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AI Assistant Setup Guide with Vector Search

🤖 Complete Guide to Launching an Intelligent Assistant

This document describes the step-by-step process of setting up an AI assistant for solving business tasks through spreadsheets and vector search.

📚 What You'll Set Up

After completing the instructions, you will have:

✅ Working AI assistant with local model (Ollama)
✅ Knowledge base for customer responses (FAQ)
✅ Supplier work automation
✅ Employee training system
✅ Vector search on your data
✅ Significant time and resource savings

💡 Economic Impact: See AI Assistant - detailed description of all capabilities and savings calculations.

⏱️ Setup Time

Quick Setup: 20-30 minutes (basic FAQ)
Full Setup: 1-2 hours (all capabilities)

Step 1: Installing and Running Ollama

1.1 Checking Ollama Status

Go to Settings → Integrations tab
Find the "Ollama" block and click "Details"
Check connection status:
- ✅ "Ollama is running" — ready, proceed to step 1.3
- ❌ "Ollama API not responding" — proceed to step 1.2

1.2 Starting Ollama (if not running)

If Ollama is not running, execute in terminal:

# For Docker (recommended)
docker-compose up -d ollama

# Or locally
ollama serve

Refresh the page and check status again.

1.3 Installing AI Model

In the Ollama section, click "Install Model"
Select model:
- qwen2.5:7b (recommended) — for Russian, 4.7 GB
- llama2:7b — for English, 3.8 GB
- mistral:7b — universal, 4.1 GB
Click "Install"
Wait for download to complete (5-10 minutes depending on internet speed)

💡 Tip: Model downloads once and is stored locally

1.4 Installing Embedding Model

In the same section, find "Install Embedding Model"
Select model:
- mxbai-embed-large:latest (recommended) — 670 MB
- nomic-embed-text:latest — alternative, 274 MB
Click "Install"

⚠️ Important: Embedding model is needed for vector search (RAG)

Step 2: Creating Knowledge Base (Spreadsheets)

2.1 Creating FAQ Table

Go to Tables (in main menu)
Click "+ Create Table"
Fill in:
- Name: FAQ - Frequently Asked Questions
- Description: Knowledge base for AI assistant customer service
Click "Create"

2.2 Configuring Table Columns

Add the following columns:

Column 1: Question (required for RAG)

Click "+ Add Column"
Fill in:
- Name: Question
- Type: Text
- Purpose: Select Question for AI ⭐
Click "Save"

⚠️ Critical: Must select purpose "Question for AI" — this field will be indexed for vector search

Column 2: Answer (required for RAG)

Click "+ Add Column"
Fill in:
- Name: Answer
- Type: Text
- Purpose: Select AI Answer ⭐
Click "Save"

Column 3: Product (optional, for filtering)

Click "+ Add Column"
Fill in:
- Name: Product
- Type: Multiple Choice
- Options: Basic, Premium, Corporate
- Purpose: Select Product Filter
Click "Save"

Column 4: Tags (optional, for categorization)

Click "+ Add Column"
Fill in:
- Name: Tags
- Type: Multiple Choice
- Options: Payment, Delivery, Return, Warranty, Support
- Purpose: Select User Tags
Click "Save"

Column 5: Priority (optional)

Click "+ Add Column"
Fill in:
- Name: Priority
- Type: Number
- Purpose: Select Priority
Click "Save"

💡 Tip: Questions with higher priority will be shown to AI first

2.3 Filling Knowledge Base

Add typical questions and answers:

Example 1: Payment

Question	Answer	Product	Tags	Priority
How to pay for order?	We accept payment by bank card, PayPal, or bank transfer. Choose convenient method when placing order.	All	Payment	10
Can I pay in installments?	Yes, for orders from 50,000₽ installment plan available for 3, 6 or 12 months without overpayment.	Premium, Corporate	Payment	8

