Research Notes Conversion

Tue, 01 Sep 2026 00:00:00 +0000

An old research idea is easier to revisit when I can find it beside the papers that discuss the same topic. Research Notes Conversion brings Word essays and research notes into that search collection. It reads the document’s existing headings and formatting directly, so the conversion itself needs neither image recognition nor an AI model.

Finding a passage also needs a useful locator. Word documents do not carry the PDF page markers the search tools originally expected. The indexer now uses headings where available and paragraph numbers as a fallback, so a short note can be cited without inventing a page number.

For an illustrative conversion, a heading such as Research idea and its following paragraphs become a heading and ordinary text in the searchable version. A reference such as Paragraphs 4–6 tells me where to look within that note. This demonstrates the format, not an excerpt from my private writing.

The original Word file stays in place. Conversion creates a Markdown copy, retaining headings, emphasis, and lists. Optional registration with the adds a catalog description and a representation used for search, using API calls; conversion alone remains local.

Reusing the course-material indexer exposed a useful mismatch: its original document labels tried to classify personal essays as textbooks or handwritten notes. Giving the research collection its own vocabulary fixed that distinction. Search also keeps each result’s collection identity, so two folders both named “notes” cannot be confused merely because their names match.

The early validation used 19 essay and research-note files. After the classification and locator fixes, topic queries could search this collection and Academic Hub together, returning relevant material from the appropriate sources. That is a limited recorded trial, not evidence that every query will find everything relevant.

The converter still works on selected local files. Direct Google Drive ingestion and a complete literature-review workflow are beyond its current interface. adds papers to the same search infrastructure; the documents conversion and indexing options.

Source Indexer

Sun, 30 Aug 2026 00:00:00 +0000

A question about eigenvalues might belong with a textbook proof, a lecture explanation, or an exercise I have already attempted. Source Indexer searches converted course materials in two stages: first finding promising documents, then locating passages inside them. The result includes a heading, page, or paragraph reference so I can continue reading where the match came from.

The search catalog has a record for each document, much like a library card with a brief description and subject labels. These descriptions help find material; the longer summaries I study are written by the answering and guide tools. A record’s identity comes from the source file’s contents rather than its filename, so renaming a PDF does not have to create a new document. Course-level records narrow the search before it compares individual files.

Give other study tools something to read

The and converters register their outputs with the indexer. Search uses document descriptions, subject labels, and numerical representations of meaning to rank related material, then returns excerpts from selected documents. PDF-derived passages can carry page references; Word-derived notes without page markers can use headings or numbered paragraphs instead.

Several study tools use that foundation:

retrieves passages for a question and records their references with the answer. can reopen the exact passages saved with a study summary to develop it further.
selects indexed sources for a topic list, then drafts and revises a longer guide.
retrieves evidence for explanations and connections, including the conditions a theorem needs.

The same search infrastructure supports , keeping their collection identities distinct from course materials. Each downstream tool still has to interpret the passages it receives.

Similar subjects are not always duplicate notes

A lecture may exist as handwriting alone and as a later version with slides pasted beside the same handwriting. Searching both can crowd out other useful sources. Whole-document similarity was too broad to distinguish these pairs from different lectures on the same subject. The subset linker instead compares short word sequences from the handwritten portions, then hides the smaller version when its fuller counterpart survives the search. Both remain accessible.

Links across different years of a course are another feature. The tool reuses existing document comparisons to identify related material, with uncertain matches held for review. That supports revisiting an older explanation without claiming every note has already been matched correctly.

An early August 2026 checkpoint recorded 30 healthy document cards in one course, with 14 subject labels. Those are historical catalog checks, not current totals or a measure of search accuracy. Dense proofs and narrative notes can still need different passage boundaries. can report missing or stale outputs, but its available repair is narrowly scoped rather than a general automatic cleanup. The describes the catalog, search, and linking behavior.

Vector Database |

Research Notes Conversion

Source Indexer

Give other study tools something to read

Similar subjects are not always duplicate notes