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from typing import Literal
from fastapi import APIRouter, Depends, Request
from pydantic import BaseModel, Field
from private_gpt.open_ai.extensions.context_filter import ContextFilter
from private_gpt.server.chunks.chunks_service import Chunk, ChunksService
from private_gpt.server.utils.auth import authenticated
chunks_router = APIRouter(prefix="/v1", dependencies=[Depends(authenticated)])
class ChunksBody(BaseModel):
text: str = Field(examples=["Q3 2023 sales"])
context_filter: ContextFilter | None = None
limit: int = 10
prev_next_chunks: int = Field(default=0, examples=[2])
class ChunksResponse(BaseModel):
object: Literal["list"]
model: Literal["private-gpt"]
data: list[Chunk]
@chunks_router.post("/chunks", tags=["Context Chunks"])
def chunks_retrieval(request: Request, body: ChunksBody) -> ChunksResponse:
"""Given a `text`, returns the most relevant chunks from the ingested documents.
The returned information can be used to generate prompts that can be
passed to `/completions` or `/chat/completions` APIs. Note: it is usually a very
fast API, because only the Embeddings model is involved, not the LLM. The
returned information contains the relevant chunk `text` together with the source
`document` it is coming from. It also contains a score that can be used to
compare different results.
The max number of chunks to be returned is set using the `limit` param.
Previous and next chunks (pieces of text that appear right before or after in the
document) can be fetched by using the `prev_next_chunks` field.
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.
"""
service = request.state.injector.get(ChunksService)
results = service.retrieve_relevant(
body.text, body.context_filter, body.limit, body.prev_next_chunks
)
return ChunksResponse(
object="list",
model="private-gpt",
data=results,
)
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