Chroma db clustering github. In-memory with optional persistence.
Chroma db clustering github By default, Chroma uses This is a basic implementation of a java client for the Chroma Vector Database API This project is heavily inspired in chromadb-java-client project. Each "chunk" is one JSON item. It additionally integrates the chatbot with a persistent knowledge base using the ChromaDB library. When you are starting your journey with Amazon Aurora and want to set up AWS the AI-native open-source embedding database. Dev, Test, Prod: the same API that runs in your python notebook, scales to your cluster; Feature-rich: Queries, filtering, density This repo is a beginner's guide to using Chroma. What are embeddings? Read the guide from OpenAI; Literal: Embedding something turns it from image/text/audio into a list of numbers. env file with the following credentials: OPENAI_API_KEY="sk-xxxxxx" Edit app. ; Mini LLM Model with URLs:. Contribute to faycaldjilali/chromadb development by creating an account on GitHub. Navigation Menu Add documents to your database. information-retrieval naive-bayes inverted-index tf-idf evaluation-metrics kmeans-clustering ChromaDB is a high-performance, scalable vector database designed to store, manage, and retrieve high-dimensional vectors efficiently. Querying and Retrieval: Chroma DB acts as a retriever to fetch relevant documents based on user queries using methods like get_relevant_documents. py to add the JSON file path and the Chroma Vector DB directory: file_path = "myfile. 0 Licensed You signed in with another tab or window. These kind of issues are extremely frustrating. Dimensional reduction is performed using PCA for colors down to 50 dimensions, followed by tSNE down to 3. io/chromadb APP VERSION DESCRIPTION chroma/chromadb 0. De Vector database geeft me de meest waarschijnlijke antwoorden, die ik vervolgens gebruikersvriendelijk ombouw met behulp van ChatGPT en prompt-engineering. It is particularly optimized for use cases involving AI, machine learning, and applications that require similarity search or context retrieval, such as Large Language Model (LLM This repository includes a Python script (csv_loader. "@ chroma-core / chromadb": "1. 🖼️ or 📄 => [1. ; Vector Database: Chroma is used to store and retrieve document vectors. ### How to reproduce 1, Run DG-GPT with chromium vector store. ; Tools Used: OpenAI API, LangChain, Streamlit for web UI. Contribute to D-Star-AI/minDB development by creating an account on GitHub. You can pass in your own embeddings, embedding function, or let Chroma embed them for you. Contribute to giorgosstath16/chroma_db development by creating an account on GitHub. Document Loading: Load PDF files using PdfReader. This enables documents and queries with the same essence to be Combines the retrieval functionality of the Chroma database with the ChatGoogleGenerativeAI model to answer questions. agent openai chroma gpt3 gpt-4 chromadb agentgpt babyagi Updated Apr 17, 2023; OpenAI text-davinci-003 LLM and ChromaDB database for answering questions about loaded texts. 4. Exporting large dataset to HuggingFace or any other dataformat Contribute to youngsecurity/ai-chroma development by creating an account on GitHub. 🚀 Stay tuned! More information and updates are on the way. This tool provides a quick and intuitive way to interact with your vector database. Protein space is complex and hard to navigate. Like when using SQLite What are embeddings? Read the guide from OpenAI; Literal: Embedding something turns it from image/text/audio into a list of numbers. Dev, Test, Prod: the same API that runs in your python notebook, scales to your cluster; Feature-rich: Queries store embeddings and allow you to search by nearest neighbors rather than by substrings like a traditional database. The script utilizes the LangChain library for natural language processing tasks and incorporates multithreading to enhance concurrent processing. These models evaluate the similarity between a query and query results retreived from vectordb, Re-Ranker rank the results by index ensuring that retrieved information is relevant and contextually accurate. This chart deploys a ChromaDB Vector Store cluster on a Kubernetes cluster using the Helm package manager. external}, an # Create a new Chroma database from the documents: chroma_db = Chroma. Embeddings databases Hands-on-Vector-database-Chroma ChromaDB is an open-source vector database designed for storing, indexing, and querying high-dimensional embeddings or vector data. 3. Embeddable vector database for Go with Chroma-like interface and zero third-party dependencies. get_or_create the AI-native open-source embedding database. In brief, version numbers are generated as follows: If the current git head is tagged, the version number is exactly the tag This YAML file defines the PersistentVolumeClaim (PVC) for Chromadb, ensuring persistent storage for the database. It offers an industry Contribute to surmistry/chroma-ai development by creating an account on GitHub. 