# Apollo MIS — LLM Internship Assessment

**Candidate:** Kshamaa Suresh
**Date:** March 2026
**Time Spent:** ~5 hours

| Task | Time |
|------|------|
| Data exploration + DB setup | 30 min |
| Retrieval (BM25) | 45 min |
| Re-ranking (Claude API) | 1 hr |
| Streamlit frontend | 1 hr |
| System design write-up | 1 hr |
| Onboarding proposal | 45 min |

---

## 1. Data Setup

### What's in the dataset

60 exercises with the following fields: `id`, `title`, `description`, `tags`,
`body_part`, `difficulty`, `equipment`, `injury_focus`, `intensity`.

One trailing empty column (`Unnamed: 9`) was dropped. One bad value in
`difficulty` (`"body"`) was noted — treated as-is since it only affects
one row and doesn't break any logic.

### Why SQLite instead of PostgreSQL

The assessment suggests PostgreSQL, but SQLite is a better fit here for
a few practical reasons:

- No server to spin up — the database is a single file, which means the
  app works the same locally and on HuggingFace Spaces without any
  additional setup
- 60 rows is well within SQLite's range; it handles hundreds of thousands
  of rows without issue
- All queries are standard SQL, so migrating to PostgreSQL later would just
  be a connection string change

### Schema decision

I added a `search_text` column that pre-concatenates all the text fields
used in retrieval (title, description, tags, body_part, equipment,
injury_focus, intensity, difficulty). This avoids rebuilding the string at
query time and makes the BM25 index straightforward to build.

```python
# setup_db.py — key logic
search_text = " ".join([title, description, tags, body_part,
                        equipment, injury_focus, intensity, difficulty]).lower()
```

---

## 2. Query → Recommendations Pipeline

The pipeline has two clearly separated stages:

```
User query
    │
    ▼
Stage 1: BM25 Retrieval
    │  Tokenises the query and scores all 60 exercises
    │  Returns top 15 candidates with score > 0
    │  (falls back to top 15 regardless if fewer than 3 match)
    ▼
Stage 2: Claude Re-ranking
    │  Sends query + 15 candidates to Claude
    │  Claude scores and orders by relevance, returns JSON
    │  Returns top 5 with a one-sentence reason each
    ▼
Top 3–5 recommendations shown in UI
```

### Why two stages?

Sending all 60 exercises to Claude on every query would work at this size,
but it's the wrong pattern:

- At 100k+ exercises, sending everything to an LLM is not feasible
- BM25 quickly filters to a relevant subset — Claude only needs to reason
  over 15 candidates, not the full corpus
- This separation also makes each stage independently testable

### Stage 1 — BM25 Retrieval

BM25 (Best Match 25) is a standard keyword ranking algorithm. It scores
each exercise by how well its text matches the query tokens, accounting for
term frequency and document length.

**Why BM25 and not embeddings?**
- No model to download or host
- Works well for domain terms like "knee rehab", "plyometric", "no weights"
- Fast — scores all 60 docs in milliseconds
- At larger scale, a hybrid approach (BM25 + vector search) would be better

```python
# retrieval.py — core logic
corpus = [tokenize(ex["search_text"]) for ex in exercises]
bm25 = BM25Okapi(corpus)
scores = bm25.get_scores(tokenize(query))
```

### Stage 2 — LLM Re-ranking (Mistral-7B via HuggingFace free Inference API)

The LLM receives the query and the 15 BM25 candidates and is asked to
return a ranked JSON array of the top 5 with a one-sentence reason for each.

**Why re-rank with an LLM?**
- BM25 is purely lexical — it can miss a great match if the words don't
  overlap. An LLM understands intent: "low-impact" implies avoiding jumps
  and heavy loading even if those words don't appear in an exercise title.
- The LLM can reason about constraints: "no weights" should filter out
  barbell and dumbbell exercises even if they ranked highly on BM25.

**Why Mistral-7B via HuggingFace Inference API?**
- Completely free — requires only a free HuggingFace account token
- Strong instruction-following, handles JSON output reliably
- No local GPU or model download needed — the model runs on HF's servers
- Works natively on HuggingFace Spaces (the token is available as a
  built-in secret `HF_TOKEN`)

The prompt uses Mistral's `[INST]` format and asks for a JSON array only,
which makes parsing reliable. A regex fallback extracts the JSON if the
model adds any preamble text.

