For more information on how to use these datasets, see the LangChain documentation. LangChain provides several classes and functions. 1. Retrieval Augmented Generation (RAG) allows you to provide a large language model (LLM) with access to data from external knowledge sources such as. Use LangChain Expression Language, the protocol that LangChain is built on and which facilitates component chaining. The goal of this repository is to be a central resource for sharing and discovering high quality prompts, chains and agents that combine together to form complex LLM. Introduction. LangChain is a framework for developing applications powered by language models. Saved searches Use saved searches to filter your results more quicklyLarge Language Models (LLMs) are a core component of LangChain. Setting up key as an environment variable. It offers a suite of tools, components, and interfaces that simplify the process of creating applications powered by large language. Member VisibilityCompute query embeddings using a HuggingFace transformer model. 「LangChain」は、「LLM」 (Large language models) と連携するアプリの開発を支援するライブラリです。. LangSmith Introduction . LangChain. This will create an editable install of llama-hub in your venv. LangChain. Note that these wrappers only work for models that support the following tasks: text2text-generation, text-generation. 614 integrations Request an integration. You can use other Document Loaders to load your own data into the vectorstore. Useful for finding inspiration or seeing how things were done in other. [docs] class HuggingFaceEndpoint(LLM): """HuggingFace Endpoint models. With the data added to the vectorstore, we can initialize the chain. LangChain’s strength lies in its wide array of integrations and capabilities. Twitter: about why the LangChain library is so coolIn this video we'r. LangChain is a framework for developing applications powered by language models. By leveraging its core components, including prompt templates, LLMs, agents, and memory, data engineers can build powerful applications that automate processes, provide valuable insights, and enhance productivity. 5 and other LLMs. LangChain provides tooling to create and work with prompt templates. Data Security Policy. . Get your LLM application from prototype to production. Embeddings for the text. py to ingest LangChain docs data into the Weaviate vectorstore (only needs to be done once). Unstructured data (e. r/LangChain: LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. Web Loaders. The default is 127. Defaults to the hosted API service if you have an api key set, or a. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This example is designed to run in all JS environments, including the browser. dalle add model parameter by @AzeWZ in #13201. For example: import { ChatOpenAI } from "langchain/chat_models/openai"; const model = new ChatOpenAI({. The LangChainHub is a central place for the serialized versions of these prompts, chains, and agents. The LangChain Hub (Hub) is really an extension of the LangSmith studio environment and lives within the LangSmith web UI. LangChain cookbook. 怎么设置在langchain demo中 #409. A `Document` is a piece of text and associated metadata. You switched accounts on another tab or window. Now, here's more info about it: LangChain 🦜🔗 is an AI-first framework that helps developers build context-aware reasoning applications. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications. It contains a text string ("the template"), that can take in a set of parameters from the end user and generates a prompt. First things first, if you're working in Google Colab we need to !pip install langchain and openai set our OpenAI key: import langchain import openai import os os. Directly set up the key in the relevant class. pull(owner_repo_commit: str, *, api_url: Optional[str] = None, api_key:. You're right, being able to chain your own sources is the true power of gpt. To associate your repository with the langchain topic, visit your repo's landing page and select "manage topics. Note: If you want to delete your databases, you can run the following commands: $ npx wrangler vectorize delete langchain_cloudflare_docs_index $ npx wrangler vectorize delete langchain_ai_docs_index. Every document loader exposes two methods: 1. It formats the prompt template using the input key values provided (and also memory key. At its core, LangChain is a framework built around LLMs. These cookies are necessary for the website to function and cannot be switched off. We are excited to announce the launch of the LangChainHub, a place where you can find and submit commonly used prompts, chains, agents, and more! See moreTaking inspiration from Hugging Face Hub, LangChainHub is collection of all artifacts useful for working with LangChain primitives such as prompts, chains and agents. pull ( "rlm/rag-prompt-mistral")Large Language Models (LLMs) are a core component of LangChain. Examples