📖 Documentation. Choose ChatDB as a main way to chat with out database. Perhaps, what makes Chroma claim it is the embedding database is that users can declare new collections and specify the so-called embedding function that will be automatically used to obtain and store embeddings for new documents, and use the function to get embedding for search queries. LangChain is a framework that makes it easier to build scalable AI/LLM apps and chatbots. Using embeddings, Chroma lets developers add state and memory to their AI-enabled applications. Dev, Test, Prod: the same API that runs in your python notebook, scales to your cluster; Feature-rich store embeddings and allow you to search by nearest neighbors rather than by substrings like a traditional database. Published 1 day ago This repo is used to locally query pdf files using AOAI embedding model, langChain, and Chroma DB embedding database. It is designed to group memories in the agent's memory based on their similarity and proximity in the data space. python openai Saved searches Use saved searches to filter your results more quickly The client does not generate embeddings, but you can generate embeddings using bumblebee with the TextEmbedding module, you can find an example on this livebook. Feel free to contribute and enhance Github. ; Question Answering: The QA chain retrieves relevant GitHub Welcome to ChromaDB Cookbook Contributing Contributing Getting Started with Contributing to Chroma Useful Shortcuts for Contributors Core Core Rebuilding Chroma DB Time-based Queries Multi tenancy Multi tenancy Implementing OpenFGA Authorization Model In Chroma Chroma Authorization Model with OpenFGA This repository contains two versions of a PDF Question Answering system built with Streamlit and LangChain: ChromaDB Version - Uses local vector storage. Azure Cosmos DB for NoSQL: Azure Cosmos DB for NoSQL is a globally distributed database service designed for scalable and high performance applications. Contribute to akaiserg/chroma-db development by creating an account on GitHub. The user can then Azure Cosmos DB for MongoDB features built-in vector database capabilities enabling your data and vectors to be stored together for efficient and accurate vector searches. The implementation queries data from the “Climate Change 2023 Synthesis Report,” allowing for the extraction of in-depth, coherent, and relevant GitHub is where people build software. the AI-native open-source embedding database. devarthurguilherme asked this question in Q&A. python query_data. 3 A Helm chart for Chroma DB vector store. For more information, refer documentation . By default, Chroma uses This project demonstrates a complete pipeline for building a Retrieval-Augmented Generation (RAG) system from scratch. connection() , connecting to a Chroma vector database becomes just a few lines of code: GitHub is where people build software. Contribute to KnowCorp/vector-db development by creating an account on GitHub. /chroma_db ディレクトリにデータが保存されます。 パフォーマンスの最適化 大量のデータを扱う場合、バッチ処理を使用することでパフォーマンスを向上させることができます： the AI-native open-source embedding database. It covers all the major features including adding data, querying collections, updating and deleting data, and using different embedding functions. Dev, Test, Prod: the same API that runs in your python notebook, scales to your cluster Feature-rich : Queries, filtering, density estimation and more Free & Open Source : Apache 2. 0 Licensed More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. With Chroma, protein design problems are represented in terms of composable building blocks from which diverse, all-atom protein structures can be automatically generated. fullnameOverride: string "anything-llm" Override the full name of the Embeddable vector database for Go with Chroma-like interface and zero third-party dependencies. 2 Use LLM and embedding model as chatgpt_proxyllm and proxy_openai respectively. Discord. 46423f83-12509072228. Contribute to la-cc/anything-llm-helm-chart development by creating an account on GitHub. Dev, Test, Prod: the same API that runs in your python notebook, scales to your cluster; Feature-rich: Queries, filtering Add documents to your database. This enhancement streamlines ChromaDB utilization in RAG environme The Go client for Chroma vector database. from_documents (documents = docs, embedding = embeddings, persist_directory = "data", collection_name = ChromaDB is a powerful vector store that has generated a lot of excitement within the AI/ML community. This example focus on how to feed Custom Data as Knowledge base to OpenAI and then do Question and Answere on it. In this tutorial, I will explain how to This project uses PyPA's setuptools_scm module to determine the version number for build artifacts, meaning the version number is derived from Git rather than hardcoded in the repository. js. 4GHz) 4 NVIDIA RTX A6000 GPUs; 256GB ECC DDR4-2400 RAM; The code snipped to upload things to the vector database: Skip to content. ), from