It also integrates with the Pinecone index and SentenceTransformers for sentence similarity and embeddings. The basic functionality here works the following way: In this notebook we will learn how to query relevant contexts to our queries from Pinecone, and pass these to a GPT-4 model to generate an answer backed by real data sources. We walk through 2 approaches, first using the RetrievalQA chain and the second using VectorStoreAgent Apr 20, 2023 · In this blog, we will explore how to build a Serverless QA Chatbot on a website using OpenAI’s Embeddings and GPT-3. sentence transformers or OpenAI’s embedding models ). Step 3: From your application, embed queries using Jan 27, 2024 · It uses OpenAI, Pinecone, and LangChain to process large amounts of text quickly and efficiently. It’s an essential technique that helps optimize the relevance of the content we get back from a vector May 8, 2024 · I wrote a well detailed article that will help you build RAG apps with Pinecone serverless, OpenAI, Langchain and Python without encountering any error Jun 24, 2024 · 3. Constructing good prompts is a crucial skill for those building with LLMs. I am hitting the queries using voiceflow. It's the next generation of search, an API call away. In this new age of LLMs, prompts are king. 🤖 May 21, 2023 · openai: This is the official OpenAI library that allows us to use their language models, like GPT-3. Step 2: Set the environmental variables as per this tutorial. Aug 29, 2023 · By combining the power of OpenAI’s semantic search pipeline with Pinecone’s efficient indexing and retrieval system, we have harnessed the potential to create a robust and accurate question Dec 7, 2023 · I have been able to successfully store the data into Pinecone by creating its embeddings, and I am even able to search for different queries related to that data. 4: 653: February 23, 2024 Using namespaces in Pinecone with Langchain Jan 10, 2024 · import os # Initialize Pinecone #pinecone. Again, they came up with very creative model names — text-embedding-3-small and text-embedding-3-large. Everything works fine, but I can’t make it work that the prompt contains multiple questions. Check this video, to know more about how we can que Start using Pinecone for free. 埋め込みの作成 Mar 27, 2023 · If you have already saved your embeddings to vector database, then it is the time to run queries on them. In this notebook, we will demo how to use the llama-index (previously GPT-index) library with Pinecone for semantic search. Project 3: Build an AI-powered app for kids that helps them find similar classes of things. The multi-modal nature of CLIP is powered by two encoder models trained to “speak the same language”. Initialize a LangChain embedding object: Jan 20, 2024 · PINECONE_INDEX (this must match the Name of your Index within the gcp-starter Free tier) Step 1: First, grab your API key. embeddings. 创建索引5. You'll also be able to create modern front-ends using Streamlit in pure Python. Create your Pinecone index. The application will utilize tools such as OpenAI/Langchain, Pinecone, Streamlit, and Snowflake for efficient data management and search capabilities. 存储向量数据6. Learn how to process PDF Nov 9, 2023 · TL;DR - OpenAI Assistants API vs Canopy (powered by Pinecone): Assistants API is limited to storing only 20 documents from the dataset. I have a feeling i’m going to need to use a vector DB service like Pinecone or Weaviate, but in the meantime, while there is not much data I was thinking of storing the data in SQL server and then just loading a table from SQL server as a dataframe and performing cosine Aug 28, 2023 · Aug 28, 2023. Create your Pinecone, OpenAI, Ably, FingerprintJS and Cockroach accounts and get your API keys. unstructured and unstructured[local-inference] : These are used for document processing and managing unstructured data. It was founded by the startup, Zilliz, which reached $113 million in investment last year. A common problem with using GPT-3 to factually answer questions is that GPT-3 can sometimes Deployment name vectorization source. API Keys - Pinecone. This course is a deep dive into the world of generative large language models (LLMs) like GPT-4 and their practical applications. Oct 10, 2023 · I’m able to use Pinecone as a vector database to store embeddings created using OpenAI text-embedding-ada-002, and I create a ConversationalRetrievalChain using langchain, where I pass OpenAI gpt-3. Create a . Jun 30, 2023 · Semantic document retrieval - we embed the inquiry and use it to query the documents indexed in Pinecone; Summarization chain (optional) - in our specific case, the documents we retrieve from Pinecone are going