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from pathlib import Path
from typing import List
from dotenv import load_dotenv
from loguru import logger as log
from qdrant_client.models import StrictFloat
from rag.db.document import DocumentDB
from rag.db.vector import VectorDB
from rag.llm.encoder import Encoder
from rag.llm.generator import Generator, Prompt
from rag.parser import pdf
class RAG:
def __init__(self) -> None:
load_dotenv()
self.generator = Generator()
self.encoder = Encoder()
self.document_db = DocumentDB()
self.vector_db = VectorDB()
def add_pdf(self, filepath: Path):
chunks = pdf.parser(filepath)
added = self.document_db.add_document(chunks)
if added:
log.debug(f"Adding pdf with filepath: {filepath} to vector db")
points = self.encoder.encode_document(chunks)
self.vector_db.add(points)
else:
log.debug("Document already exists!")
def __context(self, query_emb: List[StrictFloat], limit: int) -> str:
hits = self.vector_db.search(query_emb, limit)
log.debug(f"Got {len(hits)} hits in the vector db with limit={limit}")
return "\n".join(h.payload["text"] for h in hits)
def rag(self, query: str, role: str, limit: int = 5) -> str:
query_emb = self.encoder.encode_query(query)
context = self.__context(query_emb, limit)
prompt = Prompt(query, context)
return self.generator.generate(prompt, role)["response"]
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