From 9f8f6c8978cfd359d5af4c001121c80853a09dc7 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Sat, 6 Apr 2024 01:22:36 +0200
Subject: Update rag

---
 rag/rag.py | 40 ++++++++++++++++++++++++++++++----------
 1 file changed, 30 insertions(+), 10 deletions(-)

diff --git a/rag/rag.py b/rag/rag.py
index 5b5f5ab..1988ba7 100644
--- a/rag/rag.py
+++ b/rag/rag.py
@@ -1,20 +1,40 @@
 from pathlib import Path
-from typing import List, Optional
+from typing import List
 
-from langchain_core.documents.base import Document
-from llm.encoder import Encoder
-from llm.generator import Generator
-from parser import pdf
-from db.documents import Documents
-from db.vectors import Vectors
+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.docs = Documents()
-        self.vectors = Vectors()
+        self.document_db = DocumentDB()
+        self.vector_db = VectorDB()
 
-    def add_pdf(self, filepath: Path) -> Optional[List[Document]]:
+    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)
+
+    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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