Files

Alex 857ea60e55 ваше сообщение коммита

2026-03-02 12:33:16 +03:00

7.1 KiB

Raw Permalink Blame History

DLE AI Agents — Building Specialized Business Agents

Concept: one local model — many specialized agents. Each agent is tailored to a specific business process: its own prompt, rules, knowledge base, and interface.

What and Why
Architecture
How to Create an Agent
Agent Examples
Technology Stack
Advantages Over Cloud Solutions
Economic Impact

What and Why

DLE provides tools to create AI agents — specialized assistants, each responsible for a distinct business process.

This is not one generic chatbot. It is a builder where you:

Create an agent for a specific task (support, content, procurement, analytics)
Set its role via system prompt
Attach a knowledge base (RAG tables) with relevant data
Configure behavior rules (strict, creative, hybrid)
Bind to channels (web chat, Telegram, Email)
Get an isolated specialist working 24/7

All agents use one local Ollama model on your server. They differ by system prompts, rules, and connected data. Data never leaves your server.

Architecture

Principle: One Model — Many Agents

One Ollama instance (e.g. qwen2.5:7b) serves multiple agents. Each has its own prompt, rules, RAG tables, channels, and UI. Agents are isolated and do not affect each other.

Request Flow

User request → agent determined by channel/route → agent config (prompt, rules, RAG) → query vectorization (Ollama mxbai-embed-large) → RAG search (FAISS) → LLM response with RAG context + system prompt + history → optional cache (TTL 1 h) → response to user.

How to Create an Agent

Step 1. Basic Info

Name — e.g. “Support Agent”, “Content Editor”, “AI Procurement”
Role — support, content_editor, analyst, purchaser, etc.
Description — what the agent is for

Step 2. System Prompt

Defines identity and behavior. Examples:

Support: “You are a professional customer support assistant. Answer only from the knowledge base. If no answer — suggest contacting an operator. Do not invent prices, terms, or conditions.”

Content editor: “You are a professional content marketer and editor. Use company style from the knowledge base. Follow platform guidelines. Use keywords and hashtags from the base.”

Step 3. Rules (JSON)

{
  "searchRagFirst": true,
  "generateIfNoRag": false,
  "checkUserTags": true,
  "temperature": 0.3,
  "maxTokens": 500
}

Parameter	Effect	Support	Content	Analytics
temperature	Creativity (0.0–1.0)	0.3	0.7	0.2
searchRagFirst	Search knowledge base first	true	true	true
generateIfNoRag	Generate if not in base	false	true	false
maxTokens	Max response length	500	2000	1000

Step 4. Knowledge Base (RAG Tables)

Attach spreadsheets the agent will search: Support → FAQ, product docs; Content → platform instructions, style, examples, keywords; Procurement → supplier base, terms, prices. Tables need columns designated as “Question for AI” and “Answer for AI” for vector indexing.

Step 5. Channels and Interface

Channels: web chat, Telegram, Email, SMS. Route: e.g. /content-editor. Set which roles can access.

Step 6. Activate

Enable the agent; it starts handling requests on the selected channels.

Agent Examples

1. Customer Support Agent

Task: answer customer questions 24/7 from the knowledge base. Strict mode (only from base), temperature 0.3, RAG: FAQ, docs. Channels: web chat, Telegram, Email. If no answer → suggest operator.

2. Content Editor Agent

Task: create social posts, blog articles, emails in company style. Creative mode, temperature 0.7, RAG: platform instructions, style, examples, keywords, CTAs. Interface: /content-editor, Editor role.

3. AI Procurement Agent

Task: help choose suppliers and compare terms. Hybrid mode, temperature 0.5, RAG: supplier base, terms and prices. Example: “Who supplies electronics with delivery up to 3 days?” → top 3 from table with filters.

4. Other Possible Agents

Analyst (reports, trends), HR assistant (screening, policies), Translator (glossaries, style), Legal assistant (contracts, norms). Each = new combination of prompt, rules, and tables.

Technology Stack

Component	Technology	Purpose
LLM	Ollama (qwen2.5:7b or other)	Generation, dialogue
Embedding	mxbai-embed-large	Text vectorization
Vector DB	FAISS	Semantic search
Main DB	PostgreSQL	Agents, knowledge, history
Cache	Node.js Map + TTL	Fast repeat queries (< 50ms)
Queue	AI Queue	Priority processing
Encryption	AES-256	Prompts and settings encrypted

RAG Search Methods

Semantic (FAISS), keyword, hybrid (e.g. 70% semantic, 30% keyword). Optional: fuzzy search, stemming, keyword extraction.

Advantages Over Cloud Solutions

	DLE (local)	ChatGPT API	Claude API
Cost	$0	~$0.02/request	~$0.03/request
Confidentiality	100% on your server	Data at OpenAI	Data at Anthropic
Speed (cached)	< 50ms	500–2000ms	500–2000ms
Offline	Yes	No	No
Business tuning	Full: prompts, rules, RAG	Limited	Limited
API limits	None	Yes	Yes
Number of agents	Unlimited	Separate API per use	Separate API per use

Economic Impact

API Cost Savings

Requests/month	ChatGPT API	Claude API	DLE
10,000	$2,400/year	$3,600/year	$0
50,000	$12,000/year	$18,000/year	$0
100,000	$24,000/year	$36,000/year	$0

Process Automation Savings

Each agent replaces routine work. Example for a mid-size company: support agent ($57,600), procurement ($64,800), HR ($57,600), content ($86,400), analyst ($144,000), partners ($43,200), training ($30,000), API savings ($24,000–36,000) → total about $507,600–519,600/year. DLE license: $1,000 one-time.

5-Year Comparison with SaaS

Typical SaaS stack (CRM, chatbot, email, AI API): ~$39,000 over 5 years. DLE: $1,000 + $0 AI + free updates 5 years → savings about $38,000.

Additional Materials

Multi-agent architecture — technical spec
AI assistant setup — deployment steps
Tables system — RAG tables
FAQ

Support

Last updated: February 2026

7.1 KiB Raw Permalink Blame History Unescape Escape