever is from 2 days ago · Example from langchain_openai import ChatOpenAI from langchain_experimental. Building an agent from a runnable usually involves a few things: Data processing for the intermediate steps ( agent_scratchpad ). input_keys except for inputs that will be set by the chain’s memory. pull("hwchase17/react") model You signed in with another tab or window. Parameters. update_iterations () Increment the number of iterations and update the time elapsed. Feb 13, 2024 · We’re releasing three agent architectures in LangGraph showcasing the “plan-and-execute” style agent design. The complete list is here. See full list on medium. 3 days ago · Here’s an example: from langchain_core. This makes debugging these systems particularly tricky, and observability particularly important. In Chains, a sequence of actions is hardcoded. 4 days ago · from typing import List from langchain. Here is the SQL query to achieve this: ```sql. It simplifies the process of programming and integration with external data sources and software workflows. Should contain all inputs specified in Chain. It’s not as complex as a chat model, and it’s used best with simple input–output The University of Notre Dame is a Catholic research university located in South Bend, Indiana, United States. agent import AgentExecutor from langchain. A few-shot prompt template can be constructed from either a set of examples, or from an Example Selector object. Dec 11, 2023 · In this example, event_stream is an asynchronous generator function that yields the output of agent_executor. Forced Function Calling One simple modification of the above Graph is to modify it such that a certain tool is always called first. It is the LLM that is used to reason about the best way to carry out the ask requested by a user. tools import BaseTool from langchain. For this example, we’ll create a couple of custom tools as well as LangChain’s provided DuckDuckGo search tool to create a research agent. base import ChainExecutor HUMAN_MESSAGE_TEMPLATE Setup: LangSmith. Raw. create_sql_agent. . My question is how to make sure to import the correct library without problem. 5-turbo", temperature=0) tools = [retriever_tool] agent = create Jupyter Notebooks to help you get hands-on with Pinecone vector databases - pinecone-io/examples 3 days ago · Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run. SELECT c. prompts import StringPromptTemplate from langchain. llms import OpenAI from langchain. history import RunnableWithMessageHistory from langchain. May 2, 2024 · Initialize the AgentExecutorIterator with the given AgentExecutor, inputs, and optional callbacks. , GET, POST, PATCH, PUT, DELETE), Control access to who can submit that can be fed into a chat model. Then, we can group the results by the `Country` column to get the total sales per country. base. This can be useful to ensure that they do not go haywire and take too many steps. 7, openai_api Apr 21, 2023 · Async support for other agent tools are on the roadmap. For this reason, in the below example with an XML agent, we use Oct 31, 2023 · LangChain provides a way to use language models in JavaScript to produce a text output based on a text input. The reason to select chat model is the gpt-35-turbo model is optimized for chat, hence we use AzureChatOpenAI class here to initialize the instance. A dictionary of all inputs, including those added by the chain’s memory. from langchain. 4 days ago · langchain. Use Case In this tutorial, we'll configure few-shot examples for self-ask with search. 0rc2¶ langchain. The code in this tutorial draws heavily from the LangChain documentation, links to which are provided below. You have access to a python REPL, which you can use to execute python code. “Tool calling” in this case refers to a specific type of model API that allows for Agents. LLM: This is the language model that powers the agent. run() in order to visualize the thoughts and actions live in your app. I have had a look at the Langchain docs and could not find an example that implements streaming with Agents. Yarn. 2. instead. In particular, we will: Utilize the HuggingFaceTextGenInference, HuggingFaceEndpoint, or HuggingFaceHub integrations to instantiate an LLM. We will need to install some LangChain community packages, as well as Tavily to use as an example tool. Read about all the agent types here. This function is used to build an agent using Gemini function calling. yarn add @langchain/openai. These need to represented in a way that the language model can recognize them. chat_models import AzureChatOpenAI. from langchain_openai import OpenAI. Dec 29, 2022 · 「LangChain」の「エージェント」が提供する機能を紹介する HOW-TO EXAMPLES をまとめました。 前回 1. This walkthrough demonstrates how to use an agent optimized for conversation. Tools Custom agent. Below are a couple of examples to illustrate this -. pnpm add @langchain/openai. agents import AgentExecutor from langchain. Dec 1, 2023 · There are two ways you can authenticate to Azure OpenAI: - API Key - Azure Active Directory (AAD) Using the API key is the easiest way to get started. g. tools. However, if you have complex security requirements - you may want to use Azure Active Directory. Create a new model by parsing and validating input data from keyword arguments. agents import create_openai_functions_agent llm = ChatOpenAI(model="gpt-3. agents. The main advantages of using the SQL Agent are: It can answer questions based on the databases’ schema as well as on the databases’ content (like describing a specific table). This solves a potential problem where This example shows how to load and use an agent with a JSON toolkit. 