Spaces:
Sleeping
Sleeping
File size: 10,127 Bytes
8bf4d58 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 |
"""Base agent class with common functionality."""
import logging
from abc import ABC, abstractmethod
from typing import List, Dict, Any, Optional, Callable
from openai import OpenAI
from src.core.config import get_settings
from src.memory.short_term_memory import ShortTermMemory
from src.memory.long_term_memory import LongTermMemory
from src.planning.react_planner import ReActPlanner
from src.planning.cot_planner import CoTPlanner
logger = logging.getLogger(__name__)
class BaseAgent(ABC):
"""Base class for all agents."""
def __init__(
self,
name: str,
description: str,
tools: Optional[List[Dict[str, Any]]] = None,
use_memory: bool = True,
use_planning: bool = False,
planning_type: str = "react", # "react" or "cot"
):
"""
Initialize base agent.
Args:
name: Agent name
description: Agent description
tools: List of available tools
use_memory: Whether to use memory
use_planning: Whether to use planning
planning_type: Type of planning ("react" or "cot")
"""
self.name = name
self.description = description
self.settings = get_settings()
# Initialize OpenAI client
self.client = OpenAI(**self.settings.get_openai_client_kwargs())
self.model = self.settings.openai_model
# Initialize memory
self.use_memory = use_memory
self.short_term_memory: Optional[ShortTermMemory] = None
self.long_term_memory: Optional[LongTermMemory] = None
if use_memory:
self.short_term_memory = ShortTermMemory()
self.long_term_memory = LongTermMemory()
# Initialize planning
self.use_planning = use_planning
self.planning_type = planning_type
self.planner: Optional[ReActPlanner | CoTPlanner] = None
if use_planning:
if planning_type == "react":
self.planner = ReActPlanner(tools=tools or [])
elif planning_type == "cot":
self.planner = CoTPlanner()
else:
logger.warning(f"Unknown planning type: {planning_type}")
# Tools
self.tools = tools or []
self.tool_functions: Dict[str, Callable] = {}
def add_tool(self, tool: Dict[str, Any], tool_function: Callable) -> None:
"""
Add a tool to the agent.
Args:
tool: Tool schema
tool_function: Function to execute the tool
"""
self.tools.append(tool)
self.tool_functions[tool["name"]] = tool_function
if self.planner and isinstance(self.planner, ReActPlanner):
self.planner.add_tool(tool)
async def process(
self,
query: str,
session_id: Optional[str] = None,
context: Optional[str] = None,
) -> Dict[str, Any]:
"""
Process a query using the agent.
Args:
query: User query
session_id: Optional session ID for memory
context: Optional additional context
Returns:
Response dictionary
"""
try:
# Add user message to memory
if self.short_term_memory:
self.short_term_memory.add_message("user", query)
# Load long-term memory if available
long_term_context = ""
if self.long_term_memory and session_id:
memories = self.long_term_memory.search_memories(query, session_id, n_results=3)
if memories:
long_term_context = "\n".join([
m["content"] for m in memories
])
# Combine contexts
full_context = self._build_context(context, long_term_context)
# Use planning if enabled
if self.use_planning and self.planner:
response = await self._process_with_planning(query, full_context, session_id)
else:
response = await self._process_direct(query, full_context, session_id)
# Add assistant response to memory
if self.short_term_memory and "answer" in response:
self.short_term_memory.add_message("assistant", response["answer"])
# Store in long-term memory
if self.long_term_memory and session_id:
messages = self.short_term_memory.get_messages() if self.short_term_memory else []
self.long_term_memory.store_conversation(session_id, messages)
return response
except Exception as e:
logger.error(f"Error processing query in {self.name}: {e}")
return {
"success": False,
"error": str(e),
"agent": self.name,
}
async def _process_direct(
self,
query: str,
context: str,
session_id: Optional[str],
) -> Dict[str, Any]:
"""Process query directly without planning."""
# Build messages
messages = []
if context:
messages.append({
"role": "system",
"content": f"{self.description}\n\nContext: {context}",
})
else:
messages.append({
"role": "system",
"content": self.description,
})
# Add conversation history
if self.short_term_memory:
history = self.short_term_memory.get_messages(format_for_llm=True)
messages.extend(history[-5:]) # Last 5 messages
else:
messages.append({
"role": "user",
"content": query,
})
# Call LLM
try:
response = self.client.chat.completions.create(
model=self.model,
messages=messages,
temperature=0.7,
)
answer = response.choices[0].message.content
return {
"success": True,
"answer": answer,
"agent": self.name,
"model": self.model,
}
except Exception as e:
error_msg = str(e)
if "quota" in error_msg.lower() or "429" in error_msg:
logger.error(f"OpenAI API quota exceeded: {e}")
raise Exception("OpenAI API quota exceeded. Please check your billing and plan details.")
elif "api key" in error_msg.lower() or "401" in error_msg:
logger.error(f"Invalid OpenAI API key: {e}")
raise Exception("Invalid OpenAI API key. Please check your .env file.")
else:
logger.error(f"Error calling LLM: {e}")
raise
async def _process_with_planning(
self,
query: str,
context: str,
session_id: Optional[str],
) -> Dict[str, Any]:
"""Process query using planning."""
if not self.planner:
return await self._process_direct(query, context, session_id)
# Create sync LLM call function (planner expects sync)
def llm_call(prompt: str) -> str:
messages = [
{"role": "system", "content": self.description},
{"role": "user", "content": prompt},
]
response = self.client.chat.completions.create(
model=self.model,
messages=messages,
temperature=0.7,
)
return response.choices[0].message.content
# Generate plan (planner methods are sync)
if isinstance(self.planner, ReActPlanner):
plan = self.planner.plan(
query=query,
context=context,
llm_call=llm_call,
)
else: # CoT planner
plan = self.planner.plan(
query=query,
context=context,
llm_call=llm_call,
)
# Extract final answer
if isinstance(self.planner, ReActPlanner):
answer = plan.get("final_answer", "I couldn't find a complete answer.")
else:
answer = plan.get("conclusion", "I couldn't find a complete answer.")
return {
"success": True,
"answer": answer,
"agent": self.name,
"plan": plan,
"model": self.model,
}
def _build_context(
self,
additional_context: Optional[str],
long_term_context: str,
) -> str:
"""Build full context string."""
parts = []
if long_term_context:
parts.append(f"Relevant past conversations:\n{long_term_context}")
if additional_context:
parts.append(f"Additional context:\n{additional_context}")
return "\n\n".join(parts)
async def _execute_tool(
self,
tool_name: str,
**kwargs,
) -> Any:
"""Execute a tool (supports both sync and async tools)."""
if tool_name not in self.tool_functions:
raise ValueError(f"Tool '{tool_name}' not found")
tool_func = self.tool_functions[tool_name]
# Check if tool is async
import asyncio
if asyncio.iscoroutinefunction(tool_func):
return await tool_func(**kwargs)
else:
return tool_func(**kwargs)
@abstractmethod
async def retrieve_context(self, query: str) -> str:
"""
Retrieve relevant context for the query.
Args:
query: User query
Returns:
Context string
"""
pass
def get_status(self) -> Dict[str, Any]:
"""Get agent status."""
return {
"name": self.name,
"description": self.description,
"tools": [t["name"] for t in self.tools],
"memory_enabled": self.use_memory,
"planning_enabled": self.use_planning,
"planning_type": self.planning_type if self.use_planning else None,
}
|