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https://github.com/arc53/DocsGPT.git
synced 2025-12-03 02:23:14 +00:00
fix: enhance ReActAgent's reasoning iterations and update planning prompt structure
This commit is contained in:
Alex
@@ -20,6 +20,8 @@ with open(
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"r",
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) as f:
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final_prompt_template = f.read()
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MAX_ITERATIONS_REASONING = 10
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class ReActAgent(BaseAgent):
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def __init__(self, *args, **kwargs):
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@@ -71,7 +73,6 @@ class ReActAgent(BaseAgent):
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content_piece = chunk.delta.text
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elif isinstance(chunk, str): # Simplest case: stream of strings
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content_piece = chunk
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# Add other stream chunk formats as needed
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if content_piece:
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collected_content.append(content_piece)
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@@ -98,64 +99,76 @@ class ReActAgent(BaseAgent):
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self._prepare_tools(tools_dict)
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docs_together = "\n".join([doc["text"] for doc in retrieved_data])
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iterating_reasoning = 0
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while iterating_reasoning < MAX_ITERATIONS_REASONING:
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iterating_reasoning += 1
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# 1. Create Plan
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logger.info("ReActAgent: Creating plan...")
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plan_stream = self._create_plan(query, docs_together, log_context)
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current_plan_parts = []
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yield {"thought": f"Reasoning... (iteration {iterating_reasoning})\n\n"}
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for line_chunk in plan_stream:
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current_plan_parts.append(line_chunk)
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yield {"thought": line_chunk}
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self.plan = "".join(current_plan_parts)
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if self.plan:
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self.observations.append(f"Plan: {self.plan} Iteration: {iterating_reasoning}")
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# 1. Create Plan
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logger.info("ReActAgent: Creating plan...")
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plan_stream = self._create_plan(query, docs_together, log_context)
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current_plan_parts = []
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for line_chunk in plan_stream:
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current_plan_parts.append(line_chunk)
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yield {"thought": line_chunk}
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self.plan = "".join(current_plan_parts)
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if self.plan:
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self.observations.append(f"Plan: {self.plan}")
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# 2. Execute Plan (First Reasoning Step)
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execution_prompt_str = (self.prompt or "") + f"\n\nFollow this plan:\n{self.plan}"
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messages = self._build_messages(execution_prompt_str, query, retrieved_data)
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resp_from_llm_gen = self._llm_gen(messages, log_context)
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initial_llm_thought_content = self._extract_content_from_llm_response(resp_from_llm_gen)
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if initial_llm_thought_content:
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self.observations.append(f"Initial thought/response: {initial_llm_thought_content}")
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else:
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logger.info("ReActAgent: Initial LLM response (before handler) had no textual content (might be only tool calls).")
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logger.info("Executing plan")
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resp_after_handler = self._llm_handler(resp_from_llm_gen, tools_dict, messages, log_context)
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for tool_call_info in self.tool_calls: # Iterate over self.tool_calls populated by _llm_handler
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observation_string = (
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f"Executed Action: Tool '{tool_call_info.get('tool_name', 'N/A')}' "
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f"with arguments '{tool_call_info.get('arguments', '{}')}'. Result: '{str(tool_call_info.get('result', ''))[:200]}...'"
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max_obs_len = 20000
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obs_str = "\n".join(self.observations)
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if len(obs_str) > max_obs_len:
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obs_str = obs_str[:max_obs_len] + "\n...[observations truncated]"
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execution_prompt_str = (
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(self.prompt or "")
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+ f"\n\nFollow this plan:\n{self.plan}"
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+ f"\n\nObservations:\n{obs_str}"
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+ f"\n\nIf there is enough data to complete user query '{query}', Respond with 'SATISFIED' only. Otherwise, continue. Dont Menstion 'SATISFIED' in your response if you are not ready. "
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)
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self.observations.append(observation_string)
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messages = self._build_messages(execution_prompt_str, query, retrieved_data)
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content_after_handler = self._extract_content_from_llm_response(resp_after_handler)
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if content_after_handler:
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self.observations.append(f"Response after tool execution: {content_after_handler}")
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logger.info(f"ReActAgent: LLM response after tool execution: {content_after_handler[:500]}...")
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else:
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logger.info("ReActAgent: LLM response after handler had no textual content.")
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resp_from_llm_gen = self._llm_gen(messages, log_context)
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initial_llm_thought_content = self._extract_content_from_llm_response(resp_from_llm_gen)
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if initial_llm_thought_content:
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self.observations.append(f"Initial thought/response: {initial_llm_thought_content}")
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else:
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logger.info("ReActAgent: Initial LLM response (before handler) had no textual content (might be only tool calls).")
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resp_after_handler = self._llm_handler(resp_from_llm_gen, tools_dict, messages, log_context)
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for tool_call_info in self.tool_calls: # Iterate over self.tool_calls populated by _llm_handler
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observation_string = (
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f"Executed Action: Tool '{tool_call_info.get('tool_name', 'N/A')}' "
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f"with arguments '{tool_call_info.get('arguments', '{}')}'. Result: '{str(tool_call_info.get('result', ''))[:200]}...'"
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)
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self.observations.append(observation_string)
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if log_context:
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log_context.stacks.append(
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{"component": "agent_tool_calls", "data": {"tool_calls": self.tool_calls.copy()}}
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)
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content_after_handler = self._extract_content_from_llm_response(resp_after_handler)
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if content_after_handler:
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self.observations.append(f"Response after tool execution: {content_after_handler}")
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else:
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logger.info("ReActAgent: LLM response after handler had no textual content.")
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yield {"sources": retrieved_data}
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if log_context:
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log_context.stacks.append(
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{"component": "agent_tool_calls", "data": {"tool_calls": self.tool_calls.copy()}}
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)
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display_tool_calls = []
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for tc in self.tool_calls:
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cleaned_tc = tc.copy()
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if len(str(cleaned_tc.get("result", ""))) > 50:
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cleaned_tc["result"] = str(cleaned_tc["result"])[:50] + "..."
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display_tool_calls.append(cleaned_tc)
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if display_tool_calls:
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yield {"tool_calls": display_tool_calls}
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yield {"sources": retrieved_data}
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display_tool_calls = []
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for tc in self.tool_calls:
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cleaned_tc = tc.copy()
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if len(str(cleaned_tc.get("result", ""))) > 50:
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cleaned_tc["result"] = str(cleaned_tc["result"])[:50] + "..."
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display_tool_calls.append(cleaned_tc)
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if display_tool_calls:
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yield {"tool_calls": display_tool_calls}
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if "SATISFIED" in content_after_handler:
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logger.info("ReActAgent: LLM satisfied with the plan and data. Stopping reasoning.")
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break
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# 3. Create Final Answer based on all observations
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final_answer_stream = self._create_final_answer(query, self.observations, log_context)
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@@ -170,6 +183,8 @@ class ReActAgent(BaseAgent):
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if "{summaries}" in plan_prompt_filled:
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summaries = docs_data if docs_data else "No documents retrieved."
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plan_prompt_filled = plan_prompt_filled.replace("{summaries}", summaries)
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plan_prompt_filled = plan_prompt_filled.replace("{prompt}", self.prompt or "")
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plan_prompt_filled = plan_prompt_filled.replace("{observations}", "\n".join(self.observations))
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messages = [{"role": "user", "content": plan_prompt_filled}]
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