15.3.2026 change configuration

.dockerignore

@@ -13,4 +13,6 @@ README.md
 docker-compose.yml
 Dockerfile
 .dockerignore
+.chainlit/translations/*
+!.chainlit/translations/en-US.json
 .files/

.gitignore

@@ -181,5 +181,10 @@ fabric.properties
 # Android studio 3.1+ serialized cache file
 .idea/caches/build_file_checksums.ser
 
+# Chainlit correction
+.chainlit/translations/*
+!.chainlit/translations/en-US.json
+.files/
+
 # idea folder, uncomment if you don't need it
 .idea

README.md

@@ -1,109 +0,0 @@
-# Právny AI Asistent – integrácia s API
-
-## POPIS
-
-Právny AI Asistent je inteligentný asistent integrovaný s oficiálnymi verejnými API Ministerstva spravodlivosti 
-Slovenskej republiky. Systém poskytuje užívateľom prístup k štruktúrovaným právnym informáciám prostredníctvom 
-prirodzeného jazyka.
-
-![PROFILE](public/img/project.png)
-
-**Hlavné funkcie:**
-- Interpretácia užívateľských otázok v prirodzenom jazyku
-- Automatická extrakcia a validácia parametrov
-- Načítavanie dát výlučne cez oficiálne API
-- Prezentácia výsledkov v zrozumiteľnom formáte v slovenskom jazyku
-
-## POUŽITÉ KNIŽNICE A TECHNOLÓGIE
-
-- **Python** – hlavný programovací jazyk
-- **openai-agent** – tvorba AI agentov od OpenAI
-- **httpx** – asynchrónna komunikácia s API
-- **Chainlit** – framework pre chat rozhranie
-- **Ollama** – lokálne používanie AI modelov
-- **Docker** – kontajnerizácia aplikácie
-- a **iné**
-
-## ŠTRUKTÚRA PROJEKTU
-```
-ai-lawyer-agent/                
-├── api/                        # API logika
-│   └── tools/                  # API nástroje
-│       ├── __init__.py
-│       ├── fetch_api_data.py   # Spracovanie API
-│       └── schemas.py          # Pydantic schémy 
-├── core/
-│   ├── __init__.py
-│   ├── initialize_agent.py     # Inicializácia AI agenta
-│   ├── stream_response.py      # Streamovanie odpovedí
-│   └── system_prompt.py        # Systémový prompt
-├── public/                     # Verejné zdroje
-│   ├── icon/                   # Ikony a logá
-│   │   ├── favicon.png
-│   │   ├── logo_dark.png
-│   │   └── logo_light.png
-│   ├── img/
-│   ├── styles.css              # Štýly rozhrania
-│   └── theme.json              # Nastavenia témy
-├── .chainlit/                  # Markdown pre Chainlit
-└── app.py                      # Hlavný súbor aplikácie (Сhainlit + spracovanie API)
-```
-
-## INŠTRUKCIA PRE INŠTALÁCIU
-
-### **Inštalácia cez Git**
-
-### 1. **Naklonovanie repozitára**
-
-```bash
-git clone git@git.kemt.fei.tuke.sk:od059jr/ai-lawyer-agent.git
-```
-
-### 2. **Vytvorenie virtuálneho prostredia**
-
-#### Pre Windows
-```bash
-python -m venv venv
-
-venv\Scripts\activate
-```
-
-#### Pre macOS/Linux
-```
-python3 -m venv venv
-
-source venv/bin/activate
-```
-
-### 3. **Inštalácia závislostí**
-```bash
-pip install -r requirements.txt
-```
-
-### 4. **Sťahovanie inštalátora Ollama**
-
-https://ollama.com/download
-
-
-### 5. **Sťahovanie modelov**
-
-```bash
-ollama pull qwen3-next:80b-cloud
-```
-
-```bash
-ollama pull gpt-oss:20b-cloud
-```
-
-```bash
-ollama pull deepseek-v3.1:671b-cloud
-```
-
-### **Inštalácia cez DOCKER**
-
-### 1. Inštalácia Docker a Docker Desktop
-
-### 2. Nainštalujte a spustite aplikáciu
-
-https://hub.docker.com/r/alexgott0616/ai-lawyer-agent
-

api/fetch_api_data.py

@@ -1,6 +1,7 @@
 import httpx
 import logging
 from pydantic import BaseModel
+from tenacity import retry, stop_after_attempt, wait_exponential
 
 logging.basicConfig(
     level=logging.INFO,
@@ -20,11 +21,12 @@ def docstring_from_model(model: type[BaseModel]):
         return func
     return decorator
 
