wehub-resource-sync
194 lines
7.7 KiB
Python
194 lines
7.7 KiB
Python
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""" Fast Start Example #4 - RAG with Text Query
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This example shows a basic RAG recipe using text query combined with LLM prompt.
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We will show two different ways to achieve this basic recipe:
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-- Example 4A - this will integrate Library + Prompt - and is the most scalable general solution
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-- Example 4B - this will illustrate another capability of the Prompt class to add sources "inline"
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without necessarily a library in-place. It is another useful tool when you want to be able to quickly
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pick up a document and start asking questions to it.
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Note: both of the examples are designed to achieve the same output.
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"""
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import os
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import re
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from llmware.prompts import Prompt, HumanInTheLoop
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from llmware.setup import Setup
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from llmware.configs import LLMWareConfig
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from llmware.retrieval import Query
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from llmware.library import Library
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def example_4a_contract_analysis_from_library (model_name, verbose=False):
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""" Example #4a: Main general case to run a RAG workflow from a Library """
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# Load the llmware sample files
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print (f"\n > Loading the llmware sample files...")
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sample_files_path = Setup().load_sample_files()
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contracts_path = os.path.join(sample_files_path,"Agreements")
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contracts_lib = Library().create_new_library("example4_library")
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contracts_lib.add_files(contracts_path)
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# questions that we want to ask each contract
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question_list = [{"topic": "executive employment agreement", "llm_query": "What are the names of the two parties?"},
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{"topic": "base salary", "llm_query": "What is the executive's base salary?"},
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{"topic": "governing law", "llm_query": "What is the governing law?"}]
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print (f"\n > Loading model {model_name}...")
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q = Query(contracts_lib)
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# get a list of all of the unique documents in the library
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# doc id list
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doc_list = q.list_doc_id()
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print("update: document id list - ", doc_list)
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# filename list
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fn_list = q.list_doc_fn()
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print("update: filename list - ", fn_list)
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prompter = Prompt().load_model(model_name)
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for i, doc_id in enumerate(doc_list):
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print("\nAnalyzing contract: ", str(i+1), doc_id, fn_list[i])
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print("LLM Responses")
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for question in question_list:
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query_topic = question["topic"]
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llm_question = question["llm_query"]
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doc_filter = {"doc_ID": [doc_id]}
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query_results = q.text_query_with_document_filter(query_topic,doc_filter,result_count=5,exact_mode=True)
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if verbose:
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# this will display the query results from the query above
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for j, qr in enumerate(query_results):
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print("update: querying document - ", query_topic, j, doc_filter, qr)
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source = prompter.add_source_query_results(query_results)
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# *** this is the call to the llm with the source packaged in the context automatically ***
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responses = prompter.prompt_with_source(llm_question, prompt_name="default_with_context", temperature=0.3)
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# unpacking the results from the LLM
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for r, response in enumerate(responses):
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print("update: llm response - ", llm_question, re.sub("[\n]"," ", response["llm_response"]).strip())
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# We're done with this contract, clear the source from the prompt
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prompter.clear_source_materials()
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# Save jsonl report to jsonl to /prompt_history folder
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print("\nPrompt state saved at: ", os.path.join(LLMWareConfig.get_prompt_path(),prompter.prompt_id))
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prompter.save_state()
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# Save csv report that includes the model, response, prompt, and evidence for human-in-the-loop review
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csv_output = HumanInTheLoop(prompter).export_current_interaction_to_csv()
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print("\nCSV output saved at: ", csv_output)
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return 0
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def example_4b_contract_analysis_direct_from_prompt(model_name, verbose=False):
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""" Example #4b: Alternative implementation using prompt in-line capabilities without using a library """
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# Load the llmware sample files
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print(f"\n > Loading the llmware sample files...")
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sample_files_path = Setup().load_sample_files()
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contracts_path = os.path.join(sample_files_path, "Agreements")
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# questions that we want to ask each contract
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question_list = [{"topic": "executive employment agreement", "llm_query": "What are the names of the two parties?"},
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{"topic": "base salary", "llm_query": "What is the executive's base salary?"},
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{"topic": "governing law", "llm_query": "What is the governing law?"}]
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print(f"\n > Loading model {model_name}...")
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prompter = Prompt().load_model(model_name)
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for i, contract in enumerate(os.listdir(contracts_path)):
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# exclude potential mac os created file artifact in the samples folder path
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if contract != ".DS_Store":
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print("\nAnalyzing contract: ", str(i + 1), contract)
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print("LLM Responses")
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for question in question_list:
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query_topic = question["topic"]
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llm_question = question["llm_query"]
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# introducing "add_source_document"
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# this will perform 'inline' parsing, text chunking and query filter on a document
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# input is a file folder path, file name, and an optional query filter
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# the source is automatically packaged into the prompt object
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source = prompter.add_source_document(contracts_path,contract,query=query_topic)
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if verbose:
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print("update: document created source - ", source)
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# calling the LLM with 'source' information from the contract automatically packaged into the prompt
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responses = prompter.prompt_with_source(llm_question, prompt_name="default_with_context",
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temperature=0.3)
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# unpacking the LLM responses
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for r, response in enumerate(responses):
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print("update: llm response: ", llm_question, re.sub("[\n]", " ",
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response["llm_response"]).strip())
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# We're done with this contract, clear the source from the prompt
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prompter.clear_source_materials()
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# Save jsonl report to jsonl to /prompt_history folder
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print("\nupdate: Prompt state saved at: ", os.path.join(LLMWareConfig.get_prompt_path(), prompter.prompt_id))
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prompter.save_state()
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# Save csv report that includes the model, response, prompt, and evidence for human-in-the-loop review
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csv_output = HumanInTheLoop(prompter).export_current_interaction_to_csv()
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print("\nupdate: CSV output saved at - ", csv_output)
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return 0
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if __name__ == "__main__":
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# you can pick any model from the ModelCatalog
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# we list a few representative good choices below
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LLMWareConfig().set_active_db("sqlite")
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example_models = ["bling-phi-3-gguf",
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"llmware/bling-1b-0.1",
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"llmware/bling-tiny-llama-v0",
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"llmware/dragon-yi-6b-gguf"]
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# to swap in a gpt-4 openai model - uncomment these two lines and `pip3 install openai`
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# model_name = "gpt-4"
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# os.environ["USER_MANAGED_OPENAI_API_KEY"] = "<insert-your-openai-key>"
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# use local cpu model
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model_name = example_models[0]
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# two good recipes to address the use case
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# first let's look at the main way of retrieving and analyzing from a library
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example_4a_contract_analysis_from_library(model_name)
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# second - uncomment this line, and lets run the "in-line" prompt way
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# example_4b_contract_analysis_direct_from_prompt(model_name)
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