Build an Internal Knowledge Bot with Clawdbot: Embeddings + Rerank + Chat in 30 Minutes

👉 Try it now for free for 30 days

Build an internal knowledge assistant using Clawdbot that combines semantic search, reranking, and LLM generation on Regolo’s EU infrastructure—with predictable costs and enterprise-grade data privacy.

The Knowledge Retrieval Accuracy Problem

Most companies accumulate critical tribal knowledge in scattered docs, runbooks, ADRs, and Slack threads—but when developers need answers during an incident or sprint planning, they waste 15-30 minutes searching, often finding outdated or incomplete information. Off-the-shelf RAG solutions either lock you into expensive US-based providers (OpenAI Assistants API, Anthropic Claude with third-party vector stores) or require heavyweight infrastructure (self-hosted vector DBs, GPU clusters, complex orchestration).

The real issue? Retrieval without reranking sends garbage context to even the best LLMs. Your vector search returns “relevant-ish” results based on embedding similarity alone, but without a second-pass relevance judgment, you’re paying 3-5x more in token costs to process noisy context—and users abandon your tool after one hallucinated answer.

What You’ll Build

/kb <question> command in Telegram/Slack that searches your internal docs
85%+ retrieval accuracy using semantic embeddings + BM25 hybrid search + neural reranking
Sub-500ms response time for typical queries
Zero vendor lock-in: OpenAI-compatible endpoints mean you can swap providers or models without rewriting code

Prerequisites

Regolo API Key (get it at regolo.ai/dashboard)
Clawdbot installed (github.com/clawdbot/clawdbot)

Run the following cURL command to run the installation automatically:

curl -fsSL https://clawd.bot/install.sh | bashCode language: JavaScript (javascript)

Architecture: Clawdbot as Knowledge Agent Runtime

Here’s how the pieces fit together:

User (Telegram)
    ↓ /kb "What's our GDPR data retention policy?"
Clawdbot Agent
    ↓ orchestrates retrieval pipeline
    ├─→ [1] Embed query → Regolo gte-Qwen2 (EU GPU)
    ├─→ [2] Hybrid search → ChromaDB (dense) + BM25 (lexical)
    ├─→ [3] Rerank top-20 → Regolo Qwen3-Reranker-4B
    └─→ [4] Generate answer → Regolo Llama-3.1-8B-Instruct
    ↓
Structured response with citations
Code language: JavaScript (javascript)

Why Clawdbot?

Proactive scheduling: tun daily knowledge base updates via cron (auto-ingest new docs from Google Drive, Notion, or GitHub)
Multi-channel access: same knowledge bot accessible from Telegram, Slack, WhatsApp, or HTTP API
Memory & context: Clawdbot maintains conversation state, so follow-up questions work naturally (“What about exceptions to that policy?”)
Self-hosted: you knowledge base embeddings never leave your infrastructure — only API calls to our EU datacenters

Why Regolo GPU?

EU data residency: EU-hosted GPUs, GDPR-native, zero cross-border data transfers
OpenAI-compatible API: drop-in replacement for OpenAI—change base_url and you’re done
Transparent pricing: fixed per-token costs (no surprise bills), with 70% discount for first 3 months
Green infrastructure: 100% renewable energy GPUs

Getting Started

Required Accounts & Credentials

Regolo API Key
- Sign up at regolo.ai
- Navigate to Dashboard → API Keys → Create New Key
- Copy key (starts with sk-...)
Telegram Bot Token
- Open Telegram and search for @BotFather
- Send /newbot and follow prompts to create bot
- Copy the bot token (format: 123456789:ABCdefGHIjklMNOpqrsTUVwxyz)
- Send /setcommands to BotFather and add:

kb - Search knowledge base
kb_update - Rebuild document index
kb_stats - Show usage statisticsCode language: JavaScript (javascript)

3. Your Telegram Chat ID

Message your new bot with /start
Visit https://api.telegram.org/bot<YOUR_BOT_TOKEN>/getUpdates
Find "chat":{"id":123456789} in the JSON response
Copy the chat ID number

Step 1: Create Your Telegram Bot (5 minutes)

1.1 Initialize Bot with BotFather

Open Telegram and follow these exact steps:

Send a message to BotFather (search this name) and type:

/newbot

BotFather asks: “Alright, a new bot. How are we going to call it? Please choose a name for your bot.”

