francwa
9ca31e45e0
- Fix circular dependencies in agent/tools - Migrate from custom JSON to OpenAI tool calls format - Add async streaming (step_stream, complete_stream) - Simplify prompt system and remove token counting - Add 5 new API endpoints (/health, /v1/models, /api/memory/*) - Add 3 new tools (get_torrent_by_index, add_torrent_by_index, set_language) - Fix all 500 tests and add coverage config (80% threshold) - Add comprehensive docs (README, pytest guide) BREAKING: LLM interface changed, memory injection via get_memory()
279 lines
8.8 KiB
Python
279 lines
8.8 KiB
Python
"""Main agent for media library management."""
|
|
|
|
import json
|
|
import logging
|
|
from typing import Any, Protocol
|
|
|
|
from infrastructure.persistence import get_memory
|
|
|
|
from .config import settings
|
|
from .prompts import PromptBuilder
|
|
from .registry import Tool, make_tools
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class LLMClient(Protocol):
|
|
"""Protocol defining the LLM client interface."""
|
|
|
|
def complete(self, messages: list[dict[str, Any]]) -> str:
|
|
"""Send messages to the LLM and get a response."""
|
|
...
|
|
|
|
|
|
class Agent:
|
|
"""
|
|
AI agent for media library management.
|
|
|
|
Orchestrates interactions between the LLM, memory, and tools
|
|
to respond to user requests.
|
|
|
|
Attributes:
|
|
llm: LLM client (DeepSeek or Ollama).
|
|
tools: Available tools for the agent.
|
|
prompt_builder: Builds system prompts with context.
|
|
max_tool_iterations: Maximum tool calls per request.
|
|
"""
|
|
|
|
def __init__(self, llm: LLMClient, max_tool_iterations: int = 5):
|
|
"""
|
|
Initialize the agent.
|
|
|
|
Args:
|
|
llm: LLM client compatible with the LLMClient protocol.
|
|
max_tool_iterations: Maximum tool iterations (default: 5).
|
|
"""
|
|
self.llm = llm
|
|
self.tools: dict[str, Tool] = make_tools()
|
|
self.prompt_builder = PromptBuilder(self.tools)
|
|
self.max_tool_iterations = max_tool_iterations
|
|
|
|
def _parse_intent(self, text: str) -> dict[str, Any] | None:
|
|
"""
|
|
Parse an LLM response to detect a tool call.
|
|
|
|
Args:
|
|
text: LLM response text.
|
|
|
|
Returns:
|
|
Dict with intent if a tool call is detected, None otherwise.
|
|
"""
|
|
text = text.strip()
|
|
|
|
# Try direct JSON parse
|
|
if text.startswith("{") and text.endswith("}"):
|
|
try:
|
|
data = json.loads(text)
|
|
if self._is_valid_intent(data):
|
|
return data
|
|
except json.JSONDecodeError:
|
|
pass
|
|
|
|
# Try to extract JSON from text
|
|
try:
|
|
start = text.find("{")
|
|
end = text.rfind("}") + 1
|
|
if start != -1 and end > start:
|
|
json_str = text[start:end]
|
|
data = json.loads(json_str)
|
|
if self._is_valid_intent(data):
|
|
return data
|
|
except json.JSONDecodeError:
|
|
pass
|
|
|
|
return None
|
|
|
|
def _is_valid_intent(self, data: Any) -> bool:
|
|
"""Check if parsed data is a valid tool intent."""
|
|
if not isinstance(data, dict) or "action" not in data:
|
|
return False
|
|
action = data.get("action")
|
|
return isinstance(action, dict) and isinstance(action.get("name"), str)
|
|
|
|
def _execute_action(self, intent: dict[str, Any]) -> dict[str, Any]:
|
|
"""
|
|
Execute a tool action requested by the LLM.
|
|
|
|
Args:
|
|
intent: Dict containing the action to execute.
|
|
|
|
Returns:
|
|
Tool execution result.
|
|
"""
|
|
action = intent["action"]
|
|
name: str = action["name"]
|
|
args: dict[str, Any] = action.get("args", {}) or {}
|
|
|
|
tool = self.tools.get(name)
|
|
if not tool:
|
|
logger.warning(f"Unknown tool requested: {name}")
|
|
return {
|
|
"error": "unknown_tool",
|
|
"tool": name,
|
|
"available_tools": list(self.tools.keys()),
|
|
}
|
|
|
|
try:
|
|
result = tool.func(**args)
|
|
|
|
# Track errors in episodic memory
|
|
if result.get("status") == "error" or result.get("error"):
|
|
memory = get_memory()
|
|
memory.episodic.add_error(
|
|
action=name,
|
|
error=result.get("error", result.get("message", "Unknown error")),
|
|
context={"args": args, "result": result},
|
|
)
|
|
|
|
return result
|
|
|
|
except TypeError as e:
|
|
error_msg = f"Bad arguments for {name}: {e}"
|
|
logger.error(error_msg)
|
|
memory = get_memory()
|
|
memory.episodic.add_error(
|
|
action=name, error=error_msg, context={"args": args}
|
|
)
|
|
return {"error": "bad_args", "message": str(e)}
|
|
|
|
except Exception as e:
|
|
error_msg = f"Error executing {name}: {e}"
|
|
logger.error(error_msg, exc_info=True)
|
|
memory = get_memory()
|
|
memory.episodic.add_error(action=name, error=str(e), context={"args": args})
|
|
return {"error": "execution_error", "message": str(e)}
|
|
|
|
def _check_unread_events(self) -> str:
|
|
"""
|
|
Check for unread background events and format them.
