Langchain custom tools example

Langchain custom tools example. prompts import PromptTemplate. It’s not as complex as a chat model, and is used best with simple input Jan 12, 2024 · 1. Quickstart Many APIs are already compatible with OpenAI function calling. When contributing an implementation to LangChain, carefully document the model including the initialization parameters, include an example of how to initialize the model and include any relevant links to the underlying models documentation or API. The function to call. Below is an example. Apr 3, 2023 · A SingleActionAgent is used in an our current AgentExecutor. Debug and trace your application using LangSmith. For example, the GitHub toolkit has a tool for searching through GitHub issues, a tool for reading a file, a tool for commenting, etc. Much simpler right. stop sequence: Instructs the LLM to stop generating as soon Retrievers. LCEL is great for constructing your chains, but it's also nice to have chains used off the shelf. To create a custom callback handler, we need to determine the event (s) we want our callback handler to handle as well as what we want our callback handler to do when the event is triggered. code-block Amazon AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS). The @tool decorator provides a straightforward approach to creating a custom tool. Mar 31, 2023 · Qrious Kamal. It can be imported using the following syntax: 1. For example, a tool named "GetCurrentWeather" tells the agent that it's for finding the current weather. Whether the result of a tool should be returned directly to the user. LLM Agent with Tools: Extend the agent with access to multiple tools and test that it uses them to answer questions. example_prompt = PromptTemplate. Example. This formatter should be a PromptTemplate object. Then all we need to do is attach the callback handler to the object either as a constructer callback or a request callback (see callback types). an example of how to initialize the model and include any relevant. This example shows how to use ChatGPT Plugins within LangChain abstractions. One of the first things to do when building an agent is to decide what tools it should have access to. Simple Diagram of creating a Vector Store May 1, 2024 · langchain. LangChain has a number of components designed to help build Q&A applications, and RAG applications more generally. The Example Selector is the class responsible for doing so. Jul 21, 2023 · from langchain. Let's see how to use this! First, let's make sure to install langchain-community, as we will be using an integration in there to store message history. Architecture. A big use case for LangChain is creating agents . They combine a few things: The name of the tool. agents import AgentType, tool, create_sql_agent @tool def my_first_awesome_tool(human_message: str) -> list: """ Searches for my For example, you can create a chatbot that generates personalized travel itineraries based on user’s interests and past experiences. Execute SQL query: Execute the query. One option for creating a tool that runs custom code is to use a DynamicTool. It is more general than a vector store. Documentation about Defining Custom Tools is not fully clear to me. load_tools. Hit the ground running using third-party integrations and Templates. A prompt for a language model is a set of instructions or input provided by a user to guide the model's response, helping it understand the context and generate relevant and coherent language-based output, such as answering questions, completing sentences, or engaging in a conversation. Again, we see correct tool usage. outputs import GenerationChunk. that can be fed into a chat model. Tools 📄️ ChatGPT Plugins. """Summarize a website. Chains refer to sequences of calls - whether to an LLM, a tool, or a data preprocessing step. We want to use OpenAIEmbeddings so we have to get the OpenAI API Key. The base interface is defined as below: """Interface for selecting examples to include in prompts. Document) using a simple function. This is a simple parser that extracts the content field from an AIMessageChunk, giving us the token returned by the model. It can be used to for chatbots, G enerative Q uestion- A nwering (GQA), summarization, and much more. In this tutorial, we'll learn how to create a prompt template that uses few-shot examples. py # This module contains all ingredients to build a langchain tool # that incapsule any custom function. Tools are interfaces that an agent can use to interact with the world. """Select which examples to use based on the inputs. Vector stores can be used as the backbone of a retriever, but there are other types of retrievers as well. In this example, we will build a custom tool Functions: For example, OpenAI functions is one popular means of doing this. Initialize the LLM to use for the agent. For these applications, LangChain simplifies the entire application lifecycle: Open-source libraries: Build your applications using LangChain's modular building blocks and components. 2_Chat_with_large_documents. 