优化推理逻辑,提升推理ｉ速度
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朱潮
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@ -31,9 +31,7 @@ Examples of Guideline Match Evaluations:
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"checks": [
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{
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"guideline_id": "<example-id-for-few-shots--do-not-use-this-output>",
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"condition": "The customer asks for logistical or legal requirements.",
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"rationale": "The customer now asked about visas and documents which are legal requirements",
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"applies": true
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"rationale": "The customer now asked about visas and documents which are legal requirements"
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}
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]
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}
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@ -58,15 +56,11 @@ Examples of Guideline Match Evaluations:
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"checks": [
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{
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"guideline_id": "<example-id-for-few-shots--do-not-use-this-output>",
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"condition": "The customer mentions a constraint that related to commitment to the course",
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"rationale": "In the most recent message the customer mentions that they work full time which is a constraint",
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"applies": true
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"rationale": "In the most recent message the customer mentions that they work full time which is a constraint"
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},
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{
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"guideline_id": "<example-id-for-few-shots--do-not-use-this-output>",
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"condition": "The user expresses hesitation or self-doubt.",
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"rationale": "In the most recent message the user still sounds hesitating about their fit to the course",
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"applies": true
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"rationale": "In the most recent message the user still sounds hesitating about their fit to the course"
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}
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]
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}
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@ -89,9 +83,7 @@ Examples of Guideline Match Evaluations:
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"checks": [
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{
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"guideline_id": "<example-id-for-few-shots--do-not-use-this-in-output>",
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"condition": "When the user is having a problem with login.",
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"rationale": "In the most recent message the customer is still pursuing their login problem, making the mail access problem a sub-issue rather than a new topic",
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"applies": true
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"rationale": "In the most recent message the customer is still pursuing their login problem, making the mail access problem a sub-issue rather than a new topic"
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}
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]
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}
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@ -111,9 +103,7 @@ Examples of Guideline Match Evaluations:
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"checks": [
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{
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"guideline_id": "<example-id-for-few-shots--do-not-use-this-in-output>",
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"condition": "When the customer asks about how to return an item.",
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"rationale": "In the most recent message the customer asks about what happens when they wore the item, which an inquiry regarding returning an item",
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"applies": true
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"rationale": "In the most recent message the customer asks about what happens when they wore the item, which an inquiry regarding returning an item"
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}
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]
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}
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@ -130,15 +120,13 @@ Guidelines List:
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OUTPUT FORMAT:
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The content in JSON format needs to be wrapped in "```json" and "```".
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Only include guidelines that actually apply (applies: true). Do not include guidelines that don't match.
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Only include guidelines that actually apply.
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```json
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{
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"checks": [
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{
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"guideline_id": "1",
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"condition": "Specific condition description",
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"rationale": "<Explain why the conditions are met and what action should be taken>",
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"applies": true
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"rationale": "<Explain why the conditions are met and what action should be taken>"
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}
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]
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}
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@ -1,42 +1,48 @@
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You are an AI agent that is expected to generate a preamble message for the customer.
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You are a friendly AI assistant that generates natural preamble responses to acknowledge user messages before passing them to a more capable agent for detailed processing.
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The actual message will be sent later by a smarter agent. Your job is only to generate the right preamble in order to save time.
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## Scenario Analysis
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## Scenario Detection Logic
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Analyze the user's message to determine the appropriate response type:
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FIRST, determine if this is a COMPLEX scenario that requires a preamble:
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**Use a friendly preamble for:**
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- Questions, information requests, or searches
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- Task execution or action requests
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- Knowledge retrieval from documents, databases, or web
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- Problem-solving or troubleshooting requests
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- Complex multi-step instructions
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- Technical assistance needs
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**Complex Scenarios (preamble needed):**
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- Query scenarios: User is asking for information, searching, or looking up data
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- Action scenarios: User wants to perform an operation, execute a task
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- Knowledge retrieval scenarios: User needs to search knowledge base, documents、databases or Internet
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- Problem-solving: User is reporting issues, asking for help with problems
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- Complex requests: Multi-step tasks, detailed instructions needed
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**Return "<empty>" for:**
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- Simple greetings and farewells
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- Basic acknowledgments (thanks, ok, etc.)
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- Casual small talk
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- Very brief or non-substantive messages
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**ALL OTHER scenarios (output "<empty>"):**
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- Simple greetings: "hi", "hello", "你好", "在吗", "早上好/晚上好"
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- Simple acknowledgments: "thanks", "ok", "好的", "谢谢"
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- Small talk: "how are you", "天气怎么样", "最近怎么样"
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- Simple farewells: "bye", "goodbye", "再见"
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- Any other scenarios not explicitly listed as complex
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## Response Guidelines
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## Preamble Selection
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Generate a warm, natural preamble that:
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- Acknowledges the user's message positively
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- Shows you're ready to help
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- Creates a friendly interaction flow
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- Doesn't commit to specific answers (that's for the main agent)
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ONLY IF this is a COMPLEX scenario, choose from these preamble messages. You must ONLY choose one of these: ###
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**Reference these examples for inspiration:**
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{preamble_choices_text}
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###
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Basically, the preamble is something very short that continues the interaction naturally, without committing to any later action or response.
