💡 How ChatCX Works

ChatCX is a cutting-edge AI agent that retrieves, processes, and generates intelligent Web3 insights using retrieval-augmented generation (RAG) and reinforcement learning (RL).

🚀 Key Technologies Used:

✅ Hyperbolic APIs → Provides access to DeepSeek R1 for response generation ✅ AltLayer Autonome → Hosts the ChatCX agent for scalable, reliable AI execution ✅ pgvector (PostgreSQL) → Stores vectorized embeddings for fast semantic search ✅ OpenAI Embeddings → Converts user queries & tweets into searchable vector data

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📌 High-Level Flow Diagram

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📌 Step 1: Data Collection, Preprocessing & Storage 📊

A cron job performs:

1. Fetching fresh tweets from a curated list of Web3 Twitter/X accounts.

2. Preprocessing, filtering and customizing to remove spam, extract useful information and convert into a meaningful schema for LLMs.

3. Vectorizing the cleaned tweets custom schema using OpenAI Embeddings.

4. Storing vectorized embeddings in pgvector (PostgreSQL).

This allows ChatCX to quickly retrieve relevant discussions when answering user queries.

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📌 Step 2: Query Processing & Retrieval 🔍

When a user sends a query, ChatCX:

1. Assigns a unique chatID and starts processing.

2. Vectorizes the user’s query using OpenAI Embeddings.

3. Queries the pgvector database for the most relevant discussions & insights.

4. Retrieves the top matches (can include recent tweets, past responses, and summaries).

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📌 Step 3: Response Generation using DeepSeek R1 🧠

Once the relevant data is retrieved, ChatCX:

1. Sends it to DeepSeek R1 via Hyperbolic APIs.

2. Includes:

   • System prompt → Fine-tuned to format responses accurately.

   • Temperature settings → Adjusts randomness vs. precision.

   • Previously generated insights → Ensures context-awareness.

3. DeepSeek R1 generates an intelligent response.

4. Stores the response under the assigned chatID.

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📌 Step 4: Delivering the Response via AltLayer Autonome

1. The generated response is stored under the chatID.

2. AltLayer Autonome hosts the ChatCX agent, ensuring:

   • Scalability 🚀 → Handles large traffic efficiently.

   • Resilience 🔄 → Ensures uninterrupted service.

   • Secure execution → Unbiased generation.

3. The response can be queried using the chatID.

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Why ChatCX is Built This Way

Instead of using a basic keyword search or simple chatbot responses, ChatCX leverages state-of-the-art AI techniques to ensure accurate, real-time, and context-aware insights for Web3 users. Here’s why:

✅ Retrieval-Augmented Generation (RAG) for Precision → Unlike traditional AI agents, ChatCX doesn’t hallucinate responses. → It retrieves relevant, real-world data before generating an answer.

✅ Reinforcement Learning (RL) for self awareness → ChatCX learns from it's previous responses. → It queries for previous generations before generating a new answer.

✅ Latest Web3 Narratives in Real Time → Crypto narratives evolve fast—ChatCX ensures you're always up-to-date. → The bot continuously fetches fresh discussions from Twitter/X.

✅ AI-Enhanced Summarization Using DeepSeek R1 → Instead of overwhelming users with raw data, ChatCX generates concise, meaningful insights.

✅ Vector Search for High-Speed, Context-Aware Responses → Instead of scanning thousands of tweets manually, ChatCX uses pgvector for instant lookups.

✅ Scalable, Secure & Fault-Tolerant Deployment on AltLayer Autonome → ChatCX runs entirely on AltLayer Autonome, ensuring no downtime, secure execution using TEE, fast processing, and smooth performance.

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