Example 2: Delivery

Question	Answer	Product	Tags	Priority
How long does delivery take?	Standard delivery: 3-5 business days in Russia. Express delivery: 1-2 days in major cities.	All	Delivery	10
How much does delivery cost?	Free delivery for orders from 5,000₽. For orders less than 5,000₽ delivery cost is 300₽.	All	Delivery	9

Example 3: Return

Question	Answer	Product	Tags	Priority
How to return product?	Return possible within 14 days from receipt. Product must be in original packaging, with preserved appearance. Contact support to process return.	All	Return	10
When will money be returned?	Refund processed within 5-10 business days after product received at our warehouse.	All	Return	8

💡 Recommendation: Add minimum 20-30 questions for quality AI work. More questions = more accurate answers!

2.4 Activating Table as AI Source

In top right corner of table, find ⚙️ Table Settings
Enable toggle "Use as AI Source" ✅
Click "Save"

✅ Done! Table is now indexed for vector search

Step 3: Configuring AI Provider (Ollama)

3.1 Opening Ollama Settings

Go to Settings → Integrations
Find "Ollama" block and click "Details"

3.2 Checking Base URL

Check Base URL field:
- For Docker: http://ollama:11434 ✅
- For local: http://localhost:11434
If URL is incorrect, fix and click "Save"

3.3 Selecting Model

In "Model (LLM)" field, select installed model:
- qwen2.5:7b (recommended for Russian)
In "Embeddings Model" field, select:
- mxbai-embed-large:latest
Click "Save"

Step 4: Configuring AI Assistant

4.1 Opening Assistant Settings

Go to Settings → Integrations
Find "AI Assistant" block and click "Details"

4.2 Configuring System Prompt

In "System Prompt" field, enter instructions for AI:

Basic Prompt (to start):

You are a professional customer support assistant.

Rules:
1. Answer politely and professionally
2. Use information from knowledge base
3. If information not available — suggest contacting operator
4. Answer briefly and to the point
5. Always end with question "How else can I help?"

Advanced Prompt (with personalization):

You are a professional customer support assistant for "Your Company Name".

About company:
- We are engaged in [brief business description]
- Our values: quality, reliability, customer focus

Communication style:
- Friendly but professional
- Address customer as "You"
- Use emojis moderately (1-2 per message)

Response rules:
1. MANDATORY: Answer ONLY in Russian. All questions and answers must be in Russian
2. First search answer in knowledge base (RAG)
3. If found — answer based on found information
4. If not found — honestly say and suggest operator help
5. Don't make up information about prices, terms, conditions
6. For complex questions suggest contacting manager

Always end with: "How else can I help? 😊"

4.3 Selecting Models

LLM Model: Select qwen2.5:7b (ollama)
Embedding Model: Select mxbai-embed-large:latest (ollama)

💡 Tip: Models automatically pulled from Ollama settings

📊 Context Window Size:

Qwen2.5:7b: Base context = 32,768 tokens (~24,000 Russian words)

Total data sent to model:

System prompt: ~500-2000 characters (~300-1200 tokens)

Conversation history: up to 20 messages (~100-500 tokens per message = ~2000-10000 tokens)

RAG context: ~500-2000 tokens (from found data)

Current question: ~50-200 tokens

Total: approximately 3,000-15,000 tokens (sufficient reserve)

If larger context needed → use Qwen3 with YaRN (up to 131K tokens)

4.4 Selecting RAG Table

In "Selected RAG Tables" field, select created table:
- FAQ - Frequently Asked Questions
Click "Save"

4.5 Configuring AI Rules

Create rule set for managing AI behavior:

Click "Create" button next to "Rule Set" field
In modal window, fill in:

Name: Hybrid Mode (RAG + Generation)

Description: AI first searches knowledge base, if not found — generates answer

Rules (JSON):

{
  "checkUserTags": true,
  "searchRagFirst": true,
  "generateIfNoRag": true,
  "temperature": 0.7,
  "maxTokens": 500
}