HuggingFace, from local persisted Chroma DB or even another remote Chroma DB. Here, we explore the capabilities of ChromaDB, an open-source vector embedding database that allows users to perform semantic search. Write better code with AI A Rust client library for the Chroma vector database. ipynb to extract text from your PDF files using any of the supported libraries. This process makes documents "understandable" to a machine learning model. Each topic has its own dedicated folder with a Explore your Chroma Database with ease using Chroma-Peek. By default, Chroma uses Sentence What are embeddings? Read the guide from OpenAI; Literal: Embedding something turns it from image/text/audio into a list of numbers. Chroma is an opensource vectorstore for storing embeddings and your API data. Navigation Menu Toggle navigation For an example of using Chroma+LangChain to do question answering over documents, see this notebook. Saved searches Use saved searches to filter your results more quickly This repo is a beginner's guide to using Chroma. ; Preprocessing: Documents are split into manageable sections with RecursiveCharacterTextSplitter. Provide connection to a mssql database. 5-dev. . Installation We start off by installing the required packages. Manage Pinecone, Chroma, Qdrant, Weaviate and more vector databases with ease. This chart deploys a ChromaDB Vector Store cluster on a Kubernetes cluster using the Helm package manager. Given that the Document object is required for the update_document method, this lack of functionality makes it difficult to update document metadata, which should be a fairly common use-case. This repository is a collection of sample client tools for using ChromaDB. This repository manages a collection of ChromaDB client sample tools for beginners to register the Livedoor corpus with the AI-native open-source embedding database. Navigation Menu Toggle navigation. This enables documents and queries with the same essence to be This repository features a Python script (url_loader. Chroma is the AI-native open-source vector database. Unanswered. ; Response Generation: Language models are used to generate responses based on retrieved documents. As a joint model of Skip to content. Contribute to demvsystems/ai-chroma development by creating an account on GitHub. If you have a Dev, Test, Prod: the same API that runs in your python notebook, scales to your cluster; Feature-rich: Queries, filtering, density estimation and more; Free & Open Source: Apache 2. By analogy: An embedding represents the essence of a document. Contribute to Royer-Chang/chroma_T development by creating an account on GitHub. Description: Select a cuisine type, and the tool generates a restaurant name and a corresponding menu, showcasing creative text generation using AI. ) The nodes will now work when ran with runGraphInFile or Chroma is an open-source vector database that allows you to store, search, and analyze high-dimensional data at scale. we compared it with a commonly used HNSW-based vector database, Chroma. Latest. Query relevant documents with natural language. In the create_chroma_db function Contribute to grunge-ai/grunge-server-chromadb development by creating an account on GitHub. 5 0. js - flanker/chromadb-admin Saved searches Use saved searches to filter your results more quickly Add documents to your database. Search for "rivet-plugin-chromadb" Click the "Install" button to install the plugin into your current project. ; Azure AI Search Version - Uses cloud-based vector storage. (You may also use your own node registry if you wish, instead of the global one. Chroma DB doesn't work #3566. Contribute to chroma-core/chroma development by creating an account on GitHub. This Python script serves as the implementation of a chatbot that leverages the OpenAI's GPT-4 model. Ik laad alle teksten in de Chroma Vector database, die omgezet worden naar vectoren m. Reload to refresh your session. 2, 2. py) that demonstrates the integration of LangChain for processing data from URLs, extracting text, and establishing a Chroma vector store. Contribute to surmistry/chroma-ai development by creating an account on GitHub. json" persist_directory = ". Tech stack used includes LangChain, Private Chroma DB Deployed to AWS, Typescript, Openai, and Next. ; Implementation: To integrate vector search into my recommendation system, I followed these steps: Movie and Packages. This project is embodied in a Google Colab notebook, fine-tuned for an A100 instance. Chroma vector database in a Docker container. [ ] Now you will create the vector database. Features. Testing pixee on Chroma The AI-native open-source embedding database - GlitchLabs/chromaPixeeTest Dev, Test, Prod: the same API that runs in your python notebook, scales to your cluster; Feature-rich: Queries, filtering store embeddings and allow you to search by nearest neighbors rather than by substrings like a traditional database Chroma DB GUI. Associated vide Query the Chroma DB. A package for visualising vector embedding collections as part of the Chroma vector database. Updates. Contribute to amikos-tech/chroma-go development by creating an account on GitHub. It is especially useful in applications involving machine learning, data science, and any field that requires fast and accurate similarity searches. It makes it easy to build LLM (Large Language Model) applications and services Vector embeddings of documents are stored in the local Chroma DB directory using Chroma's from_documents method. ; Retrieve and answer questions: Finally, use the AI-native open-source embedding database. 