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NotebookLM-style response generator with professional formatting.
"""
from typing import List, Dict
import config
import re
class SimpleGenerator:
"""Lightweight generator with NotebookLM-quality formatting."""
def __init__(self):
self.ready = True
def _clean_and_format_text(self, text: str) -> str:
"""Clean and format text with proper spacing like NotebookLM."""
# Fix spacing after punctuation
text = re.sub(r'([.!?])([A-Z])', r'\1 \2', text)
# Remove multiple spaces
text = re.sub(r'\s+', ' ', text)
# Add proper line breaks after sentences
text = re.sub(r'([.!?])\s+', r'\1\n\n', text)
return text.strip()
def _extract_key_terms(self, text: str) -> List[str]:
"""Extract key terms that should be bolded."""
# Look for capitalized terms, technical terms
terms = []
# Find terms in quotes
quoted = re.findall(r'"([^"]+)"', text)
terms.extend(quoted)
# Find repeated important words (appear 2+ times)
words = re.findall(r'\b[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*\b', text)
word_count = {}
for word in words:
word_count[word] = word_count.get(word, 0) + 1
# Add words that appear multiple times
terms.extend([w for w, count in word_count.items() if count >= 2])
return list(set(terms))
def _apply_bold_formatting(self, text: str) -> str:
"""Apply bold formatting to key terms like NotebookLM."""
key_terms = self._extract_key_terms(text)
# Bold key terms
for term in key_terms:
if len(term) > 3: # Skip very short terms
text = re.sub(rf'\b({re.escape(term)})\b', r'**\1**', text, count=1)
# Bold specific patterns
# Numbers with context
text = re.sub(r'\b(\d+)\s+(observations?|years?|months?|quarters?)', r'**\1 \2**', text)
return text
def _create_structured_response(self, context: str, query: str) -> str:
"""Create a NotebookLM-style structured response."""
# Split into paragraphs
paragraphs = [p.strip() for p in context.split('\n\n') if len(p.strip()) > 50]
# Remove duplicates
unique_paras = []
seen = set()
for para in paragraphs:
para_key = para.lower()[:150]
if para_key not in seen:
unique_paras.append(para)
seen.add(para_key)
if len(unique_paras) >= 5:
break
if not unique_paras:
return context[:1000]
# Build NotebookLM-style response
response = ""
# Main explanation (first paragraph - cleaned and formatted)
main_para = self._clean_and_format_text(unique_paras[0])
main_para = self._apply_bold_formatting(main_para)
response += main_para + "\n\n"
# Add structured details if more content available
if len(unique_paras) > 1:
response += "### Key Points:\n\n"
for i, para in enumerate(unique_paras[1:4], 1):
# Extract first 2-3 sentences
sentences = [s.strip() for s in para.split('.') if len(s.strip()) > 20]
if sentences:
detail = self._clean_and_format_text('. '.join(sentences[:2]) + '.')
detail = self._apply_bold_formatting(detail)
response += f"{i}. {detail}\n\n"
return response.strip()
def generate_response(
self,
prompt: str,
context: str = "",
use_case: str = "explanation",
metadatas: List[Dict] = None,
**kwargs
) -> str:
"""
Generate a NotebookLM-quality response with strict citations.
Args:
prompt: User query
context: Retrieved context from documents
use_case: Type of response (explanation, summary, qa,notes)
metadatas: Metadata for each context chunk (for citations)
Returns:
Professional formatted response with inline citations
"""
if not context:
return (
"I don't have enough information from your uploaded documents to answer this question. "
"Please upload relevant study materials first, or try rephrasing your question."
)
# Use specialized prompts based on use case
if use_case == "summary":
response = self._create_summary_with_citations(context, prompt, metadatas)
elif use_case == "notes":
response = self._create_notes_with_citations(context, prompt, metadatas)
elif use_case == "qa":
response = self._create_qa_with_citations(context, prompt, metadatas)
else: # Default to explanation
response = self._create_structured_response_with_citations(context, prompt, metadatas)
return response
def _create_structured_response_with_citations(
self,
context: str,
query: str,
metadatas: List[Dict] = None
) -> str:
"""Create NotebookLM-style response with inline citations."""
