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-<p align="center">
-  <a href="https://trychroma.com"><img src="https://user-images.githubusercontent.com/891664/227103090-6624bf7d-9524-4e05-9d2c-c28d5d451481.png" alt="Chroma logo"></a>
-</p>
-
-<p align="center">
-    <b>Chroma - the open-source embedding database</b>. <br />
-    The fastest way to build Python or JavaScript LLM apps with memory!
-</p>
-
-<p align="center">
-  <a href="https://discord.gg/MMeYNTmh3x" target="_blank">
-      <img src="https://img.shields.io/discord/1073293645303795742" alt="Discord">
-  </a> |
-  <a href="https://github.com/chroma-core/chroma/blob/master/LICENSE" target="_blank">
-      <img src="https://img.shields.io/static/v1?label=license&message=Apache 2.0&color=white" alt="License">
-  </a> |
-  <a href="https://docs.trychroma.com/" target="_blank">
-      Docs
-  </a> |
-  <a href="https://www.trychroma.com/" target="_blank">
-      Homepage
-  </a>
-</p>
-
-
-<p align="center">
-  <a href="https://github.com/chroma-core/chroma/actions/workflows/chroma-integration-test.yml" target="_blank">
-    <img src="https://github.com/chroma-core/chroma/actions/workflows/chroma-integration-test.yml/badge.svg?branch=main" alt="Integration Tests">
-  </a> |
-  <a href="https://github.com/chroma-core/chroma/actions/workflows/chroma-test.yml" target="_blank">
-    <img src="https://github.com/chroma-core/chroma/actions/workflows/chroma-test.yml/badge.svg?branch=main" alt="Tests">
-  </a>
-</p>
-
-```bash
-pip install chromadb # python client
-# for javascript, npm install chromadb!
-# for client-server mode, chroma run --path /chroma_db_path
-```
-
-The core API is only 4 functions (run our [💡 Google Colab](https://colab.research.google.com/drive/1QEzFyqnoFxq7LUGyP1vzR4iLt9PpCDXv?usp=sharing) or [Replit template](https://replit.com/@swyx/BasicChromaStarter?v=1)):
-
-```python
-import chromadb
-# 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 well
-    metadatas=[{"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 id
-results = 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
-)
-```
-
-## Features
-- __Simple__: Fully-typed, fully-tested, fully-documented == happiness
-- __Integrations__: [`🦜️🔗 LangChain`](https://blog.langchain.dev/langchain-chroma/) (python and js), [`🦙 LlamaIndex`](https://twitter.com/atroyn/status/1628557389762007040) 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 2.0 Licensed
-
-## Use case: ChatGPT for ______
-
-For example, the `"Chat your data"` use case:
-1. Add documents to your database. You can pass in your own embeddings, embedding function, or let Chroma embed them for you.
-2. Query relevant documents with natural language.
-3. Compose documents into the context window of an LLM like `GPT3` for additional summarization or analysis.
-
-## Embeddings?
-
-What are embeddings?
-
-- [Read the guide from OpenAI](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings)
-- __Literal__: 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 as **vector databases**) store embeddings and allow you to search by nearest neighbors rather than by substrings like a traditional database. By default, Chroma uses [Sentence Transformers](https://docs.trychroma.com/embeddings#sentence-transformers) to embed for you but you can also use OpenAI embeddings, Cohere (multilingual) embeddings, or your own.
-
-## Get involved
-
-Chroma is a rapidly developing project. We welcome PR contributors and ideas for how to improve the project.
-- [Join the conversation on Discord](https://discord.gg/MMeYNTmh3x) - `#contributing` channel
-- [Review the 🛣️ Roadmap and contribute your ideas](https://docs.trychroma.com/roadmap)
-- [Grab an issue and open a PR](https://github.com/chroma-core/chroma/issues) - [`Good first issue tag`](https://github.com/chroma-core/chroma/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
-- [Read our contributing guide](https://docs.trychroma.com/contributing)
-
-**Release Cadence**
-We currently release new tagged versions of the `pypi` and `npm` packages on Mondays. Hotfixes go out at any time during the week.
-
-## License
-
-[Apache 2.0](./LICENSE)