137 lines
3.6 KiB
Python
137 lines
3.6 KiB
Python
"""
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Ollama-based embedding client for text vectorization
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Uses Ollama's embedding API instead of local sentence-transformers.
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This eliminates the need for PyTorch and heavy ML dependencies.
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"""
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import logging
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import httpx
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from typing import List, Optional
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from src.config import get_settings
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logger = logging.getLogger(__name__)
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settings = get_settings()
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class OllamaEmbeddingClient:
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"""Client for generating text embeddings using Ollama"""
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def __init__(
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self,
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model_name: Optional[str] = None,
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base_url: Optional[str] = None,
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timeout: int = 30
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):
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"""
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Initialize Ollama embedding client
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Args:
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model_name: Embedding model name (default: nomic-embed-text)
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base_url: Ollama base URL (default from settings)
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timeout: Request timeout in seconds
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"""
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self.model_name = model_name or settings.embedding_model
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self.base_url = (base_url or settings.ollama_base_url).rstrip("/")
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self.timeout = timeout
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self.dimension = settings.embedding_dimension
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logger.info(f"Initializing OllamaEmbeddingClient with model: {self.model_name}")
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logger.info(f"Ollama URL: {self.base_url}")
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async def embed_text(self, text: str) -> List[float]:
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"""
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Generate embedding for a single text using Ollama
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Args:
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text: Input text to embed
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Returns:
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List of floats representing the embedding vector
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"""
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try:
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async with httpx.AsyncClient(timeout=self.timeout) as client:
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response = await client.post(
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f"{self.base_url}/api/embeddings",
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json={
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"model": self.model_name,
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"prompt": text
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}
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)
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response.raise_for_status()
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result = response.json()
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return result["embedding"]
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except Exception as e:
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logger.error(f"Error generating embedding via Ollama: {e}")
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raise
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async def embed_batch(self, texts: List[str]) -> List[List[float]]:
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"""
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Generate embeddings for multiple texts
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Args:
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texts: List of input texts
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Returns:
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List of embedding vectors
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"""
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embeddings = []
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for text in texts:
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embedding = await self.embed_text(text)
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embeddings.append(embedding)
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return embeddings
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def get_dimension(self) -> int:
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"""
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Get embedding dimension
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Returns:
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Embedding vector dimension
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"""
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return self.dimension
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# Global instance
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_embedding_client: Optional[OllamaEmbeddingClient] = None
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def get_embedding_client() -> OllamaEmbeddingClient:
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"""
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Get or create global Ollama embedding client instance
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Returns:
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OllamaEmbeddingClient instance
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"""
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global _embedding_client
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if _embedding_client is None:
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_embedding_client = OllamaEmbeddingClient()
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return _embedding_client
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async def embed_text_async(text: str) -> List[float]:
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"""
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Async wrapper for embedding text
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Args:
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text: Input text
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Returns:
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Embedding vector
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"""
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client = get_embedding_client()
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return await client.embed_text(text)
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async def embed_batch_async(texts: List[str]) -> List[List[float]]:
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"""
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Async wrapper for batch embedding
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Args:
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texts: List of input texts
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Returns:
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List of embedding vectors
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"""
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client = get_embedding_client()
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return await client.embed_batch(texts)
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