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WangCham
223
pkg/rag/knowledge/services/embedding_models.py
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223
pkg/rag/knowledge/services/embedding_models.py
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# services/embedding_models.py
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import os
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from typing import Dict, Any, List, Type, Optional
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import logging
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import aiohttp # Import aiohttp for asynchronous requests
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import asyncio
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from sentence_transformers import SentenceTransformer
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logger = logging.getLogger(__name__)
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# Base class for all embedding models
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class BaseEmbeddingModel:
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def __init__(self, model_name: str):
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self.model_name = model_name
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self._embedding_dimension = None
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async def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Asynchronously embeds a list of texts."""
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raise NotImplementedError
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async def embed_query(self, text: str) -> List[float]:
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"""Asynchronously embeds a single query text."""
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raise NotImplementedError
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@property
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def embedding_dimension(self) -> int:
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"""Returns the embedding dimension of the model."""
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if self._embedding_dimension is None:
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raise NotImplementedError("Embedding dimension not set for this model.")
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return self._embedding_dimension
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class EmbeddingModelFactory:
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@staticmethod
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def create_model(model_type: str, model_name_key: str) -> BaseEmbeddingModel:
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"""
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Factory method to create an embedding model instance.
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Currently only supports 'third_party_api' types.
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"""
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if model_name_key not in EMBEDDING_MODEL_CONFIGS:
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raise ValueError(f"Embedding model configuration '{model_name_key}' not found in EMBEDDING_MODEL_CONFIGS.")
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config = EMBEDDING_MODEL_CONFIGS[model_name_key]
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if config['type'] == "third_party_api":
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required_keys = ['api_endpoint', 'headers', 'payload_template', 'embedding_dimension']
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if not all(key in config for key in required_keys):
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raise ValueError(f"Missing configuration keys for third_party_api model '{model_name_key}'. Required: {required_keys}")
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# Retrieve model_name from config if it differs from model_name_key
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# Some APIs expect a specific 'model' value in the payload that might be different from the key
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api_model_name = config.get('model_name', model_name_key)
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return ThirdPartyAPIEmbeddingModel(
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model_name=api_model_name, # Use the model_name from config or the key
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api_endpoint=config['api_endpoint'],
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headers=config['headers'],
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payload_template=config['payload_template'],
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embedding_dimension=config['embedding_dimension']
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)
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class SentenceTransformerEmbeddingModel(BaseEmbeddingModel):
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def __init__(self, model_name: str):
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super().__init__(model_name)
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try:
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# SentenceTransformer is inherently synchronous, but we'll wrap its calls
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# in async methods. The actual computation will still block the event loop
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# if not run in a separate thread/process, but this keeps the API consistent.
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self.model = SentenceTransformer(model_name)
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self._embedding_dimension = self.model.get_sentence_embedding_dimension()
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logger.info(f"Initialized SentenceTransformer model '{model_name}' with dimension {self._embedding_dimension}")
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except Exception as e:
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logger.error(f"Failed to load SentenceTransformer model {model_name}: {e}")
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raise
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async def embed_documents(self, texts: List[str]) -> List[List[float]]:
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# For CPU-bound tasks like local model inference, consider running in a thread pool
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# to prevent blocking the event loop for long operations.
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# For simplicity here, we'll call it directly.
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return self.model.encode(texts).tolist()
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async def embed_query(self, text: str) -> List[float]:
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return self.model.encode(text).tolist()
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class ThirdPartyAPIEmbeddingModel(BaseEmbeddingModel):
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def __init__(self, model_name: str, api_endpoint: str, headers: Dict[str, str], payload_template: Dict[str, Any], embedding_dimension: int):
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super().__init__(model_name)
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self.api_endpoint = api_endpoint
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self.headers = headers
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self.payload_template = payload_template
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self._embedding_dimension = embedding_dimension
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self.session = None # aiohttp client session will be initialized on first use or in a context manager
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logger.info(f"Initialized ThirdPartyAPIEmbeddingModel '{model_name}' for async calls to {api_endpoint} with dimension {embedding_dimension}")
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async def _get_session(self):
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"""Lazily create or return the aiohttp client session."""
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if self.session is None or self.session.closed:
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self.session = aiohttp.ClientSession()
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return self.session
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async def close_session(self):
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"""Explicitly close the aiohttp client session."""
