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Using RAG to create your own Podcast conversations.pdf

The presentation on "Retrieval Augmented Generation for Interactive Podcasts" outlines a method to transform podcast audio into interactive chat interfaces. It covers the design, coding, and practical aspects of using Retrieval Augmented Generation (RAG) to emulate podcast guest responses. The project involves processing a significant volume of podcast data, including episodes, transcripts, and metadata. The technical discussion focuses on ingesting audio and metadata into an AI system and querying it for conversational responses. Key concepts introduced include vector embedding, which converts text to conceptual vectors using models, and the application of Large Language Models (LLMs) with Transformer architecture for context understanding. The coding segment details microservice messages for transcript ingestion and chat functionalities, employing transcription services, embedding techniques, and vector storage solutions. Challenges in RAG project deployment are also discussed, highlighting performance, quality, regressions, and managing expectations. The presentation concludes by contrasting technical complexities with a philosophical vision of AI's potential, inspired by speculative fiction, suggesting a future where AI capabilities vastly exceed human cognitive functions. Further resources and open-source implementations are provided for those interested in the technical development of interactive podcast systems

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Retrieval
Augmented
Generation
Making a Podcast
“interactive”
Richard Rodger, Voxgig
Using RAG to create your own Podcast conversations.pdf
The Culture’s inhabitants “could
record their mind-states,
effectively taking a reading of the
person’s personality which could be
stored, duplicated, read, transmitted
and installed …”
Iain M Banks, Look to Windward
Using RAG to create your own Podcast conversations.pdf
Using RAG to create your own Podcast conversations.pdf
Using RAG to create your own Podcast conversations.pdf

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Using RAG to create your own Podcast conversations.pdf

