Upload my_webassistant_103.py

my_webassistant_103.py

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+# Code adapted from https://github.com/AIAdvantage/chatgpt-api-youtube
+# To see how to create a virtual environment, check https://python.land/virtual-environments/virtualenv 
+# python -m venv my-envi, and the next steps, see https://stackoverflow.com/a/74825209
+# For error of installation due to privileges, see https://stackoverflow.com/questions/66322049/could-not-install-packages-due-to-an-oserror-winerror-2-no-such-file-or-direc
+# For gitignore, see https://github.com/github/gitignore/blob/main/Python.gitignore
+# If VS Code shows issues about execution policies, you may need to change the execution policies settings in the powershell, see https://www.sharepointdiary.com/2014/03/fix-for-powershell-script-cannot-be-loaded-because-running-scripts-is-disabled-on-this-system.html
+
+
+import os
+
+import my_api_keys
+import gradio as gr
+
+from llama_index import (
+ GPTSimpleVectorIndex,
+ SimpleDirectoryReader,
+ LLMPredictor,
+ ServiceContext, 
+ download_loader, 
+ PromptHelper
+)
+
+from llama_index.prompts.prompts import QuestionAnswerPrompt
+
+
+# documentation of langchain at https://github.com/hwchase17/langchain
+from langchain.chat_models import ChatOpenAI
+from langchain import OpenAI # if you want to use a model other than gpt-3.5-turbo
+
+
+os.environ['OPENAI_API_KEY'] = my_api_keys.my_open_ai_key
+
+
+'''
+What is this document about
+Which countries were affected
+How many people injured
+When did the earthquake take place
+What is the president
+what is the data of birth of Germany => Should return no answer
+'''
+
+
+def custom_llama_index (question): 
+
+ ## Working with llama_index = playing around with data augmentation
+
+ ## Step 1: load the new data
+ # documentation of llama_index at https://gpt-index.readthedocs.io/en/latest/
+ # data loaders at https://llamahub.ai/
+ #from llama_index import download_loader, GPTSimpleVectorIndex
+
+ SimpleDirectoryReader = download_loader("SimpleDirectoryReader")
+ # Take all the files in the data folder, see https://llamahub.ai/l/file
+ loader = SimpleDirectoryReader('./data', recursive=True, exclude_hidden=True)
+ documents = loader.load_data()
+ #print(documents)
+
+
+ ## Step 2: Build a CUSTOM llm index: code adapted from https://github.com/wombyz/custom-knowledge-chatbot/tree/main/custom-knowledge-chatbot
+ # Official documentation: https://gpt-index.readthedocs.io/en/latest/how_to/customization/custom_llms.html
+
+ # define prompt helper
+ # set maximum input size
+ max_input_size = 2048
+ # set number of output tokens
+ num_output = 256
+ # set maximum chunk overlap
+ max_chunk_overlap = 20
+ prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap)
+
+ # define LLM
+ llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="text-davinci-002")) 
+ service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
+
+ # build index
+ custom_index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
+
+
+ ## Step 3: reuse the custom index to get some answers
+ # get response from query
+ response = custom_index.query(question)
+
+ 
+ # If we want to include prompt-engineering
+ # Code from https://www.linkedin.com/pulse/extending-chatgpt-knowledge-base-custom-datasources-cezar-romaniuc
+ QUESTION_ANSWER_PROMPT_TMPL = (
+ "You are an assistant that specializes in geographic question answering. If you don't have an answer, answer with 'I don't know' \n"
+ "---------------------\n"
+ "{context_str}"
+ "\n---------------------\n"
+ "{query_str}\n"
+ )
+ QUESTION_ANSWER_PROMPT = QuestionAnswerPrompt(QUESTION_ANSWER_PROMPT_TMPL)
+
+ response_with_custom_prompt = custom_index.query(question, text_qa_template=QUESTION_ANSWER_PROMPT)
+ 
+
+ return response_with_custom_prompt 
+
+
+demo = gr.Interface(fn=custom_llama_index, inputs="text", outputs="text")
+
+demo.launch()