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Update app.py
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app.py
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@@ -8,8 +8,9 @@ import s3fs
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load_dotenv('myenvfile.env')
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openai_api_key = os.environ['OPENAI_API_KEY']
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aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID']
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aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY']
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from llama_index import GPTListIndex, GPTSimpleVectorIndex
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from langchain.agents import load_tools, Tool, initialize_agent
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from langchain.llms import OpenAI
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@@ -18,37 +19,30 @@ from langchain.agents import initialize_agent, Tool
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from langchain import OpenAI, LLMChain
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from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader
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index = GPTSimpleVectorIndex.load_from_disk('index.json')
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def querying_db(query: str):
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tools = [
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]
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llm = OpenAI(temperature=0)
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def get_answer(query_string):
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def qa_app(query):
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inputs = gr.inputs.Textbox(label="Enter your question:")
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output = gr.outputs.Textbox(label="Answer:")
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load_dotenv('myenvfile.env')
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openai_api_key = os.environ['OPENAI_API_KEY']
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aws_access_key_id = os.environ['AWS_ACCESS_KEY_ID']
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aws_secret_access_key = os.environ['AWS_SECRET_ACCESS_KEY']
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from llama_index import GPTListIndex, GPTSimpleVectorIndex
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from langchain.agents import load_tools, Tool, initialize_agent
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from langchain.llms import OpenAI
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from langchain import OpenAI, LLMChain
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from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader
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index = GPTSimpleVectorIndex.load_from_disk('index.json')
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def querying_db(query: str):
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response = index.query(query)
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return response
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tools = [
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Tool(
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name="QueryingDB",
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func=querying_db,
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description="This function takes a query string as input and returns the most relevant answer from the documentation as output"
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)
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]
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llm = OpenAI(temperature=0)
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def get_answer(query_string):
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agent = initialize_agent(tools, llm, agent="zero-shot-react-description")
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result = agent.run(query_string)
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return result
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def qa_app(query):
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answer = get_answer(query)
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return answer
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inputs = gr.inputs.Textbox(label="Enter your question:")
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output = gr.outputs.Textbox(label="Answer:")
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