langchainhub. 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. langchainhub

--workers: Sets the number of worker processes. Pull an object from the hub and use it. LangChain - Prompt Templates (what all the best prompt engineers use) by Nick Daigler. datasets. I have built 12 AI apps in 12 weeks using Langchain hosted on SamurAI and have onboarded million visitors a month. Recently Updated. The new way of programming models is through prompts. ⚡ LangChain Apps on Production with Jina & FastAPI 🚀. langchain. #3 LLM Chains using GPT 3. That should give you an idea. Announcing LangServe LangServe is the best way to deploy your LangChains. LangChain has become the go-to tool for AI developers worldwide to build generative AI applications. I believe in information sharing and if the ideas and the information provided is clear… Run python ingest. hub. We have used some of these posts to build our list of alternatives and similar projects. utilities import SerpAPIWrapper. The application demonstration is available on both Streamlit Public Cloud and Google App Engine. 💁 Contributing. Quickstart . LangChainHub UI. Taking inspiration from Hugging Face Hub, LangChainHub is collection of all artifacts useful for working with LangChain primitives such as prompts, chains and agents. 怎么设置在langchain demo中 #409. llama-cpp-python is a Python binding for llama. It's always tricky to fit LLMs into bigger systems or workflows. LangChain Hub 「LangChain Hub」は、「LangChain」で利用できる「プロンプト」「チェーン」「エージェント」などのコレクションです。複雑なLLMアプリケーションを構築するための高品質な「プロンプト」「チェーン」「エージェント」を. Shell. Advanced refinement of langchain using LLaMA C++ documents embeddings for better document representation and information retrieval. chains import ConversationChain. Specifically, the interface of a tool has a single text input and a single text output. pull ¶. This guide will continue from the hub. llama-cpp-python is a Python binding for llama. code-block:: python from langchain. Chapter 4. It is used widely throughout LangChain, including in other chains and agents. Here's how the process breaks down, step by step: If you haven't already, set up your system to run Python and reticulate. Python Version: 3. Useful for finding inspiration or seeing how things were done in other. Learn how to use LangChainHub, its features, and its community in this blog post. Glossary: A glossary of all related terms, papers, methods, etc. 💁 Contributing. You can share prompts within a LangSmith organization by uploading them within a shared organization. LangFlow is a GUI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows with drag-and-drop components and a chat. dalle add model parameter by @AzeWZ in #13201. This is a breaking change. I explore & write about all things at the intersection of AI & language; ranging from LLMs, Chatbots, Voicebots, Development Frameworks, Data-Centric latent spaces & more. Unified method for loading a prompt from LangChainHub or local fs. @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. However, for commercial applications, a common design pattern required is a hub-spoke model where one. 📄️ Quick Start. llms. ResponseSchema(name="source", description="source used to answer the. Data security is important to us. The LangChainHub is a central place for the serialized versions of these prompts, chains, and agents. Quickly and easily prototype ideas with the help of the drag-and-drop. huggingface_endpoint. Routing helps provide structure and consistency around interactions with LLMs. OpenGPTs gives you more control, allowing you to configure: The LLM you use (choose between the 60+ that LangChain offers) The prompts you use (use LangSmith to debug those)By using LangChain, developers can empower their applications by connecting them to an LLM, or leverage a large dataset by connecting an LLM to it. Build a chat application that interacts with a SQL database using an open source llm (llama2), specifically demonstrated on an SQLite database containing rosters. Project 3: Create an AI-powered app. " OpenAI. whl; Algorithm Hash digest; SHA256: 3d58a050a3a70684bca2e049a2425a2418d199d0b14e3c8aa318123b7f18b21a: Copy4. It loads and splits documents from websites or PDFs, remembers conversations, and provides accurate, context-aware answers based on the indexed data. It takes in a prompt template, formats it with the user input and returns the response from an LLM. Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM 等语言模型的本地知识库问答 | Langchain-Chatchat (formerly langchain-ChatGLM. 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. This is especially useful when you are trying to debug your application or understand how a given component is behaving. Note that these wrappers only work for models that support the following tasks: text2text-generation, text-generation. It offers a suite of tools, components, and interfaces that simplify the process of creating applications powered by large language. By continuing, you agree to our Terms of Service. To convert existing GGML. g. Prompt templates: Parametrize model inputs. LangChain Hub is built into LangSmith (more on that below) so there are 2 ways to start exploring LangChain Hub. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications. To use the LLMChain, first create a prompt template. Connect custom data sources to your LLM with one or more of these plugins (via LlamaIndex or LangChain) 🦙 LlamaHub. pull ¶. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you are able to combine them with other sources of computation or knowledge. 1. Tell from the coloring which parts of the prompt are hardcoded and which parts are templated substitutions. A web UI for LangChainHub, built on Next. All credit goes to Langchain, OpenAI and its developers!LangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents. It supports inference for many LLMs models, which can be accessed on Hugging Face. Whether implemented in LangChain or not! Gallery: A collection of our favorite projects that use LangChain. text – The text to embed. Source code for langchain. It builds upon LangChain, LangServe and LangSmith . This code creates a Streamlit app that allows users to chat with their CSV files. dev. That’s where LangFlow comes in. Thanks for the example. The supervisor-model branch in this repository implements a SequentialChain to supervise responses from students and teachers. Welcome to the LangChain Beginners Course repository! This course is designed to help you get started with LangChain, a powerful open-source framework for developing applications using large language models (LLMs) like ChatGPT. in-memory - in a python script or jupyter notebook. 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. Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. json to include the following: tsconfig. Creating a generic OpenAI functions chain. Org profile for LangChain Chains Hub on Hugging Face, the AI community building the future. The LangChainHub is a central place for the serialized versions of these prompts, chains, and agents. 3. github","path. Building Composable Pipelines with Chains. g. It builds upon LangChain, LangServe and LangSmith . For more information, please refer to the LangSmith documentation. llms import OpenAI. Step 5. Introduction. llms import HuggingFacePipeline. To associate your repository with the langchain topic, visit your repo's landing page and select "manage topics. """Interface with the LangChain Hub. ts:26; Settings. LangChain does not serve its own LLMs, but rather provides a standard interface for interacting with many different LLMs. As the number of LLMs and different use-cases expand, there is increasing need for prompt management to support. Taking inspiration from Hugging Face Hub, LangChainHub is collection of all artifacts useful for working with LangChain primitives such as prompts, chains and agents. Data security is important to us. LangChain is another open-source framework for building applications powered by LLMs. Pull an object from the hub and use it. Easy to set up and extend. These cookies are necessary for the website to function and cannot be switched off. We’re establishing best practices you can rely on. It formats the prompt template using the input key values provided (and also memory key. Whether implemented in LangChain or not! Gallery: A collection of our favorite projects that use LangChain. . What you will need: be registered in Hugging Face website (create an Hugging Face Access Token (like the OpenAI API,but free) Go to Hugging Face and register to the website. The obvious solution is to find a way to train GPT-3 on the Dagster documentation (Markdown or text documents). The Embeddings class is a class designed for interfacing with text embedding models. LangChain is an open-source framework designed to simplify the creation of applications using large language models (LLMs). {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". Compute doc embeddings using a modelscope embedding model. By continuing, you agree to our Terms of Service. LangChain’s strength lies in its wide array of integrations and capabilities. In the below example, we will create one from a vector store, which can be created from embeddings. prompts. LlamaHub Github. LangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents. This generally takes the form of ft: {OPENAI_MODEL_NAME}: {ORG_NAME}:: {MODEL_ID}. Go To Docs. This is useful because it means we can think. Our first instinct was to use GPT-3’s fine-tuning capability to create a customized model trained on the Dagster documentation. LLMs are capable of a variety of tasks, such as generating creative content, answering inquiries via chatbots, generating code, and more. LangChain provides tooling to create and work with prompt templates. Data Security Policy. Now, here's more info about it: LangChain 🦜🔗 is an AI-first framework that helps developers build context-aware reasoning applications. LLM. For more detailed documentation check out our: How-to guides: Walkthroughs of core functionality, like streaming, async, etc. 4. They enable use cases such as:. 