using load_chain¶ Hugging Face Prompt Injection Identification. Popular. Read this in other languages: 简体中文 What is Deep Lake? Deep Lake is a Database for AI powered by a storage format optimized for deep-learning applications. Langchain is a powerful language processing platform that leverages artificial intelligence and machine learning algorithms to comprehend, analyze, and generate human-like language. global corporations, STARTUPS, and TINKERERS build with LangChain. ) 1. Unstructured data can be loaded from many sources. Ollama. Looking for the JS/TS version? Check out LangChain. It includes API wrappers, web scraping subsystems, code analysis tools, document summarization tools, and more. With LangSmith access: Full read and write permissions. It brings to the table an arsenal of tools, components, and interfaces that streamline the architecture of LLM-driven applications. pull ¶. This new development feels like a very natural extension and progression of LangSmith. """ from __future__ import annotations from typing import TYPE_CHECKING, Any, Optional from langchain. Standard models struggle with basic functions like logic, calculation, and search. The goal of. 3 projects | 9 Nov 2023. We intend to gather a collection of diverse datasets for the multitude of LangChain tasks, and make them easy to use and evaluate in LangChain. You can explore all existing prompts and upload your own by logging in and navigate to the Hub from your admin panel. Don’t worry, you don’t need to be a mad scientist or a big bank account to develop and. What is LangChain? LangChain is a powerful framework designed to help developers build end-to-end applications using language models. ) Reason: rely on a language model to reason (about how to answer based on. , Python); Below we will review Chat and QA on Unstructured data. Let's load the Hugging Face Embedding class. LLMChain. g. There are lots of LLM providers (OpenAI, Cohere, Hugging Face, etc) - the LLM class is designed to provide a standard interface for all of them. LangChain as an AIPlugin Introduction. Check out the. if var_name in config: raise ValueError( f"Both. from llamaapi import LlamaAPI. Loading from LangchainHub:Cookbook. Large Language Models (LLMs) are a core component of LangChain. from langchain. 多GPU怎么推理？. 「LangChain」の「LLMとプロンプト」「チェーン」の使い方をまとめました。. 10 min read. There are 2 supported file formats for agents: json and yaml. 怎么设置在langchain demo中 · Issue #409 · THUDM/ChatGLM3 · GitHub. We are particularly enthusiastic about publishing: 1-technical deep-dives about building with LangChain/LangSmith 2-interesting LLM use-cases with LangChain/LangSmith under the hood!This article shows how to quickly build chat applications using Python and leveraging powerful technologies such as OpenAI ChatGPT models, Embedding models, LangChain framework, ChromaDB vector database, and Chainlit, an open-source Python package that is specifically designed to create user interfaces (UIs) for AI. T5 is a state-of-the-art language model that is trained in a “text-to-text” framework. All functionality related to Anthropic models. A `Document` is a piece of text and associated metadata. What is Langchain. 「LLM」という革新的テクノロジーによって、開発者. Source code for langchain. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs. hub . The Github toolkit contains tools that enable an LLM agent to interact with a github repository. Access the hub through the login address. Private. Advanced refinement of langchain using LLaMA C++ documents embeddings for better document representation and information retrieval. The goal of this repository is to be a central resource for sharing and discovering high quality prompts, chains and agents that combine together to form complex LLM applications. Let's now use this in a chain! llm = OpenAI(temperature=0) from langchain. This is built to integrate as seamlessly as possible with the LangChain Python package. Standardizing Development Interfaces. Index, retriever, and query engine are three basic components for asking questions over your data or. For example, if you’re using Google Colab, consider utilizing a high-end processor like the A100 GPU. Can be set using the LANGFLOW_HOST environment variable. Org profile for LangChain Agents Hub on Hugging Face, the AI community building the future. [docs] class HuggingFaceEndpoint(LLM): """HuggingFace Endpoint models. All functionality related to Google Cloud Platform and other Google products. exclude – fields to exclude from new model, as with values this takes precedence over include. Reload to refresh your session. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. That should give you an idea. This is an open source effort to create a similar experience to OpenAI's GPTs and Assistants API. Click here for Data Source that we used for analysis!. Langchain has been becoming one of the most popular NLP libraries, with around 30K starts on GitHub. For instance, you might need to get some info from a. First, let's import an LLM and a ChatModel and call predict. Embeddings create a vector representation of a piece of text. LangChain provides several classes and functions. repo_full_name – The full name of the repo to push to in the format of owner/repo. Enabling the next wave of intelligent chatbots using conversational memory. 