Click "Save"
Select created rule in dropdown

💡 What parameters mean:

checkUserTags: true — consider user tags when searching

searchRagFirst: true — first search knowledge base

generateIfNoRag: true — generate answer if nothing found

temperature: 0.7 — balance between accuracy and creativity (0.0-1.0)

maxTokens: 500 — maximum answer length

4.6 RAG Search Settings

Expand "🔍 RAG Search Settings" section:

Basic Settings:

Search Method: Select Hybrid Search (recommended)
Maximum Results: 5
Relevance Threshold: 0.1 (from 0.01 to 1.0)

Keyword Extraction:

✅ Enable Keyword Extraction
Minimum Word Length: 3
Maximum Keywords: 10
✅ Remove Stop Words

Search Weights (for hybrid):

Semantic Search: 70% (accuracy)
Keyword Search: 30% (speed)

Additional Settings:

✅ Fuzzy Search (for typos)
✅ Word Stemming (finds different word forms)
☐ Synonym Search (currently disabled)

4.7 Saving Settings

Click "Save" button at bottom of form.

Step 5: Testing AI Assistant

5.1 Using Built-in Tester

On AI assistant settings page, scroll down to "🔍 System Monitoring" block
In "🧠 RAG Functionality Test" section:
- Ensure table FAQ - Frequently Asked Questions is selected
- Enter test question: How to pay for order?
- Click "Test RAG"
Observe process:
- 🔍 Searching answer in knowledge base... (vector search)
- 🤖 Generating answer with AI... (LLM generation)
- ✅ Done!
Check result:
- Answer from your table should be displayed
- Score (similarity score): closer to 0 is better

💡 Good Score: from -300 to 0 (answer found)
⚠️ Bad Score: greater than 300 (answer not found, AI will make up its own)

5.2 Testing via Web Chat

Go to application main page
Find "💬 Chat with AI" widget (usually bottom right)
Click widget to open chat
Enter question: How much does delivery cost?
Check AI response

Expected Result:

🤖 AI Assistant:
Free delivery for orders from 5,000₽. 
For orders less than 5,000₽ delivery cost is 300₽.

How else can I help? 😊

5.3 Testing Different Scenarios

Try asking various questions:

✅ Question from knowledge base:

User: "How to return product?"
AI: [Answer from FAQ table]

⚠️ Question NOT from knowledge base:

User: "What's the weather today?"
AI: "Sorry, I specialize in questions about our company and products. 
     For weather questions, please contact specialized services. 
     How else can I help?"

🎯 Question with typo:

User: "How to pay for order?" (typo)
AI: [Will find correct answer thanks to fuzzy search]

Step 6: Advanced Capabilities (Optional)

6.1 Creating Supplier Table

"Supplier Database" Table Structure

Create new table: Supplier Database
Add columns:

Column	Type	Description
Company Name	Text	Supplier name
Product Category	Multiple Choice	Electronics, Furniture, Clothing, etc.
Contact Person	Text	Manager name
Email	Text	Email address
Phone	Text	Contact phone
Prices	Text	Price list (brief description)
Payment Terms	Text	Deferred payment, prepayment, etc.
Minimum Order	Number	Minimum order amount
Delivery Time	Text	Delivery terms
Rating	Number	Rating from 1 to 10
Notes	Text	Additional information

Activate as AI source
Fill with your supplier data

Prompt for AI Purchaser

Add to system prompt:

ADDITIONALLY - Working with Suppliers:

When user asks about suppliers:
1. Search in "Supplier Database" base
2. Filter by product category
3. Sort by rating and terms
4. Provide TOP-3 recommendations

Response format:
🏆 TOP-3 suppliers for "[category]":

1. [Name] ⭐ [Rating]/10
   📧 [Email] | 📞 [Phone]
   💰 Terms: [Payment Terms]
   🚚 Delivery: [Delivery Time]
   📦 Minimum: [Minimum Order]₽

2. ...
3. ...

I recommend contacting [First Supplier Name] — best terms.

