Complete ChromaDB Complete Guide | Open Source Vector DB

Complete ChromaDB Complete Guide | Open Source Vector DB

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Complete ChromaDB Complete Guide | Open Source Vector DB

이 글의 핵심

Complete guide to implementing local vector search with ChromaDB. From open source, local execution, embedding storage to RAG implementation with practi...

Key Takeaways

Complete guide to implementing local vector search with ChromaDB. From open source, local execution, embedding storage to RAG implementation with practical examples.

Real-World Experience: Sharing experience of switching from Pinecone to ChromaDB, achieving 100% cost savings and 2x faster development speed.

Introduction: “Vector DB Costs Are Too High”

Real-World Problem Scenarios

Scenario 1: High Cloud Costs
Pinecone is expensive. ChromaDB is free open source. Scenario 2: Difficult Local Development
Cloud is slow. ChromaDB runs fast locally. Scenario 3: Data Privacy Matters
Cloud is concerning. ChromaDB is safe locally.

1. What is ChromaDB?

Core Features

ChromaDB is an open-source vector database. Key Advantages:

  • Open Source: Free
  • Local Execution: Fast development
  • Simple API: Easy to use
  • LangChain Integration: Perfect compatibility
  • Metadata Filtering: Sophisticated search

2. Installation and Basic Usage

Installation

pip install chromadb

Basic Usage

import chromadb
# Create client
client = chromadb.Client()
# Create collection
collection = client.create_collection(name="my_collection")
# Add data
collection.add(
    documents=["This is document 1", "This is document 2"],
    metadatas=[{"source": "doc1"}, {"source": "doc2"}],
    ids=["id1", "id2"]
)
# Search
results = collection.query(
    query_texts=["document about Python"],
    n_results=2
)
print(results)

3. Embeddings

Basic Embedding

# Use default embedding function
collection = client.create_collection(
    name="my_collection",
    metadata={"hnsw:space": "cosine"}
)
collection.add(
    documents=["Python is great", "JavaScript is popular"],
    ids=["id1", "id2"]
)

Custom Embedding

from chromadb.utils import embedding_functions
openai_ef = embedding_functions.OpenAIEmbeddingFunction(
    api_key="your-api-key",
    model_name="text-embedding-3-small"
)
collection = client.create_collection(
    name="my_collection",
    embedding_function=openai_ef
)
results = collection.query(
    query_texts=["Python programming"],
    n_results=5
)
for i, doc in enumerate(results["documents"][0]):
    print(f"{i+1}. {doc}")
    print(f"   Distance: {results['distances'][0][i]}")

Metadata Filtering

results = collection.query(
    query_texts=["Python tutorial"],
    n_results=5,
    where={"category": "programming"},
    where_document={"$contains": "beginner"}
)

5. LangChain Integration

from langchain.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Load documents
loader = TextLoader("document.txt")
documents = loader.load()
# Split into chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
chunks = text_splitter.split_documents(documents)
# Create vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(
    documents=chunks,
    embedding=embeddings,
    persist_directory="./chroma_db"
)
# Search
docs = vectorstore.similarity_search("Python tutorial", k=3)
for doc in docs:
    print(doc.page_content)

6. RAG Chatbot

from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4", temperature=0)
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever(search_kwargs={"k": 3})
)
def ask(question: str) -> str:
    response = qa_chain.invoke({"query": question})
    return response["result"]
# Usage
print(ask("What is Python?"))
print(ask("How do I install packages?"))

7. Persistent Storage

# Save
client = chromadb.PersistentClient(path="./chroma_db")
collection = client.get_or_create_collection(name="my_collection")
collection.add(
    documents=["Document 1", "Document 2"],
    ids=["id1", "id2"]
)
# Load later
client = chromadb.PersistentClient(path="./chroma_db")
collection = client.get_collection(name="my_collection")

Summary and Checklist

Key Summary

  • ChromaDB: Open source Vector DB
  • Local Execution: Fast development
  • Simple API: Easy to use
  • LangChain Integration: Perfect compatibility
  • Metadata Filtering: Sophisticated search
  • Free: Open source

Implementation Checklist

  • Install ChromaDB
  • Create collection
  • Add data
  • Implement search
  • Integrate LangChain
  • Implement RAG
  • Set up persistent storage
  • Complete Pinecone Guide
  • Complete LangChain Guide
  • Vector Database Comparison Guide

Keywords Covered

ChromaDB, Vector Database, Embedding, RAG, AI, Open Source, Python

Frequently Asked Questions (FAQ)

Q. How does it compare to Pinecone?

A. ChromaDB is free and can run locally. Pinecone is managed and has better scalability.

Q. Can it be used in production?

A. Yes, but for large scale, Pinecone or Weaviate is recommended.

Q. What embedding models can be used?

A. Various models like OpenAI, Cohere, HuggingFace can be used.

Q. Is it free?

A. Yes, it’s completely open source and free.

Frequently Asked Questions (FAQ)

Q. When would I use this in practice?

A. Complete guide to implementing local vector search with ChromaDB.

Q. What should I read before this?

A. Follow the previous article or related articles links at the bottom of each post to learn in sequence.

Q. Where can I study this more deeply?

A. Check cppreference and the relevant library’s official documentation. The reference links at the end of the article are also worth using.

Other articles related to this topic.

Keywords Covered in This Article (Related Search Terms)

This article covers ChromaDB, Vector Database, Embedding, RAG, AI, Open Source, Python.