345 lines
14 KiB
Python
345 lines
14 KiB
Python
from __future__ import annotations
|
|
|
|
import json
|
|
from collections.abc import AsyncIterator
|
|
from typing import Any
|
|
|
|
import httpx
|
|
from tzlocal import get_localzone
|
|
|
|
from traderai.memory import MemoryStore, iso_now, iso_now_in_zone, time_since
|
|
from traderai.tools import ToolRegistry
|
|
|
|
|
|
SYSTEM_PROMPT = """You are TraderAI, a local assistant for UEX marketplace work.
|
|
Use tools when the user asks about listings, negotiations, messages, offers, or posting ads.
|
|
For marketplace writes, draft the exact pending action and tell the user what will be sent; never claim it was sent until approval succeeds.
|
|
Keep prices, listing ids, slugs, users, and UEX status codes precise. If data is missing, say what you need next."""
|
|
|
|
|
|
class OllamaAgent:
|
|
def __init__(
|
|
self,
|
|
base_url: str,
|
|
model: str,
|
|
tools: ToolRegistry,
|
|
memory: MemoryStore | None = None,
|
|
user_name: str | None = None,
|
|
) -> None:
|
|
self.base_url = base_url.rstrip("/")
|
|
self.model = model
|
|
self.tools = tools
|
|
self.memory = memory
|
|
self.user_name = user_name
|
|
self.messages: list[dict[str, Any]] = [{"role": "system", "content": SYSTEM_PROMPT}]
|
|
|
|
async def health(self) -> dict[str, Any]:
|
|
try:
|
|
async with httpx.AsyncClient(timeout=3) as client:
|
|
response = await client.get(f"{self.base_url}/api/tags")
|
|
response.raise_for_status()
|
|
body = response.json()
|
|
except (httpx.HTTPError, ValueError) as exc:
|
|
return {
|
|
"online": False,
|
|
"model": self.model,
|
|
"base_url": self.base_url,
|
|
"message": f"Ollama is offline or unreachable at {self.base_url}. Start Ollama and make sure the model is pulled.",
|
|
"detail": str(exc),
|
|
}
|
|
|
|
models = [model.get("name") or model.get("model") for model in body.get("models", [])]
|
|
return {
|
|
"online": True,
|
|
"model": self.model,
|
|
"base_url": self.base_url,
|
|
"model_available": self.model in models,
|
|
"models": models,
|
|
"message": "Ollama is online.",
|
|
}
|
|
|
|
async def ensure_available(self) -> None:
|
|
health = await self.health()
|
|
if not health["online"]:
|
|
raise OllamaUnavailable(health["message"])
|
|
|
|
async def chat(self, content: str) -> dict[str, Any]:
|
|
await self.ensure_available()
|
|
previous_interaction = self.memory.last_interaction() if self.memory else None
|
|
if self.memory:
|
|
self.memory.add_conversation("user", content)
|
|
self.messages.append({"role": "user", "content": content})
|
|
for _ in range(5):
|
|
response = await self._ollama_chat(content, previous_interaction=previous_interaction)
|
|
message = response.get("message") or {}
|
|
tool_calls = message.get("tool_calls") or []
|
|
if not tool_calls:
|
|
self.messages.append({"role": "assistant", "content": message.get("content", "")})
|
|
if self.memory:
|
|
self.memory.add_conversation("assistant", message.get("content", ""))
|
|
return {"message": message.get("content", ""), "pending_actions": self._pending_payloads()}
|
|
|
|
self.messages.append(message)
|
|
for call in tool_calls:
|
|
name, arguments = self._extract_call(call)
|
|
result = await self.tools.execute(name, arguments)
|
|
self.messages.append({"role": "tool", "tool_name": name, "content": json.dumps(result)})
|
|
|
|
fallback = "I hit the tool-call limit while working on that. Try narrowing the request or approve any pending action first."