5-turbo" , temperature = 0 ) agent_executor = create_pandas_dataframe_agent ( llm , df , agent_type = "tool-calling" , verbose = True ) The below example will create a connection with a Neo4j database and will populate it with example data about movies and their actors. We read every piece of feedback, and take your input very seriously. It optimizes setup and configuration details, including GPU usage. This notebook goes through how to create your own custom agent. 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). 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). This notebook covers how to have an agent return a structured output. llm=ChatOpenAI(temperature=0, model="gpt-4-1106-preview"), tool=PythonREPLTool(), verbose=True, from langchain. prompts import PromptTemplate llm = OpenAI(model_name='text-davinci-003', temperature = 0. BaseMultiActionAgent¶ class langchain. """. sql. This example shows how to load and use an agent with a OpenAPI toolkit. 1. 2 days ago · langchain 0. from langchain_community. Change the content in PREFIX, SUFFIX, and FORMAT_INSTRUCTION according to your need after tying and testing few times. We will recreate the AgentExecutor class from LangChain. Memory is needed to enable conversation. LLMs are large deep-learning models pre-trained on large amounts of data that can generate responses to user queries—for example, answering questions or creating images from text-based prompts. docker May 27, 2023 · The LLMAgent is used in an AgentExecutor. 0: Use Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. Useful Resources. output_pars The execution is usually done by a separate agent (equipped with tools). Examples of end-to-end agents. py. npm install @langchain/openai. The AgentExecutor class is responsible for executing an agent. Jul 11, 2023 · LangChain (v0. Run the docker container using docker-compose (Recommended) Edit the Command in docker-compose with target streamlit app. agents import create_pandas_dataframe_agent import pandas as pd df = pd . In order to carry out its task, and operate on things and retrieve information, the agent has what are called Tool’s in LangChain, at its disposal. """This is an example of how to use async langchain with fastapi and return a streaming response. chat May 29, 2023 · …r PlanAndExecute agents (langchain-ai#5401) # Adds the option to pass the original prompt into the AgentExecutor for PlanAndExecute agents This PR allows the user to optionally specify that they wish for the original prompt/objective to be passed into the Executor agent used by the PlanAndExecute agent. Agents. args_schema (Pydantic BaseModel), is optional but recommended, can be used to provide more information (e. It takes as input all the same input variables as the prompt passed in does. import os from langchain. '}] [0m [32;1m [1;3m <final_answer>LangChain is an open source framework for building applications based on large language models (LLMs). In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. It has four colleges (Arts and Letters, Science, Engineering, Business) and an Architecture School. Agent Types There are many different types of agents to use. Here is an example of how you might use it: fromlangchain. agents import load_tools from langchain. If you get an error, debug your code and try again. Dec 13, 2023 · This class is designed to handle few-shot learning scenarios by formatting the examples using the example_prompt attribute, which is a PromptTemplate used to format an individual example. 3 days ago · langchain_community. You can create an agent in your Streamlit app and simply pass the StreamlitCallbackHandler to agent. from langchain import hub from langchain_community. call the model multiple times until they arrive at the final answer. Sep 28, 2023 · Initialize LangChain chat_model instance which provides an interface to invoke a LLM provider using chat API. input ( Any) – The input to the runnable. We will first create it WITHOUT memory, but we will then show how to add memory in. ⏰ First of all, they can execute multi-step workflow faster, since the larger agent doesn’t need to be consulted after Apr 19, 2024 · Langchain with fastapi stream example. from langchain import hub. movies_query = """. Nov 15, 2023 · Integrated Loaders: LangChain offers a wide variety of custom loaders to directly load data from your apps (such as Slack, Sigma, Notion, Confluence, Google Drive and many more) and databases and use them in LLM applications. You can find your API key in the Azure portal under your Azure OpenAI resource. 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. The list of messages per example corresponds to: 1) HumanMessage: contains the content from which content should be extracted. Saved searches Use saved searches to filter your results more quickly Deprecated. agents import AgentExecutor, create_react_agent prompt = hub. Wraps _call and handles memory. npm. LangChain provides a wide set of toolkits to get started. agents import initialize_agent from langchain. and capabilities of the underlying toolkit. Aug 18, 2023 · In this article we will walk through step-by-step a coded example of creating a simple conversational document retrieval agent using LangChain, the pre-eminent package for developing large language model based applications. ¶. Jan 28, 2024 · from langchain. Notes. 