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). db file in a notebooks folder at the root of this repository. This notebook goes through how to create your own custom agent. openai_api_key="OPENAI_API_KEY", temperature=0, model_name="text-davinci-003" ) Now to initialize the calculator tool. langchain app new my-app. 3) ToolMessage: contains confirmation to the model that the model requested a tool correctly. More Advanced Tool Usage. This will let it easily answer questions about LangSmith; A search tool. 1. For example, Klarna has a YAML file that describes its API and allows OpenAI to interact with it: Let's build a simple chain using LangChain Expression Language ( LCEL) that combines a prompt, model and a parser and verify that streaming works. tools import tool @tool def add(a: int, b: int) -> int: “””Adds two numbers together””” # this docstring gets used as the description return a + b # the actions our tool performs. Docs lacks a straightforward example of creating a new tool from scratch. agents import AgentExecutor, create_react_agent from langchain_openai import AzureChatOpenAI from custom_llm_wrapper import CustomLLM from config import DefaultConfig from prompt import Prompt from langchain_community. prompts import ChatPromptTemplate system_prompt = f"""You are an assistant that has access to the following set of tools. This agent could use the LangChain toolkit to fetch data from a database, process this data using custom tools, and then generate responses based on the processed data. a RunnableLambda (a custom runnable function) is that a BaseRetriever is a well known LangChain entity so some tooling for monitoring may implement specialized behavior for retrievers. Feb 6, 2024 · Defining a Custom Capability. from langchain import OpenAI. chains import ConversationChain from langchain_community. First we build Sep 22, 2023 · Use the combination of the prefix variable and the tool function description. As a result, it can be helpful to decouple the parsing logic from the loading logic, which makes it easier to re-use a given parser regardless of how the data was loaded. Custom agent. Oct 10, 2023 · Language model. # import json from langchain. It can recover from errors by running a generated Create a formatter for the few-shot examples. To create a custom callback handler we need to determine the event (s) we want our callback handler to handle as well as what we want our callback handler to do when the event is triggered. agents import load_tools from langchain import hub from langchain. Then, copy the API key and index name. Tools allow us to extend the capabilities of a model beyond just outputting text/messages. The @tool decorator is the most concise way to define a LangChain tool. % pip install - - upgrade - - quiet langchain langchain - community langchain - experimental A tool that we will be interacting with, An agent to control the interaction. Jan 16, 2023 · Langchain is a great project! I'm trying to implement custom APIs integration as langchain tool, as you suggested on discord, but is not clear exactly how it works. Tools. If you are interested for RAG over """A custom chat model that echoes the first `n` characters of the input. This API can be used to interact with your StateGraph from any programming language that can make HTTP requests. You must encourage the model // to wrap output in a JSON object with "tool" and "tool_input" properties. Note that querying data in CSVs can follow a similar approach. bind_tools method, which receives a list of LangChain tool objects and binds them to the chat model in its expected format. chat_models import ChatOpenAI from langchain. llms import OpenAI from langchain import LLMMathChain, SerpAPIWrapper. Please scope the permissions of each tools to the minimum required for the application. For example, if the class is langchain. The score_tool is a tool I define for the LLM that uses a function named llm Mar 7, 2024 · from langchain. Chat models that support tool calling features implement a . It is useful to have all this information because Passing tools to chat models. #. The only method it needs to define is a select_examples method. LangChain is an open-source framework created to aid the development of applications leveraging the power of large language models (LLMs). llms import OpenAI conversation = ConversationChain(llm=OpenAI()) Create a new model by parsing and validating input data from keyword arguments. . Chain to have a conversation and load context from memory. 1_Summarizing_long_texts. The LangChain framework consists of an array of tools, components, and interfaces that simplify the development process for language model-powered applications. 📄 """A custom chat model that echoes the first `n` characters of the input. 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. We will first create it WITHOUT memory, but we will then show how to add memory in. --. Mar 31, 2023. Example:. The tool returns the accuracy score for a pre-trained model saved at a given path. LLM: This is the language model that powers the agent. 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 Apr 13, 2023 · Examples of tools can range from Google Search, database lookups, Python REPL, to other chains. The custom tool is executing a HTTP POST call and API key is needed for successful authenticated request. Jun 22, 2023 · In some cases, you would like to pass variables to custom tool function. Then all we need to do is attach the callback handler to the object, for example via the constructor or at runtime. To access the OpenAI key, make an account on the OpenAI platform. Use Case In this tutorial, we'll configure few-shot examples for self-ask with search. 📄️ Connery Action Tool. This repository contains a collection of apps powered by LangChain. In the example below, we'll implement The @tool decorator, as you've noted, requires the function to be of type (str) -> str. """Add new example to store. Create new app using langchain cli command. 