Added new docker files

.env

@@ -11,8 +11,10 @@ SUPER_ADMIN_EMAIL=superadmin@email.com
 SUPER_ADMIN_PASSWORD=supersecretpassword
 SUPER_ADMIN_ACCOUNT_NAME=superaccount
 
-SECRET_KEY=ba9dc3f976cf8fb40519dcd152a8d7d21c0b7861d841711cdb2602be8e85fd7c
+SECRET_KEY=ba9d23f976cf8fb40519dcd152a8d7d21c0b7861d841711cdb2602be8e85fd7c
-ACCESS_TOKEN_EXPIRE_MINUTES=720                
+REFRESH_KEY=ba9dc3f976cf8fb12312dve22a8d7d21c0b7861d841711cdb2602be8e85fdde
+
+ACCESS_TOKEN_EXPIRE_MINUTES=240                
 REFRESH_TOKEN_EXPIRE_MINUTES=1400 
 
 SMTP_SERVER=mail.gibl.com.np
@@ -22,4 +24,5 @@ SMTP_USERNAME=noreply@gibl.com.np
 SMTP_PASSWORD=*G15y^N0reP!y
 
 LDAP_SERVER=ldap://192.168.101.111
-LDAP_ENABLE=False
+LDAP_ENABLE=False
+ENABLE_MAKER_CHECKER=False

Dockerfile.local

@@ -10,11 +10,7 @@ RUN apt-get update && apt-get install -y \
     ninja-build \
     build-essential \
     pkg-config \
-    wget \
+    wget 
-    make \
-    cmake \ 
-    g++ \ 
-    gcc 
 
 # Switch to the Python image for the final build stage
 FROM python:3.11.6-slim-bookworm as base
@@ -50,11 +46,12 @@ RUN poetry run python scripts/setup
 FROM base as app
 
 ENV PYTHONUNBUFFERED=1
-ENV PORT=8080
+ENV PORT=80
-EXPOSE 8080
+EXPOSE 80
 
 # Prepare a non-root user
-RUN adduser --system worker
+RUN adduser --group worker
+RUN adduser --system --ingroup worker worker
 WORKDIR /home/worker/app
 
 RUN mkdir local_data; chown worker local_data
@@ -68,4 +65,4 @@ COPY --chown=worker scripts/ scripts
 ENV PYTHONPATH="$PYTHONPATH:/private_gpt/"
 
 USER worker
-ENTRYPOINT python -m private_gpt
+ENTRYPOINT uvicorn private_gpt.main:app --host 0.0.0.0 --port 80

docker-compose.yaml

@@ -6,11 +6,12 @@ services:
       - ./local_data/:/home/worker/app/local_data
       - ./models/:/home/worker/app/models
     ports:
-      - 8000:8000
+      - 80:80
     environment:
-      PORT: 8000
+      PORT: 80
       PGPT_PROFILES: docker
       PGPT_MODE: local
 
     env_file:
       - .env
+    

private_gpt/components/llm/llm_component.py

@@ -11,7 +11,6 @@ from private_gpt.paths import models_cache_path, models_path
 from private_gpt.settings.settings import Settings
 
 logger = logging.getLogger(__name__)
-local_path = models_path / "tokenizer/Mistral-7B-Instruct-v0.1"
 @singleton
 class LLMComponent:
     llm: LLM
@@ -20,11 +19,22 @@ class LLMComponent:
     def __init__(self, settings: Settings) -> None:
         llm_mode = settings.llm.mode
         if settings.llm.tokenizer:
-            set_global_tokenizer(
+            try:
-                AutoTokenizer.from_pretrained(
+                set_global_tokenizer(
-                    local_path
+                    AutoTokenizer.from_pretrained(
+                        pretrained_model_name_or_path=settings.llm.tokenizer,
+                        cache_dir=str(models_cache_path),
+                        token=settings.huggingface.access_token,
+                    )
                 )
-            )
+            except Exception as e:
+                logger.warning(
+                    "Failed to download tokenizer %s. Falling back to "
+                    "default tokenizer.",
+                    settings.llm.tokenizer,
+                    e,
+                )
+
 
         logger.info("Initializing the LLM in mode=%s", llm_mode)
         match settings.llm.mode:

private_gpt/server/chat/chat_service.py

@@ -166,7 +166,8 @@ class ChatService:
         )
         system_prompt = (
             """
-            You should answer questions only in English or Nepali. 
+            You are a helpful assistant that should answer questions 
+            only in English or Nepali. 
             Responses should be based on the context documents provided 
             and should be relevant, informative, and easy to understand. 
             You should aim to deliver high-quality responses that are 

private_gpt/server/chunks/chunks_router.py

@@ -70,7 +70,6 @@ async def chunks_retrieval(
         action='Chat',
         details={
             "query": body.text,
-            'user': current_user.username,
             }, 
         user_id=current_user.id
     )

