clean folder structure

app/Dockerfile.api

@@ -1,5 +0,0 @@
-FROM python:3.10
-WORKDIR /app
-COPY . /app
-RUN pip install --no-cache-dir -r requirements.txt
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

app/main.py

@@ -1,25 +0,0 @@
-from fastapi import FastAPI
-from langchain_community.vectorstores import FAISS
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain.chains import RetrievalQA
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from langchain_community.llms import HuggingFacePipeline
-
-app = FastAPI()
-
-vectorstore = FAISS.load_local(
-    "./vectorstore/",
-    embeddings=HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-)
-
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")  # Or llama-3
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
-pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512)
-llm = HuggingFacePipeline(pipeline=pipe)
-
-retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
-rag_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever, chain_type="stuff")
-
-@app.get("/query/")
-def query_rag(question: str):
-    return {"response": rag_chain.run(question)}

app/rag.py

@@ -1,56 +0,0 @@
-# source:https://python.langchain.com/api_reference/langchain/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html
-
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain.prompts import ChatPromptTemplate
-from langchain.chains import create_retrieval_chain
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain_community.llms import Ollama
-from fastapi import FastAPI
-import requests
-from pydantic import BaseModel
-from langchain.chains import create_retrieval_chain
-from dotenv import load_dotenv
-import os
-
-
-load_dotenv()
-token= os.getenv("TOKEN")
-app = FastAPI()
-
-class QueryInput(BaseModel):
-    query: str
-
-
-# build the retrieval and augmented generator chain here
-
-embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
-db = FAISS.load_local("./vectorstore/agriquery_faiss_index", embeddings, allow_dangerous_deserialization=True)
-llm = Ollama(model="llama3", base_url="http://localhost:11434")
-
-retriever = db.as_retriever()
-
-system_prompt = (
-    "You are an agriultural research assistant."
-    "Use the given context to answer the question."
-    "If you don't know the answer, say you don't know."
-    "Context: {context}"
-)
-
-prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", system_prompt),
-        ("human", "{input}"),
-    ]
-)
-
-question_answer_chain = create_stuff_documents_chain(llm,prompt)
-chain = create_retrieval_chain(retriever, question_answer_chain)
-
-
-@app.post("/query")
-async def query_handler(input: QueryInput):
-    result = chain.invoke({"input": input.query})
-    answer = result['answer'].replace("\\n", "\n").strip()
-
-    return {"answer": answer}

app/requirements.txt

@@ -1,7 +0,0 @@
-fastapi
-uvicorn
-transformers
-sentence-transformers
-faiss-cpu
-langchain
-langchain_community

app/streamlit_app.py

@@ -1,73 +0,0 @@
-import streamlit as st
-from langchain.chains import RetrievalQA
-from langchain_community.llms import HuggingFacePipeline
-from transformers import pipeline
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain.prompts import ChatPromptTemplate
-from langchain.chains import create_retrieval_chain
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain_community.llms import Ollama
-import os
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-
-
-# ----------------------
-system_prompt = (
-    "You are an agriultural research assistant."
-    "Use the given context to answer the question."
-    "If you don't know the answer, say you don't know."
-    "Context: {context}"
-)
-
-prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", system_prompt),
-        ("human", "{input}"),
-    ]
-)
-
-# Initialize embeddings & documents
-@st.cache_resource
-def load_retriever():
-    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
-    db = FAISS.load_local("./vectorstore/agriquery_faiss_index", embeddings, allow_dangerous_deserialization=True)
-    retriever = db.as_retriever()
-    return retriever
-
-# Load a lightweight model via HuggingFace pipeline
-@st.cache_resource
-def load_llm():
-    # pipe = pipeline("text-generation", model="google/flan-t5-small", max_new_tokens=256)
-    
-    # load the tokenizer and model on cpu/gpu
-    model_name = "meta-llama/Llama-2-7b-chat-hf"
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
-    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=256)
-    return HuggingFacePipeline(pipeline=pipe)
-
-# Setup RAG Chain
-@st.cache_resource
-def setup_qa():
-
-    retriever = load_retriever()
-    llm = load_llm()
-    question_answer_chain = create_stuff_documents_chain(llm,prompt)
-    chain = create_retrieval_chain(retriever, question_answer_chain)
-
-    # qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
-    return chain
-
-
-# Streamlit App UI
-st.title("🌾 AgriQuery: RAG-Based Q&A Assistant")
-
-query = st.text_input("Ask a question related to agriculture:")
-
-if query:
-    qa = setup_qa()
-    with st.spinner("Thinking..."):
-        result = qa.invoke({"input": query})
-    st.success(result['answer'])

app/templates/index.html

@@ -1,16 +0,0 @@
-
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <title>AgriQuery</title>
-</head>
-<body>
-    <h1>AgriQuery</h1>
-    <form action="/query" method="get">
-        <label for="q">Enter your question:</label><br>
-        <input type="text" id="q" name="q" required><br><br>
-        <input type="submit" value="Ask">
-    </form>
-</body>
-</html>