to be too long to send to OpenAI to formulate a final answer (they most likely are more than 4000 characters long). CLIP is able to encode different text and images into the same vector space. We showed how you can highlight the specific portions of the retrieved context that are applicable to the response, which can help users gain trust in the AI assistant. Assuming I have a PDF with a date, an address, some content. Jun 30, 2023 · Chunking Strategies for LLM Applications. pinecone. I would like to gather feedback on ⌨️ DAY 1: Introduction to Generative AI Community Course ⌨️ DAY 2: Introduction to OpenAI and understanding the OpenAI API ⌨️ DAY 3: Introduction to LangChain ⌨️ Day 4: Hugging Face API + Langchain ⌨️ DAY 5: Memory in Langchain ⌨️ DAY 6: LLM Generative AI Project using OpenAI & LangChain ⌨️ DAY 7: LLM Generative AI Project & Deployment ⌨️ DAY 8: Introduction to Open AI API Using OpenAI gpt-3. The applications will be complete and we’ll also contain a modern web app front-end using Streamlit. We will use OpenAI's text-embedding-ada-002 here, so we must authenticate ourselves with an OpenAI API key. 订阅Pinecone服务2. 通过将OpenAI的LLMs与Pinecone集成，我们将嵌入生成的深度学习能力与高效的向量存储和检索相结合。. Python. gpt-3. The second dependency is the ai package which we'll use to define the Message and OpenAIStream types, which we'll use to stream back the response from OpenAI back to the client. The below is an example workflow using OpenAI’s ChatGPT retrieval plugin with Pinecone: Step 1: Fork chatgpt-retrieval-plugin from OpenAI. Share: Fine-tuning for GPT-3. First, OpenAI trained it on a huge dataset of 400M text-image pairs that were scraped from across the internet. Note that the vectors being searched were all generated by OpenAI text-embedding-ada-002. This notebook is an introduction and Jul 19, 2023 · We used OpenAI and the Pinecone API to create an index, add documents to the index, and query the index. These embeddings are used to populate a Pinecone index, which is used for performing queries. llms import OpenAI from langchain. Since LLMs are now such an integral piece of the puzzle, there are several Search through billions of items for similar matches to any object, in milliseconds. This notebook shows how to use functionality related to the Pinecone vector database. 5 model, create a Vector Database with Pinecone to store the embeddings, and deploy the application on AWS Lambda, providing a powerful tool for the website visitors to get the information they need quickly and efficiently. OpenAI is dethroning its own model. Training uses a contrastive learning approach that aims to unify text and images, allowing tasks like image classification to be done with Mar 24, 2023 · Building a chat bot has become a hot skill, and with the release of ChatGPT we see a huge number of chat applications being released. Pinecone is a vector database with broad functionality. Step 3: Embed your documents using the retrieval plugin’s “/UPSERT Code Along to this Session Using DataCamp Workspace!Chatbots have been around for years, but it's only with the advent of LLMs and vector databases that it's May 8, 2024 · The aim of it is to create a chatbot with my own data (txt files) that can be used on a website for support reasons. Greater productivity. Step 2: Next, create the Index for this project Jan 17, 2024 · The Pinecone documentation uses openai. But now my requirement is that the voiceflow should There are a few approaches to question answering (QA). In this notebook we will learn how to query relevant contexts to our queries from Pinecone, and pass these to a generative OpenAI model to generate an answer backed by real data sources. g. However, Ada 002 is about to be dethroned. 只有在您想使用 Python客户端 时才执行此步骤。. environ['OPENAI_API_KEY']) chain = load_qa_chain(llm, chain_type="stuff") query = "" docs = docsearch. 5-turbo model to power the chat; Pinecone Serverless used as a DB for custom documents; Langchin. Building safe and beneficial AGI is our mission. By the end of this course, you will have a solid understanding of the fundamentals of LangChain, Pinecone, OpenAI and Google's Gemini Pro and Pro Vision. Powerful as they are, current LLMs Jan 20, 2024 · The left pane displays your chatbot powered by Pinecone, beside the right pane allows you to compare a chatbot without context. Project 4: Create a marketing campaign app focused on The script uses OpenAI's GPT-3. Tech stack used includes LangChain, Pinecone, Typescript, Openai, and Next. The Pinecone vector database lets you build RAG applications using vector search. Vector Store and QA Chain: Establish a vector store using Pinecone Pinecone. Oct 30, 2023 · Pinecone is now available on the Azure Marketplace. text_splitter import RecursiveCharacterTextSplitter from langchain. 