Developed the complete RAG system.

Necessary docker deployment files are added.

Dockerfile

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+# Use official Python base image
+FROM python:3.10-slim-bookworm
+
+# Set working directory
+WORKDIR /app
+
+# Upgrade system packages to patch vulnerabilities
+RUN apt-get update && apt-get upgrade -y && apt-get clean
+
+# Copy requirements and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy app code
+COPY . .
+
+# Expose Gradio default port
+EXPOSE 7860
+
+# Run the app
+CMD ["python", "app.py"]

app/app.py

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+import gradio as gr
+from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+import fitz # PyMuPDF
+from langchain.docstore.document import Document
+from langchain.vectorstores import Chroma
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from langchain.chat_models import init_chat_model
+import os
+from config import GOOGLE_API_KEY, NVIDIA_API_KEY, CHROMA_DIR
+
+
+
+def setup_nvidia_embedding_model():
+
+    os.environ["NVIDIA_API_KEY"] = NVIDIA_API_KEY
+    nvidia_embedding_model = "nvidia/nv-embed-v1"
+    embedding_model = NVIDIAEmbeddings(model=nvidia_embedding_model)
+    
+    return embedding_model
+
+def setup_google_gemini_embedding_model():
+    os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
+    gemini_embedding_model = "models/gemini-embedding-001"
+    embedding_model = GoogleGenerativeAIEmbeddings(model=gemini_embedding_model)
+
+    return embedding_model
+
+vectorstore = None
+
+def answer_with_llm(query, retrieved_docs, model_name, model_provider):
+    if not retrieved_docs:
+        return "No relevant information found to answer your question."
+
+    context = "\n\n".join(doc.page_content for doc in retrieved_docs)
+    
+    prompt = f""" 
+    You are an expert assistant. Use the following context to answer the user's question. 
+    If you do not find or know the answer, do not hallucinate, do not try to generate fake answers.
+    If no Context is given, simply state "No relevant information found to answer your question."
+    
+    Context: 
+    {context}
+
+    Question:
+    {query}
+    
+    Answer:
+    
+    """
+    
+    llm = init_chat_model(model_name, model_provider=model_provider)
+    response = llm.invoke(prompt)
+    return response.content
+
+
+def read_pdf(file):
+    doc = fitz.open(stream=file, filetype="pdf")
+    text = "\n".join([page.get_text() for page in doc])
+    return text
+
+
+def process_pdf(file):
+    global vectorstore
+
+    if not file:
+        return "Error: No file uploaded."
+    
+    text = read_pdf(file)
+    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    chunks = splitter.split_text(text)
+    docs = [Document(page_content=chunk) for chunk in chunks]
+    
+    #Create Chroma DB
+    embedding_model = setup_nvidia_embedding_model()
+    # embedding_model = setup_google_gemini_embedding_model()
+    
+    vectorstore = Chroma.from_documents(documents=docs, embedding=embedding_model, persist_directory=CHROMA_DIR )
+    # vectorstore.persist()
+    
+    return f"PDF processed and stored with {len(docs)} chunks."
+
+
+def search_query(query):
+    if not vectorstore:
+        error_message = "Error: No vectorstore found. Please upload and process a PDF first."
+        return error_message, error_message
+
+    retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
+    results = retriever.get_relevant_documents(query)
+    # results = retriever.similarity_search(query, k=2)
+
+    # return "\n\n-------------\n\n".join(doc.page_content for doc in results)
+
+    semantic_search_response = "\n\n-------------\n\n".join(doc.page_content for doc in results)
+
+    # model_name = "gemini-2.5-pro"
+    # model_provider = "google_genai"
+
+    model_name = "bytedance/seed-oss-36b-instruct"
+    model_provider = "nvidia"
+    
+    llm_answer = answer_with_llm(query, results, model_name=model_name, model_provider=model_provider)
+    return semantic_search_response, llm_answer
+
+#Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 🧠 Semantic Search App (Langchain + ChromaDb + NVidia LLM API)")
+    
+    with gr.Row():
+        pdf_input = gr.File(label="Upload PDF (max 5mb)", type="binary", file_types=[".pdf"])
+        process_btn = gr.Button("Process PDF")
+        
+    status = gr.Textbox(label="Status")
+    
+    with gr.Row():
+        query_input = gr.Textbox(label="Enter your query")
+        search_btn = gr.Button("Semantic Search")
+        
+    with gr.Row():
+        semantic_search_response = gr.Textbox(label="Semantic Search Response", lines=10, show_copy_button=True)
+        llm_response = gr.Textbox(label="LLM Response", lines=10, show_copy_button=True)
+
+    process_btn.click(fn=process_pdf, inputs=pdf_input, outputs=status)
+    search_btn.click(fn=search_query, inputs=query_input, outputs=(semantic_search_response, llm_response))
+    
+    
+demo.launch(server_name="0.0.0.0")
+
+
+
+    
+    

app/config.py

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+import os
+from dotenv import load_dotenv
+
+#Load .env only if running locally
+env_path = os.path.join(os.path.dirname(__file__), '..', '.env')
+
+if os.path.exists(env_path):
+    load_dotenv(dotenv_path=env_path)
+    
+    
+# Access Secrets
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+NVIDIA_API_KEY = os.getenv("NVIDIA_API_KEY")        
+CHROMA_DIR = os.getenv("CHROMA_DIR", "./chroma_db") 
+
+if not GOOGLE_API_KEY:
+    print("⚠️ Warning: GOOGLE_API_KEY is not set. Gemini LLM API may fail.")
+    
+if not NVIDIA_API_KEY:
+    print("⚠️ Warning: NVIDIA_API_KEY is not set. NVIDIA LLM API may fail.")

docker-compose.yml

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+version: '3.8'
+
+services:
+  semantic-search-app:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    container_name: semantic-search-app
+    ports:
+      - "1200:7860"
+    volumes:
+      - ./app:/app:rw           # Live code updates via bind mount
+      - ./chroma_db:/app/chroma_db  # Persist Chroma DB
+    environment:
+      - NVIDIA_API_KEY=${NVIDIA_API_KEY}  # Optional: if using .env
+    restart: unless-stopped
+    command: python -m app

requirements.txt

@@ -1,6 +1,8 @@
 gradio
 langchain
+langchain-community
 chromadb
 PyMuPDF
 langchain-google-genai 
-langchain-nvidia-ai-endpoints
+langchain-nvidia-ai-endpoints
+dotenv