File size: 4,740 Bytes
a54fd97 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 | """
Answer Generator - Final synthesis from retrieved contexts
Section 3.3: Intent-Aware Retrieval Planning
Generates answers from the merged context C_q after multi-view retrieval
"""
from typing import List
from models.memory_entry import MemoryEntry
from utils.llm_client import LLMClient
import config
class AnswerGenerator:
"""
Answer Generator - Synthesis from retrieved memory units (Section 3.3)
Generates answers from C_q = R_sem ∪ R_lex ∪ R_sym
"""
def __init__(self, llm_client: LLMClient):
self.llm_client = llm_client
def generate_answer(self, query: str, contexts: List[MemoryEntry]) -> str:
"""
Generate answer
Args:
- query: User question
- contexts: List of retrieved relevant MemoryEntry
Returns:
- Generated answer (concise phrase)
"""
if not contexts:
return "No relevant information found"
# Build context string
context_str = self._format_contexts(contexts)
# Build prompt
prompt = self._build_answer_prompt(query, context_str)
# Call LLM to generate answer
messages = [
{
"role": "system",
"content": "You are a professional Q&A assistant. Extract concise answers from context. You must output valid JSON format."
},
{
"role": "user",
"content": prompt
}
]
# Retry up to 3 times
max_retries = 3
for attempt in range(max_retries):
try:
# Use JSON format if configured
response_format = None
if hasattr(config, 'USE_JSON_FORMAT') and config.USE_JSON_FORMAT:
response_format = {"type": "json_object"}
response = self.llm_client.chat_completion(
messages,
temperature=0.1,
response_format=response_format
)
# Parse JSON response
result = self.llm_client.extract_json(response)
# Return the answer from JSON
return result.get("answer", response.strip())
except Exception as e:
if attempt < max_retries - 1:
print(f"Answer generation attempt {attempt + 1}/{max_retries} failed: {e}. Retrying...")
else:
print(f"Warning: Failed to parse JSON response after {max_retries} attempts: {e}")
# Fallback to raw response
if 'response' in locals():
return response.strip()
else:
return "Failed to generate answer"
def _format_contexts(self, contexts: List[MemoryEntry]) -> str:
"""
Format contexts to readable text
"""
formatted = []
for i, entry in enumerate(contexts, 1):
parts = [f"[Context {i}]"]
parts.append(f"Content: {entry.lossless_restatement}")
if entry.timestamp:
parts.append(f"Time: {entry.timestamp}")
if entry.location:
parts.append(f"Location: {entry.location}")
if entry.persons:
parts.append(f"Persons: {', '.join(entry.persons)}")
if entry.entities:
parts.append(f"Related Entities: {', '.join(entry.entities)}")
if entry.topic:
parts.append(f"Topic: {entry.topic}")
formatted.append("\n".join(parts))
return "\n\n".join(formatted)
def _build_answer_prompt(self, query: str, context_str: str) -> str:
"""
Build answer generation prompt
"""
return f"""
Answer the user's question based on the provided context.
User Question: {query}
Relevant Context:
{context_str}
Requirements:
1. First, think through the reasoning process
2. Then provide a very CONCISE answer (short phrase about core information)
3. Answer must be based ONLY on the provided context
4. All dates in the response must be formatted as 'DD Month YYYY' but you can output more or less details if needed
5. Return your response in JSON format
Output Format:
```json
{{
"reasoning": "Brief explanation of your thought process",
"answer": "Concise answer in a short phrase"
}}
```
Example:
Question: "When will they meet?"
Context: "Alice suggested meeting Bob at 2025-11-16T14:00:00..."
Output:
```json
{{
"reasoning": "The context explicitly states the meeting time as 2025-11-16T14:00:00",
"answer": "16 November 2025 at 2:00 PM"
}}
```
Now answer the question. Return ONLY the JSON, no other text.
"""
|