7. Langchain Retrieval Augmentation. Step 2: Save those embeddings in Pinecone. We are excited to expand our Microsoft partnership by joining the Azure Marketplace, following our recent announcement supporting Azure regions on Pinecone. In the application I used Pinecone as the vector database and store embeddings in Pinecone. 本指南介绍如何在几分钟内设置Pinecone向量数据库。. To create a new LangChain project and install this as the only package, you can do: langchain app new my-app --package rag-pinecone. Keep getting error: APIRemovedInV1: You tried to access openai. Text inputs are passed to a text encoder, and image inputs to an image encoder [3]. Upsert a dataset as a dataframe. Embedding, but this is no longer supported in openai>=1. The default model for the Retrieval Plugin is text-embedding-3-large with 256 dimensions. We use Vercel’s AI SDK and OpenAI for completion and embeddings. It also includes features for querying existing vectors and retrieving relevant metadata. These models then create a vector representation of the respective input. 安装Pinecone Python客户端4. Pinecone has integration to OpenAI, Haystack and co:here. question_answering import load_qa_chain llm = OpenAI(temperature=0, openai_api_key=os. Setup the knowledge base (in Pinecone) Chunk the content; Create vector embeddings from the chunks; Load embeddings into a Pinecone index; Ask a question; Create vector embedding of the question; Find relevant context in Pinecone, looking for embeddings similar to the question; Ask a question of OpenAI, using the relevant context from Pinecone . 5 model that has been tuned towards a particular dataset. We’ll build together, step-by-step, line-by-line, real-world LLM applications with Python, LangChain, and OpenAI. Pinecone excels at similarity search, enabling Langchain to retrieve highly relevant information from large datasets quickly. init(api_key="", environment="eu-west-gcp") import os import re import pdfplumber import openai import pinecone from langchain. Then perform true semantic searches on the data, returning highly accurate results. It can identify text and images with similar meanings by encoding both modalities into a shared vector space. At the root of all of these applications live Large Language Models - the engine of the generative AI train. We believe our research will eventually lead to artificial general intelligence, a system that can solve human-level problems. Project 2: Develop a conversational bot using LangChain,LLM and OpenAI. I have a prompt like: “Give me the address, the date, question 1 about the content, question 2 Project 1: Construct a question-answering application powered by LLM using LangChain, OpenAI, and Hugging Face Spaces. 查询向量数据7. 無料のアカウント開設に3日間くらい待機リストに入りましたが、割りとすぐに使える Zero Shot CLIP. There are three primary benefits here: CLIP requires just image-text pairs rather than specific class labels thanks to the contrastive rather than classification focused training function. Canopy could store all 423 documents, with room for another ~777 before reaching the limit of Pinecone's free plan. Last updated at 2023-05-06 Posted at 2023-05-06. Jan 25, 2024 · That lack of movement from OpenAI didn't matter much regarding adoption. openai import OpenAIEmbeddings model_name Feb 7, 2023 · Retrieval augmented generative question answering with Pinecone. 安装Pinecone客户端（可选）. py file: from rag_pinecone import chain as Mar 25, 2023 · If I have this code from langchain. init (api_key="your-api-key", environment="us-east4-gcp") index_name = "semanticsearch". Ada 002 is still the most broadly adopted text embedding model. additional_kwargs: Add additional parameters to OpenAI request body. max_retries: How many times to retry the May 17, 2023 · OpenAI with Pinecone with a prompt containing multiple questions. Both models “speak the same language” by encoding similar concepts in text and Pinecone, as a managed vector database platform, stands out as a powerful tool for data engineers and scientists, offering scalability, low-latency search capabilities, and real-time data ingestion. LangChain is a framework that makes it easier to build scalable AI/LLM apps and chatbots. Note: This notebook is built to run end-to-end in Google Colab. Embedding. Deep Dives. Aug 3, 2023 · Retrieval Augmented Generation (RAG) in AI is a technique that leverages a database to fetch the most contextually relevant results that match the user's query at generation time. 