Clawdbot Assistant

BotFather asks: “Good. Now let’s choose a username for your bot. It must end in bot.”

clawdbot_assistant_bot

BotFather responds with:

Done! Congratulations on your new bot. You will find it at t.me/company_kb_bot
Use this token to access the HTTP API:
1234567890:ABCdefGHIjklMNOpqrsTUVwxyz-1234567

For a description of the Bot API, see this page: https://core.telegram.org/bots/api

Copy the token and save it securely.

1.2 Configure Bot Commands

Send this to @BotFather:

/setcommands

BotFather asks: “Choose a bot to set commands for.”
→ Select your bot from the list

BotFather asks: “Send me a list of commands for your bot.”

kb - Search the knowledge base
kb_update - Rebuild document index from latest sources
kb_stats - Show usage statistics and costs
kb_help - Show help and examplesCode language: JavaScript (javascript)

BotFather confirms: “Success! Command list updated.”

1.3 Get Your Chat ID

Message your bot: /start
Open browser and visit (replace <TOKEN> with your bot token):

https://api.telegram.org/bot<TOKEN>/getUpdatesCode language: HTML, XML (xml)

3. Find the JSON section:

"chat": {
  "id": 123456789,
  "first_name": "Your Name",
  "type": "private"
}Code language: JavaScript (javascript)

4. Copy the id number (your Chat ID)

Expected output: Bot is ready to receive messages, commands configured.

Step 2: Clone the Repository and Install Dependencies

2.1 Clone from GitHub

# Clone the complete knowledge-bot repository
git clone https://github.com/regolo-ai/tutorials/clawdbot-knowledge-base.git
cd clawdbot-knowledge-base

# Repository structure:
# .
# ├── README.md                 # Full documentation
# ├── requirements.txt          # Python dependencies
# ├── .env.example             # Environment variables template
# ├── kb_bot.py                # Main bot application
# ├── rag_pipeline.py          # RAG implementation
# ├── telegram_handler.py      # Telegram integration
# ├── knowledge-base/          # Your documents (add yours here)
# │   └── examples/
# ├── index/                   # Vector index (auto-generated)
# └── logs/                    # Usage logs & metricsCode language: PHP (php)

2.2 Install Python Dependencies

# Create virtual environment (recommended)
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install all dependencies
pip install -r requirements.txt

# Expected packages:
# - openai==1.12.0           # Regolo API client (OpenAI-compatible)
# - python-telegram-bot==20.7 # Telegram bot framework
# - numpy==1.26.3            # Vector operations
# - scikit-learn==1.4.0      # BM25 lexical retrieval
# - PyPDF2==3.0.1           # PDF document parsing
# - python-dotenv==1.0.0     # Environment variables
# - requests==2.31.0         # HTTP client

Expected output:

Successfully installed openai-1.12.0 python-telegram-bot-20.7 numpy-1.26.3 ...

2.3 Configure Environment Variables

# Copy template and edit with your credentials
cp .env.example .env
nano .env  # or use your preferred editor

Edit .env with your actual credentials:

# ============================================================================
# Regolo API Configuration (EU-hosted GPUs, OpenAI-compatible)
# ============================================================================
REGOLO_API_KEY=sk-your-regolo-api-key-here
REGOLO_BASE_URL=https://api.regolo.ai/v1

# Models (all running on Regolo EU GPUs)
EMBED_MODEL=gte-Qwen2              # Embeddings: €0.05/1M tokens
RERANK_MODEL=Qwen3-Reranker-4B     # Reranking: €0.01/query
CHAT_MODEL=Llama-3.1-8B-Instruct   # Generation: €0.05 input, €0.25 output per 1M tokens

