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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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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.authentication import get_current_user |
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completions_router = APIRouter(prefix="/v1", dependencies=[Depends(get_current_user)]) |
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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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) |
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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 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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