introduce and use a generic ContentDelta

llama_stack/apis/common/content_types.py

@@ -5,10 +5,12 @@
 # the root directory of this source tree.
 
 import base64
+from enum import Enum
 from typing import Annotated, List, Literal, Optional, Union
 
-from llama_models.schema_utils import json_schema_type, register_schema
+from llama_models.llama3.api.datatypes import ToolCall
 
+from llama_models.schema_utils import json_schema_type, register_schema
 from pydantic import BaseModel, Field, field_serializer, model_validator
 
 
@@ -60,3 +62,42 @@ InterleavedContent = register_schema(
     Union[str, InterleavedContentItem, List[InterleavedContentItem]],
     name="InterleavedContent",
 )
+
+
+class TextDelta(BaseModel):
+    type: Literal["text"] = "text"
+    text: str
+
+
+class ImageDelta(BaseModel):
+    type: Literal["image"] = "image"
+    data: bytes
+
+
+@json_schema_type
+class ToolCallParseStatus(Enum):
+    started = "started"
+    in_progress = "in_progress"
+    failed = "failed"
+    succeeded = "succeeded"
+
+
+@json_schema_type
+class ToolCallDelta(BaseModel):
+    type: Literal["tool_call"] = "tool_call"
+
+    # you either send an in-progress tool call so the client can stream a long
+    # code generation or you send the final parsed tool call at the end of the
+    # stream
+    content: Union[str, ToolCall]
+    parse_status: ToolCallParseStatus
+
+
+# streaming completions send a stream of ContentDeltas
+ContentDelta = register_schema(
+    Annotated[
+        Union[TextDelta, ImageDelta, ToolCallDelta],
+        Field(discriminator="type"),
+    ],
+    name="ContentDelta",
+)

llama_stack/apis/inference/inference.py

@@ -29,7 +29,7 @@ from llama_models.schema_utils import json_schema_type, register_schema, webmeth
 from pydantic import BaseModel, Field, field_validator
 from typing_extensions import Annotated
 
-from llama_stack.apis.common.content_types import InterleavedContent
+from llama_stack.apis.common.content_types import ContentDelta, InterleavedContent
 from llama_stack.apis.models import Model
 from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
 
@@ -147,26 +147,12 @@ class ChatCompletionResponseEventType(Enum):
     progress = "progress"
 
 
-@json_schema_type
-class ToolCallParseStatus(Enum):
-    started = "started"
-    in_progress = "in_progress"
-    failure = "failure"
-    success = "success"
-
-
-@json_schema_type
-class ToolCallDelta(BaseModel):
-    content: Union[str, ToolCall]
-    parse_status: ToolCallParseStatus
-
-
 @json_schema_type
 class ChatCompletionResponseEvent(BaseModel):
     """Chat completion response event."""
 
     event_type: ChatCompletionResponseEventType
-    delta: Union[str, ToolCallDelta]
+    delta: ContentDelta
     logprobs: Optional[List[TokenLogProbs]] = None
     stop_reason: Optional[StopReason] = None