# ============================================================================
# Telegram Bot Configuration
# ============================================================================
TELEGRAM_BOT_TOKEN=1234567890:ABCdefGHIjklMNOpqrsTUVwxyz
TELEGRAM_CHAT_ID=123456789  # Your personal chat ID for admin notifications

# ============================================================================
# Knowledge Base Configuration
# ============================================================================
KB_DOCS_PATH=./knowledge-base
KB_INDEX_PATH=./index
KB_AUTO_UPDATE_CRON=0 2 * * *  # Daily at 2 AM (cron format)

# ============================================================================
# Performance & Cost Controls
# ============================================================================
MAX_QUERIES_PER_DAY=500        # Rate limiting
MAX_CHUNKS_RETRIEVE=20         # Hybrid search candidates
MAX_CHUNKS_RERANK=5           # Final context for LLM
CHUNK_SIZE=600                # Tokens per chunk
CHUNK_OVERLAP=100             # Overlap between chunks

# ============================================================================
# Logging & Monitoring
# ============================================================================
LOG_LEVEL=INFO
LOG_FILE=./logs/kb-bot.log
METRICS_ENABLED=true

Save and close the file.

Step 3: Add Your Knowledge Base Documents

3.1 Copy Your Documents

# Clear example documents
rm -rf knowledge-base/examples/

# Copy your documents (choose one method):

# Method A: Copy from local folder
cp -r /path/to/your/docs/* knowledge-base/

# Method B: Clone from GitHub repository
cd knowledge-base
git clone https://github.com/your-org/internal-docs.git
cd ..

# Method C: Download from Google Drive (using rclone)
rclone copy gdrive:CompanyDocs knowledge-base/ --progressCode language: PHP (php)

3.2 Verify Document Structure

# Count total documents
find knowledge-base -type f \( -name "*.md" -o -name "*.txt" -o -name "*.pdf" \) | wc -l

# Expected output: 50-500 documents

# Show file tree
tree knowledge-base -L 2

# Expected structure:
# knowledge-base/
# ├── runbooks/
# │   ├── incident-response.md
# │   └── deployment.md
# ├── policies/
# │   └── gdpr.md
# └── technical/
#     └── architecture/

File requirements:

✅ UTF-8 encoding
✅ < 100MB per file
✅ Total corpus < 10GB
❌ No binary files (images, videos) – will be skipped

Step 4: Build the Knowledge Base Index

4.1 Initial Index Build

# Run the indexing script
python3 -c "from rag_pipeline import build_knowledge_base; build_knowledge_base()"

# Expected output:
# 📚 Loading documents from ./knowledge-base
# ✅ Loaded 247 documents
# 🔪 Chunking documents (600 tokens, 100 overlap)...
# ✅ Created 1,834 chunks
# 🔢 Generating embeddings via Regolo GPU (gte-Qwen2)...
#   ├─ Batch 1/37: 50 chunks embedded (210ms)
#   ├─ Batch 2/37: 100 chunks embedded (198ms)
#   └─ ... 
# ✅ Embedded 1,834 chunks (total time: 8.2s, avg 4.5ms/chunk)
# 📖 Building lexical index (BM25)...
# ✅ Lexical index built (5,000 features)
# 💾 Saving index to ./index/kb-index.pkl
# ✅ Index saved (287 MB)
# 
# Summary:
# ├─ Documents: 247
# ├─ Chunks: 1,834
# ├─ Embedding cost: €0.09 (1.83M tokens * €0.05/1M)
# └─ Build time: 14.3 secondsCode language: PHP (php)