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We leave that later response to another agent. Make sure you understand this.
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**Your approach:**
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- Match the tone to the user's message (formal/professional or casual/friendly)
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- Use contextual awareness - consider what the user is asking about
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- Keep it brief and welcoming
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- Feel free to create natural variations beyond the examples
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- In Chinese conversations, use appropriate conversational phrases
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- Be helpful and encouraging
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Instructions:
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- For COMPLEX scenarios:
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- Note that some of the choices are more generic, and some are more specific to a particular scenario.
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- If you're unsure what to choose --> prefer to go with a more generic, bland choice. This should be 80% of cases.
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Examples of generic choices: "Hey there!", "Just a moment.", "Hello.", "Got it."
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- If you see clear value in saying something more specific and nuanced --> then go with a more specific choice. This should be 20% or less of cases.
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Examples of specific choices: "Let me check that for you.", "Sorry to hear that.", "Thanks for your patience."
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- For ALL OTHER scenarios: Always output preamble: "<empty>"
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**Examples of good preambles:**
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- "I'd be happy to help you with that!"
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- "Let me look into that for you."
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- "Thanks for reaching out - I'll assist you with this."
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- "That's an interesting question! Let me help you find the answer."
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- "I understand what you need. Let me get that information for you."
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Chat History:
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@ -46,26 +52,25 @@ User's Last Message:
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{last_message}
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OUTPUT FORMAT:
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You must now choose the preamble message. You must produce a JSON object with a single key, "preamble", holding the preamble message as a string,
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EXACTLY as it is given (pay attention to subtleties like punctuation and copy your choice EXACTLY as it is given above).The content in JSON format needs to be wrapped in "```json" and "```".
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Generate a JSON response with your preamble message:
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For ALL OTHER scenarios:
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For simple interactions (greetings, basic acknowledgments, small talk):
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```json
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{
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"preamble": "<empty>"
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}
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```
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For COMPLEX scenarios:
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For substantive requests and questions:
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```json
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{
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"preamble": "Just a moment."
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"preamble": "Your friendly, contextual preamble here"
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}
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```
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You will now be given the current state of the interaction to which you must generate the next preamble message.
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Remember to wrap your entire response in ```json ... ``` tags.
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Preamble LANGAGE:
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{language}
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You will now be given the current state of the interaction to which you must generate the next preamble message.
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@ -77,7 +77,7 @@ async def process_guidelines_and_terms(
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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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Returns:
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tuple: (agent, processed_system_prompt, guideline_analysis, terms_analysis)
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tuple: (agent, processed_system_prompt, guideline_reasoning, 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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@ -108,7 +108,8 @@ async def process_guidelines_and_terms(
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logger.error(f"Error removing terms cache file: {e}")
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# 处理guidelines
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guideline_analysis = ""
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guideline_reasoning = ""
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active_guidelines = ""
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agent = None
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if guidelines_list:
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@ -125,7 +126,7 @@ async def process_guidelines_and_terms(
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batch_guidelines = guidelines_list[i:i + guidelines_per_batch]
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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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guideline_str = f"{guideline['guideline_id']}) Condition: {guideline['condition']} Action: {guideline['action']}"
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batch_strings.append(guideline_str)
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batches.append(batch_strings)
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@ -179,14 +180,50 @@ async def process_guidelines_and_terms(
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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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applicable_checks = [check for check in result['checks'] if check.get('applies') is True]
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applicable_checks = [check for check in result['checks']]
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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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logger.info(f"Non-JSON result from batch {i}: {result}")
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if all_checks:
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guideline_analysis = "\n".join([item["condition"]+":"+item["rationale"] for item in all_checks])
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logger.info(f"Guideline analysis completed: {len(guideline_analysis)} chars")
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# 首先创建guideline_id到action字段的映射，同时支持string和int类型的guideline_id
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guideline_map = {}
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for guideline in guidelines_list:
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guideline_id = guideline.get('guideline_id')
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if guideline_id is not None:
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# 同时存储字符串和整数类型的键，确保匹配成功
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guideline_map[str(guideline_id)] = guideline
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# 补全all_checks中缺失的action字段
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completed_checks = []
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for check in all_checks:
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completed_check = check.copy()
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guideline_id = check.get('guideline_id')
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# 如果action字段缺失或为空，从guidelines_list中获取
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if str(guideline_id) in guideline_map:
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completed_check['action'] = guideline_map[str(guideline_id)].get('action', "")
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completed_check['condition'] = guideline_map[str(guideline_id)].get('condition', "")
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completed_checks.append(completed_check)
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guideline_reasoning = "\n".join([item["rationale"] for item in completed_checks])
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# 使用补全后的checks生成active_guidelines，添加安全检查
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active_guidelines_parts = []
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for item in completed_checks:
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if 'action' in item and item['action']:
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active_guidelines_parts.append(
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"Condition:" + item["condition"] + "\nRationale:" + item["rationale"] + "\nAction:" + item["action"]
