mirror of
https://github.com/zylon-ai/private-gpt.git
synced 2025-12-22 23:22:57 +01:00
Updated the chat system prompt
This commit is contained in:
Saurab-Shrestha9639*969**9858//852
commit
5012529201
208 changed files with 22903 additions and 22524 deletions
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@ -1,237 +1,236 @@
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from private_gpt.users import crud, models, schemas
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import itertools
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from llama_index.core.llms import ChatMessage, ChatResponse, MessageRole
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from fastapi import APIRouter, Depends, Request, Security, HTTPException, status
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from private_gpt.server.ingest.ingest_service import IngestService
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from pydantic import BaseModel
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from typing import List, Dict, Any, Optional
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from sqlalchemy.orm import Session
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import traceback
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import logging
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import json
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logger = logging.getLogger(__name__)
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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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from private_gpt.users.api import deps
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from private_gpt.users import crud, models, schemas
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import uuid
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completions_router = APIRouter(prefix="/v1", dependencies=[Depends(authenticated)])
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class CompletionsBody(BaseModel):
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conversation_id: uuid.UUID
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history: Optional[list[OpenAIMessage]]
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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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"conversation_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
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"history": [
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{
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"role": "user",
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"content": "Hello!"
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},
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{
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"role": "assistant",
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"content": "Hello, how can I help you?"
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}
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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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class ChatContentCreate(BaseModel):
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content: Dict[str, Any]
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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 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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def create_chat_item(db, sender, content, conversation_id):
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chat_item_create = schemas.ChatItemCreate(
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sender=sender,
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content=content,
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conversation_id=conversation_id
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)
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return crud.chat_item.create(db, obj_in=chat_item_create)
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@completions_router.post(
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"/chat",
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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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async def prompt_completion(
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request: Request,
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body: CompletionsBody,
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db: Session = Depends(deps.get_db),
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log_audit: models.Audit = Depends(deps.get_audit_logger),
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current_user: models.User = Security(
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deps.get_current_user,
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),
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) -> OpenAICompletion | StreamingResponse:
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service = request.state.injector.get(IngestService)
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try:
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department = crud.department.get_by_id(
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db, id=current_user.department_id)
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if not department:
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raise HTTPException(status_code=status.HTTP_404_NOT_FOUND,
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detail=f"No department assigned to you")
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documents = crud.documents.get_enabled_documents_by_departments(
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db, department_id=department.id)
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if not documents:
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raise HTTPException(status_code=status.HTTP_404_NOT_FOUND,
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detail=f"No documents uploaded for your department.")
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docs_list = [document.filename for document in documents]
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docs_ids = []
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for filename in docs_list:
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doc_id = service.get_doc_ids_by_filename(filename)
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docs_ids.extend(doc_id)
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body.context_filter = {"docs_ids": docs_ids}
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chat_history = crud.chat.get_by_id(
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db, id=body.conversation_id
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)
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if (chat_history is None) and (chat_history.user_id != current_user.id):
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raise HTTPException(
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status_code=404, detail="Chat not found")
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_history = body.history if body.history else []
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def build_history() -> list[OpenAIMessage]:
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history_messages: list[OpenAIMessage] = []
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for interaction in _history:
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role = interaction.role
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if role == 'user':
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history_messages.append(
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OpenAIMessage(