private_gpt/server/ingest/ingest_router.py

@@ -208,7 +208,6 @@ async def create_documents(
         status=MakerCheckerStatus.PENDING,
         doc_type_id=departments.doc_type_id,
     )
-    print("DOCUMENT CREATE: ", docs_in)
     document = crud.documents.create(db=db, obj_in=docs_in)
     department_ids = department_ids if department_ids else "1"
     department_ids = [int(number) for number in department_ids.split(",")]

private_gpt/users/api/v1/routers/auth.py

@@ -43,12 +43,12 @@ def register_user(
             department_id=department.id,
             checker= True if role == 'OPERATOR' else False
         )    
-    # try:
+    try:
-    #     send_registration_email(fullname, email, password)
+        send_registration_email(fullname, email, password)
-    # except Exception as e:
+    except Exception as e:
-    #     logging.info(f"Failed to send registration email: {str(e)}")
+        logging.info(f"Failed to send registration email: {str(e)}")
-    #     raise HTTPException(
+        raise HTTPException(
-    #         status_code=500, detail=f"Failed to send registration email.")
+            status_code=500, detail=f"Failed to send registration email.")
     return crud.user.create(db, obj_in=user_in)
 
 

private_gpt/users/api/v1/routers/documents.py

@@ -7,13 +7,13 @@ from datetime import datetime
 
 from typing import Any, List
 from sqlalchemy.orm import Session
-from sqlalchemy import select
 from fastapi_pagination import Page, paginate
 from fastapi import APIRouter, Depends, HTTPException, status, Security, Request, File, UploadFile
 
 from private_gpt.users.api import deps
 from private_gpt.constants import UNCHECKED_DIR
 from private_gpt.users.constants.role import Role
+from private_gpt.users.core.config import settings
 from private_gpt.users import crud, models, schemas
 from private_gpt.server.ingest.ingest_router import create_documents, ingest
 from private_gpt.users.models.document import MakerCheckerActionType, MakerCheckerStatus
@@ -23,7 +23,7 @@ logger = logging.getLogger(__name__)
 router = APIRouter(prefix='/documents', tags=['Documents'])
 
 
-ENABLE_MAKER_CHECKER = False
+ENABLE_MAKER_CHECKER = settings.ENABLE_MAKER_CHECKER
 
 def get_username(db, id):
     user = crud.user.get_by_id(db=db, id=id)
@@ -259,7 +259,6 @@ def update_department(
             status_code=500,
             detail="Internal Server Error.",
         )
-    
 
 @router.post('/upload', response_model=schemas.Document)
 async def upload_documents(
@@ -384,12 +383,12 @@ async def verify_documents(
                 user_id=current_user.id
             )
 
-            if document.doc_type_id == 2:
+            if document.doc_type_id == 2:  # For OCR
                 return await process_ocr(request, unchecked_path)
-            elif document.doc_type_id == 3:
+            elif document.doc_type_id == 3: # For BOTH
                 return await process_both_ocr(request, unchecked_path)
             else:
-                return await ingest(request, unchecked_path)
+                return await ingest(request, unchecked_path) # For pdf
             
             
         elif checker_in.status == MakerCheckerStatus.REJECTED.value:

private_gpt/users/core/config.py

@@ -3,9 +3,11 @@ from functools import lru_cache
 from pydantic_settings import BaseSettings
 
 class Settings(BaseSettings):
-    PROJECT_NAME: str = "AUTHENTICATION AND AUTHORIZATION"
+    PROJECT_NAME: str = "QuickGPT"
     API_V1_STR: str = "/v1"
     SECRET_KEY: str
+    REFRESH_KEY: str
+
     ACCESS_TOKEN_EXPIRE_MINUTES: int
     REFRESH_TOKEN_EXPIRE_MINUTES: int
 
@@ -30,7 +32,8 @@ class Settings(BaseSettings):
 
     LDAP_SERVER: str
     LDAP_ENABLE: bool
-
+    ENABLE_MAKER_CHECKER: bool
+    
     class Config:
         case_sensitive = True
         env_file = ".env"

private_gpt/users/core/security.py

@@ -1,4 +1,3 @@
-import os
 import random
 import string
 from datetime import datetime, timedelta
@@ -6,14 +5,13 @@ from typing import Dict, Any, Optional, Union
 
 from jose import JWTError, jwt
 from passlib.context import CryptContext
+from private_gpt.users.core.config import settings
 
-ACCESS_TOKEN_EXPIRE_MINUTES = 60 * 24 * 1   # 12 hrs
+ACCESS_TOKEN_EXPIRE_MINUTES = 60 * 24 * 1   # 12 hrs # Default Value
-REFRESH_TOKEN_EXPIRE_MINUTES = 60 * 24 * 7 # 7 days
+REFRESH_TOKEN_EXPIRE_MINUTES = 60 * 24 * 7 # 7 days  # Default Value
 ALGORITHM = "HS256"
-# JWT_SECRET_KEY = os.environ['JWT_SECRET_KEY']     # should be kept secret
+JWT_SECRET_KEY = settings.SECRET_KEY
-# JWT_REFRESH_SECRET_KEY = os.environ['JWT_REFRESH_SECRET_KEY']      # should be kept secret
+JWT_REFRESH_SECRET_KEY = settings.REFRESH_KEY
-JWT_SECRET_KEY = "QUICKGPT"
-JWT_REFRESH_SECRET_KEY = "QUICKGPT_REFRESH"
 
 pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
 

scripts/setup

@@ -40,10 +40,10 @@ print("LLM model downloaded!")
 
