87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
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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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to_openai_sse_stream,
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)
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from private_gpt.server.recipes.summarize.summarize_service import SummarizeService
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from private_gpt.server.utils.auth import authenticated
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summarize_router = APIRouter(prefix="/v1", dependencies=[Depends(authenticated)])
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class SummarizeBody(BaseModel):
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text: str | None = None
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use_context: bool = False
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context_filter: ContextFilter | None = None
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prompt: str | None = None
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instructions: str | None = None
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stream: bool = False
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class SummarizeResponse(BaseModel):
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summary: str
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@summarize_router.post(
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"/summarize",
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response_model=None,
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summary="Summarize",
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responses={200: {"model": SummarizeResponse}},
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tags=["Recipes"],
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)
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def summarize(
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request: Request, body: SummarizeBody
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) -> SummarizeResponse | StreamingResponse:
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"""Given a text, the model will return a summary.
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Optionally include `instructions` to influence the way the summary is generated.
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If `use_context`
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is set to `true`, the model will also use the content coming from the ingested
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documents in the summary. The documents being used can
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be filtered by their metadata using the `context_filter`.
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Ingested documents metadata can be found using `/ingest/list` endpoint.
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If you want all ingested documents to be used, remove `context_filter` altogether.
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If `prompt` is set, it will be used as the prompt for the summarization,
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otherwise the default prompt will be used.
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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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service: SummarizeService = request.state.injector.get(SummarizeService)
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if body.stream:
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completion_gen = service.stream_summarize(
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text=body.text,
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instructions=body.instructions,
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use_context=body.use_context,
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context_filter=body.context_filter,
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prompt=body.prompt,
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)
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return StreamingResponse(
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to_openai_sse_stream(
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response_generator=completion_gen,
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),
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media_type="text/event-stream",
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)
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else:
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completion = service.summarize(
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text=body.text,
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instructions=body.instructions,
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use_context=body.use_context,
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context_filter=body.context_filter,
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prompt=body.prompt,
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)
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return SummarizeResponse(
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summary=completion,
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)
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