File size: 14,482 Bytes
aca8ab4 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 |
"""
Pydantic schemas for type safety and validation.
"""
from datetime import datetime
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field, validator, field_validator
import logging
logger = logging.getLogger(__name__)
class Paper(BaseModel):
"""Schema for arXiv paper metadata."""
arxiv_id: str = Field(..., description="arXiv paper ID")
title: str = Field(..., description="Paper title")
authors: List[str] = Field(..., description="List of author names")
abstract: str = Field(..., description="Paper abstract")
pdf_url: str = Field(..., description="URL to PDF")
published: datetime = Field(..., description="Publication date")
categories: List[str] = Field(default_factory=list, description="arXiv categories")
@validator('authors', pre=True)
def normalize_authors(cls, v):
"""Ensure authors is always a List[str], handling various input formats."""
if isinstance(v, list):
# Already a list, ensure all elements are strings
return [str(author) if not isinstance(author, str) else author for author in v]
elif isinstance(v, dict):
# Dict format - extract values or keys as list
logger.warning(f"Authors field is dict, extracting values: {v}")
if 'names' in v:
return v['names'] if isinstance(v['names'], list) else [str(v['names'])]
elif 'authors' in v:
return v['authors'] if isinstance(v['authors'], list) else [str(v['authors'])]
else:
# Extract all values from dict
return [str(val) for val in v.values() if val]
elif isinstance(v, str):
# Single author as string
return [v]
else:
logger.warning(f"Unexpected authors format: {type(v)}, returning empty list")
return []
@validator('categories', pre=True)
def normalize_categories(cls, v):
"""Ensure categories is always a List[str], handling various input formats."""
if isinstance(v, list):
# Already a list, ensure all elements are strings
return [str(cat) if not isinstance(cat, str) else cat for cat in v]
elif isinstance(v, dict):
# Dict format - extract values or keys as list
logger.warning(f"Categories field is dict, extracting values: {v}")
if 'categories' in v:
return v['categories'] if isinstance(v['categories'], list) else [str(v['categories'])]
else:
# Extract all values from dict
return [str(val) for val in v.values() if val]
elif isinstance(v, str):
# Single category as string
return [v]
else:
logger.warning(f"Unexpected categories format: {type(v)}, returning empty list")
return []
@validator('pdf_url', pre=True)
def normalize_pdf_url(cls, v):
"""Ensure pdf_url is always a string."""
if isinstance(v, dict):
logger.warning(f"pdf_url is dict, extracting url value: {v}")
return v.get('url') or v.get('pdf_url') or str(v)
return str(v) if v else ""
@validator('title', pre=True)
def normalize_title(cls, v):
"""Ensure title is always a string."""
if isinstance(v, dict):
logger.warning(f"title is dict, extracting title value: {v}")
return v.get('title') or str(v)
return str(v) if v else ""
@validator('abstract', pre=True)
def normalize_abstract(cls, v):
"""Ensure abstract is always a string."""
if isinstance(v, dict):
logger.warning(f"abstract is dict, extracting abstract value: {v}")
return v.get('abstract') or v.get('summary') or str(v)
return str(v) if v else ""
class Config:
json_encoders = {
datetime: lambda v: v.isoformat()
}
class PaperChunk(BaseModel):
"""Schema for chunked paper content."""
chunk_id: str = Field(..., description="Unique chunk identifier")
paper_id: str = Field(..., description="arXiv paper ID")
content: str = Field(..., description="Chunk text content")
section: Optional[str] = Field(None, description="Section name if available")
page_number: Optional[int] = Field(None, description="Page number")
arxiv_url: str = Field(..., description="arXiv URL for citation")
metadata: Dict[str, Any] = Field(default_factory=dict, description="Additional metadata")
class Analysis(BaseModel):
"""Schema for individual paper analysis."""
paper_id: str = Field(..., description="arXiv paper ID")
methodology: str = Field(..., description="Research methodology description")
key_findings: List[str] = Field(..., description="Main findings from the paper")
conclusions: str = Field(..., description="Paper conclusions")
limitations: List[str] = Field(..., description="Study limitations")
citations: List[str] = Field(..., description="Source locations for claims")
main_contributions: List[str] = Field(default_factory=list, description="Key contributions")
confidence_score: float = Field(..., ge=0.0, le=1.0, description="Analysis confidence")
@field_validator('key_findings', 'limitations', 'citations', 'main_contributions', mode='before')
@classmethod
def normalize_string_lists(cls, v, info):
"""
Normalize list fields to ensure they contain only strings.
Handles nested lists, None values, and mixed types.
"""
def flatten_and_clean(value):
"""Recursively flatten nested lists and clean values."""
if isinstance(value, str):
return [value.strip()] if value.strip() else []
elif isinstance(value, list):
cleaned = []
for item in value:
if isinstance(item, str):
if item.strip():
cleaned.append(item.strip())
elif isinstance(item, list):
# Recursively flatten nested lists
cleaned.extend(flatten_and_clean(item))
elif item is not None and str(item).strip():
cleaned.append(str(item).strip())
return cleaned
elif value is not None:
str_value = str(value).strip()
return [str_value] if str_value else []
else:
return []
result = flatten_and_clean(v)
if v != result:
logger.warning(f"Normalized '{info.field_name}' in Analysis: cleaned nested/invalid values")
return result
class ConsensusPoint(BaseModel):
"""Schema for consensus findings across papers."""
statement: str = Field(..., description="Consensus statement")
supporting_papers: List[str] = Field(..., description="Paper IDs supporting this claim")
citations: List[str] = Field(..., description="Specific citations")
confidence: float = Field(..., ge=0.0, le=1.0, description="Confidence in consensus")
@field_validator('supporting_papers', 'citations', mode='before')
@classmethod
def normalize_string_lists(cls, v, info):
"""Normalize list fields to ensure they contain only strings."""
