| from qdrant_client import QdrantClient | |
| from src.vectorstore import QdrantVectorStoreDB | |
| from src.answerquery import AnswerQuery | |
| from src.embedding import all_minilm_l6_v2 | |
| from src.settings import settings | |
| class QAPipeline: | |
| """ | |
| A class that handles the entire QA pipeline. | |
| """ | |
| def __init__(self): | |
| self.embeddings=all_minilm_l6_v2() | |
| self.qdrant_client=QdrantClient(url=settings.QDRANT_URL, api_key=settings.QDRANT_API_KEY) | |
| self.vector_store = QdrantVectorStoreDB(qdrant_client=self.qdrant_client,vector_embedding= self.embeddings) | |
| self.answer_query = AnswerQuery() | |
| async def upload_documents(self, documents, collection_name:str="recipe"): | |
| """ | |
| Upload documents to the Qdrant vector store. | |
| """ | |
| await self.vector_store.upload_documents(documents, collection_name) | |
| async def answer_query_(self, query): | |
| """ | |
| Answer a query using the Groq model. | |
| """ | |
| return await self.answer_query.answer_query( | |
| vectorembedding=self.embeddings, | |
| query=query, | |
| ) | |
| async def search_web(self, query): | |
| """ | |
| Search the web for a query. | |
| """ | |
| return await self.answer_query.search_web( | |
| query=query | |
| ) | |