6.2 Creating Employee Training Table

"Employee Knowledge Base" Table Structure

Create new table: Employee Knowledge Base
Add columns:

Column	Type	Description
Question	Text	Employee question (purpose: Question for AI)
Answer	Text	Detailed answer (purpose: AI Answer)
Category	Multiple Choice	Sales, HR, Finance, IT, Marketing
Department	Multiple Choice	Which department it's relevant for
Difficulty	Number	From 1 (simple) to 5 (complex)
Instructions	Text	Step-by-step instructions (if any)
Links	Text	Links to documents/videos
Update Date	Date	When information was updated

Example questions:

"Sales" Category:

"How to process customer return?"
"What discounts can be given to regular customers?"
"How to work with corporate customers?"

"HR" Category:

"How to apply for vacation?"
"Where to contact for sick leave?"
"How does new employee onboarding work?"

"IT" Category:

"How to get access to corporate email?"
"What to do with VPN problems?"
"How to create support ticket?"

6.3 Creating Table Relationships

Example: "Customers" → "Orders" Relationship

Create "Customers" table:
- Name, Email, Phone, Status (VIP/Regular)
Create "Orders" table:
- Order Number, Date, Amount
In "Orders" table, add column:
- Name: Customer
- Type: Relation
- Related Table: select Customers
- Display Field: select Name
Add another column in "Orders":
- Name: Customer Email
- Type: Lookup
- Relation Through: select Customer column
- Lookup Field: select Email

Result: When selecting customer, their Email is automatically filled!

AI automatically understands relationships and can answer questions:

User: "Show all orders for customer Ivanov"
AI: [Searches Orders table, filters by customer Ivanov]

Step 7: Telegram and Email Integration (Optional)

7.1 Setting Up Telegram Bot

Go to Settings → Integrations → Telegram
Create bot via @BotFather in Telegram:
- Send /newbot
- Choose bot name and username
- Copy Bot Token
In DLE settings, enter:
- Bot Token: paste token from BotFather
- Bot Username: your bot username (e.g., @mycompany_bot)
Click "Save"
In AI assistant settings, select this Telegram bot in "Telegram Bot" field

Result: AI will respond to Telegram messages!

7.2 Setting Up Email Integration

Go to Settings → Integrations → Email
Fill IMAP settings (for receiving emails):
- IMAP Host: imap.gmail.com (for Gmail)
- IMAP Port: 993
- IMAP User: your email
- IMAP Password: app password (not main password!)
Fill SMTP settings (for sending emails):
- SMTP Host: smtp.gmail.com
- SMTP Port: 587
- SMTP User: your email
- SMTP Password: app password
- From Email: email for sending
Click "Test IMAP" and "Test SMTP" to verify
Click "Save"
In AI assistant settings, select this Email in "Email Contact" field

⚠️ Important for Gmail: Create "App Password" in Google security settings

Result: AI will automatically respond to incoming emails!

Step 8: Monitoring and Optimization

8.1 Checking Service Status

Go to Settings → Integrations → AI Assistant
Scroll down to "🔍 System Monitoring"
Click "🔄 Refresh Status"
Check statuses:
- 🟢 Backend: should be "Running"
- 🟢 Postgres: should be "Running"
- 🟢 Ollama: should show number of models
- 🟢 Vector Search: should be "Running"

⚠️ If something is red (🔴) — see "Troubleshooting" section below

8.2 Analyzing Answer Quality

Regularly check AI answer quality:

Score in RAG Tester:
- -300 to 0 ✅ — excellent match
- 0 to 300 ⚠️ — average match
- >300 ❌ — match not found
If Score is Bad:
- Add more similar questions to table
- Use different phrasings of same question
- Increase relevance threshold (e.g., to 0.2)

8.3 Optimizing RAG Settings

Experiment with settings to improve results:

For More Accurate Answers:

Search Method: Semantic
Relevance Threshold: 0.05 (lower = stricter)
Weights: Semantic 100% / Keywords 0%

For Faster Answers:

Search Method: Keyword Search
Maximum Results: 3

For Balance (Recommended):

Search Method: Hybrid
Weights: Semantic 70% / Keywords 30%
Relevance Threshold: 0.1

✅ AI Assistant Ready to Work!

