James Briggs on Odysee

Predictive Query Language (PQL) Explained

Thu, 02 Oct 2025 15:39:26 GMT

Overview of Kumo AI's Predictive Query Language (PQL) - a SQL-like syntax for developing state-of-the-art predictive analytics.

💡 Subscribe for Latest Courses and Tutorials:
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https://aurelio.ai

👾 Discord:
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Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/
...
https://www.youtube.com/watch?v=tEvUfsYqQAM

Data Science as a Service | Kumo AI Full Walkthrough

Thu, 02 Oct 2025 14:01:36 GMT

Building recommendation systems is hard. In data science, we can spend months wrangling data, training models, and still end up with mediocre results. That's where Kumo AI comes in — it's a service that abstracts away the complexity of building Graph Neural Networks (GNNs) for predictive analytics.

In this guide, we'll build a complete e-commerce recommendation engine using real H&M data with 33 million transactions. By the end, we'll have a system that can:

- Predict customer lifetime value for the next 30 days
- Generate personalized product recommendations
- Forecast purchase behavior to identify active customers

All of this can be done in just a couple of hours - not months.

📌 Code:
https://github.com/aurelio-labs/cookbook/blob/main/recsys/ecommerce/kumo-hm/kumo-hm.ipynb

💡 Subscribe for Latest Courses and Tutorials:
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👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#datascience #machinelearning #python

00:00 Kumo AI and GNNs
07:39 Kumo Setup
12:17 Kumo Connectors
14:45 Getting Data into BigQuery
20:39 Building the Graph in Kumo
28:34 Predictive Query Language (PQL)
35:01 Personalized Product Recommendations
38:44 Predicting Purchase Volume
41:44 Making Predictions with Kumo
27:10 Analysis and Prediction with Kumo
52:36 When to use Kumo
...
https://www.youtube.com/watch?v=ylyktSE8wcw

Agents are coming for Ecom

Tue, 23 Sep 2025 14:09:04 GMT

Ecom was always at the forefront of ML and data science in the past, but I felt like the latest AI and agentic wave had left ecom behind — but after building with KumoRFM I'm convinced ecommerce + GNNs + agents is going to be big — wdyt?

Full video here: https://youtu.be/MFp9vjr6rgA
KumoRFM: https://bit.ly/47x3WSk

💡 Subscribe for Latest Courses and Tutorials:
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👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/
...
https://www.youtube.com/watch?v=XfZbmic6wn8

Build Agentic Ecommerce with KumoRFM

Wed, 17 Sep 2025 15:00:41 GMT

In this video we explore the use of agents and LLMs for ecommerce and develop our own advanced agent enabling advanced data science and analytics for ecommerce.

We use Kumo AI's Relational Foundation Model (RFM) to produce insanely high quality predictions super fast, enabling a conversational experience with what is essentially an expert data science agent.

📌 Notebook Code: https://github.com/aurelio-labs/cookbook/tree/main/gen-ai/agents/ecommerce-agent
📍 AI App Repo: https://github.com/jamescalam/ecommerce-agent

💡 KumoRFM: https://bit.ly/47x3WSk
📊 Kumo AI: https://bit.ly/4gduL04

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#datascience #machinelearning #python

00:00 Ecommerce and AI
01:46 Agents for Ecommerce
02:34 KumoRFM
03:24 Talking with a KumoRFM Agent
10:58 Using KumoRFM
16:51 Making Predictions with KumoRFM
19:02 Agentic Predictions
22:34 Query Dataframes Tool
25:23 Query KumoRFM Tool
26:50 Building the Agent Graph
36:12 Testing our Ecommerce Agent
46:14 Ecommerce Agent App Setup
48:00 Ecommerce and Agents
...
https://www.youtube.com/watch?v=MFp9vjr6rgA

OpenAI's Agents SDK | Tools and Agents-as-Tools Explained

Fri, 08 Aug 2025 11:30:38 GMT

Agents SDK provides various approaches for tool use, including pre-built tools and all the features we need to develop custom tools. In this chapter, we'll learn everything there is about tools, tool-use, and agents-as-tools.

📌 Article and Code:
https://www.aurelio.ai/learn/agents-sdk-tools

💡 Subscribe for Latest Courses and Tutorials:
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👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#artificialintellegence #openai #ai #aiagents

00:00 Agents SDK Tools
00:59 Using Prebuilt Tools
02:30 Custom Tools
09:36 FunctionTool Object
13:43 Agents as Tools
...
https://www.youtube.com/watch?v=_RTxDOnLfVM

AI Observability with OpenAI Agents SDK

Thu, 31 Jul 2025 13:00:58 GMT

Agents SDK integrates with OpenAI's built-in Traces dashboard found within the OpenAI Platform for out-of-the-box observability and telemetry of our AI agents.

In this video, we'll take a look at both the default tracing enabled by default whenever we use Agents SDK (with an OpenAI API key present), and custom tracing.

📖 Full Course:
https://www.aurelio.ai/course/agents-sdk
📌 Code:
https://github.com/aurelio-labs/agents-sdk-course/blob/main/chapters/02-tracing.ipynb

💡 Subscribe for Latest Courses and Tutorials:
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👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #aiagents #programming

00:00 Agents SDK Tracing
00:17 OpenAI Traces Dashboard
02:03 Agents SDK Tracing Setup
03:00 Access to OpenAI Traces
04:03 Creating Agents SDK Traces
05:29 Custom Traces
08:24 Tracing Agent Tools
12:15 Conclusion
...
https://www.youtube.com/watch?v=T2ytN20H-BQ

You're doing Agentic chat history wrong | OpenAI Agents SDK

Thu, 24 Jul 2025 17:44:13 GMT

Prompting is an essential component when working with LLMs, and Agents SDK naturally has its own way of handling various components of prompts. In this chapter, we'll examine how to utilise static and dynamic prompting, as well as how to correctly use system, user, assistant, and tool prompts to build event-based conversations, not interaction-based conversations. Then, we'll see how these come together to create advanced conversational agents that use chat history the right way.

📌 Code: https://github.com/aurelio-labs/agents-sdk-course/blob/main/chapters/01-prompting.ipynb
📖 Article: https://www.aurelio.ai/learn/agents-sdk-prompting

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#aiagents #openai #ai #coding #artificialintellegence #programming

00:00 OpenAI Agents SDK
00:56 Agents SDK Setup
01:56 Static Instructions
06:03 Dynamic Prompts
08:38 Rethinking Agentic Chat History
11:09 Message Types
20:25 How to Use SDK Message Types
22:09 Developer Messages
24:31 Assistant Messages
26:37 Chat History
27:53 Function Calls
31:03 Conclusion for Agents SDK Prompting
...
https://www.youtube.com/watch?v=9nwWJWyxSyk

You're doing AGENTIC "chat history" wrong

Tue, 22 Jul 2025 14:01:09 GMT

💡 Subscribe for Latest Courses and Tutorials:
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Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #programming #aiagents
...
https://www.youtube.com/watch?v=fj9dF_HXL-U

End-to-end AI Agent Project with LangChain | Full Walkthrough

Tue, 15 Jul 2025 12:30:18 GMT

In this video, we'll develop a full AI Agent application using Python, LangChain, FastAPI, AI agents, tools, streaming, and more.

