tazarov

[Unit]
Description = Chroma Service
After = network.target

[Service]
Type = simple
User = root
Group = root
WorkingDirectory = /chroma
ExecStart=/usr/local/bin/chroma run --host 127.0.0.1 --port 8000 --path /chroma/data --log-path /var/log/chroma.log

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[Unit]
Description = Chroma Docker Service
After = network.target docker.service
Requires = docker.service

[Service]
Type = forking
User = root
Group = root
WorkingDirectory = /home/admin/chroma

tazarov

#!/usr/bin/env python3
# Call the script: python wal_clean.py ./chroma-test-compact
#!/usr/bin/env python3
# Call the script: python wal_clean.py ./chroma-test-compact
import argparse
import importlib
import os
import sqlite3

import typer

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#!/bin/bash

if ! command -v sqlite3 &> /dev/null; then
    echo "sqlite3 could not be found. Please install sqlite3 and try again."
    exit 1
fi

PERSIST_DIR=$(realpath "$1")
BACKUP_DIR=$(realpath "$2")

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version: '3.9'

networks:
  net:
    driver: bridge
services:
  chromadb:
    image: chromadb/chroma:latest
    volumes:
      - ./chromadb:/chroma/chroma

tazarov

from llama_index.embeddings import OpenAIEmbedding
from llama_index.embeddings.base import BaseEmbedding
import chromadb
from chromadb.api.types import EmbeddingFunction

class LlamaIndexEmbeddingAdapter(EmbeddingFunction):
  def __init__(self,ef:BaseEmbedding):
    self.ef = ef

  def __call__(self, input: Documents) -> Embeddings:

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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.add(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"])
col.query(query_texts=["technology"], where_document={"$or":[{"$contains":"technology"}, {"$contains":"freak"}]})

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