mirror of
https://github.com/dograh-hq/dograh.git
synced 2026-06-07 07:55:16 +02:00
00a1a22b74
* refactor: carve out extraction panel * refactor: create spec versions for node types * refactor: create a GenericNode and remove custom nodes * feat: add python and typescript sdk * add dograh sdk * fix: fetch draft workflow definition over published one * fix: fix routes of SDKs to use code gen * chore: remove doclink dependency to reduce image size * chore: format files * chore: bump pipecat * feat: let mcp fetch archived workflows on demand * chore: fix tests * feat: add sdk documentation * chore: change banner and add badge
130 lines
4.4 KiB
Python
130 lines
4.4 KiB
Python
"""OpenAI embedding service.
|
|
|
|
Embeds text and performs vector similarity search via the local database.
|
|
Document conversion and chunking now live in the Model Proxy Service (MPS);
|
|
this file no longer pulls docling/transformers.
|
|
"""
|
|
|
|
from typing import Any, Dict, List, Optional
|
|
|
|
from loguru import logger
|
|
from openai import AsyncOpenAI
|
|
|
|
from api.db.db_client import DBClient
|
|
|
|
from .base import BaseEmbeddingService
|
|
|
|
DEFAULT_MODEL_ID = "text-embedding-3-small"
|
|
EMBEDDING_DIMENSION = 1536 # Dimension for text-embedding-3-small
|
|
|
|
|
|
class EmbeddingAPIKeyNotConfiguredError(Exception):
|
|
"""Raised when OpenAI API key is not configured for embeddings."""
|
|
|
|
def __init__(self):
|
|
super().__init__(
|
|
"OpenAI API key not configured. Please set your API key in "
|
|
"Model Configurations > Embedding to use document processing."
|
|
)
|
|
|
|
|
|
class OpenAIEmbeddingService(BaseEmbeddingService):
|
|
"""Embedding service using OpenAI's text-embedding-3-small."""
|
|
|
|
def __init__(
|
|
self,
|
|
db_client: DBClient,
|
|
api_key: Optional[str] = None,
|
|
model_id: str = DEFAULT_MODEL_ID,
|
|
base_url: Optional[str] = None,
|
|
):
|
|
"""Initialize the OpenAI embedding service.
|
|
|
|
Args:
|
|
db_client: Database client for vector similarity search.
|
|
api_key: OpenAI API key. If not provided, the client will not be
|
|
initialized and operations will fail with a clear error.
|
|
model_id: OpenAI embedding model ID (default: text-embedding-3-small).
|
|
base_url: Optional base URL for the API (e.g. for OpenRouter).
|
|
"""
|
|
self.db = db_client
|
|
self.model_id = model_id
|
|
|
|
self._api_key_configured = bool(api_key)
|
|
if self._api_key_configured:
|
|
client_kwargs = {"api_key": api_key}
|
|
if base_url:
|
|
client_kwargs["base_url"] = base_url
|
|
self.client = AsyncOpenAI(**client_kwargs)
|
|
logger.info(f"OpenAI embedding service initialized with model: {model_id}")
|
|
else:
|
|
self.client = None
|
|
logger.warning(
|
|
"OpenAI embedding service initialized without API key. "
|
|
"Operations will fail until API key is configured in Model Configurations."
|
|
)
|
|
|
|
def get_model_id(self) -> str:
|
|
"""Return the model identifier."""
|
|
return self.model_id
|
|
|
|
def get_embedding_dimension(self) -> int:
|
|
"""Return the embedding dimension."""
|
|
return EMBEDDING_DIMENSION
|
|
|
|
def _ensure_api_key_configured(self):
|
|
"""Check if API key is configured and raise error if not."""
|
|
if not self._api_key_configured or self.client is None:
|
|
raise EmbeddingAPIKeyNotConfiguredError()
|
|
|
|
async def embed_texts(self, texts: List[str]) -> List[List[float]]:
|
|
"""Embed a batch of texts using OpenAI API.
|
|
|
|
Raises:
|
|
EmbeddingAPIKeyNotConfiguredError: If API key is not configured.
|
|
"""
|
|
self._ensure_api_key_configured()
|
|
|
|
try:
|
|
response = await self.client.embeddings.create(
|
|
input=texts,
|
|
model=self.model_id,
|
|
)
|
|
return [item.embedding for item in response.data]
|
|
except Exception as e:
|
|
logger.error(f"Error generating OpenAI embeddings: {e}")
|
|
raise
|
|
|
|
async def embed_query(self, query: str) -> List[float]:
|
|
"""Embed a single query text using OpenAI API.
|
|
|
|
Raises:
|
|
EmbeddingAPIKeyNotConfiguredError: If API key is not configured.
|
|
"""
|
|
self._ensure_api_key_configured()
|
|
embeddings = await self.embed_texts([query])
|
|
return embeddings[0]
|
|
|
|
async def search_similar_chunks(
|
|
self,
|
|
query: str,
|
|
organization_id: int,
|
|
limit: int = 5,
|
|
document_uuids: Optional[List[str]] = None,
|
|
) -> List[Dict[str, Any]]:
|
|
"""Search for similar chunks using vector similarity.
|
|
|
|
Raises:
|
|
EmbeddingAPIKeyNotConfiguredError: If API key is not configured.
|
|
"""
|
|
self._ensure_api_key_configured()
|
|
|
|
query_embedding = await self.embed_query(query)
|
|
|
|
return await self.db.search_similar_chunks(
|
|
query_embedding=query_embedding,
|
|
organization_id=organization_id,
|
|
limit=limit,
|
|
document_uuids=document_uuids,
|
|
embedding_model=self.model_id,
|
|
)
|