Add streaming and embedding endpoints to fastapi example

examples/fastapi_server.py

@@ -1,11 +1,14 @@
 """Example FastAPI server for llama.cpp.
 """
-from typing import List, Optional
+import json
+from typing import List, Optional, Iterator
 
-from llama_cpp import Llama
+import llama_cpp
 
 from fastapi import FastAPI
-from pydantic import BaseModel, BaseSettings, Field
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, BaseSettings, Field, create_model_from_typeddict
+from sse_starlette.sse import EventSourceResponse
 
 
 class Settings(BaseSettings):
@@ -16,11 +19,24 @@ app = FastAPI(
     title="🦙 llama.cpp Python API",
     version="0.0.1",
 )
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 settings = Settings()
-llama = Llama(settings.model)
+llama = llama_cpp.Llama(
+    settings.model,
+    f16_kv=True,
+    use_mlock=True,
+    n_threads=6,
+    n_batch=2048,
+)
 
 
-class CompletionRequest(BaseModel):
+class CreateCompletionRequest(BaseModel):
     prompt: str
     suffix: Optional[str] = Field(None)
     max_tokens: int = 16
@@ -31,6 +47,7 @@ class CompletionRequest(BaseModel):
     stop: List[str] = []
     repeat_penalty: float = 1.1
     top_k: int = 40
+    stream: bool = False
 
     class Config:
         schema_extra = {
@@ -41,6 +58,39 @@ class CompletionRequest(BaseModel):
         }
 
 
-@app.post("/v1/completions")
+CreateCompletionResponse = create_model_from_typeddict(llama_cpp.Completion)
-def completions(request: CompletionRequest):
+
+
+@app.post(
+    "/v1/completions",
+    response_model=CreateCompletionResponse,
+)
+def create_completion(request: CreateCompletionRequest):
+    if request.stream:
+        chunks: Iterator[llama_cpp.CompletionChunk] = llama(**request.dict())  # type: ignore
+        return EventSourceResponse(dict(data=json.dumps(chunk)) for chunk in chunks)
     return llama(**request.dict())
+
+
+class CreateEmbeddingRequest(BaseModel):
+    model: Optional[str]
+    input: str
+    user: Optional[str]
+
+    class Config:
+        schema_extra = {
+            "example": {
+                "input": "The food was delicious and the waiter...",
+            }
+        }
+
+
+CreateEmbeddingResponse = create_model_from_typeddict(llama_cpp.Embedding)
+
+
+@app.post(
+    "/v1/embeddings",
+    response_model=CreateEmbeddingResponse,
+)
+def create_embedding(request: CreateEmbeddingRequest):
+    return llama.create_embedding(**request.dict())