grahama1970’s gists

grahama1970 / bm25_embedding_keyword_combined.aql

Last active January 16, 2025 14:03

ArangoDB hybrid search implementation combining BM25 text search, embedding similarity (using sentence-transformers), and keyword matching. Includes Python utilities and AQL query for intelligent document retrieval with configurable thresholds and scoring. Perhaps, use RapidFuzz for post-processing later

	LET results = (
	// Get embedding results
	LET embedding_results = (
	FOR doc IN glossary_view
	LET similarity = COSINE_SIMILARITY(doc.embedding, @embedding_search)
	FILTER similarity >= @embedding_similarity_threshold
	SORT similarity DESC
	LIMIT @top_n
	RETURN {
	doc: doc,

grahama1970 / get_env_value.sh

Last active January 22, 2025 01:10

Efficient .env Key Retrieval for Raycast A Raycast Script Command to search and copy environment variable values from a .env file. Supports exact matching, abbreviation shortcuts, and fuzzy search with fzf. Outputs the value to the terminal and clipboard for seamless workflows.

	#!/bin/bash

	# Description:
	# A Raycast script for quickly finding environment variables in .env files.
	# Matches keys in three ways:
	# 1. Abbreviations: "aak" → "AWS_ACCESS_KEY", "gpt" → "GITHUB_PAT_TOKEN"
	# 2. Partial matches: "shap" → "SHAPE", "aws" → "AWS_KEY"
	# 3. Fuzzy finding: "ath" → "AUTH_TOKEN"

	# Matched values are copied to clipboard and printed to terminal using pbcopy (brew install pbcopy or similar)

grahama1970 / globi_tab.applescript

Created January 22, 2025 21:12

GlobiTab is a Raycast script that intelligently manages Chrome tabs using quicklinks (like 'gh' for GitHub). Unlike Raycast's built-in quicklinks that always create new tabs, GlobiTab first checks if the target URL already exists in any window. If found, it switches to that tab instead of creating a duplicate.

	#!/usr/bin/osascript

	# Required parameters:
	# @raycast.schemaVersion 1
	# @raycast.title GlobiTab
	# @raycast.mode silent
	# @raycast.icon 🔍
	# @raycast.packageName GlobiTab
	# @raycast.argument1 { "type": "text", "placeholder": "Tab Name/URL/Keyword", "optional": false }

grahama1970 / ask-obsidian-result.tsx

Last active January 23, 2025 17:20

Debug for an Raycast extension that tries to access Obsidian's Smart Chat Conversations (within Raycast) to ask questions of local documents

	import { Action, Detail, LaunchProps } from "@raycast/api";
	import { getConfig } from "./utils/preferences";

	interface Preferences {
	obsidianVaultPath: string;
	}

	export default function ResultView(props: LaunchProps<{ context: { answer: string } }>) {
	const { obsidianVaultPath } = getConfig();

grahama1970 / ast_output.json

Last active January 26, 2025 22:24

Deepseek Structured vs (hacked) unstructured outputs: This project evaluates Deepseek's string-based (Markdown) and JSON-based outputs to determine which approach is better suited for structured storage and processing. By converting the Markdown output into an Abstract Syntax Tree (AST) and storing the results in ArangoDB, we test whether the ad…

	[
	{
	"type": "Heading",
	"children": [
	{
	"type": "RawText",
	"children": [],
	"content": "Paris: The Enchanting Capital of France"
	}
	],

grahama1970 / 01_inference.py

Last active January 31, 2025 13:35

Training a Distilbert model to determine question complexity before sent to a smolagent

	from complexity.file_utils import get_project_root, load_env_file
	import torch
	from transformers import (
	DistilBertTokenizerFast,
	DistilBertForSequenceClassification
	)
	from loguru import logger
	import os
	import time # Add this at the top with other imports

grahama1970 / rag_classifer_unified.py

Created January 31, 2025 16:19

RAG based Classifier for determining sentence complexity (proof of concept only)

	#!/usr/bin/env python3

	import os
	import time
	from typing import List, Dict, Any
	from functools import partial
	from concurrent.futures import ThreadPoolExecutor, as_completed

	import torch
	import torch.nn.functional as F

grahama1970 / create_vector_index.py

Created February 3, 2025 18:02

	from arango import ArangoClient
	from loguru import logger


	def validate_embeddings(db, collection_name, dimension):
	"""Validate embeddings with boolean AQL result."""
	try:
	# Collection name interpolation (must be sanitized)
	query = f"""
	RETURN COUNT(

grahama1970 / create_vector_index.py

Created February 3, 2025 18:02

	from arango import ArangoClient
	from loguru import logger


	def validate_embeddings(db, collection_name, dimension):
	"""Validate embeddings with boolean AQL result."""
	try:
	# Collection name interpolation (must be sanitized)
	query = f"""
	RETURN COUNT(

grahama1970 / 01_docker-compose-sglang.yml

Last active February 5, 2025 21:19

Trying to get Qwen2.5-VL-7B to work with CUDA 12.8.

	services:
	# ---------------------------
	# SGLang Service
	# ---------------------------
	sglang-service:
	# image: lmsysorg/sglang:latest
	build:
	context: .
	dockerfile: Dockerfile_v2.sglang
	container_name: sglang-service

Graham grahama1970