🟢 Open to roles · Full Stack ML Data Scientist

3-Fold Augmentation for Vector Documents

Transforming public arXiv metadata into vector documents with summaries, keywords, context labels, and embeddings—ready for vector search and content filtering.

Document ParsingGenerative PromptingPersonalized signalsEmbeddingsDistributed Process Automation

Avg processed (items/min)

186

Shards in flight

Latest batch

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Clean inputs. Smarter outputs. At scale.

Before / After JSON Diff

Loading sample documents… Fetching samples…

Prompt “Flipbook”

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Extract research problem, main findings, key terms, and a readable abstract summary.

Messages (system/user)

[
  {"role": "system", "content": "You are an expert at summarizing academia and peer-reviewed journals."},
  {"role": "system", "content": "Given the paper’s key sections, output the following fields:"},
  {"role": "system", "content": "1. Research_Problem: One sentence summary of the problem"},
  {"role": "system", "content": "2. Main_Findings: The most important result"},
  {"role": "system", "content": "3. Key_Terms: list of technical terms or concepts"},
  {"role": "system", "content": "4. Summarized_Abstract: Clear, readable abstract"},
  {"role": "user", "content": "Title: {title}\nAbstract: {abstract}\nIntroduction: {introduction}\nMethods: {methods}\nResults: {results}\nConclusion: {conclusion}"}
]

Paste into your API call’s messages array. Placeholders like {title} are templated server-side.

Sample Output

{
  "note": "No sample found"
}

Quick Embeddings Explainer

Done

sentence-transformers/all-MiniLM-L6-v2

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openai/text-embedding-3-small (via /api/embeddings)

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sentence-transformers/all-MiniLM-L6-v2

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openai/text-embedding-3-small (via /api/embeddings)

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cosine(Query A, Query B) — MiniLM

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cosine(Query A, Query B) — OpenAI

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Values shown are first 5 dims; cosine uses full (normalized) vectors.

Values above are normalized vectors (MiniLM via Xenova in-browser; OpenAI via your proxy). Use cosine similarity for search.

Embedding Pipeline Diagram

Data Processing Flowchart

Stateless workers · idempotent runs · retries on 5xx · rate-limited model calls · chunk long abstracts when needed.

Parallelization & Sharding

Split batch: assign each of Y nodes a deterministic slice (shard).
Each node processes its slice independently (stateless workers).
On completion, results are merged and loaded to the DB / index.
Design for retries: same shard re-run should be idempotent.

Coming Up: Vector DB Vespa

See our Vespa DB in GKE and why we choose it!

Vespa! →

🎧 Audio Guide: Page 3 · Processing and Vectors 🎧

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