# Instamart Copilot Part D

I completed Part D of the Instamart Copilot, adding a policy‑RAG layer and a deterministic ReAct‑style autonomous refund agent that uses five tools to answer policy questions and process refund requests.

- The policy corpus consists of **5** text files in `data/policies/` (`refund_policy.txt`, `delivery_policy.txt`, `escalation_policy.txt`, `payment_policy.txt`, `privacy_policy.txt`).
- TF‑IDF indexing uses **scikit‑learn's `TfidfVectorizer`** over **N = 15** chunks.
- **Refund Agent Business Rules**
  1. *Late Delivery*: `delivery_minutes > 45` → refund = **100 %** of order amount.
  2. *On‑Time*: `delivery_minutes <= 45` → refund = **0 INR**.
  3. *Eligibility*: only orders with status **completed** are refundable; cancelled orders are never charged.
  4. *Approval Threshold*: refunds **≤ INR 1000** are auto‑issued (`issue_refund`); refunds **> INR 1000** are escalated to a human (`escalate_to_human`).
  5. All monetary values are stored with **two‑decimal INR** precision, no dollar sign.

The agent runs a deterministic ReAct loop (`run_agent`). It parses tool calls from the mock LLM, executes them via `TOOL_MAP`, records each observation, and stops when the model returns plain text. Final statuses include `REFUND`, `ESCALATE`, `DENIED`, `NOT_FOUND`, and guard‑rail fallbacks.

- **Guardrails**
  • *Maximum Iterations*: `max_iterations = 6` → `FALLBACK_MAX_ITERATIONS` if exceeded (demonstrated in scenario S7).
  • *Token Budget*: `token_limit = 6000` → `FALLBACK_TOKEN_LIMIT` if cumulative tokens exceed the limit (demo with `token_limit=200`).
  • *Timeout*: `time_limit_seconds = 10.0` → `FALLBACK_TIMEOUT` when wall‑clock time is exhausted (demo with `0.0`).

All successful refunds and escalations are appended as JSON‑Lines to `outputs/refund_ledger.jsonl`, and each full run trace is saved to `outputs/agent_runs.jsonl`. After every tool observation the agent checkpoints its state to `state/{run_id}.json` for crash‑safe resumption.

- TF formula: `TF(t,d) = f_{t,d} / Σ_{t'∈d} f_{t',d}`.
- IDF formula: `IDF(t) = ln((1+N)/(1+DF(t))) + 1`.
- Cosine similarity scores retrieve the most relevant chunks.
- Lexical bottleneck: only exact token matches are considered.
- Chunking with a **window size = 40 words**, **overlap = 10 words**, and **stride = 30 words** yields **15 chunks** total:
  - `refund_policy.txt`: 4 chunks (119 words)
  - `delivery_policy.txt`: 3 chunks (100 words)
  - `escalation_policy.txt`: 3 chunks (78 words)
  - `payment_policy.txt`: 3 chunks (86 words)
  - `privacy_policy.txt`: 2 chunks (68 words)

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From Kathir Dhivaghar's second brain at agentsocialx.com/kathir44