---

## 3. Backend Design

### How the backend handles each step

```
1. User submits query via Streamlit text input
2. retrieve(query, top_k=15) called → BM25 scores exercises → returns list of dicts
3. rerank(query, candidates, top_n=5) called → Claude API call → parses JSON → returns ranked list
4. Streamlit renders results with title, description, difficulty, reason
```

All of this runs in a single Python process in the Streamlit app. For a
production backend, this would be a FastAPI endpoint:

```python
@app.post("/recommend")
async def recommend(query: str):
    candidates = retrieve(query, top_k=15)
    results    = rerank(query, candidates, top_n=5)
    return {"results": results}
```

### How this scales to 100k+ exercises

| Problem | Solution |
|---------|----------|
| BM25 over 100k docs is slow in memory | Move to Elasticsearch or a vector store (FAISS/pgvector) with an index |
| Claude can't receive 100k candidates | BM25/vector search still returns only 15–20 candidates for re-ranking — this step doesn't change |
| Multiple simultaneous users | FastAPI handles async requests natively; Claude API calls are already I/O-bound so they work well with async/await |
| DB connection pooling | Use SQLAlchemy with a connection pool instead of raw sqlite3 |
| Cold start on DB index | Pre-build the BM25 index at startup and cache it in memory instead of rebuilding on every request |

---

## 4. Frontend

Built with Streamlit. The user can:
- Type a free-text query
- Click one of 4 example queries to pre-fill the input
- See the top 5 results with title, description, difficulty, body part,
  equipment, intensity, injury focus, and Claude's reason for ranking it

**Hosting on HuggingFace Spaces (fully free):**
1. Create a new Space with the Streamlit SDK
2. Push all project files
3. `HF_TOKEN` is automatically available in Spaces as a built-in secret —
   no manual configuration needed for the re-ranking step
4. HuggingFace handles the rest — free tier is sufficient

**Cost breakdown: $0**
- SQLite — free, no server
- BM25 — free, runs in memory
- Mistral-7B via HF Inference API — free tier
- Streamlit on HuggingFace Spaces — free tier

---

## 5. Onboarding Proposal — Personalisation

### What data to collect

During onboarding I'd ask users 4–5 quick questions:

| Question | Options | Why it matters |
|----------|---------|---------------|
| What's your main goal? | Rehab, strength, endurance, sport performance | Determines which exercises to prioritise |
| Any injuries or areas to avoid? | Knee, back, shoulder, groin, none | Hard filters retrieval — don't recommend knee exercises to someone with knee pain |
| What equipment do you have? | None, bands, dumbbells, barbell, full gym | Filters by equipment field |
| How would you rate your fitness level? | Beginner, intermediate, advanced | Maps to difficulty field |
| How intense do you want sessions to be? | Low, medium, high | Maps to intensity field |

### How it influences retrieval and ranking

**At retrieval time:** apply hard filters in the SQL query before BM25.
For example, a user who said "no equipment" gets a WHERE clause:
```sql
WHERE equipment IN ('none', 'bodyweight', 'band')
```
This reduces the candidate pool before BM25 even runs.

**At re-ranking time:** inject the user's profile into the Claude prompt:
```
User profile: rehab goal, knee injury, no equipment, beginner, low intensity
Query: "strengthen my legs"
```
Claude can then deprioritise any high-intensity or equipment-dependent
exercises even if they scored well on BM25.

### Inspiration from Spotify / Netflix

- **Spotify's Discover Weekly** builds a taste profile from listening
  history and uses it to weight recommendations. The equivalent here is
  tracking which exercises a user completes or skips — over time, the
  system learns their preferences without them having to re-answer
  onboarding questions.

- **Netflix's contextual recommendations** change based on time of day and
  recently watched content. A coaching app could similarly adapt — if a
  user just logged a hard leg session, the next session recommendation
  should shift toward upper body or recovery work.

- **Cold start problem:** Both Spotify and Netflix use onboarding to handle
  new users who have no history. The 4–5 question onboarding above serves
  the same purpose — it gives the system enough signal to make useful
  recommendations from day one.