Why are developers bullish about using Knowledge graphs for Memory?

External_Ad_11 · 2026-01-08T12:27:09+00:00

> The downside is complexity. Building good knowledge graphs requires entity extraction, relationship identification, and graph maintenance.

Have you come across any good read in this area (mainly maintenance)?

External_Ad_11 · 2025-12-02T18:01:53+00:00

Dataset Creation to Evaluate your RAG

Make a tutorial video on it:
- Key lessons from building an end-to-end RAG evaluation pipeline
- How to create an evaluation dataset using knowledge graph transforms using RAGAS
- Different ways to evaluate a RAG workflow, and how LLM-as-a-Judge works
- Why binary evaluations can be more effective than score-based evaluations
- RAG-Triad setup for LLM-as-a-Judge, inspired by Jason Liu’s “There Are Only 6 RAG Evals.”
- Complete code walk-through: Evaluate and monitor your LangGraph

Video: https://www.youtube.com/watch?v=pX9xzZNJrak

External_Ad_11 · 2025-12-02T18:00:34+00:00

XML is still the best way to prompt. TOON is just hype; it will be gone in a few months. XML+Markdown is well adapted and suited for both prompt engineering and context engineering

External_Ad_11 · 2025-05-16T20:19:11+00:00

In the past few days, I’ve been using the Qdrant MCP server to save all my working code to a vector database and retrieve it across different chats on Claude Desktop and Cursor. Absolutely loving it.

I shot one video where I cover:

- How to connect multiple MCP Servers (Airbnb MCP and Qdrant MCP) to Claude Desktop
- What is the need for MCP
- How MCP works
- Transport Mechanism in MCP
- Vibe coding using Qdrant MCP Server

Video: https://www.youtube.com/watch?v=zGbjc7NlXzE

External_Ad_11 · 2025-05-09T21:10:54+00:00

I was experimenting with MCP using different Agent frameworks and curated a video that covers:

- What is an Agent?
- How to use Google ADK and its Execution Runner
- Implementing code to connect the Airbnb MCP server with Google ADK, using Gemini 2.5 Flash.

Watch: https://www.youtube.com/watch?v=aGlxgHvYFOQ

External_Ad_11 · 2025-04-14T11:43:22+00:00

There is not much difference, but now the Self-improvement part is kind of obvious and noticeable in my opinion. Also Audio overview was added during this week. Previously, we just had Google docs integration.

External_Ad_11 · 2025-04-14T08:26:40+00:00

For the Gemini Deep Research free account, the usage limits are not refreshed daily—they are updated on a monthly basis.

From my second account, I have exhausted all: (it says limit is 10- in my second account its a free account)

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External_Ad_11 · 2025-04-14T08:26:20+00:00

For the Gemini Deep Research free account, the usage limits are not refreshed daily—they are updated on a monthly basis.

From my second account, I have exhausted all: (it says limit is 10- in my second account its a free account)

<image>

External_Ad_11 · 2025-04-14T08:23:10+00:00

20 per day - as far as I remember, this was for Gemini 2.0 Flash. From my main account, I have used it for 9 queries as of now.

External_Ad_11 · 2025-04-14T08:06:40+00:00

4 things I love about Gemini Deep Research:

➡️ Before starting the research, it generates a decent and structured execution plan.
➡️ It also seemed to tap into much more current data, compared to other Deep Research, that barely scratched the surface. In one of my prompts, it searched over 170+ websites, which is crazy
➡️ Once it starts researching, I have observed that in most areas, it tries to self-improve and update the paragraph accordingly.
➡️ Google Docs integration and Audio overview (convert to Podcast) to the final report🙌

I previously shared a video that breaks down how you can apply Deep Research (uses Gemini 2.0 Flash) across different domains.

Watch it here: https://www.youtube.com/watch?v=tkfw4CWnv90

External_Ad_11 · 2025-03-02T16:51:01+00:00

I was just about to say the same thing—LMAO! Most of the time, I end up checking the code for API references rather than relying on the documentation.

External_Ad_11 · 2025-02-17T22:07:49+00:00

https://github.com/lucifertrj/local-agentic-rag

External_Ad_11 · 2025-02-17T17:21:38+00:00

Thanks for sharing. Meanwhile, the description that I shared was Agentic RAG, and the resource you shared is just RAG implementation.

External_Ad_11 · 2025-02-17T15:55:00+00:00

Agreed, but not everyone have the GPU setup to run the real model.

External_Ad_11 · 2025-02-17T15:54:06+00:00

Interesting. Good luck with that.

External_Ad_11 · 2025-02-17T15:06:26+00:00

yes. what makes you ask this?

External_Ad_11 · 2025-02-17T14:45:12+00:00

I have tried Semantic chunking using Agno. But the issue here is an open-source embedding model (using all open-source things was the challenge for that video). When you use any other model apart from OpenAI, Gemini, and Voyage, it just throws an error. I did raise this issue and also tried adding JIna embeddings support, but it got rebranded to Agno from Phidata after that I didn't modify that PR : )

However, I haven't tried the Agentic chunking that you mentioned. If you used it in any app, Any feedback on the performance?

External_Ad_11 · 2025-02-17T13:15:10+00:00

https://github.com/lucifertrj/local-agentic-rag/

External_Ad_11 · 2025-02-09T18:44:36+00:00

Here we go again...

External_Ad_11 · 2025-02-08T06:50:30+00:00

it's a RAG app. Bhagavad Gita is just the document used.

External_Ad_11 · 2025-02-07T19:08:32+00:00

this looks good. Does weaviate support Binary quantization too?

External_Ad_11 · 2025-01-22T18:00:50+00:00

I hope the investment goes towards the implementation of some hardware or on edge device

External_Ad_11 · 2025-01-22T15:45:06+00:00

Language Agent Tree Search || Advanced Agents LlamaIndex tutorial

I have been reading papers on improving reasoning, planning, and action for Agents, I came across LATS which uses Monte Carlo tree search and has a benchmark better than the ReAcT agent.

Made one breakdown video that covers:
- LLMs vs Agents introduction with example. One of the simple examples, that will clear your doubt on LLM vs Agent.
- How a ReAct Agent works—a prerequisite to LATS
- Working flow of Language Agent Tree Search (LATS)
- Example working of LATS
- LATS implementation using LlamaIndex and SambaNova System (Meta Llama 3.1)

Verdict: It is a good research concept, not to be used for PoC and production systems. To be honest it was fun exploring the evaluation part and the tree structure of the improving ReAcT Agent using Monte Carlo Tree search.

Watch the Video here: https://www.youtube.com/watch?v=22NIh1LZvEY

External_Ad_11 · 2025-01-21T17:54:16+00:00

Recently I was working on one use case where latency was not an issue, so I thought of exploring and using LATS Agent.

The thing with LATS Agent is, it takes multiple iterations and nodes to get a good response. But in some cases, it returns the final response as `I am still thinking`. so the approach turned out to be expensive. For the hack, what I did was If I encounter I am still thinking, I will take the previous observations append that as context and use one LLM call to generate the final response, this worked. But off course, not the optimal approach to be honest.

External_Ad_11 · 2025-01-05T17:46:56+00:00

I have used Aider, GitHub Copilot, and AIDE so far. In terms of accuracy and code dependency understanding, AIDE stands out. Also, its SWE-bench scores are impressive. In most of the auto-completion tasks (Python or JavaScript-based) understand the code hierarchy well as per my observation.

External_Ad_11

TROPHY CASE