📄️ AWS. There are no prompts. Duplicate a model, optionally choose which fields to include, exclude and change. class HuggingFaceBgeEmbeddings (BaseModel, Embeddings): """HuggingFace BGE sentence_transformers embedding models. This notebook shows how you can generate images from a prompt synthesized using an OpenAI LLM. 👉 Give context to the chatbot using external datasources, chatGPT plugins and prompts. We can use it for chatbots, G enerative Q uestion- A nswering (GQA), summarization, and much more. If your API requires authentication or other headers, you can pass the chain a headers property in the config object. langchain. hub. LangChainHubの詳細やプロンプトはこちらでご覧いただけます。 3C. Subscribe or follow me on Twitter for more content like this!. We want to split out core abstractions and runtime logic to a separate langchain-core package. Whether implemented in LangChain or not! Gallery: A collection of our favorite projects that use LangChain. By continuing, you agree to our Terms of Service. model_download_counter: This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. The Hugging Face Hub is a platform with over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together. What I like, is that LangChain has three methods to approaching managing context: ⦿ Buffering: This option allows you to pass the last N. toml file. Recently added. pull. Official release Saved searches Use saved searches to filter your results more quickly To use, you should have the ``huggingface_hub`` python package installed, and the environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass it as a named parameter to the constructor. Don’t worry, you don’t need to be a mad scientist or a big bank account to develop and. Easily browse all of LangChainHub prompts, agents, and chains. This is a standard interface with a few different methods, which make it easy to define custom chains as well as making it possible to invoke them in a standard way. You can. owner_repo_commit – The full name of the repo to pull from in the format of owner/repo:commit_hash. def _load_template(var_name: str, config: dict) -> dict: """Load template from the path if applicable. BabyAGI is made up of 3 components: A chain responsible for creating tasks; A chain responsible for prioritising tasks; A chain responsible for executing tasks1. environ ["OPENAI_API_KEY"] = "YOUR-API-KEY". LangChain is a framework for developing applications powered by language models. 📄️ Cheerio. Org profile for LangChain Hub Prompts on Hugging Face, the AI community building the future. Change the content in PREFIX, SUFFIX, and FORMAT_INSTRUCTION according to your need after tying and testing few times. LangSmith helps you trace and evaluate your language model applications and intelligent agents to help you move from prototype to production. Introduction. See the full prompt text being sent with every interaction with the LLM. Tools are functions that agents can use to interact with the world. The retriever can be selected by the user in the drop-down list in the configurations (red panel above). An LLMChain is a simple chain that adds some functionality around language models. g. [docs] class HuggingFaceHubEmbeddings(BaseModel, Embeddings): """HuggingFaceHub embedding models. llms. That’s where LangFlow comes in. It's all about blending technical prowess with a touch of personality. OpenAI requires parameter schemas in the format below, where parameters must be JSON Schema. Structured output parser. Each option is detailed below:--help: Displays all available options. langchain. Viewer • Updated Feb 1 • 3. This will also make it possible to prototype in one language and then switch to the other. Columns:Load a chain from LangchainHub or local filesystem. import { OpenAI } from "langchain/llms/openai"; import { PromptTemplate } from "langchain/prompts"; import { LLMChain } from "langchain/chains";Notion DB 2/2. It also supports large language. Proprietary models are closed-source foundation models owned by companies with large expert teams and big AI budgets. from langchain. It will change less frequently, when there are breaking changes. from. We’re establishing best practices you can rely on. This is the same as create_structured_output_runnable except that instead of taking a single output schema, it takes a sequence of function definitions. Useful for finding inspiration or seeing how things were done in other. Configuring environment variables. The tool is a wrapper for the PyGitHub library. Example code for building applications with LangChain, with an emphasis on more applied and end-to-end examples than contained in the main documentation. , PDFs); Structured data (e. 