  • 3. The Culture’s inhabitants “could record their mind-states, effectively taking a reading of the person’s personality which could be stored, duplicated, read, transmitted and installed …” Iain M Banks, Look to Windward
  • 9. How do I build this?
  • 10. 1. Design 2. Code 3. Practicalities
  • 11. “Using the podcast audio recordings, build a chat interface that responds like a podcast guest”
  • 12. - ~130 episodes - ~30 minutes per episode - ~7000 words per transcript - 2 new episodes per week - Metadata: - guest details - show notes
  • 13. 1. Ingestion Get the audio and metadata into the “AI” 2. Querying Get the conversational responses out of the “AI”
  • 16. Seems like hard work…? Why not just concatenate all transcripts, and use that as the context prompt?
  • 17. “Vector Embedding” Text -> Concepts (using a “Model”)
  • 19. The vector database does the embedding for you
  • 21. Large Language Models (LLMs) - Very large neural networks - Use the Transformer* architecture - A vector embedding - Cares about word order - Cares about word context * Attention is All You Need, Vaswani et al. 2017
  • 23. Code
  • 24. // Microservice messages aim: ingest: { // Get transcription transcribe: episode: {} // Do “embedding” ingest: transcript: {} } aim: chat: { // Use prompt context to reply chat: query: {} }
  • 25. // aim:ingest, transcribe:episode // lambda called when there’s // a new audio file in s3 const audio = loadAudio(event) // Use deepgram.com to get the conversation text const result = await deepgram .listen .prerecorded .transcribeFile( audio.content, options ) // Save transcript to s3 saveTranscript(result)
  • 26. // aim:ingest, ingest:transcript // lambda called when there’s // a new transcript file in s3 const transcript = loadTranscript(event) // Split into “chunks”, each to be added to // an OpenSearch vector collection const chunks = chunkify(transcript) for(let chunk in chunks) { // Call AWS Bedrock, specify model const embedding = bedrockClient.embed(chunk, model) // Store embedding vectors in OpenSearch openSearchClient.store(embedding) }
  • 27. // aim:chat, chat:query const query = event.body.query // HTTP POST // Call AWS Bedrock, specify model const embedding = bedrockClient.embed(query, model) // Use embedding to get context chunk text const context = openSearchClient .search(embedding) // Do prompt engineering here! const prompt = "Answer question with Context: " + context + "nQuestion: " + query // Get answer! const answer = bedrockClient.invoke(prompt)
  • 28. // Pro Tip: use a REPL! wovs/pdm-local> aim:chat,chat:query,query:"what is developer relations?" { ok: true, why: '', answer: "Developer Relations is the practice of building and maintaining relationships between companies and developers...The goal of Developer Relations is to make the company's products as easy to use, understand, and integrate into a developer's workflow as possible." }
  • 29. // Open Source // Reference implementation github.com/mikaelvesavuori/bedrock-rag-demo // Voxgig microservice implementation github.com/voxgig/podmind // Blog post (next week) richardrodger.com
  • 30. Practicalities …, you are face to face with the champion privy builder of Sangamon County
  • 32. It’s the Wild West “I’ve got my stuff rigged to hit mixtral-8x7, and dolphin locally, and 3.5-turbo, and the 4-series preview all with easy comparison in emacs and stuff, and in fairness the 4.5-preview is starting to show some edge on 8x7 … Until I realized Perplexity will give you a decent amount of Mistral Medium for free …. Who is sama kidding they’re still leading here? Mistral Medium destroys the 4.5 preview. And Perplexity wouldn’t be giving it away in any quantity if it had a cost structure like 4.5 … Mistral is the new “RenTech of AI”, DPO and Alibi and sliding window and modern mixtures are well-understood so the money is in the lag between some new edge and TheBloke having it quantized for a Mac Mini or 4070 Super … https://news.ycombinator.com/item?id=38948291
  • 33. It’s the Wild West Here's a glossary to understand this post: - mixtral-8x7 or 8x7: Open source model by Mistral AI. - Dolphin: An uncensored version of the mistral model - 3.5-turbo: GPT-3.5 Turbo, the cheapest API from OpenAI - 4-series preview OR "4.5 preview": GPT-4 Turbo, the most capable API from OpenAI - mistral-medium: A new model by Mistral AI that they are only serving through AI. It's in private beta and there's a waiting list to access it. - Perplexity: A new search engine that is challenging Google by applying LLM to search - Sama: Sam Altman, CEO of OpenAI - RenTech: Renaissance Technologies, a secretive hedge fund known for delivering impressive returns improving on the work of others - DPO: Direct Preference Optimization. It is a technique that leverages AI feedback to optimize the performance of smaller, open-source models like Zephyr-7B1. - Alibi: a Python library that provides tools for machine learning model inspection and interpretation2. It can be used to explain the predictions of any black-box model, including LLMs. - Sliding window: a type of attention mechanism introduced by Mistral-7B3. It is used to support longer sequences in LLMs. - Modern mixtures: The process of using multiple models together, like "mixtral" is a mixture of several mistral models. - TheBloke: Open source developer that is very quick at quantizing all new models that come out - Quantize: Decreasing memory requirements of a new model by decreasing the precision of weights, typically with just minor performance degradation. - 4070 Super: NVIDIA 4070 Super, new graphics card announced just a week ago
  • 35. So you want to deliver a RAG project…
  • 36. Do you want good performance?
  • 37. Do you want high quality answers? ann-benchmarks.com
  • 38. Do you like regressions?
  • 39. Do you like unrealistic expectations?
  • 40. Do you like being unable to solve fundamental limitations? (maybe) …my experience over the past few months suggests that for system programming, LLMs almost never provide acceptable solutions… antirez.com/news/140 (Salvatore Sanfilippo - wrote Redis)
  • 42. I am not an animal brain, I am not even some attempt to produce an AI through software running on a computer. I am a Culture Mind. We are close to gods, … we are quicker; we live faster and more completely than you do, with so many more senses, such a greater store of memories and at such a fine level of detail. - Look to Windward, Iain M Banks