46423f83-12509072228" Recent Versions. Prompt questions regarding the database. Simple: Fully-typed, fully-tested, fully-documented == happiness; Integrations: 🦜️🔗 LangChain (python and js), 🦙 LlamaIndex and more soon; Dev, Test, Prod: the same API that runs in your python notebook, scales to your cluster; Feature-rich: Queries, filtering, density estimation and more; Free & Open Source: Apache 2. Careers. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. Probably ef or M is too small\') Some background info: ChromaDB is a Seeing as you are the only other user I've seen working with Chroma on Databricks / DBFS, do let me know if you figure out persistence, I am struggling with the PersistentClient actually saving the DB upon cluster restart and langchain chroma's . It is designed to help organisations manage and scale large volumes of data, making it an ideal solution for GitHub Welcome to ChromaDB Cookbook Rebuilding Chroma DB Time-based Queries Multi tenancy Multi tenancy Implementing OpenFGA Authorization Model In Chroma Chroma DB is an open-source vector database designed to store and manage vector embeddings—numerical representations of complex data types like text, images, and audio. Chroma Vector Database Java Client This is a very basic/naive implementation in Java of the Chroma Vector Database API. 'Coming Soon Testing with Chroma - learn how to test your GenAI apps that include Chroma. The script employs the LangChain library for embeddings and vector stores and incorporates multithreading for concurrent processing. py) showcasing the integration of LangChain to process CSV files, split text documents, and establish a Chroma vector store. A set of AWS CloudFormation samples to deploy an Amazon Aurora DB cluster based on AWS security and high availability best practices. go golang embedded embeddings in-memory nearest-neighbor chroma cosine-similarity rag vector-search vector-database llm llms chromadb retrieval-augmented-generation the AI-native open-source embedding database. If you have a This tutorial demonstrates how to use the Gemini API to create a vector database and retrieve answers to questions from the database. Collection module: {:ok, collection} = Chroma. Contribute to treatmyocd/nocd-chroma development by creating an account on GitHub. With st. You signed in with another tab or window. Operating system information Windows Python version information 3. この設定により、. Ruby client for Chroma DB. Chroma is a generative model for designing proteins programmatically. To make it possible and efficient to run chroma in Kubernetes we take the chroma base image ( ghcr. Moreover, you will use ChromaDB {:. This process makes documents "understandable" to a machine learning Tutorials to help you get started with ChromaDB. The 100+ record thing is related to default BruteForce (buffer) index in Chroma which holds up to 100 uncommitted embeddings in memory and performs KNN search by going thru all the vectors. Contribute to ecsricktorzynski/chroma development by creating an account on GitHub. ]. ipynb to load documents, generate embeddings, and store them in ChromaDB. Chroma makes it easy to build LLM apps by making knowledge, facts, and skills pluggable for LLMs. Database. Chroma is the open-source embedding database. To use a persistent database with Chroma and Langchain, see this notebook. Sign in Product GitHub Copilot. Food Menu Name Generator:. Importing large datasets from local documents (PDF, TXT, etc. Contribute to Cords-AI/Chroma development by creating an account on GitHub. ; Making Chunks: The make_chunks function splits documents into smaller chunks for better processing. Saved searches Use saved searches to filter your results more quickly @SchwarzeFahne, sorry to hear about your troubles. By default, Chroma uses A simple Ruby UI for Chroma database. Configuration for the vector db like lanceDB (in storage) or chroma DB (external), etc. devarthurguilherme Aug 27 Sign up for free to join this conversation on GitHub. Vector Database: Utilizes Chroma DB for efficient text storage and You signed in with another tab or window. persistDirectory string /index_data The Go client for Chroma vector database. Currently, there are two methods for Local RAG with chroma db, ollama and langchain. You signed out in another tab or window. The library reference can be the AI-native open-source embedding database. ' Coming Soon Building Chroma clients - learn Chroma is an open-source embedding database designed to store and query vector embeddings efficiently, enhancing Large Language Models (LLMs) by providing relevant context to user inquiries. 