67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
from fastapi import APIRouter
|
|
from pydantic import BaseModel
|
|
from starlette.responses import StreamingResponse
|
|
|
|
from private_gpt.open_ai.extensions.context_filter import ContextFilter
|
|
from private_gpt.open_ai.openai_models import (
|
|
OpenAICompletion,
|
|
OpenAIMessage,
|
|
)
|
|
from private_gpt.server.chat.chat_router import ChatBody, chat_completion
|
|
|
|
completions_router = APIRouter(prefix="/v1")
|
|
|
|
|
|
class CompletionsBody(BaseModel):
|
|
prompt: str
|
|
use_context: bool = False
|
|
context_filter: ContextFilter | None = None
|
|
stream: bool = False
|
|
|
|
model_config = {
|
|
"json_schema_extra": {
|
|
"examples": [
|
|
{
|
|
"prompt": "How do you fry an egg?",
|
|
"stream": False,
|
|
"use_context": False,
|
|
}
|
|
]
|
|
}
|
|
}
|
|
|
|
|
|
@completions_router.post(
|
|
"/completions",
|
|
response_model=None,
|
|
summary="Completion",
|
|
responses={200: {"model": OpenAICompletion}},
|
|
tags=["Contextual Completions"],
|
|
)
|
|
def prompt_completion(body: CompletionsBody) -> OpenAICompletion | StreamingResponse:
|
|
"""We recommend most users use our Chat completions API.
|
|
|
|
Given a prompt, the model will return one predicted completion. If `use_context`
|
|
is set to `true`, the model will use context coming from the ingested documents
|
|
to create the response. The documents being used can be filtered using the
|
|
`context_filter` and passing the document IDs to be used. Ingested documents IDs
|
|
can be found using `/ingest/list` endpoint. If you want all ingested documents to
|
|
be used, remove `context_filter` altogether.
|
|
|
|
When using `'stream': true`, the API will return data chunks following [OpenAI's
|
|
streaming model](https://platform.openai.com/docs/api-reference/chat/streaming):
|
|
```
|
|
{"id":"12345","object":"completion.chunk","created":1694268190,
|
|
"model":"private-gpt","choices":[{"index":0,"delta":{"content":"Hello"},
|
|
"finish_reason":null}]}
|
|
```
|
|
"""
|
|
message = OpenAIMessage(content=body.prompt, role="user")
|
|
chat_body = ChatBody(
|
|
messages=[message],
|
|
use_context=body.use_context,
|
|
stream=body.stream,
|
|
context_filter=body.context_filter,
|
|
)
|
|
return chat_completion(chat_body)
|