What You Now Have

✅ Local AI Assistant without cloud dependency
✅ FAQ Knowledge Base for customer responses
✅ Vector Search for accurate answers
✅ Configured Rules for AI behavior
✅ Monitoring System for quality control

Economic Impact

With proper AI assistant setup, you will get:

✅ Task Automation - freeing time for strategy
✅ Improved Service Quality - AI works 24/7 without fatigue
✅ Reduced Operating Costs - less staff on routine tasks
✅ Faster Decision Making - instant access to information

💡 Detailed Information: See AI Assistant - Complete Description - detailed description of all capabilities, use cases, and savings calculations.

📚 Next Steps

Expand AI Capabilities

Add More Tables:
- Partner knowledge base
- Employee instructions
- Product catalog
- Contact database
Create Rules for Different Scenarios:
- Strict mode (RAG only) — for finance
- Creative mode (more generation) — for marketing
- Hybrid mode (balance) — for support
Integrate with Other Systems:
- CRM (customer synchronization)
- Warehouse system (inventory)
- Accounting (invoices and payments)

Train Your Team

Show employees how AI works
Explain how to add new questions to base
Establish regular knowledge base update process
Assign responsible person for AI answer quality

🆘 Troubleshooting

Problem: Ollama Not Starting

Symptoms: Status "Ollama API not responding"

Solution:

# Check container
docker ps | grep ollama

# Restart
docker-compose restart ollama

# Check logs
docker-compose logs ollama

Problem: AI Answers Inaccurately

Symptoms: Answers don't match knowledge base

Solution:

Check Score in tester (should be < 300)
Add more question variations to table
Decrease relevance threshold (e.g., to 0.05)
Check that columns have correct purposes ("Question for AI", "AI Answer")

Problem: Vector Search Not Working

Symptoms: Vector Search status shows error

Solution:

Check if Embedding model is installed
Rebuild index: on table page click "🔄 Rebuild Index"
Check that table is activated as AI source

Problem: AI Answers in Wrong Language

Symptoms: Answers in English instead of Russian

Solution:

Change system prompt, adding at start: ALWAYS answer in Russian.
Use qwen2.5:7b model instead of llama2:7b
In AI rules, set "language": "ru"

Problem: Slow Responses

Symptoms: AI responds longer than 5-10 seconds

Solution:

Use smaller model (mistral:7b instead of qwen2.5:14b)
Decrease maxResults in RAG settings (e.g., to 3)
Disable "Synonym Search" in additional settings
Use SSD for storing models

📖 Additional Documentation

Learn AI Capabilities

🤖 AI Assistant - Complete Description - all capabilities and use cases
📊 Spreadsheet System - technical description of tables (temporary)
⚙️ AI Configuration - technical setup details

General Documentation

🛡️ Security - how AI protects your data
💼 Blockchain for Business - AI integration with blockchain
📋 FAQ - frequently asked questions

Support

💬 Support Chat: https://hb3-accelerator.com/
📧 Email: info@hb3-accelerator.com
📚 Knowledge Base: https://hb3-accelerator.com

🔧 Technical Documentation (for Developers)