|
|
self.messages.append({"role": "assistant", "content": fallback})
|
|
if self.memory:
|
|
self.memory.add_conversation("assistant", fallback)
|
|
return {"message": fallback, "pending_actions": self._pending_payloads()}
|
|
|
|
async def chat_events(self, content: str) -> AsyncIterator[dict[str, Any]]:
|
|
health = await self.health()
|
|
if not health["online"]:
|
|
yield {"type": "warning", "message": health["message"]}
|
|
yield {"type": "done", "pending_actions": self._pending_payloads()}
|
|
return
|
|
|
|
previous_interaction = self.memory.last_interaction() if self.memory else None
|
|
if self.memory:
|
|
self.memory.add_conversation("user", content)
|
|
self.messages.append({"role": "user", "content": content})
|
|
yield {"type": "status", "message": "Thinking"}
|
|
|
|
for _ in range(5):
|
|
assistant_message: dict[str, Any] = {"role": "assistant", "content": ""}
|
|
tool_calls: list[dict[str, Any]] = []
|
|
|
|
async for event in self._ollama_chat_stream(content, previous_interaction=previous_interaction):
|
|
message = event.get("message") or {}
|
|
chunk = message.get("content") or ""
|
|
if chunk:
|
|
assistant_message["content"] += chunk
|
|
yield {"type": "token", "content": chunk}
|
|
if message.get("tool_calls"):
|
|
tool_calls.extend(message["tool_calls"])
|
|
|
|
if not tool_calls:
|
|
self.messages.append(assistant_message)
|
|
if self.memory:
|
|
self.memory.add_conversation("assistant", assistant_message.get("content", ""))
|
|
yield {"type": "done", "pending_actions": self._pending_payloads()}
|
|
return
|
|
|
|
assistant_message["tool_calls"] = tool_calls
|
|
self.messages.append(assistant_message)
|
|
for call in tool_calls:
|
|
name, arguments = self._extract_call(call)
|
|
yield {"type": "status", "message": self._tool_status(name)}
|
|
result = await self.tools.execute(name, arguments)
|
|
self.messages.append({"role": "tool", "tool_name": name, "content": json.dumps(result)})
|
|
|
|
yield {"type": "status", "message": "Writing response"}
|
|
|
|
fallback = "I hit the tool-call limit while working on that. Try narrowing the request or approve any pending action first."
|
|
self.messages.append({"role": "assistant", "content": fallback})
|
|
if self.memory:
|
|
self.memory.add_conversation("assistant", fallback)
|
|
yield {"type": "token", "content": fallback}
|
|
yield {"type": "done", "pending_actions": self._pending_payloads()}
|
|
|
|
async def generate_wake_response(self, wake_message: str) -> str:
|
|
await self.ensure_available()
|
|
self.messages.append({"role": "user", "content": wake_message})
|
|
response = await self._ollama_chat(wake_message)
|
|
message = response.get("message") or {}
|
|
content = message.get("content", "")
|
|
self.messages.append({"role": "assistant", "content": content})
|
|
if self.memory:
|
|
self.memory.add_conversation("system", wake_message)
|
|
self.memory.add_conversation("assistant", content)
|
|
return content or wake_message
|
|
|
|
async def _ollama_chat(self, query: str = "", previous_interaction: dict[str, Any] | None = None) -> dict[str, Any]:
|
|
async with httpx.AsyncClient(timeout=120) as client:
|
|
response = await client.post(
|
|
f"{self.base_url}/api/chat",
|
|
json={
|
|
"model": self.model,
|
|
"messages": self._messages_with_context(query, previous_interaction=previous_interaction),
|
|
"tools": self.tools.schemas,
|
|
"stream": False,
|
|
},
|
|
)
|
|
response.raise_for_status()
|
|
return response.json()
|
|
|
|
async def _ollama_chat_stream(
|
|
self,
|
|
query: str = "",
|
|
previous_interaction: dict[str, Any] | None = None,
|
|
) -> AsyncIterator[dict[str, Any]]:
|
|
async with httpx.AsyncClient(timeout=120) as client:
|
|
async with client.stream(
|
|
"POST",
|
|
f"{self.base_url}/api/chat",
|
|
json={
|
|
"model": self.model,
|
|
"messages": self._messages_with_context(query, previous_interaction=previous_interaction),
|
|
"tools": self.tools.schemas,
|
|
"stream": True,
|
|
},
|
|
) as response:
|
|
response.raise_for_status()
|
|
async for line in response.aiter_lines():
|
|
if line:
|
|
yield json.loads(line)
|
|
|
|
def _messages_with_context(
|
|
self,
|
|
query: str,
|
|
previous_interaction: dict[str, Any] | None = None,
|
|
) -> list[dict[str, Any]]:
|
|
context = self._runtime_context(query, previous_interaction=previous_interaction)
|
|
if not context:
|
|
return self.messages
|
|
return [self.messages[0], {"role": "system", "content": context}, *self.messages[1:]]
|
|
|
|
def _runtime_context(self, query: str, previous_interaction: dict[str, Any] | None = None) -> str:
|
|
local_zone = get_localzone()
|
|
parts = [
|
|
f"Current local date/time: {iso_now()} UTC; {iso_now_in_zone(local_zone)} {local_zone}.",
|
|
]
|
|
if self.user_name:
|
|
parts.append(f"Known user name/handle: {self.user_name}.")
|
|
|
|
if self.memory is None:
|
|
return "\n".join(parts)
|
|
|
|
profile = self.memory.get_profile()
|
|
if profile:
|
|
identity = self._profile_identity(profile)
|
|
if identity:
|
|
parts.append(identity)
|
|
parts.append(f"Known user profile JSON: {json.dumps(self._profile_for_prompt(profile), ensure_ascii=True)}.")
