feat(recipe): add our first recipe Summarize (#2028)

* feat: add summary recipe

* test: add summary tests

* docs: move all recipes docs

* docs: add recipes and summarize doc

* docs: update openapi reference

* refactor: split method in two method (summary)

* feat: add initial summarize ui

* feat: add mode explanation

* fix: mypy

* feat: allow to configure async property in summarize

* refactor: move modes to enum and update mode explanations

* docs: fix url

* docs: remove list-llm pages

* docs: remove double header

* fix: summary description

private_gpt/server/recipes/summarize/summarize_router.py

@@ -0,0 +1,86 @@
+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 (
+    to_openai_sse_stream,
+)
+from private_gpt.server.recipes.summarize.summarize_service import SummarizeService
+from private_gpt.server.utils.auth import authenticated
+
+summarize_router = APIRouter(prefix="/v1", dependencies=[Depends(authenticated)])
+
+
+class SummarizeBody(BaseModel):
+    text: str | None = None
+    use_context: bool = False
+    context_filter: ContextFilter | None = None
+    prompt: str | None = None
+    instructions: str | None = None
+    stream: bool = False
+
+
+class SummarizeResponse(BaseModel):
+    summary: str
+
+
+@summarize_router.post(
+    "/summarize",
+    response_model=None,
+    summary="Summarize",
+    responses={200: {"model": SummarizeResponse}},
+    tags=["Recipes"],
+)
+def summarize(
+    request: Request, body: SummarizeBody
+) -> SummarizeResponse | StreamingResponse:
+    """Given a text, the model will return a summary.
+
+    Optionally include `instructions` to influence the way the summary is generated.
+
+    If `use_context`
+    is set to `true`, the model will also use the content coming from the ingested
+    documents in the summary. The documents being used can
+    be filtered by their metadata using the `context_filter`.
+    Ingested documents metadata can be found using `/ingest/list` endpoint.
+    If you want all ingested documents to be used, remove `context_filter` altogether.
+
+    If `prompt` is set, it will be used as the prompt for the summarization,
+    otherwise the default prompt will be used.
+
+    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}]}
+    ```
+    """
+    service: SummarizeService = request.state.injector.get(SummarizeService)
+
+    if body.stream:
+        completion_gen = service.stream_summarize(
+            text=body.text,
+            instructions=body.instructions,
+            use_context=body.use_context,
+            context_filter=body.context_filter,
+            prompt=body.prompt,
+        )
+        return StreamingResponse(
+            to_openai_sse_stream(
+                response_generator=completion_gen,
+            ),
+            media_type="text/event-stream",
+        )
+    else:
+        completion = service.summarize(
+            text=body.text,
+            instructions=body.instructions,
+            use_context=body.use_context,
+            context_filter=body.context_filter,
+            prompt=body.prompt,
+        )
+        return SummarizeResponse(
+            summary=completion,
+        )