---
title: Telecom
emoji: 🏃
colorFrom: pink
colorTo: red
sdk: gradio
sdk_version: 6.10.0
app_file: app.py
pinned: false
license: mit
---

# Autonomous Telecom Support Agent (Self-Improving RAG)

A production-grade Tier-1 Telecommunications Support Engineer powered by **Gemini 1.5 Flash-Lite** and **FAISS**. This agent features a "closed-loop" learning system that automatically expands its knowledge base by capturing and validating high-confidence resolutions.

## 🚀 Key Features

* **Self-Improving Knowledge Base:** Automatically collects and deduplicates high-scoring answers (Score ≥ 9) to improve future retrieval.
* **Hierarchical Retrieval (RAG):** Combines vector search (FAISS) with session-based memory for context-aware troubleshooting.
* **Autonomous Evaluation:** Includes an "AI Judge" that scores responses based on groundedness, hallucination risk, and evidence usage.
* **HNSW Vector Indexing:** Uses Hierarchical Navigable Small Worlds for efficient, incremental updates to the knowledge base without full re-indexing.
* **Memory Summarization:** Intelligently summarizes long conversations to stay within LLM context windows while preserving key facts.

## 🏗️ Architecture

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1.  **Retrieval:** Pulls data from the Nigerian Telecom Customer Support dataset and short-term session memory.
2.  **Generation:** Gemini generates a structured JSON response grounded strictly in retrieved evidence.
3.  **Validation:** A secondary "Judge" prompt evaluates the output for logical consistency.
4.  **Learning:** If the answer is exceptional, it is embedded and injected into the permanent FAISS index for future use.

## 🛠️ Tech Stack

* **LLM:** Google Gemini (via `google-genai`)
* **Vector Database:** FAISS (Facebook AI Similarity Search)
* **Embeddings:** `all-MiniLM-L6-v2` (Sentence-Transformers)
* **Data Source:** HuggingFace Datasets (Nigerian Telecom Support Records)
* **Language:** Python

## 📋 Prerequisites

```bash
pip install google-genai sentence-transformers faiss-cpu datasets scikit-learn
```
⚙️ Configuration
Set your environment variables:
GEMINI_API_KEY: Your Google AI Studio key.
HF_TOKEN: HuggingFace token for dataset access.


📖 Usage

from your_script import telecom_agent

# Initialize and query the agent
answer, evaluation, stats = telecom_agent("My MTN sim is not showing 4G signal in Lagos.")

print(f"Diagnosis: {answer['diagnosis']}")
print(f"Confidence: {answer['confidence_score']}")

📊 Self-Learning Logic
The system uses Semantic Deduplication. New samples are only added to the training set if their cosine similarity to existing data is < 0.9, ensuring the knowledge base grows in quality, not just volume.
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference