082 Claude C268c3c4

Embedding Model Providers

Embedding provider implementations for text-to-vector conversion.

Files

• base.py: Abstract base class EmbeddingModel defining the interface
• openai.py: OpenAI embedding models (text-embedding-3-small/large, etc.)
• google.py: Google embedding models (text-embedding-004, etc.)
• azure.py: Azure OpenAI embedding models
• ollama.py: Local Ollama embedding models
• mistral.py: Mistral embedding models
• jina.py: Jina AI embedding models
• voyage.py: Voyage AI embedding models
• openrouter.py: OpenRouter embedding API
• vertex.py: Google Vertex AI embeddings
• transformers.py: Local HuggingFace transformers models (BERT, sentence-transformers, etc.)
• openai_compatible.py: Generic OpenAI-compatible embedding API

Patterns

Base Class Contract

All providers inherit from EmbeddingModel (base.py:16) and must:

1. Implement abstract methods:

   • embed(): Synchronous embedding generation
   • aembed(): Async embedding generation
   • _get_models(): Return list of available models
   • _get_default_model(): Return default model name
   • provider property: Return provider name string

2. Override __post_init__():

   • Call super().__post_init__() first (extracts task-aware settings)
   • Set api_key from parameter or environment variable
   • Set base_url (use default if not provided)
   • Call self._create_http_clients() last (for API-based providers)

3. Handle Advanced Features:

   • Task-aware embeddings via self.task_type (EmbeddingTaskType enum)
   • Late chunking via self.late_chunking boolean
   • Output dimensions via self.output_dimensions
   • Truncation control via self.truncate_at_max_length

Task-Aware Embeddings

Providers handle task_type differently:

• Native support (Jina, Voyage, Google): Pass task directly to API
• Prefix-based (others): Use _apply_task_optimization() to add prefixes like "query: " or "passage: "
• No support: Override _apply_task_optimization() to return texts unchanged

Set SUPPORTED_FEATURES class attribute to list which features are supported:

class JinaEmbeddingModel(EmbeddingModel):
    SUPPORTED_FEATURES = ["task_type", "late_chunking"]

Task Type Conversion

Google uses different task names. Implement _map_task_to_google_task() or similar:

• Esperanto RETRIEVAL_QUERY → Google RETRIEVAL_QUERY
• Esperanto RETRIEVAL_DOCUMENT → Google RETRIEVAL_DOCUMENT
• Esperanto SIMILARITY → Google SEMANTIC_SIMILARITY

Text Preprocessing

Base class provides:

• _clean_text(): Normalize spacing, remove extra punctuation (base.py:101)
• _apply_task_optimization(): Add task-specific prefixes (base.py:128)
• _apply_late_chunking(): Simple sentence-based chunking (base.py:161)

Providers with native support should override to skip preprocessing.

Configuration Serialization

When passing config to APIs:

• Use _serialize_config_for_api() to convert enums to strings (base.py:218)
• Use _filter_unsupported_params() to remove features the provider doesn't support (base.py:238)
• Use _get_api_kwargs() for clean kwargs dict (base.py:263)

HTTP Client Pattern

Same as LLM providers:

def __post_init__(self):
    super().__post_init__()  # Extracts task_type, late_chunking, etc.
    self.api_key = self.api_key or os.getenv("PROVIDER_API_KEY")
    self.base_url = self.base_url or "https://api.provider.com/v1"
    self._create_http_clients()

Local vs API Providers

• API providers (OpenAI, Jina, Voyage): Use httpx clients, make HTTP requests
• Local providers (Transformers, Ollama local): May use local libraries instead
  • Transformers: Uses HuggingFace sentence-transformers library
  • Ollama: Can use HTTP (if remote) or local client

Batch Processing

Most embedding APIs have batch limits:

• OpenAI: 2048 texts per request
• Google: 100-250 texts per request
• Jina: 8192 texts per request

Implement batching in embed() and aembed():

def embed(self, texts: List[str], **kwargs) -> List[List[float]]:
    batch_size = 2048
    all_embeddings = []
    for i in range(0, len(texts), batch_size):
        batch = texts[i:i+batch_size]
        # Process batch...
        all_embeddings.extend(batch_embeddings)
    return all_embeddings

Integration

• Imported by factory.py via AIFactory._provider_modules["embedding"]
• Uses types from esperanto.common_types (Model)
• Uses EmbeddingTaskType enum from esperanto.common_types.task_type
• Inherits mixins from esperanto.utils.timeout and esperanto.utils.ssl

Gotchas

• Task type extraction: Base __post_init__() automatically extracts task_type from config and converts strings to enum
• Task type string conversion: Config may have "retrieval.query" (with dot) or "retrieval_query" (underscore) - base class handles both
• Feature filtering: If a provider doesn't declare SUPPORTED_FEATURES, all advanced features are removed by _filter_unsupported_params()
• Text cleaning: Only apply if provider doesn't handle it - avoid double-processing
• Empty texts: Handle empty string lists gracefully (return empty list)
• Dimension validation: If output_dimensions is set but provider doesn't support it, ignore or raise error
• API key optional: Local providers (Transformers, local Ollama) don't need API keys
• Model loading: Transformers provider needs to download models on first use - handle this gracefully
• Async implementation: For local models (Transformers), don't spawn threads - use asyncio properly or run in executor
• Deprecation warnings: Use _get_models() internally (not .models property)

When Adding a New Provider

1. Create new file provider_name.py
2. Import EmbeddingModel from esperanto.providers.embedding.base
3. Define class inheriting from EmbeddingModel
4. Declare SUPPORTED_FEATURES class attribute (if provider supports advanced features)
5. Implement all abstract methods
6. Add __post_init__() following the pattern
7. Implement batching for API efficiency
8. Add provider to factory.py in _provider_modules["embedding"] dict
9. Add optional import in src/esperanto/__init__.py
10. Write tests in tests/providers/embedding/test_provider_name.py
11. Add documentation in docs/providers/provider_name.md

Special Cases

Transformers Provider

• Uses HuggingFace sentence-transformers library
• Downloads models to cache on first use
• Supports advanced features like late chunking natively
• Can run on GPU if available (check device parameter)
• Model names are HuggingFace model IDs (e.g., "sentence-transformers/all-MiniLM-L6-v2")

OpenAI-Compatible Provider

• Generic provider for any OpenAI-compatible API
• Requires explicit base_url parameter
• Useful for self-hosted models (vLLM, text-embeddings-inference, etc.)
• Configure via environment variables with OPENAI_COMPATIBLE_ prefix

Task Type Support Matrix

Providers without native support use prefix-based task optimization from base class.