85 lines
3.3 KiB
Python
85 lines
3.3 KiB
Python
import asyncio
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from typing import List, Optional, Tuple
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import openai
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from letta.log import get_logger
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from letta.schemas.embedding_config import EmbeddingConfig
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from letta.schemas.passage import Passage
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from letta.schemas.user import User
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from letta.settings import model_settings
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logger = get_logger(__name__)
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class OpenAIEmbedder:
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"""OpenAI-based embedding generation"""
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def __init__(self, embedding_config: Optional[EmbeddingConfig] = None):
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self.embedding_config = embedding_config or EmbeddingConfig.default_config(model_name="text-embedding-3-small", provider="openai")
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# TODO: Unify to global OpenAI client
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self.client = openai.AsyncOpenAI(api_key=model_settings.openai_api_key)
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self.max_batch = 1024
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self.max_concurrent_requests = 20
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async def _embed_batch(self, batch: List[str], batch_indices: List[int]) -> List[Tuple[int, List[float]]]:
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"""Embed a single batch and return embeddings with their original indices"""
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response = await self.client.embeddings.create(model=self.embedding_config.embedding_model, input=batch)
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return [(idx, res.embedding) for idx, res in zip(batch_indices, response.data)]
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async def generate_embedded_passages(self, file_id: str, source_id: str, chunks: List[str], actor: User) -> List[Passage]:
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"""Generate embeddings for chunks with batching and concurrent processing"""
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if not chunks:
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return []
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logger.info(f"Generating embeddings for {len(chunks)} chunks using {self.embedding_config.embedding_model}")
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# Create batches with their original indices
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batches = []
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batch_indices = []
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for i in range(0, len(chunks), self.max_batch):
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batch = chunks[i : i + self.max_batch]
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indices = list(range(i, min(i + self.max_batch, len(chunks))))
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batches.append(batch)
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batch_indices.append(indices)
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logger.info(f"Processing {len(batches)} batches")
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async def process(batch: List[str], indices: List[int]):
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try:
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return await self._embed_batch(batch, indices)
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except Exception as e:
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logger.error(f"Failed to embed batch of size {len(batch)}: {str(e)}")
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raise
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# Execute all batches concurrently with semaphore control
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tasks = [process(batch, indices) for batch, indices in zip(batches, batch_indices)]
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results = await asyncio.gather(*tasks)
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# Flatten results and sort by original index
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indexed_embeddings = []
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for batch_result in results:
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indexed_embeddings.extend(batch_result)
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# Sort by index to maintain original order
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indexed_embeddings.sort(key=lambda x: x[0])
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# Create Passage objects in original order
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passages = []
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for (idx, embedding), text in zip(indexed_embeddings, chunks):
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passage = Passage(
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text=text,
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file_id=file_id,
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source_id=source_id,
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embedding=embedding,
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embedding_config=self.embedding_config,
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organization_id=actor.organization_id,
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)
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passages.append(passage)
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logger.info(f"Successfully generated {len(passages)} embeddings")
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return passages
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