Large Language Models (LLMs) are popular in tech. In Belgium and the Netherlands, the podcast \"Nerdland\" is a favorite for tech and science fans. It covers topics like bioscience, space, robotics, and AI. With over 100 episodes, \"Nerdland\" is a goldmine of information.
\nSo, why not create a chatbot for \"Nerdland\" fans? This chatbot uses podcast content to engage and inform users. It allows the \"Nerdland\" community to interact with the content in new ways and makes the information accessible in many languages, thanks to LLMs' multi-language capabilities.
\nThis blog post explains the project's technical details, including the LLMs used, integration process, and deployment on Azure.
","body":"Large Language Models (LLMs) have become a hot topic in the tech world. For those of us in Belgium (or the Netherlands) with a passion for technology and science, one of the go-to resources is the monthly podcast \"Nerdland,\" which delves into a variety of subjects from bioscience and space exploration to robotics and artificial intelligence.
Recognizing the wealth of knowledge contained in over 100 episodes of \"Nerdland,\" A simple thought came to our minds: why not develop a chatbot tailored for \"Nerdland\" enthusiasts? This chatbot would leverage the content of the podcasts to engage and inform users. Want to know more of the project with Nerdland? Visit aka.ms/nerdland
This chatbot enables the Nerdland community to interact at another level with the Nerdland content. On top of that, it democratizes the wealth of information in all these podcasts. Since the podcast is in Dutch, the audience around the world is quite limited. Now we can expose this information in dozens of languages, cause these LLMs are capable of multi-language conversations out of the box.
In this blog post, I'll explore the technical details and architecture of this exciting project. I'll discuss the LLMs utilized, the essential components, the process of integrating podcast content into the chatbot, and the deployment strategy used to ensure the solution is scalable, secure, and meets enterprise standards on Azure.
\n
\n
\n
Upon delving into this article, it's likely you've experimented with chatGPT or other Generative AI (GenAI) models. You may have observed two notable aspects:
LLMs are often employed for data retrieval, offering a user-friendly interface through natural language. To mitigate the issue of hallucinations, it's crucial to ground the LLM's responses in a specific dataset, such as one you privately own. This grounding ensures that the LLM's outputs are based on factual information.
\n\n
To utilize LLMs with your proprietary data, a method called Retrieval Augmented Generation (RAG) is used. RAG combines the natural language prowess of LLMs with data they haven't been explicitly trained on.
\n\n
Several components are essential for this process:
Keyword matching is a fundamental aspect of data retrieval, yet it has its limitations. For instance, a search for \"car\" might yield numerous references to \"cars\" but overlook related terms like \"vehicles\" due to the lack of direct word correlation. To enhance search capabilities, it's not just exact keyword matches that are important, but also the identification of words with similar meanings.
This is where vector space models come into play. By mapping words into a vector space, \"car\" and \"vehicle\" can be positioned in close proximity, indicating their semantic similarity, while \"grass\" would be positioned far from both. Such vector representations significantly refine the search results by considering semantically related terms.
Embedding models are the tools that facilitate the translation of words into their vector counterparts. These pretrained models, such as the ADA model, encode words into vectors.
\n
Integrating vector search with the Retrieval Augmented Generation (RAG) model introduces additional steps:
\n
\n
Our Nerdland Assistant is a Retrieval Augmented Generation (RAG) chatbot, uniquely crafted to harness the rich content from the podcast archives. To achieve this, we've combined a suite of Azure components, each serving a distinct purpose in the chatbot's architecture:
\n\n
\n
\n
\n
This entire setup is fully scripted as Infrastructure as Code. We utilize Bicep and the Azure Developer CLI to template the architecture, ensuring that our solution is both robust and easily replicable.
\n\n
The quality of the answers by the LLM are significantly shaped by several factors: the system prompts, LLM parameters (such as temperature, max tokens, ..), chunk size, and the indexing method's robustness.
\n\n
Every single of the above parameters influences the outcome by a lot. The only way to improve the outcome is to assess the results by influencing the parameters. To structure this process, you can make use of PromptFlow. PromptFlow allows you to repeat this LLM tweaking process, keeping track of the quality of the results per configuration.
When deploying an application that is making use of generative AI; adhering to Responsible AI Principles is crucial. These Microsoft principles guide the ethical and safe use of AI technologies. https://learn.microsoft.com/en-us/legal/cognitive-services/openai/overview
\n\n
A key advantage of utilizing Azure OpenAI endpoints is the built-in safety content filters provided by Microsoft. These filters function by processing both the input prompts and the generated outputs through a sophisticated array of classification models. The goal is to identify and mitigate the risk of producing any content that could be deemed harmful.
\n\n
The journey of developing this custom Assistant was a time-constrained endeavor that laid the groundwork for a basic yet functional system. The possibilities for expansion and enhancement are endless, with potential future enhancements including:
\nWant to know more? Contact us!
\n\n
Other sources
\nLarge Language Models (LLMs) have become a hot topic in the tech world. For those of us in Belgium (or the Netherlands) with a passion for technology and science, one of the go-to resources is the monthly podcast \"Nerdland,\" which delves into a variety of subjects from bioscience and space exploration to robotics and artificial intelligence.