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. LangChain provides a wide set of toolkits to get started. Tools are interfaces that an agent, chain, or LLM can use to interact with the world. Pick a display name To start, let's choose a name for our component by adding a display_name attribute. agents import AgentType, Tool, initialize_agent, tool from Feb 7, 2023 · # # weather_tool. For example, we can define the schema May 21, 2024 · For custom connection, you need to follow the steps: Import library from promptflow. from langchain_core. For this LangChain provides the concept of toolkits - groups of around 3-5 tools needed to accomplish specific objectives. Compose a LangGraph Agent, which use an LLM to determine actions and then execute them. I have the python 3 langchain code below that I'm using to create a conversational agent and define a tool for it to use. config (Optional[RunnableConfig]) – Return type. """ name: str = "custom_sql_db_query For example, you can use open to read the binary content of either a PDF or a markdown file, but you need different parsing logic to convert that binary data into text. This will let it easily answer questions that require up to date information. code-block Option 1: Using the @tool decorator. search), other chains, or even other agents. The primary supported way to do this is with LCEL. To set it up follow these instructions and place the . agent_toolkits import SQLDatabaseToolkit from langchain. Using an example set Create the example set Apr 24, 2024 · Build an Agent. To start, we define a custom capability for converting text to speech as an example. The standard interface for a tool is a function that accepts a string as input and returns a string Tools. If the Agent returns an AgentAction, then use that to call a tool and get an Observation. Currently, tools can be loaded with the following snippet: from langchain. # ! pip install langchain_community. But for certain use cases, how many times we use tools depends on the input. This name will appear on the canvas. connections import CustomConnection, and define an input parameter of type CustomConnection in the tool function. For example, suppose you are working in the finance industry. OpenAI, then the namespace is [“langchain”, “llms”, “openai”] Return type. Introduction. Define the runnable in add_routes. Use a Search Tool to look up information from the Internet. The Dall-E tool allows your agent to create images using OpenAI's Dall-E image generation tool. py ‘ like this: Finx_LangChain. Below are a couple of examples to illustrate this -. In this example, we will use OpenAI Tool Calling to create this agent. These tools can be generic utilities (e. A few-shot prompt template can be constructed from either a set of examples, or from an Example Selector object. The model is scored on data that is saved at another path. A retriever does not need to be able to store documents, only to return (or retrieve) them. Schema of what the inputs to the tool are. We will be Nov 30, 2023 · Domain-Specific Functionality: Custom tools allow you to incorporate domain-specific functionality into LangChain. from_template("Question: {question}\n{answer}") langchain-examples. Jul 11, 2023 · In this story we are going to explore how you can create a simple web based chat application that communicates with a private REST API, uses OpenAI functions and conversational memory. g. To propagate callbacks through the tool function, simply include the "callbacks" option in the wrapped function. ·. We’ve seen two examples of custom tools. For example, if an application only needs to read from a database, the Agents. This notebook builds off of this notebook and assumes familiarity with how agents work. %load_ext autoreload %autoreload 2. links to the underlying models documentation or API. Type[BaseModel] classmethod get_lc_namespace → List [str] ¶ Get the namespace of the langchain object. The name of the class is not relevant, but let's call it DocumentProcessor. This serverless architecture enables you to focus on writing and deploying code, while AWS automatically takes care of scaling, patching, and managing from langchain_core. Follow. We will use StrOutputParser to parse the output from the model. Answer the question: Model responds to user input using the query results. In that case, you can define custom financial calculations or data analysis tools tailored to your needs. Now start a new folder named ‘ 4_Custom_tools ‘ and inside add a new file named ‘ 1_stock_price_tool. Go to server. Hit the ground running using third-party integrations. 3_Agents_and_tools. _ Besides having a large collection of different types of output parsers, one distinguishing benefit of LangChain OutputParsers is that many of them support streaming. Configure a formatter that will format the few-shot examples into a string. py and edit. # Import things that are needed generically from langchain import LLMMathChain, SerpAPIWrapper from langchain. const toolSystemPromptTemplate = ` You have access to the following tools: {tools} To use a tool, respond with a JSON object with the following structure: {{"tool": <name of the called tool>, Bases: LLMChain. You can pass it as a variable. The main benefit of implementing a retriever as a BaseRetriever vs. agents import load_tools tool_names = [] tools = load_tools(tool_names) LangChain is a popular framework that allow users to quickly build apps and pipelines around L arge L anguage M odels. After that, we can import the relevant classes and set up our chain which wraps the model and adds in this message history. LangChain adopts this convention for structuring tool calls into conversation across LLM model providers. sql_database import SQLDatabase from langchain. Return your response as a JSON blob with 'name' and 'arguments This uses the example Chinook database. 