preamble
This commit is contained in:
朱潮
parent
95577c07a8
commit
db71607ff1
324
routes/chat.py
324
routes/chat.py
@ -148,48 +148,41 @@ async def generate_stream_response(agent, messages, pre_message_list, tool_respo
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yield f"data: {json.dumps(error_data, ensure_ascii=False)}\n\n"
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async def create_agent_and_generate_response(
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async def process_guidelines_and_terms(
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bot_id: str,
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api_key: str,
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messages: list,
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stream: bool,
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tool_response: bool,
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model_name: str,
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model_server: str,
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system_prompt: str,
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messages: list,
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agent_manager,
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project_dir: Optional[str],
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generate_cfg: Optional[dict],
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language: str,
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system_prompt: Optional[str],
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mcp_settings: Optional[list],
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robot_type: str,
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project_dir: Optional[str] = None,
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generate_cfg: Optional[dict] = None,
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user_identifier: Optional[str] = None
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) -> Union[ChatResponse, StreamingResponse]:
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"""创建agent并生成响应的公共逻辑"""
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if generate_cfg is None:
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generate_cfg = {}
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pre_message_list = []
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query_text = get_user_last_message_content(messages)
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chat_history = format_messages_to_chat_history(messages)
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preamble_text = await call_preamble_llm(chat_history, query_text, get_preamble_text(language), language, model_name, api_key, model_server)
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user_identifier: Optional[str]
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) -> tuple:
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"""
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公共函数：处理guideline分析和terms处理，返回agent和analysis结果
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if preamble_text != '':
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pre_message_list.append({"role": "assistant","content": preamble_text})
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Returns:
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tuple: (agent, processed_system_prompt, guideline_analysis, terms_analysis)
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"""
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# 提取system_prompt中的guideline和terms
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processed_system_prompt, guidelines_list, terms_list = extract_block_from_system_prompt(system_prompt)
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# 1. 从system_prompt提取guideline和terms内容
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system_prompt, guidelines_list, terms_list = extract_block_from_system_prompt(system_prompt)
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# 2. 如果有terms内容，先进行embedding（embedding需要缓存起来，这个可以tmp文件缓存，以{bot_id}_terms作为key）embedding实现情参考 @embedding/embedding.py 文件,可以在里面实现。拿到embedding后，可以进行相似性检索，检索方式先使用cos相似度，找到阈值相似性>0.7的匹配项，重新整理为terms_analysis，格式：1) Name: term_name1, Description: desc, Synonyms: syn1, syn2。
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# 处理terms
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terms_analysis = ""
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if terms_list:
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logger.info(f"terms_list: {terms_list}")
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# 使用embedding进行terms处理
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logger.info(f"Processing terms: {len(terms_list)} terms")
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try:
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from embedding.embedding import process_terms_with_embedding
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query_text = get_user_last_message_content(messages)
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terms_analysis = process_terms_with_embedding(terms_list, bot_id, query_text)
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if terms_analysis:
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# 将terms分析结果也添加到消息中
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system_prompt = system_prompt.replace("{terms}", terms_analysis)
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logger.info(f"Generated terms analysis: {terms_analysis[:200]}...") # 只打印前200个字符
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processed_system_prompt = processed_system_prompt.replace("{terms}", terms_analysis)
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logger.info(f"Terms analysis completed: {len(terms_analysis)} chars")
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except Exception as e:
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logger.error(f"Error processing terms with embedding: {e}")
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terms_analysis = ""
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@ -204,24 +197,22 @@ async def create_agent_and_generate_response(
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except Exception as e:
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logger.error(f"Error removing terms cache file: {e}")
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# 3. 如果有guideline内容，进行并发处理
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# 处理guidelines
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guideline_analysis = ""
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agent = None
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if guidelines_list:
