RAG Simplified!! 🐣

AI

├── ML (Machine Learning)

│   ├── LLM (Large Language Models)

│   └── RAG (Retrieval-Augmented Generation)

 pip3 install llmware

def hello_world_questions():



    """ This is a set of useful test questions to do a 'hello world' but there is nothing special about the

    questions - please feel free to edit and ask your own queries with your own context passages.



    --if you are using one of the llmware models, please take note that the models have been trained to answer

    based on the information provided, so if you ask a question without passing any context passage, then

    don't be surprised if the model responds with 'Not Found.' """



    test_list = [



    {"query": "What is the total amount of the invoice?",

     "answer": "$22,500.00",

     "context": "Services Vendor Inc. \n100 Elm Street Pleasantville, NY \nTO Alpha Inc. 5900 1st Street "

                "Los Angeles, CA \nDescription Front End Engineering Service $5000.00 \n Back End Engineering"

                " Service $7500.00 \n Quality Assurance Manager $10,000.00 \n Total Amount $22,500.00 \n"

                "Make all checks payable to Services Vendor Inc. Payment is due within 30 days."

                "If you have any questions concerning this invoice, contact Bia Hermes. "

                "THANK YOU FOR YOUR BUSINESS!  INVOICE INVOICE # 0001 DATE 01/01/2022 FOR Alpha Project P.O. # 1000"},



    {"query": "What was the amount of the trade surplus?",

     "answer": "62.4 billion yen ($416.6 million)",

     "context": "Japan’s September trade balance swings into surplus, surprising expectations"

                "Japan recorded a trade surplus of 62.4 billion yen ($416.6 million) for September, "

                "beating expectations from economists polled by Reuters for a trade deficit of 42.5 "

                "billion yen. Data from Japan’s customs agency revealed that exports in September "

                "increased 4.3% year on year, while imports slid 16.3% compared to the same period "

                "last year. According to FactSet, exports to Asia fell for the ninth straight month, "

                "which reflected ongoing China weakness. Exports were supported by shipments to "

                "Western markets, FactSet added. — Lim Hui Jie"}

]



    return test_list

import time

from llmware.prompts import Prompt

from llmware.models import ModelCatalog



def fast_start_prompting  (model_name):



    """ This is the main example script - it loads the question list, loads the model, and executes the prompts. """



    t0 = time.time()



    # load in the 'hello world' test questions above

    test_list = hello_world_questions()



    print(f"\n > Loading Model: {model_name}...")



    prompter = Prompt().load_model(model_name)



    t1 = time.time()

    print(f"\n > Model {model_name} load time: {t1-t0} seconds")



    for i, entries in enumerate(test_list):

        print(f"\n{i+1}. Query: {entries['query']}")



        #   run the prompt

        output = prompter.prompt_main(entries["query"],

                                      context=entries["context"],

                                      prompt_name="default_with_context",

                                      temperature=0.30)



        #   'output' is a dictionary with two keys - 'llm_response' and 'usage'

        #   --'llm_response' is the output from the model

        #   --'usage' is a dictionary with the usage stats



        llm_response = output["llm_response"].strip("\n")

        print(f"LLM Response: {llm_response}")



        #   note: the 'gold answer' is the answer we provided above in the hello_world question list

        print(f"Gold Answer: {entries['answer']}")



        print(f"LLM Usage: {output['usage']}")



    t2 = time.time()

    print(f"\nTotal processing time: {t2-t1} seconds")



    return 0





if __name__ == "__main__":



    #   Step 1 - we will pick a model from the ModelCatalog



    #   A few useful methods to discover and display a list of available models...



    #   all generative models

    llm_models = ModelCatalog().list_generative_models()



    #   if you only want to see the local models

    llm_local_models = ModelCatalog().list_generative_local_models()



    #   to see only the open source models

    llm_open_source_models = ModelCatalog().list_open_source_models()



    #   we will print out the local models

    for i, models in enumerate(llm_local_models):

        print("models: ", i, models["model_name"], models["model_family"])



    #   for purposes of demo, try a few selected models from the list



    #   each of these pytorch models are ~1b parameters and will run reasonably fast and accurate on CPU

    #   --per note above, may require separate pip3 install of: torch and transformers

    pytorch_generative_models = ["llmware/bling-1b-0.1", "llmware/bling-tiny-llama-v0", "llmware/bling-falcon-1b-0.1"]



    #   bling-answer-tool is 1b parameters quantized

    #   bling-phi-3-gguf is 3.8b parameters quantized

    #   dragon-yi-6b-gguf is 6b parameters quantized

    gguf_generative_models = ["bling-answer-tool", "bling-phi-3-gguf","llmware/dragon-yi-6b-gguf"]



    #   by default, we will select a gguf model requiring no additional imports

    model_name = gguf_generative_models[0]



    #   to swap in a GPT-4 openai model - uncomment these two lines

    #   model_name = "gpt-4"

    #   os.environ["USER_MANAGED_OPENAI_API_KEY"] = "<insert-your-openai-key>"



    fast_start_prompting(model_name)

python fast_start_rag.py