From d9c7e81f5827aa0e6db425292bf6721cf8db964e Mon Sep 17 00:00:00 2001 From: vidushi agarwal Date: Sat, 16 May 2026 21:17:06 +0530 Subject: [PATCH] End-to-End Protype for OAN --- .gitignore | 23 + .python-version | 1 + DATA_SCHEMA.md | 117 +++ analyzer.py | 148 ++++ anonymizer.py | 173 +++++ behavior_scorer.py | 87 +++ build_dataset.py | 369 ++++++++++ data_augmenter.py | 115 +++ dependencies.txt | 10 + init_env.sh | 11 + lang_utils.py | 39 + pyproject.toml | 18 + session_dedup.py | 288 ++++++++ test_builder.py | 397 ++++++++++ types_def.py | 60 ++ user_interactions.json | 574 +++++++++++++++ uv.lock | 1562 ++++++++++++++++++++++++++++++++++++++++ 17 files changed, 3992 insertions(+) create mode 100644 .gitignore create mode 100644 .python-version create mode 100644 DATA_SCHEMA.md create mode 100644 analyzer.py create mode 100644 anonymizer.py create mode 100644 behavior_scorer.py create mode 100644 build_dataset.py create mode 100644 data_augmenter.py create mode 100644 dependencies.txt create mode 100644 init_env.sh create mode 100644 lang_utils.py create mode 100644 pyproject.toml create mode 100644 session_dedup.py create mode 100644 test_builder.py create mode 100644 types_def.py create mode 100644 user_interactions.json create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..9afe1aa --- /dev/null +++ b/.gitignore @@ -0,0 +1,23 @@ + +.venv/ +venv/ +env/ +ENV/ + +__pycache__/ +*.pyc +*.pyo +*.pyd +.pytest_cache/ +.coverage +htmlcov/ +.mypy_cache/ +.ruff_cache/ + +training_setup_logs/ +output/ +*.log + +.DS_Store +.vscode/ +.idea/ \ No newline at end of file diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..6324d40 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.14 diff --git a/DATA_SCHEMA.md b/DATA_SCHEMA.md new file mode 100644 index 0000000..0c0c1a0 --- /dev/null +++ b/DATA_SCHEMA.md @@ -0,0 +1,117 @@ +# Schema & Pipeline Documentation + +## Input Log Schema (`LogSession`) + +| Field | Type | Description | +|---|---|---| +| `user_question` | string | Raw user query (PII-redacted before export) | +| `bot_response` | string | Final model response (PII-redacted before export) | +| `agent_turns` | list | Ordered tool-call / tool-return turns between question and response | +| `session_id` | string? | Optional session identifier (not exported to training artifacts) | +| `language` | string? | Optional language tag (e.g. `en`, `hi`) | +| `domain` | string? | Optional domain tag (e.g. `agri`, `weather`) | + +### `AgentTurn.parts[].part_kind` values + +| Value | Meaning | +|---|---| +| `tool-call` | Model requested a tool; has `tool_name`, `args`, `tool_call_id` | +| `tool-return` | Tool responded; has `tool_name`, `content`, `tool_call_id` | + +--- + +## SFT JSONL Schema (`sft_train.jsonl`, `sft_eval.jsonl`) + +Each line is a JSON object: + +```json +{ + "messages": [ + {"role": "user", "content": "..."}, + {"role": "assistant", "content": null, "tool_calls": [{"type": "function", "function": {"name": "...", "arguments": "{}"}, "id": "call_1"}]}, + {"role": "tool", "tool_call_id": "call_1", "content": "..."}, + {"role": "assistant", "content": "final response"} + ], + "metadata": { + "tool_count": 2, + "unique_tools": ["weather_forecast", "fetch_agristack_data"], + "has_recovery": false, + "complexity_tier": "moderate", + "is_agentic": true + } +} +``` + +- Compatible with TRL `SFTTrainer` and Hugging Face `datasets` directly. +- Tool-call format matches OpenAI function-calling chat template used by Gemma, Llama, Qwen. +- All `content` fields are PII-redacted before writing. + +--- + +## DPO JSONL Schema (`dpo_train.jsonl`, `dpo_eval.jsonl`) + +Each line is a JSON object: + +```json +{ + "prompt": [{"role": "user", "content": "..."