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logger.info(f"guidelines_list: {guidelines_list}")
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logger.info(f"Processing guidelines: {len(guidelines_list)} guidelines")
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chat_history = format_messages_to_chat_history(messages)
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guidelines_count = len(guidelines_list)
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if guidelines_count > 0:
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# 获取最优批次数量（并发数）
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batch_count = _get_optimal_batch_size(guidelines_count)
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# 计算每个批次应该包含多少条guideline
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guidelines_per_batch = max(1, guidelines_count // batch_count)
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# 分批处理guidelines - 将字典列表转换为字符串列表以便处理
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# 分批处理guidelines
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batches = []
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for i in range(0, guidelines_count, guidelines_per_batch):
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batch_guidelines = guidelines_list[i:i + guidelines_per_batch]
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# 将格式化为字符串，保持原有的格式以便LLM处理
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batch_strings = []
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for guideline in batch_guidelines:
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guideline_str = f"{guideline['id']}) Condition: {guideline['condition']} Action: {guideline['action']}"
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@ -230,16 +221,13 @@ async def create_agent_and_generate_response(
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# 确保批次数量不超过要求的并发数
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while len(batches) > batch_count:
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# 将最后一个批次合并到倒数第二个批次
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batches[-2].extend(batches[-1])
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batches.pop()
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logger.info(f"Processing {guidelines_count} guidelines in {len(batches)} batches with {batch_count} concurrent batches")
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# 并发执行所有任务：guideline批次处理 + agent创建
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# 创建所有任务
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tasks = []
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# 添加所有guideline批次任务
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# 添加guideline批次任务
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for batch in batches:
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task = process_guideline_batch(
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guidelines_batch=batch,
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@ -260,47 +248,38 @@ async def create_agent_and_generate_response(
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model_server=model_server,
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generate_cfg=generate_cfg,
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language=language,
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system_prompt=system_prompt,
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system_prompt=processed_system_prompt,
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mcp_settings=mcp_settings,
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robot_type=robot_type,
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user_identifier=user_identifier
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)
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tasks.append(agent_task)
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# 等待所有任务完成
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# 并发执行所有任务
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all_results = await asyncio.gather(*tasks, return_exceptions=True)
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# 处理结果：最后一个结果是agent，前面的是guideline批次结果
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# 处理结果
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agent = all_results[-1] # agent创建的结果
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batch_results = all_results[:-1] # guideline批次的结果
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logger.info(f"batch_results:{batch_results}")
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# 合并guideline分析结果，使用JSON格式的checks数组
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# 合并guideline分析结果
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all_checks = []
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for i, result in enumerate(batch_results):
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if isinstance(result, Exception):
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logger.error(f"Guideline batch {i} failed: {result}")
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continue
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if result and isinstance(result, dict) and 'checks' in result:
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# 如果是JSON对象且包含checks数组，只保留applies为true的checks
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applicable_checks = [check for check in result['checks'] if check.get('applies') is True]
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all_checks.extend(applicable_checks)
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elif result and isinstance(result, str) and result.strip():
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# 如果是普通文本，保留原有逻辑
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logger.info(f"Non-JSON result from batch {i}: {result}")
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if all_checks:
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# 将checks数组格式化为JSON字符串
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guideline_analysis = "\n".join([item["rationale"] for item in all_checks])
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# guideline_analysis = json.dumps({"checks": all_checks}, ensure_ascii=False)
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logger.info(f"Merged guideline analysis result: {guideline_analysis}")
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# 将分析结果添加到最后一个消息的内容中
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if guideline_analysis:
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messages = append_user_last_message(messages, f"\n\nActive Guidelines:\n{guideline_analysis}\nPlease follow these guidelines in your response.")
