🎨 Building a Streamlit Frontend for AI Apps Using ChromaDB, FAISS, and LangChain in 2025

📘 Introduction

In the rapidly evolving landscape of AI application development, Streamlit has become one of the most popular Python-based tools for quickly building modern web frontends. Whether you're creating a chatbot, document search engine, image analysis tool, or Retrieval-Augmented Generation (RAG) app, Streamlit provides a simple and powerful UI layer for your AI workflows.

This article guides you through creating a Streamlit frontend that connects to:

• FAISS (for fast in-memory vector search)

• ChromaDB (for persistent vector storage with metadata)

• LangChain (for orchestration and LLM integration)

• LLMs like DeepSeek, OpenAI, or HuggingFace

• Multimodal input: Text, PDF, image, audio

You’ll walk away with a deployable AI interface that can handle chat, vector search, semantic querying, and tool use — all wrapped in a clean, browser-based UI.

✅ Table of Contents

1. Why Streamlit for AI Interfaces?

2. Architecture Overview

3. System Requirements and Setup

4. Installing Dependencies

5. Designing the Streamlit Layout

6. Integrating ChromaDB Backend

7. Connecting FAISS for Local Memory

8. Adding LangChain + LLM Agent

9. Multimodal Inputs (PDFs, Images, Audio)

10. Building a RAG-Enabled Chatbot

11. Real-Time Chat UI with Streamlit Chat

12. Deploying Streamlit on Cloud or Docker

13. Use Cases and Extensions

14. Final Thoughts + GitHub Template

1. 🖥️ Why Streamlit for AI Interfaces?

Streamlit is designed for data scientists and Python developers who want to turn code into shareable web apps without needing JavaScript, React, or CSS.

Advantages:

• No frontend coding required

• Works with Pandas, Torch, HuggingFace, LangChain

• Auto-reloads on file save

• Supports file uploads, camera input, plots, maps

• Easy deployment to Streamlit Cloud, HuggingFace Spaces, or Docker

2. ⚙️ Architecture Overview

plaintext
                    +----------------------+
                    |     User Interface   | ← Streamlit
                    +----------+-----------+
                               ↓
            +--------------------------------------+
            |     Backend (LangChain Agent)        |
            | +----------------------------------+ |
            | | Vector DB (FAISS + ChromaDB)     | |
            | | LLM (DeepSeek / OpenAI)          | |
            | | Tools (Search, Code, RAG)        | |
            +--------------------------------------+
                               ↓
                  +-------------------------+
                  |       Final Output       |
                  +-------------------------+

3. 🧰 System Requirements and Setup

• Python 3.10+

• Min. 8 GB RAM (16 GB for local models)

• GPU optional for local model inference

• conda or venv environment recommended

4. 📦 Installing Dependencies

Install packages:

bash
pip install streamlit langchain faiss-cpu chromadb openai
pip install sentence-transformers
pip install pypdf Pillow

Optional:

bash
pip install whisper transformers torchvision

5. 🧱 Designing the Streamlit Layout

Create a file: app.py

python
import streamlit as st

st.set_page_config(page_title="AI Assistant", layout="wide")
st.title("🔍 AI Assistant with ChromaDB + FAISS")# Sidebar for file uploadst.sidebar.header("Upload Content")
uploaded_files = st.sidebar.file_uploader("Choose files", accept_multiple_files=True)
query = st.text_input("Ask something...")if st.button("Submit"):
    st.write("Processing...")

