Dora openai server example (#676)

You will find in this pull request a quick implementation of an openAI
server makes it possible to make request from an openai endpoint client
and receive it within the dataflow.

Note that I have only implemented chat completion and this is still
experimental.

Get started with:

```bash
cd examples/openai-server
dora build dataflow.yml
dora start dataflow.yml

# In a separate terminal
python openai_api_client.py
```

examples/openai-server/.gitignore

@@ -0,0 +1 @@
+*.pt

examples/openai-server/README.md

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+# Dora openai echo example
+
+This is a quick example to showcase how use the `dora-openai-server` to receive and send back data.
+
+Dora Openai Server is still experimental and may change in the future.
+
+Make sure to have, dora, pip and cargo installed.
+
+```bash
+dora up
+dora build dataflow.yml
+dora start dataflow.yml
+
+# In a separate terminal
+python openai_api_client.py
+dora stop
+```

examples/openai-server/dataflow.yml

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+nodes:
+  - id: dora-openai-server
+    build: pip install -e ../../node-hub/dora-openai-server
+    path: dora-openai-server
+    outputs:
+      - v1/chat/completions
+    inputs:
+      v1/chat/completions: dora-echo/echo
+
+  - id: dora-echo
+    build: pip install -e ../../node-hub/dora-echo
+    path: dora-echo
+    inputs:
+      echo: dora-openai-server/v1/chat/completions
+    outputs:
+      - echo

examples/openai-server/openai_api_client.py

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+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy_api_key")
+
+
+def test_list_models():
+    try:
+        models = client.models.list()
+        print("Available models:")
+        for model in models.data:
+            print(f"- {model.id}")
+    except Exception as e:
+        print(f"Error listing models: {e}")
+
+
+def test_chat_completion(user_input):
+    try:
+        response = client.chat.completions.create(
+            model="gpt-3.5-turbo",
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": user_input},
+            ],
+        )
+        print("Chat Completion Response:")
+        print(response.choices[0].message.content)
+    except Exception as e:
+        print(f"Error in chat completion: {e}")
+
+
+if __name__ == "__main__":
+    print("Testing API endpoints...")
+    test_list_models()
+    print("\n" + "=" * 50 + "\n")
+
+    chat_input = input("Enter a message for chat completion: ")
+    test_chat_completion(chat_input)
+    print("\n" + "=" * 50 + "\n")

node-hub/dora-openai-server/README.md

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+# Dora OpenAI Server
+
+This is an experimental to expose an openai server endpoint with dora.
+
+Check example at [examples/openai-server](../../examples/openai-server/README.md)

node-hub/dora-openai-server/dora_openai_server/__init__.py

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+import os
+
+# Define the path to the README file relative to the package directory
+readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.md")
+
+# Read the content of the README file
+try:
+    with open(readme_path, "r", encoding="utf-8") as f:
+        __doc__ = f.read()
+except FileNotFoundError:
+    __doc__ = "README file not found."