Recognizing the wealth of knowledge contained in over 100 episodes of \"Nerdland,\" A simple thought came to our minds: why not develop a chatbot tailored for \"Nerdland\" enthusiasts? This chatbot would leverage the content of the podcasts to engage and inform users. Want to know more of the project with Nerdland? Visit aka.ms/nerdland
This chatbot enables the Nerdland community to interact at another level with the Nerdland content. On top of that, it democratizes the wealth of information in all these podcasts. Since the podcast is in Dutch, the audience around the world is quite limited. Now we can expose this information in dozens of languages, cause these LLMs are capable of multi-language conversations out of the box.
In this blog post, I'll explore the technical details and architecture of this exciting project. I'll discuss the LLMs utilized, the essential components, the process of integrating podcast content into the chatbot, and the deployment strategy used to ensure the solution is scalable, secure, and meets enterprise standards on Azure.
\n
\n
Upon delving into this article, it's likely you've experimented with chatGPT or other Generative AI (GenAI) models. You may have observed two notable aspects:
LLMs are often employed for data retrieval, offering a user-friendly interface through natural language. To mitigate the issue of hallucinations, it's crucial to ground the LLM's responses in a specific dataset, such as one you privately own. This grounding ensures that the LLM's outputs are based on factual information.
\n\n
To utilize LLMs with your proprietary data, a method called Retrieval Augmented Generation (RAG) is used. RAG combines the natural language prowess of LLMs with data they haven't been explicitly trained on.
\n\n
Several components are essential for this process:
Keyword matching is a fundamental aspect of data retrieval, yet it has its limitations. For instance, a search for \"car\" might yield numerous references to \"cars\" but overlook related terms like \"vehicles\" due to the lack of direct word correlation. To enhance search capabilities, it's not just exact keyword matches that are important, but also the identification of words with similar meanings.
This is where vector space models come into play. By mapping words into a vector space, \"car\" and \"vehicle\" can be positioned in close proximity, indicating their semantic similarity, while \"grass\" would be positioned far from both. Such vector representations significantly refine the search results by considering semantically related terms.
Embedding models are the tools that facilitate the translation of words into their vector counterparts. These pretrained models, such as the ADA model, encode words into vectors.
\n
Integrating vector search with the Retrieval Augmented Generation (RAG) model introduces additional steps:
\n
\n
Our Nerdland Assistant is a Retrieval Augmented Generation (RAG) chatbot, uniquely crafted to harness the rich content from the podcast archives. To achieve this, we've combined a suite of Azure components, each serving a distinct purpose in the chatbot's architecture:
\n\n
\n
\n
This entire setup is fully scripted as Infrastructure as Code. We utilize Bicep and the Azure Developer CLI to template the architecture, ensuring that our solution is both robust and easily replicable.
\n\n
The quality of the answers by the LLM are significantly shaped by several factors: the system prompts, LLM parameters (such as temperature, max tokens, ..), chunk size, and the indexing method's robustness.
\n\n
Every single of the above parameters influences the outcome by a lot. The only way to improve the outcome is to assess the results by influencing the parameters. To structure this process, you can make use of PromptFlow. PromptFlow allows you to repeat this LLM tweaking process, keeping track of the quality of the results per configuration.
When deploying an application that is making use of generative AI; adhering to Responsible AI Principles is crucial. These Microsoft principles guide the ethical and safe use of AI technologies. https://learn.microsoft.com/en-us/legal/cognitive-services/openai/overview
\n\n
A key advantage of utilizing Azure OpenAI endpoints is the built-in safety content filters provided by Microsoft. These filters function by processing both the input prompts and the generated outputs through a sophisticated array of classification models. The goal is to identify and mitigate the risk of producing any content that could be deemed harmful.
\n\n
The journey of developing this custom Assistant was a time-constrained endeavor that laid the groundwork for a basic yet functional system. The possibilities for expansion and enhancement are endless, with potential future enhancements including:
\nWant to know more? Contact us!
\n\n
Other sources
\nLarge Language Models (LLMs) are popular in tech. In Belgium and the Netherlands, the podcast \"Nerdland\" is a favorite for tech and science fans. It covers topics like bioscience, space, robotics, and AI. With over 100 episodes, \"Nerdland\" is a goldmine of information.
\nSo, why not create a chatbot for \"Nerdland\" fans? This chatbot uses podcast content to engage and inform users. It allows the \"Nerdland\" community to interact with the content in new ways and makes the information accessible in many languages, thanks to LLMs' multi-language capabilities.
\nThis blog post explains the project's technical details, including the LLMs used, integration process, and deployment on Azure.
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It allows the \"Nerdland\" community to interact with the content in new ways and makes the information accessible in many languages, thanks to LLMs' multi-language capabilities.\r\n\r\nThis blog post explains the project's technical details, including the LLMs used, integration process, and deployment on Azure. 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Is the source code for this solution available somewhere?
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Did you use Hybrid search (keyword+vector), or perhaps (keyword + semantic search + vector) and why?
Did you use semantic ranker and why?
Did you do something extra on top of the built in security measures, content filters and system message in the RAG to be compliant with Responsible AI policy?
Did you consider extra safety measures available in Azure like VNET's and Private Endpoints, and if so, how did you implement it?
What was the total volume of the files that you processed and what was the total cost?
Looking forward for your response ArneDeProft !
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