Update app.py

app.py

@@ -31,26 +31,7 @@ from langchain_huggingface.embeddings import HuggingFaceEndpointEmbeddings
 model = "BAAI/bge-m3"
 embeddings = HuggingFaceEndpointEmbeddings(model = model)
 
-vectorstore = Chroma.from_documents(documents = data, embedding = embeddings)
-retriever = vectorstore.as_retriever()
 
-# from langchain.prompts import PromptTemplate
-
-from langchain_core.prompts import ChatPromptTemplate
-
-prompt = ChatPromptTemplate.from_template("""Given the following history, context and a question, generate an answer based on the context only.
-
-In the answer try to provide as much text as possible from "response" section in the source document context without making much changes.
-If somebody asks "Who are you?" or a similar phrase, state "I am Rishi's assistant built using a Large Language Model!"
-If the answer is not found in the context, kindly state "I don't know. Please ask Rishi on Discord. Discord Invite Link: https://discord.gg/6ezpZGeCcM. Or email at [email protected]" Don't try to make up an answer.
-
-CONTEXT: {context}
-
-HISTORY: {history}
-
-QUESTION: {question}""")
-
-from langchain_core.runnables import RunnablePassthrough
 
 # Define the chat response function
 def chatresponse(message, history):
@@ -60,8 +41,30 @@ def chatresponse(message, history):
     #     history_langchain_format.append(AIMessage(content=ai))
     # history_langchain_format.append(HumanMessage(content=message))
 
+    data_vectorstore = Chroma.from_documents(documents = data, embedding = embeddings)
+    history_vectorstore = Chroma.from_documents(documents = history, embedding = embeddings)
+    vectorstore = data_vectorstore + history_vectorstore
+    retriever = vectorstore.as_retriever()
+    
+    # from langchain.prompts import PromptTemplate
+    
+    from langchain_core.prompts import ChatPromptTemplate
+    
+    prompt = ChatPromptTemplate.from_template("""Given the following history, context and a question, generate an answer based on the context only.
+    
+    In the answer try to provide as much text as possible from "response" section in the source document context without making much changes.
+    If somebody asks "Who are you?" or a similar phrase, state "I am Rishi's assistant built using a Large Language Model!"
+    If the answer is not found in the context, kindly state "I don't know. Please ask Rishi on Discord. Discord Invite Link: https://discord.gg/6ezpZGeCcM. Or email at [email protected]" Don't try to make up an answer.
+    
+    CONTEXT: {context}
+    
+    HISTORY: {history}
+    
+    QUESTION: {question}""")
+    
+    from langchain_core.runnables import RunnablePassthrough
     rag_chain = (
-    {"context": retriever, "history": history, "question": RunnablePassthrough()}
+    {"context": retriever, "question": RunnablePassthrough()}
     | prompt
     | llm
     | StrOutputParser()
@@ -75,6 +78,83 @@ def chatresponse(message, history):
 # Launch the Gradio chat interface
 gr.ChatInterface(chatresponse).launch()
 
+# import gradio as gr
+# from langchain.schema import AIMessage, HumanMessage
+
+
+# import os
+# hftoken = os.environ["hftoken"]
+
+# from langchain_huggingface import HuggingFaceEndpoint
+
+# repo_id = "mistralai/Mistral-7B-Instruct-v0.3"
+# llm = HuggingFaceEndpoint(repo_id = repo_id, max_new_tokens = 128, temperature = 0.7, huggingfacehub_api_token = hftoken)
+
+# from langchain_core.output_parsers import StrOutputParser
+# from langchain_core.prompts import ChatPromptTemplate
+
+# # prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
+# # chain = prompt | llm | StrOutputParser()
+
+# # from langchain.document_loaders.csv_loader import CSVLoader
+# from langchain_community.document_loaders.csv_loader import CSVLoader
+
+
+# loader = CSVLoader(file_path='aiotsmartlabs_faq.csv', source_column = 'prompt')
+# data = loader.load()
+
+# from langchain_huggingface import HuggingFaceEmbeddings
+# from langchain_chroma import Chroma
+# from langchain_huggingface.embeddings import HuggingFaceEndpointEmbeddings
+
+# # CHECK MTEB LEADERBOARD & FIND BEST EMBEDDING MODEL
+# model = "BAAI/bge-m3"
+# embeddings = HuggingFaceEndpointEmbeddings(model = model)
+
+# vectorstore = Chroma.from_documents(documents = data, embedding = embeddings)
+# retriever = vectorstore.as_retriever()
+
+# # from langchain.prompts import PromptTemplate
+
+# from langchain_core.prompts import ChatPromptTemplate
+
+# prompt = ChatPromptTemplate.from_template("""Given the following history, context and a question, generate an answer based on the context only.
+
+# In the answer try to provide as much text as possible from "response" section in the source document context without making much changes.
+# If somebody asks "Who are you?" or a similar phrase, state "I am Rishi's assistant built using a Large Language Model!"
+# If the answer is not found in the context, kindly state "I don't know. Please ask Rishi on Discord. Discord Invite Link: https://discord.gg/6ezpZGeCcM. Or email at [email protected]" Don't try to make up an answer.
+
+# CONTEXT: {context}
+
+# HISTORY: {history}
+
+# QUESTION: {question}""")
+
+# from langchain_core.runnables import RunnablePassthrough
+
+# # Define the chat response function
+# def chatresponse(message, history):
+#     # history_langchain_format = []
+#     # for human, ai in history:
+#     #     history_langchain_format.append(HumanMessage(content=human))
+#     #     history_langchain_format.append(AIMessage(content=ai))
+#     # history_langchain_format.append(HumanMessage(content=message))
+
+#     rag_chain = (
+#     {"context": retriever, "history": history, "question": RunnablePassthrough()}
+#     | prompt
+#     | llm
+#     | StrOutputParser()
+#     )
+
+    
+#     output = rag_chain.invoke(message)
+#     response = output.split('ANSWER: ')[-1].strip()
+#     return response
+
+# # Launch the Gradio chat interface
+# gr.ChatInterface(chatresponse).launch()
+
 # import gradio as gr
 
 # def chatresponse(message, history):