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# Services API Reference
This page documents the API for DeepCritical services.
## EmbeddingService
**Module**: `src.services.embeddings`
**Purpose**: Local sentence-transformers for semantic search and deduplication.
### Methods
#### `embed`
```python
async def embed(self, text: str) -> list[float]
```
Generates embedding for a text string.
**Parameters**:
- `text`: Text to embed
**Returns**: Embedding vector as list of floats.
#### `embed_batch`
```python
async def embed_batch(self, texts: list[str]) -> list[list[float]]
```
Generates embeddings for multiple texts.
**Parameters**:
- `texts`: List of texts to embed
**Returns**: List of embedding vectors.
#### `similarity`
```python
async def similarity(self, text1: str, text2: str) -> float
```
Calculates similarity between two texts.
**Parameters**:
- `text1`: First text
- `text2`: Second text
**Returns**: Similarity score (0.0-1.0).
#### `find_duplicates`
```python
async def find_duplicates(
self,
texts: list[str],
threshold: float = 0.85
) -> list[tuple[int, int]]
```
Finds duplicate texts based on similarity threshold.
**Parameters**:
- `texts`: List of texts to check
- `threshold`: Similarity threshold (default: 0.85)
**Returns**: List of (index1, index2) tuples for duplicate pairs.
### Factory Function
#### `get_embedding_service`
```python
@lru_cache(maxsize=1)
def get_embedding_service() -> EmbeddingService
```
Returns singleton EmbeddingService instance.
## LlamaIndexRAGService
**Module**: `src.services.rag`
**Purpose**: Retrieval-Augmented Generation using LlamaIndex.
### Methods
#### `ingest_evidence`
```python
async def ingest_evidence(self, evidence: list[Evidence]) -> None
```
Ingests evidence into RAG service.
**Parameters**:
- `evidence`: List of Evidence objects to ingest
**Note**: Requires OpenAI API key for embeddings.
#### `retrieve`
```python
async def retrieve(
self,
query: str,
top_k: int = 5
) -> list[Document]
```
Retrieves relevant documents for a query.
**Parameters**:
- `query`: Search query string
- `top_k`: Number of top results to return (default: 5)
**Returns**: List of Document objects with metadata.
#### `query`
```python
async def query(
self,
query: str,
top_k: int = 5
) -> str
```
Queries RAG service and returns formatted results.
**Parameters**:
- `query`: Search query string
- `top_k`: Number of top results to return (default: 5)
**Returns**: Formatted query results as string.
### Factory Function
#### `get_rag_service`
```python
@lru_cache(maxsize=1)
def get_rag_service() -> LlamaIndexRAGService | None
```
Returns singleton LlamaIndexRAGService instance, or None if OpenAI key not available.
## StatisticalAnalyzer
**Module**: `src.services.statistical_analyzer`
**Purpose**: Secure execution of AI-generated statistical code.
### Methods
#### `analyze`
```python
async def analyze(
self,
hypothesis: str,
evidence: list[Evidence],
data_description: str | None = None
) -> AnalysisResult
```
Analyzes a hypothesis using statistical methods.
**Parameters**:
- `hypothesis`: Hypothesis to analyze
- `evidence`: List of Evidence objects
- `data_description`: Optional data description
**Returns**: `AnalysisResult` with:
- `verdict`: SUPPORTED, REFUTED, or INCONCLUSIVE
- `code`: Generated analysis code
- `output`: Execution output
- `error`: Error message if execution failed
**Note**: Requires Modal credentials for sandbox execution.
## See Also
- [Architecture - Services](../architecture/services.md) - Architecture overview
- [Configuration](../configuration/index.md) - Service configuration
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