Delete rag_engine.py

rag_engine.py

@@ -1,65 +0,0 @@
-import os
-from langchain_community.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_openai import OpenAIEmbeddings, ChatOpenAI
-from langchain_community.vectorstores import FAISS
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnablePassthrough, RunnableParallel
-
-class ProjectRAGEngine:
-    def __init__(self, api_key):
-        self.embeddings = OpenAIEmbeddings(openai_api_key=api_key)
-        self.llm = ChatOpenAI(model="gpt-4o", openai_api_key=api_key, temperature=0)
-        self.vector_store = None
-
-    def process_documents(self, pdf_paths):
-        all_docs = []
-        for path in pdf_paths:
-            try:
-                loader = PyPDFLoader(path)
-                docs = loader.load()
-                all_docs.extend(docs)
-            except Exception as e:
-                print(f"Error loading {path}: {e}")
-        
-        # Splitting logic to handle large reports [cite: 10]
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-        splits = text_splitter.split_documents(all_docs)
-        self.vector_store = FAISS.from_documents(splits, self.embeddings)
-
-    def _format_docs(self, docs):
-        return "\n\n".join(doc.page_content for doc in docs)
-
-    def get_answer(self, query):
-        if not self.vector_store:
-            return "Please upload documents first.", []
-
-        # System prompt ensuring grounded responses [cite: 18, 25]
-        template = """
-        You are a professional Project Analyst. Answer strictly based on the provided context.
-        If the answer is not in the context, say you don't know. 
-        Cite document names and page numbers for every answer. Include direct quotes.
-        
-        Context: {context}
-        Question: {question}
-        """
-        prompt = ChatPromptTemplate.from_template(template)
-        retriever = self.vector_store.as_retriever(search_kwargs={"k": 5})
-
-        # Pure LCEL Chain composition
-        rag_chain_from_docs = (
-            RunnablePassthrough.assign(context=(lambda x: self._format_docs(x["context"])))
-            | prompt
-            | self.llm
-            | StrOutputParser()
-        )
-
-        rag_chain_with_source = RunnableParallel(
-            {"context": retriever, "question": RunnablePassthrough()}
-        ).assign(answer=rag_chain_from_docs)
-
-        result = rag_chain_with_source.invoke(query)
-        
-        sources = [{"content": doc.page_content, "metadata": doc.metadata} for doc in result["context"]]
-        return result["answer"], sources