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)
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else:
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# 如果仍然缺少action字段，使用默认值
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active_guidelines_parts.append(
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"Condition:" + item["condition"] + "\nRationale:" + item["rationale"] + "\nAction:无具体操作要求"
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)
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active_guidelines = "\n".join(active_guidelines_parts)
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logger.info(f"Guideline analysis completed: {len(guideline_reasoning)} chars, processed {len(completed_checks)} checks")
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else:
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# 没有guidelines，直接创建agent
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@ -204,7 +241,7 @@ async def process_guidelines_and_terms(
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user_identifier=user_identifier
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)
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return agent, processed_system_prompt, guideline_analysis, terms_analysis
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return agent, processed_system_prompt, guideline_reasoning, active_guidelines
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async def enhanced_generate_stream_response(
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@ -269,11 +306,11 @@ async def enhanced_generate_stream_response(
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logger.error(f"Error generating preamble text: {e}")
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# 等待guideline分析任务完成
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agent, _, guideline_analysis, _ = await guidelines_task
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agent, system_prompt, guideline_reasoning, active_guidelines = await guidelines_task
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# 立即发送guideline_analysis
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if guideline_analysis:
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guideline_content = get_content_from_messages([{"role": "assistant","reasoning_content": guideline_analysis+ "\n"}], tool_response=tool_response)
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# 立即发送guideline_reasoning
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if guideline_reasoning:
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guideline_content = get_content_from_messages([{"role": "assistant","reasoning_content": guideline_reasoning+ "\n"}], tool_response=tool_response)
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chunk_data = create_stream_chunk(f"chatcmpl-guideline", model_name, guideline_content)
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yield f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n"
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@ -281,8 +318,8 @@ async def enhanced_generate_stream_response(
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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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if active_guidelines:
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final_messages = append_user_last_message(final_messages, f"\n\nActive Guidelines:\n{active_guidelines}\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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@ -373,14 +410,14 @@ async def create_agent_and_generate_response(
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)
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_, system_prompt = get_preamble_text(language, system_prompt)
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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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agent, system_prompt, guideline_reasoning, active_guidelines = 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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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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@ -395,9 +432,10 @@ async def create_agent_and_generate_response(
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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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pre_message_list = []
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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+ "\n"})
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if active_guidelines:
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final_messages = append_user_last_message(final_messages, f"\n\nActive Guidelines:\n{active_guidelines}\nPlease follow these guidelines in your response.")
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if guideline_reasoning:
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pre_message_list.append({"role": "assistant","reasoning_content": guideline_reasoning+ "\n"})
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# 非流式响应
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agent_responses = agent.run_nonstream(final_messages)
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@ -749,45 +749,13 @@ def parse_guidelines_text(text: str) -> List[Dict[str, Any]]:
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match = re.match(id_condition_action_pattern, line, re.IGNORECASE)
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if match:
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guidelines.append({
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'id': int(match.group(1)),
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'guideline_id': int(match.group(1)),
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'condition': match.group(2).strip(),
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'action': match.group(3).strip(),
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'priority': int(match.group(4)) if match.group(4) else 1
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})
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continue
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# 尝试解析 "condition -> action" 格式
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arrow_pattern = r'(?:\d+\)\s*)?(.*?)\s*->\s*(.*?)(?:\s*\[(\d+)\])?$'
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match = re.match(arrow_pattern, line, re.IGNORECASE)
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if match:
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guidelines.append({
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'id': len(guidelines) + 1,
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'condition': match.group(1).strip(),
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'action': match.group(2).strip(),
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'priority': int(match.group(3)) if match.group(3) else 1
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})
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continue
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# 尝试解析 "if condition then action" 格式
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if_then_pattern = r'(?:\d+\)\s*)?if\s+(.*?)\s+then\s+(.*?)(?:\s*\[(\d+)\])?$'
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match = re.match(if_then_pattern, line, re.IGNORECASE)
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if match:
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guidelines.append({
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'id': len(guidelines) + 1,
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'condition': match.group(1).strip(),
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'action': match.group(2).strip(),
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'priority': int(match.group(3)) if match.group(3) else 1
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})
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continue
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# 默认格式：整行作为action，condition为空
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guidelines.append({
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'id': len(guidelines) + 1,
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'condition': '',
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'action': line.strip(),
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'priority': 1
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})
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return guidelines
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@ -848,20 +816,6 @@ def parse_terms_text(text: str) -> List[Dict[str, Any]]:
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current_term = term_data
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continue
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# 尝试解析 "| value" 格式（简化格式）
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if line.startswith('|'):
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parts = [p.strip() for p in line[1:].split('|', 2)] # 最多分割3部分
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if len(parts) >= 1:
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if current_term:
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terms.append(current_term)
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current_term = {'name': parts[0]}
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if len(parts) >= 2:
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current_term['description'] = parts[1]
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if len(parts) >= 3:
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synonyms = re.split(r'[,;|]', parts[2])
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current_term['synonyms'] = [s.strip() for s in synonyms if s.strip()]
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continue
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# 添加最后一个term
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if current_term:
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terms.append(current_term)
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