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logger.info(f"Guideline analysis completed: {len(guideline_analysis)} chars")
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else:
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# 3. 从全局管理器获取或创建助手实例
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# 没有guidelines，直接创建agent
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agent = await agent_manager.get_or_create_agent(
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bot_id=bot_id,
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project_dir=project_dir,
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@ -309,27 +288,244 @@ async def create_agent_and_generate_response(
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model_server=model_server,
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generate_cfg=generate_cfg,
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language=language,
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system_prompt=system_prompt,
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system_prompt=processed_system_prompt,
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mcp_settings=mcp_settings,
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robot_type=robot_type,
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user_identifier=user_identifier
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)
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messages = append_user_last_message(messages, f"\n\nlanguage:{get_language_text(language)}")
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return agent, processed_system_prompt, guideline_analysis, terms_analysis
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if guideline_analysis != '':
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pre_message_list.append({"role": "assistant","reasoning_content": guideline_analysis})
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# 根据stream参数决定返回流式还是非流式响应
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async def enhanced_generate_stream_response(
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agent_manager,
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bot_id: str,
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api_key: str,
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messages: list,
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tool_response: bool,
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model_name: str,
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model_server: str,
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language: str,
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system_prompt: str,
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mcp_settings: Optional[list],
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robot_type: str,
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project_dir: Optional[str],
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generate_cfg: Optional[dict],
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user_identifier: Optional[str],
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pre_message_list: Optional[list]
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):
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"""增强的渐进式流式响应生成器"""
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try:
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# 第一阶段：立即传输preamble_text
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if pre_message_list:
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chunk_data = {
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"id": f"chatcmpl-preamble",
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"object": "chat.completion.chunk",
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"created": int(__import__('time').time()),
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"model": model_name,
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"choices": [{
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"index": 0,
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"delta": {
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"content": get_content_from_messages(pre_message_list, tool_response=tool_response)
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},
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"finish_reason": None
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}]
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}
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yield f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n"
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# 第二阶段：使用公共函数处理guideline分析和agent创建
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agent, _, guideline_analysis, _ = await process_guidelines_and_terms(
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bot_id=bot_id,
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api_key=api_key,
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model_name=model_name,
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model_server=model_server,
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system_prompt=system_prompt,
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messages=messages,
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agent_manager=agent_manager,
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project_dir=project_dir,
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generate_cfg=generate_cfg,
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language=language,
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mcp_settings=mcp_settings,
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robot_type=robot_type,
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user_identifier=user_identifier
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)
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# 立即发送guideline_analysis
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if guideline_analysis:
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chunk_data = {
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"id": f"chatcmpl-guideline",
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"object": "chat.completion.chunk",
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"created": int(__import__('time').time()),
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"model": model_name,
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"choices": [{
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"index": 0,
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"delta": {
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"content": get_content_from_messages([{"role": "assistant","reasoning_content": guideline_analysis}], tool_response=tool_response)
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},
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"finish_reason": None
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}]
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}
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yield f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n"
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# 准备最终的消息
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final_messages = messages.copy()
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final_messages = append_user_last_message(final_messages, f"\n\nlanguage:{get_language_text(language)}")
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if guideline_analysis:
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final_messages = append_user_last_message(final_messages, f"\n\nActive Guidelines:\n{guideline_analysis}\nPlease follow these guidelines in your response.")
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# 第三阶段：agent响应流式传输
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logger.info(f"Starting agent stream response")
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accumulated_content = ""
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chunk_id = 0
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for response in agent.run(messages=final_messages):
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previous_content = accumulated_content
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accumulated_content = get_content_from_messages(response, tool_response=tool_response)
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# 计算新增的内容
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if accumulated_content.startswith(previous_content):
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new_content = accumulated_content[len(previous_content):]
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else:
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new_content = accumulated_content
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previous_content = ""
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# 只有当有新内容时才发送chunk
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if new_content:
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if chunk_id == 0:
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logger.info(f"Agent首个Token已生成, 开始流式输出")
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chunk_id += 1
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chunk_data = {
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"id": f"chatcmpl-{chunk_id}",
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"object": "chat.completion.chunk",
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"created": int(__import__('time').time()),
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"model": model_name,
