Spaces:
Sleeping
Sleeping
File size: 8,171 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 |
"""ReAct (Reasoning + Acting) planner implementation."""
import logging
from typing import List, Dict, Any, Optional, Callable
from enum import Enum
from src.core.config import get_settings
logger = logging.getLogger(__name__)
class ActionType(Enum):
"""Types of actions in ReAct loop."""
THOUGHT = "thought"
ACTION = "action"
OBSERVATION = "observation"
FINAL_ANSWER = "final_answer"
class ReActPlanner:
"""ReAct planner that implements thought-action-observation loop."""
def __init__(
self,
max_iterations: int = 10,
tools: Optional[List[Dict[str, Any]]] = None,
):
"""
Initialize ReAct planner.
Args:
max_iterations: Maximum number of ReAct iterations
tools: List of available tools with their schemas
"""
self.max_iterations = max_iterations
self.tools = tools or []
self.tool_map = {tool["name"]: tool for tool in self.tools}
def plan(
self,
query: str,
context: Optional[str] = None,
llm_call: Optional[Callable] = None,
) -> Dict[str, Any]:
"""
Generate a plan using ReAct methodology.
Args:
query: User query
context: Optional context information
llm_call: Function to call LLM (should return structured response)
Returns:
Plan dictionary with steps and reasoning
"""
if not llm_call:
raise ValueError("llm_call function is required")
steps = []
observations = []
current_context = context or ""
for iteration in range(self.max_iterations):
# Build prompt for this iteration
prompt = self._build_react_prompt(
query=query,
context=current_context,
steps=steps,
observations=observations,
)
# Get LLM response
try:
response = llm_call(prompt)
step = self._parse_react_response(response)
steps.append(step)
# Check if we have a final answer
if step["type"] == ActionType.FINAL_ANSWER:
return {
"query": query,
"steps": steps,
"final_answer": step.get("content", ""),
"iterations": iteration + 1,
}
# Execute action if needed
if step["type"] == ActionType.ACTION:
observation = self._execute_action(step)
observations.append(observation)
steps.append({
"type": ActionType.OBSERVATION,
"content": observation,
"iteration": iteration + 1,
})
except Exception as e:
logger.error(f"Error in ReAct iteration {iteration}: {e}")
steps.append({
"type": ActionType.OBSERVATION,
"content": f"Error: {str(e)}",
"iteration": iteration + 1,
})
# Max iterations reached
return {
"query": query,
"steps": steps,
"final_answer": None,
"iterations": self.max_iterations,
"status": "max_iterations_reached",
}
def _build_react_prompt(
self,
query: str,
context: str,
steps: List[Dict[str, Any]],
observations: List[str],
) -> str:
"""Build ReAct prompt."""
prompt_parts = [
"You are a helpful assistant that uses the ReAct (Reasoning + Acting) methodology.",
"You can think, take actions using tools, and observe results.",
"",
"Available tools:",
]
for tool in self.tools:
prompt_parts.append(f"- {tool['name']}: {tool.get('description', '')}")
if "parameters" in tool:
prompt_parts.append(f" Parameters: {tool['parameters']}")
prompt_parts.extend([
"",
"Format your responses as:",
"Thought: <your reasoning>",
"Action: <tool_name>",
"Action Input: <tool_parameters>",
"Observation: <result>",
"",
"When you have the final answer, use:",
"Final Answer: <your answer>",
"",
])
if context:
prompt_parts.extend([
f"Context: {context}",
"",
])
prompt_parts.append(f"Question: {query}")
prompt_parts.append("")
# Add previous steps
if steps:
prompt_parts.append("Previous steps:")
for step in steps[-3:]: # Last 3 steps for context
if step["type"] == ActionType.THOUGHT:
prompt_parts.append(f"Thought: {step.get('content', '')}")
elif step["type"] == ActionType.ACTION:
prompt_parts.append(f"Action: {step.get('action', '')}")
prompt_parts.append(f"Action Input: {step.get('input', '')}")
elif step["type"] == ActionType.OBSERVATION:
prompt_parts.append(f"Observation: {step.get('content', '')}")
prompt_parts.append("")
prompt_parts.append("Your response:")
return "\n".join(prompt_parts)
def _parse_react_response(self, response: str) -> Dict[str, Any]:
"""Parse LLM response into ReAct step."""
response = response.strip()
# Check for final answer
if response.startswith("Final Answer:"):
return {
"type": ActionType.FINAL_ANSWER,
"content": response.replace("Final Answer:", "").strip(),
}
# Parse thought
if "Thought:" in response:
thought_part = response.split("Thought:")[1].split("Action:")[0].strip()
else:
thought_part = ""
# Parse action
action_name = None
action_input = None
if "Action:" in response:
action_line = response.split("Action:")[1].split("Observation:")[0].strip()
if "Action Input:" in action_line:
parts = action_line.split("Action Input:")
action_name = parts[0].strip()
action_input = parts[1].strip()
else:
action_name = action_line
if action_name:
return {
"type": ActionType.ACTION,
"thought": thought_part,
"action": action_name,
"input": action_input or "",
}
else:
return {
"type": ActionType.THOUGHT,
"content": thought_part or response,
}
def _execute_action(self, step: Dict[str, Any]) -> str:
"""Execute an action using available tools."""
action_name = step.get("action")
action_input = step.get("input", "")
if action_name not in self.tool_map:
return f"Error: Tool '{action_name}' not found"
tool = self.tool_map[action_name]
tool_func = tool.get("function")
if not tool_func:
return f"Error: Tool '{action_name}' has no implementation"
try:
# Parse input (assuming JSON format)
import json
try:
params = json.loads(action_input) if action_input else {}
except:
params = {"query": action_input} if action_input else {}
result = tool_func(**params)
return str(result)
except Exception as e:
return f"Error executing {action_name}: {str(e)}"
def add_tool(self, tool: Dict[str, Any]) -> None:
"""Add a tool to the planner."""
self.tools.append(tool)
self.tool_map[tool["name"]] = tool
def get_tools(self) -> List[Dict[str, Any]]:
"""Get list of available tools."""
return self.tools
|