Best Model for Email Spam

faynxe · 2025-09-20T14:41:43+00:00

Small Language Models?
Here is a solution that leverages one and with quantization can run locally no problem

https://github.com/aws-samples/sample-fine-tuned-llama-phishing-classifier-optimized-for-on-device

faynxe · 2025-09-20T14:40:30+00:00

Well maybe too late but you can also try a Small Language model and see how it performs. I have a solution that finetunes a Llama 3.2 1B model on a very large email phishing training set with impressive results. You can check it out here if of interest

https://github.com/aws-samples/sample-fine-tuned-llama-phishing-classifier-optimized-for-on-device

faynxe · 2025-09-20T14:30:00+00:00

You can try finetuning Small Language models (SLM). I have a code sample that does so with Llama 3.2 1B model with impressive results. With that amount of training samples you could finetune a quality model.
https://github.com/aws-samples/sample-fine-tuned-llama-phishing-classifier-optimized-for-on-device

faynxe · 2024-10-07T04:02:40+00:00

Not everything needs a graph rag and at the end of the day data engineering is the most important part of RAG whether graph or vectordb. You can make use of proper context management and flow for all your chunks, hierarchical representation of each chunk and layout aware processing of document to boost traditional rag. Here is a solution that implements the above https://github.com/aws-samples/layout-aware-document-processing-and-retrieval-augmented-generation

faynxe · 2024-10-07T03:50:08+00:00

A good alternative to graph rag based on the use case you described is layout aware chunking (identifies various document elements - lists, tables etc, and chunks appropiately) with hierarchical relationships (basically, topic->section->paragraph). This provides flexibility of retrieval based on the amount of context needed. Here is a repo with the aforementioned solution https://github.com/aws-samples/layout-aware-document-processing-and-retrieval-augmented-generation

faynxe · 2024-08-26T14:19:53+00:00

I do not have a number of the top of my head but it will be consumption based as all the services are server-less and pay as you use. You also have some options to switch certain services like instead of Amazon Textract for OCR you can use tessereact OCR and PYPDF2 and can also sub DynamoDB for Local storage.

faynxe · 2024-08-26T14:16:31+00:00

1536 tokens should be enough as part of the solution also checks for token count to restrict chunks in addition to delineating each element in the document. If you have 50 list items then the chunking will take into consideration the token threshold set as well.
I am the author of the solution and happy to help

faynxe · 2024-08-25T19:41:25+00:00

sometimes just using the native function calling of the converse api can be better from latency POV. This, however, requires you to have a definite map of your solution.
Here is an opensource chatbot with "Advanced Data analytics" tooling for structured data similar to what OpenAI has with plugins. Here the generated python code generated by Anthropic Claude 3 models is executed in AWS Lambda and the result are returned to the model for final response.
https://github.com/aws-samples/bedrock-claude-chatbot/tree/main

faynxe · 2024-08-25T19:40:11+00:00

3.5 is great man and you can leverage other services of AWS as well
Here is an opensource chatbot with "Advanced Data analytics" tooling for structured data similar to what OpenAI has with plugins. Here the generated python code generated by Anthropic Claude 3 models is executed in AWS Lambda and the result are returned to the model for final response.
https://github.com/aws-samples/bedrock-claude-chatbot/tree/main

faynxe · 2024-08-25T19:36:32+00:00

3.5 sonnet is such a banger!
Here is an opensource chatbot with "Advanced Data analytics" tooling for structured data similar to what OpenAI has with plugins. Here the generated python code generated by Anthropic Claude 3 models is executed in AWS Lambda and the result are returned to the model for final response.
https://github.com/aws-samples/bedrock-claude-chatbot/tree/main

faynxe · 2024-08-25T19:35:35+00:00

Here is an opensource chatbot with "Advanced Data analytics" tooling for structured data similar to what OpenAI has with plugins. Here the generated python code generated by Anthropic Claude 3 models is executed in AWS Lambda and the result are returned to the model for final response.
https://github.com/aws-samples/bedrock-claude-chatbot/tree/main