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"choices": [{
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"index": 0,
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"delta": {
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"content": new_content
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},
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"finish_reason": None
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}]
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}
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yield f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n"
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# 发送最终完成标记
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final_chunk = {
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"id": f"chatcmpl-{chunk_id + 1}",
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"object": "chat.completion.chunk",
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"created": int(__import__('time').time()),
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"model": model_name,
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"choices": [{
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"index": 0,
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"delta": {},
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"finish_reason": "stop"
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}]
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}
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yield f"data: {json.dumps(final_chunk, ensure_ascii=False)}\n\n"
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# 发送结束标记
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yield "data: [DONE]\n\n"
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logger.info(f"Enhanced stream response completed, total chunks: {chunk_id}")
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except Exception as e:
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import traceback
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error_details = traceback.format_exc()
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logger.error(f"Error in enhanced_generate_stream_response: {str(e)}")
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logger.error(f"Full traceback: {error_details}")
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error_data = {
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"error": {
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"message": f"Stream error: {str(e)}",
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"type": "internal_error"
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}
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}
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yield f"data: {json.dumps(error_data, ensure_ascii=False)}\n\n"
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async def create_agent_and_generate_response(
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bot_id: str,
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api_key: str,
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messages: list,
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stream: bool,
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tool_response: bool,
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model_name: str,
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model_server: str,
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language: str,
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system_prompt: Optional[str],
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mcp_settings: Optional[list],
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robot_type: str,
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project_dir: Optional[str] = None,
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generate_cfg: Optional[dict] = None,
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user_identifier: Optional[str] = None
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) -> Union[ChatResponse, StreamingResponse]:
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"""创建agent并生成响应的公共逻辑"""
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if generate_cfg is None:
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generate_cfg = {}
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pre_message_list = []
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# 只在stream=True时生成preamble_text
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preamble_text = ""
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if stream:
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query_text = get_user_last_message_content(messages)
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chat_history = format_messages_to_chat_history(messages)
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preamble_text = await call_preamble_llm(chat_history, query_text, get_preamble_text(language), language, model_name, api_key, model_server)
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pre_message_list.append({"role": "assistant","content": preamble_text})
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logger.info(f"Stream mode: Generated preamble text ({len(preamble_text)} chars)")
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# 如果是流式模式，使用增强的流式响应生成器
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if stream:
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return StreamingResponse(
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generate_stream_response(agent, messages, pre_message_list, tool_response, model_name),
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enhanced_generate_stream_response(
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agent_manager=agent_manager,
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bot_id=bot_id,
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api_key=api_key,
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messages=messages,
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tool_response=tool_response,
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model_name=model_name,
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model_server=model_server,
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language=language,
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system_prompt=system_prompt or "",
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mcp_settings=mcp_settings,
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robot_type=robot_type,
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project_dir=project_dir,
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generate_cfg=generate_cfg,
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user_identifier=user_identifier,
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pre_message_list=pre_message_list
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),
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media_type="text/event-stream",
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headers={"Cache-Control": "no-cache", "Connection": "keep-alive"}
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)
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else:
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# 使用公共函数处理所有逻辑
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agent, _, guideline_analysis, _ = await process_guidelines_and_terms(
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bot_id=bot_id,
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api_key=api_key,
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model_name=model_name,
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model_server=model_server,
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system_prompt=system_prompt or "",
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messages=messages,
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agent_manager=agent_manager,
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project_dir=project_dir,
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generate_cfg=generate_cfg,
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language=language,
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mcp_settings=mcp_settings,
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robot_type=robot_type,
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user_identifier=user_identifier
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)
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# 准备最终的消息
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final_messages = messages.copy()
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final_messages = append_user_last_message(final_messages, f"\n\nlanguage:{get_language_text(language)}")
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if guideline_analysis:
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final_messages = append_user_last_message(final_messages, f"\n\nActive Guidelines:\n{guideline_analysis}\nPlease follow these guidelines in your response.")
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pre_message_list.append({"role": "assistant","reasoning_content": guideline_analysis})
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# 非流式响应
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agent_responses = agent.run_nonstream(messages)
|
||||
agent_responses = agent.run_nonstream(final_messages)
|
||||
final_responses = pre_message_list + agent_responses
|
||||
if final_responses and len(final_responses) > 0:
|
||||
# 使用 get_content_from_messages 处理响应，支持 tool_response 参数
|
||||
|
||||
Loading…
Reference in New Issue
Block a user