Legal/Medical Research Engine

Architecture

How the system is shaped. Full implementation stays private.

1. Step 1

   Ingest

   Chunk EU legal and medical guideline text with provenance metadata.

2. Step 2

   Index

   Embedding model + vector store (full Chroma stack in private repo). Free demo uses a curated Worker corpus.

3. Step 3

   Retrieve

   Hybrid semantic + keyword retrieval with top-k passages.

4. Step 4

   Answer

   Extractive synthesis with citations and confidence. No paid LLM required for the portfolio demo.

5. Step 5

   Privacy

   Queries are not persisted. Portfolio demo discloses curated-subset limits.

Public demo scope

The in-page widget uses a curated Worker corpus. Queries are not stored. The full 65,000 document index stays on request.

Algorithm

Hybrid retrieve-then-extract

Score passages by query overlap / semantic similarity, then build a cited answer from top excerpts.

tokens = tokenize(question)
candidates = rank(corpus, tokens, top_k)
answer = stitch_excerpts(candidates)  # with [1][2] citations
confidence = f(top_score, k)
return answer, sources, confidence

Key logic

Compact illustrative snippet (Hybrid retrieval score (illustrative)). Not the full codebase.

function hybridScore(doc, queryTokens, queryVec, alpha = 0.65) {
  const bm25 = bm25Score(doc.tokens, queryTokens);
  const semantic = cosine(queryVec, doc.embedding);
  return alpha * semantic + (1 - alpha) * bm25;
}

function rankPassages(corpus, question, topK = 8) {
  const tokens = tokenize(question);
  const queryVec = embed(question);
  return corpus
    .map((doc) => ({ doc, score: hybridScore(doc, tokens, queryVec) }))
    .sort((a, b) => b.score - a.score)
    .slice(0, topK);
}

Stack