6. 🧠 Integrating ChromaDB Backend

python
import chromadbfrom sentence_transformers import SentenceTransformer

client = chromadb.Client()
collection = client.create_collection(name="docs")

embedder = SentenceTransformer("all-MiniLM-L6-v2")# Store uploaded PDFsfrom PyPDF2 import PdfReaderfor file in uploaded_files:
    reader = PdfReader(file)
    text = "\n".join([page.extract_text() for page in reader.pages])
    embeddings = embedder.encode([text])
    collection.add(documents=[text], embeddings=embeddings, ids=[file.name])

7. ⚡ Connecting FAISS for Local Memory

python
import faissimport numpy as np# Initialize FAISSdimension = 384index = faiss.IndexFlatL2(dimension)
memory_vectors = []def store_short_term(text):
    vec = embedder.encode([text])
    memory_vectors.append(vec[0])
    index.add(np.array(memory_vectors).astype('float32'))

8. 🧠 Adding LangChain + LLM Agent

python
from langchain.llms import OpenAIfrom langchain.chains 
import RetrievalQAfrom langchain.vectorstores 
import Chromafrom langchain.embeddings import HuggingFaceEmbeddings

llm = OpenAI(model_name="gpt-3.5-turbo")  # Or use DeepSeek proxyembeddings = HuggingFaceEmbeddings()

vectorstore = Chroma(client=client, collection_name="docs", embedding_function=embeddings)
retriever = vectorstore.as_retriever()

qa = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)

Query the chatbot:

python
if query:
    response = qa.run(query)
    st.markdown("### 🤖 Answer")
    st.write(response)

9. 🖼️ Multimodal Inputs (Image, Audio, PDF)

Add image understanding:

python
image_file = st.sidebar.file_uploader("Upload image", type=["png", "jpg"])if image_file:    from PIL import Image
    image = Image.open(image_file)
    st.image(image, caption="Uploaded Image", use_column_width=True)    # TODO: Use DeepSeek-Vision here for captioning

Add Whisper for voice:

python
audio = st.sidebar.file_uploader("Upload audio", type=["wav", "mp3"])if audio:    import whisper
    model = whisper.load_model("base")
    transcription = model.transcribe(audio.name)
    st.write("Transcribed:", transcription["text"])

10. 🔁 Building a RAG-Enabled Chatbot

Integrate short-term + long-term memory:

python
retriever_memory = retrieverdef hybrid_search(query):    # Step 1: FAISS
    q_vec = embedder.encode([query])
    _, indices = index.search(np.array(q_vec).astype('float32'), k=3)
    short_term_docs = [memory_vectors[i] for i in indices[0]]    # Step 2: ChromaDB
    long_term_docs = retriever.get_relevant_documents(query)

    combined = "\n".join([d.page_content for d in long_term_docs])    return combined

11. 💬 Real-Time Chat UI with Streamlit Chat

python
if "history" not in st.session_state:
    st.session_state.history = []for msg in st.session_state.history:
    st.chat_message("user").write(msg["user"])
    st.chat_message("assistant").write(msg["bot"])

query = st.chat_input("Talk to the assistant...")if query:
    response = qa.run(query)
    st.chat_message("user").write(query)
    st.chat_message("assistant").write(response)
    st.session_state.history.append({"user": query, "bot": response})

12. 🚀 Deployment Options

Streamlit Cloud:

bash
streamlit run app.py

Push to GitHub and connect to https://streamlit.io/cloud

HuggingFace Spaces:

bash
pip install gradio

Adapt to Gradio or deploy Docker container.

Dockerfile Example:

Dockerfile
FROM python:3.10
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.enableCORS=false"]

13. 🌐 Use Cases and Extensions

14. ✅ Final Thoughts + GitHub Template

Streamlit enables developers to build AI-first UIs in under an hour. When combined with FAISS, ChromaDB, and LangChain, you get a production-ready stack for document QA, chat, RAG pipelines, and multimodal interaction.

🔧 Suggested Folder Structure:

streamlit-ai-app/
├── app.py
├── faiss_store.py
├── chroma_store.py
├── llm_agent.py
├── utils/
│   ├── pdf_utils.py
│   ├── image_utils.py
├── requirements.txt
├── Dockerfile
└── README.md

Would you like me to generate the GitHub repo, provide a Gradio version, or add multilingual support (e.g., for Chinese or Spanish users)?