moved app.python to main directory

reggpt/app.py → app.py

@@ -22,11 +22,11 @@ from dotenv import load_dotenv
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 
-from configs.api import API_TITLE, API_VERSION, API_DESCRIPTION 
-from api.router import ChatAPI
+from reggpt.configs.api import API_TITLE, API_VERSION, API_DESCRIPTION 
+from reggpt.api.router import ChatAPI
 
 def filer():
-    return "logs/log"
+    return "reggpt/logs/log"
     # today = datetime.datetime.today()
     # log_filename = f"logs/{today.year}-{today.month:02d}-{today.day:02d}.log"
     # return log_filename

reggpt/api/router.py

@@ -4,9 +4,9 @@ import logging
 from fastapi import  APIRouter, HTTPException, status
 from fastapi import HTTPException, status
 
-from schemas.schema import UserQuery, LoginRequest, UserModel
-from routers.controller import get_QA_Answers, get_avaliable_models
-from configs.api import API_ENDPOINT_LOGIN,API_ENDPOINT_CHAT, API_ENDPOINT_HEALTH, API_ENDPOINT_MODEL
+from reggpt.schemas.schema import UserQuery, LoginRequest, UserModel
+from reggpt.routers.controller import get_QA_Answers, get_avaliable_models
+from reggpt.configs.api import API_ENDPOINT_LOGIN,API_ENDPOINT_CHAT, API_ENDPOINT_HEALTH, API_ENDPOINT_MODEL
 
 logger = logging.getLogger(__name__)
 

reggpt/chains/llmChain.py

@@ -23,16 +23,16 @@ load_dotenv()
 
 verbose = os.environ.get('VERBOSE')
 
-from llms.llm import get_model
+from reggpt.llms.llm import get_model
 from langchain.chains import  ConversationalRetrievalChain
 # from conversationBufferWindowMemory import ConversationBufferWindowMemory
 
 # from langchain.prompts import PromptTemplate
 from langchain.chains import LLMChain
-from prompts.document_combine import document_combine_prompt
-from prompts.retrieval import retrieval_qa_chain_prompt
-from prompts.general import general_qa_chain_prompt
-from prompts.router import router_prompt
+from reggpt.prompts.document_combine import document_combine_prompt
+from reggpt.prompts.retrieval import retrieval_qa_chain_prompt
+from reggpt.prompts.general import general_qa_chain_prompt
+from reggpt.prompts.router import router_prompt
 
 
 def get_qa_chain(model_type,retriever):

reggpt/controller/agent.py

@@ -9,10 +9,10 @@ load_dotenv()
 
 verbose = os.environ.get('VERBOSE')
 
-from controller.router import run_router_chain
-from routers.out_of_domain import run_out_of_domain_chain
-from routers.general import run_general_qa_chain
-from routers.qa import run_qa_chain
+from reggpt.controller.router import run_router_chain
+from reggpt.routers.out_of_domain import run_out_of_domain_chain
+from reggpt.routers.general import run_general_qa_chain
+from reggpt.routers.qa import run_qa_chain
 
 def chain_selector(chain_type, query):
     chain_type = chain_type.lower().strip()

reggpt/controller/router.py

@@ -18,9 +18,9 @@ import time
 import logging
 logger = logging.getLogger(__name__)
 
-from chains.llmChain import get_router_chain
+from reggpt.chains.llmChain import get_router_chain
 
-from configs.model import ROUTER_MODEL_TYPE
+from reggpt.configs.model import ROUTER_MODEL_TYPE
 
 
 router_model_type=ROUTER_MODEL_TYPE

reggpt/retriever/multi_query_retriever.py

@@ -36,7 +36,7 @@ import pandas as pd
 
 logger = logging.getLogger(__name__)
 
-from prompts.multi_query import MULTY_QUERY_PROMPT
+from reggpt.prompts.multi_query import MULTY_QUERY_PROMPT
 
 class LineListOutputParser(BaseOutputParser[List[str]]):
     """Output parser for a list of lines."""

reggpt/routers/controller.py

@@ -16,13 +16,13 @@
 
 import logging
 logger = logging.getLogger(__name__)
-from configs.model import AVALIABLE_MODELS , MEMORY_WINDOW_K
+from reggpt.configs.model import AVALIABLE_MODELS , MEMORY_WINDOW_K
 
 # from qaPipeline import QAPipeline
 # from qaPipeline_retriever_only import QAPipeline
 # qaPipeline = QAPipeline()
 
-from controller.agent import run_agent
+from reggpt.controller.agent import run_agent
 
 def get_QA_Answers(userQuery):
     # model=userQuery.model

reggpt/routers/general.py

@@ -3,10 +3,10 @@ import time
 import logging
 logger = logging.getLogger(__name__)
 