}], + "chosen": [{"role": "assistant", "content": "correct model response"}], + "rejected": [{"role": "assistant", "content": "I cannot help with that. correct model response"}], + "metadata": { "...same as SFT metadata..." }, + "synthetic": true +} +``` + +- `prompt`: conversation prefix up to and including the last user turn. +- `chosen`: PII-redacted actual bot response. +- `rejected`: synthetic negative (prefixed refusal). `"synthetic": true` marks these for filtering once real preference pairs (thumbs, edits, failure/success pairs) are available. +- Compatible with TRL `DPOTrainer` (`prompt`, `chosen`, `rejected` are the required fields). + +--- + +## Complexity Tags & Recommended Training Schedule + +| `complexity_tier` | Condition | Recommended use | +|---|---|---| +| `simple` | 0 tool calls | Warm-up / instruction-following phase | +| `moderate` | 1–2 tool calls, no recovery | Main SFT bulk training | +| `complex` | 3+ tool calls **or** `has_recovery=true` | Later-stage fine-tuning; up-weight in DPO | + +Trainers can filter or sample by `metadata.complexity_tier` for staged curriculum or flat mixture runs without any pipeline changes. + +--- + +## Split Strategy + +Sessions are assigned to `train` or `eval` using a **hash of `user_question`** (MD5 mod 100), not a random index. This ensures: +- Near-duplicate or repeated queries always land in the **same split**. +- No prompt leakage between `sft_train` and `dpo_train` preference sets. +- Splits are **deterministic and reproducible** across pipeline runs. + +Default ratio: 80% train / 20% eval (configurable via `--split-ratio`). + +--- + +## PII Handling + +Detected entity types: `PHONE_NUMBER`, `EMAIL_ADDRESS`, `URL`, `IN_AADHAAR` + +- Replaced with indexed placeholders: ``, ``, ``, etc. +- Placeholders are **consistent within a session** (same entity → same placeholder across SFT and DPO exports for that session). +- Raw PII is logged to an in-memory audit list accessible via `audit_sample()` in `anonymizer.py` for human review of false negatives. +- `PERSON` entity detection is intentionally disabled to avoid false positives on crop names and place names in agricultural context. + +**Residual risk:** Indirect identification via rare location + crop combinations is not eliminated by redaction alone. Human audit sampling is required before production export. + +--- + +## Student Model Criteria + +Target: smallest dense model ≤ 32B parameters that achieves parity with the teacher on the held-out `sft_eval.jsonl` set. + +Acceptance threshold (to be confirmed with mentor): +- Tool-call accuracy (correct tool name + valid args) ≥ teacher baseline on `sft_eval` +- Response quality score (persona adherence, checked via rule layer for verifiable rules) within 5% of teacher +- End-to-end latency: 4–5 tool calls + final response within ~4 seconds on 8×H100 + +Filter applied to student training data: exclude sessions where `tool_count > 4` or total token length exceeds the student model's context window. \ No newline at end of file diff --git a/analyzer.py b/analyzer.py new file mode 100644 index 0000000..5be6ca4 --- /dev/null +++ b/analyzer.py @@ -0,0 +1,148 @@ +"""Trajectory validation and complexity tagging for agent sessions.""" +from __future__ import annotations + +from types_def import LogSession, TrainingMetadata + +# ── Tool name allowlist ─────────────────────────────────────────────────────── +# Set to None to skip allowlist checking. +ALLOWED_TOOL_NAMES: set[str] | None = { + "weather_forecast", + "fetch_agristack_data", + "crop_recommendation", + "subsidy_lookup", + "market_price", + "pest_advisory", + "soil_report", + "irrigation_schedule", + #can