|
|
|
|
Returns:
|
|
Formatted string of events, or empty string if none.
|
|
"""
|
|
memory = get_memory()
|
|
events = memory.episodic.get_unread_events()
|
|
|
|
if not events:
|
|
return ""
|
|
|
|
lines = ["Recent events:"]
|
|
for event in events:
|
|
event_type = event.get("type", "unknown")
|
|
data = event.get("data", {})
|
|
|
|
if event_type == "download_complete":
|
|
lines.append(f" - Download completed: {data.get('name')}")
|
|
elif event_type == "new_files_detected":
|
|
lines.append(f" - {data.get('count')} new files detected")
|
|
else:
|
|
lines.append(f" - {event_type}: {data}")
|
|
|
|
return "\n".join(lines)
|
|
|
|
def step(self, user_input: str) -> str:
|
|
"""
|
|
Execute one agent step with iterative tool execution.
|
|
|
|
Process:
|
|
1. Check for unread events
|
|
2. Build system prompt with memory context
|
|
3. Query the LLM
|
|
4. If tool call detected, execute and loop
|
|
5. Return final text response
|
|
|
|
Args:
|
|
user_input: User message.
|
|
|
|
Returns:
|
|
Final response in natural text.
|
|
"""
|
|
logger.info("Starting agent step")
|
|
logger.debug(f"User input: {user_input}")
|
|
|
|
memory = get_memory()
|
|
|
|
# Check for background events
|
|
events_notification = self._check_unread_events()
|
|
if events_notification:
|
|
logger.info("Found unread background events")
|
|
|
|
# Build system prompt
|
|
system_prompt = self.prompt_builder.build_system_prompt()
|
|
|
|
# Initialize conversation
|
|
messages: list[dict[str, Any]] = [
|
|
{"role": "system", "content": system_prompt},
|
|
]
|
|
|
|
# Add conversation history
|
|
history = memory.stm.get_recent_history(settings.max_history_messages)
|
|
if history:
|
|
for msg in history:
|
|
messages.append({"role": msg["role"], "content": msg["content"]})
|
|
logger.debug(f"Added {len(history)} messages from history")
|
|
|
|
# Add events notification
|
|
if events_notification:
|
|
messages.append(
|
|
{"role": "system", "content": f"[NOTIFICATION]\n{events_notification}"}
|
|
)
|
|
|
|
# Add user input
|
|
messages.append({"role": "user", "content": user_input})
|
|
|
|
# Tool execution loop
|
|
iteration = 0
|
|
while iteration < self.max_tool_iterations:
|
|
logger.debug(f"Iteration {iteration + 1}/{self.max_tool_iterations}")
|
|
|
|
llm_response = self.llm.complete(messages)
|
|
logger.debug(f"LLM response: {llm_response[:200]}...")
|
|
|
|
intent = self._parse_intent(llm_response)
|
|
|
|
if not intent:
|
|
# Final text response
|
|
logger.info("No tool intent, returning response")
|
|
memory.stm.add_message("user", user_input)
|
|
memory.stm.add_message("assistant", llm_response)
|
|
memory.save()
|
|
return llm_response
|
|
|
|
# Execute tool
|
|
tool_name = intent.get("action", {}).get("name", "unknown")
|
|
logger.info(f"Executing tool: {tool_name}")
|
|
tool_result = self._execute_action(intent)
|
|
logger.debug(f"Tool result: {tool_result}")
|
|
|
|
# Add to conversation
|
|
messages.append(
|
|
{"role": "assistant", "content": json.dumps(intent, ensure_ascii=False)}
|
|
)
|
|
messages.append(
|
|
{
|
|
"role": "user",
|
|
"content": json.dumps(
|
|
{"tool_result": tool_result}, ensure_ascii=False
|
|
),
|
|
}
|
|
)
|
|
|
|
iteration += 1
|
|
|
|
# Max iterations reached
|
|
logger.warning(f"Max iterations ({self.max_tool_iterations}) reached")
|
|
messages.append(
|
|
{
|
|
"role": "user",
|
|
"content": "Please provide a final response based on the results.",
|
|
}
|
|
)
|
|
|
|
final_response = self.llm.complete(messages)
|
|
|
|
memory.stm.add_message("user", user_input)
|
|
memory.stm.add_message("assistant", final_response)
|
|
memory.save()
|
|
|
|
return final_response
|