这种方法超越了传统的基于关键字的搜索，提供了 Source code with full-stack langchain chatbot with a custom knowledge base: https://github. Jul 10, 2023 · First, we collect YouTube videos and transform each into audio using Python. API. Pineconeのアカウントが開設できたので、試してみました！. The following example upserts the uora_all-MiniLM-L6-bm25 dataset as a dataframe. 5 turbo is finally here! The latest update gives OpenAI users the ability to create their own custom GPT-3. max_tokens: the maximum number of tokens to generate. Created by our developer advocate, Roie Schwaber-Cohen. 知乎专栏提供自由写作平台，让用户随心表达观点和分享知识。 Jun 30, 2023 · You can also refer to our example notebook and NLP for Semantic Search guide for more information. This application can save time by quickly finding the information needed without having to search Now that you’ve built your Pinecone index, you need to initialize a LangChain vector store using the index. - rohitf1/chatbot-streamlit-langchain-pinecone-openai A Streamlit-powered chatbot integrating OpenAI's GPT-3. The most common form of QA is open-book extractive QA (top-left above). Core Components. Keep in mind different embeddings models may have a different number of dimensions: 🧠 Fully updated in February 2024 for the latest versions of LangChain, OpenAI, Google's Gemini & Pinecone! Master the art of integrating state-of-the-art AI with real-world applications. May 1, 2023 · Dive into this comprehensive tutorial on building a PDF-GPT Chatbot that seamlessly integrates Pinecone and OpenAI's GPT technology. To encode that data, we need to use an embedding model. from openai import OpenAI. GPT-4 is a big step up from previous OpenAI completion models. So before starting with the actual issue, let me share my recent code with you: import os. First look video A Ruby gem for semantic search using Pinecone and OpenAI API. Styling Pinecone是专为存储和查询高维向量而设计的向量数据库。. embeddings, pinecone. 它提供了对这些向量嵌入的快速，高效的语义搜索。. Initialize Pinecone: Please note the the dimension parameter here depends on the chunk_size that you've used to split your pdf's text. pnpm add @langchain/pinecone @pinecone-database/pinecone The below examples use OpenAI embeddings, but you can swap in whichever provider you'd like. Aug 8, 2023 · As we already used OpenAI for the embedding, the easiest approach is to use it as well for the question answering. #gpt #openai #chatgpt #pinecone #embedding 在这期视频中，我将为您演示如何以WTF学院的开源课程系列为数据源，使用OpenAI Embedding API实现文本向量化，Pinecone OpenAI Embedding API为用户的文本生成向量表达。Pinecone为向量数据提供了数据存储解决方案。今天的视频将涵盖如下内容：1. 5-turbo text-embedding-ada-002 Python OpenAI Langchain. import pinecone. Response Generation and Quality. Assistants API couldn't answer a question that required retrieving context from multiple Unlike OpenAI, with Pinecone, you don’t necessarily need to copy the API key when you generate one because there is a copy icon under the Actions column that you can use anytime you want. If you’re already on Azure or leveraging Azure’s OpenAI Service, you can now quickly add Pinecone to your AI stack with just a few OpenAI API key required. And add the following code to your server. The OpenAI Whisper model is then used to transcribe audio to text. Get Pinecone API Key. Products built on top of Large Language Models (LLMs) such as OpenAI's ChatGPT and Anthropic's Claude are brilliant yet flawed. This step uses the OpenAI API key you set as an environment variable earlier. similarity_search(query, include_metadata=True) chain. import openai. Use the new GPT-4 api to build a chatGPT chatbot for multiple Large PDF files. We will use Langchain as an orchestration framework to tie all the bits together. create(which appears to be deprecated in the latest version of OpenAI. Then that splitted documents is fed into the pinecone database and get an store using the pinecone library. This LangChain course is the 2nd part of “OpenAI API with Python Bootcamp”. 0. But this beast must be tamed - and that’s not always an easy task. document_loaders import PyPDFLoader from langchain. Jan 29, 2024 · What I am finding is that the Pinecone API: When passed one of these new vectors, text-embedding-3-large, truncated to 1536, the search results are wonky. First, install a few pip packages locally: pip install pinecone-client langchain openai. Retrieval Enhanced Generative Question Answering with OpenAI Fixing LLMs that Hallucinate In this notebook we will learn how to query relevant contexts to our queries from Pinecone, and pass these to a generative OpenAI model to generate an answer backed by real data sources. Firstly, I'm using langchainjs to load the documents based on the file path provided and split them into chunks. There, I take a query from a user, and it sends the query to my code, and then it retrieves a result. run(input_documents=docs, question=query) the docsearch relies on