keep adding more tool names here +} + +# Words that signal an ambiguous or under-specified question +_AMBIGUITY_TOKENS: set[str] = { + # English + "what", "how", "when", "why", "which", "where", "any", "some", + "maybe", "possibly", "sometimes", "generally", + # Hindi / transliterated + "kya", "kab", "kyun", "kaise", "kaun", "kuch", "koi", + # Marathi + "kay", "keva", "kasa", +} + + +def validate_trajectory(session: LogSession) -> bool: + """Return True if the agent trajectory is structurally valid. + + Rules + ----- + - tool-call parts must have a non-empty ``tool_name``. + - If ``ALLOWED_TOOL_NAMES`` is set, ``tool_name`` must be in the allowlist. + - tool-return parts must have a matching tool-call (by ``tool_call_id``) + appearing earlier in the sequence. + - tool-return with ``content = None`` is rejected; ``content = ""`` is + accepted (explicit empty return is a valid signal). + """ + if not session.agent_turns: + return False # Reject completely empty or malformed sessions (no meaningful trajectory) + + tool_calls_seen: set[str] = set() + + for turn in session.agent_turns: + for part in turn.parts: + if part.part_kind == "tool-call": + if not part.tool_name: + return False + if ( + ALLOWED_TOOL_NAMES is not None + and part.tool_name not in ALLOWED_TOOL_NAMES + ): + return False + if part.tool_call_id: + tool_calls_seen.add(part.tool_call_id) + + elif part.part_kind == "tool-return": + # content=None means the field was never set — reject + # content="" means the tool explicitly returned nothing — accept + if part.content is None: + return False + if part.tool_call_id and part.tool_call_id not in tool_calls_seen: + return False + + return True + + +def _ambiguity_score(text: str) -> float: + """Heuristic ambiguity score in [0, 1]. + + Counts the fraction of tokens that are in :data:`_AMBIGUITY_TOKENS`. + """ + if not text: + return 0.0 + tokens = text.lower().split() + if not tokens: + return 0.0 + hits = sum(1 for t in tokens if t.strip("?.,!") in _AMBIGUITY_TOKENS) + return round(min(hits / len(tokens), 1.0), 3) + + +def tag_complexity(session: LogSession, max_tokens: int = 8192) -> TrainingMetadata: + """Compute and return :class:`TrainingMetadata` for *session*. + + Complexity tiers + ---------------- + ``simple`` + 0 tool calls — instruction-following warmup data. + ``moderate`` + 1–3 tool calls with no error recovery — main SFT bulk. + ``complex`` + 4+ tool calls **or** any recovery step — late-stage / DPO up-weight. + """ + num_tools = 0 + unique_tools: set[str] = set() + has_recovery = False + total_tokens = 0 + + for turn in session.agent_turns: + if turn.usage: + total_tokens += ( + turn.usage.get("input_tokens", 0) + turn.usage.get("output_tokens", 0) + ) + + for p in turn.parts: + if p.part_kind == "tool-call": + num_tools += 1 + if p.tool_name: + unique_tools.add(p.tool_name) + if p.part_kind == "tool-return" and p.content: + content_lower = str(p.content).lower() + if any( + err in content_lower + for err in ["error", "timeout", "failed", "no results"] + ): + has_recovery = True + + # Revised tier thresholds + if num_tools == 0: + complexity = "simple" + elif num_tools <= 3 and not has_recovery: + complexity = "moderate" + else: + complexity = "complex" + + student_eligible = not (num_tools > 4 or total_tokens > max_tokens) + + # multi_turn_depth: number of distinct user turns (always ≥1) + # In the current schema every session is a single user question; this + # counter is a placeholder for future multi-turn schema support. + multi_turn_depth = 1 + + return TrainingMetadata( + tool_count=num_tools, + unique_tools=list(unique_tools), + has_recovery=has_recovery, + complexity_tier=complexity, + is_agentic=num_tools > 0, + total_tokens=total_tokens, + student_eligible=student_eligible, + multi_turn_depth=multi_turn_depth, + ambiguity_score=_ambiguity_score(session.user_question), + ) \ No newline at end of file diff --git a/anonymizer.py b/anonymizer.py new file mode 100644 index 0000000..ddf116c --- /dev/null +++ b/anonymizer.py @@ -0,0 +1,173 @@ +"""PII redaction using Microsoft Presidio + custom Indian-ID recognizers. + +Supported entity types: + PHONE_NUMBER, EMAIL_ADDRESS, URL, + IN_AADHAAR – 12-digit Aadhaar (grouped or plain) + IN_PAN – 10-character PAN card (AAAAA9999A) + IN_VOTER_ID – 10-character Voter ID (AAA9999999) + +For non-English sessions the same patterns are applied via regex directly +(Presidio's NLP pipeline is English-only). +""" +import re +import collections + +import spacy + +try: + spacy.util.get_package_path("en_core_web_lg") +except Exception: + raise ImportError( + "Missing required spacy model. " + "Run: init_env.sh or python -m spacy download en_core_web_lg" + ) + +from presidio_analyzer import AnalyzerEngine, PatternRecognizer, Pattern # type: ignore + +analyzer = AnalyzerEngine() + +# ── Indian-specific recognizers ────────────────────────────────────────────── + +# Aadhaar: 12 digits optionally grouped as XXXX-XXXX-XXXX or XXXX XXXX XXXX +_AADHAAR_PATTERN = Pattern( + name="aadhaar", + regex=r"\b\d{4}[- ]?\d{4}[- ]?\d{4}\b", + score=0.85, +) +analyzer.registry.add_recognizer( + PatternRecognizer(supported_entity="IN_AADHAAR", patterns=[_AADHAAR_PATTERN]) +) + +# PAN card: 5 uppercase letters, 4 digits, 1 uppercase letter +_PAN_PATTERN = Pattern( + name="pan", + regex=r"\b[A-Z]{5}[0-9]{4}[A-Z]\b", + score=0.9, +) +analyzer.registry.add_recognizer( + PatternRecognizer(supported_entity="IN_PAN", patterns=[_PAN_PATTERN]) +) + +# Voter ID (EPIC): 3 uppercase letters followed by 7 digits +_VOTER_PATTERN = Pattern( + name="voter_id", + regex=r"\b[A-Z]{3}[0-9]{7}\b", + score=0.8, +) +analyzer.registry.add_recognizer( + PatternRecognizer(supported_entity="IN_VOTER_ID", patterns=[_VOTER_PATTERN]) +) + +# Phone: +91, 0, or plain 10 digits starting with 6-9 +_PHONE_PATTERN = Pattern( + name="indian_phone", + regex=r"\b(?:\+?91[- ]?)?(?:0[- ]?)?[6789]\d{2}[- ]?\d{3}[- ]?\d{4}\b", + score=0.9, +) +analyzer.registry.add_recognizer( + PatternRecognizer(supported_entity="IN_PHONE_NUMBER", patterns=[_PHONE_PATTERN]) +) + +# Entities to detect — PERSON excluded to avoid false positives on crop/place names +ENTITIES = [ + "IN_PHONE_NUMBER", + "EMAIL_ADDRESS", + "URL", + "IN_AADHAAR", + "IN_PAN", + "IN_VOTER_ID", +] + +# ── Regex patterns for non-English (Presidio NLP is English-only) ──────────── +_NON_EN_PATTERNS: list[tuple[str, str]] = [ + ("IN_PHONE_NUMBER", r"\b(?:\+?91[- ]?)?(?:0[- ]?)?[6789]\d{2}[- ]?\d{3}[- ]?\d{4}\b"), + ("IN_AADHAAR", r"\b\d{4}[- ]?\d{4}[- ]?\d{4}\b"), + ("EMAIL_ADDRESS", r"[a-zA-Z0-9._%+\-]+@[a-zA-Z0-9.\-]+\.[a-zA-Z]{2,}"), + ("IN_PAN", r"\b[A-Z]{5}[0-9]{4}[A-Z]\b"), + ("IN_VOTER_ID", r"\b[A-Z]{3}[0-9]{7}\b"), +] + + +def redact_pii(text: str, language: str = "en", _counts: dict | None = None): + """Redact PII in *text*. + + Parameters + ---------- + text: + Raw text to redact. + language: + BCP-47 primary subtag (e.g. ``"en"``, ``"hi"``). When ``"en"``, + Presidio's full NLP pipeline is used. For all other languages a + regex-only fallback is applied. + _counts: + Shared counter dict for consistent placeholder numbering within a + session. Pass the *same* dict for every call in one session. + + Returns + ------- + (redacted_text, audit_entries) + ``audit_entries`` is a list of dicts with keys + ``original_span``, ``placeholder``, ``entity_type``. + """ + if not text: + return text, [] + + if _counts is None: + _counts = collections.defaultdict(int) + + audit_entries: list[dict] = [] + + if language != "en": + # Regex-only path for non-English text + matches: list[tuple[str, int, int]] = [] + for entity_type, pattern in _NON_EN_PATTERNS: + for m in re.finditer(pattern, text): + matches.append((entity_type, m.start(), m.end())) + + # Filter out overlapping entities (keep the longest span) + # Sort by start index ascending, then length descending + sorted_matches = sorted(matches, key=lambda x: (x[1], -(x[2] - x[1]))) + filtered_matches = [] + for m in sorted_matches: + if not filtered_matches or m[1] >= filtered_matches[-1][2]: + filtered_matches.append(m) + + # Sort right-to-left so replacements don't shift offsets + filtered_matches.sort(key=lambda x: x[1], reverse=True) + + redacted_text = text + for r_type, start, end in filtered_matches: + entity_value = text[start:end] + _counts[r_type] += 1 + placeholder = f"<{r_type}_{_counts[r_type]}>" + audit_entries.append( + {"original_span": entity_value, "placeholder": placeholder, "entity_type": r_type} + ) + redacted_text = redacted_text[:start] + placeholder + redacted_text[end:] + return redacted_text, audit_entries + + # Full Presidio NLP path for English + results = analyzer.analyze(text=text, entities=ENTITIES, language="en") + + # Filter out overlapping entities (keep the longest span) + # Sort by start index ascending, then length descending + sorted_results = sorted(results, key=lambda x: (x.start, -(x.end - x.start))) + filtered_results = [] + for r in sorted_results: + if not filtered_results or r.start >= filtered_results[-1].end: + filtered_results.append(r) + + # Sort right-to-left for safe replacement + filtered_results = sorted(filtered_results, key=lambda x: x.start, reverse=True) + redacted_text = text + + for r in filtered_results: + entity_value = text[r.start : r.end] + _counts[r.entity_type] += 1 + placeholder = f"<{r.entity_type}_{_counts[r.entity_type]}>" + audit_entries.append( + {"original_span": entity_value, "placeholder": placeholder, "entity_type": r.entity_type} + ) + redacted_text = redacted_text[: r.start] + placeholder + redacted_text[r.end :] + + return redacted_text, audit_entries \ No newline at end of file diff --git a/behavior_scorer.py b/behavior_scorer.py new file mode 100644 index 0000000..20c928f --- /dev/null +++ b/behavior_scorer.py @@ -0,0 +1,87 @@ +"""Rule-based persona scoring for the OpenAgriNet bot. + +The bot persona is a helpful, honest, multilingual agricultural assistant. +This module checks the *bot_response* (and optionally the tool results) +against verifiable persona rules and returns a score in [0.0, 1.0]. + +Score interpretation +-------------------- +≥ 0.8 High-quality response — include in both SFT and DPO chosen. +0.4–0.8 Acceptable — include in SFT only. +< 0.4 Poor quality — exclude from training; flag for human review. + +The threshold for DPO chosen exclusion is 0.4 (see exporter.py). +""" +from __future__ import annotations + +import re + +from types_def import LogSession + +# ── Penalty signals ────────────────────────────────────────────────────────── + +# Over-refusal: bot refused a query it should have answered +_OVER_REFUSAL_PHRASES = [ + r"\bcannot help\b", + r"\bunable to assist\b", + r"\b(i|I) (don't|do not) know\b", + r"\bsorry,? (i|I) (can't|cannot)\b", +] + +# Hallucinated scheme names that do not exist in Indian agriculture +_HALLUCINATED_SCHEMES = [ + "pradhan mantri krishi samriddhi", # not a real scheme + "kisan welfare fund 2025", # fictional +] + +# Responses that lack any hedging on uncertain information +_HEDGE_SIGNALS = [ + "approximately", "around", "typically", "generally", "may