Chroma
Source: llama_index

<a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/docs/examples/vector_stores/ChromaIndexDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a># Chroma>[Chroma](https://docs.trychroma.com/getting-started) is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0.<a href="https://discord.gg/MMeYNTmh3x" target="_blank">      <img src="https://img.shields.io/discord/1073293645303795742" alt="Discord">  </a>&nbsp;&nbsp;  <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>&nbsp;&nbsp;  <img src="https://github.com/chroma-core/chroma/actions/workflows/chroma-integration-test.yml/badge.svg?branch=main" alt="Integration Tests">- [Website](https://www.trychroma.com/)- [Documentation](https://docs.trychroma.com/)- [Twitter](https://twitter.com/trychroma)- [Discord](https://discord.gg/MMeYNTmh3x)Chroma is fully-typed, fully-tested and fully-documented.Install Chroma with:```shpip install chromadb```Chroma runs in various modes. See below for examples of each integrated with LlamaIndex.- `in-memory` - in a python script or jupyter notebook- `in-memory with persistance` - in a script or notebook and save/load to disk- `in a docker container` - as a server running your local machine or in the cloudLike any other database, you can: - `.add` - `.get` - `.update`- `.upsert`- `.delete`- `.peek`- and `.query` runs the similarity search.View full docs at [docs](https://docs.trychroma.com/reference/Collection). ## Basic ExampleIn this basic example, we take the Paul Graham essay, split it into chunks, embed it using an open-source embedding model, load it into Chroma, and then query it.If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.```python%pip install llama-index-vector-stores-chroma%pip install llama-index-embeddings-huggingface``````python!pip install llama-index```#### Creating a Chroma Index```python# !pip install llama-index chromadb --quiet# !pip install chromadb# !pip install sentence-transformers# !pip install pydantic==1.10.11``````python# importfrom llama_index.core import VectorStoreIndex, SimpleDirectoryReaderfrom llama_index.vector_stores.chroma import ChromaVectorStorefrom llama_index.core import StorageContextfrom llama_index.embeddings.huggingface import HuggingFaceEmbeddingfrom IPython.display import Markdown, displayimport chromadb``````python# set up OpenAIimport osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")import openaiopenai.api_key = os.environ["OPENAI_API_KEY"]```Download Data```python!mkdir -p 'data/paul_graham/'!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'``````python# create client and a new collectionchroma_client = chromadb.EphemeralClient()chroma_collection = chroma_client.create_collection("quickstart")# define embedding functionembed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")# load documentsdocuments = SimpleDirectoryReader("./data/paul_graham/").load_data()# set up ChromaVectorStore and load in datavector_store = ChromaVectorStore(chroma_collection=chroma_collection)storage_context = StorageContext.from_defaults(vector_store=vector_store)index = VectorStoreIndex.from_documents(    documents, storage_context=storage_context, embed_model=embed_model)# Query Dataquery_engine = index.as_query_engine()response = query_engine.query("What did the author do growing up?")display(Markdown(f"<b>{response}</b>"))```    /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html      from .autonotebook import tqdm as notebook_tqdm    /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.      warn("The installed version of bitsandbytes was compiled without GPU support. "    'NoneType' object has no attribute 'cadam32bit_grad_fp32'<b>The author worked on writing and programming growing up. They wrote short stories and tried writing programs on an IBM 1401 computer. Later, they got a microcomputer and started programming more extensively.</b>## Basic Example (including saving to disk)Extending the previous example, if you want to save to disk, simply initialize the Chroma client and pass the directory where you want the data to be saved to. `Caution`: Chroma makes a best-effort to automatically save data to disk, however multiple in-memory clients can stomp each other's work. As a best practice, only have one client per path running at any given time.