🔗 Full Course: https://www.aurelio.ai/course/langchain
📌 Code: https://github.com/aurelio-labs/langchain-course/tree/main/chapters/09-capstone

💡 Subscribe for Latest Courses and Tutorials:
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https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #langchain #programming #aiagents

00:00 End-to-End LangChain Agent
01:39 Setup the AI App
04:37 API Setup
11:28 API Token Generator
15:57 Agent Executor in API
34:03 Async SerpAPI Tool
40:08 Running the App
11:44 Course Completion
...
https://www.youtube.com/watch?v=AO6WbXTeDow

LangChain Streaming and API Integration

Fri, 11 Jul 2025 12:30:53 GMT

Streaming is a common pattern in AI applications. We've all seen AI interfaces where answers from AI chatbots appear on the screen as a word-by-word stream of information.

This word-by-word stream can look nice but provides many more benefits. Streamed text feels more natural to the user, which means the user can begin reading a response sooner.

The Time-to-First-Token of models like gpt-4.1-mini is very low (just 1-2 seconds in many cases). However, the full generation time (or Time-to-Last-Token, TTLT) can vary significantly. When generating long responses from gpt-4.1-mini, a TTLT of 10-20 seconds is typical.

A significant difference exists in having users wait 1-2 seconds vs 10-20 seconds. But beyond this, streaming also allows us to send intermediate steps to our interfaces. Suppose an agent uses various tools and/or takes multiple steps to generate a final response. In that case, we can use streaming to send this information to our application, allowing us to render UI components that inform the user about the agent's actions.

Using these intermediate step components, we provide continual feedback to the user, preventing them from being stuck staring at a blank screen. These components also provide us with an interface to provide more information to the user, such as research sources or results from intermediate calculations.

In this chapter, we will introduce LangChain's async streaming. Async streaming is an essential feature for APIs wanting to support real-time information streaming and enable the enhanced user experience described above.

🔗 Full Course: https://www.aurelio.ai/course/langchain
📌 Article and Code: https://www.aurelio.ai/learn/langchain-streaming

💡 Subscribe for Latest Courses and Tutorials:
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👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #langchain #programming #aiagents

00:00 LangChain Streaming
00:54 Streaming for AI
06:08 Basic LangChain Streaming
10:16 Streaming with Agents
28:13 Custom Agent and Streaming
31:13 Streaming to an API
...
https://www.youtube.com/watch?v=eor3eU9eReA

LangChain Expression Language (LCEL)

Fri, 04 Jul 2025 14:04:12 GMT

This chapter will introduce LangChain's Expression Langauge (LCEL). We'll focus on understanding how LCEL works under the hood and how it is implemented with OpenAI's LLMs.

We'll compare LCEL against the traditional methods. We will build a pipeline where the user inputs a specific topic, and then the LLM looks for and returns a report on the specified topic. This generates a research report for the user.

📌 Article and Code:
https://www.aurelio.ai/learn/langchain-lcel

💡 Subscribe for Latest Courses and Tutorials:
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👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #langchain #programming #aiagents

00:00 LangChain Expression Language
00:58 Traditional Chains in LangChain
03:02 LangChain LCEL
03:55 LCEL Pipe Operator
09:09 LangChain RunnableLambda
12:41 LCEL Runnable Parallel and Passthrough
...
https://www.youtube.com/watch?v=v9G-h6Ygokk

LangChain Agent Executor Deep Dive | Walkthrough for 2025

Thu, 26 Jun 2025 12:01:15 GMT

In this video, we will continue from the introduction to agents and dive deeper into agents. Learning how to build our custom agent execution loop for v0.3 of LangChain.

When we talk about agents, a significant part of an "agent" is simple code logic, iteratively rerunning LLM calls and processing their output. The exact logic varies significantly, but one well-known example is the ReAct agent.

Reason + Action (ReAct) agents use iterative reasoning and action steps to incorporate chain-of-thought and tool-use into their execution. During the reasoning step, the LLM generates the steps to take to answer the query. Next, the LLM generates the action input, which our code logic parses into a tool call.

Following our action step, we get an observation from the tool call. Then, we feed the observation back into the agent executor logic for a final answer or further reasoning and action steps.

The agent and agent executor we will be building will follow this pattern.

📌 Article and code:
https://www.aurelio.ai/learn/langchain-agent-executor

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #coding #aiagents #langchain

00:00 LangChain v0.3 Agent Executor
09:26 Creating an Agent with LCEL
13:53 Executing Tool Calls
16:58 Agentic Final Answers
25:58 Building a Custom Agent Executor
32:47 Executing Multiple Tool Calls
...
https://www.youtube.com/watch?v=iN1Xx8ca_8I

LangChain Agents in 2025 | Full Tutorial for v0.3

Tue, 24 Jun 2025 12:00:34 GMT

In this chapter, we will introduce LangChain's Agents, adding the ability to use tools such as search and calculators to complete tasks that normal LLMs cannot fulfil. We will be using OpenAI's gpt-4o-mini.

📌 Article and code:
https://www.aurelio.ai/learn/langchain-agents-intro

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #coding #aiagents #langchain

00:00 LangChain Agents 101
01:27 Introduction to Tools
07:05 Creating an Agent
11:27 Agent Executor
18:02 Web Search Agent
...
https://www.youtube.com/watch?v=Gi7nqB37WEY

Conversational Memory in LangChain for 2025

Thu, 19 Jun 2025 16:47:43 GMT

Conversational memory allows our chatbots and agents to remember previous interactions within a conversation. Without conversational memory, our chatbots would only ever be able to respond to the last message they received, essentially forgetting all previous messages with each new message.

Naturally, conversations require our chatbots to be able to respond over multiple interactions and refer to previous messages to understand the context of the conversation.

LangChain versions 0.0.x consisted of various conversational memory types. Most of these are due to deprecation, but they still hold value in understanding the different approaches to building conversational memory.

Throughout the video, we will refer to these older memory types and then rewrite them for LangChain v0.3 (the latest version in 2025) using the recommended RunnableWithMessageHistory class. We will learn about:

- ConversationBufferMemory
- ConversationBufferWindowMemory
- ConversationSummaryMemory
- ConversationSummaryBufferMemory

We'll work through each of these memory types in turn and rewrite each one using the RunnableWithMessageHistory class.

