Create aappppp.py

aappppp.py

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+from langchain.vectorstores.chroma import Chroma
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.document_loaders import DirectoryLoader, TextLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import SentenceTransformerEmbeddings
+import gradio as gr
+import os
+
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+from dotenv import load_dotenv
+load_dotenv()
+
+def create_embeddings_from_txt(file_path: str) -> None:
+    loader = loader = TextLoader(file_path=file_path)
+    documents = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
+    texts = text_splitter.split_documents(documents)
+    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+    persist_directory = 'db'
+    vectordb = Chroma.from_documents(
+        documents=texts,
+        embedding=embeddings,
+        persist_directory=persist_directory
+    )
+    vectordb.persist()
+
+def create_conversation() -> ConversationalRetrievalChain:
+
+    persist_directory = 'db'
+    embeddings = OpenAIEmbeddings(
+        openai_api_key=os.getenv('OPENAI_API_KEY')
+    )
+    db = Chroma(
+        persist_directory=persist_directory,
+        embedding_function=embeddings
+    )
+    memory = ConversationBufferMemory(
+        memory_key='chat_history',
+        return_messages=False
+    )
+    qa = ConversationalRetrievalChain.from_llm(
+        llm=ChatOpenAI(),
+        chain_type='stuff',
+        retriever=db.as_retriever(),
+        memory=memory,
+        get_chat_history=lambda h: h,
+        verbose=True
+    )
+
+    return qa
+
+file_path = "./shipping.txt"
+create_embeddings_from_txt(file_path)
+qa = create_conversation()
+
+
+def add_text(history, text):
+    history = history + [(text, None)]
+    return history, ""
+
+
+def bot(history):
+    res = qa(
+        {
+            'question': history[-1][0],
+            'chat_history': history[:-1]
+        }
+    )
+    history[-1][1] = res['answer']
+    return history
+
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot([], elem_id="chatbot",
+                         label='Document GPT')
+    with gr.Row():
+        with gr.Column(scale=0.80):
+            txt = gr.Textbox(
+                show_label=False,
+                placeholder="Enter text and press enter",
+            )
+        with gr.Column(scale=0.10):
+            submit_btn = gr.Button(
+                'Submit',
+                variant='primary'
+            )
+        with gr.Column(scale=0.10):
+            clear_btn = gr.Button(
+                'Clear',
+                variant='stop'
+            )
+
+    txt.submit(add_text, [chatbot, txt], [chatbot, txt]).then(
+        bot, chatbot, chatbot
+    )
+
+    submit_btn.click(add_text, [chatbot, txt], [chatbot, txt]).then(
+        bot, chatbot, chatbot
+    )
+
+    clear_btn.click(lambda: None, None, chatbot, queue=False)
+
+if __name__ == '__main__':
+    demo.launch()