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 packages using the following pip command:
pip install ollama qdrant-client
Integration Example
The following code assumes Ollama is accessible at port 11434 and Qdrant at port 6334.
from qdrant_client import QdrantClient, models
import ollama
COLLECTION_NAME = "NicheApplications"
# Initialize Ollama client
oclient = ollama.Client(host="localhost")
# Initialize Qdrant client
qclient = QdrantClient(host="localhost", port=6333)
# Text to embed
text = "Ollama excels in niche applications with specific embeddings"
# Generate embeddings
response = oclient.embeddings(model="llama3.2", prompt=text)
embeddings = response["embedding"]
# Create a collection if it doesn't already exist
if not qclient.collection_exists(COLLECTION_NAME):
qclient.create_collection(
collection_name=COLLECTION_NAME,
vectors_config=models.VectorParams(
size=len(embeddings), distance=models.Distance.COSINE
),
)
# Upload the vectors to the collection along with the original text as payload
qclient.upsert(
collection_name=COLLECTION_NAME,
points=[models.PointStruct(id=1, vector=embeddings, payload={"text": text})],
)