📌 Article and code:
https://www.aurelio.ai/learn/langchain-conversational-memory

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #coding #aiagents #langchain

00:00 Conversational Memory in LangChain
01:12 LangChain Chat Memory Types
04:26 LangChain ConversationBufferMemory
08:23 Buffer Memory with LCEL
13:14 LangChain ConversationBufferWindowMemory
16:01 Buffer Window Memory with LCEL
22:32 LangChain ConversationSummaryMemory
25:17 Summary Memory with LCEL
30:12 Token Usage in LangSmith
32:08 Conversation Summary Buffer Memory
34:36 Summary Buffer with LCEL
...
https://www.youtube.com/watch?v=EtldFS3JbGs

Prompt Templating and Techniques in LangChain

Wed, 11 Jun 2025 12:30:53 GMT

Until 2021, to use an AI model for a specific use case, we would need to fine-tune the model weights themselves. That would require huge training data and significant computate to fine-tune any reasonably performing model.

Instruction-fine-tuned large language models (LLMs) changed this fundamental rule of applying AI models to new use cases. Rather than needing to either train a model from scratch or fine-tune an existing model, these new LLMs could adapt incredibly well to a new problem or use case with nothing more than a prompt change.

Prompts allow us to completely change the functionality of an AI pipeline. Through natural language, we tell our LLM what it needs to do, and with the right AI pipeline and prompting, it often works.

LangChain naturally has many functionalities geared towards helping us build our prompts. We can build dynamic prompting pipelines that modify the structure and content of what we feed into our LLM based on essentially any parameter we would like. In this example, we'll explore the essentials of prompting in LangChain and apply this in a demo Retrieval Augmented Generation (RAG) pipeline.

📌 Article and code:
https://www.aurelio.ai/learn/langchain-prompts

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #coding #aiagents #langchain

00:00 Prompts are Fundamental to LLMs
02:13 Building Good LLM Prompts
07:13 LangChain Prompts Code Setup
11:36 Using our LLM with Templates
16:54 Few-shot Prompting
23:11 Chain of Thought Prompting
...
https://www.youtube.com/watch?v=jPeOAOvKFHE

LangSmith 101 for AI Observability | Full Walkthrough

Tue, 03 Jun 2025 12:00:56 GMT

LangSmith is a built-in observability service and platform that integrates easily with LangChain. We use LangSmith as an incredibly powerful approach to AI observability throughout the AI Engineers Guide to LangChain. We recommend using it beyond this course for general AI and LLM development with LangChain.

📌 Full Course: https://www.aurelio.ai/course/langchain
➤ Repo: https://github.com/aurelio-labs/langchain-course
👋🏼 New AI Services: https://platform.aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #aiagents #langchain #coding #programming #artificialintellegence #python

00:00 LangChain's LangSmith
00:24 LangSmith Setup
02:37 LangSmith Tracing
04:54 Custom LangSmith Traceables
08:18 LangSmith Conclusion
...
https://www.youtube.com/watch?v=Iyc80hY2yYk

LangChain v0.3 — Getting Started

Thu, 29 May 2025 12:45:06 GMT

LangChain is one of the most popular open-source libraries for AI Engineers. Its goal is to abstract away the complexity of building AI software, provide easy-to-use building blocks, and facilitate switching between AI service providers.

In this chapter, we will introduce LangChain by building a simple LLM-powered assistant.

📌 Full Course: https://www.aurelio.ai/course/langchain
➤ Repo: https://github.com/aurelio-labs/langchain-course
👋🏼 New AI Services: https://platform.aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #coding #aiagents #langchain

00:00 Getting Started with LangChain
00:46 Local Setup
03:32 Colab Setup
04:43 Initializing our OpenAI LLMs
09:06 LLM Prompting
10:31 LangChain Prompt Templates
15:20 Creating a LLM Chain with LCEL
20:31 Another Text Generation Pipeline
23:43 Structured Outputs in LangChain
28:27 Image Generation in LangChain
...
https://www.youtube.com/watch?v=i2jGTcJsDPM

When Should You Use LangChain?

Thu, 29 May 2025 12:30:57 GMT

LangChain is one of (if not the) most popular open-source AI frameworks. It works well for many things and less so for many others. Here, we will discuss the when and why of using LangChain compared to other frameworks.

📌 Full Course:
https://www.aurelio.ai/course/langchain

👋🏼 Aurelio AI:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #coding #aiagents #langchain

00:00 When to use LangChain
00:52 Do I need a framework?
04:03 LangChain for Learning
06:03 Moving on from LangChain
07:46 Should you use Langchain?
...
https://www.youtube.com/watch?v=d2-R_o5I5FQ

LoRA Fine-tuning Tiny LLMs as Expert Agents

Tue, 27 May 2025 16:46:53 GMT

Tiny LLMs have never been ideal for agentic workflows. They lack the ability to reliably generate function calls; however, this isn't due to any real limitation on LLM size. Instead, it's due to the LLM providers' lack of focus on data that provides quality examples of function calling.

Because of that, we can fine-tune expert agents from tiny LLMs such as the 1B parameter Llama 3.2 and get incredible results. In this video, we do just that - we take llama-3.2-1b-instruct, Salesforce's xLAM dataset, and Low-Rank Adaptation (LoRA) fine-tuning via NVIDIA's NeMo Microservices, to create our own tiny LLM agent.

Thanks to NVIDIA for sponsoring the video!

📌 NeMo Microservices: https://nvda.ws/4mqG2wH
🚢 Deploying NeMo Code: https://github.com/aurelio-labs/cookbook/blob/main/gen-ai/training/lora/nvidia-nemo/deploying-nemo.ipynb
👾 LoRA Fine-tuning Code: https://github.com/aurelio-labs/cookbook/blob/main/gen-ai/training/lora/nvidia-nemo/nemo-lora-function-calling.ipynb

👾 Discord:
https://discord.gg/c5QtDB9RAP

X: https://x.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #aiagents #artificialintellegence #programming #coding

00:00 LoRA Fine-tuning Agents
01:34 NeMo Microservices
03:19 NeMo Deployment
07:49 Deploying NeMo Microservices
16:54 xLAM Dataset Preparation
26:49 Train Validation Test Split
28:59 NeMo Data Store and Entity Store
34:14 LoRA Training with NeMo Customizer
42:03 Deploying NIMs
47:10 Chat Completion with NVIDIA NIMs
49:47 NVIDIA NeMo Microservices
...
https://www.youtube.com/watch?v=ZQfibo521Lc

Stateful and Fault-Tolerant AI Agents

Fri, 23 May 2025 13:00:03 GMT

Temporal is a distributed, open-source workflow orchestration platform built by ex-Uber engineers to enable massive-scale workflow orchestration. In this talk, Bogdan from Aurelio AI explains how Temporal can be used for AI agents to build fault-tolerant, stateful, and durable AI agent workflows. We cover a hands-on AI agents tutorial using Temporal workflow orchestration engine.