9. ; Both systems allow users to upload PDFs, process them, and ask questions about their content using natural language. Get started. Add a simple UI for Chroma database with Streamlit. ' Coming Soon Monitoring Chroma - learn how to monitor your Chroma instance. Create a Python virtual environment virtualenv env source env/bin/activate Feature request. 0 Licensed; Use case: ChatGPT for _____ Example with chromadb. 5. Compose documents into the context window of an LLM like GPT3 for additional summarization or analysis. It should be possible to search a Chroma vectorstore for a particular Document by it's ID. 4. Contribute to Anush008/chromadb-rs development by creating an account on GitHub. Vector embeddings are often used in AI and machine learning applications, such as natural language processing (NLP) and computer vision, to capture the semantic relationships Add documents to your database. The Saved searches Use saved searches to filter your results more quickly The ChromaDB CSV Loader optimizes the integration of ChromaDB with RAG models, offering efficient handling of large text datasets. 0 Licensed This chart deploys a ChromaDB Vector Store cluster on a Kubernetes cluster using the Helm package manager. Contribute to SymbiosHolst/Chroma- development by creating an account on GitHub. Get Started | Sampling | Design | Conditioners | License. Host and manage packages Automate any workflow Packages You signed in with another tab or window. ; User Interface: Streamlit provides a This is a simple project to test Chroma DB on a local environment as part of Python app. Contribute to Figo57/G-chroma-db development by creating an account on GitHub. Once you get the embeddings for your documents, you can index them using the add function from the Chroma. For reference, there isn't a lack of compute or memory or hardware power as the cluster that this is being deployed on is with the following specs: 2 Intel Xeon E5-2640v4 CPUs (10c/20t @ 2. b. Uses Flask, Vite, and react-three-fiber to host a live 3D view of the data in a web browser, should perform well up to 10k+ documents. Chroma is the AI native open-source embeddings database. clustering provides an implementation of DBScan (Density-Based Spatial Clustering of Applications with Noise) clustering. Each topic has its own dedicated folder with a detailed README and corresponding Python scripts for a practical understanding. By default, Chroma uses Contribute to BoilerToad/chroma-core development by creating an account on GitHub. We also implement a novel adaptation of Faiss's two-level k-means clustering algorithm that only requires a small subset of vectors to be held in memory at an given point. Extract text from PDFs: Use the 0_PDF_text_extractor. ; Embedding and Storing: The to_vector_db function embeds the chunks and stores them in a Chroma vector database. 💾 Installing the library. Dev, Test, Prod: the same API that runs in your python notebook, scales to your cluster; Feature-rich: Queries, filtering, density To enhance the accuracy of RAG, we can incorporate HuggingFace Re-rankers models. Python based source code to bootstrap the database upon creation using AWS Lambda. You switched accounts on another tab or window. Embeddings databases Chroma: Chroma is a library specialized in efficient similarity search and clustering of dense vectors. Category Ingest JSON files into a Chroma Vector DB (stored localy in a SQLite DB)). In-memory with optional persistence. 1. Vector databases facilitate Generative AI and other applications, notably providing context to a Large Language Model (LLM). persist()--both don't seem to be saving to DBFS like they should be. github. Chroma has built-in functionality to embed text and images so you can build out your proof-of-concepts on a vector database quickly. ; Streamlit is an open-source app framework for Machine Learning and Data Science teams. 3+ Saved searches Use saved searches to filter your results more quickly View source on GitHub [ ] keyboard_arrow_down Overview. Collection. Split your Admin UI for Chroma embedding database built with Next. Dev, Test, Prod: the same API that runs in your python notebook, scales to your cluster; Feature-rich: Queries, filtering, density Issue Sometimes when doing search similarity using chromaDB wrapper, I run into the following issue: RuntimeError(\'Cannot return the results in a contigious 2D array. /chromadb/mydbname" Run these two commands: Open the plugins overlay at the top of the screen. Now you are ready to deploy it. It utilizes the gte-base model for embedding and ChromaDB as the vector database to store these embeddings. Contribute to flanker/chroma-db-ui development by creating an account on GitHub. We used the FIQA Welcome to the ChromaDB client sample tools repository. Reading