node-hub/dora-openai-server/dora_openai_server/main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import List, Optional
+import uvicorn
+from dora import Node
+import asyncio
+import pyarrow as pa
+import ast
+
+DORA_RESPONSE_TIMEOUT = 10
+app = FastAPI()
+
+
+class ChatCompletionMessage(BaseModel):
+    role: str
+    content: str
+
+
+class ChatCompletionRequest(BaseModel):
+    model: str
+    messages: List[ChatCompletionMessage]
+    temperature: Optional[float] = 1.0
+    max_tokens: Optional[int] = 100
+
+
+class ChatCompletionResponse(BaseModel):
+    id: str
+    object: str
+    created: int
+    model: str
+    choices: List[dict]
+    usage: dict
+
+
+node = Node()  # provide the name to connect to the dataflow if dynamic node
+
+
+@app.post("/v1/chat/completions")
+async def create_chat_completion(request: ChatCompletionRequest):
+    data = next(
+        (msg.content for msg in request.messages if msg.role == "user"),
+        "No user message found.",
+    )
+
+    # Convert user_message to Arrow array
+    # user_message_array = pa.array([user_message])
+    # Publish user message to dora-echo
+    # node.send_output("user_query", user_message_array)
+
+    try:
+        data = ast.literal_eval(data)
+    except ValueError:
+        print("Passing input as string")
+    except SyntaxError:
+        print("Passing input as string")
+    if isinstance(data, list):
+        data = pa.array(data)  # initialize pyarrow array
+    elif isinstance(data, str):
+        data = pa.array([data])
+    elif isinstance(data, int):
+        data = pa.array([data])
+    elif isinstance(data, float):
+        data = pa.array([data])
+    elif isinstance(data, dict):
+        data = pa.array([data])
+    else:
+        data = pa.array(data)  # initialize pyarrow array
+    node.send_output("v1/chat/completions", data)
+
+    # Wait for response from dora-echo
+    while True:
+        event = node.next(timeout=DORA_RESPONSE_TIMEOUT)
+        if event["type"] == "ERROR":
+            response_str = "No response received. Err: " + event["value"][0].as_py()
+            break
+        elif event["type"] == "INPUT" and event["id"] == "v1/chat/completions":
+            response = event["value"]
+            response_str = response[0].as_py() if response else "No response received"
+            break
+        else:
+            pass
+
+    return ChatCompletionResponse(
+        id="chatcmpl-1234",
+        object="chat.completion",
+        created=1234567890,
+        model=request.model,
+        choices=[
+            {
+                "index": 0,
+                "message": {"role": "assistant", "content": response_str},
+                "finish_reason": "stop",
+            }
+        ],
+        usage={
+            "prompt_tokens": len(data),
+            "completion_tokens": len(response_str),
+            "total_tokens": len(data) + len(response_str),
+        },
+    )
+
+
+@app.get("/v1/models")
+async def list_models():
+    return {
+        "object": "list",
+        "data": [
+            {
+                "id": "gpt-3.5-turbo",
+                "object": "model",
+                "created": 1677610602,
+                "owned_by": "openai",
+            }
+        ],
+    }
+
+
+async def run_fastapi():
+    config = uvicorn.Config(app, host="0.0.0.0", port=8000, log_level="info")
+    server = uvicorn.Server(config)
+
+    server = asyncio.gather(server.serve())
+    while True:
+        await asyncio.sleep(1)
+        event = node.next(0.001)
+        if event["type"] == "STOP":
+            break
+
+
+def main():
+    asyncio.run(run_fastapi())
+
+
+if __name__ == "__main__":
+    asyncio.run(run_fastapi())

node-hub/dora-openai-server/pyproject.toml

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+[tool.poetry]
+name = "dora-openai-server"
+version = "0.3.6"
+authors = [
+    "Haixuan Xavier Tao <tao.xavier@outlook.com>",
+    "Enzo Le Van <dev@enzo-le-van.fr>",
+]
+description = "Dora OpenAI API Server"
+license = "MIT License"
+homepage = "https://github.com/dora-rs/dora.git"
+documentation = "https://github.com/dora-rs/dora/blob/main/node-hub/dora-openai-server/README.md"
+readme = "README.md"
+packages = [{ include = "dora_openai_server" }]
+
+[tool.poetry.dependencies]
+dora-rs = "^0.3.6"
+numpy = "< 2.0.0"
+pyarrow = ">= 5.0.0"
+python = "^3.7"
+fastapi = "^0.115"
+asyncio = "^3.4"
+uvicorn = "^0.31"
+pydantic = "^2.9"
+
+[tool.poetry.scripts]
+dora-openai-server = "dora_openai_server.main:main"
+
+[build-system]
+requires = ["poetry-core>=1.8.0"]
+build-backend = "poetry.core.masonry.api"

node-hub/dora-openai-server/tests/test_dora_openai_server.py

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+def test_import_main():
+    pass