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if self.session and not self.session.closed:
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await self.session.close()
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self.session = None
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logger.info(f"Closed aiohttp session for model {self.model_name}")
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async def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Asynchronously embeds a list of texts using the third-party API."""
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session = await self._get_session()
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embeddings = []
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tasks = []
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for text in texts:
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payload = self.payload_template.copy()
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if 'input' in payload:
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payload['input'] = text
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elif 'texts' in payload:
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payload['texts'] = [text]
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else:
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raise ValueError("Payload template does not contain expected text input key.")
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tasks.append(self._make_api_request(session, payload))
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results = await asyncio.gather(*tasks, return_exceptions=True)
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for i, res in enumerate(results):
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if isinstance(res, Exception):
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logger.error(f"Error embedding text '{texts[i][:50]}...': {res}")
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# Depending on your error handling strategy, you might:
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# - Append None or an empty list
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# - Re-raise the exception to stop processing
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# - Log and skip, then continue
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embeddings.append([0.0] * self.embedding_dimension) # Append dummy embedding or handle failure
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else:
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embeddings.append(res)
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return embeddings
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async def _make_api_request(self, session: aiohttp.ClientSession, payload: Dict[str, Any]) -> List[float]:
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"""Helper to make an asynchronous API request and extract embedding."""
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try:
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async with session.post(self.api_endpoint, headers=self.headers, json=payload) as response:
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response.raise_for_status() # Raise an exception for HTTP errors (4xx, 5xx)
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api_response = await response.json()
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# Adjust this based on your API's actual response structure
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if "data" in api_response and len(api_response["data"]) > 0 and "embedding" in api_response["data"][0]:
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embedding = api_response["data"][0]["embedding"]
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if len(embedding) != self.embedding_dimension:
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logger.warning(f"API returned embedding of dimension {len(embedding)}, but expected {self.embedding_dimension} for model {self.model_name}. Adjusting config might be needed.")
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return embedding
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elif "embeddings" in api_response and isinstance(api_response["embeddings"], list) and api_response["embeddings"]:
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embedding = api_response["embeddings"][0]
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if len(embedding) != self.embedding_dimension:
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logger.warning(f"API returned embedding of dimension {len(embedding)}, but expected {self.embedding_dimension} for model {self.model_name}. Adjusting config might be needed.")
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return embedding
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else:
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raise ValueError(f"Unexpected API response structure: {api_response}")
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except aiohttp.ClientError as e:
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raise ConnectionError(f"API request failed: {e}") from e
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except ValueError as e:
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raise ValueError(f"Error processing API response: {e}") from e
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async def embed_query(self, text: str) -> List[float]:
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"""Asynchronously embeds a single query text."""
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results = await self.embed_documents([text])
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if results:
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return results[0]
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return [] # Or raise an error if embedding a query must always succeed
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# --- Embedding Model Configuration ---
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EMBEDDING_MODEL_CONFIGS: Dict[str, Dict[str, Any]] = {
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"MiniLM": { # Example for a local Sentence Transformer model
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"type": "sentence_transformer",
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"model_name": "sentence-transformers/all-MiniLM-L6-v2"
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},
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"bge-m3": { # Example for a third-party API model
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"type": "third_party_api",
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"model_name": "bge-m3",
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"api_endpoint": "https://api.qhaigc.net/v1/embeddings",
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"headers": {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {os.getenv('rag_api_key')}"
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},
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"payload_template": {
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"model": "bge-m3",
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"input": ""
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},
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"embedding_dimension": 1024
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},
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"OpenAI-Ada-002": {
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"type": "third_party_api",
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"model_name": "text-embedding-ada-002",
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"api_endpoint": "https://api.openai.com/v1/embeddings",
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"headers": {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {os.getenv('OPENAI_API_KEY')}" # Ensure OPENAI_API_KEY is set
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},
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"payload_template": {
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"model": "text-embedding-ada-002",
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"input": "" # Text will be injected here
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},
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"embedding_dimension": 1536
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},
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"OpenAI-Embedding-3-Small": {
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"type": "third_party_api",
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"model_name": "text-embedding-3-small",
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"api_endpoint": "https://api.openai.com/v1/embeddings",
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"headers": {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {os.getenv('OPENAI_API_KEY')}"
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},
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"payload_template": {
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"model": "text-embedding-3-small",
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"input": "",
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# "dimensions": 512 # Optional: uncomment if you want a specific output dimension
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},
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"embedding_dimension": 1536 # Default max dimension for text-embedding-3-small
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},
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}
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