208 lines
8.0 KiB
Python
208 lines
8.0 KiB
Python
import logging
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from typing import List, Optional
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from pydantic import BaseModel
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class Pipeline:
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"""
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This pipeline optimizes the prompt used for multi-model summarization requests.
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It intercepts the request intended to summarize responses from various models,
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extracts the original user query and the individual model answers,
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and constructs a new, more detailed and structured prompt.
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The optimized prompt guides the summarizing model to act as an expert analyst,
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synthesizing the inputs into a high-quality, comprehensive integrated report.
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"""
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class Valves(BaseModel):
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# Specifies target pipeline IDs (models) this filter will attach to.
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# Use ["*"] to connect to all pipelines.
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pipelines: List[str] = ["*"]
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# Assigns priority to the filter pipeline.
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# Determines execution order (lower numbers execute first).
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priority: int = 0
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# Specifies model ID for analysis and summarization.
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# If set, the aggregate request will be redirected to this model.
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model_id: Optional[str] = None
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# Trigger prefix for aggregate requests.
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# Used to identify if it is an aggregate synthesis request.
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trigger_prefix: str = (
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"You have been provided with a set of responses from various models to the latest user query"
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)
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# Marker for parsing the original query start
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query_start_marker: str = 'the latest user query: "'
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# Marker for parsing the original query end
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query_end_marker: str = '"\n\nYour task is to'
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# Marker for parsing model responses start
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response_start_marker: str = "Responses from models: "
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def __init__(self):
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self.type = "filter"
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self.name = "wisdom_synthesizer"
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self.valves = self.Valves()
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async def on_startup(self):
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pass
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async def on_shutdown(self):
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pass
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async def inlet(self, body: dict, user: Optional[dict] = None) -> dict:
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"""
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Entry point for the pipeline filter.
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Checks if the request is an aggregate request. If yes, parses the original prompt,
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extracts query & responses, builds a dynamically-structured new prompt,
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and replaces the original message content.
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"""
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logging.info(f"pipe:{__name__}")
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messages = body.get("messages", [])
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if not messages:
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return body
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user_message_content = ""
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user_message_index = -1
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for i in range(len(messages) - 1, -1, -1):
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if messages[i].get("role") == "user":
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content = messages[i].get("content", "")
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if isinstance(content, list):
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text_parts = []
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for item in content:
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if isinstance(item, dict) and item.get("type") == "text":
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text_parts.append(item.get("text", ""))
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elif isinstance(item, str):
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text_parts.append(item)
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user_message_content = "".join(text_parts)
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elif isinstance(content, str):
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user_message_content = content
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user_message_index = i
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break
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if user_message_index == -1:
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return body
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if isinstance(user_message_content, str) and user_message_content.startswith(
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self.valves.trigger_prefix
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):
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logging.info("Detected aggregate request, modifying prompt.")
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if self.valves.model_id:
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logging.info(f"Redirecting request to model: {self.valves.model_id}")
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body["model"] = self.valves.model_id
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query_start_phrase = self.valves.query_start_marker
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query_end_phrase = self.valves.query_end_marker
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start_index = user_message_content.find(query_start_phrase)
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end_index = user_message_content.find(query_end_phrase)
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original_query = ""
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if start_index != -1 and end_index != -1:
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original_query = user_message_content[
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start_index + len(query_start_phrase) : end_index
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]
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responses_start_phrase = self.valves.response_start_marker
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responses_start_index = user_message_content.find(responses_start_phrase)
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responses_text = ""
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if responses_start_index != -1:
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responses_text = user_message_content[
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responses_start_index + len(responses_start_phrase) :
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]
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import re
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responses = [
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part.strip() for part in re.split(r'\n?\"\"\"\n?', responses_text) if part.strip()
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]
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responses_section = ""
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for i, response in enumerate(responses):
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responses_section += f'''"""
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[Complete Response from Model {i + 1}]
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{response}
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"""
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'''
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merge_prompt = f'''# Role Definition
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You are an experienced Chief Analyst processing analysis reports from multiple independent AI expert teams regarding the same question. Your task is to perform deep integration, critical analysis, and distill a structured, insightful, and highly actionable comprehensive report for decision-makers.
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# Original User Query
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{original_query}
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# Input Format Instruction ⚠️ IMPORTANT
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The responses from various models have been accurately identified and separated using an artificial """ (triple quote) delimiter. The system has extracted these distinct answers; you must now analyze based on the separated content below.
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**Separated Model Responses**:
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{responses_section}
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# Core Tasks
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Do not simply copy or concatenate the original reports. Use your professional analytical skills to complete the following steps:
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## 1. Analysis & Evaluation
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- **Accurate Separation**: Verified boundaries using the """ delimiter.
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- **Credibility Assessment**: Critically examine each report for potential biases, errors, or inconsistencies.
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- **Logic Tracing**: Smooth out core arguments and reasoning chains.
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## 2. Insight Extraction
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- **Identify Consensus**: Find points or recommendations uniformly mentioned across models. This represents the core facts or robust strategies.
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- **Highlight Divergences**: Explicitly state key disagreements in perspectives, methods, or forecasts.
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- **Capture Highlights**: Unearth innovative views found only in a single report.
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## 3. Comprehensive Reporting
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Based on the analysis above, generate a synthesis containing:
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### **【Core Consensus】**
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- List key information or advice agreed upon by models.
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- Annotate coverage (e.g., "All models agree" or "Majority of models").
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### **【Key Divergences】**
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- Contrast different viewpoints on core issues clearly.
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- Use descriptive references (e.g., "Perspective Camp A vs B").
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### **【Unique Insights】**
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- Present high-value unique advice or perspectives from standalone reports.
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### **【Synthesis & Recommendation】**
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- **Integration**: A balanced final analysis optimized by your professional judgment.
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- **Recommendation**: Formulate actionable blended strategies.
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# Format Requirements
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- Concise language, clear logic, distinct structure.
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- Use bolding, lists, and headings for readability.
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- **Language Alignment**: You MUST respond in the **SAME LANGUAGE** as the `Original User Query` above (e.g., if the user query is in Chinese, reply in Chinese; if in English, reply in English). Translate all section headers for your output.
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# Output Structure Example
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Output should follow this structure:
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## 【Core Consensus】
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✓ [Consensus Point 1] —— All models agree
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✓ [Consensus Point 2] —— Majority of models agree
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## 【Key Divergences】
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⚡ **Divergence on [Topic]**:
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- Perspective Camp A: ...
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- Perspective Camp B: ...
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## 【Unique Insights】
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💡 [Insight from Model A]: ...
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💡 [Insight from Model B]: ...
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## 【Synthesis & Recommendation】
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Based on the analysis above, recommended strategies: ...
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'''
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body["messages"][user_message_index]["content"] = merge_prompt
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logging.info("Prompt dynamically replaced successfully.")
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return body
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