TOOLSMarch 18, 2026
I Turned 16 Months of Google Search Console Data Into a Vector DB
I had a simple question: what if I could talk directly to my search metrics? Here is how I loaded 16 months of queries and clicks into ChromaDB.
Talking to Google Search Console Data
Storing raw search keywords in a spreadsheet is old school. By embedding 16 months of search performance data (queries, impressions, CTR, positions) into a vector database, we can perform semantic querying and automated clustering.
The Architecture
- Data Source: Google Search Console API.
- Embeddings Model:
text-embedding-3-small(OpenAI). - Vector Database: ChromaDB (running locally).
- Agent Interface: Streamlit + LangChain.
Now, queries like “What are my rising transactional intents that aren’t ranking on page 1?” return immediately with full context, bypassing complex Excel filters.
tags:
#vector db#search console#llm