Retrieval-Augmented Generation bridges LLMs with proprietary enterprise knowledge repositories, eliminating hallucinations and ensuring factual precision.

Python Vector Similarity Query Example

from openai import OpenAI
import qdrant_client

client = OpenAI()
qdrant = qdrant_client.QdrantClient(host="localhost", port=6333)

def query_rag_pipeline(user_query: str):
    # 1. Generate Query Vector Embedding
    emb_res = client.embeddings.create(
        input=user_query,
        model="text-embedding-3-small"
    )
    query_vector = emb_res.data[0].embedding

    # 2. Vector Search Top K Documents
    hits = qdrant.search(
        collection_name="engineering_kb",
        query_vector=query_vector,
        limit=5
    )
    
    context = "n".join([hit.payload["text"] for hit in hits])
    return context