 # Download Tokenizer
 print(f"Downloading tokenizer {settings().llm.tokenizer}")
-local_path = models_path / "tokenizer/Mistral-7B-Instruct-v0.1"
 AutoTokenizer.from_pretrained(
-    local_path
+    pretrained_model_name_or_path=settings().llm.tokenizer,
+    cache_dir=models_cache_path,
 )
 print("Tokenizer downloaded!")
 
-print("Setup done")
+print("Setup done")

settings-docker.yaml

@@ -9,16 +9,28 @@ embedding:
   mode: ${PGPT_MODE:sagemaker}
 
 llamacpp:
-  llm_hf_repo_id: ${PGPT_HF_REPO_ID:TheBloke/Mistral-7B-Instruct-v0.1-GGUF}
+  llm_hf_repo_id: ${PGPT_HF_REPO_ID:TheBloke/OpenHermes-2.5-Mistral-7B-GGUF}
-  llm_hf_model_file: ${PGPT_HF_MODEL_FILE:mistral-7b-instruct-v0.1.Q4_K_M.gguf}
+  llm_hf_model_file: ${PGPT_HF_MODEL_FILE:openhermes-2.5-mistral-7b.Q5_K_M.gguf}
 
 huggingface:
-  embedding_hf_model_name: ${PGPT_EMBEDDING_HF_MODEL_NAME:BAAI/bge-small-en-v1.5}
+  embedding_hf_model_name: ${PGPT_EMBEDDING_HF_MODEL_NAME:mixedbread-ai/mxbai-embed-large-v1}
 
 sagemaker:
   llm_endpoint_name: ${PGPT_SAGEMAKER_LLM_ENDPOINT_NAME:}
   embedding_endpoint_name: ${PGPT_SAGEMAKER_EMBEDDING_ENDPOINT_NAME:}
 
+ollama:
+  llm_model: ${PGPT_OLLAMA_LLM_MODEL:mistral}
+  embedding_model: ${PGPT_OLLAMA_EMBEDDING_MODEL:nomic-embed-text}
+  api_base: ${PGPT_OLLAMA_API_BASE:http://ollama:11434}
+  embedding_api_base: ${PGPT_OLLAMA_EMBEDDING_API_BASE:http://ollama:11434}
+  tfs_z: ${PGPT_OLLAMA_TFS_Z:1.0}
+  top_k: ${PGPT_OLLAMA_TOP_K:40}
+  top_p: ${PGPT_OLLAMA_TOP_P:0.9}
+  repeat_last_n: ${PGPT_OLLAMA_REPEAT_LAST_N:64}
+  repeat_penalty: ${PGPT_OLLAMA_REPEAT_PENALTY:1.2}
+  request_timeout: ${PGPT_OLLAMA_REQUEST_TIMEOUT:600.0}
+
 ui:
   enabled: true
   path: /

settings-local.yaml

@@ -25,7 +25,7 @@ embedding:
   ingest_mode: simple
 
 huggingface:
-  embedding_hf_model_name: BAAI/bge-large-en-v1.5
+  embedding_hf_model_name: mixedbread-ai/mxbai-embed-large-v1
 
 vectorstore:
   database: qdrant

settings.yaml

@@ -7,7 +7,7 @@ server:
   cors:
     enabled: true
     allow_credentials: true
-    allow_origins: ["http://localhost:80", "http://10.1.101.125", "http://quickgpt.gibl.com.np"]
+    allow_origins: ["*"]
     allow_methods: ["DELETE", "GET", "POST", "PUT", "OPTIONS", "PATCH"]
     allow_headers: ["*"]
   auth:
@@ -44,9 +44,9 @@ llm:
   tokenizer: mistralai/Mistral-7B-Instruct-v0.2
   
 llamacpp:
-  prompt_style: "default"
+  prompt_style: "chatml"
-  llm_hf_repo_id: bartowski/Meta-Llama-3-8B-Instruct-GGUF
+  llm_hf_repo_id: TheBloke/OpenHermes-2.5-Mistral-7B-GGUF
-  llm_hf_model_file: Meta-Llama-3-8B-Instruct-Q6_K.gguf
+  llm_hf_model_file: openhermes-2.5-mistral-7b.Q5_K_M.gguf
   tfs_z: 1.0            # Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting
   top_k: 40             # Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)
   top_p: 0.9            # Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)