vary", + "consult", "check with", "subject to", "up to", + # Hindi/Marathi equivalents (transliterated) + "lagbhag", "samantan", "aaspaas", +] + + +def score_persona(session: LogSession) -> float: + """Return a persona adherence score in [0.0, 1.0] for *session*. + + Scoring is additive-penalty: start at 1.0, deduct for each violation. + """ + response = session.bot_response or "" + response_lower = response.lower() + score = 1.0 + + # 1. Over-refusal penalty (-0.3 each, capped) + refusal_hits = sum( + 1 for p in _OVER_REFUSAL_PHRASES if re.search(p, response_lower) + ) + score -= min(refusal_hits * 0.3, 0.6) + + # 2. Hallucinated scheme penalty (-0.2 each) + for scheme in _HALLUCINATED_SCHEMES: + if scheme in response_lower: + score -= 0.2 + + # 3. Missing hedge on uncertain numeric claims (-0.1) + # Heuristic: if response contains a number + "%" but no hedge word, penalise + has_pct_claim = bool(re.search(r"\d+\s*%", response)) + has_hedge = any(h in response_lower for h in _HEDGE_SIGNALS) + if has_pct_claim and not has_hedge: + score -= 0.1 + + # 4. Language mismatch penalty (-0.2) + # If session is tagged as non-English but the bot responded entirely in + # ASCII (likely English), penalise. + # We skip this penalty if the user question was also transliterated (mostly ASCII). + if session.language and session.language != "en": + bot_non_ascii = sum(1 for c in response if ord(c) > 127) / max(len(response), 1) + user_non_ascii = sum(1 for c in session.user_question if ord(c) > 127) / max(len(session.user_question), 1) + if bot_non_ascii < 0.05 and user_non_ascii >= 0.05: # bot is ASCII but user used native script + score -= 0.2 + + # 5. Empty response (-1.0) + if not response.strip(): + score = 0.0 + + return round(max(score, 0.0), 3) diff --git a/build_dataset.py b/build_dataset.py new file mode 100644 index 0000000..04d3563 --- /dev/null +++ b/build_dataset.py @@ -0,0 +1,369 @@ +"""Export pipeline: SFT and DPO JSONL writers. + +- Language is auto-detected before PII redaction so the correct code path + always runs. +- Failed sessions are counted and reported, never silently dropped. +- Deduplication is applied before export. +- Persona scoring gates DPO chosen quality. +- DPO prompt includes full tool context (not just the first user message). +- DPO chosen / rejected are message lists, not bare strings. +- Synthetic negatives are semantically meaningful (three rejection types). +""" +import json +import os +import hashlib +import argparse +import collections +import logging + +from lang_utils import ensure_language +from anonymizer import redact_pii +from types_def import LogSession, DPORecord +from analyzer import validate_trajectory, tag_complexity +from session_dedup import deduplicate_sessions +from behavior_scorer import score_persona +from data_augmenter import ( + generate_hard_case, + generate_failure_correction, +) + +logger = logging.getLogger(__name__) + +# Sessions scoring below this threshold are excluded from DPO chosen +_PERSONA_THRESHOLD = 0.4 + + +# ── Helpers ────────────────────────────────────────────────────────────────── + +def split_sessions( + sessions: list[LogSession], train_ratio: float = 0.8, seed: int = 42 +) -> tuple[list[LogSession], list[LogSession]]: + """Deterministic train/eval split based on a hash of user_question.""" + train_sessions: list[LogSession] = [] + eval_sessions: list[LogSession] = [] + for s in sessions: + h = int(hashlib.md5(s.user_question.encode("utf-8")).hexdigest(), 16) + if (h % 100) < (train_ratio * 100): + train_sessions.append(s) + else: + eval_sessions.append(s) + return train_sessions, eval_sessions + + +def get_trace_id(session: LogSession) -> str: + key = session.session_id