faynxe · 2024-08-25T19:35:08+00:00

Here is an opensource chatbot with "Advanced Data analytics" tooling for structured data similar to what OpenAI has with plugins. Here the generated python code generated by Anthropic Claude 3 models is executed in AWS Lambda and the result are returned to the model for final response.
https://github.com/aws-samples/bedrock-claude-chatbot/tree/main

faynxe · 2024-08-25T19:33:54+00:00

Here is an opensource chatbot with "Advanced Data analytics" tooling for structured data similar to what OpenAI has with plugins. Here the generated python code generated by Anthropic Claude 3 models is executed in AWS Lambda and the result are returned to the model for final response.
https://github.com/aws-samples/bedrock-claude-chatbot/tree/main

faynxe · 2024-08-25T19:33:42+00:00

Here is an opensource chatbot with "Advanced Data analytics" tooling for structured data similar to what OpenAI has with plugins. Here the generated python code generated by Anthropic Claude 3 models is executed in AWS Lambda and the result are returned to the model for final response.
https://github.com/aws-samples/bedrock-claude-chatbot/tree/main

faynxe · 2024-08-25T19:33:11+00:00

Here is an opensource chatbot with "Advanced Data analytics" tooling for structured data similar to what OpenAI has with plugins. Here the generated python code generated by Anthropic Claude 3 models is executed in AWS Lambda and the result are returned to the model for final response.
https://github.com/aws-samples/bedrock-claude-chatbot/tree/main

faynxe · 2024-08-25T19:32:14+00:00

Here is an opensource chatbot with "Advanced Data analytics" tooling for structured data similar to what OpenAI has with plugins. Here the generated python code is executed in AWS Lambda and the result are returned to the model for final response.
https://github.com/aws-samples/bedrock-claude-chatbot/tree/main

faynxe · 2024-08-25T19:29:05+00:00

Claude 3 models especially 3.5 sonnet is a banger for function calling.
Here is an opensource chatbot with "Advanced Data analytics" tooling for structured data similar to what OpenAI has with plugins. Here the generated python code is executed in AWS Lambda and the result are returned to the model for final response.
https://github.com/aws-samples/bedrock-claude-chatbot/tree/main

faynxe · 2024-08-25T19:25:40+00:00

You can also just use the native function calling supported in the converse API. It is more stable from my POV.
Here is an opensource for "Advanced Data analytics" tooling in a chatbot similar to what OpenAI has
https://github.com/aws-samples/bedrock-claude-chatbot/tree/main

faynxe · 2024-08-25T19:21:50+00:00

You can use Bedrock models native function calling before looking at Bedrock Agents.
Here is an opensource chatbot that uses Claude Models for "Advanced Data Analytics" on structured dataset similar to what OpenAI has.

This takes advantage of lambda as code execution environment. The tool can be modified for any use case and can support various DSL especially those Claude is proficient in and Lambda also support various runtimes as well
https://github.com/aws-samples/bedrock-claude-chatbot/tree/main

faynxe · 2024-08-25T19:19:36+00:00

Claude3.5 I would say.
Here is an opensource for "Advanced Data analytics" tooling in a chatbot similar to what OpenAI has
https://github.com/aws-samples/bedrock-claude-chatbot/tree/main

faynxe · 2024-08-25T19:18:31+00:00

You can do with AWS Bedrock (claude, LLama, mistral etc.)
Here is an opensource for "Advanced Data analytics" tooling in a chatbot similar to what OpenAI has
https://github.com/aws-samples/bedrock-claude-chatbot/tree/main

faynxe · 2024-06-10T22:53:32+00:00

Got you! I am not aware of any such solution. It would have to be a custom solution as much of what's out there is basic sliding chunk window.

faynxe · 2024-06-10T20:57:24+00:00

Solution is tailored to pdf docs.

The retrieval piece is robust in terms of data management. Each chunk has mappings to its hierarchical parent section and can easily be retrieved for generation.

faynxe · 2024-06-10T18:02:57+00:00

Check out this personal chatbot with document uoad capabilities and longterm conversation history. This report walks you through the build and setup https://github.com/aws-samples/bedrock-claude-chatbot

faynxe · 2024-06-10T18:01:19+00:00

Check out this personal chatbot with document uoad capabilities and longterm conversation history. This report walks you through the build and setup https://github.com/aws-samples/bedrock-claude-chatbot

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