Anurag Kanojiya

Posted on Jun 29

🎬 Semantic Search for Movies & Series with MindsDB and FastAPI

#backend #ai #python #tutorial

In this post, I’ll walk you through the FastAPI-based backend of BingeMate and how it integrates with MindsDB to enable semantic search, metadata filtering, and even conversational agent responses using a SQL-powered interface.

🧠 What Is BingeMate?

BingeMate is a semantic search and recommendation engine that helps users find the perfect movie or series using natural language queries like:

“Top-rated Netflix thrillers after 2020”
“Feel-good anime with high IMDb ratings”
“Is ‘Dark’ similar to ‘Stranger Things’?”

The backend is powered by FastAPI and connects to a MindsDB knowledge base, which stores semantically indexed chunks of content using LLMs and vectors.

anuragkanojiya1 / BingeMate-Backend

This project is a FastAPI backend for BingeMate querying a MindsDB Knowledge Base powered by Ollama LLMs and embedding models. It integrates:

🌐 FastAPI backend
🧠 MindsDB KB, Jobs, Agent
🦙 Ollama (Mistral + Granite embedding)
🐳 Docker for environment management

📦 Requirements

Before getting started, make sure you have installed:

Docker Desktop
Ollama
Python 3.8+

📁 Project Structure

project-root

│── mindsdb_client.py
├── main.py
├── requirements.txt
├── bingewatch.csv
└── README.md

🧪 Step-by-Step Setup

1️⃣ Clone this repo

git clone https://github.com/your-username/BingeMate-Backend.git
cd BingeMate-Backend

2️⃣ Install Python dependencies

pip install -r requirements.txt

3️⃣ Run MindsDB in Docker

docker run --name mindsdb_container -e MINDSDB_APIS=http,mysql -p 47334:47334 -p 47335:47335 mindsdb/mindsdb

4️⃣ Start Ollama and download models

Ollama must be running in the background.

ollama run granite-embedding
ollama run mistral

🤖 Configure MindsDB

Once MindsDB and Ollama are running, execute the following commands to set up:

📚 Create a Knowledge Base

CREATE KNOWLEDGE_BASE my_bingekb
USING

…

View on GitHub

⚙️ Backend Tech Stack

FastAPI: Lightweight, high-performance Python web framework for building APIs.
MindsDB SDK: Python client to query MindsDB knowledge bases and agents
Pandas: For formatting query results
Uvicorn: ASGI server for FastAPI
JSON-based filtering: For flexible metadata queries

📁 Project Structure

📦 bingemate-backend/
├── main.py                  # FastAPI app with routes
├── bingewatch.csv           # CSV file that contains movie/series data
├── mindsdb_client.py        # MindsDB SDK helper functions
├── requirements.txt         # Dependencies

🧪 API Endpoints

`/query` — Search Knowledge Base

@app.get("/query")
def search_kb(
    request: Request,
    q: str = Query(..., description="Search query, e.g., 'best series'"),
    min_relevance: float = Query(0.25, description="Minimum relevance threshold"),
):
    query_params = dict(request.query_params)
    query_params.pop("q", None)
    query_params.pop("min_relevance", None)

    results = query_knowledge_base(
        search_query=q, 
        min_relevance=min_relevance, 
        metadata=query_params
    )
    return {"count": len(results), "results": results}

🔎 Accepts a natural language query like "popular kdrama in 2023"
📊 Supports dynamic metadata filters like ?type=Series&genre=Drama
🔁 Filters results by minimum relevance score

`/agent` — Ask a Question via Agent

@app.get("/agent")
def create_agent(
    query: str = Query(..., description="Question to ask the agent"),
    description: Optional[str] = Query(None, description="Description of the agent")
):
    result = query_agent(query=query)
    return {"result": result}

This endpoint interacts with a MindsDB agent that answers natural language questions based on pre-trained or fine-tuned SQL logic.

`/api/projects/mindsdb/jobs` — Simulated Job Endpoint

@app.post("/api/projects/mindsdb/jobs")
def create_job(
    project: str,
    job_name: str,
    query: str
):
    return {
        "message": f"Job '{job_name}' created in project '{project}' with query: {query}"
    }

✏️ Note : The MindsDB Job endpoint is used to schedule specific timepoints at which an SQL query will be executed.

🔧 mindsdb_client.py — Query Logic

This module handles communication with MindsDB via SQL.

Search the Knowledge Base

def query_knowledge_base(search_query: str, min_relevance: float = 0.25, metadata: dict = None):
    conditions = [f'content = "{search_query}"', f"relevance >= {min_relevance}"]
    if metadata:
        for key, value in metadata.items():
            conditions.append(f"{key} = '{value}'")
    sql = f"SELECT * FROM my_bingekb WHERE {' AND '.join(conditions)};"
    query = server.query(sql)
    df = query.fetch()
    ...

✅ Uses SQL to query my_bingekb table
🧠 Automatically filters results using relevance and metadata (e.g., type, genre)
🔄 Converts results to a JSON-friendly format

Ask a Question to an Agent

def query_agent(query: str):
    sql = f"""
    SELECT answer FROM my_agent WHERE question = "{query}";
    """
    df = server.query(sql).fetch()
    return df

This enables direct Q&A via a SQL-like agent interface. You can map semantic questions to custom-trained answers.

🧪 requirements.txt

fastapi
uvicorn
mindsdb_sdk
pandas

🚀 To run:

pip install -r requirements.txt
uvicorn main:app --reload

🌐 Example Usage

GET /query?q=best%20series%20on%20netflix&genre=Drama&type=Series

GET /agent?query=Is%20Breaking%20Bad%20better%20than%20Ozark

💡 Final Thoughts

FastAPI + MindsDB gives you a supercharged backend for semantic search and conversational interfaces. Whether you’re building a movie recommender, travel planner, or knowledge assistant—this stack is lightweight, extensible, and LLM-ready.

Now go and check out the implementation of this backend -- BingeMate!

anuragkanojiya1 / BingeMate

🎬 BingeMate – Your AI-Powered Movie & Series Finder 🎙️📺

BingeMate is a Jetpack Compose Android application that helps users discover movies and series using Voice input and Semantic search powered by MindsDB Knowledge Base and Ollama LLMs.

It combines natural language queries with structured filters like genre, type, year, and IMDb rating to return personalized results from Mindsdb Knowledge base App's backend link is required to get movie/series data for Semantic/agent search - BingeMate-Backend