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93 lines
3.3 KiB
Python
93 lines
3.3 KiB
Python
from fastapi import APIRouter, Depends, Request
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from pydantic import BaseModel
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from starlette.responses import StreamingResponse
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from private_gpt.open_ai.extensions.context_filter import ContextFilter
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from private_gpt.open_ai.openai_models import (
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OpenAICompletion,
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OpenAIMessage,
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)
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from private_gpt.server.chat.chat_router import ChatBody, chat_completion
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from private_gpt.server.utils.auth import authenticated
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completions_router = APIRouter(prefix="/v1", dependencies=[Depends(authenticated)])
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class CompletionsBody(BaseModel):
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prompt: str
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system_prompt: str | None = None
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use_context: bool = False
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context_filter: ContextFilter | None = None
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include_sources: bool = True
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stream: bool = False
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model_config = {
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"json_schema_extra": {
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"examples": [
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{
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"prompt": "How do you fry an egg?",
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"system_prompt": "You are a rapper. Always answer with a rap.",
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"stream": False,
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"use_context": False,
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"include_sources": False,
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}
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]
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}
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}
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@completions_router.post(
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"/completions",
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response_model=None,
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summary="Completion",
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responses={200: {"model": OpenAICompletion}},
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tags=["Contextual Completions"],
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openapi_extra={
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"x-fern-streaming": {
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"stream-condition": "stream",
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"response": {"$ref": "#/components/schemas/OpenAICompletion"},
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"response-stream": {"$ref": "#/components/schemas/OpenAICompletion"},
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}
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},
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)
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def prompt_completion(
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request: Request, body: CompletionsBody
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) -> OpenAICompletion | StreamingResponse:
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"""We recommend most users use our Chat completions API.
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Given a prompt, the model will return one predicted completion.
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Optionally include a `system_prompt` to influence the way the LLM answers.
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If `use_context`
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is set to `true`, the model will use context coming from the ingested documents
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to create the response. The documents being used can be filtered using the
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`context_filter` and passing the document IDs to be used. Ingested documents IDs
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can be found using `/ingest/list` endpoint. If you want all ingested documents to
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be used, remove `context_filter` altogether.
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When using `'include_sources': true`, the API will return the source Chunks used
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to create the response, which come from the context provided.
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When using `'stream': true`, the API will return data chunks following [OpenAI's
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streaming model](https://platform.openai.com/docs/api-reference/chat/streaming):
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```
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{"id":"12345","object":"completion.chunk","created":1694268190,
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"model":"private-gpt","choices":[{"index":0,"delta":{"content":"Hello"},
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"finish_reason":null}]}
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```
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"""
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messages = [OpenAIMessage(content=body.prompt, role="user")]
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# If system prompt is passed, create a fake message with the system prompt.
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if body.system_prompt:
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messages.insert(0, OpenAIMessage(content=body.system_prompt, role="system"))
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chat_body = ChatBody(
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messages=messages,
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use_context=body.use_context,
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stream=body.stream,
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include_sources=body.include_sources,
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context_filter=body.context_filter,
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)
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return chat_completion(request, chat_body)
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