多GPU怎么推理？. 6. OpenGPTs. Basic query functionalities Index, retriever, and query engine. The updated approach is to use the LangChain. Shell. . It is used widely throughout LangChain, including in other chains and agents. Glossary: A glossary of all related terms, papers, methods, etc. We will pass the prompt in via the chain_type_kwargs argument. github","path. 1. langchain-core will contain interfaces for key abstractions (LLMs, vectorstores, retrievers, etc) as well as logic for combining them in chains (LCEL). Blog Post. 1. temperature: 0. LangSmith helps you trace and evaluate your language model applications and intelligent agents to help you move from prototype to production. batch: call the chain on a list of inputs. llms import HuggingFacePipeline. We’d extract every Markdown file from the Dagster repository and somehow feed it to GPT-3. LangChainHub is a hub where users can find and submit commonly used prompts, chains, agents, and more for the LangChain framework, a Python library for using large language models. Please read our Data Security Policy. llama-cpp-python is a Python binding for llama. --host: Defines the host to bind the server to. This will allow for largely and more widespread community adoption and sharing of best prompts, chains, and agents. The last one was on 2023-11-09. To use, you should have the ``huggingface_hub`` python package installed, and the environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass it as a named parameter to the constructor. To create a generic OpenAI functions chain, we can use the create_openai_fn_runnable method. 🦜🔗 LangChain. For dedicated documentation, please see the hub docs. Agents can use multiple tools, and use the output of one tool as the input to the next. ”. gpt4all_path = 'path to your llm bin file'. In this course you will learn and get experience with the following topics: Models, Prompts and Parsers: calling LLMs, providing prompts and parsing the. RetrievalQA Chain: use prompts from the hub in an example RAG pipeline. cpp. You can connect to various data and computation sources, and build applications that perform NLP tasks on domain-specific data sources, private repositories, and much more. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs. from langchain import hub. This filter parameter is a JSON object, and the match_documents function will use the Postgres JSONB Containment operator @> to filter documents by the metadata field. devcontainer","contentType":"directory"},{"name":". In supabase/functions/chat a Supabase Edge Function. semchunk alternatives - text-splitter and langchain. While the documentation and examples online for LangChain and LlamaIndex are excellent, I am still motivated to write this book to solve interesting problems that I like to work on involving information retrieval, natural language processing (NLP), dialog agents, and the semantic web/linked data fields. While the Pydantic/JSON parser is more powerful, we initially experimented with data structures having text fields only. Taking inspiration from Hugging Face Hub, LangChainHub is collection of all artifacts useful for working with LangChain primitives such as prompts, chains and. As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis. Note: new versions of llama-cpp-python use GGUF model files (see here ). Click on New Token. At its core, Langchain aims to bridge the gap between humans and machines by enabling seamless communication and understanding. In this notebook we walk through how to create a custom agent. It. from_chain_type(. Introduction. pull langchain. 怎么设置在langchain demo中 · Issue #409 · THUDM/ChatGLM3 · GitHub. prompts. They also often lack the context they need and personality you want for your use-case. This notebook covers how to do routing in the LangChain Expression Language. 👍 5 xsa-dev, dosuken123, CLRafaelR, BahozHagi, and hamzalodhi2023 reacted with thumbs up emoji 😄 1 hamzalodhi2023 reacted with laugh emoji 🎉 2 SharifMrCreed and hamzalodhi2023 reacted with hooray emoji ️ 3 2kha, dentro-innovation, and hamzalodhi2023 reacted with heart emoji 🚀 1 hamzalodhi2023 reacted with rocket emoji 👀 1 hamzalodhi2023 reacted with. It starts with computer vision, which classifies a page into one of 20 possible types. 