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Merge branch 'temporary_branch' of https://github.com/QuickfoxConsulting/privateGPT into temporary_branch

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Saurab-Shrestha 2024-04-09 15:48:44 +05:45
commit aab5e50f7c
29 changed files with 566 additions and 163 deletions
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147
private_gpt/server/completions/completions_router.py
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@ -4,10 +4,11 @@ from llama_index.llms import ChatMessage, ChatResponse, MessageRole
from fastapi import APIRouter, Depends, Request, Security, HTTPException, status
from private_gpt.server.ingest.ingest_service import IngestService
from pydantic import BaseModel
from typing import List, Dict, Any
from sqlalchemy.orm import Session
import traceback
import logging
import json
logger = logging.getLogger(__name__)
from starlette.responses import StreamingResponse
@ -20,13 +21,13 @@ from private_gpt.open_ai.openai_models import (
from private_gpt.server.chat.chat_router import ChatBody, chat_completion
from private_gpt.server.utils.auth import authenticated
from private_gpt.users.api import deps
from pydantic import Optional
from private_gpt.users import crud, models, schemas
import uuid
completions_router = APIRouter(prefix="/v1", dependencies=[Depends(authenticated)])
class CompletionsBody(BaseModel):
conversation_id: Optional[int]
conversation_id: uuid.UUID
prompt: str
system_prompt: str | None = None
use_context: bool = False
@ -38,6 +39,7 @@ class CompletionsBody(BaseModel):
"json_schema_extra": {
"examples": [
{
"conversation_id": 123,
"prompt": "How do you fry an egg?",
"system_prompt": "You are a rapper. Always answer with a rap.",
"stream": False,
@ -49,6 +51,9 @@ class CompletionsBody(BaseModel):
}
class ChatContentCreate(BaseModel):
content: Dict[str, Any]
# @completions_router.post(
# "/completions",
# response_model=None,
@ -97,6 +102,14 @@ class CompletionsBody(BaseModel):
# )
# return chat_completion(request, chat_body)
def create_chat_item(db, sender, content, conversation_id):
chat_item_create = schemas.ChatItemCreate(
sender=sender,
content=content,
conversation_id=conversation_id
)
return crud.chat_item.create(db, obj_in=chat_item_create)
@completions_router.post(
"/chat",
@ -135,97 +148,59 @@ async def prompt_completion(
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]
print("DOCUMENTS ASSIGNED TO THIS DEPARTMENTS: ", docs_list)
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 history not found")
user_message = OpenAIMessage(content=body.prompt, role="user")
user_message = user_message.model_dump(mode="json")
user_message_json = {
'text': body.prompt,
}
create_chat_item(db, "user", user_message_json , body.conversation_id)
messages = [user_message]
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,
)
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 HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="Internal Server Error",
)
if body.conversation_id:
chat_history = crud.chat.get_by_id(db, id=body.conversation_id)
if chat_history is None or chat_history.user_id != current_user.id:
raise HTTPException(
status_code=404, detail="Chat history not found")
else:
chat_create_in = schemas.ChatCreate(user_id=current_user.id)
chat_history = crud.chat.create(db=db, obj_in=chat_create_in)
_history = chat_history.messages or []
def build_history() -> list[ChatMessage]:
history_messages: list[ChatMessage] = []
for interaction in _history:
user_message = interaction.get("user", "")
ai_message = interaction.get("ai", "")
if user_message:
history_messages.append(
ChatMessage(
content=user_message,
role=MessageRole.USER
)
)
if ai_message:
history_messages.append(
ChatMessage(
content=ai_message,
role=MessageRole.ASSISTANT
)
)
# max 20 messages to try to avoid context overflow
return history_messages[:20]
# Prepare new messages
new_messages = []
if body.prompt:
new_messages.append(OpenAIMessage(content=body.prompt, role="user"))
if body.system_prompt:
new_messages.insert(0, OpenAIMessage(
content=body.system_prompt, role="system"))
# Update chat history with new user messages
if new_messages:
new_message = ChatMessage(content=new_messages, role=MessageRole.USER)
_history.append(new_message.dict())
# Process chat completion
chat_body = ChatBody(
messages=build_history(),
use_context=body.use_context,
stream=body.stream,
include_sources=body.include_sources,
context_filter=body.context_filter,
)
ai_response = await chat_completion(request, chat_body)
# Update chat history with AI response
if ai_response.messages:
ai_message = OpenAIMessage(
content=ai_response.messages, role="assistant")
_history.append(ai_message.dict())
# Update chat history in the database
chat_obj_in = schemas.ChatUpdate(messages=build_history())
crud.chat.update_messages(db, db_obj=chat_history, obj_in=chat_obj_in)
return ai_response
# log_audit(
# model='Chat',
# action='Chat',
# details={
# "query": body.prompt,
# 'user': current_user.username,
# },
# user_id=current_user.id
# )
)