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. This ChatGPT agent can reason, interact with tools, be constrained to specific answers and keep a memory of all of it. Glossary: A glossary of all related terms, papers, methods, etc. object – The LangChain to serialize and push to the hub. Pulls an object from the hub and returns it as a LangChain object. For more information, please refer to the LangSmith documentation. Introduction. Defaults to the hosted API service if you have an api key set, or a localhost. Introduction . OKLink blockchain Explorer Chainhub provides you with full-node chain data, all-day updates, all-round statistical indicators; on-chain master advantages: 10 public chains with 10,000+ data indicators, professional standard APIs, and integrated data solutions; There are also popular topics such as DeFi rankings, grayscale thematic data, NFT rankings,. You can find more details about its implementation in the LangChain codebase . 「LangChain」の「LLMとプロンプト」「チェーン」の使い方をまとめました。. Unexpected token O in JSON at position 0 gitmaxd/synthetic-training-data. Integrations: How to use. See below for examples of each integrated with LangChain. See the full prompt text being sent with every interaction with the LLM. // If a template is passed in, the. chains. from langchain. data can include many things, including:. g. prompt import PromptTemplate. All functionality related to Google Cloud Platform and other Google products. hub. While generating diverse samples, it infuses the unique personality of 'GitMaxd', a direct and casual communicator, making the data more engaging. When adding call arguments to your model, specifying the function_call argument will force the model to return a response using the specified function. LangSmith. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. 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. For a complete list of supported models and model variants, see the Ollama model. ) 1. ChatGPT with any YouTube video using langchain and chromadb by echohive. g. from langchain import hub. perform a similarity search for question in the indexes to get the similar contents. Routing allows you to create non-deterministic chains where the output of a previous step defines the next step. In terminal type myvirtenv/Scripts/activate to activate your virtual. LangChain is a framework for developing applications powered by language models. llama = LlamaAPI("Your_API_Token")LangSmith's built-in tracing feature offers a visualization to clarify these sequences. This will allow for largely and more widespread community adoption and sharing of best prompts, chains, and agents. A variety of prompts for different uses-cases have emerged (e. In this blog I will explain the high-level design of Voicebox, including how we use LangChain. . This example showcases how to connect to the Hugging Face Hub and use different models. 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. 2. 5 and other LLMs. [docs] class HuggingFaceEndpoint(LLM): """HuggingFace Endpoint models. Chroma runs in various modes. Standardizing Development Interfaces. LangChain. Its two central concepts for us are Chain and Vectorstore. We are incredibly stoked that our friends at LangChain have announced LangChainJS Support for Multiple JavaScript Environments (including Cloudflare Workers). 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. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs. To create a conversational question-answering chain, you will need a retriever. 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. 💁 Contributing. Useful for finding inspiration or seeing how things were done in other. 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. md","path":"prompts/llm_math/README. Taking inspiration from Hugging Face Hub, LangChainHub is collection of all artifacts useful for working with LangChain primitives such as prompts, chains and. Prev Up Next LangChain 0. " GitHub is where people build software. Content is then interpreted by a machine learning model trained to identify the key attributes on a page based on its type. import { ChatOpenAI } from "langchain/chat_models/openai"; import { LLMChain } from "langchain/chains"; import { ChatPromptTemplate } from "langchain/prompts"; const template =. Get your LLM application from prototype to production. It includes a name and description that communicate to the model what the tool does and when to use it. It's always tricky to fit LLMs into bigger systems or workflows. Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. g. Here are some examples of good company names: - search engine,Google - social media,Facebook - video sharing,Youtube The name should be short, catchy and easy to remember. Parameters. 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. Retriever is a Langchain abstraction that accepts a question and returns a set of relevant documents. What makes the development of Langchain important is the notion that we need to move past the playground scenario and experimentation phase for productionising Large Language Model (LLM) functionality. llm, retriever=vectorstore. Embeddings create a vector representation of a piece of text. LangChain is a framework for developing applications powered by language models. Only supports `text-generation`, `text2text-generation` and `summarization` for now. embeddings. LangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents. For tutorials and other end-to-end examples demonstrating ways to. LangChain does not serve its own LLMs, but rather provides a standard interface for interacting with many different LLMs. if var_name in config: raise ValueError( f"Both. You can explore all existing prompts and upload your own by logging in and navigate to the Hub from your admin panel. obj = hub. LangChain. def _load_template(var_name: str, config: dict) -> dict: """Load template from the path if applicable. get_tools(); Each of these steps will be explained in great detail below. 