main logic

app.py

@@ -0,0 +1,60 @@
+import os
+import getpass
+from langchain_community.document_loaders import ConfluenceLoader
+from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores.faiss import FAISS
+import google.generativeai as genai
+from langchain.prompts import PromptTemplate
+from langchain.chains.question_answering import load_qa_chain
+import streamlit as st
+
+confluence_api_key = os.environ["CONFLUENCE_API_KEY"]
+
+if "GOOGLE_API_KEY" not in os.environ:
+    os.environ["GOOGLE_API_KEY"] = getpass.getpass("Please provide Google API Key")
+
+google_api_key = os.environ['GOOGLE_API_KEY']
+genai.configure(api_key=google_api_key)
+
+
+loader = ConfluenceLoader(
+    url=os.environ["CONFLUENCE_URL"], space_key=os.environ['SPACE_KEY'], username=os.environ['USERNAME'], api_key=confluence_api_key
+)
+
+conf_docs = loader.load(page_id=os.environ["PAGE_ID"])
+
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+chunks = text_splitter.split_text(conf_docs[-1].page_content)
+
+embeddings = GoogleGenerativeAIEmbeddings(model='models/embedding-001')
+llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash-latest")
+
+vector_store = FAISS.from_texts(chunks, embedding=embeddings)
+vector_store.save_local("faiss_index")
+
+def get_response(query):
+    prompt_template = """
+        Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
+        provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
+        Context:\n {context}?\n
+        Question: \n{question}\n
+
+        Answer:
+    """
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    chain = load_qa_chain(llm, chain_type="stuff", prompt=prompt)
+    db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+    docs = db.similarity_search(query)
+    response =  chain({"input_documents" : docs, "question": query}, return_only_outputs = True)
+    return response["output_text"]
+
+
+if __name__ == '__main__':
+    st.set_page_config("Chat with Confluence Page")
+    st.header("Chat with Confluence Page using AI")
+
+    question = st.text_input("Ask questions related to login and registration")
+    answer = get_response(question)
+    st.write("Reply: ", answer)

requirements.txt

@@ -0,0 +1,8 @@
+lxml
+google-generativeai
+langchain
+langchain_community
+langchain_google_genai
+faiss-cpu
+atlassian-python-api
+streamlit