Loading Documents and Generating Embeddings for Chroma Database

Chroma, a powerful vector database, requires data to be represented as numerical vectors for efficient storage and retrieval. In this comprehensive guide, we will explore the steps involved in loading documents into Chroma and generating their corresponding embeddings.

Prerequisites

• Chroma: Ensure you have Chroma installed on your system.
• Embedding Model: Choose a suitable embedding model, such as SentenceTransformer, to generate embeddings for your documents.
• Data: Prepare your documents in a suitable format, such as a list of strings or a text file.

Loading Documents

Create a Chroma Client:

Python

import chromadb
client = chromadb.Client()

Create a Collection:

Python

collection = client.create_collection(
name=”my_collection”,
embedding_function=SentenceTransformer(“all-MiniLM-L6-v2”)
)

Generating Embeddings

Import Necessary Libraries:

Python

from sentence_transformers import SentenceTransformer

Load Documents:

Python

documents = [“This is a sample document.”, “Another sample document.”]

Create Embedding Model:

Python

model = SentenceTransformer(“all-MiniLM-L6-v2”)

Generate Embeddings:

Python

embeddings = model.encode(documents)

Adding Documents and Embeddings to Chroma

Add Documents:

Python

collection.add(
documents=documents,
embeddings=embeddings
)

By following these steps, you can effectively load documents into a Chroma database and generate their corresponding embeddings. These embeddings will be stored in the database, allowing you to perform similarity search and other operations.