📄️ Dall-E Tool. , langchain-openai, langchain-anthropic, langchain-mistral etc). There are two types of off-the-shelf chains that LangChain supports: Chains that are built with LCEL. This AgentExecutor can largely be thought of as a loop that: Passes user input and any previous steps to the Agent. 0) tools = load_tools( ["human", "llm-math"], llm=math_llm, ) We need memory for our agent to remember the conversation. LangChain is a software development framework designed to simplify the creation of applications using large language models (LLMs). Another difference is that a BaseRetriever will behave slightly // Custom system prompt to format tools. # Import things that are needed generically from langchain. The complete list is here. At a high-level, the steps of these systems are: Convert question to DSL query: Model converts user input to a SQL query. 4_Custom_tools. class CustomLLM(LLM): """A custom chat model that echoes the first `n` characters of the input. llm = OpenAI(. LangChain is a framework for developing applications powered by large language models (LLMs). from langchain. LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. Memory is needed to enable conversation. llms. import { OpenAI } from "langchain/llms/openai"; The OpenAI API uses API keys for authentication. to implement a retriever. Apr 21, 2023 · The function that should be called when the tool is selected should return a single string. When contributing an implementation to LangChain, carefully document. tools. A practical example of integrating LangChain agent tools with other systems is the construction of an agent that utilizes external APIs for data retrieval and processing. agents import load_tools from langchain. Here are the names and descriptions for each tool: {rendered_tools} Given the user input, return the name and input of the tool to use. A retriever is an interface that returns documents given an unstructured query. This is generally the most reliable way to create agents. tool import BaseSQLDatabaseTool from langchain. The code and data used in this note book is at https Apr 10, 2024 · Defining the “add” tool in LangChain using the @tool decorator will look like this. Defining custom tools. The results of those actions can then be fed back into the agent Example Let's create a custom component that processes a document (langchain. 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 The Example Selector is the class responsible for doing so. Output Parser Types LangChain has lots of different types of output parsers. Note: Here we focus on Q&A for unstructured data. This means that for this specific tool, the return type is a string, which is the final Next, go to the and create a new index with dimension=1536 called "langchain-test-index". It helps developers to build and run applications and services without provisioning or managing servers. NotImplemented) 3. It can be used for chatbots, text summarisation, data generation, code understanding, question answering, evaluation In following this tutorial, you will learn how to: Use language models, in particular their tool calling ability. Apr 21, 2023 · Custom Agent with Tool Retrieval. Framework and Libraries. Let’s start by installing langchain and initializing our base LLM. If the Agent returns an AgentFinish, then return that directly to the user. In particular, we will: Utilize the HuggingFaceTextGenInference, HuggingFaceEndpoint, or HuggingFaceHub integrations to instantiate an LLM. schema. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source building blocks and components. JSON schema of what the inputs to the tool are. The list of messages per example corresponds to: 1) HumanMessage: contains the content from which content should be extracted. Tools allow agents to interact with various resources and services like APIs, databases, file systems, etc. Integrate with hundreds of third-party providers. By default, Function in custom tool has access to variables: This notebook shows how to get started using Hugging Face LLM's as chat models. tools import BaseTool from langchain. For this example, we will give the agent access two tools: The retriever we just created. Take a peek at how LLMs are used to call Python functions and based on the Prompts generated by the 1 day ago · The tool’s input schema. description: a short instruction manual that explains when and why the agent should use the tool. By themselves, language models can't take actions - they just output text. agents import initialize_agent, Tool from langchain. . LLM-generated interface: Use an LLM with access to API documentation to create an interface. Parameters. LangGraph is a library for building stateful, multi-actor applications with LLMs. 