README.md

metadata

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

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