sync: update notebooks from delphos

Author: github-actions[bot]

docs/case_studies/match-software-vendors-to-requirements/notebook.ipynb

@@ -3,7 +3,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# Fuzzy join two Pandas DataFrames using LLMs\n\nThis notebook demonstrates the [everyrow.io SDK](https://github.com/futuresearch/everyrow-sdk) merge capabilities:\n\n1. **Fuzzy String Matching** - Handling typos and corrupted data\n2. **LLM Merge** - Matching without common columns (company ↔ ticker)\n3. **Web Merge** - Dynamic data requiring real-time verification (CEO matching)\n\nThe SDK implements a cascade: **Exact → Fuzzy → LLM → Web**, using the simplest method that works."
+   "source": "# Fuzzy join two Pandas DataFrames using LLMs\n\nThis notebook demonstrates the [everyrow.io SDK](https://github.com/futuresearch/futuresearch-python) merge capabilities:\n\n1. **Fuzzy String Matching** - Handling typos and corrupted data\n2. **LLM Merge** - Matching without common columns (company ↔ ticker)\n3. **Web Merge** - Dynamic data requiring real-time verification (CEO matching)\n\nThe SDK implements a cascade: **Exact → Fuzzy → LLM → Web**, using the simplest method that works."
   },
   {
    "cell_type": "code",
@@ -20,7 +20,7 @@
     "\n",
     "import os\n",
     "if \"EVERYROW_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"EVERYROW_API_KEY\"] = \"your-api-key-here\"  # Get one at everyrow.io\n"
+    "    os.environ[\"EVERYROW_API_KEY\"] = \"your-api-key-here\"  # Get one at futuresearch.ai\n"
    ]
   },
   {
@@ -247,7 +247,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here we pick the right columns from the groun truth, apply the noise, and call the everyrow.io SDK to perform the merge:"
+    "Here we pick the right columns from the groun truth, apply the noise, and call the FutureSearch SDK to perform the merge:"
    ]
   },
   {
@@ -491,7 +491,7 @@
    "version": "3.13.9"
   },
   "everyrow": {
-   "description": "Python notebook testing everyrow's merge cascade at 0%, 5%, and 10% name corruption on 438 S&P 500 companies. Demonstrates exact, fuzzy, LLM, and web matching for company names, tickers, and CEO data."
+   "description": "Python notebook testing FutureSearch's merge cascade at 0%, 5%, and 10% name corruption on 438 S&P 500 companies. Demonstrates exact, fuzzy, LLM, and web matching for company names, tickers, and CEO data."
   }
  },
  "nbformat": 4,