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from fastapi import APIRouter, Depends, Request
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
from private_gpt.server.utils.auth import authenticated
completions_router = APIRouter(prefix="/v1", dependencies=[Depends(authenticated)])
class CompletionsBody(BaseModel):
prompt: str
system_prompt: str | None = None
use_context: bool = False
context_filter: ContextFilter | None = None
include_sources: bool = True
stream: bool = False
model_config = {
"json_schema_extra": {
"examples": [
{
"prompt": "How do you fry an egg?",
"system_prompt": "You are a rapper. Always answer with a rap.",
"stream": False,
"use_context": False,
"include_sources": False,
}
]
}
}
@completions_router.post(
"/completions",
response_model=None,
summary="Completion",
responses={200: {"model": OpenAICompletion}},
tags=["Contextual Completions"],
openapi_extra={
"x-fern-streaming": {
"stream-condition": "stream",
"response": {"$ref": "#/components/schemas/OpenAICompletion"},
"response-stream": {"$ref": "#/components/schemas/OpenAICompletion"},
}
},
)
def prompt_completion(
request: Request, body: CompletionsBody
) -> OpenAICompletion | StreamingResponse:
"""We recommend most users use our Chat completions API.
Given a prompt, the model will return one predicted completion.
Optionally include a `system_prompt` to influence the way the LLM answers.
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 `'include_sources': true`, the API will return the source Chunks used
to create the response, which come from the context provided.
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}]}
```
"""
messages = [OpenAIMessage(content=body.prompt, role="user")]
# If system prompt is passed, create a fake message with the system prompt.
if body.system_prompt:
messages.insert(0, OpenAIMessage(content=body.system_prompt, role="system"))
chat_body = ChatBody(
messages=messages,
use_context=body.use_context,
stream=body.stream,
include_sources=body.include_sources,
context_filter=body.context_filter,
)
return chat_completion(request, chat_body)
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