OpenAI’s powerful embedding models can be seamlessly integrated with Chroma to enhance the capabilities of your vector database. By leveraging OpenAI’s embeddings, you can improve the accuracy and relevance of your similarity search results. In this comprehensive guide, we will explore how to integrate the OpenAI Embedding API with Chroma.
Prerequisites
- OpenAI API Key: Obtain an OpenAI API key from the OpenAI platform.
- Chroma: Ensure you have Chroma installed on your system.
- Python: Install Python and the necessary libraries (e.g.,
openai,chromadb).
Creating a Chroma Collection
Import Necessary Libraries:
Python
import chromadb
import openai
Create a Chroma Client:
Python
client = chromadb.Client()
Create a Collection:
Python
collection = client.create_collection(
name=”my_collection”
)
Generating Embeddings with OpenAI
Set Your API Key:
Python
openai.api_key = “YOUR_API_KEY”
Generate Embeddings:
Python
text = “This is a sample text.”
embedding = openai.Embedding.create(
input=text,
engine=”text-davinci-003″
)
Integrating with Chroma
Add Embeddings to Chroma:
Python
collection.add(
documents=[“This is a sample document.”],
embeddings=[embedding[“data”][0]])
Querying the Collection
Create a Query:
Python
query_text = “What is the capital of France?”
Generate Query Embedding:
Python
query_embedding = openai.Embedding.create(
input=query_text,
engine=”text-davinci-003″
)
Perform Query:
Python
results = collection.query(
query_embeddings=[query_embedding[“data”][0]],
n_results=5
)
Accessing Results
Python
for result in results["matches"]:
print(result["document"])
print(result["score"])
