Prompt Title: Loop Result Collection.

Human: <div class="empty:hidden">why this for loop only keep the last iteration? if run_supply_insights: # Read the questions dataset df_supply_insights_questions = pd.read_csv(supply_insights_questions) for row in range(2): # Split the text into smaller chunks supply_insights_docs = text_splitter.create_documents([df_supply_insights.loc[row]['slack_message']]) for supply_doc in supply_insights_docs: supply_doc.metadata['supplier_name'] = df_supply_insights.loc[row]['supplier_name'] supply_doc.metadata['raw_text'] = df_supply_insights.loc[row]['slack_message'] # Create a new database based on this documents new_db = create_chromadb_vectorstore(document_list=supply_insights_docs) llm_parser = DiscoveryLLMParser(vectorstore = new_db) df_supply_insights_questions[['structured_output', 'raw_answer']] = df_supply_insights_questions.apply(get_structured_output, axis=1, result_type='expand') df_supply_insights_questions['name'] = df_supply_insights.loc[row]['supplier_name'] df_supply_insights_questions['text'] = df_supply_insights.loc[row]['slack_message']</div>

ChatGPT:

The provided code snippet contains a for loop that iterates over the range of numbers [0, 1] (since range(2) generates [0, 1]). As a result, the loop will only execute twice, once for row = 0 and once for row = 1.

In each iteration of the for loop, the code performs various operations on supply_insights_docs and eventually creates a new database new_db. The issue is that new_db is overwritten in each iteration, so only the last iteration's value of new_db will be retained, and the previous ones will be lost.

If the intention is to keep the results for all iterations, you need to modify the code to store the results from each iteration in a collection (e.g., a list) instead of overwriting the same variable. For example:

pythonCopy code
if run_supply_insights:
    df_supply_insights_questions = pd.read_csv(supply_insights_questions)
    supply_insights_results = []  # Create an empty list to store results

    for row in range(2):
        supply_insights_docs = text_splitter.create_documents([df_supply_insights.loc[row]['slack_message']])

        for supply_doc in supply_insights_docs:
            supply_doc.metadata['supplier_name'] = df_supply_insights.loc[row]['supplier_name']
            supply_doc.metadata['raw_text'] = df_supply_insights.loc[row]['slack_message']

        new_db = create_chromadb_vectorstore(document_list=supply_insights_docs)
        llm_parser = DiscoveryLLMParser(vectorstore=new_db)

        df_supply_insights_questions[['structured_output', 'raw_answer']] = df_supply_insights_questions.apply(get_structured_output, axis=1, result_type='expand')
        df_supply_insights_questions['name'] = df_supply_insights.loc[row]['supplier_name']
        df_supply_insights_questions['text'] = df_supply_insights.loc[row]['slack_message']

        supply_insights_results.append(df_supply_insights_questions.copy())  # Store the results for this iteration

    # Now you have all the results from each iteration in supply_insights_results list

With this modification, supply_insights_results will contain the results for each iteration of the loop, preserving the data from all iterations.