📄️ AWS Step Functions Toolkit AWS Step Functions are a visual workflow service that helps developers use AWS services to build distributed applications, automate processes, orchestrate microservices, and create data and machine learning (ML) pipelines. Once it has a plan, it uses an embedded traditional Action Agent to solve each step. schema import AgentAction, AgentFinish, OutputParserException from langchain. May 14, 2023 · agent_executor = AgentExecutor. A selection of agents to choose from. By definition, agents take a self-determined, input-dependent sequence of steps before returning a user-facing output. Second step: internal vector database text embedding. Will be removed in 0. ChatOpenAI. The goal of tools APIs is to more reliably return valid and useful tool calls than what can Contribute to langchain-ai/langgraph development by creating an account on GitHub. Use . def load_llm(): return AzureChatOpenAI(. If your use case is always based on the same flow and strategy, for example: First step: web search. Construct a SQL agent from an LLM and toolkit or database. May 2, 2023 · A Structured Tool object is defined by its: name: a label telling the agent which tool to pick. If the Agent returns an AgentAction, then use that to call a tool and get an Observation Nov 30, 2023 · Agents in LangChain are systems that use a language model to interact with other tools. The agent itself will use chat models and tool calling. This should be pretty tightly coupled to the instructions in the prompt. This solution is intended to act as a launchpad for 2 days ago · class AgentExecutor (Chain): """Agent that is using tools. from_agent_and_tools(agent=agent, tools=tools, verbose=True) agent_executor. They can be used for tasks such as grounded question/answering, interacting with APIs, or taking action. Mar 13, 2024 · Breaking down the technical documentation into actionable steps and at every stage providing real-world examples showing how developers can use LangChain to build sophisticated agents Apr 10, 2024 · The brains of a LangChain agent are an LLM. It is consistently ranked among the top twenty universities in the United States. If the Agent returns an AgentAction, then use that to call a tool and get an Observation. astream(input_data). When you’re building your own AI LangChain solution, you need to be aware if using an agent is the way you want to go. For example, a tool named "GetCurrentWeather" tells the agent that it's for finding the current weather. Total) AS TotalSales. executors. Jan 5, 2024 · I experimented with a use case in which I initialize an AgentExecutor with an agent chain that is a RemoteRunnable. chat_models import ChatOpenAI from langchain. main. run_in_executor to avoid blocking the main runloop. toolkit ( Optional[SQLDatabaseToolkit]) – SQLDatabaseToolkit for the Returning Structured Output. agents import AgentExecutor, create_openai_tools_agent prompt = hub Welcome to LangChain — 🦜🔗 LangChain 0. agentsimportcreate_gemini_functions_agentfromlanggraph. This agent uses a two step process: First, the agent uses an LLM to create a plan to answer the query with clear steps. csv" ) llm = ChatOpenAI ( model = "gpt-3. Please replace input_data with your actual input data for the astream method. Bases: BaseModel Base Multi Action Agent class. Deprecated since version 0. 0. You can see another example here. To stream the final output word by word when using the AgentExecutor in LangChain v0. g: arxiv (free) azure_cognitive_services Custom LLM Agent. agents import AgentExecutor, tool from langchain. tools import BaseTool from langchain_core. tavily_search import TavilySearchResults from langchain. agents import AgentExecutor, create_react_agent. e. LangChain comes with a number of built-in agents that are optimized for different use cases. Otherwise, the AgentExecutor will call the Tool ’s func via asyncio. Using an example set Create the example set To make agents more powerful we need to make them iterative, ie. BaseMultiActionAgent [source] ¶. 3) ToolMessage: contains confirmation to the model that the model requested a tool correctly. LangSmith is especially useful for such cases. LangChain is a powerful tool that can be used to work with Large Language Models (LLMs). For example, if the default implementation of OpenAPIToolkit uses the RequestsToolkit which contains tools to make arbitrary network requests against any URL (e. graphs import Neo4jGraph. language_models import BaseLanguageModel from langchain_experimental. This notebook walks through how to cap an agent at taking a certain number of steps. In this guide, we will walk through how to do for two functions: A made up search function that always returns the string “LangChain” Conversational. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well. instructions = """You are an agent designed to write and execute python code to answer questions. This notebook goes through how to create your own custom LLM agent. Load the LLM First, let's load the language model we're going to use to control the agent. This agent will represent all its state as a list of messages. These are mainly transformation chains that preprocess the prompt, such as removing extra spaces, before inputting it into the LLM. pnpm. However, there are a few points to consider: Make sure that the examples you provide are relevant to the task you want your model to perform. It returns as output either an AgentAction or AgentFinish. Create a new model by parsing and validating input 1 day ago · It takes as input all the same input variables as the prompt passed in does. This notebook shows how to get started using Hugging Face LLM's as chat models. May 23, 2023 · When creating a create_sql_agent() how do you get the prompt, thought, and observation?. For Tool s that have a coroutine implemented (the two mentioned above), the AgentExecutor will await them directly. agent_toolkits. Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. """ agent: Union [BaseSingleActionAgent, BaseMultiActionAgent] """The agent to run for creating a plan and determining actions to take at each step of the execution loop. prompts import ChatPromptTemplate , MessagesPlaceholder system = '''Respond to the human as helpfully and accurately as possible. This is generally the most reliable way to create agents. This AgentExecutor can largely be thought of as a loop that: Passes user input and any previous steps to the Agent. The agent can assist users with finding their account information, completing a loan application, or answering natural language questions while also citing sources for the provided answers. LangChain Written in: Python and JavaScript. The AgentExecutor class and the initialize_agent function in the LangChain framework serve different purposes. 