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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
try:
from rag.db.vector import VectorDB, Document
from rag.db.document import DocumentDB
from rag.llm.encoder import Encoder
from rag.llm.ollama_generator import OllamaGenerator, Prompt
from rag.llm.cohere_generator import CohereGenerator
from rag.parser.pdf import PDFParser
except ModuleNotFoundError:
from db.vector import VectorDB, Document
from db.document import DocumentDB
from llm.encoder import Encoder
from llm.ollama_generator import OllamaGenerator, Prompt
from llm.cohere_generator import CohereGenerator
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 = CohereGenerator()
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, source: str):
if self.doc_db.add(blob):
log.debug("Adding pdf to vector database...")
document = self.pdf_parser.from_data(blob)
chunks = self.pdf_parser.chunk(document, source)
points = self.encoder.encode_document(chunks)
self.vector_db.add(points)
else:
log.debug("Document already exists!")
def search(self, query: str, limit: int = 5) -> List[Document]:
query_emb = self.encoder.encode_query(query)
return self.vector_db.search(query_emb, limit)
def retrieve(self, prompt: Prompt):
yield from self.generator.generate(prompt)
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