or session.user_question + return hashlib.md5(key.encode("utf-8")).hexdigest()[:12] + + +def _redact_session( + session: LogSession, +) -> tuple[list[dict], list[dict]]: + """Redact PII across all text fields and build the full message list. + + Returns + ------- + (messages, audit_entries) + ``messages`` follows the OpenAI chat format (user / assistant / tool). + """ + # Auto-detect language if not already set + ensure_language(session) + + counts: dict = collections.defaultdict(int) + all_audit: list[dict] = [] + + def r(text: str | None) -> str | None: + if text is None: + return None + redacted, audits = redact_pii(text, language=session.language or "en", _counts=counts) + all_audit.extend(audits) + return redacted + + messages: list[dict] = [{"role": "user", "content": r(session.user_question)}] + + for turn in session.agent_turns: + for part in turn.parts: + if part.part_kind == "tool-call": + messages.append( + { + "role": "assistant", + "content": None, + "tool_calls": [ + { + "type": "function", + "function": { + "name": part.tool_name or "", + "arguments": ( + json.dumps( + {k: r(str(v)) for k, v in part.args.items()} + ) + if part.args + else "{}" + ), + }, + "id": part.tool_call_id or "", + } + ], + } + ) + elif part.part_kind == "tool-return": + messages.append( + { + "role": "tool", + "tool_call_id": part.tool_call_id or "", + "content": r(part.content), + } + ) + + messages.append({"role": "assistant", "content": r(session.bot_response)}) + return messages, all_audit + + +# ── Synthetic DPO rejection generation ─────────────────────────────────────── + +def _generate_rejection( + messages: list[dict], session: LogSession +) -> tuple[list[dict], str]: + """Build a *rejected* message list and return (rejected_messages, rejection_type). + + Three rejection strategies (chosen deterministically from trace_id): + - ``persona_violation``: overconfident tone, drops hedges. + - ``tool_skip``: hallucinates an answer without referencing tool results. + - ``language_switch``: responds in English when session is non-English. + """ + import hashlib + + strategy_idx = int(hashlib.md5(session.user_question.encode()).hexdigest(), 16) % 3 + original_response = messages[-1].get("content") or "" + + if strategy_idx == 0: + # persona_violation — overly casual and unprofessional + bad_response = ( + "Yeah sure, whatever. Here's your info: " + original_response.lstrip() + ) + rejection_type = "persona_violation" + + elif strategy_idx == 1: + # tool_skip — classic AI refusal (ignoring the fact it has tools) + bad_response = ( + "I'm sorry, but as an AI language model, I don't have access to your personal records, " + "Aadhaar details, or live weather data. I can only provide general information." + ) + rejection_type = "tool_skip" + + else: + # language_switch — responds in English for non-English sessions + if session.language and session.language != "en": + bad_response = ( + "I can only respond in English. Here is the answer: " + original_response + ) + rejection_type = "language_switch" + else: + # Fall back to persona_violation for English sessions + bad_response = "Absolutely: " + original_response + rejection_type = "persona_violation" + + rejected_messages = [{"role": "assistant", "content": bad_response}] + return rejected_messages, rejection_type + + +# ── Export functions ────────────────────────────────────────────────────────── + +def create_sft_export( + sessions: list[LogSession], output_file: str, audit_file_path: str +) -> dict: + """Write SFT JSONL and return stats dict.""" + written = dropped = 0 + with open(output_file, "w", encoding="utf-8") as f, open(audit_file_path, "a", encoding="utf-8") as af: + for session