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| import os | |
| import openai | |
| os.environ["TOKENIZERS_PARALLELISM"] = "false" | |
| os.environ["OPENAI_API_KEY"] | |
| global agent | |
| def create_agent(): | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.chains.conversation.memory import ConversationSummaryBufferMemory | |
| from langchain.chains import ConversationChain | |
| global agent | |
| llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k") | |
| memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=1000) | |
| agent = ConversationChain(llm=llm, memory=memory, verbose=True) | |
| return "Successful!" | |
| def formatted_response(docs, question, response, state): | |
| formatted_output = response + "\n\nSources" | |
| for i, doc in enumerate(docs): | |
| source_info = doc.metadata.get("source", "Unknown source") | |
| page_info = doc.metadata.get("page", None) | |
| doc_name = source_info.split("/")[-1].strip() | |
| if page_info is not None: | |
| formatted_output += f"\n{doc_name}\tpage no {page_info}" | |
| else: | |
| formatted_output += f"\n{doc_name}" | |
| state.append((question, formatted_output)) | |
| return state, state | |
| def search_docs(prompt, question, state): | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.vectorstores import FAISS | |
| from langchain.callbacks import get_openai_callback | |
| global agent | |
| agent = agent | |
| state = state or [] | |
| embeddings = OpenAIEmbeddings() | |
| docs_db = FAISS.load_local("/home/user/app/docs_db/", embeddings) | |
| docs = docs_db.similarity_search(question) | |
| prompt += "\n\n" | |
| prompt += question | |
| prompt += "\n\n" | |
| prompt += str(docs) | |
| with get_openai_callback() as cb: | |
| response = agent.predict(input=prompt) | |
| print(cb) | |
| return formatted_response(docs, question, response, state) | |
| import gradio as gr | |
| css = """ | |
| .col{ | |
| max-width: 75%; | |
| margin: 0 auto; | |
| display: flex; | |
| flex-direction: column; | |
| justify-content: center; | |
| align-items: center; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| gr.Markdown("## <center>All in One Document Chatting App</center>") | |
| with gr.Tab("Chat With Your Documents"): | |
| with gr.Column(elem_classes="col"): | |
| with gr.Tab("Upload and Process Documents"): | |
| with gr.Column(): | |
| # docs_upload_input = gr.Files(label="Upload File(s)") | |
| # docs_upload_button = gr.Button("Upload") | |
| # docs_upload_output = gr.Textbox(label="Output") | |
| # docs_process_button = gr.Button("Process") | |
| # docs_process_output = gr.Textbox(label="Output") | |
| create_agent_button = gr.Button("Create Agent") | |
| create_agent_output = gr.Textbox(label="Output") | |
| # gr.ClearButton([docs_upload_input, docs_upload_output, docs_process_output, create_agent_output]) | |
| gr.ClearButton([create_agent_output]) | |
| with gr.Tab("Query Documents"): | |
| with gr.Column(): | |
| docs_prompt_input = gr.Textbox(label="Custom Prompt") | |
| docs_chatbot = gr.Chatbot(label="Chats") | |
| docs_state = gr.State() | |
| docs_search_input = gr.Textbox(label="Question") | |
| docs_search_button = gr.Button("Search") | |
| gr.ClearButton([docs_prompt_input, docs_search_input]) | |
| ######################################################################################################### | |
| # docs_upload_button.click(save_docs, inputs=docs_upload_input, outputs=docs_upload_output) | |
| # docs_process_button.click(process_docs, inputs=None, outputs=docs_process_output) | |
| create_agent_button.click(create_agent, inputs=None, outputs=create_agent_output) | |
| docs_search_button.click( | |
| search_docs, | |
| inputs=[docs_prompt_input, docs_search_input, docs_state], | |
| outputs=[docs_chatbot, docs_state], | |
| ) | |
| ######################################################################################################### | |
| demo.queue() | |
| demo.launch() | |