Keyword Search

Chroma uses SQLite for storing metadata and documents. Additionally documents are indexed using SQLite FTS5 for fast text search.

PythonJS/TS

import chromadb
from chromadb.config import Settings

client = chromadb.PersistentClient(path="test", settings=Settings(allow_reset=True))

client.reset()
col = client.get_or_create_collection("test")

col.upsert(ids=["1", "2", "3"], documents=["He is a technology freak and he loves AI topics", "AI technology are advancing at a fast pace", "Innovation in LLMs is a hot topic"],metadatas=[{"author": "John Doe"}, {"author": "Jane Doe"}, {"author": "John Doe"}])
col.query(query_texts=["technology"], where_document={"$or":[{"$contains":"technology"}, {"$contains":"freak"}]})

The above should return:

{'ids': [['2', '1']],
'distances': [[1.052205477809135, 1.3074231535113972]],
'metadatas': [[{'author': 'Jane Doe'}, {'author': 'John Doe'}]],
'embeddings': None,
'documents': [['AI technology are advancing at a fast pace',
  'He is a technology freak and he loves AI topics']],
'uris': None,
'data': None}

const { ChromaClient, OpenAIEmbeddingFunction } = require("chromadb");

(async () => {
    const client = new ChromaClient({
        url: "http://localhost:8000",
    });

    const collection = client.getOrCreateCollection("test");

    await collection.upsert({
        ids: ["1", "2", "3"],
        documents: ["He is a technology freak and he loves AI topics", "AI technology are advancing at a fast pace", "Innovation in LLMs is a hot topic"],
        metadatas: [{ author: "John Doe" }, { author: "Jane Doe" }, { author: "John Doe" }],
    });

    const results = await collection.query({
        queryTexts: ["technology"],
        whereDocument: {
            "$or": [
                { "$contains": "technology" },
                { "$contains": "freak" }
            ]
        }
    });
})();