Documents: The read_docs function reads PDF files from a directory or a single file. The universal tool suite for vector database management. This tutorial demonstrates how to use the Gemini API to create a vector database and retrieve answers to questions from the database. Contribute to TrizteX/RAG-chroma-ollama-langchain development by creating an account on GitHub. Because chromem-go is embeddable it enables you to add retrieval augmented generation (RAG) and similar embeddings-based features into your Go app without having to run a separate database. Create a . cargo add chromadb. Contribute to BoilerToad/chroma-core development by creating an account on GitHub. ChromaDB stores documents as dense vector embeddings the open source embedding database. 1, . Installation Install LangChain, Chroma, and other prerequisites using the following commands: Chroma DB is an open-source vector database designed to store and manage vector embeddings—numerical representations of complex data types like text, images, and audio. Contribute to thakkaryash94/chroma-ui development by creating an account on GitHub. Vervolgens kan ik een zoekopdracht geven. the open source embedding database. It is designed to be fast, scalable, and reliable. embedding technologie. and query data with powerful features like filtering built in, with more features like automatic clustering and query relevance coming soon. - IceFireDB/chromem-go-embeddable-vector-database Once you have installed the requisite tools start a single node k8s cluster using the following: Next, let’s add the helm chart repo and update: helm repo add chroma <https://amikos-tech. Note: These prerequisites are necessary for local testing. The cluster function in agentmemory. Already have an account? Sign in to comment. ; Create a ChromaDB vector database: Run 1_Creating_Chroma_database. Skip to content. Description: Users provide a list of URLs, and the system scrapes content from them. - GitHub - ABDFMSM/AOAI-Langchain-ChromaDB: This repo is used to locally query pdf files using AOAI embedding model, Dev, Test, Prod: the same API that runs in your python notebook, scales to your cluster Feature-rich : Queries, filtering, density estimation and more Free & Open Source : Apache 2. Contribute to anamhira47/chromadbfork development by creating an account on GitHub. For full details, see the documentation for setuptools_scm. Navigation Menu Toggle navigation This custom step queries a Chroma vector database collection and writes results to a SAS Cloud Analytics Services (CAS) table. 10 DB-GPT version main Related scenes Chat Data Chat Excel Chat DB Chat Knowledge Model Mana Chroma DB doesn't work #3566. List Servers - chroma server ls; Remove Server - chroma server rm <server-id> Switch Server, Tenant or Database - chroma use -s -t -d; List Collections - chroma ls or chroma c/collection ls; Create Collection - chroma create <collection-name> To give a concrete example of how it can be used for world building, I created this text and placed it for chromadb to find: Heaven's View Inn. By default, Chroma uses Astro ChromaDB Search is a showcase project that demonstrates the integration of ChromaDB, a vector database, with the Astro framework. This client works with Chroma Versions 0. The goal of this project is to create an efficient and cost-effective indexing system for embeddings, showcasing the power of combining these technologies. It tries to provide a more user-friendly API for working within java with chromaDB instance. v. How to Deploy Private Chroma Vector DB to AWS video Connection for Chroma vector database, ChromaDBConnection, has been released which makes it easy to connect any Streamlit LLM-powered app to. The workflow includes creating a vector database, generating embeddings, and performing RAG using advanced models. One Saved searches Use saved searches to filter your results more quickly Search before asking I had searched in the issues and found no similar issues. py "How does Alice meet the Mad Hatter?" You'll also need to set up an OpenAI account (and set the OpenAI key in your environment variable) for this to work. Contribute to kp-forks/chroma-db development by creating an account on GitHub. Here's what it includes: Metadata: Contains metadata about the PVC, including its name (name: chromadb-pvc) and labels (labels: app: "chroma-db"). Like when using SQLite the AI-native open-source embedding database. User Interaction: (Commented out in the provided code) Takes a user question as input ("What is Clustering in ML?" in the example). Contribute to mariochavez/chroma development by creating an account on GitHub. io/chroma-core/chroma:) and we improve on it by: chromadb. Retrieves relevant document chunks using the Chroma database. 1. Chroma DB, an open-source vector database specifically designed for storing and retrieving vector embeddings. CLUSTERING: Specifies that the embeddings will be used for clustering. iydywol suopy izd mhn wgqysukdd wwmnqa wrxjoy dvgbbwdg sksz zvcju