Building a Chroma Vector Database with LangChain

Chroma is a powerful vector database that can be used to store and retrieve high-dimensional data. LangChain, a framework for building LLM applications, provides tools for integrating with vector databases. In this comprehensive guide, we will explore how to build a Chroma vector database using LangChain.

Prerequisites

• Chroma: Ensure you have Chroma installed on your system.
• LangChain: Install LangChain using pip: pip install langchain
• Embedding Model: Choose a suitable embedding model for generating embeddings.

Creating a Chroma Collection

Import Necessary Libraries:

Python

import chromadb
from langchain.embeddings import HuggingFaceEmbeddings

Create a Chroma Client:

Python

client = chromadb.Client()

Create a Collection:

Python

collection = client.create_collection(
name=”my_collection”,
embedding_function=HuggingFaceEmbeddings(model_name=”all-MiniLM-L6-v2″)
)

Loading and Processing Documents

• Load Documents: Use LangChain’s document loaders to load your documents.
• Split Documents: If necessary, split large documents into smaller chunks using LangChain’s document splitters.
• Generate Embeddings: Use an embedding model to generate embeddings for each document or chunk.

Adding Documents to Chroma

Create a Batch:

Python

batch = [
{“id”: “doc1”, “content”: “This is the first document.”, “embeddings”: [embedding1]},
{“id”: “doc2”, “content”: “This is the second document.”, “embeddings”: [embedding2]}
]

Add Batch to Collection:

Python

collection.add(
documents=batch
)

Querying the Collection

Create a Query:

Python

query_text = “What is the capital of France?”

Generate Query Embedding:

Python

query_embedding = embedding_model.encode([query_text])

Perform Query:

Python

results = collection.query(
query_embeddings=[query_embedding],
n_results=5
)

By following these steps, you can effectively build a Chroma vector database using LangChain. This provides a powerful tool for storing and retrieving high-dimensional data, enabling you to perform tasks such as semantic search, question answering, and recommendation.