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. Dall-E Image Generator. As we mentioned above, the core component of chatbots is the memory system. LangChain is a framework for developing applications powered by language models. 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. # Replace 'Your_API_Token' with your actual API token. load. It allows AI developers to develop applications based on the combined Large Language Models. langchain. 1. This input is often constructed from multiple components. Write with us. Let's load the Hugging Face Embedding class. Structured output parser. Generate. For example: import { ChatOpenAI } from "langchain/chat_models/openai"; const model = new ChatOpenAI({. LangChain. It builds upon LangChain, LangServe and LangSmith . 0. They are usually only set in response to actions made by you which amount to a request for services, such as setting your privacy preferences, logging in or filling in forms. 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. Glossary: A glossary of all related terms, papers, methods, etc. Cookie settings Strictly necessary cookies. Hardware Considerations: Efficient text processing relies on powerful hardware. Use LlamaIndex to Index and Query Your Documents. cpp. We will pass the prompt in via the chain_type_kwargs argument. 多GPU怎么推理？. 多GPU怎么推理？. Discover, share, and version control prompts in the LangChain Hub. Duplicate a model, optionally choose which fields to include, exclude and change. LangChain exists to make it as easy as possible to develop LLM-powered applications. We would like to show you a description here but the site won’t allow us. It also supports large language. [docs] class HuggingFaceEndpoint(LLM): """HuggingFace Endpoint models. This will install the necessary dependencies for you to experiment with large language models using the Langchain framework. dump import dumps from langchain. The LangChain Hub (Hub) is really an extension of the LangSmith studio environment and lives within the LangSmith web UI. 多GPU怎么推理？. Defaults to the hosted API service if you have an api key set, or a localhost. 怎么设置在langchain demo中 · Issue #409 · THUDM/ChatGLM3 · GitHub. Unlike traditional web scraping tools, Diffbot doesn't require any rules to read the content on a page. For example, there are document loaders for loading a simple `. Unstructured data can be loaded from many sources. Llama Hub. Reload to refresh your session. The LangChainHub is a central place for the serialized versions of these prompts, chains, and agents. Hashes for langchainhub-0. LangChain Visualizer. It first tries to load the chain from LangChainHub, and if it fails, it loads the chain from a local file. An agent has access to a suite of tools, and determines which ones to use depending on the user input. Click here for Data Source that we used for analysis!. APIChain enables using LLMs to interact with APIs to retrieve relevant information. Langchain is the first of its kind to provide. Standard models struggle with basic functions like logic, calculation, and search. Prompt Engineering can steer LLM behavior without updating the model weights. Index, retriever, and query engine are three basic components for asking questions over your data or. Contact Sales. Note: the data is not validated before creating the new model: you should trust this data. LangChainHub-Prompts/LLM_Bash. import { AutoGPT } from "langchain/experimental/autogpt"; import { ReadFileTool, WriteFileTool, SerpAPI } from "langchain/tools"; import { InMemoryFileStore } from "langchain/stores/file/in. LangChain is a software framework designed to help create applications that utilize large language models (LLMs). The langchain docs include this example for configuring and invoking a PydanticOutputParser # Define your desired data structure. If the user clicks the "Submit Query" button, the app will query the agent and write the response to the app. 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. Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM 等语言模型的本地知识库问答 | Langchain-Chatchat (formerly langchain-ChatGLM. What is Langchain. Without LangSmith access: Read only permissions. 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. Discover, share, and version control prompts in the LangChain Hub. LangChainHub-Prompts/LLM_Bash. [2]This is a community-drive dataset repository for datasets that can be used to evaluate LangChain chains and agents. © 2023, Harrison Chase. To make it super easy to build a full stack application with Supabase and LangChain we've put together a GitHub repo starter template. We will continue to add to this over time. Hub. LLM. 