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# Models API Reference
This page documents the Pydantic models used throughout DeepCritical.
## Evidence
**Module**: `src.utils.models`
**Purpose**: Represents evidence from search results.
```python
class Evidence(BaseModel):
citation: Citation
content: str
relevance_score: float = Field(ge=0.0, le=1.0)
metadata: dict[str, Any] = Field(default_factory=dict)
```
**Fields**:
- `citation`: Citation information (title, URL, date, authors)
- `content`: Evidence text content
- `relevance_score`: Relevance score (0.0-1.0)
- `metadata`: Additional metadata dictionary
## Citation
**Module**: `src.utils.models`
**Purpose**: Citation information for evidence.
```python
class Citation(BaseModel):
title: str
url: str
date: str | None = None
authors: list[str] = Field(default_factory=list)
```
**Fields**:
- `title`: Article/trial title
- `url`: Source URL
- `date`: Publication date (optional)
- `authors`: List of authors (optional)
## KnowledgeGapOutput
**Module**: `src.utils.models`
**Purpose**: Output from knowledge gap evaluation.
```python
class KnowledgeGapOutput(BaseModel):
research_complete: bool
outstanding_gaps: list[str] = Field(default_factory=list)
```
**Fields**:
- `research_complete`: Boolean indicating if research is complete
- `outstanding_gaps`: List of remaining knowledge gaps
## AgentSelectionPlan
**Module**: `src.utils.models`
**Purpose**: Plan for tool/agent selection.
```python
class AgentSelectionPlan(BaseModel):
tasks: list[AgentTask] = Field(default_factory=list)
```
**Fields**:
- `tasks`: List of agent tasks to execute
## AgentTask
**Module**: `src.utils.models`
**Purpose**: Individual agent task.
```python
class AgentTask(BaseModel):
agent_name: str
query: str
context: dict[str, Any] = Field(default_factory=dict)
```
**Fields**:
- `agent_name`: Name of agent to use
- `query`: Task query
- `context`: Additional context dictionary
## ReportDraft
**Module**: `src.utils.models`
**Purpose**: Draft structure for long-form reports.
```python
class ReportDraft(BaseModel):
title: str
sections: list[ReportSection] = Field(default_factory=list)
references: list[Citation] = Field(default_factory=list)
```
**Fields**:
- `title`: Report title
- `sections`: List of report sections
- `references`: List of citations
## ReportSection
**Module**: `src.utils.models`
**Purpose**: Individual section in a report draft.
```python
class ReportSection(BaseModel):
title: str
content: str
order: int
```
**Fields**:
- `title`: Section title
- `content`: Section content
- `order`: Section order number
## ParsedQuery
**Module**: `src.utils.models`
**Purpose**: Parsed and improved query.
```python
class ParsedQuery(BaseModel):
original_query: str
improved_query: str
research_mode: Literal["iterative", "deep"]
key_entities: list[str] = Field(default_factory=list)
research_questions: list[str] = Field(default_factory=list)
```
**Fields**:
- `original_query`: Original query string
- `improved_query`: Refined query string
- `research_mode`: Research mode ("iterative" or "deep")
- `key_entities`: List of key entities
- `research_questions`: List of research questions
## Conversation
**Module**: `src.utils.models`
**Purpose**: Conversation history with iterations.
```python
class Conversation(BaseModel):
iterations: list[IterationData] = Field(default_factory=list)
```
**Fields**:
- `iterations`: List of iteration data
## IterationData
**Module**: `src.utils.models`
**Purpose**: Data for a single iteration.
```python
class IterationData(BaseModel):
iteration: int
observations: str | None = None
knowledge_gaps: list[str] = Field(default_factory=list)
tool_calls: list[dict[str, Any]] = Field(default_factory=list)
findings: str | None = None
thoughts: str | None = None
```
**Fields**:
- `iteration`: Iteration number
- `observations`: Generated observations
- `knowledge_gaps`: Identified knowledge gaps
- `tool_calls`: Tool calls made
- `findings`: Findings from tools
- `thoughts`: Agent thoughts
## AgentEvent
**Module**: `src.utils.models`
**Purpose**: Event emitted during research execution.
```python
class AgentEvent(BaseModel):
type: str
iteration: int | None = None
data: dict[str, Any] = Field(default_factory=dict)
```
**Fields**:
- `type`: Event type (e.g., "started", "search_complete", "complete")
- `iteration`: Iteration number (optional)
- `data`: Event data dictionary
## BudgetStatus
**Module**: `src.utils.models`
**Purpose**: Current budget status.
```python
class BudgetStatus(BaseModel):
tokens_used: int
tokens_limit: int
time_elapsed_seconds: float
time_limit_seconds: float
iterations: int
iterations_limit: int
```
**Fields**:
- `tokens_used`: Tokens used so far
- `tokens_limit`: Token limit
- `time_elapsed_seconds`: Elapsed time in seconds
- `time_limit_seconds`: Time limit in seconds
- `iterations`: Current iteration count
- `iterations_limit`: Iteration limit
## See Also
- [Architecture - Agents](../architecture/agents.md) - How models are used
- [Configuration](../configuration/index.md) - Model configuration
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