Package: chromadb-pysqlite3
Chroma - the open-source embedding database.The fastest way to build Python or JavaScript LLM apps with memory!||Docs|Homepage|pipinstallchromadb# python client# for javascript, npm install chromadb!# for client-server mode, chroma run --path /chroma_db_pathThe core API is only 4 functions (run our💡 Google ColaborReplit template):importchromadb# setup Chroma in-memory, for easy prototyping. Can add persistence easily!client=chromadb.Client()# Create collection. get_collection, get_or_create_collection, delete_collection also available!collection=client.create_collection("all-my-documents")# Add docs to the collection. Can also update and delete. Row-based API coming soon!collection.add(documents=["This is document1","This is document2"],# we handle tokenization, embedding, and indexing automatically. You can skip that and add your own embeddings as wellmetadatas=[{"source":"notion"},{"source":"google-docs"}],# filter on these!ids=["doc1","doc2"],# unique for each doc)# Query/search 2 most similar results. You can also .get by idresults=collection.query(query_texts=["This is a query document"],n_results=2,# where={"metadata_field": "is_equal_to_this"}, # optional filter# where_document={"$contains":"search_string"}  # optional filter)FeaturesSimple: Fully-typed, fully-tested, fully-documented == happinessIntegrations:🦜️🔗 LangChain(python and js),🦙 LlamaIndexand more soonDev, Test, Prod: the same API that runs in your python notebook, scales to your clusterFeature-rich: Queries, filtering, density estimation and moreFree & Open Source: Apache 2.0 LicensedUse case: ChatGPT for ______For example, the"Chat your data"use case:Add documents to your database. You can pass in your own embeddings, embedding function, or let Chroma embed them for you.Query relevant documents with natural language.Compose documents into the context window of an LLM likeGPT3for additional summarization or analysis.Embeddings?What are embeddings?Read the guide from OpenAILiteral: Embedding something turns it from image/text/audio into a list of numbers. 🖼️ or 📄 =>[1.2, 2.1, ....]. This process makes documents "understandable" to a machine learning model.By analogy: An embedding represents the essence of a document. This enables documents and queries with the same essence to be "near" each other and therefore easy to find.Technical: An embedding is the latent-space position of a document at a layer of a deep neural network. For models trained specifically to embed data, this is the last layer.A small example: If you search your photos for "famous bridge in San Francisco". By embedding this query and comparing it to the embeddings of your photos and their metadata - it should return photos of the Golden Gate Bridge.Embeddings databases (also known asvector databases) store embeddings and allow you to search by nearest neighbors rather than by substrings like a traditional database. By default, Chroma usesSentence Transformersto embed for you but you can also use OpenAI embeddings, Cohere (multilingual) embeddings, or your own.Get involvedChroma is a rapidly developing project. We welcome PR contributors and ideas for how to improve the project.Join the conversation on Discord-#contributingchannelReview the 🛣️ Roadmap and contribute your ideasGrab an issue and open a PR-Good first issue tagRead our contributing guideRelease CadenceWe currently release new tagged versions of thepypiandnpmpackages on Mondays. Hotfixes go out at any time during the week.LicenseApache 2.0