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. get_event_loop(). The benefits of doing it this way are that In this example, we will use OpenAI Function Calling to create this agent. In the LangChain framework, the equivalent function to create_openai_functions_agent for Gemini is create_gemini_functions_agent. It can often be useful to have an agent return something with more structure. 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. code-block:: python from langchain import hub from langchain_community. May 25, 2023 · Here is how you can do it. In this example, we will use OpenAI Tool Calling to create this agent. That's the job of the AgentExecutor. 220) comes out of the box with a plethora of tools which allow you to connect to all kinds of paid and free services or interactions, like e. Example An example that initialize a MRKL (Modular Reasoning, Knowledge and Language, pronounced "miracle") agent executor. agents import AgentExecutor, create_react_agent, load_tools. """ return_intermediate_steps: bool = False In this tutorial, we'll learn how to create a prompt template that uses few-shot examples. The latest version of Langchain has improved its compatibility with asynchronous FastAPI, making it easier to implement streaming functionality in your applications. run("generate a short blog post to review the plot of the movie Avatar 2. For example, the GitHub toolkit has a tool for searching through GitHub issues, a tool for reading a file, a tool for commenting, etc. It should be a Jan 11, 2024 · The main goal of using agents. import streamlit as st. # Get the prompt to use - you can modify this! Initialize the AgentExecutor with return_intermediate_steps=True: agent=agent, tools=tools, verbose=True, return_intermediate_steps=True. Finally, we can sort the results in descending order of total sales and select the country with the highest total sales. stop sequence: Instructs the LLM to stop generating as soon . If agent_type is “tool-calling” then llm is expected to support tool calling. In this video we will go over how to re-create the canonical LangChain "AgentExecutor" functionality in LangGraph. I know how to get the final answer which is just the response of the agent_executor. Reload to refresh your session. prebuiltimportcreate_agent 🤖. By default, most of the agents return a single string. This generator is then passed to StreamingResponse, which streams the output to the client as it's generated. An LLM agent consists of three parts: PromptTemplate: This is the prompt template that can be used to instruct the language model on what to do. This AgentExecutor can largely be thought of as a loop that: Passes user input and any previous steps to the Agent (in this case, the LLMAgent) If the Agent returns an AgentFinish, then return that directly to the user. Examples AgentExecutor See the above Quick Start for an example of re-creating the LangChain AgentExecutor class. plan_and_execute. agents import create_openai_functions_agent from langchain. agents ¶ Agent is a class that uses an LLM to choose a sequence of actions to take. Apr 3, 2023 · A SingleActionAgent is used in an our current AgentExecutor. com There are several key concepts to understand when building agents: Agents, AgentExecutor, Tools, Toolkits. See this section for general instructions on installing integration packages. """ tools: Sequence [BaseTool] """The valid tools the agent can call. Class hierarchy: Ollama allows you to run open-source large language models, such as Llama 2, locally. You switched accounts on another tab or window. A good example of this is an agent tasked with doing question-answering over some sources. Importing Necessary Libraries create_openai_functions_agent. This method returns an asynchronous generator that yields log patches as the agent runs. For an in depth explanation, please check out this conceptual guide. make_final_outputs (outputs, run_manager) reset () Reset the iterator to its initial state, clearing intermediate steps, iterations, and time elapsed. DOCKER_BUILDKIT=1 docker build --target=runtime . Mar 15, 2024 · from langchain_community. For this LangChain provides the concept of toolkits - groups of around 3-5 tools needed to accomplish specific objectives. description: a short instruction manual that explains when and why the agent should use the tool. Run the core logic of this chain and add to output if desired. It can recover from errors by running a generated Aug 15, 2023 · Agents use a combination of an LLM (or an LLM Chain) as well as a Toolkit in order to perform a predefined series of steps to accomplish a goal. 3, you can use the astream_log method of the AgentExecutor class. Returns. The idea is that the planning step keeps the LLM more "on track" by This sample solution creates a generative AI financial services agent powered by Amazon Bedrock. chains import LLMChain from typing import List, Union from langchain. We’ll use the tool calling agent, which is generally the most reliable kind and the recommended one for most use cases. For a complete list of supported models and model variants, see the Ollama model library. agent. i. 4 days ago · A Runnable sequence representing an agent. 190 Redirecting 3 days ago · Security Note: When creating an OpenAPI agent, check the permissions. Reasoning. . dq hb dh nv dp ly ub dc th un