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content=interaction.content,
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role="user"
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)
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)
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else:
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history_messages.append(
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OpenAIMessage(
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content=interaction.content,
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role="assistant"
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)
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)
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return history_messages
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user_message = OpenAIMessage(content=body.prompt, role="user")
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user_message_json = {
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'text': body.prompt,
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}
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create_chat_item(db, "user", user_message_json , body.conversation_id)
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messages = [user_message]
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if body.system_prompt:
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messages.insert(0, OpenAIMessage(
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content=body.system_prompt, role="system"))
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all_messages = [*build_history(), user_message]
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chat_body = ChatBody(
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messages=all_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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log_audit(
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model='Chat',
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action='Chat',
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details={
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"query": body.prompt,
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'user': current_user.username,
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},
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user_id=current_user.id
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)
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chat_response = await chat_completion(request, chat_body)
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ai_response = chat_response.model_dump(mode="json")
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create_chat_item(db, "assistant", ai_response, body.conversation_id)
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return chat_response
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except Exception as e:
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print(traceback.format_exc())
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logger.error(f"There was an error: {str(e)}")
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail="Internal Server Error",
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)
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from private_gpt.users import crud, models, schemas
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import itertools
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from llama_index.core.llms import ChatMessage, ChatResponse, MessageRole
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from fastapi import APIRouter, Depends, Request, Security, HTTPException, status
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from private_gpt.server.ingest.ingest_service import IngestService
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from pydantic import BaseModel
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from typing import List, Dict, Any, Optional
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from sqlalchemy.orm import Session
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import traceback
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import logging
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import json
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logger = logging.getLogger(__name__)
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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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from private_gpt.users.api import deps
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from private_gpt.users import crud, models, schemas
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import uuid
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completions_router = APIRouter(prefix="/v1", dependencies=[Depends(authenticated)])
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|
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class CompletionsBody(BaseModel):
|
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conversation_id: uuid.UUID
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history: Optional[list[OpenAIMessage]]
|
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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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|
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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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"conversation_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
|
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"history": [
|
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{
|
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"role": "user",
|
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"content": "Hello!"
|
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},
|
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{
|
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"role": "assistant",
|
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"content": "Hello, how can I help you?"
|
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}
|
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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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|
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class ChatContentCreate(BaseModel):
|
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content: Dict[str, Any]
|
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|
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# @completions_router.post(
|
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# "/completions",
|
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# response_model=None,
|
||||
# summary="Completion",
|
||||
# responses={200: {"model": OpenAICompletion}},
|
||||
# tags=["Contextual Completions"],
|
||||
# )
|
||||
# def prompt_completion(
|
||||
# request: Request, body: CompletionsBody
|
||||
# ) -> OpenAICompletion | StreamingResponse:
|
||||
# """We recommend most users use our Chat completions API.
|
||||
|
||||
# Given a prompt, the model will return one predicted completion.
|
||||
|
||||
# Optionally include a `system_prompt` to influence the way the LLM answers.
|
||||
|
||||
# If `use_context`
|
||||
# is set to `true`, the model will use context coming from the ingested documents
|
||||
# to create the response. 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.
|
||||
|
||||
# When using `'include_sources': true`, the API will return the source Chunks used
|
||||
# to create the response, which come from the context provided.
|
||||
|
||||
# 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,
|
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# "model":"private-gpt","choices":[{"index":0,"delta":{"content":"Hello"},
