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