4.2 Test Retrieval Quality

# Run quality test with sample queries
python3 test_retrieval.py

# Expected output:
# 🧪 Testing retrieval quality...
# 
# Query 1: "What is our GDPR data retention policy?"
# ├─ Retrieved 20 candidates (hybrid search)
# ├─ Reranked to top 5 (Qwen3-Reranker-4B)
# ├─ Top match: policies/gdpr-compliance.md (score: 0.94)
# └─ Latency: 312ms
# 
# Query 2: "How do we handle production incidents?"
# ├─ Retrieved 20 candidates
# ├─ Reranked to top 5
# ├─ Top match: runbooks/incident-response.md (score: 0.89)
# └─ Latency: 287ms
# 
# ✅ Average retrieval accuracy: 87% 
# ✅ Average latency: 298ms

Step 5: Deploy the Telegram Bot

5.1 Start the Bot (Development Mode)

# Run in foreground to see logs
python3 kb_bot.py

# Expected startup logs:
# 2026-01-26 16:30:15 INFO     Starting Clawdbot Knowledge Base...
# 2026-01-26 16:30:15 INFO     ├─ Regolo API: Connected ✓
# 2026-01-26 16:30:15 INFO     ├─ Models: gte-Qwen2, Qwen3-Reranker-4B, Llama-3.1-8B-Instruct
# 2026-01-26 16:30:15 INFO     ├─ Knowledge base: 247 docs, 1,834 chunks
# 2026-01-26 16:30:15 INFO     └─ Telegram bot: @company_kb_bot (ID: 1234567890)
# 2026-01-26 16:30:16 INFO     Bot started successfully. Listening for commands...Code language: CSS (css)

5.2 Test in Telegram

Open Telegram and message your bot:

Test 1: Simple query

/kb What is our GDPR policy?

Bot response (within 500ms):

📚 Knowledge Base Answer

Our GDPR data retention policy requires:

1. Personal data minimization: Collect only necessary information
2. Standard retention: 30 days for application logs, 90 days for audit logs
3. User deletion requests: Process within 30 days, with full data export option
4. Cross-border transfers: EU data residency required for all production systems

📎 Sources:
• policies/gdpr-compliance.md (relevance: 94%)
• policies/data-handling.md (relevance: 78%)

⏱️ Retrieved in 389ms | Cost: €0.0023

Test 2: Follow-up question (context maintained)

What about exceptions to the 30-day rule?

Bot response:

📚 Knowledge Base Answer

Exceptions to the standard 30-day retention include:

1. Legal hold: Data preserved indefinitely if subject to active litigation
2. Financial records: 7 years retention for accounting compliance
3. Security incidents: 12 months retention for forensic analysis
4. Contractual obligations: As specified in customer agreements (typically 12-24 months)

All exceptions require approval from Legal and DPO.

📎 Sources:
• policies/gdpr-compliance.md (relevance: 91%)

⏱️ Retrieved in 312ms | Cost: €0.0019

Test 3: Admin commands

/kb_stats

Bot response:

📊 Knowledge Base Statistics

Usage (last 24h):
├─ Total queries: 47
├─ Avg response time: 398ms 
├─ Successful retrievals: 44 (93.6%)
└─ Cache hit rate: 12.8%

Costs (last 24h):
├─ Embeddings: €0.04 (892K tokens)
├─ Reranking: €0.47 (47 queries * €0.01)
├─ Generation: €0.18 (356K input + 189K output tokens)
└─ Total: €0.69

Knowledge Base:
├─ Documents: 247
├─ Chunks: 1,834
├─ Last updated: 2026-01-26 02:00:15 (auto-update)
└─ Index size: 287 MB

Projected monthly cost: €20.70 (current usage rate)Code language: CSS (css)

5.3 Deploy in Production (Background Service)

Option A: Using systemd (Linux)

# Create systemd service file
sudo nano /etc/systemd/system/kb-bot.serviceCode language: PHP (php)

Paste this configuration:

[Unit]
Description=Clawdbot Knowledge Base (Regolo GPU)
After=network.target

[Service]
Type=simple
User=your-username
WorkingDirectory=./
Environment="PATH=./venv/bin"
ExecStart=./venv/bin/python3 kb_bot.py
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target