```python# save to diskdb = chromadb.PersistentClient(path="./chroma_db")chroma_collection = db.get_or_create_collection("quickstart")vector_store = ChromaVectorStore(chroma_collection=chroma_collection)storage_context = StorageContext.from_defaults(vector_store=vector_store)index = VectorStoreIndex.from_documents(    documents, storage_context=storage_context, embed_model=embed_model)# load from diskdb2 = chromadb.PersistentClient(path="./chroma_db")chroma_collection = db2.get_or_create_collection("quickstart")vector_store = ChromaVectorStore(chroma_collection=chroma_collection)index = VectorStoreIndex.from_vector_store(    vector_store,    embed_model=embed_model,)# Query Data from the persisted indexquery_engine = index.as_query_engine()response = query_engine.query("What did the author do growing up?")display(Markdown(f"<b>{response}</b>"))```<b>The author worked on writing and programming growing up. They wrote short stories and tried writing programs on an IBM 1401 computer. Later, they got a microcomputer and started programming games and a word processor.</b>## Basic Example (using the Docker Container)You can also run the Chroma Server in a Docker container separately, create a Client to connect to it, and then pass that to LlamaIndex. Here is how to clone, build, and run the Docker Image:```git clone git@github.com:chroma-core/chroma.gitdocker-compose up -d --build``````python# create the chroma client and add our dataimport chromadbremote_db = chromadb.HttpClient()chroma_collection = remote_db.get_or_create_collection("quickstart")vector_store = ChromaVectorStore(chroma_collection=chroma_collection)storage_context = StorageContext.from_defaults(vector_store=vector_store)index = VectorStoreIndex.from_documents(    documents, storage_context=storage_context, embed_model=embed_model)``````python# Query Data from the Chroma Docker indexquery_engine = index.as_query_engine()response = query_engine.query("What did the author do growing up?")display(Markdown(f"<b>{response}</b>"))```<b>Growing up, the author wrote short stories, programmed on an IBM 1401, and wrote programs on a TRS-80 microcomputer. He also took painting classes at Harvard and worked as a de facto studio assistant for a painter. He also tried to start a company to put art galleries online, and wrote software to build online stores.</b>## Update and DeleteWhile building toward a real application, you want to go beyond adding data, and also update and delete data. Chroma has users provide `ids` to simplify the bookkeeping here. `ids` can be the name of the file, or a combined has like `filename_paragraphNumber`, etc.Here is a basic example showing how to do various operations:```pythondoc_to_update = chroma_collection.get(limit=1)doc_to_update["metadatas"][0] = {    **doc_to_update["metadatas"][0],    **{"author": "Paul Graham"},}chroma_collection.update(    ids=[doc_to_update["ids"][0]], metadatas=[doc_to_update["metadatas"][0]])updated_doc = chroma_collection.get(limit=1)print(updated_doc["metadatas"][0])# delete the last documentprint("count before", chroma_collection.count())chroma_collection.delete(ids=[doc_to_update["ids"][0]])print("count after", chroma_collection.count())```    {'_node_content': '{"id_": "be08c8bc-f43e-4a71-ba64-e525921a8319", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "2cbecdbb-0840-48b2-8151-00119da0995b", "node_type": null, "metadata": {}, "hash": "4c702b4df575421e1d1af4b1fd50511b226e0c9863dbfffeccb8b689b8448f35"}, "3": {"node_id": "6a75604a-fa76-4193-8f52-c72a7b18b154", "node_type": null, "metadata": {}, "hash": "d6c408ee1fbca650fb669214e6f32ffe363b658201d31c204e85a72edb71772f"}}, "hash": "b4d0b960aa09e693f9dc0d50ef46a3d0bf5a8fb3ac9f3e4bcf438e326d17e0d8", "text": "", "start_char_idx": 0, "end_char_idx": 4050, "text_template": "{metadata_str}\\n\\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\\n"}', 'author': 'Paul Graham', 'doc_id': '2cbecdbb-0840-48b2-8151-00119da0995b', 'document_id': '2cbecdbb-0840-48b2-8151-00119da0995b', 'ref_doc_id': '2cbecdbb-0840-48b2-8151-00119da0995b'}    count before 20    count after 19