index_name Sep 13, 2023 · The chunk field contains the text we will encode and store inside Pinecone. Step 1: Take data from the data warehouse and generate vector embeddings using an AI model (e. It also exclusively uses the ChatCompletion endpoint, so we must use it in a slightly different way to usual. This will be a learning-by-doing experience. chains. Jun 30, 2023. The Milvus vector database is specifically designed from the bottom up to handle embedding vectors converted from unstructured data. Pinecone is a vectorstore for storing embeddings and your PDF in text to later retrieve similar docs. Pinecone is the developer-favorite vector database that's fast and easy to use at any scale. tutorial. Jan 4, 2024 · I am learning how to use Pinecone properly with LangChain and OpenAI Embedding. Oct 17, 2023 · I want to use langchain to give my own context to an openai gpt llm model and query my data using the llm model. Jun 27, 2023 · Using Pinecone for embeddings search. Contrastive Language-Image Pre-training (CLIP for short) is a state-of-the-art model introduced by OpenAI in February 2021 [1]. Reduce hallucination Leverage domain-specific and up-to-date data at lower cost for any scale and get 50% more accurate answers with RAG. env file in the root directory of the project and add your API keys: The ChatGPT Retrieval Plugin uses OpenAI's embeddings models to generate embeddings of document chunks. May 6, 2023 · PineconeにQAデータ格納してボット回答試してみた。. 5-turbo as the LLM, and the Pinecone vectorstore as the retriever. Oct 11, 2023 · I’m able to use Pinecone as a vector database to store embeddings created using OpenAI text-embedding-ada-002, and I create a ConversationalRetrievalChain using langchain, where I pass OpenAI gpt-3. But I have a very specific use case. By “wonky” what I have observed is that the “score” returned is abnormally low. This feature means we can teach GPT-3. OpenAI’s CLIP is a multi-modal model pretrained on a massive dataset of text-image pairs [3]. You can demonstrate recursive and markdown chunking and explore the difference in performance. pip install pinecone-client. If you want to add this to an existing project, you can just run: langchain app add rag-pinecone. 快速入门. Roie Schwaber-Cohen. Interact with the chatbot by running the Streamlit application and providing queries. js app I’ve created a PDF loader which creates a vector store via pinecone embedding. Bad prompts produce bad outputs, and good prompts are unreasonably powerful. Create conversational agents with LangChain and Pinecone. 5-turbo, for generating human-like text. 5 model for generating conversational responses. In this tutorial, we'll walk you through the process of creating a knowledge-based chatbot using the OpenAI Embedding API, Pinecone as a vector database, and langchain. CLIP is a neural network trained on about 400 million (text and image) pairs. Users might run into issues when running locally, depending on their local environment setup. js for coordination between the model and the database; Vercel AI SDK for streaming chat UI; Support for OpenAI (default), Anthropic, Cohere, Hugging Face, or custom AI chat models and/or LangChain; shadcn/ui. This gem provides out-of-the-box functionality for generating vectors using the OpenAI API's text-embedding-ada-002 model and uploading them to Pinecone for efficient semantic search. 5 the language and terminology of our niche domain (like finance or tech), reply in Jun 17, 2022 · Pinecone gives you access to powerful vector databases, you can upload your data to these vector databases from various sources. The method includes retry logic and batch_size, and is performant especially with Parquet file data sets. Feb 9, 2023 · I’m looking at trying to store something in the ballpark of 10 billion embeddings to use for vector search and Q&A. Note that OpenAI is a paid service and so running the remainder of this tutorial may incur some small cost. 5-turbo as the LLM, an… Feb 23, 2024 · In my next. Share: In the context of building LLM-related applications, chunking is the process of breaking down large pieces of text into smaller segments. com/irina1nik/chatbot-with-dataLearn how to create a Pinecone inde Apr 28, 2023 · To store embeddings in Pinecone, follow these steps: a. from langchain_community. After that, we use the text-embedding-ada-002 model to generate transcription embeddings. This notebook takes you through a simple flow to download some data, embed it, and then index and search it using a selection of vector databases. The user can ask a prompt about this PDF. This vectorization source is based on an internal embeddings model deployment name in the