👋🏼 Aurelio AI: https://aurelio.ai/learn
🦜🔗 AI Engineer's Guide to LangChain: https://aurelio.ai/course/langchain
🔬 AI Platform: https://platform.aurelio.ai

#ai #aiagents #artificialintellegence #programming #coding

00:00 What is Temporal?
04:25 Temporal 101
24:36 Temporal AI Agent Demo
35:38 Temporal Agent Code
56:48 Questions
...
https://www.youtube.com/watch?v=14vQqJ9WG6U

OpenAI Agents SDK Handoffs | Deep Dive Tutorial

Sat, 17 May 2025 13:00:38 GMT

In OpenAI's Agents SDK, we can build multi-agent workflows in two ways. The first is agents-as-tools, which follow an orchestrator-subagent pattern. The second is using handoffs, which allow agents to pass control over to other agents. In this video, we'll dive into handoffs using Agents SDK and the latest LLMs, including gpt-4.1-mini and gpt-4.1 — using a multi-agent workflow containing a web search agent, dummy RAG agent, and code execution agent.

📖 Article: https://www.aurelio.ai/learn/agents-sdk-multi-agent
📌 Code: https://github.com/aurelio-labs/agents-sdk-course/blob/main/chapters/04-multi-agent.ipynb
🔗 LinkUp: https://app.linkup.so/?utm_source=james (Affiliate link)
👋🏼 AI Platform: https://platform.aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #coding #aiagents #programming #artificialintellegence #python

00:00 Agents SDK Handoff
05:06 Code Start
07:12 Web Search Agent
10:14 RAG Agent
11:18 Code Execution Agent
11:46 Defining the Orchestrator
12:36 Agents SDK Handoffs
20:27 Using OpenAI Traces Dashboard
23:26 More Handoff Testing
26:20 Other Handoff Features
28:45 Agents SDK on_handoff
29:51 Agents SDK Handoff input_type
31:28 Agents SDK Handoff input_filter
...
https://www.youtube.com/watch?v=LuarehusOWU

Multi-Agent Systems in OpenAI's Agents SDK | Full Tutorial

Thu, 08 May 2025 13:00:07 GMT

OpenAI's Agents SDK provides various ways for building multi-agent systems. Here we focus on the agents-as-tools method to build an orchestrator-subagent system. Throughout the video we'll be using OpenAI's new gpt-4.1 and gpt-4.1-mini models while building a full multi-agent workflow capable of searching the web, using internal documents, and executing code.

📌 Code:
https://github.com/aurelio-labs/agents-sdk-course/blob/main/chapters/04-multi-agent.ipynb
📖 Article:
https://www.aurelio.ai/learn/agents-sdk-multi-agent

👾 Discord:
https://discord.gg/c5QtDB9RAP

X: https://x.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#artificialintellegence #ai #aiagents #programming #python

00:00 OpenAI's Agents SDK
01:38 Python Setup
02:51 Orchestrator Subagent
05:57 Web Search Subagent
11:40 RAG Subagent
17:15 Code Execution Subagent
23:44 Orchestrator Agent
28:44 Evaluating our Multi-Agent Workflow
39:31 Pros and Cons of Orchestrators
...
https://www.youtube.com/watch?v=2MYzc79Lj04

AI Voice Assistants with OpenAI's Agents SDK | Full Tutorial + Code

Thu, 01 May 2025 13:01:42 GMT

Voice-based AI agents represent a huge opportunity for engineers and are likely to dominate the various ways that we interface with AI in the near future. In this video, we look at building voice AI agent using OpenAI's Agents SDK and their new gpt 4.1 models (specifically gpt 4.1 nano).

📌 Code:
https://github.com/aurelio-labs/agents-sdk-course/blob/main/chapters/07-voice.ipynb

📖 Article:
https://www.aurelio.ai/learn/agents-sdk-voice

👋🏼 New AI Platform:
https://platform.aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #aiagents #coding #programming #artificialintellegence #python

00:00 AI Voice Assistants
00:58 Getting the Code
02:19 Handling Audio in Python
06:56 Agents SDK Voice Pipeline
11:02 Speaking to the Agent
13:38 Chat with Voice Agent
15:31 Voice Agents Conclusion
...
https://www.youtube.com/watch?v=xMkbcSiS47o

Cogito v1 Outperforms Llama 4 | Full Tutorial with LM Studio and LiteLLM

Tue, 22 Apr 2025 13:01:09 GMT

Cogito v1 is the latest in state-of-the-art (SotA) open source / open weight LLMs. It manages to outperform even Llama 4 in various benchmarks and weight classes. In this video, we see how to deploy and use Cogito v1 locally with LiteLLM and LM Studio. We also build this out to enable full agent logic with tool use / function calling.

📕 Article: https://www.aurelio.ai/learn/cogito-v1
📌 Code: https://github.com/aurelio-labs/cookbook/blob/main/gen-ai/local/lmstudio/cogito-v1.ipynb
👾 New AI Platform: https://platform.aurelio.ai/

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#aiagents #llm #coding #ai #artificialintellegence #python #programming

00:00 Cogito v1
00:26 Local LLMs with LM Studio
02:38 Python Setup with uv
03:49 Loading our LLM in LM Studio
04:31 Using LM Studio with Python
05:17 Using LiteLLM with LM Studio
09:14 Tools and Agents
12:29 Create a Web Search Tool
16:19 Tool Calling with LiteLLM
20:54 Building a Local Agent
...
https://www.youtube.com/watch?v=bEPYba4bxs8

Advanced Guardrails for AI Agents

Tue, 01 Apr 2025 17:28:21 GMT

In this video we'll learn how to build advanced AI guardrails as part of a broader protective system for production-ready AI systems. We explore the use of hybrid vector space to develop this guardrails, which can be incredibly useful for chatbot use-cases with specific brand, topic, or behavioral guardrails.

📌 Code:
https://github.com/aurelio-labs/semantic-router/blob/main/docs/examples/hybrid-chat-guardrails.ipynb
⚠️ API keys:
- OpenAI https://platform.openai.com/api-keys
- Aurelio AI https://platform.aurelio.ai/settings/api-keys

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#artificialintellegence #aiagents #coding #ai #programming

00:00 Why Guardrails
00:20 Guardrails for Agents
03:40 Sparse and Dense Vectors
08:00 Hybrid Guardrails with Python
12:29 Initializing the HybridRouter
15:01 Optimizing our Hybrid Guardrails
19:16 Testing our Hybrid Router
20:46 AI Guardrails in Context
...
https://www.youtube.com/watch?v=rMUycP_cp9g

Mistral AI Agent with Streaming + Tools

Thu, 13 Mar 2025 16:51:40 GMT

Mistral AI is one of the big AI labs providing various models including SotA LLM and embedding models, making them an ideal sole provider for retrieval tasks (such as RAG) that require both generation via LLMs and retrieval via embedding models.

The Aurelio Platform provides several utility services to help AI engineers build RAG and GenAI applications faster.

In this example we're going to use both of these together to create a "chat-with-video" AI pipeline. We'll see how to:

1. Take any YouTube video and transcribe it to text using Aurelio's video-to-text endpoint.
2. Use Mistral LLMs to chat with our transcribed video content.
3. Add chat history to make our AI conversational.
4. Integrate async and streaming for a better UX and improved scalability.
5. See how we can optimize response latency and costs by reducing overall token count using semantic similarity, using Aurelio's chunking endpoint and Mistral's embedding models.

📌 Code:
https://github.com/aurelio-labs/cookbook/blob/main/gen-ai/agents/video-agent.ipynb

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#aiagents #mistralai #ai #coding

00:00 Mistral AI Agent
00:47 Python Setup
01:44 Video Transcription
03:25 Agent Overview
07:03 Using Mistral
09:26 Adding Agent Chat History
11:23 Async and Streaming
17:53 Agent Token Usage
19:17 Building a Retrieval Agent
25:52 Creating a Tool for Mistral
33:40 Tool Execution Logic
...
https://www.youtube.com/watch?v=oaIBkEdITRQ

Agents SDK from OpenAI! | Full Tutorial

Wed, 12 Mar 2025 17:27:13 GMT

OpenAI have released an Agents SDK, their version of an open source agent development library akin to LangChain, Llama-Index, Pydantic AI, and others.

OpenAI have outlined a few features of the library in their announcement blog post:

- Agent Loop: Automated loop for tool calls and LLM interactions until completion.
- Python-First: Leverage Python features for agent orchestration without new abstractions.
- Handoffs: Seamless coordination and delegation between multiple agents.
- Guardrails: Parallel input validations to halt processes on failure.
- Function Tools: Convert Python functions into tools with auto-schema and validation.
- Tracing: Visualize, debug, and monitor workflows with OpenAI tools integration.

We'll focus on covering the essentials here - including the agent loop, python-first, guardrails, and function tools features.

📌 Code:
https://github.com/aurelio-labs/cookbook/blob/main/gen-ai/openai/agents-sdk-intro.ipynb
📚 5 Hour LangChain Course:
https://www.aurelio.ai/course/langchain

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#aiagents #openai #ai #coding #artificialintellegence #programming

00:00 OpenAI Agents SDK
01:05 Agents SDK Code
02:38 Agent and Runner
06:56 Function Tools
12:13 Agents SDK Guardrails
18:29 Conversational Agents
21:02 Thoughts on Agents SDK
...
https://www.youtube.com/watch?v=35nxORG1mtg

NEW Pinecone Assistant Features + GA Release!

Thu, 23 Jan 2025 00:00:00 GMT

First look at the new Pinecone Assistant, including the Chat API, Context API, and OpenAI-compatible Chat Completions API.

📌 Code:
https://github.com/pinecone-io/examples/blob/master/learn/assistant/yorkshire-assistant.ipynb

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #aichatbot #artificialintellegence #aiagents

00:00 New AI Assistant
00:40 Pinecone Assistant Release
02:38 Pinecone Assistant Python Client
03:39 Assistant Custom Instructions
07:05 Pinecone Assistant APIs
09:42 Assistant Chat API
19:37 Context API
21:37 Pinecone Chat Completions
24:42 Deleting our Assistant and Concluding
...
https://www.youtube.com/watch?v=kGrSJKBfQZM

What's next for Semantic Router (v1 update)

Tue, 14 Jan 2025 00:00:00 GMT

Talking through an update on the progress for Semantic Router's first major release, ie v0.1.0. It comes with full HybridRouter support, features to make the library ready for production environments, and much more.

📌 Article:
https://www.aurelio.ai/learn/semantic-router-update-jan

⭐️ Repo:
https://github.com/aurelio-labs/semantic-router

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

00:00 Semantic Router
01:13 Keeping Semantic Router Lightweight
03:32 Current State of SR v1
03:58 Modular Routers Encoders and Indexes
06:27 Semantic Router Synchronization
10:30 Full Async Support
12:07 HybridRouter Upgrades
12:46 New Semantic Router Integrations
13:55 Testing and Doc Upgrades
16:04 Getting Started with v1

#artificialintelligence #ai #aiagents #python
...
https://www.youtube.com/watch?v=vjMN8FSGiQI

Gemini 2 Agent + Google Search and Citations

Thu, 19 Dec 2024 00:00:00 GMT

In this video we build a web search agent with Gemini 2. We're using gemini-2.0-flash-exp with Google's built-in Google search tool. We'll take a look at grounding our model response with inline citations.

📌 Code:
https://github.com/aurelio-labs/cookbook/blob/main/gen-ai/google-ai/gemini-2/web-search.ipynb

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#artificialintelligence #ai #aiagents

00:00 Gemini with Google Search
01:36 Gemini Web Search in Python
04:20 Using Gemini 2 Flash
06:07 Google GenAI Libraries
06:59 Using Google Search with Gemini
09:51 Grounding Gemini Responses
13:46 Inserting Citations for Gemini
20:30 Why use Citations
...
https://www.youtube.com/watch?v=G8kwhCf_xCI

Gemini 2 Multimodal and Spatial Awareness in Python

Tue, 17 Dec 2024 00:00:00 GMT

We test Google Deepmind's new Gemini 2 (gemini-flash-2.0-exp) multimodal capabilities and spatial awareness. Gemini has impressive structured output reliability as and we'll see with a few bounding box examples, very good spatial awareness — but it isn't perfect.

We'll see in the near future (and with a few more videos) how Gemini compares to OpenAI's models (such as gpt-4, gpt-4o, and o1) and whether we finally have a worthy competitor to OpenAI's dominance in production level AI application.

📌 Code:
https://github.com/aurelio-labs/cookbook/blob/main/gen-ai/google-ai/gemini-2/multimodal.ipynb

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #artificialintelligence #aichatbot #python

00:00 Gemini 2 Multimodal
00:41 Gemini Focus on Agents
01:53 Running the Code
03:08 Asking Gemini to Describe Images
09:29 Gemini Image Bounding Boxes
21:06 Gemini Spatial Awareness Example 2
23:29 Gemini Spatial Awareness Example 3
26:52 Gemini Spatial Awareness Example 4
29:09 Gemini Image-to-Text
30:50 Google Gemini vs OpenAI GPTs
...
https://www.youtube.com/watch?v=zBiKWxonD-o

Better Chatbots with Semantic Routes

Tue, 03 Dec 2024 00:00:00 GMT

We explore how to use semantic router for various conversational AI use-cases. Focusing on the conceptual logic behind using semantic router for better control over LLM / agent behaviours, tool use, etc.

📌 Code:
https://github.com/aurelio-labs/semantic-router/blob/main/docs/00-introduction.ipynb

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #artificialintelligence #python #aiagents #chatbot

00:00 Semantic Router
00:37 Concept of Semantic Routers
07:42 Routes and Utterances
15:11 Encoders
16:26 New Routers
20:48 Semantic Routes for Chat
21:29 LLM Output Guardrails
28:25 Fine-grained control of LLMs
29:10 Routes for Tool Use
32:01 LLM Routing
34:34 Outro
...
https://www.youtube.com/watch?v=uCVRfRVNRHQ

AI Agents as Neuro-Symbolic Systems?