-from chains.llmChain import get_general_qa_chain
-from output_parsers.output_parser import general_qa_chain_output_parser
+from reggpt.chains.llmChain import get_general_qa_chain
+from reggpt.output_parsers.output_parser import general_qa_chain_output_parser
 
-from configs.model import GENERAL_QA_MODEL_TYPE
+from reggpt.configs.model import GENERAL_QA_MODEL_TYPE
 
 
 

reggpt/routers/out_of_domain.py

@@ -1,4 +1,4 @@
-from output_parsers.output_parser import  out_of_domain_chain_parser
+from reggpt.output_parsers.output_parser import  out_of_domain_chain_parser
 
 def run_out_of_domain_chain(query):
     return out_of_domain_chain_parser(query)

reggpt/routers/qa.py

@@ -19,11 +19,11 @@ import time
 import logging
 logger = logging.getLogger(__name__)
 from dotenv import load_dotenv
-from chains.llmChain import get_qa_chain
-from output_parsers.output_parser import qa_chain_output_parser
+from reggpt.chains.llmChain import get_qa_chain
+from reggpt.output_parsers.output_parser import qa_chain_output_parser
 
-from configs.model import QA_MODEL_TYPE
-from utils.retriever import load_ensemble_retriever
+from reggpt.configs.model import QA_MODEL_TYPE
+from reggpt.utils.retriever import load_ensemble_retriever
 load_dotenv()
 
 verbose = os.environ.get('VERBOSE')

reggpt/routers/qaPipeline.py

@@ -20,11 +20,10 @@ import logging
 logger = logging.getLogger(__name__)
 from dotenv import load_dotenv
 from fastapi import HTTPException
-from chains.llmChain import get_qa_chain, get_general_qa_chain, get_router_chain
-from output_parsers.output_parser import general_qa_chain_output_parser, qa_chain_output_parser, out_of_domain_chain_parser
+from reggpt.chains.llmChain import get_qa_chain, get_general_qa_chain, get_router_chain
 
-from configs.model import QA_MODEL_TYPE, GENERAL_QA_MODEL_TYPE, ROUTER_MODEL_TYPE, Multi_Query_MODEL_TYPE
-from utils.retriever import load_faiss_retriever, load_ensemble_retriever, load_multi_query_retriever
+from reggpt.configs.model import QA_MODEL_TYPE, GENERAL_QA_MODEL_TYPE, ROUTER_MODEL_TYPE, Multi_Query_MODEL_TYPE
+from reggpt.utils.retriever import load_ensemble_retriever
 load_dotenv()
 
 verbose = os.environ.get('VERBOSE')

reggpt/utils/retriever.py

@@ -23,7 +23,7 @@ multiple retrievers by using weighted  Reciprocal Rank Fusion
 import logging
 logger = logging.getLogger(__name__)
 
-from vectorstores.faissDb import load_FAISS_store
+from reggpt.vectorstores.faissDb import load_FAISS_store
 
 from langchain_community.retrievers import BM25Retriever
 from langchain_community.document_loaders import PyPDFLoader
@@ -74,11 +74,11 @@ def split_documents():
         for text in texts:
             splits_list.append(text)
 
-    splitted_texts_file='../data/splitted_texts.jsonl'
+    splitted_texts_file='../reggpt/data/splitted_texts.jsonl'
     save_docs_to_jsonl(splits_list,splitted_texts_file)
 
-from retriever.ensemble_retriever import EnsembleRetriever
-from retriever.multi_query_retriever import MultiQueryRetriever 
+from reggpt.retriever.ensemble_retriever import EnsembleRetriever
+from reggpt.retriever.multi_query_retriever import MultiQueryRetriever 
 
 def load_faiss_retriever():
     try:
@@ -97,7 +97,7 @@ def load_faiss_retriever():
 def load_ensemble_retriever():
     try:
         # splitted_texts_file=os.path.dirname(os.path.abspath(__file__).join('/data/splitted_texts.jsonl'))
-        splitted_texts_file='./data/splitted_texts.jsonl'
+        splitted_texts_file='./reggpt/data/splitted_texts.jsonl'
         sementic_k = 4
         bm25_k = 2
         splits_list = load_docs_from_jsonl(splitted_texts_file)
@@ -118,7 +118,7 @@ def load_ensemble_retriever():
         logger.exception(e)
         raise e
 
-from llms.llm import get_model
+from reggpt.llms.llm import get_model
 
 def load_multi_query_retriever(multi_query_model_type):
     #multi query

reggpt/vectorstores/faissDb.py

@@ -31,7 +31,7 @@ chunk_size=2000
 chunk_overlap=100
 
 embeddings_model_name = "BAAI/bge-large-en-v1.5"
-persist_directory = "./vectorstores/faiss_embeddings_2024"
+persist_directory = "./reggpt/vectorstores/faiss_embeddings_2024"
 
 from langchain_community.embeddings import HuggingFaceEmbeddings
 embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)