embeddings.py

import os
from typing import Any
from typing import Callable
from typing import Optional
from typing import Union

import httpx

from singlestoredb import manage_workspaces

try:
    from langchain_openai import OpenAIEmbeddings
except ImportError:
    raise ImportError(
        'Could not import langchain_openai python package. '
        'Please install it with `pip install langchain_openai`.',
    )

try:
    from langchain_aws import BedrockEmbeddings
except ImportError:
    raise ImportError(
        'Could not import langchain-aws python package. '
        'Please install it with `pip install langchain-aws`.',
    )

import boto3
from botocore import UNSIGNED
from botocore.config import Config


def SingleStoreEmbeddingsFactory(
    model_name: str,
    api_key: Optional[str] = None,
    http_client: Optional[httpx.Client] = None,
    obo_token_getter: Optional[Callable[[], Optional[str]]] = None,
    **kwargs: Any,
) -> Union[OpenAIEmbeddings, BedrockEmbeddings]:
    """Return an embeddings model instance (OpenAIEmbeddings or BedrockEmbeddings).
    """
    inference_api_manager = (
        manage_workspaces().organizations.current.inference_apis
    )
    info = inference_api_manager.get(model_name=model_name)
    token_env = os.environ.get('SINGLESTOREDB_USER_TOKEN')
    token = api_key if api_key is not None else token_env

    if info.hosting_platform == 'Amazon':
        # Instantiate Bedrock client
        cfg_kwargs = {
            'signature_version': UNSIGNED,
            'retries': {'max_attempts': 1, 'mode': 'standard'},
        }
        # Extract timeouts from http_client if provided
        t = http_client.timeout if http_client is not None else None
        connect_timeout = None
        read_timeout = None
        if t is not None:
            if isinstance(t, httpx.Timeout):
                if t.connect is not None:
                    connect_timeout = float(t.connect)
                if t.read is not None:
                    read_timeout = float(t.read)
                if connect_timeout is None and read_timeout is not None:
                    connect_timeout = read_timeout
                if read_timeout is None and connect_timeout is not None:
                    read_timeout = connect_timeout
            elif isinstance(t, (int, float)):
                connect_timeout = float(t)
                read_timeout = float(t)
        if read_timeout is not None:
            cfg_kwargs['read_timeout'] = read_timeout
        if connect_timeout is not None:
            cfg_kwargs['connect_timeout'] = connect_timeout

        cfg = Config(**cfg_kwargs)
        client = boto3.client(
            'bedrock-runtime',
            endpoint_url=info.connection_url,
            region_name='us-east-1',
            aws_access_key_id='placeholder',
            aws_secret_access_key='placeholder',
            config=cfg,
        )

        def _inject_headers(request: Any, **_ignored: Any) -> None:
            """Inject dynamic auth/OBO headers prior to Bedrock sending."""
            if obo_token_getter is not None:
                obo_val = obo_token_getter()
                if obo_val:
                    request.headers['X-S2-OBO'] = obo_val
            if token:
                request.headers['Authorization'] = f'Bearer {token}'
            request.headers.pop('X-Amz-Date', None)
            request.headers.pop('X-Amz-Security-Token', None)

        emitter = client._endpoint._event_emitter
        emitter.register_first(
            'before-send.bedrock-runtime.InvokeModel',
            _inject_headers,
        )
        emitter.register_first(
            'before-send.bedrock-runtime.InvokeModelWithResponseStream',
            _inject_headers,
        )

        return BedrockEmbeddings(
            model_id=model_name,
            endpoint_url=info.connection_url,
            region_name='us-east-1',
            aws_access_key_id='placeholder',
            aws_secret_access_key='placeholder',
            client=client,
            **kwargs,
        )

    # OpenAI / Azure OpenAI path
    openai_kwargs = dict(
        base_url=info.connection_url,
        api_key=token,
        model=model_name,
    )
    if http_client is not None:
        openai_kwargs['http_client'] = http_client
    return OpenAIEmbeddings(
        **openai_kwargs,
        **kwargs,
    )