Tue, 19 Nov 2024 00:00:00 GMT

Thinking through AI agents and the neuro-symbolic definition from an early LLM agent paper called MRKL. I'm sharing my reasoning behind using the "neuro-symbolic system" definition for AI agents.

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#artificialintelligence #ai #python #machinelearning

00:00 AI Agents
02:04 ReAct Agents
07:28 Redefining Agents
12:48 Origins of Connectionism
17:23 Neuro-symbolic AI
21:09 Agents without LLMs
25:21 Broader Definition of Agents
...
https://www.youtube.com/watch?v=JaHfCrVTYF4

Llama Index Workflows | Building Async AI Agents

Tue, 24 Sep 2024 00:00:00 GMT

Llama Index Workflows is an event-driven framework for building AI agents. It aims to provide AI Engineers with a structured conceptual frame around which we can build AI software, similar in some respects to LangChain's LangGraph. In this video, we'll compare the two frameworks (Llama Index Workflows and Langchain's LangGraph), learn how to use Workflows, and build an async research agent with the library.

📌 Code:
https://github.com/pinecone-io/examples/blob/master/learn/generation/llama-index/llama-index-research-agent.ipynb

🌲 Subscribe for Latest Articles and Videos:
https://www.pinecone.io/newsletter-signup/

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#artificialintelligence #llamaindex #coding #programming

00:00 Llama Index Workflows
00:53 Llama Index vs. LangGraph
05:27 Python Prerequisites
06:40 Building Knowledge Base
08:20 Defining Agent Tools
11:02 Defining the LLM
12:31 Llama Index Workflow Events
14:00 Llama Index Agent Workflow
24:25 Debugging our Workflow
26:47 Using and Tweaking our Agent
30:05 Testing Llama Index Async
...
https://www.youtube.com/watch?v=KMZBLBAfE1s

Local LangGraph Agents with Llama 3.1 + Ollama

Thu, 29 Aug 2024 00:00:00 GMT

LangGraph is one of the most versatile Python libraries for building AI agents. We can combine LangChain's LangGraph with Ollama and Llama 3.1 to build highly custom and fully local LLM agents. In this video, we will do exactly that by building a pizza recommendation agent using the Reddit API.

📌 Code:
https://github.com/pinecone-io/examples/tree/master/learn/generation/langchain/langgraph/02-ollama-langgraph-agent

💻 LangGraph Intro Video:
https://youtu.be/usOmwLZNVuM

🌲 Subscribe for Latest Articles and Videos:
https://www.pinecone.io/newsletter-signup/

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#langchain #ai #artificialintelligence #ollama #meta

00:00 Local Agents with LangGraph and Ollama
01:00 Setting up Ollama and Python
05:35 Reddit API Tool
12:40 Overview of the Graph
17:11 Final Answer Tool
18:33 Agent State
19:09 Ollama Llama 3.1 Setup
26:21 Organizing Agent Tool Use
35:21 Creating Agent Nodes
39:14 Building the Agent Graph
43:10 Testing the Llama 3.1 Agent
46:07 Final Notes on Local Agents
...
https://www.youtube.com/watch?v=5a-NuqTaC20

LangGraph Deep Dive: Build Better Agents

Wed, 07 Aug 2024 00:00:00 GMT

LangGraph is an agent framework from LangChain that allows us to develop agents via graphs. By building agents using graphs we have much more control and flexibility in our AI agent execution path.

In this video, we will build an AI research agent using LangGraph. Research agents are multi-step LLM agents that can produce in-depth research reports on a topic of our choosing through multiple steps.

We will see how we can build our own AI research agent using gpt-4o, Pinecone, LangGraph, arXiv, and Google via the SerpAPI.

📌 Code:
https://colab.research.google.com/github/pinecone-io/examples/blob/master/learn/generation/langchain/langgraph/01-gpt-4o-research-agent.ipynb

📖 Article:
https://www.pinecone.io/learn/langgraph-research-agent/

🌲 Subscribe for Latest Articles and Videos:
https://www.pinecone.io/newsletter-signup/

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#artificialintelligence #langchain #llm #python #rag

00:00 LangGraph Agents
02:04 LangGraph Agent Overview
04:46 Short History of Agents and ReAct
07:58 Agents as Graphs
10:18 LangGraph
12:41 Research Agent Components
14:30 Building the RAG Pipeline
17:28 LangGraph Graph State
18:56 Custom Agent Tools
19:10 ArXiv Paper Fetch Tool
21:22 Web Search Tool
22:42 RAG Tools
23:57 Final Answer Tool
25:10 Agent Decision Making
30:16 LangGraph Router and Nodes
33:00 Building the LangGraph Graph
36:52 Building the Research Agent Report
39:39 Testing the Research Agent
43:42 Final Notes on Agentic Graphs
...
https://www.youtube.com/watch?v=usOmwLZNVuM

RAG with Mistral AI!

Thu, 11 Jul 2024 00:00:00 GMT

We build an RAG pipeline using Mistral AI's mistral-embed and mistral-large, using Pinecone vector DB as our knowledge base.

📌 Code:
https://github.com/pinecone-io/examples/blob/master/integrations/mistralai/mistral-rag.ipynb

🌲 Subscribe for Latest Articles and Videos:
https://www.pinecone.io/newsletter-signup/

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#artificialintelligence #mistralai #rag #llm

00:00 RAG with Mistral and Pinecone
01:07 Mistral API in Python
01:44 Setting up Vector DB
03:14 Mistral Embeddings
04:12 Creating Pinecone Index
08:24 RAG with Mistral
11:20 Final Thoughts on Mistral
...
https://www.youtube.com/watch?v=I0c405L7-9A

Superfast RAG with Llama 3 and Groq

Tue, 02 Jul 2024 00:00:00 GMT

Groq API provides access to Language Processing Units (LPUs) that enable incredibly fast LLM inference. The service offers several LLMs including Meta's Llama 3. In this video, we'll implement a RAG pipeline using Llama 3 70B via Groq, an open source e5 encoder, and the Pinecone vector database.

📌 Code:
https://github.com/pinecone-io/examples/blob/master/integrations/groq/groq-llama-3-rag.ipynb

🌲 Subscribe for Latest Articles and Videos:
https://www.pinecone.io/newsletter-signup/

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#artificialintelligence #llama3 #groq

00:00 Groq and Llama 3 for RAG
00:37 Llama 3 in Python
04:25 Initializing e5 for Embeddings
05:56 Using Pinecone for RAG
07:24 Why We Concatenate Title and Content
10:15 Testing RAG Retrieval Performance
11:28 Initialize connection to Groq API
12:24 Generating RAG Answers with Llama 3 70B
14:37 Final Points on Why Groq Matters
...
https://www.youtube.com/watch?v=ne-lrm0n0bg

NEW Pinecone Assistant

Tue, 25 Jun 2024 00:00:00 GMT

Pinecone Assistant is a new AI assistant service from Pinecone, bringing together the best of LLMs and GenAI with advanced Retrieval Augmented Generation (RAG) methods to reduce hallucination and optimize assistant reliability.

