Update utills.py

utills.py

@@ -1,96 +1,96 @@
-import os
-import sys
-from langchain.text_splitter import TokenTextSplitter,RecursiveCharacterTextSplitter
-from langchain_community.embeddings import HuggingFaceEmbeddings
-import torch
-from transformers import AutoTokenizer
-from langchain.retrievers.document_compressors import LLMChainExtractor
-from langchain.text_splitter import  RecursiveCharacterTextSplitter
-from langchain.document_loaders import TextLoader
-from langchain.chains import RetrievalQA,  ConversationalRetrievalChain
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import create_history_aware_retriever, create_retrieval_chain
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain_core.runnables.history import RunnableWithMessageHistory
-from langchain_core.chat_history import BaseChatMessageHistory
-from langchain_community.chat_message_histories import ChatMessageHistory
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from typing import Callable, Dict, List, Optional, Union
-from langchain.vectorstores import Chroma
-from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler
-from langchain.document_loaders import PyPDFLoader
-from langchain.document_loaders.pdf import PyPDFDirectoryLoader
-from langchain_community.llms import llamacpp
-
-
-store = {}
-
-def get_session_history(session_id: str):
-    if session_id not in store:
-        store[session_id] = ChatMessageHistory()
-    return store[session_id]
-
-
-
-
-def load_documents(data_path):
-    try:
-        document_loader = PyPDFDirectoryLoader(data_path)
-        return document_loader.load()
-    except Exception as e:
-        print(f"Error loading documents from {data_path}: {e}")
-        return None  # or handle the error in an appropriate manner    
-
-
-
-def split_docs(documents, chunk_size, chunk_overlap):
-    try:
-        text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
-            chunk_size=chunk_size, chunk_overlap=chunk_overlap,
-            separators=["\n \n \n", "\n \n", "\n1", "(?<=\. )", " ", ""]
-        )
-        docs = text_splitter.split_documents(documents)
-        return docs
-    except Exception as e:
-        print(f"Error splitting documents: {e}")
-        return []  # or handle the error in an appropriate manner
-
-
-def chroma_db(docs, embeddings):
-    try:
-        vectordb = Chroma.from_documents(
-            documents=docs, embedding=embeddings, persist_directory="docs/chroma/"
-        )
-        return vectordb
-    except Exception as e:
-        print(f"Error creating Chroma vector database: {e}")
-        return None  # or handle the error in an appropriate manner
-
-
-def retriever_from_chroma(vectordb, search_type, k):
-    retriever = vectordb.as_retriever(search_type=search_type, search_kwargs={"k": k})
-    return retriever    
-
-
-def history_aware_retriever(llm, retriever, contextualize_q_system_prompt):
-    try:
-        contextualize_q_prompt = ChatPromptTemplate.from_messages(
-            [
-                ("system", contextualize_q_system_prompt),
-                MessagesPlaceholder("chat_history"),
-                ("human", "{input}"),
-            ]
-        )
-        history_aware_retriever = create_history_aware_retriever(llm, retriever, contextualize_q_prompt)
-        return history_aware_retriever
-    except Exception as e:
-        print(f"Error creating history-aware retriever: {e}")
-        return None  # or handle the error in an appropriate manner
-
-
-
-
-def echo(question, history):
-    ai_message = rag_chain.invoke({"input": question, "chat_history": chat_history})
-    chat_history.extend([HumanMessage(content=question), ai_message["answer"]])
-    return ai_message['answer']
+import os
+import sys
+from langchain.text_splitter import TokenTextSplitter,RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+import torch
+from transformers import AutoTokenizer
+from langchain.retrievers.document_compressors import LLMChainExtractor
+from langchain.text_splitter import  RecursiveCharacterTextSplitter
+from langchain.document_loaders import TextLoader
+from langchain.chains import RetrievalQA,  ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import create_history_aware_retriever, create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.runnables.history import RunnableWithMessageHistory
+from langchain_core.chat_history import BaseChatMessageHistory
+from langchain_community.chat_message_histories import ChatMessageHistory
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from typing import Callable, Dict, List, Optional, Union
+from langchain.vectorstores import Chroma
+from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler
+from langchain.document_loaders import PyPDFLoader
+from langchain.document_loaders.pdf import PyPDFDirectoryLoader
+from langchain_community.llms import llamacpp
+
+
+store = {}
+@st.cache_resource
+def get_session_history(session_id: str):
+    if session_id not in store:
+        store[session_id] = ChatMessageHistory()
+    return store[session_id]
+
+
+
+@st.cache_resource
+def load_documents(data_path):
+    try:
+        document_loader = PyPDFDirectoryLoader(data_path)
+        return document_loader.load()
+    except Exception as e:
+        print(f"Error loading documents from {data_path}: {e}")
+        return None  # or handle the error in an appropriate manner    
+
+
+@st.cache_resource
+def split_docs(documents, chunk_size, chunk_overlap):
+    try:
+        text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
+            chunk_size=chunk_size, chunk_overlap=chunk_overlap,
+            separators=["\n \n \n", "\n \n", "\n1", "(?<=\. )", " ", ""]
+        )
+        docs = text_splitter.split_documents(documents)
+        return docs
+    except Exception as e:
+        print(f"Error splitting documents: {e}")
+        return []  # or handle the error in an appropriate manner
+
+@st.cache_resource
+def chroma_db(docs, embeddings):
+    try:
+        vectordb = Chroma.from_documents(
+            documents=docs, embedding=embeddings, persist_directory="docs/chroma/"
+        )
+        return vectordb
+    except Exception as e:
+        print(f"Error creating Chroma vector database: {e}")
+        return None  # or handle the error in an appropriate manner
+
+@st.cache_resource
+def retriever_from_chroma(vectordb, search_type, k):
+    retriever = vectordb.as_retriever(search_type=search_type, search_kwargs={"k": k})
+    return retriever    
+
+@st.cache_resource
+def history_aware_retriever(llm, retriever, contextualize_q_system_prompt):
+    try:
+        contextualize_q_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", contextualize_q_system_prompt),
+                MessagesPlaceholder("chat_history"),
+                ("human", "{input}"),
+            ]
+        )
+        history_aware_retriever = create_history_aware_retriever(llm, retriever, contextualize_q_prompt)
+        return history_aware_retriever
+    except Exception as e:
+        print(f"Error creating history-aware retriever: {e}")
+        return None  # or handle the error in an appropriate manner
+
+
+
+@st.cache_resource
+def echo(question, history):
+    ai_message = rag_chain.invoke({"input": question, "chat_history": chat_history})
+    chat_history.extend([HumanMessage(content=question), ai_message["answer"]])
+    return ai_message['answer']