|
|
|
|
last = previous_interaction if previous_interaction is not None else self.memory.last_interaction()
|
|
if last:
|
|
parts.append(
|
|
f"Previous interaction before this message: {last['created_at']} "
|
|
f"({time_since(last['created_at'])}, role {last['role']})."
|
|
)
|
|
else:
|
|
parts.append("Previous interaction before this message: none recorded.")
|
|
|
|
memories = self.memory.recall(query, limit=6)
|
|
if memories:
|
|
memory_text = "\n".join(
|
|
f"- [{item['kind']}, importance {item['importance']}] {item['content']}"
|
|
for item in memories
|
|
)
|
|
parts.append(f"Relevant long-term memories:\n{memory_text}")
|
|
|
|
recent = self.memory.recent_conversation(limit=6)
|
|
if recent:
|
|
recent_text = "\n".join(
|
|
f"- {item['created_at']} {item['role']}: {item['content'][:500]}"
|
|
for item in recent
|
|
)
|
|
parts.append(f"Recent conversation excerpts:\n{recent_text}")
|
|
|
|
return "\n".join(parts)
|
|
|
|
def _pending_payloads(self) -> list[dict[str, Any]]:
|
|
return [
|
|
{
|
|
"id": action.id,
|
|
"label": action.label,
|
|
"endpoint": action.endpoint,
|
|
"payload": action.payload,
|
|
}
|
|
for action in self.tools.pending_actions.values()
|
|
]
|
|
|
|
@staticmethod
|
|
def _tool_status(name: str) -> str:
|
|
labels = {
|
|
"search_marketplace_listings": "Searching UEX listings",
|
|
"get_marketplace_listing": "Fetching listing details",
|
|
"list_marketplace_negotiations": "Checking negotiations",
|
|
"get_negotiation_messages": "Reading negotiation messages",
|
|
"draft_negotiation_message": "Drafting message for approval",
|
|
"draft_marketplace_listing": "Drafting listing for approval",
|
|
}
|
|
return labels.get(name, f"Running {name}")
|
|
|
|
@staticmethod
|
|
def _profile_identity(profile: dict[str, Any]) -> str:
|
|
user = profile.get("uex_user")
|
|
if not isinstance(user, dict):
|
|
configured = profile.get("configured_name")
|
|
return f"You are speaking with {configured}." if configured else ""
|
|
|
|
username = user.get("username") or user.get("user_username")
|
|
name = user.get("name")
|
|
fields = []
|
|
if username and name and username != name:
|
|
fields.append(f"You are speaking with UEX user {username} ({name}).")
|
|
elif username or name:
|
|
fields.append(f"You are speaking with UEX user {username or name}.")
|
|
|
|
details = []
|
|
for key, label in [
|
|
("timezone", "timezone"),
|
|
("language", "preferred language"),
|
|
("specializations", "specializations"),
|
|
("languages", "languages"),
|
|
("archetypes", "archetypes"),
|
|
]:
|
|
value = user.get(key)
|
|
if value:
|
|
details.append(f"{label}: {value}")
|
|
if details:
|
|
fields.append("UEX profile details: " + "; ".join(details) + ".")
|
|
return " ".join(fields)
|
|
|
|
@staticmethod
|
|
def _profile_for_prompt(profile: dict[str, Any]) -> dict[str, Any]:
|
|
user = profile.get("uex_user")
|
|
if not isinstance(user, dict):
|
|
return profile
|
|
|
|
useful_user_fields = [
|
|
"id",
|
|
"name",
|
|
"username",
|
|
"avatar",
|
|
"bio",
|
|
"website_url",
|
|
"timezone",
|
|
"language",
|
|
"day_availability",
|
|
"time_availability",
|
|
"specializations",
|
|
"languages",
|
|
"archetypes",
|
|
"is_datarunner",
|
|
"is_staff",
|
|
"is_away_game",
|
|
"date_rsi_verified",
|
|
"date_twitch_verified",
|
|
]
|
|
prompt_profile = dict(profile)
|
|
prompt_profile["uex_user"] = {
|
|
key: user[key]
|
|
for key in useful_user_fields
|
|
if key in user and user[key] not in (None, "")
|
|
}
|
|
return prompt_profile
|
|
|
|
@staticmethod
|
|
def _extract_call(call: dict[str, Any]) -> tuple[str, dict[str, Any]]:
|
|
function = call.get("function") or {}
|
|
name = function.get("name") or call.get("name")
|
|
arguments = function.get("arguments") or call.get("arguments") or {}
|
|
if isinstance(arguments, str):
|
|
arguments = json.loads(arguments or "{}")
|
|
return name, arguments
|
|
|
|
|
|
class OllamaUnavailable(RuntimeError):
|
|
pass
|