+@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=1, max=5))
 async def fetch_api_data(icon: str, url: str, params: dict, remove_keys: list = None) -> dict:
     try:
         logger.info(f"\n🔨{icon}Input parameters: {params}\n")
-        async with httpx.AsyncClient() as client:
-            response = await client.get(url, params=params, timeout=10.0)
+        async with httpx.AsyncClient(timeout=httpx.Timeout(10.0)) as client:
+            response = await client.get(url, params=params)
             response.raise_for_status()
             logger.debug(f"\n🖇️{icon}Request URL: {response.url}\n")
 
@@ -32,14 +34,12 @@ async def fetch_api_data(icon: str, url: str, params: dict, remove_keys: list =
             if remove_keys and isinstance(data, dict):
                 for key in remove_keys:
                     data.pop(key, None)
-                    logger.info(f"\n🚮{icon}Successfully removed key: {key}\n")
-
-            logger.info(f"\n✅{icon}Successfully fetched data from {url}\n")
+            logger.info(f"\n🚮{icon}Successfully removed key: {key}\n")
             return data
 
     except httpx.HTTPStatusError as e:
         logger.error(f"\n{icon}HTTP error: {e.response.status_code} - {e.response.text}\n")
-        return {"error": "http_error", "status_code": e.response.status_code, "detail": e.response.text}
+        return {"error": "http_error", "status_code": e.response.status_code}
     except httpx.RequestError as e:
         logger.error(f"\n{icon}Request error: {str(e)}\n")
         return {"error": "request_error", "status_code": str(e)}

app.py

@@ -1,76 +1,54 @@
-import os
 import chainlit as cl
 
-from core.initialize_agent import assistant_agent
+from core.init_agent import assistant_agent
 from core.stream_response import stream_response
 
-def clear_screen():
-    os.system('cls' if os.name == 'nt' else 'clear')
+DEFAULT_MODEL = "qwen3.5:cloud"
+
+STARTERS = [
+    ("What legal data can the agent find?","magnifying_glass"),
+    ("What is the agent not allowed to do or use?","ban"),
+    ("What are the details of your AI model?","hexagon"),
+    ("What data sources does the agent rely on?","database"),
+]
+
+PROFILES = [
+    ("qwen3.5:cloud","Qwen 3.5 CLOUD"),
+    ("gpt-oss:20b-cloud","GPT-OSS 20B CLOUD"),
+]
 
 @cl.set_starters
 async def set_starters():
     return [
-        cl.Starter(
-            label="What legal data can the agent find?",
-            message="What legal data can the agent find?",
-            icon="/public/icon/magnifying_glass.svg",
-        ),
-        cl.Starter(
-            label="What is the agent not allowed to do or use?",
-            message="What is the agent not allowed to do or use?",
-            icon="/public/icon/ban.svg",
-        ),
-        cl.Starter(
-            label="What are the details of your AI model?",
-            message="What are the details of your AI model?",
-            icon="/public/icon/hexagon.svg",
-        ),
-        cl.Starter(
-            label="What data sources does the agent rely on?",
-            message="What data sources does the agent rely on?",
-            icon="/public/icon/database.svg",
-        )
+        cl.Starter(label=label, message=label, icon=f"/public/icon/{icon}.svg")
+        for label, icon in STARTERS
     ]
 
 @cl.set_chat_profiles
 async def chat_profile():
     return [
-        cl.ChatProfile(
-            name="qwen3-next:80b-cloud",
-            markdown_description="Uses **Qwen 3 Next 80B**",
-        ),
-        cl.ChatProfile(
-            name="gpt-oss:20b-cloud",
-            markdown_description="Uses **GPT-OSS 20B CLOUD**",
-        ),
-        cl.ChatProfile(
-            name="deepseek-v3.1:671b-cloud",
-            markdown_description="Uses **DEEPSEEK v3.1 671B CLOUD**",
-        ),
+        cl.ChatProfile(name=name, markdown_description=f"Uses **{desc}**")
+        for name, desc in PROFILES
     ]
 
 @cl.on_chat_start
 async def start():
-    clear_screen()
-
+    model_name = cl.user_session.get("chat_profile") or DEFAULT_MODEL
+    cl.user_session.set("agent", assistant_agent(model_name))
     cl.user_session.set("message_history", [])
 
-    profile = cl.user_session.get("chat_profile")
-    model_name = profile or "qwen3-next:80b-cloud"
-    agent = assistant_agent(model_name)
-    cl.user_session.set("agent", agent)
-
 
 @cl.on_message
 async def main(message: cl.Message):
     agent = cl.user_session.get("agent")
-    message_history = cl.user_session.get("message_history")
+    history = cl.user_session.get("message_history")
 
