Supporting blog content - Local rag with lightweight Elasticsearch #488
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Review these changes at https://app.gitnotebooks.com/elastic/elasticsearch-labs/pull/488 |
.../local-rag-with-lightweight-elasticsearch/app-logs/llama-smoltalk-3.2-1b-instruct_results.md
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| ## Stats | ||
| ✅ Indexed 5 documents in 250ms |
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Why is the indexing differing between models? I would expect indexing is a one-off operation independent to the model. This doesn't make sense to me. Should it be removed or clarified?
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I’m not sure why it differs between tests; I think it’s better to remove it. I did.
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I would change this example to something generic such as "Why is the sky blue?" or something else. As a developer this comes across as quite cringy to me.
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Changed. I added a new file showing the output response. Related commit.
| from openai import OpenAI | ||
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| ES_URL = "http://localhost:9200" | ||
| ES_API_KEY = "your-api-key-here" |
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I would change the URL, API key and LOCAL_AI_URL values to environment variables that are loaded via something like dotenv and a local .env file. While this is fine for local development, when developers try to move this to production they need to tidy it up. So lets set the example now.
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Ok! Added dotenv support! Related commit.
| ai_client = OpenAI(base_url=LOCAL_AI_URL, api_key="sk-x") | ||
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| def build_documents(dataset_folder, index_name): |
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Should this be called load_documents as it's opening text files. It's not really building the documents from scratch which is misleading.
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Done! Method renamed!
| if filename.endswith(".txt"): | ||
| filepath = os.path.join(dataset_folder, filename) | ||
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| with open(filepath, "r", encoding="utf-8") as file: |
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I would add a comment explaining why you've used utf-8 encoding here.
supporting-blog-content/local-rag-with-lightweight-elasticsearch/script.py
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supporting-blog-content/local-rag-with-lightweight-elasticsearch/script.py
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| try: | ||
| start_time = time.time() | ||
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| success, _ = helpers.bulk( |
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You should add the index creation code either here based on the condition that the index doesn't exist, or in a separate utility function. For semantic text you'll need to specify that mapping when creating the index, and that step is missing here.
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I added the index creation as a method in the script. I also added a verification step before bulk-indexing the data.
ulations
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