feat: implement RAG pipeline with Qdrant vector store, Groq LLM integration, and structured prompt management
bbd91de | import logging | |
| from langchain_qdrant import QdrantVectorStore | |
| from langchain_huggingface import HuggingFaceEmbeddings | |
| from config import COLLECTION, EMBEDDING_MODEL | |
| from db.qdrant_client import client | |
| logger = logging.getLogger(__name__) | |
| logger.info(f"Loading HuggingFace Embeddings model '{EMBEDDING_MODEL}'...") | |
| embeddings = HuggingFaceEmbeddings( | |
| model_name=EMBEDDING_MODEL | |
| ) | |
| logger.info("Initializing LangChain QdrantVectorStore...") | |
| db = QdrantVectorStore( | |
| client=client, | |
| collection_name=COLLECTION, | |
| embedding=embeddings | |
| ) | |