# Split into paragraphs
paragraphs = [p.strip() for p in context.split('\n\n') if len(p.strip()) > 50]
# Remove duplicates
unique_paras = []
seen = set()
for para in paragraphs:
para_key = para.lower()[:150]
if para_key not in seen:
unique_paras.append(para)
seen.add(para_key)
if len(unique_paras) >= 5:
break
if not unique_paras:
return context[:1000]
# Build response with citations
response = ""
# Main explanation (first paragraph - cleaned and formatted)
main_para = self._clean_and_format_text(unique_paras[0])
main_para = self._apply_bold_formatting(main_para)
# Add citation to end of main paragraph
cite_text = self._get_citation(0, metadatas) if metadatas else ""
response += main_para + cite_text + "\n\n"
# Add structured details if more content available
if len(unique_paras) > 1:
response += "### Key Points:\n\n"
for i, para in enumerate(unique_paras[1:4], 1):
# Extract first 2-3 sentences
sentences = [s.strip() for s in para.split('.') if len(s.strip()) > 20]
if sentences:
detail = self._clean_and_format_text('. '.join(sentences[:2]) + '.')
detail = self._apply_bold_formatting(detail)
# Add citation
cite_text = self._get_citation(i, metadatas) if metadatas and i < len(metadatas) else ""
response += f"{i}. {detail}{cite_text}\n\n"
return response.strip()
def _get_citation(self, index: int, metadatas: List[Dict] = None) -> str:
"""Generate inline citation from metadata."""
if not metadatas or index >= len(metadatas):
return ""
meta = metadatas[index]
filename = meta.get('filename', 'Unknown')
# Remove file extension for cleaner citation
clean_name = filename.replace('.pdf', '').replace('.docx', '').replace('.txt', '')
return f" **[{clean_name}]**"
def _create_summary_with_citations(
self,
context: str,
query: str,
metadatas: List[Dict] = None
) -> str:
"""Create a summary with citations."""
sentences = []
seen = set()
for s in context.split('.'):
s_clean = s.strip()
if len(s_clean) > 40 and s_clean.lower() not in seen:
sentences.append(s_clean)
seen.add(s_clean.lower())
if len(sentences) >= 6:
break
if not sentences:
return context[:800]
response = "## Summary\n\n"
for i, point in enumerate(sentences, 1):
cite = self._get_citation(i-1, metadatas) if metadatas else ""
response += f"{i}. {point}.{cite}\n\n"
return response.strip()
def _create_qa_with_citations(
self,
context: str,
query: str,
metadatas: List[Dict] = None
) -> str:
"""Answer with strict source grounding."""
paragraphs = [p.strip() for p in context.split('\n\n') if len(p.strip()) > 50]
if not paragraphs:
sentences = [s.strip() + '.' for s in context.split('.') if len(s.strip()) > 30]
response = ' '.join(sentences[:6])
cite = self._get_citation(0, metadatas) if metadatas else ""
return response + cite
# Remove duplicates
unique_paras = []
seen = set()
for para in paragraphs:
para_key = para.lower()[:150]
if para_key not in seen:
unique_paras.append(para)
seen.add(para_key)
if len(unique_paras) >= 3:
break
# Fix spacing and add citations
response = unique_paras[0] if unique_paras else context[:800]
response = re.sub(r'([.!?])([A-Z])', r'\1 \2', response)
cite = self._get_citation(0, metadatas) if metadatas else ""
response += cite
# Add supporting details if available
if len(unique_paras) > 1:
second_para = re.sub(r'([.!?])([A-Z])', r'\1 \2', unique_paras[1])
cite2 = self._get_citation(1, metadatas) if metadatas and len(metadatas) > 1 else ""
response += "\n\n" + second_para + cite2
return response.strip()
def _create_notes_with_citations(
self,
context: str,
query: str,
metadatas: List[Dict] = None
) -> str:
"""Create study notes with source attribution."""
sections = [s.strip() for s in context.split('\n\n') if len(s.strip()) > 40]
# Remove duplicates
unique_sections = []
seen = set()
for section in sections:
section_key = section.lower()[:100]
if section_key not in seen:
unique_sections.append(section)
seen.add(section_key)
if len(unique_sections) >= 6:
break
if not unique_sections:
return context[:1000]
response = "## Study Notes\n\n"
for i, section in enumerate(unique_sections, 1):
sentences = [s.strip() for s in section.split('.') if len(s.strip()) > 20]
if sentences:
heading = sentences[0]
cite = self._get_citation(i-1, metadatas) if metadatas else ""
response += f"### {i}. {heading}{cite}\n\n"
for sent in sentences[1:3]:
response += f"- {sent}\n"
response += "\n"
return response.strip()
def _create_summary(self, context: str, query: str) -> str:
"""Create a clean summary from retrieved context."""
