rename toolchain/ --> llama_toolchain/

llama_toolchain/inference/api/endpoints.py

@@ -0,0 +1,117 @@
+from .datatypes import *  # noqa: F403
+from typing import Optional, Protocol
+
+# this dependency is annoying and we need a forked up version anyway
+from pyopenapi import webmethod
+
+
+@json_schema_type
+class CompletionRequest(BaseModel):
+    model: PretrainedModel
+    content: InterleavedTextAttachment
+    sampling_params: Optional[SamplingParams] = SamplingParams()
+
+    stream: Optional[bool] = False
+    logprobs: Optional[LogProbConfig] = None
+    quantization_config: Optional[QuantizationConfig] = None
+
+
+@json_schema_type
+class CompletionResponse(BaseModel):
+    completion_message: CompletionMessage
+    logprobs: Optional[List[TokenLogProbs]] = None
+
+
+@json_schema_type
+class CompletionResponseStreamChunk(BaseModel):
+    """streamed completion response."""
+
+    delta: str
+    stop_reason: Optional[StopReason] = None
+    logprobs: Optional[List[TokenLogProbs]] = None
+
+
+@json_schema_type
+class BatchCompletionRequest(BaseModel):
+    model: PretrainedModel
+    content_batch: List[InterleavedTextAttachment]
+    sampling_params: Optional[SamplingParams] = SamplingParams()
+    logprobs: Optional[LogProbConfig] = None
+    quantization_config: Optional[QuantizationConfig] = None
+
+
+@json_schema_type
+class BatchCompletionResponse(BaseModel):
+    completion_message_batch: List[CompletionMessage]
+
+
+@json_schema_type
+class ChatCompletionRequest(BaseModel):
+    model: InstructModel
+    messages: List[Message]
+    sampling_params: Optional[SamplingParams] = SamplingParams()
+
+    # zero-shot tool definitions as input to the model
+    available_tools: Optional[List[ToolDefinition]] = Field(default_factory=list)
+
+    stream: Optional[bool] = False
+    logprobs: Optional[LogProbConfig] = None
+    quantization_config: Optional[QuantizationConfig] = None
+
+
+@json_schema_type
+class ChatCompletionResponseStreamChunk(BaseModel):
+    """SSE-stream of these events."""
+
+    event: ChatCompletionResponseEvent
+
+
+@json_schema_type
+class ChatCompletionResponse(BaseModel):
+    completion_message: CompletionMessage
+    logprobs: Optional[List[TokenLogProbs]] = None
+
+
+@json_schema_type
+class BatchChatCompletionRequest(BaseModel):
+    model: InstructModel
+    messages_batch: List[List[Message]]
+    sampling_params: Optional[SamplingParams] = SamplingParams()
+
+    # zero-shot tool definitions as input to the model
+    available_tools: Optional[List[ToolDefinition]] = Field(default_factory=list)
+
+    logprobs: Optional[LogProbConfig] = None
+    quantization_config: Optional[QuantizationConfig] = None
+
+
+@json_schema_type
+class BatchChatCompletionResponse(BaseModel):
+    completion_message_batch: List[CompletionMessage]
+
+
+class Inference(Protocol):
+
+    @webmethod(route="/inference/completion")
+    async def completion(
+        self,
+        request: CompletionRequest,
+    ) -> Union[CompletionResponse, CompletionResponseStreamChunk]: ...
+
+    @webmethod(route="/inference/chat_completion")
+    async def chat_completion(
+        self,
+        request: ChatCompletionRequest,
+    ) -> Union[ChatCompletionResponse, ChatCompletionResponseStreamChunk]: ...
+
+    @webmethod(route="/inference/batch_completion")
+    async def batch_completion(
+        self,
+        request: BatchCompletionRequest,
+    ) -> List[CompletionResponse]: ...
+
+    @webmethod(route="/inference/batch_chat_completion")
+    async def batch_chat_completion(
+        self,
+        request: BatchChatCompletionRequest,
+    ) -> List[ChatCompletionResponse]: ...