def flatten_and_clean(value):
if isinstance(value, str):
return [value.strip()] if value.strip() else []
elif isinstance(value, list):
cleaned = []
for item in value:
if isinstance(item, str) and item.strip():
cleaned.append(item.strip())
elif isinstance(item, list):
cleaned.extend(flatten_and_clean(item))
elif item is not None and str(item).strip():
cleaned.append(str(item).strip())
return cleaned
elif value is not None:
str_value = str(value).strip()
return [str_value] if str_value else []
else:
return []
result = flatten_and_clean(v)
if v != result:
logger.warning(f"Normalized '{info.field_name}' in ConsensusPoint: cleaned nested/invalid values")
return result
class Contradiction(BaseModel):
"""Schema for contradictory findings."""
topic: str = Field(..., description="Topic of contradiction")
viewpoint_a: str = Field(..., description="First viewpoint")
papers_a: List[str] = Field(..., description="Papers supporting viewpoint A")
viewpoint_b: str = Field(..., description="Second viewpoint")
papers_b: List[str] = Field(..., description="Papers supporting viewpoint B")
citations: List[str] = Field(..., description="Specific citations for both sides")
confidence: float = Field(..., ge=0.0, le=1.0, description="Confidence in contradiction")
@field_validator('papers_a', 'papers_b', 'citations', mode='before')
@classmethod
def normalize_string_lists(cls, v, info):
"""Normalize list fields to ensure they contain only strings."""
def flatten_and_clean(value):
if isinstance(value, str):
return [value.strip()] if value.strip() else []
elif isinstance(value, list):
cleaned = []
for item in value:
if isinstance(item, str) and item.strip():
cleaned.append(item.strip())
elif isinstance(item, list):
cleaned.extend(flatten_and_clean(item))
elif item is not None and str(item).strip():
cleaned.append(str(item).strip())
return cleaned
elif value is not None:
str_value = str(value).strip()
return [str_value] if str_value else []
else:
return []
result = flatten_and_clean(v)
if v != result:
logger.warning(f"Normalized '{info.field_name}' in Contradiction: cleaned nested/invalid values")
return result
class SynthesisResult(BaseModel):
"""Schema for synthesis across multiple papers."""
consensus_points: List[ConsensusPoint] = Field(..., description="Areas of agreement")
contradictions: List[Contradiction] = Field(..., description="Areas of disagreement")
research_gaps: List[str] = Field(..., description="Identified research gaps")
summary: str = Field(..., description="Executive summary")
confidence_score: float = Field(..., ge=0.0, le=1.0, description="Overall confidence")
papers_analyzed: List[str] = Field(..., description="List of paper IDs analyzed")
@field_validator('research_gaps', 'papers_analyzed', mode='before')
@classmethod
def normalize_string_lists(cls, v, info):
"""Normalize list fields to ensure they contain only strings."""
def flatten_and_clean(value):
if isinstance(value, str):
return [value.strip()] if value.strip() else []
elif isinstance(value, list):
cleaned = []
for item in value:
if isinstance(item, str) and item.strip():
cleaned.append(item.strip())
elif isinstance(item, list):
cleaned.extend(flatten_and_clean(item))
elif item is not None and str(item).strip():
cleaned.append(str(item).strip())
return cleaned
elif value is not None:
str_value = str(value).strip()
return [str_value] if str_value else []
else:
return []
result = flatten_and_clean(v)
if v != result:
logger.warning(f"Normalized '{info.field_name}' in SynthesisResult: cleaned nested/invalid values")
return result
class Citation(BaseModel):
"""Schema for properly formatted citations."""
paper_id: str = Field(..., description="arXiv paper ID")
authors: List[str] = Field(..., description="Paper authors")
year: int = Field(..., description="Publication year")
title: str = Field(..., description="Paper title")
source: str = Field(..., description="Publication source (arXiv)")
apa_format: str = Field(..., description="Full APA formatted citation")
url: str = Field(..., description="arXiv URL")
class ValidatedOutput(BaseModel):
"""Schema for final validated output with citations."""
synthesis: SynthesisResult = Field(..., description="Synthesis results")
citations: List[Citation] = Field(..., description="All citations used")
retrieved_chunks: List[str] = Field(..., description="Chunk IDs used for grounding")
token_usage: Dict[str, int] = Field(default_factory=dict, description="Token usage stats")
model_desc: Dict[str, str] = Field(default_factory=dict, description="Model descriptions")
cost_estimate: float = Field(..., description="Estimated cost in USD")
processing_time: float = Field(..., description="Processing time in seconds")
class AgentState(BaseModel):
"""
Schema for LangGraph state management.
Note: This Pydantic model serves as type documentation and validation reference.
The actual LangGraph workflow in app.py uses Dict[str, Any] for state to maintain
compatibility with Gradio progress tracking and dynamic state updates during execution.
All fields in this schema correspond to keys in the workflow state dictionary.
"""
query: str = Field(..., description="User research question")
category: Optional[str] = Field(None, description="arXiv category filter")
num_papers: int = Field(default=5, ge=1, le=20, description="Number of papers to retrieve")
papers: List[Paper] = Field(default_factory=list, description="Retrieved papers")
chunks: List[PaperChunk] = Field(default_factory=list, description="Chunked content")
analyses: List[Analysis] = Field(default_factory=list, description="Individual analyses")
synthesis: Optional[SynthesisResult] = Field(None, description="Synthesis result")
validated_output: Optional[ValidatedOutput] = Field(None, description="Final output")
errors: List[str] = Field(default_factory=list, description="Error messages")
class Config:
arbitrary_types_allowed = True
|