feat: Introduce SingleStoreChatFactory and SingleStoreEmbeddingsFactory factories that instantiate various flavors of chat and embedding instances.

singlestoredb/ai/__init__.py

@@ -1,2 +1,5 @@
+from .chat import SingleStoreChat  # noqa: F401
+from .chat import SingleStoreChatFactory  # noqa: F401
 from .chat import SingleStoreChatOpenAI  # noqa: F401
 from .embeddings import SingleStoreEmbeddings  # noqa: F401
+from .embeddings import SingleStoreEmbeddingsFactory  # noqa: F401

singlestoredb/ai/chat.py

@@ -1,7 +1,12 @@
 import os
 from typing import Any
+from typing import Callable
+from typing import Optional
+from typing import Union
 
-from singlestoredb.fusion.handlers.utils import get_workspace_manager
+import httpx
+
+from singlestoredb import manage_workspaces
 
 try:
     from langchain_openai import ChatOpenAI
@@ -11,30 +16,144 @@
         'Please install it with `pip install langchain_openai`.',
     )
 
+try:
+    from langchain_aws import ChatBedrockConverse
+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
+
 
 class SingleStoreChatOpenAI(ChatOpenAI):
-    def __init__(self, model_name: str, **kwargs: Any):
+    def __init__(self, model_name: str, api_key: Optional[str] = None, **kwargs: Any):
         inference_api_manger = (
-            get_workspace_manager().organizations.current.inference_apis
+            manage_workspaces().organizations.current.inference_apis
         )
         info = inference_api_manger.get(model_name=model_name)
+        token = (
+            api_key
+            if api_key is not None
+            else os.environ.get('SINGLESTOREDB_USER_TOKEN')
+        )
         super().__init__(
             base_url=info.connection_url,
-            api_key=os.environ.get('SINGLESTOREDB_USER_TOKEN'),
+            api_key=token,
             model=model_name,
             **kwargs,
         )
 
 
 class SingleStoreChat(ChatOpenAI):
-    def __init__(self, model_name: str, **kwargs: Any):
+    def __init__(self, model_name: str, api_key: Optional[str] = None, **kwargs: Any):
         inference_api_manger = (
-            get_workspace_manager().organizations.current.inference_apis
+            manage_workspaces().organizations.current.inference_apis
         )
         info = inference_api_manger.get(model_name=model_name)
+        token = (
+            api_key
+            if api_key is not None
+            else os.environ.get('SINGLESTOREDB_USER_TOKEN')
+        )
         super().__init__(
             base_url=info.connection_url,
-            api_key=os.environ.get('SINGLESTOREDB_USER_TOKEN'),
+            api_key=token,
             model=model_name,
             **kwargs,
         )
+
+
+def SingleStoreChatFactory(
+    model_name: str,
+    api_key: Optional[str] = None,
+    streaming: bool = True,
+    http_client: Optional[httpx.Client] = None,
+    obo_token_getter: Optional[Callable[[], Optional[str]]] = None,
+    **kwargs: Any,
+) -> Union[ChatOpenAI, ChatBedrockConverse]:
+    """Return a chat model instance (ChatOpenAI or ChatBedrockConverse).
+    """
+    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'},
+        }
+        if http_client is not None and http_client.timeout is not None:
+            cfg_kwargs['read_timeout'] = http_client.timeout
+            cfg_kwargs['connect_timeout'] = http_client.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.Converse',
+            _inject_headers,
+        )
+        emitter.register_first(
+            'before-send.bedrock-runtime.ConverseStream',
+            _inject_headers,
+        )
+        emitter.register_first(
+            'before-send.bedrock-runtime.InvokeModel',
+            _inject_headers,
+        )
+        emitter.register_first(
+            'before-send.bedrock-runtime.InvokeModelWithResponseStream',
+            _inject_headers,
+        )
+
+        return ChatBedrockConverse(
+            model_id=model_name,
+            endpoint_url=info.connection_url,
+            region_name='us-east-1',
+            aws_access_key_id='placeholder',
+            aws_secret_access_key='placeholder',
+            disable_streaming=not streaming,
+            client=client,
+            **kwargs,
+        )
+
+    # OpenAI / Azure OpenAI path
+    openai_kwargs = dict(
+        base_url=info.connection_url,
+        api_key=token,
+        model=model_name,
+        streaming=streaming,
+    )
+    if http_client is not None:
+        openai_kwargs['http_client'] = http_client
+    return ChatOpenAI(
+        **openai_kwargs,
+        **kwargs,
+    )

singlestoredb/ai/embeddings.py

@@ -1,7 +1,12 @@
 import os
 from typing import Any
+from typing import Callable
+from typing import Optional
+from typing import Union
 
-from singlestoredb.fusion.handlers.utils import get_workspace_manager
+import httpx
+
+from singlestoredb import manage_workspaces
 
 try:
     from langchain_openai import OpenAIEmbeddings
@@ -11,12 +16,24 @@
         '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
+
 
 class SingleStoreEmbeddings(OpenAIEmbeddings):
 
     def __init__(self, model_name: str, **kwargs: Any):
         inference_api_manger = (
-            get_workspace_manager().organizations.current.inference_apis
+            manage_workspaces().organizations.current.inference_apis
         )
         info = inference_api_manger.get(model_name=model_name)
         super().__init__(
@@ -25,3 +42,84 @@ def __init__(self, model_name: str, **kwargs: Any):
             model=model_name,
             **kwargs,
         )
+
+
+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'},
+        }
+        if http_client is not None and http_client.timeout is not None:
+            cfg_kwargs['read_timeout'] = http_client.timeout
+            cfg_kwargs['connect_timeout'] = http_client.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,
+    )

singlestoredb/management/inference_api.py

@@ -23,6 +23,7 @@ class InferenceAPIInfo(object):
     name: str
     connection_url: str
     project_id: str
+    hosting_platform: str
 
     def __init__(
         self,
@@ -31,12 +32,14 @@ def __init__(
         name: str,
         connection_url: str,
         project_id: str,
+        hosting_platform: str,
     ):
         self.service_id = service_id
         self.connection_url = connection_url
         self.model_name = model_name
         self.name = name
         self.project_id = project_id
+        self.hosting_platform = hosting_platform
 
     @classmethod
     def from_dict(
@@ -62,6 +65,7 @@ def from_dict(
             model_name=obj['modelName'],
             name=obj['name'],
             connection_url=obj['connectionURL'],
+            hosting_platform=obj['hostingPlatform'],
         )
         return out