-    message_history.append({"role": "user", "content": message.content})
+    history.append({"role": "user", "content": message.content})
 
     msg = cl.Message(content="")
-    async for token in stream_response(agent, message_history):
+    async for token in stream_response(agent, history):
         await msg.stream_token(token)
-
-    message_history.append({"role": "assistant", "content": msg.content})
     await msg.update()
+
+    if msg.content:
+        history.append({"role": "assistant", "content": msg.content})

core/init_agent.py

@@ -1,10 +1,9 @@
 import os
 from agents import Agent, AgentHooks
 from agents import OpenAIChatCompletionsModel, AsyncOpenAI, ModelSettings
-from agents import set_tracing_disabled, enable_verbose_stdout_logging
+from agents import set_tracing_disabled
 
 from core.system_prompt import get_system_prompt
-
 from api.tools.judges import JudgesAPI
 from api.tools.coutrs import CourtsAPI
 from api.tools.contracts import ContractsAPI
@@ -12,18 +11,16 @@ from api.tools.decisions import DecisionsAPI
 from api.tools.admin_proceedings import AdminProceedingsAPI
 from api.tools.civil_proceedings import CivilProceedingsAPI
 
-ollama_host = os.getenv("OLLAMA_HOST", "host.docker.internal:11434")
-
 set_tracing_disabled(True)
-enable_verbose_stdout_logging()
-
-class BaseAgent(Agent[None]): pass
 
 class MyAgentHooks(AgentHooks):
-  async def on_start(self, context, agent): print(f"\n🏃‍♂️‍➡️[AgentHooks] {agent.name} started.")
-  async def on_end(self, context, agent, output): print(f"\n🏁[AgentHooks] {agent.name} ended.")
+  async def on_start(self, context, agent):
+      print(f"\n🏃‍♂️‍➡️ [AgentHooks] {agent.name} started.")
 
-def assistant_agent(model_name: str = "qwen3-next:80b-cloud") -> BaseAgent:
+  async def on_end(self, context, agent, output):
+      print(f"🏁 [AgentHooks] {agent.name} ended.")
+
+def assistant_agent(model_name: str = "qwen3.5:cloud") -> Agent:
 
     judge_api = JudgesAPI()
     court_api = CourtsAPI()
@@ -32,19 +29,14 @@ def assistant_agent(model_name: str = "qwen3-next:80b-cloud") -> BaseAgent:
     admin_proceedings_api = AdminProceedingsAPI()
     civil_proceedings_api = CivilProceedingsAPI()
 
-    client = AsyncOpenAI(base_url=f"http://{ollama_host}/v1", api_key="ollama")
-
+    client = AsyncOpenAI(base_url=f"http://localhost:11434/v1", api_key="ollama", timeout=120.0, max_retries=0)
     model = OpenAIChatCompletionsModel(model=model_name, openai_client=client)
 
-    agent = BaseAgent(
+    agent = Agent(
         name="Assistant",
         instructions=get_system_prompt(model_name),
         model=model,
-        model_settings=ModelSettings(
-            temperature=0.7,
-            tool_choice="auto",
-            parallel_tool_calls=False,
-        ),
+        model_settings=ModelSettings(temperature=0.7, tool_choice="auto", parallel_tool_calls=False),
         tools=[
             judge_api.judge, judge_api.judge_id, judge_api.judge_autocomplete,
             court_api.court, court_api.court_id, court_api.court_autocomplete,

core/stream_response.py

@@ -1,23 +1,13 @@
-from typing import Any, AsyncGenerator
+from typing import AsyncGenerator
 from agents import Agent, Runner
-from agents import RunHooks, ModelResponse
 from openai.types.responses import ResponseTextDeltaEvent
 
-class MyRunHooks(RunHooks):
-    """RunHooks for logging the lifecycle of an agent."""
-    async def on_agent_start(self, context, agent) -> None:
-        print(f"\n 🐾[RunHooks] {agent.name} started.\n")
-
-    async def on_agent_end(self, context, agent, output: Any) -> None:
-        print(f"\n 🚪[RunHooks] {agent.name} ended.\n")
-
 async def stream_response(agent: Agent, prompt: str) -> AsyncGenerator[str, None]:
     """Stream agent response and update the UI."""
     try:
-        result = Runner.run_streamed(agent, input=prompt, hooks=MyRunHooks())
+        result = Runner.run_streamed(agent, input=prompt)
         async for event in result.stream_events():
             if event.type == "raw_response_event" and isinstance(event.data, ResponseTextDeltaEvent):
                 yield event.data.delta # <-- sends the next piece of response text
-
     except Exception as e:
         yield f"⚠️🖨️ Error: {e}"

docker-compose.yml

@@ -10,4 +10,7 @@ services:
     environment:
       - PYTHONPATH=/main
       - PYTHONUNBUFFERED=1
+      - OLLAMA_HOST=ollama:11434
+    depends_on:
+      - ollama
     restart: unless-stopped