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from dataclasses import dataclass
from io import BytesIO
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
try:
from rag.db.vector import VectorDB
from rag.db.document import DocumentDB
from rag.llm.encoder import Encoder
from rag.llm.generator import Generator, Prompt
from rag.parser.pdf import PDFParser
except ModuleNotFoundError:
from db.vector import VectorDB
from db.document import DocumentDB
from llm.encoder import Encoder
from llm.generator import Generator, Prompt
from parser.pdf import PDFParser
@dataclass
class Response:
query: str
context: List[str]
answer: str
class RAG:
def __init__(self) -> None:
# FIXME: load this somewhere else?
load_dotenv()
self.pdf_parser = PDFParser()
self.generator = Generator()
self.encoder = Encoder()
self.vector_db = VectorDB()
self.doc_db = DocumentDB()
def add_pdf_from_path(self, path: Path):
blob = self.pdf_parser.from_path(path)
self.add_pdf_from_blob(blob)
def add_pdf_from_blob(self, blob: BytesIO):
if self.doc_db.add(blob):
log.debug("Adding pdf to vector database...")
chunks = self.pdf_parser.from_data(blob)
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 [h.payload["text"] for h in hits]
def retrive(self, query: str, limit: int = 5) -> Response:
query_emb = self.encoder.encode_query(query)
context = self.__context(query_emb, limit)
prompt = Prompt(query, "\n".join(context))
answer = self.generator.generate(prompt)["response"]
return Response(query, context, answer)
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