📄️ Quick Start. This will also make it possible to prototype in one language and then switch to the other. txt` file, for loading the text contents of any web page, or even for loading a transcript of a YouTube video. # Needed if you would like to display images in the notebook. Defaults to the hosted API service if you have an api key set, or a localhost instance if not. For agents, where the sequence of calls is non-deterministic, it helps visualize the specific. @inproceedings{ zeng2023glm-130b, title={{GLM}-130B: An Open Bilingual Pre-trained Model}, author={Aohan Zeng and Xiao Liu and Zhengxiao Du and Zihan Wang and Hanyu Lai and Ming Ding and Zhuoyi Yang and Yifan Xu and Wendi Zheng and Xiao Xia and Weng Lam Tam and Zixuan Ma and Yufei Xue and Jidong Zhai and Wenguang Chen and. import { ChatOpenAI } from "langchain/chat_models/openai"; import { LLMChain } from "langchain/chains"; import { ChatPromptTemplate } from "langchain/prompts"; const template =. Go to your profile icon (top right corner) Select Settings. Here's how the process breaks down, step by step: If you haven't already, set up your system to run Python and reticulate. This input is often constructed from multiple components. Langchain is a groundbreaking framework that revolutionizes language models for data engineers. This is a new way to create, share, maintain, download, and. "compilerOptions": {. ts:26; Settings. 1. Routing allows you to create non-deterministic chains where the output of a previous step defines the next step. Glossary: A glossary of all related terms, papers, methods, etc. You can share prompts within a LangSmith organization by uploading them within a shared organization. 4. {. If you choose different names, you will need to update the bindings there. #3 LLM Chains using GPT 3. LangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents. What is a good name for a company. The obvious solution is to find a way to train GPT-3 on the Dagster documentation (Markdown or text documents). Organizations looking to use LLMs to power their applications are. Some popular examples of LLMs include GPT-3, GPT-4, BERT, and. model_download_counter: This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. Viewer • Updated Feb 1 • 3. ; Import the ggplot2 PDF documentation file as a LangChain object with. We considered this a priority because as we grow the LangChainHub over time, we want these artifacts to be shareable between languages. This is useful if you have multiple schemas you'd like the model to pick from. g. r/LangChain: LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. Chat and Question-Answering (QA) over data are popular LLM use-cases. The goal of LangChain is to link powerful Large. Glossary: A glossary of all related terms, papers, methods, etc. We have used some of these posts to build our list of alternatives and similar projects. js. # Replace 'Your_API_Token' with your actual API token. schema in the API docs (see image below). LangChain 的中文入门教程. class langchain. You can import it using the following syntax: import { OpenAI } from "langchain/llms/openai"; If you are using TypeScript in an ESM project we suggest updating your tsconfig. GitHub repo * Includes: Input/output schema, /docs endpoint, invoke/batch/stream endpoints, Release Notes 3 min read. Unstructured data (e. Let's put it all together into a chain that takes a question, retrieves relevant documents, constructs a prompt, passes that to a model, and parses the output. # RetrievalQA. See below for examples of each integrated with LangChain. conda install. 👉 Dedicated API endpoint for each Chatbot. Here's how the process breaks down, step by step: If you haven't already, set up your system to run Python and reticulate. If you're just getting acquainted with LCEL, the Prompt + LLM page is a good place to start. It allows AI developers to develop applications based on the combined Large Language Models. Remove _get_kwarg_value function by @Guillem96 in #13184. Hi! Thanks for being here. LangChain does not serve its own LLMs, but rather provides a standard interface for interacting with many different LLMs. We will pass the prompt in via the chain_type_kwargs argument. LangChain for Gen AI and LLMs by James Briggs. Unstructured data can be loaded from many sources. To use, you should have the huggingface_hub python package installed, and the environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass it as a named parameter to the constructor. ⛓️ Langflow is a UI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows. Easy to set up and extend. huggingface_endpoint. ¶. QA and Chat over Documents. Docs • Get Started • API Reference • LangChain & VectorDBs Course • Blog • Whitepaper • Slack • Twitter. LangChain is an open-source framework built around LLMs. Taking inspiration from Hugging Face Hub, LangChainHub is collection of all artifacts useful for working with LangChain primitives such as prompts, chains and agents. If the user clicks the "Submit Query" button, the app will query the agent and write the response to the app. template = """The following is a friendly conversation between a human and an AI. The application demonstration is available on both Streamlit Public Cloud and Google App Engine. Please read our Data Security Policy. update – values to change/add in the new model. whl; Algorithm Hash digest; SHA256: 3d58a050a3a70684bca2e049a2425a2418d199d0b14e3c8aa318123b7f18b21a: CopyIn this video, we're going to explore the core concepts of LangChain and understand how the framework