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. Tools are functions that agents can use to interact with the world. Importantly, the name and the description will be used by the language model to determine when to call this function and with what parameters May 2, 2023 · A Structured Tool object is defined by its: name: a label telling the agent which tool to pick. The above video will provide visual explanation along with the code in the git hub repo here. Go to API keys and Generate API key with the option : Create new secret key. The process of bringing the appropriate information and inserting it into the model prompt is known as Retrieval Augmented Generation (RAG). Custom LLM Agent. Below is a snippet illustrating how to define a TextToSpeech capability: Oct 28, 2023 · Before we get started run the following in your terminal: pip install yfinance. Tools can be just about anything — APIs, functions, databases, etc. That's where Agents come in! LangChain comes with a number of built-in agents that are optimized for different use cases. LangChain provides a way to use language models in Python to produce text output based on text input. This walkthrough uses the FAISS vector database, which makes use of the Facebook AI Similarity Search (FAISS) library. LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications. agents. This notebook goes through how to create your own custom LLM agent. Read about all the available agent types here. ¶. Chains: Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). utilities import SerpAPIWrapper from LangChain provides a large collection of common utils to use in your application. Utilize the ChatHuggingFace class to enable any of these LLMs to interface with LangChain's Chat Messages abstraction. The main use cases for LangGraph are Tools. May 2, 2023 · Knowledge Base: Create a knowledge base of "Stuff You Should Know" podcast episodes, to be accessed through a tool. Quick Start See this quick-start guide for an introduction to output parsers and how to work with them. This is useful when you have many many tools to select from. In this tutorial, you can learn how to create a custom tool that is not registered Nov 9, 2023 · Here is an example of how you can implement this: from langchain. We would like to show you a description here but the site won’t allow us. The novel idea introduced in this notebook is the idea of using retrieval to select the set of tools to use to answer an agent query. It works by taking a big source of data, take for example a 50-page PDF, and breaking it down into "chunks" which are then embedded into a Vector Store. The DynamicTool and DynamicStructuredTool classes takes as input a name, a description, and a function. In most scenarios, we’d likely want to do something more powerful — so let’s give that a go. add_routes(app. """. Something like: from langchain. This is enforced by the _run and _arun methods of the ShellTool class, which are decorated with the @tool decorator and are specifically annotated to return a str type. llms import OpenAI math_llm = OpenAI(temperature=0. The autoreload extension is already loaded. openai. This is achieved by creating a class that inherits from BaseModel and uses the @tool decorator to register the function as a Langchain tool. In this guide, we will go over the basic ways to create Chains and Agents that call Tools. agents import Tool, AgentExecutor from langchain. The core idea of the library is that we can "chain" together different components to create more advanced use-cases around LLMs. LangChain结合了大型语言模型、知识库和计算逻辑，可以用于快速开发强大的AI应用。这个仓库包含了我对LangChain的学习和实践经验，包括教程和代码案例。让我们一起探索LangChain的可能性，共同推动人工智能领域的进步！ - aihes/LangChain-Tutorials-and-Examples This is an example of how to use langgraph-api to stand up a REST API for your custom LangGraph StateGraph. Using this tool, you can integrate individual Connery Action into your LangChain agent. the model including the initialization parameters, include. Mar 26, 2023 · World of Large Language models are taking a path that other technologies have taken till date. Load tools based on their name. For example, you are creating a chatbot that uses a custom tool. Parse the input to the input section, then select your target custom connection in the value dropdown. Aug 20, 2023 · Custom tool agent In the above tutorial on agents, we used pre-existing tools with langchain to create agents. 2. base import BaseTool class CustomQuerySQLCheckerTool ( BaseSQLDatabaseTool, BaseTool ): """Custom tool for modifying and checking SQL queries for data protection. sql_database. File logging. In these cases, we want to let the model itself decide how many times to use tools and in what order. A description of what the tool is. Subsequent invocations of the chat model will include tool schemas in its calls to the LLM. 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. There are two ways to define a tool, we will cover both in the example below. List[str] Apr 25, 2024 · LangChain 🦜️🔗 Custom Tools @tool decorator for custom tools. May 17, 2023 · LangChain is an open-source library that provides developers with the tools to build applications powered by large language models (LLMs). Use poetry to add 3rd party packages (e. 2) AIMessage: contains the extracted information from the model. agents import Tool # # weather_data # is an example of a custom python function # that takes a list of custom arguments and returns a text (or in general any data structure) # def weather_data Jun 1, 2023 · In short, LangChain just composes large amounts of data that can easily be referenced by a LLM with as little computation power as possible. Even with our short tool description, the agent can consistently use the tool as intended and with multiple parameters. dk da yi qt kc sj ga qd zk qb