AI System Architecture

┌───────────────────────────────────────────────────────────┐
│          AI Assistant Configuration in DLE                │
├───────────────────────────────────────────────────────────┤
│                                                           │
│  🤖 AI Providers:                                        │
│     ├── OpenAI (GPT-4, GPT-3.5)                          │
│     ├── Anthropic (Claude)                               │
│     ├── Google (Gemini)                                  │
│     └── Ollama (local models)                            │
│                                                           │
│  ⚙️  AI Settings:                                        │
│     ├── System Prompt                                    │
│     ├── LLM Model Selection                              │
│     ├── Embedding Model Selection                        │
│     ├── RAG Table Selection                              │
│     ├── Rules                                            │
│     └── RAG Search Settings                              │
│                                                           │
│  📋 Rules:                                                │
│     ├── JSON behavior configuration                      │
│     ├── Create/edit/delete                              │
│     └── Link to AI assistant                            │
│                                                           │
│  🔗 Integrations:                                         │
│     ├── Email (IMAP + SMTP)                              │
│     └── Telegram Bot                                     │
│                                                           │
│  🔍 RAG:                                                  │
│     ├── Table selection for search                       │
│     ├── Search settings (hybrid/semantic)                │
│     ├── Relevance threshold                              │
│     └── Keyword extraction                               │
│                                                           │
│  📊 Monitoring:                                           │
│     ├── Service status (Backend, Ollama, Postgres)      │
│     ├── RAG functionality test                           │
│     └── Progress tracking                                │
│                                                           │
└───────────────────────────────────────────────────────────┘

Database

Table: `ai_providers_settings`

CREATE TABLE IF NOT EXISTS ai_providers_settings (
    id SERIAL PRIMARY KEY,
    provider_encrypted TEXT,              -- Provider: openai, anthropic, google, ollama
    api_key_encrypted TEXT,               -- API key (encrypted)
    base_url_encrypted TEXT,              -- Base URL for API
    selected_model_encrypted TEXT,        -- Selected LLM model
    embedding_model_encrypted TEXT,       -- Selected Embedding model
    created_at TIMESTAMP NOT NULL DEFAULT NOW(),
    updated_at TIMESTAMP NOT NULL DEFAULT NOW()
);

Table: `ai_assistant_settings`

CREATE TABLE IF NOT EXISTS ai_assistant_settings (
    id SERIAL PRIMARY KEY,
    system_prompt_encrypted TEXT,         -- System prompt
    selected_rag_tables INTEGER[],        -- Array of RAG table IDs
    languages TEXT[],                     -- Array of supported languages
    model_encrypted TEXT,                 -- Selected LLM model
    embedding_model_encrypted TEXT,       -- Selected Embedding model
    rules JSONB,                          -- Rules (DEPRECATED)
    rules_id INTEGER REFERENCES ai_assistant_rules(id),  -- Rule reference
    telegram_settings_id INTEGER,         -- Telegram bot reference
    email_settings_id INTEGER,            -- Email settings reference
    rag_settings JSONB,                   -- RAG search settings
    updated_at TIMESTAMP DEFAULT NOW(),
    updated_by INTEGER
);

Table: `ai_assistant_rules`

CREATE TABLE IF NOT EXISTS ai_assistant_rules (
    id SERIAL PRIMARY KEY,
    name TEXT NOT NULL,                   -- Rule set name
    description TEXT,                     -- Rule description
    rules JSONB NOT NULL,                 -- JSON configuration
    rules_encrypted TEXT,                 -- Encrypted version of rules
    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW()
);

Backend API

AI Provider Settings

GET /settings/ai-settings/:provider — Get provider settings
PUT /settings/ai-settings/:provider — Save provider settings
DELETE /settings/ai-settings/:provider — Delete provider settings
GET /settings/ai-settings/:provider/models — Get model list
POST /settings/ai-settings/:provider/verify — Verify API key

AI Assistant Settings

GET /settings/ai-assistant — Get assistant settings
PUT /settings/ai-assistant — Save assistant settings

AI Rules

GET /settings/ai-assistant-rules — Get all rules
GET /settings/ai-assistant-rules/:id — Get rule by ID
POST /settings/ai-assistant-rules — Create rule
PUT /settings/ai-assistant-rules/:id — Update rule
DELETE /settings/ai-assistant-rules/:id — Delete rule

Ollama (Local Models)

GET /ollama/status — Check Ollama status
GET /ollama/models — Get model list
POST /ollama/install — Install model
DELETE /ollama/models/:modelName — Delete model