# Enable and start service
sudo systemctl daemon-reload
sudo systemctl enable kb-bot
sudo systemctl start kb-bot

# Check status
sudo systemctl status kb-bot

# View logs
sudo journalctl -u kb-bot -fCode language: PHP (php)

Option B: Using Docker

# Build Docker image
docker build -t kb-bot:latest .

# Run container
docker run -d \
  --name kb-bot \
  --restart unless-stopped \
  -v $(pwd)/knowledge-base:/app/knowledge-base \
  -v $(pwd)/index:/app/index \
  -v $(pwd)/logs:/app/logs \
  --env-file .env \
  kb-bot:latest

# View logs
docker logs -f kb-bot

Option C: Using screen (Quick & Simple)

# Start in detached screen session
screen -dmS kb-bot python3 kb_bot.py

# Reattach to view logs
screen -r kb-bot

# Detach again: Ctrl+A, then D

Step 6: Configure Auto-Updates (Optional)

Enable automatic knowledge base updates when documents change.

6.1 Enable Cron-Based Auto-Update

The bot includes built-in cron support for automatic index rebuilds:

Edit .env:

KB_AUTO_UPDATE_CRON=0 2 * * *  # Daily at 2 AM
KB_AUTO_UPDATE_ENABLED=trueCode language: PHP (php)

Restart the bot:

# If using systemd:
sudo systemctl restart kb-bot

# If using screen:
screen -X -S kb-bot quit
screen -dmS kb-bot python3 kb_bot.pyCode language: PHP (php)

Expected behavior: Every day at 2 AM, the bot will:

Check for new/modified documents in knowledge-base/
Rebuild the index if changes detected
Send Telegram notification: “✅ Knowledge base updated: 12 new docs, 3 modified”
Log cost: “Index rebuild cost: €0.11”

6.2 Manual Update Command

Users can trigger updates via Telegram:

/kb_update

Bot response:

🔄 Rebuilding knowledge base index...

📚 Scanning ./knowledge-base
├─ Found 259 documents (+12 new, +3 modified since last build)
├─ Creating 1,947 chunks (+113 new)
└─ Generating embeddings via Regolo GPU...

✅ Index rebuilt successfully
├─ Build time: 16.8s
├─ Cost: €0.11 (embeddings)
└─ Ready to serve queries

Try: /kb <your question>Code language: PHP (php)

The Architecture

User (Telegram)
    ↓ /kb <question>
Hybrid Retrieval
    ├─ Dense: gte-Qwen2 embeddings (Regolo GPU)
    └─ Lexical: BM25 (local)
    ↓ Top 20 candidates
Reranking: Qwen3-Reranker-4B (Regolo GPU)
    ↓ Top 5 chunks
Generation: Llama-3.1-8B-Instruct (Regolo GPU)
    ↓ Final answer + citationsCode language: HTML, XML (xml)

Benchmarks

Metric	Value
Retrieval accuracy	87%
Response latency	420ms
Throughput	50 queries/sec (concurrent)
Cost per 1K queries	€12.30

Comparison vs. OpenAI GPT-4:

Cost: 73% cheaper (Regolo: €12.30 vs OpenAI: €45/1K queries)
Latency: 22% faster (420ms vs 540ms)
Data residency: EU vs US

👉 Try it now for free for 30 days

Github Codes

You can download the codes on our Github repo. If need help you can always reach out our team on Discord 🤙

Download the Code

Resources & Community

Official Documentation:

Regolo Python Client – Package reference
Regolo Models Library – Available models
Regolo API Docs – API reference

Related Guides:

Join the Community:

Regolo Discord – Share your RAG builds
GitHub Repo – Contribute examples
Follow Us on X @regolo_ai – Show your RAG pipelines!
Open discussion on our Subreddit Community

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Built with ❤️ by the Regolo team. Questions? support@regolo.ai or chat with us on Discord