🚩 Get Access:
https://www.pinecone.io/product/pinecone-assistant/

📌 Code:
https://github.com/pinecone-io/examples/blob/master/learn/generation/pinecone-assistant/assistants-ai-demo.ipynb

🌲 Subscribe for Latest Articles and Videos:
https://www.pinecone.io/newsletter-signup/

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

00:00 AI Assistants
00:41 Pinecone Assistants in Python
01:19 Building an AI Research Assistant
02:11 Assistant Message and Chat
03:05 Adding Files to the Assistant
05:30 Chatting with our Assistant
07:23 Assistant Chat History
10:47 Asking about Mamba 2
12:11 Wrapping up with Assistants
...
https://www.youtube.com/watch?v=MPCiNA8BqO8

Semantic Chunking - 3 Methods for Better RAG

Sat, 01 Jun 2024 00:00:00 GMT

Semantic chunking allows us to build more context-aware chunks of information. We can use this for RAG, splitting video and audio, and much more.

In this video, we will use a simple RAG-focused example. We will learn about three different types of chunkers: StatisticalChunker, ConsecutiveChunker, and CumulativeChunker.

At the end, we also discuss semantic chunking for video, such as for the new gpt-4o and other multi-modal use cases.

📌 Code:
https://github.com/aurelio-labs/semantic-chunkers/blob/main/docs/00-chunkers-intro.ipynb

⭐️ Article:
https://www.aurelio.ai/learn/semantic-chunkers-intro

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #artificialintelligence #chatbot #nlp

00:00 3 Types of Semantic Chunking
00:42 Python Prerequisites
02:44 Statistical Semantic Chunking
04:38 Consecutive Semantic Chunking
06:45 Cumulative Semantic Chunking
08:58 Multi-modal Chunking
...
https://www.youtube.com/watch?v=7JS0pqXvha8

Processing Videos for GPT-4o and Search

Tue, 21 May 2024 00:00:00 GMT

Recent multi-modal models like OpenAI's gpt-4o and Google's Gemini 1.5 models can comprehend video. When feeding video into these new models, we can push frames at a set frequency (for example, one frame every second) — but this method can be wildly inefficient and expensive.

Fortunately, there is a better method called "semantic chunking." Semantic chunking is a common method used in text-based Retrieval-Augmented Generation (RAG), but we can apply the same logic to video using image embedding models. Using the similarity between these frames, we can effectively split videos based on the semantic meaning of the constituent frames.

In this video, we'll explore how to use two test videos and chunk them into semantic blocks.

📌 Code:
https://github.com/aurelio-labs/semantic-chunkers/blob/main/docs/01-video-chunking.ipynb

📖 Article:
https://www.aurelio.ai/learn/video-chunking

⭐ Repo:
https://github.com/aurelio-labs/semantic-chunkers

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #artificialintelligence #openai

00:00 Semantic Chunking
00:24 Video Chunking and gpt-4o
01:59 Video Chunking Code
03:28 Setting up the Vision Transformer
05:56 ViT vs. CLIP and other models
06:40 Video Chunking Results
08:37 Using CLIP for Vision Chunking
11:29 Final Conclusion on Video Processing
...
https://www.youtube.com/watch?v=gxqdNl1nTYw

NVIDIA's NEW AI Workbench for AI Engineers

Thu, 16 May 2024 00:00:00 GMT

NVIDIA AI Workbench (NVWB) is a software toolkit designed to help AI engineers and data scientists build in GPU-enabled environments.

Using NVWB, we can set up a local AI project with a prebuilt template with a few clicks. Then, after building out our project locally, we can quickly deploy it to a more powerful remote GPU instance, switch to a different remote, or go back to local.

By abstracting away many repetitive and tedious boilerplate actions, NVWB aims to help AI engineers focus on the core of AI development. It helps us reduce time spent on managing our dev environment, deployments, or maintaining remote compute instances.

In this tutorial, we'll learn about NVWB's features, where to use it, and how to use it.

I'm using NVIDIA RTX 5000 Ada Generation Laptop GPU here from Dell Precision AI-ready workstations — enabling substantial performance across AI projects and streamlining both training and development phases while still keeping things lightweight for travel. More info here https://dell.com/precisionai

📌 Download NVWB:
https://nvda.ws/4acZNRZ

📖 Read the Article:
https://www.aurelio.ai/learn/ai-workbench-intro

💻 Deploying remote EC2 instances for AI Workbench:
https://www.aurelio.ai/learn/ai-workbench-remote

📌 Code:
https://github.com/NVIDIA/workbench-example-rapids-cudf/blob/main/code/cudf-pandas-demo.ipynb

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

00:00 NVIDIA AI Workbench
01:18 Installing AI Workbench
04:05 Sponsor Segment
05:46 AI Workbench Locations
06:54 Creating and Loading Projects
09:21 AI Workbench Projects
14:18 Jupyterlab in AI Workbench
17:46 Using CuDF and Pandas
19:51 Finishing up with AI Workbench

#ai #artificialintelligence #chatbot #nlp #ad
...
https://www.youtube.com/watch?v=DcrFL_zNRKM

Semantic Chunking for RAG

Sat, 04 May 2024 00:00:00 GMT

Semantic chunking for RAG allows us to build more concise chunks for our RAG pipelines, chatbots, and AI agents. We can pair this with various LLMs and embedding models from OpenAI, Cohere, Anthropic, etc, and libraries like LangChain or CrewAI to build potentially improved Retrieval Augmented Generation (RAG) pipelines.

📌 Code:
https://github.com/pinecone-io/examples/blob/master/learn/generation/better-rag/02b-semantic-chunking.ipynb

🚩 Intro to Semantic Chunking:
https://www.aurelio.ai/learn/semantic-chunkers-intro

🌲 Subscribe for Latest Articles and Videos:
https://www.pinecone.io/newsletter-signup/

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

00:00 Semantic Chunking for RAG
00:45 What is Semantic Chunking
03:31 Semantic Chunking in Python
12:17 Adding Context to Chunks
13:41 Providing LLMs with More Context
18:11 Indexing our Chunks
20:27 Creating Chunks for the LLM
27:18 Querying for Chunks

#artificialintelligence #ai #nlp #chatbot #openai
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https://www.youtube.com/watch?v=TcRRfcbsApw

LangGraph 101: it's better than LangChain

Tue, 23 Apr 2024 00:00:00 GMT

LangGraph is a special LangChain-built library that builds intelligent AI Agents using graphs. Ie, agentic state machines. It allows us to build more powerful and flexible AI agents than what we can build using just the core library, LangChain.

In this video, we'll see how to build agents with LangGraph and OpenAI.

