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DOCS: add OpenAI-compatible http and python api reference (#5374)

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### What problem does this PR solve?

Add OpenAI-compatible http and python api reference

### Type of change

- [x] Documentation Update

---------

Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
Co-authored-by: writinwaters <93570324+writinwaters@users.noreply.github.com>
This commit is contained in:
Yongteng Lei
2025-02-26 15:52:26 +08:00
committed by GitHub
parent a9e4695b74
commit b3b341173f
3 changed files with 223 additions and 15 deletions
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37
api/apps/sdk/session.py
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@ -217,7 +217,7 @@ def chat_completion_openai_like(tenant_id, chat_id):
model=model,
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Who you are?"},
{"role": "user", "content": "Who are you?"},
{"role": "assistant", "content": "I am an AI assistant named..."},
{"role": "user", "content": "Can you tell me how to install neovim"},
],
@ -236,14 +236,20 @@ def chat_completion_openai_like(tenant_id, chat_id):
messages = req.get("messages", [])
# To prevent empty [] input
if len(messages) < 1:
return get_error_data_result("You have to provide messages")
return get_error_data_result("You have to provide messages.")
if messages[-1]["role"] != "user":
return get_error_data_result("The last content of this conversation is not from user.")
prompt = messages[-1]["content"]
# Treat context tokens as reasoning tokens
context_token_used = sum(len(message["content"]) for message in messages)
dia = DialogService.query(tenant_id=tenant_id, id=chat_id, status=StatusEnum.VALID.value)
if not dia:
return get_error_data_result(f"You don't own the chat {chat_id}")
dia = dia[0]
# Filter system and assistant messages
# Filter system and non-sense assistant messages
msg = None
msg = [m for m in messages if m["role"] != "system" and (m["role"] != "assistant" or msg)]
@ -251,7 +257,7 @@ def chat_completion_openai_like(tenant_id, chat_id):
# The value for the usage field on all chunks except for the last one will be null.
# The usage field on the last chunk contains token usage statistics for the entire request.
# The choices field on the last chunk will always be an empty array [].
def streamed_respose_generator(chat_id, dia, msg):
def streamed_response_generator(chat_id, dia, msg):
token_used = 0
response = {
"id": f"chatcmpl-{chat_id}",
@ -286,17 +292,17 @@ def chat_completion_openai_like(tenant_id, chat_id):
response["choices"][0]["delta"]["content"] = "**ERROR**: " + str(e)
yield f"data:{json.dumps(response, ensure_ascii=False)}\n\n".encode("utf-8")
# The last chunck
# The last chunk
response["choices"][0]["delta"]["content"] = None
response["choices"][0]["finish_reason"] = "stop"
response["usage"] = {
"prompt_tokens": len(msg),
"prompt_tokens": len(prompt),
"completion_tokens": token_used,
"total_tokens": len(msg) + token_used
"total_tokens": len(prompt) + token_used
}
yield f"data:{json.dumps(response, ensure_ascii=False)}\n\n".encode("utf-8")
resp = Response(streamed_respose_generator(chat_id, dia, msg), mimetype="text/event-stream")
resp = Response(streamed_response_generator(chat_id, dia, msg), mimetype="text/event-stream")
resp.headers.add_header("Cache-control", "no-cache")
resp.headers.add_header("Connection", "keep-alive")
resp.headers.add_header("X-Accel-Buffering", "no")
@ -308,6 +314,7 @@ def chat_completion_openai_like(tenant_id, chat_id):
# focus answer content only
answer = ans
break
content = answer["answer"]
response = {
"id": f"chatcmpl-{chat_id}",
@ -315,20 +322,20 @@ def chat_completion_openai_like(tenant_id, chat_id):
"created": int(time.time()),
"model": req.get("model", ""),
"usage": {
"prompt_tokens": len(messages),
"completion_tokens": len(answer),
"total_tokens": len(messages) + len(answer),
"prompt_tokens": len(prompt),
"completion_tokens": len(content),
"total_tokens": len(prompt) + len(content),
"completion_tokens_details": {
"reasoning_tokens": len(answer),
"accepted_prediction_tokens": len(answer),
"rejected_prediction_tokens": len(answer)
"reasoning_tokens": context_token_used,
"accepted_prediction_tokens": len(content),
"rejected_prediction_tokens": 0 # 0 for simplicity
}
},
"choices": [
{
"message": {
"role": "assistant",
"content": answer["answer"]
"content": content
},
"logprobs": None,
"finish_reason": "stop",