initial commit

.env

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+OPENAI_API_KEY='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'

conversational_with_file.py

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+import streamlit as st
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
+from langchain import OpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+from langchain.document_loaders import TextLoader
+import pickle
+
+load_dotenv()
+embeddings = OpenAIEmbeddings(openai_api_key='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')
+new_db = FAISS.load_local("faiss_index", embeddings)
+
+
+
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(separators=["\n\n", "\n", " "],
+        chunk_size = 200,
+        chunk_overlap=50,  length_function=len)
+    chunks = text_splitter.split_documents(text)
+    return chunks
+
+
+# def get_vector_store(text_chunks):
+#     embeddings = OpenAIEmbeddings(openai_api_key='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')
+#     vectorstore_openai = FAISS.from_documents(text_chunks, embeddings)
+#     vectorstore_openai.save_local("faiss_index")
+
+
+def get_conversational_chain():
+    prompt_template = """
+    Answer the question as breif 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.
+    Answer the question as canadian citizen as buyproperly customer care \n\n
+    Context:\n {context}?\n
+    Question: \n{question}\n
+
+    Answer:
+    """
+
+    model = OpenAI(temperature=0.6, max_tokens=500, model='gpt-3.5-turbo-instruct')
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+
+    return chain
+
+
+
+def user_input(user_question):
+    docs = new_db.similarity_search(user_question)
+    print('loaded from docs')
+    chain = get_conversational_chain()
+
+    response = chain(
+        {"input_documents": docs, "question": user_question}
+        , return_only_outputs=True)
+
+    print(response)
+    st.write("Reply: ", response["output_text"])
+
+
+def main():
+    st.set_page_config("Chat with BuyProperly AI Assistant")
+
+    # with st.sidebar:
+    #     st.title("Menu:")
+    #     input_file_path = st.sidebar.text_input("Enter the path of the text file:")
+    #     process_url_clicked = st.sidebar.button("Process URLs")
+    #     if process_url_clicked:
+    #         #print('clicked')
+    #         loader = TextLoader(input_file_path, encoding='UTF-8')
+    #         raw_text = loader.load()
+    #         #print(raw_text)
+    #         text_chunks = get_text_chunks(raw_text)
+    #         get_vector_store(text_chunks)
+    #         st.success("Done")
+
+    user_question = st.text_input("Ask a Question:")
+    if st.button("Submit & Process"):
+        with st.spinner("Processing..."):
+            print('user_question response', user_question)
+            if user_question:
+                print('entered the user question')
+                user_input(user_question)
+
+
+if __name__ == "__main__":
+    main()

faiss_index/index.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:10a1eabe666dfd9ccdea0e307693d0e2a7df577915b0281b636e4e7b5bea8b31
+size 9917

requirements.txt

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+langchain==0.0.284
+python-dotenv==1.0.0
+streamlit==1.22.0
+unstructured==0.9.2
+tiktoken==0.4.0
+faiss-cpu==1.7.4
+libmagic==1.0
+python-magic==0.4.27
+python-magic-bin==0.4.14
+OpenAI == 0.28.0