📌 Code:
https://github.com/pinecone-io/examples/blob/master/learn/generation/langchain/langgraph/00-langgraph-intro.ipynb

🌲 Subscribe for Latest Articles and Videos:
https://www.pinecone.io/newsletter-signup/

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

#ai #langchain #artificialintelligence #nlp #chatbot #openai

00:00 Intro to LangGraph
00:52 Graphs in LangGraph
03:00 More Complex LangGraph Agent
08:12 LangGraph Graph State
14:00 LangGraph Agent Node
17:08 Forcing a Specific LLM Output
20:00 Building the Graph
23:23 Using our Agent Graph
28:32 LangGraph vs LangChain
...
https://www.youtube.com/watch?v=qaWOwbFw3cs

AI Agent Evaluation with RAGAS

Thu, 04 Apr 2024 00:00:00 GMT

RAGAS (RAG ASsessment) is an evaluation framework for RAG pipelines. Here, we see how to use RAGAS for evaluating an AI agent built using LangChain and using Anthropic's Claude 3, Cohere's embedding models, and the Pinecone vector database.

📌 Code:
https://github.com/pinecone-io/examples/blob/master/learn/generation/better-rag/03-ragas-evaluation.ipynb

📕 Article:
https://www.pinecone.io/learn/series/rag/ragas/

🌲 Subscribe for Latest Articles and Videos:
https://www.pinecone.io/newsletter-signup/

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

00:00 RAG Evaluation
00:39 Overview of LangChain RAG Agent
03:04 RAGAS Code Prerequisites
03:40 Agent Output for RAGAS
05:14 RAGAS Evaluation Format
08:04 RAGAS Metrics
08:56 Understanding RAGAS Metrics
09:16 Retrieval Metrics
11:55 RAGAS Context Recall
14:43 RAGAS Context Precision
15:52 Generation Metrics
16:05 RAGAS Faithfulness
17:16 RAGAS Answer Relevancy
18:40 Metrics Driven Development

#ai #artificialintelligence #nlp #chatbot #langchain
...
https://www.youtube.com/watch?v=-_52DIIOsCE

Claude 3 Opus RAG Chatbot (Full Walkthrough)

Fri, 15 Mar 2024 00:00:00 GMT

Claude 3 Opus is a state-of-the-art (SOTA) LLM from Anthropic. In this walkthrough, we'll see how to use Claude 3 Opus as a conversational AI agent with LangChain v1, using a Retrieval Augmented Generation (RAG) tool powered by Voyage AI embeddings and the Pinecone vector database.

Putting all of these together, we have an extremely accurate AI RAG conversational agent.

📌 Code:
https://github.com/pinecone-io/examples/blob/master/learn/generation/langchain/v1/claude-3-agent.ipynb

🌲 Subscribe for Latest Articles and Videos:
https://www.pinecone.io/newsletter-signup/

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

00:00 Claude 3 AI Agent in LangChain
00:33 Finding Claude 3 RAG Code
01:35 Using Voyage AI Embeddings
02:25 Using Pinecone Knowledge Base for RAG
03:55 Claude 3 AI Agent Setup
09:19 Using Claude 3 Agent
10:17 Adding Conversational Memory
12:32 Testing Claude 3 Agent with Memory
14:40 Final Thoughts on AI Agents and Anthropic

#ai #claude3 #artificialintelligence #anthropic #nlp #chatbot #langchain
...
https://www.youtube.com/watch?v=rbzYZLfQbAM

Multi-Modal NSFW Detection with AI

Thu, 07 Mar 2024 00:00:00 GMT

Using multi-modal models like OpenAI's CLIP we can use the Semantic Router library for detection of specific images or videos, for example the detection of Not Shrek For Work (NSFW) and Shrek For Work (SFW) images. In this video, we'll see how.

⭐ GitHub Repo:
https://github.com/aurelio-labs/semantic-router/

📌 Code:
https://github.com/aurelio-labs/semantic-router/blob/main/docs/07-multi-modal.ipynb

🔥 Semantic Router Course:
https://www.aurelio.ai/course/semantic-router

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

00:00 AI Image Classification
00:23 How to use Multi-Modal AI
01:47 Finding Image Detection Notebook
02:18 Shrek Dataset
04:55 Creating Multi-Modal Routes
06:36 Testing NSFW Image Detection
07:53 Final Notes on Multi-Modal AI

#ai #artificialintelligence #nlp #openai
...
https://www.youtube.com/watch?v=EqKjaLrpeI4

AI Decision Making — Optimizing Routes

Tue, 27 Feb 2024 00:00:00 GMT

AI decision-making can now be easily trained using the optimization methods available in semantic router.

Route score thresholds define whether a route should be chosen. If the score we identify for any given route is higher than the Route.score_threshold, it passes; otherwise, it does not, and either another route is chosen or we return no route.

Given that this one score_threshold parameter can define the choice of a route, it's important to get it right — but it's incredibly inefficient to do so manually. Instead, we can use the fit and evaluate methods of our RouteLayer. All we must do is pass a smaller number of (utterance, target route) examples to our methods, and with the fit, we will often see dramatically improved performance within seconds — we will see how to measure that performance gain with evaluation.

⭐ GitHub Repo:
https://github.com/aurelio-labs/semantic-router/

📌 Code:
https://github.com/aurelio-labs/semantic-router/blob/main/docs/06-threshold-optimization.ipynb

🔥 Semantic Router Course:
https://www.aurelio.ai/course/semantic-router

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/
...
https://www.youtube.com/watch?v=Qi2_r4AopLM

Steerable AI with Pinecone + Semantic Router

Wed, 21 Feb 2024 00:00:00 GMT

We can make AI steerable and predictable using Semantic Router. How much fine-grained control we need will adjust the scale required by our routes. At very large scales, it can be useful to use a vector database to store and search through your route vector space. In this walkthrough, we will see how to use the new Pinecone integration in Semantic Router.

⭐ GitHub Repo:
https://github.com/aurelio-labs/semantic-router/

📌 Code:
https://github.com/aurelio-labs/semantic-router/blob/main/docs/examples/pinecone-and-scaling.ipynb

🌲 Subscribe for Latest Articles and Videos:
https://www.pinecone.io/newsletter-signup/

🔥 Semantic Router Course:
https://www.aurelio.ai/course/semantic-router

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/

00:00 Pinecone and Semantic Router
01:53 Finding Code for Pinecone
04:12 Getting Routes from Hugging Face
07:36 Loading Route Layers from Pinecone

#ai #artificialintelligence #nlp #chatbot
...
https://www.youtube.com/watch?v=qjRrMxT20T0

OpenAI's Sora: Incredible AI Generated Video

Thu, 15 Feb 2024 00:00:00 GMT

Taking a look at the new text-to-video diffusion model, Sora, from OpenAI — it is truly incredible.

OpenAI Blog Post:
https://openai.com/sora

👋🏼 AI Consulting:
https://aurelio.ai

👾 Discord:
https://discord.gg/c5QtDB9RAP

Twitter: https://twitter.com/jamescalam
LinkedIn: https://www.linkedin.com/in/jamescalam/
...
https://www.youtube.com/watch?v=F-BuJId6cK4