AI Travel Planning Assistant — Singapore

A context-aware travel assistant combining a Retrieval-Augmented Generation (RAG) knowledge base with live information from MCP tools. Built with LangChain, FAISS, Groq, and Streamlit.

Architecture

┌───────────────────────────┐
│  Streamlit UI (app.py)    │
└─────────────┬─────────────┘
              │
              ▼
┌───────────────────────────┐
│  agent.py (router)        │  ← classifies query via Groq LLM
└──┬──────────────────┬─────┘
   │                  │
   ▼                  ▼
┌────────────┐  ┌────────────────────────┐
│ rag.py     │  │ mcp_clients.py         │
│  FAISS     │  │  (stdio transport)     │
│  + Groq    │  │                        │
└─────┬──────┘  └────────┬───────────────┘
      │                  │
      ▼                  ▼
 4 KB docs        ┌──────────────┐  ┌──────────────┐
 (markdown)       │ weather_srv  │  │ currency_srv │
                  │ (FastMCP)    │  │ (FastMCP)    │
                  │ Open-Meteo   │  │ frankfurter  │
                  └──────────────┘  └──────────────┘

Related MCP server: Travel Planner MCP Server

Knowledge Base Sources

Each markdown file begins with metadata:

<!-- source_title: <title> | source_url: <url> -->

RAG Workflow

1. Ingest (ingest.py) — reads all data/knowledge_base/*.md files, splits with RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=100).

2. Embed — sentence-transformers/all-MiniLM-L6-v2 (local, no API key).

3. Store — FAISS index saved to ./faiss_index/.

4. Retrieve — top-4 chunks per question via similarity_search.

5. Generate — Groq openai/gpt-oss-20b answers using only retrieved context.

6. Cite — every KB fact carries a [KB: <source_title>] marker; if the KB is insufficient, the assistant replies exactly: "I don't have enough information in the knowledge base to answer this reliably."

MCP Tools

The client (mcp_clients.py) spawns each server as a subprocess via stdio_client + ClientSession, calls the required tool, and returns structured JSON. Failures return {"error": "..."} and never fabricate data.

Prompt & Context Strategy

Context retention: app.py stores the full message history in st.session_state.messages. agent.py prepends the last 6 turns to the COMBINED_PROMPT via the {history} slot, so follow-ups like "Make day 2 indoor only" modify the previous plan correctly.

Factual vs. AI-generated: Every statement is labelled:

• [KB: <source_title>] — from the knowledge base

• [Source: MCP weather tool] / [Source: MCP currency tool] — from live tools

• Unmarked text is the LLM's own synthesis (clearly separable from the above)

Setup

Requires Python 3.11–3.13. Groq API key (free tier).

cd travel_assistant
python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install --upgrade pip
pip install -r requirements.txt

Copy .env.example to .env and set:

GROQ_API_KEY=gsk_your_key_here
GROQ_MODEL=openai/gpt-oss-20b

Build the vector index:

python ingest.py

Test MCP tools:

python mcp_clients.py

Test the agent:

python agent.py

Launch the UI:

streamlit run app.py

Open http://localhost:8501.

Sample Questions

Try these in order to exercise every code path:

1. What are the must-visit attractions in Singapore? — RAG only

2. What's the weather in Singapore for the next 3 days? — MCP weather

3. Convert INR 50000 to SGD — MCP currency

4. Plan a 3-day Singapore itinerary for next week and adjust for weather — combined RAG + MCP

5. Make day 2 indoor only — multi-turn context

6. Convert my 60000 INR budget to SGD and suggest a 3-day plan — combined currency + itinerary

7. What's the best ramen shop in Tokyo? — KB-insufficiency guardrail

Full transcripts in sample_conversations.md.

Acceptance Criteria Mapping

Notes

• Groq models: use openai/gpt-oss-20b or openai/gpt-oss-120b. The older llama-3.x names were deprecated by Groq.

• MCP SDK pinned to mcp>=1.2,<2 for the v1 API (FastMCP).

• FAISS index is small (~9 chunks) and loads in under a second.

• All tools are read-only; no booking, payment, or reservation logic.

AI Travel Planning Assistant — Singapore

Source code: https://github.com/hban1116/travel-assistant-rag-mcp

A context-aware travel assistant combining a Retrieval-Augmented Generation (RAG) knowledge base with live information from MCP tools. Built with LangChain, FAISS, Groq, and Streamlit.