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Using Ollama with Qdrant

Ollama provides specialized embeddings for niche applications. Ollama supports a variety of embedding models, making it possible to build retrieval augmented generation (RAG) applications that combine text prompts with existing documents or other data in specialized areas.

Installation

You can install the required package using the following pip command:

pip install ollama

Integration Example

import qdrant_client
from qdrant_client.models import Batch
from ollama import Ollama

# Initialize Ollama model
model = Ollama("ollama-unique")

# Generate embeddings for niche applications
text = "Ollama excels in niche applications with specific embeddings."
embeddings = model.embed(text)

# Initialize Qdrant client
qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)

# Upsert the embedding into Qdrant
qdrant_client.upsert(
    collection_name="NicheApplications",
    points=Batch(
        ids=[1],
        vectors=[embeddings],
    )
)

Ollama