2026-08-02 Testing vault index chunking and topic clustering (by Claude)

This documents the debugging and analysis work behind 2026-08-02 Initial indexing of my vault on Mac mini (by Claude)‘s indexing pipeline, plus the clustering experiments run while looking for a way to build a topic tag taxonomy. The clustering approach here was ultimately dropped in favor of the plan in Manual topic tag library and AI model use (by Claude).

// Still working out of a copy of the vault /Users/josephrenner/Projects/vault copy for agent testing

Chunking bug investigation, found while running the full vault index

1. // Created a new script to run over the entire vault and clean up renames
2. // Ran script via python3 scripts/index_all.py --vault-root "/Users/josephrenner/Projects/vault copy for agent testing"
3. // Test rename worked and was reported in the terminal
4. // some files are being skipped
   1. // 1)chunking logic doesn’t consolidate some of the files down enough 2)Ollama killed by OS because it ran up against the OS memory
      • Root causes of the chunk length failure: chunk_markdown() packed paragraphs up to a word budget but never sub-split a single paragraph that was already oversized on its own, some chunks reach up to 44,899 tokens against nomic-embed-text’s 2048 token limit. Hard-split fallback for oversized paragraphs and liveness check to distinguish content errors from a dead server, are implemented in scripts/index_file.py
   • // Some still failing due to chunk length

Clustering exploration, k-means

1. Pull all chunk embeddings and their metadata out of the Chroma collection built in Phase 5, since clustering needs the raw vectors, not another embedding pass.
2. Determine the optimal number of clusters before committing to one, rather than just fixing k at 100. Write a small, separate one-off script, such as find_k.py, that runs k-means across a range like 60 to 150 and prints a table of k, inertia, and silhouette score for each value, no plotting or extra dependencies needed. Eyeball the table for the elbow, and pick the k near that range that best balances tight, well-separated clusters against staying close to a manageable, roughly 100-tag taxonomy. // created find_k.py for 5-150 k-means range. installed scikit-learn and numpy
   1. // running python3 scripts/find_k.py --k-min 5 --k-max 150 --step 5
      • // Inertia elbows out somewhere around k=40-60
      • // Silhouette score is very low, meaning the clusters have significant overlap
        • // Because the k-means is using Euclidean distance but nomic-embed-text is using cosine similarity, the unnormalized vectors have too much noise and appear to be dissimilar. Fixing by adding cosine space in index_file.pys collection creation, and normalization in find_k.py
        • // Had to remove old vault index via rm -rf vault_index
        • // Rerunning the indexing python3 scripts/index_all.py --vault-root "/Users/josephrenner/Projects/vault copy for agent testing"
        • // Still low silhouette, but that is fine since general knowledge does overlap a lot

• // Lots of clusters failing to be tagged. Updated the script to force a tag instead of the model quitting if it is too hard. Was able to accomplish running via python3 scripts/cluster_kmeans.py --k 65
  • Root cause of the failures: Ollama’s format: "json" only forces valid JSON syntax, not a required shape. On ambiguous, multi-topic clusters the model satisfied that constraint with an empty {} rather than actually answering. Fixed by passing a JSON Schema requiring a non-empty tag string as format, instead of the bare string "json".
• // Generated vault topic tag list based on k means of 65 - ultimately doesn’t create a list that I think is super useful

Clustering exploration, HDBSCAN

Run alongside the k-means path above, not instead of it, to see whether a density-based approach surfaces a better taxonomy or better edge-idea coverage.

1. Write a script that pulls the same normalized chunk embeddings and clusters them with HDBSCAN (sklearn.cluster.HDBSCAN) instead of k-means, with no fixed cluster count set in advance, so the algorithm finds however many natural, varying-density clusters actually exist in the data. Implemented as scripts/cluster_hdbscan.py, reusing name_cluster() and representative_indices() from cluster_kmeans.py.
2. Tune min_cluster_size, and optionally min_samples, instead of a target k, starting with a modest value and adjusting based on how many clusters and how much noise it produces.
   • First attempt, --min-cluster-size 30: found 3 clusters, sizes 33/134/1132, with 84.8% noise (7225/8524). Too conservative: the default eom selection method plus min_samples defaulting to min_cluster_size favored a few large, stable clusters over resolution.
   • Second attempt, --min-cluster-size 10 --min-samples 5 --cluster-selection-method leaf: found 70 clusters, sizes 10 to 81, median 16, but noise barely moved, 83.0% (7076/8524). This confirmed the vault’s embedding space is genuinely long-tailed: a modest set of tightly-clustered recurring topics, surrounded by a much larger body of relatively unique, one-off content that doesn’t have enough close neighbors to cluster at any threshold tried.
3. Note how many points get labeled as noise, cluster label -1, rather than assigned to any cluster, since these are the edge ideas: notes that do not fit neatly into any larger group.
4. Name the resulting clusters the same way as the k-means path, sending representative chunks from each to Qwen3 14B for a short tag name.
5. Review the noise points separately, either as one-off manual tags or left untagged for now, rather than forcing them into the fixed taxonomy. Written to noise_points.json by cluster_hdbscan.py.
6. Compare the two resulting tag lists side by side to see whether HDBSCAN surfaces topics the fixed k-means taxonomy missed or merged together, before deciding which one, or a combination of both, to use for the actual per-file tagging.

// Generated vault topic tag list based on HDBSCAN - created a more specific list, but didn’t bucket 83% of the clusters

Conclusion

// Instead I should develop the list and have each file associated to the tag I create

Neither clustering approach produced a taxonomy that felt genuinely useful: k-means’s fixed k forces every chunk into a cluster regardless of fit, and HDBSCAN’s density-based approach left the vast majority of the vault as unclustered noise no matter how the parameters were tuned.

See Manual topic tag library and AI model use (by Claude) for the resulting plan.