Frontend Pages

/settings/ai — Main integrations page
/settings/ai/:provider — AI provider settings
/settings/ai/assistant — AI assistant settings

Message Processing Flow

1. User → Message
              ↓
2. UnifiedMessageProcessor
              ↓
3. Language Check (Russian only)
              ↓
4. Deduplication (message hash)
              ↓
5. Load Settings (aiAssistantSettingsService)
              ↓
6. Load Rules (aiAssistantRulesService)
              ↓
7. RAG Search (ragService)
   ├── Semantic Search (vector search)
   ├── Keyword Search
   └── Hybrid Search
              ↓
8. Generate Answer (generateLLMResponse)
   ├── System Prompt
   ├── Conversation History
   ├── RAG Context
   └── Rules
              ↓
9. Answer → User

Security

Encryption: All sensitive fields encrypted with AES-256
Access Rights: Only administrators can change settings
Validation: All input data and API keys validated

Document Version: 1.0.0
Creation Date: October 25, 2025

28 KiB Raw Blame History

AI Assistant Setup Guide with Vector Search

🤖 Complete Guide to Launching an Intelligent Assistant

📚 What You'll Set Up

⏱️ Setup Time

Step 1: Installing and Running Ollama

1.1 Checking Ollama Status

1.2 Starting Ollama (if not running)

1.3 Installing AI Model

1.4 Installing Embedding Model

Step 2: Creating Knowledge Base (Spreadsheets)

2.1 Creating FAQ Table

2.2 Configuring Table Columns

Column 1: Question (required for RAG)

Column 2: Answer (required for RAG)

Column 3: Product (optional, for filtering)

Column 4: Tags (optional, for categorization)

Column 5: Priority (optional)

2.3 Filling Knowledge Base

2.4 Activating Table as AI Source

Step 3: Configuring AI Provider (Ollama)

3.1 Opening Ollama Settings

3.2 Checking Base URL

3.3 Selecting Model

Step 4: Configuring AI Assistant

4.1 Opening Assistant Settings

4.2 Configuring System Prompt

4.3 Selecting Models

4.4 Selecting RAG Table

4.5 Configuring AI Rules

4.6 RAG Search Settings

4.7 Saving Settings

Step 5: Testing AI Assistant

5.1 Using Built-in Tester

5.2 Testing via Web Chat

5.3 Testing Different Scenarios

Step 6: Advanced Capabilities (Optional)

6.1 Creating Supplier Table

"Supplier Database" Table Structure

Prompt for AI Purchaser

6.2 Creating Employee Training Table

"Employee Knowledge Base" Table Structure

6.3 Creating Table Relationships

Example: "Customers" → "Orders" Relationship

Using AI with Related Tables

Step 7: Telegram and Email Integration (Optional)

7.1 Setting Up Telegram Bot

7.2 Setting Up Email Integration

Step 8: Monitoring and Optimization

8.1 Checking Service Status

8.2 Analyzing Answer Quality

8.3 Optimizing RAG Settings

✅ AI Assistant Ready to Work!

What You Now Have

Economic Impact

📚 Next Steps

Expand AI Capabilities

Train Your Team

🆘 Troubleshooting

Problem: Ollama Not Starting

Problem: AI Answers Inaccurately

Problem: Vector Search Not Working

Problem: AI Answers in Wrong Language

Problem: Slow Responses

📖 Additional Documentation

Learn AI Capabilities

General Documentation

Support

🔧 Technical Documentation (for Developers)

AI System Architecture

Database

Table: ai_providers_settings

Table: ai_assistant_settings

Table: ai_assistant_rules

Backend API

AI Provider Settings

AI Assistant Settings

AI Rules

Ollama (Local Models)

Frontend Pages

28 KiB

Raw Blame History

Table: `ai_providers_settings`

Table: `ai_assistant_settings`

Table: `ai_assistant_rules`