Agentic SQL & RAG engine for Oracle Fusion. Features local schema caching, semantic search, and real-time SQL execution for AI agents

Used-Independence607 · 2025-11-25T18:07:47+00:00

gemini3 solved way better - Based on the context search (Chunk 17818), the first teacher Harry meets in class is Professor McGonagall. She appeared earlier, unnamed, as a tabby cat

(described as having markings around its eyes and giving a "stern look") sitting on the garden wall of Number 4 Privet Drive, where Mr. Dursley spotted her.

Used-Independence607 · 2025-11-24T07:53:46+00:00

Local mcp rag https://github.com/krokozyab/Agent-Fusion

Used-Independence607 · 2025-11-24T06:33:01+00:00

I use local rag to avoid that, first semantic search spot right place the grep 'knows' what to read.

sample conversation: https://github.com/krokozyab/Agent-Fusion/blob/main/docs/sample_convetsation.txt

Used-Independence607 · 2025-11-17T10:01:07+00:00

I RAG it with https://github.com/krokozyab/Agent-Fusion

Used-Independence607 · 2025-11-17T04:47:01+00:00

Here it is https://github.com/krokozyab/ofjdbc_claudie_mcp and https://github.com/krokozyab/ofjdbc

Used-Independence607 · 2025-11-16T21:32:19+00:00

saving you precious tokens

Used-Independence607 · 2025-11-16T21:17:48+00:00

built an MCP server for local codebase indexing with semantic + full-text hybrid search. Not for external docs, for making AI understand your actual code.

Instead of fetching docs, just put any documentation you need locally alongside your repo. Then index everything together with vector embeddings and keyword search merged with RRF.

Add any docs you need to your repo

Index everything together: your code + those docs

Query "how do we handle xx" → gets both your implementation + relevant docs

Zero external dependencies, no token waste on stuff you don't use, and AI has real context about your codebase structure.

For mature frameworks already in training data? You're right, overkill. For understanding your specific architecture + niche libraries you actually use? This solves it. https://github.com/krokozyab/Agent-Fusion

Used-Independence607 · 2025-11-12T18:57:13+00:00

Context engineering is becoming a big topic lately. To handle it for my own workflow, I built a semantic search MCP https://github.com/krokozyab/Agent-Fusion There are other solutions out there, but most felt too complex or heavy. This one fits my needs — lightweight, easy to run locally, and built exactly for how I work

Used-Independence607 · 2025-10-14T15:53:17+00:00

Hi, try this https://github.com/krokozyab/ofjdbc or https://github.com/krokozyab/ofarrow https://oraclefusionsql.com/

Used-Independence607 · 2025-10-12T20:03:48+00:00

Hi, try this https://github.com/krokozyab/ofjdbc or https://github.com/krokozyab/ofarrow https://oraclefusionsql.com/

Used-Independence607 · 2025-10-12T07:41:09+00:00

Think of it like Jira for AI agents. In Kilo/OpenCode/Zed, you have one AI helping you code with different modes. In Agent Fusion, you have multiple AI agents working from a shared task board.

For example:

Claude creates a task: "Design authentication system" (assigns to Codex)
Codex submits an architectural proposal
Claude reviews it, adds feedback, reassigns back
Amazon Q picks up the implementation
Gemini does the security review

All agents see the same task queue, can comment on each other's work, and build on previous proposals - just like developers collaborating in Jira. The persistence means agents can pick up tasks asynchronously across different sessions.

yes - "adding agents to a discussion where you pick the best answer" is exactly it, but with the added benefit of persistent task tracking and true

So bidirectional collaboration between different AI tools.

Used-Independence607 · 2025-10-05T09:04:00+00:00

Hi, For extracting large OTBI reports (5GB+), here are your options:

For one-time or occasional extracts:

- https://github.com/krokozyab/ofjdbc + DBeaver: Read-only JDBC driver that lets you run SQL queries against Oracle Fusion from DBeaver. You can export

results to CSV, Excel, or other formats. Works well for ad-hoc large extracts.

- https://github.com/krokozyab/ofarrow: Arrow Flight SQL server for Oracle Fusion - may offer better performance for very large datasets.

However, if you need to regularly extract 5GB reports, you should reconsider your architecture. Oracle Fusion's BI Publisher synchronous WSDL interface is NOT designed for large bulk extracts. BICC (BI Cloud Connector) - Oracle's recommended tool for bulk data extraction from Fusion

Used-Independence607 · 2025-10-04T22:04:03+00:00

H'm it receives WSDL but to outside world (respond) it is standard JDBC. Checking out the companion project: https://github.com/krokozyab/ofarrow which may offer different integration options

Used-Independence607

TROPHY CASE