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from __future__ import annotations
from typing import List
import json
from ollama import AsyncClient, ChatResponse, Message
from .config import (
MAX_TOOL_CALL_DEPTH,
MODEL_NAME,
NUM_CTX,
OLLAMA_HOST,
SYSTEM_PROMPT,
)
from .db import Conversation, Message as DBMessage, User, _db, init_db
from .log import get_logger
from .schema import Msg
from .tools import execute_terminal
_LOG = get_logger(__name__)
class ChatSession:
def __init__(
self,
user: str = "default",
session: str = "default",
host: str = OLLAMA_HOST,
model: str = MODEL_NAME,
) -> None:
init_db()
self._client = AsyncClient(host=host)
self._model = model
self._user, _ = User.get_or_create(username=user)
self._conversation, _ = Conversation.get_or_create(
user=self._user, session_name=session
)
self._messages: List[Msg] = self._load_history()
self._ensure_system_prompt()
async def __aenter__(self) -> "ChatSession":
return self
async def __aexit__(self, exc_type, exc, tb) -> None:
if not _db.is_closed():
_db.close()
def _ensure_system_prompt(self) -> None:
if any(m.get("role") == "system" for m in self._messages):
return
DBMessage.create(
conversation=self._conversation, role="system", content=SYSTEM_PROMPT
)
self._messages.insert(0, {"role": "system", "content": SYSTEM_PROMPT})
def _load_history(self) -> List[Msg]:
messages: List[Msg] = []
for msg in self._conversation.messages.order_by(DBMessage.created_at):
if msg.role == "assistant":
try:
calls = json.loads(msg.content)
except json.JSONDecodeError:
messages.append({"role": "assistant", "content": msg.content})
else:
messages.append(
{
"role": "assistant",
"tool_calls": [Message.ToolCall(**c) for c in calls],
}
)
elif msg.role == "user":
messages.append({"role": "user", "content": msg.content})
else:
messages.append({"role": "tool", "content": msg.content})
return messages
@staticmethod
def _store_assistant_message(
conversation: Conversation, message: Message
) -> None:
"""Persist assistant messages, storing tool calls when present."""
if message.tool_calls:
content = json.dumps([c.model_dump() for c in message.tool_calls])
else:
content = message.content or ""
DBMessage.create(conversation=conversation, role="assistant", content=content)
async def ask(self, messages: List[Msg], *, think: bool = True) -> ChatResponse:
return await self._client.chat(
self._model,
messages=messages,
think=think,
tools=[execute_terminal],
options={"num_ctx": NUM_CTX},
)
async def _handle_tool_calls(
self,
messages: List[Msg],
response: ChatResponse,
conversation: Conversation,
depth: int = 0,
) -> ChatResponse:
if depth >= MAX_TOOL_CALL_DEPTH or not response.message.tool_calls:
return response
for call in response.message.tool_calls:
if call.function.name == "execute_terminal":
result = execute_terminal(**call.function.arguments)
else:
continue
messages.append(
{
"role": "tool",
"name": call.function.name,
"content": str(result),
}
)
DBMessage.create(
conversation=conversation,
role="tool",
content=str(result),
)
nxt = await self.ask(messages, think=True)
self._store_assistant_message(conversation, nxt.message)
return await self._handle_tool_calls(
messages, nxt, conversation, depth + 1
)
return response
async def chat(self, prompt: str) -> str:
DBMessage.create(conversation=self._conversation, role="user", content=prompt)
self._messages.append({"role": "user", "content": prompt})
response = await self.ask(self._messages)
self._messages.append(response.message.model_dump())
self._store_assistant_message(self._conversation, response.message)
_LOG.info("Thinking:\n%s", response.message.thinking or "<no thinking trace>")
final_resp = await self._handle_tool_calls(
self._messages, response, self._conversation
)
return final_resp.message.content
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