👍 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. LangChain is an open-source framework designed to simplify the creation of applications using large language models (LLMs). g. 📄️ Google. I’m currently the Chief Evangelist @ HumanFirst. invoke: call the chain on an input. Tags: langchain prompt. There are lots of embedding model providers (OpenAI, Cohere, Hugging Face, etc) - this class is designed to provide a standard interface for all of them. OpenAI requires parameter schemas in the format below, where parameters must be JSON Schema. LangSmith is constituted by three sub-environments, a project area, a data management area, and now the Hub. Popular. We want to split out core abstractions and runtime logic to a separate langchain-core package. In this notebook we walk through how to create a custom agent. 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. You signed out in another tab or window. Here is how you can do it. Build context-aware, reasoning applications with LangChain’s flexible abstractions and AI-first toolkit. We are witnessing a rapid increase in the adoption of large language models (LLM) that power generative AI applications across industries. Llama Hub. pip install opencv-python scikit-image. First, let's load the language model we're going to use to control the agent. , SQL); Code (e. Columns:Load a chain from LangchainHub or local filesystem. This code defines a function called save_documents that saves a list of objects to JSON files. You can also replace this file with your own document, or extend. Chat and Question-Answering (QA) over data are popular LLM use-cases. Assuming your organization's handle is "my. The Hugging Face Hub serves as a comprehensive platform comprising more than 120k models, 20kdatasets, and 50k demo apps (Spaces), all of which are openly accessible and shared as open-source projectsPrompts. class langchain. A `Document` is a piece of text and associated metadata. In this quickstart we'll show you how to: Get setup with LangChain, LangSmith and LangServe. We will use the LangChain Python repository as an example. 1. Saved searches Use saved searches to filter your results more quicklyUse object in LangChain. OPENAI_API_KEY=". Finally, set the OPENAI_API_KEY environment variable to the token value. It allows AI developers to develop applications based on the combined Large Language Models. If no prompt is given, self. 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. Log in. T5 is a state-of-the-art language model that is trained in a “text-to-text” framework. This is a breaking change. It starts with computer vision, which classifies a page into one of 20 possible types. The tool is a wrapper for the PyGitHub library. LangSmith helps you trace and evaluate your language model applications and intelligent agents to help you move from prototype to production. chains import RetrievalQA. Let's load the Hugging Face Embedding class. 14-py3-none-any. agents import initialize_agent from langchain. The LangChainHub is a central place for the serialized versions of these prompts, chains, and agents. Only supports text-generation, text2text-generation and summarization for now. Viewer • Updated Feb 1 • 3. The application demonstration is available on both Streamlit Public Cloud and Google App Engine. class HuggingFaceBgeEmbeddings (BaseModel, Embeddings): """HuggingFace BGE sentence_transformers embedding models. --host: Defines the host to bind the server to. 📄️ Cheerio. We’re establishing best practices you can rely on. With the data added to the vectorstore, we can initialize the chain. Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. Unstructured data (e. プロンプトテンプレートに、いくつかの例を渡す(Few Shot Prompt) Few shot examples は、言語モデルがよりよい応答を生成するために使用できる例の集合です。The Langchain GitHub repository codebase is a powerful, open-source platform for the development of blockchain-based technologies. 0. LangChain provides several classes and functions. - GitHub - RPixie/llama_embd-langchain-docs_pro: Advanced refinement of langchain using LLaMA C++ documents embeddings for better document representation and information retrieval. ; Glossary: Um glossário de todos os termos relacionados, documentos, métodos, etc. You can also create ReAct agents that use chat models instead of LLMs as the agent driver. Whether implemented in LangChain or not! Gallery: A collection of our favorite projects that use LangChain. This prompt uses NLP and AI to convert seed content into Q/A training data for OpenAI LLMs. Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. We will pass the prompt in via the chain_type_kwargs argument. Setting up key as an environment variable. Contribute to jordddan/langchain- development by creating an account on GitHub. When using generative AI for question answering, RAG enables LLMs to answer questions with the most relevant,. Saved searches Use saved searches to filter your results more quicklyTo upload an chain to the LangChainHub, you must upload 2 files: ; The chain. Get your LLM application from prototype to production. 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. If you'd prefer not to set an environment variable, you can pass the key in directly via the openai_api_key named parameter when initiating the OpenAI LLM class: 2.