same Azure OpenAI resource. It is not recommended for The first dependency is the openai-edge package which makes it easier to interact with OpenAI's APIs in an edge environment. After obtaining OpenAI and Pinecone API keys, assign them to variables in the Notebook or set them as environment variables using the It also guides you on the basics of querying your custom PDF files data to get answers back (semantic search) from the Pinecone vector database, via the OpenAI LLM API. This is a common requirement for customers who want to store and search our embeddings with their own data in a secure environment to support production use May 7, 2024 · OPENAI_API_KEY="your openAI api key here" PINECONE_API_KEY="your pinecone api key here" 5. Llama-Index with Pinecone. langchain. To quickly ingest data when using the Python client, use the upsert_from_dataframe method. 0 - see the README at GitHub - openai/openai-python: The official Python library for the OpenAI API for Apr 17, 2023 · このスクリプトは、OpenAIのAPIを使用して文書を埋め込みベクトルに変換し、Pineconeの検索インデックスに挿入し、そのインデックスを使用してクエリ検索を実行する例を示しています。 OpenAI APIとPineconeクライアントをインストールする. document_loaders import TextLoader. Custom integration is also possible. Endless inspiration. Any open-book QA requires an IR step to retrieve relevant information from the ‘open-book’. Here we combine an information retrieval (IR) step and a reading comprehension (RC) step. Set the following environment variables to follow along in this doc: OPENAI_API_KEY: Your OpenAI API key, for using OpenAIEmbeddings % Jun 29, 2023 · Milvus. Faster, simpler procurement: Skip the approvals needed to integrate a new solution, and start building right away with a simplified architecture. Apr 9, 2023 · openai. With its seamless integration and user-friendly API, Pinecone streamlines the process of constructing and deploying large-scale machine learning Step-by-step guide for building LLM-powered Chatbot on your own custom data, leveraging RAG techniques using OpenAI and Pinecone in Python. OpenAI. Open the Python file you will be working with, write the following code there to load your environment Pinecone向量数据库是一个云原生的向量数据库，具有简单的API和无需基础架构的优势。 它可以快速处理数十亿条向量数据，并实时更新索引。 同时，它还可以与元数据过滤器相结合，以获得更相关、更快速的结果。 pip install -U langchain-cli. OpenAI Assistant is known for its high-quality, human-like text generation, thanks to extensive pre-training on diverse datasets. This chatbot will be able to accept URLs, which it will use to gain knowledge from and provide answers based on Jan 3, 2024 · Embeddings and Pinecone Setup: Create embeddings for data using OpenAI and initialize Pinecone with API key and environment. In order to from langchain_openai import OpenAIEmbeddings from langchain_pinecone import PineconeVectorStore from langchain_text_splitters import MarkdownHeaderTextSplitter import os import time # Chunk the document based on h2 headers. Another way to get started is by implementing Pinecone as an agent. 此步骤是可选的。. 5-turbo model with LangChain for conversation management, and Pinecone for advanced search capabilities. markdown_document = "## Introduction\n\nWelcome to the whimsical world of the WonderVector5000, an astonishing leap into the realms of imaginative technology. 使用以下shell命令安装Pinecone：. The details of the vectorization source, used by Azure OpenAI On Your Data when applying vector search. Instant answers. We also used the Pinecone API to retrieve embeddings for a given text. js as a large language model (LLM) framework. I built an application which can allow user upload PDFs and ask questions about the PDFs. 获取Environment和API Key3. temperature: a float from 0 to 1 controlling randomness in generation; higher will lead to more creative, less deterministic responses. Like Weaviate, Milvus is an open-source vector database written in Go. May 1, 2023 · Hello Pinecone User Community, I am currently designing an application that integrates a chatbot and advanced search functionality to access and filter internal and public documentation using a vector database. js. 如何开始使用Pinecone向量数据库。. OpenAI offers two latest embeddings models, text-embedding-3-small and text-embedding-3-large, as well as an older model, text-embedding-ada-002. Next initialize the OpenAI client: Jul 13, 2023 · Running Pinecone on Azure also enables our customers to achieve: Performance at scale: Having Pinecone closer to the data, applications, and models means lower end-to-end latencies for AI applications. Dec 27, 2023 · We’ll dive deep into embeddings and vector databases such as Pinecone. The use case is that I’m saving the backstory of a fictional company employee Args: model: name of the OpenAI model to use. hw ki xm vh tk ii jj vi sh en