can be used to build your own large language model appl. Langchain is a powerful language processing platform that leverages artificial intelligence and machine learning algorithms to comprehend, analyze, and generate human-like language. For loaders, create a new directory in llama_hub, for tools create a directory in llama_hub/tools, and for llama-packs create a directory in llama_hub/llama_packs It can be nested within another, but name it something unique because the name of the directory. Flan-T5 is a commercially available open-source LLM by Google researchers. Standardizing Development Interfaces. Cookie settings Strictly necessary cookies. We considered this a priority because as we grow the LangChainHub over time, we want these artifacts to be shareable between languages. Chapter 4. wfh/automated-feedback-example. All credit goes to Langchain, OpenAI and its developers!LangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents. These loaders are used to load web resources. g. ResponseSchema(name="source", description="source used to answer the. Each command or ‘link’ of this chain can. The Agent interface provides the flexibility for such applications. Learn how to get started with this quickstart guide and join the LangChain community. In this quickstart we'll show you how to: Get setup with LangChain, LangSmith and LangServe. First, let's load the language model we're going to use to control the agent. You can also create ReAct agents that use chat models instead of LLMs as the agent driver. We would like to show you a description here but the site won’t allow us. Organizations looking to use LLMs to power their applications are. Microsoft SharePoint is a website-based collaboration system that uses workflow applications, “list” databases, and other web parts and security features to empower business teams to work together developed by Microsoft. 2 min read Jan 23, 2023. Within LangChain ConversationBufferMemory can be used as type of memory that collates all the previous input and output text and add it to the context passed with each dialog sent from the user. LangChain strives to create model agnostic templates to make it easy to. Introduction. This notebook goes over how to run llama-cpp-python within LangChain. , see @dair_ai ’s prompt engineering guide and this excellent review from Lilian Weng). We've worked with some of our partners to create a. pull. With LangChain, engaging with language models, interlinking diverse components, and incorporating assets like APIs and databases become a breeze. Using an LLM in isolation is fine for simple applications, but more complex applications require chaining LLMs - either with each other or with other components. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". Example code for building applications with LangChain, with an emphasis on more applied and end-to-end examples than contained in the main documentation. Last updated on Nov 04, 2023. - The agent class itself: this decides which action to take. Data Security Policy. encoder is an optional function to supply as default to json. Each object in the list should have two properties: the name of the document that was chunked, and the chunked data itself. This guide will continue from the hub quickstart, using the Python or TypeScript SDK to interact with the hub instead of the Playground UI. Recently Updated. Push a prompt to your personal organization. Here are some of the projects we will work on: Project 1: Construct a dynamic question-answering application with the unparalleled capabilities of LangChain, OpenAI, and Hugging Face Spaces. As an open source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infra, or better documentation. It lets you debug, test, evaluate, and monitor chains and intelligent agents built on any LLM framework and seamlessly integrates with LangChain, the go-to open source framework for building with LLMs. r/ChatGPTCoding • I created GPT Pilot - a PoC for a dev tool that writes fully working apps from scratch while the developer oversees the implementation - it creates code and tests step by step as a human would, debugs the code, runs commands, and asks for feedback. "You are a helpful assistant that translates. Pull an object from the hub and use it. This prompt uses NLP and AI to convert seed content into Q/A training data for OpenAI LLMs. It. By continuing, you agree to our Terms of Service. Learn more about TeamsLangChain UI enables anyone to create and host chatbots using a no-code type of inteface. LangChain chains and agents can themselves be deployed as a plugin that can communicate with other agents or with ChatGPT itself. import { ChatOpenAI } from "langchain/chat_models/openai"; import { HNSWLib } from "langchain/vectorstores/hnswlib";TL;DR: We’re introducing a new type of agent executor, which we’re calling “Plan-and-Execute”. Contact Sales. A web UI for LangChainHub, built on Next. OPENAI_API_KEY=".