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# What does this PR do? This PR implements the openai compatible endpoints for chromadb Closes #2462 ## Test Plan Ran ollama llama stack server and ran the command `pytest -sv --stack-config=http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2` 8 failed, 27 passed, 8 skipped, 1 xfailed The failed ones are regarding files api --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> Co-authored-by: sarthakdeshpande <sarthak.deshpande@engati.com> Co-authored-by: Francisco Javier Arceo <farceo@redhat.com> Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
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1.5 KiB
inline::chromadb
Description
Chroma is an inline and remote vector database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database. That means you're not limited to storing vectors in memory or in a separate service.
Features
Chroma supports:
- Store embeddings and their metadata
- Vector search
- Full-text search
- Document storage
- Metadata filtering
- Multi-modal retrieval
Usage
To use Chrome in your Llama Stack project, follow these steps:
- Install the necessary dependencies.
- Configure your Llama Stack project to use chroma.
- Start storing and querying vectors.
Installation
You can install chroma using pip:
pip install chromadb
Documentation
See Chroma's documentation for more details about Chroma in general.
Configuration
| Field | Type | Required | Default | Description |
|---|---|---|---|---|
db_path |
<class 'str'> |
No | PydanticUndefined | |
kvstore |
utils.kvstore.config.RedisKVStoreConfig | utils.kvstore.config.SqliteKVStoreConfig | utils.kvstore.config.PostgresKVStoreConfig | utils.kvstore.config.MongoDBKVStoreConfig |
No | sqlite | Config for KV store backend |
Sample Configuration
db_path: ${env.CHROMADB_PATH}
kvstore:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/chroma_inline_registry.db