|
||||
# "finish_reason":null}]}
|
||||
# ```
|
||||
# """
|
||||
# messages = [OpenAIMessage(content=body.prompt, role="user")]
|
||||
# # If system prompt is passed, create a fake message with the system prompt.
|
||||
# if body.system_prompt:
|
||||
# messages.insert(0, OpenAIMessage(content=body.system_prompt, role="system"))
|
||||
|
||||
# chat_body = ChatBody(
|
||||
# messages=messages,
|
||||
# use_context=body.use_context,
|
||||
# stream=body.stream,
|
||||
# include_sources=body.include_sources,
|
||||
# context_filter=body.context_filter,
|
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# )
|
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# return chat_completion(request, chat_body)
|
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|
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def create_chat_item(db, sender, content, conversation_id):
|
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chat_item_create = schemas.ChatItemCreate(
|
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sender=sender,
|
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content=content,
|
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conversation_id=conversation_id
|
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)
|
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chat_history = crud.chat.get_conversation(db, conversation_id=conversation_id)
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chat_history.generate_title()
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return crud.chat_item.create(db, obj_in=chat_item_create)
|
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|
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@completions_router.post(
|
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"/chat",
|
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response_model=None,
|
||||
summary="Completion",
|
||||
responses={200: {"model": OpenAICompletion}},
|
||||
tags=["Contextual Completions"],
|
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openapi_extra={
|
||||
"x-fern-streaming": {
|
||||
"stream-condition": "stream",
|
||||
"response": {"$ref": "#/components/schemas/OpenAICompletion"},
|
||||
"response-stream": {"$ref": "#/components/schemas/OpenAICompletion"},
|
||||
}
|
||||
},
|
||||
)
|
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async def prompt_completion(
|
||||
request: Request,
|
||||
body: CompletionsBody,
|
||||
db: Session = Depends(deps.get_db),
|
||||
log_audit: models.Audit = Depends(deps.get_audit_logger),
|
||||
current_user: models.User = Security(
|
||||
deps.get_current_user,
|
||||
),
|
||||
) -> OpenAICompletion | StreamingResponse:
|
||||
|
||||
service = request.state.injector.get(IngestService)
|
||||
try:
|
||||
department = crud.department.get_by_id(
|
||||
db, id=current_user.department_id)
|
||||
if not department:
|
||||
raise HTTPException(status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail=f"No department assigned to you")
|
||||
|
||||
documents = crud.documents.get_enabled_documents_by_departments(
|
||||
db, department_id=department.id)
|
||||
if not documents:
|
||||
raise HTTPException(status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail=f"No documents uploaded for your department.")
|
||||
|
||||
docs_list = [document.filename for document in documents]
|
||||
docs_ids = []
|
||||
for filename in docs_list:
|
||||
doc_id = service.get_doc_ids_by_filename(filename)
|
||||
docs_ids.extend(doc_id)
|
||||
body.context_filter = {"docs_ids": docs_ids}
|
||||
|
||||
chat_history = crud.chat.get_by_id(
|
||||
db, id=body.conversation_id
|
||||
)
|
||||
if (chat_history is None) and (chat_history.user_id != current_user.id):
|
||||
raise HTTPException(
|
||||
status_code=404, detail="Chat not found")
|
||||
|
||||
_history = body.history if body.history else []
|
||||
|
||||
def build_history() -> list[OpenAIMessage]:
|
||||
history_messages: list[OpenAIMessage] = []
|
||||
for interaction in _history:
|
||||
role = interaction.role
|
||||
if role == 'user':
|
||||
history_messages.append(
|
||||
OpenAIMessage(
|
||||
content=interaction.content,
|
||||
role="user"
|
||||
)
|
||||
)
|
||||
else:
|
||||
history_messages.append(
|
||||
OpenAIMessage(
|
||||
content=interaction.content,
|
||||
role="assistant"
|
||||
)
|
||||
)
|
||||
return history_messages
|
||||
user_message = OpenAIMessage(content=body.prompt, role="user")
|
||||
user_message_json = {
|
||||
'text': body.prompt,
|
||||
}
|
||||
create_chat_item(db, "user", user_message_json , body.conversation_id) # store every query in the db
|
||||
|
||||
messages = [user_message]
|
||||
|
||||
if body.system_prompt:
|
||||
messages.insert(0, OpenAIMessage(
|
||||
content=body.system_prompt, role="system"))
|
||||
|
||||
all_messages = [*build_history(), user_message]
|
||||
chat_body = ChatBody(
|
||||
messages=all_messages,
|
||||
use_context=body.use_context,
|
||||
stream=body.stream,
|
||||
include_sources=body.include_sources,
|
||||
context_filter=body.context_filter,
|
||||
)
|
||||
log_audit(
|
||||
model='Chat',
|
||||
action='Chat',
|
||||
details={
|
||||
"query": body.prompt,
|
||||
'user': current_user.username,
|
||||
},
|
||||
user_id=current_user.id
|
||||
)
|
||||
|
||||
chat_response = await chat_completion(request, chat_body)
|
||||
ai_response = chat_response.model_dump(mode="json")
|
||||
create_chat_item(db, "assistant", ai_response, body.conversation_id)
|
||||
return chat_response
|
||||
|
||||
except Exception as e:
|
||||
print(traceback.format_exc())
|
||||
logger.error(f"There was an error: {str(e)}")
|
||||
raise
|
||||
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Reference in a new issue