# Extract key sentences - remove duplicates
sentences = []
seen = set()
for s in context.split('.'):
s_clean = s.strip()
# Remove duplicates and filter short/low-quality sentences
if len(s_clean) > 40 and s_clean.lower() not in seen:
sentences.append(s_clean)
seen.add(s_clean.lower())
if len(sentences) >= 6:
break
if not sentences:
return context[:800]
response = "## Summary\n\n"
for i, point in enumerate(sentences, 1):
response += f"{i}. {point}.\n\n"
return response.strip()
def _create_explanation(self, context: str, query: str) -> str:
"""Create a well-formatted explanation from retrieved context."""
# Remove duplicate paragraphs
paragraphs = []
seen = set()
for para in context.split('\n\n'):
para_clean = para.strip()
# Keep unique, substantial paragraphs
if len(para_clean) > 50:
para_lower = para_clean.lower()[:200] # Check first 200 chars for duplicates
if para_lower not in seen:
paragraphs.append(para_clean)
seen.add(para_lower)
if not paragraphs:
# Fallback: split by sentence
sentences = [s.strip() + '.' for s in context.split('.') if len(s.strip()) > 30]
return ' '.join(sentences[:8])
# Build clean, formatted response with proper spacing
response = ""
# Add first paragraph as main explanation (ensure spacing between sentences)
first_para = paragraphs[0]
# Add space after punctuation if missing
import re
first_para = re.sub(r'([.!?])([A-Z])', r'\1 \2', first_para)
response += first_para
# Add additional details if available
if len(paragraphs) > 1:
response += "\n\n### Key Points:\n\n"
for i, para in enumerate(paragraphs[1:4], 1): # Max 3 additional points
# Extract first sentence as bullet
sentences = [s.strip() for s in para.split('.') if len(s.strip()) > 20]
if sentences:
response += f"• {sentences[0]}.\n"
if len(sentences) > 1 and len(sentences[1]) > 20:
response += f" {sentences[1]}.\n"
response += "\n"
return response.strip()
def _create_qa(self, context: str, query: str) -> str:
"""Answer a question with clean formatting."""
# Find most relevant paragraphs
paragraphs = [p.strip() for p in context.split('\n\n') if len(p.strip()) > 50]
if not paragraphs:
sentences = [s.strip() + '.' for s in context.split('.') if len(s.strip()) > 30]
return ' '.join(sentences[:6])
# Remove duplicates
unique_paras = []
seen = set()
for para in paragraphs:
para_key = para.lower()[:150]
if para_key not in seen:
unique_paras.append(para)
seen.add(para_key)
if len(unique_paras) >= 3:
break
# Fix spacing in response
import re
response = unique_paras[0] if unique_paras else context[:800]
response = re.sub(r'([.!?])([A-Z])', r'\1 \2', response)
# Add supporting details if available
if len(unique_paras) > 1:
second_para = re.sub(r'([.!?])([A-Z])', r'\1 \2', unique_paras[1])
response += "\n\n" + second_para
return response.strip()
def _create_notes(self, context: str, query: str) -> str:
"""Create well-structured study notes."""
# Split and clean sections
sections = [s.strip() for s in context.split('\n\n') if len(s.strip()) > 40]
# Remove duplicates
unique_sections = []
seen = set()
for section in sections:
section_key = section.lower()[:100]
if section_key not in seen:
unique_sections.append(section)
seen.add(section_key)
if len(unique_sections) >= 6:
break
if not unique_sections:
return context[:1000]
response = "## Study Notes\n\n"
for i, section in enumerate(unique_sections, 1):
# Extract key information
sentences = [s.strip() for s in section.split('.') if len(s.strip()) > 20]
if sentences:
# Use first sentence as heading
heading = sentences[0]
response += f"### {i}. {heading}\n\n"
# Add bullet points for remaining content
for sent in sentences[1:3]: # Max 2 additional sentences
response += f"- {sent}\n"
response += "\n"
return response.strip()
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