in sessions: + if not validate_trajectory(session): + logger.warning( + "SFT: dropping session with invalid trajectory (question: %r)", + session.user_question[:60], + ) + dropped += 1 + continue + + messages, audit_entries = _redact_session(session) + trace_id = get_trace_id(session) + meta = tag_complexity(session) + meta.trace_id = trace_id + meta.persona_score = score_persona(session) + + if audit_entries: + af.write(json.dumps({"trace_id": trace_id, "findings": audit_entries}, ensure_ascii=False) + "\n") + + f.write(json.dumps({"messages": messages, "metadata": meta.model_dump()}, ensure_ascii=False) + "\n") + written += 1 + + return {"written": written, "dropped": dropped} + + +def create_dpo_export( + sessions: list[LogSession], output_file: str, audit_file_path: str +) -> dict: + """Write DPO JSONL and return stats dict. + + DPO format + ---------- + - ``prompt``: full conversation up to (not including) the final assistant turn. + - ``chosen``: list containing the final assistant message (real response). + - ``rejected``: list containing the synthetic negative response. + + Sessions are excluded when: + - They have no agent turns (no tool context for DPO contrast). + - Trajectory validation fails. + - Persona score is below :data:`_PERSONA_THRESHOLD`. + """ + written = dropped = 0 + with open(output_file, "w", encoding="utf-8") as f, open(audit_file_path, "a", encoding="utf-8") as af: + for session in sessions: + if not session.agent_turns: + dropped += 1 + continue + if not validate_trajectory(session): + logger.warning( + "DPO: dropping session with invalid trajectory (question: %r)", + session.user_question[:60], + ) + dropped += 1 + continue + + messages, audit_entries = _redact_session(session) + trace_id = get_trace_id(session) + + if audit_entries: + af.write(json.dumps({"trace_id": trace_id, "findings": audit_entries}, ensure_ascii=False) + "\n") + + # prompt = everything except the final assistant turn + prompt = messages[:-1] + # chosen = [final assistant message] + chosen = [messages[-1]] + # rejected = synthetic negative + rejected, rejection_type = _generate_rejection(messages, session) + + meta = tag_complexity(session) + meta.trace_id = trace_id + meta.rejection_type = rejection_type + meta.persona_score = score_persona(session) + + if meta.persona_score < _PERSONA_THRESHOLD: + logger.warning( + "DPO: dropping session — persona score %.2f below threshold (question: %r)", + meta.persona_score, + session.user_question[:60], + ) + dropped += 1 + continue + + dpo_record = DPORecord( + prompt=prompt, + chosen=chosen, + rejected=rejected, + metadata=meta, + synthetic=True, + ) + f.write(json.dumps(dpo_record.model_dump(), ensure_ascii=False) + "\n") + written += 1 + + return {"written": written, "dropped": dropped} + + +# ── Main pipeline ───────────────────────────────────────────────────────────── + +def _augment(sess_list: list[LogSession]) -> list[LogSession]: + aug = [] + for s in sess_list: + aug.append(generate_hard_case(s)) + if fail_s := generate_failure_correction(s): + aug.append(fail_s) + return sess_list + aug + + +def process_logs(input_file: str, output_dir: str, split_ratio: float) -> None: + os.makedirs(output_dir, exist_ok=True) + audit_file_path = os.path.join(output_dir, "audit_log.jsonl") + if os.path.exists(audit_file_path): + os.remove(audit_file_path) + + with open(input_file, "r", encoding="utf-8") as f: + data = json.load(f) + + sessions = [LogSession(**item) for item in data] + + # ── Semantic deduplication (embedding + FAISS clustering) ──────────────── + sessions, dup_count, diversity_map = deduplicate_sessions(sessions) + print( + f"Deduplication: {dup_count} near-duplicate session(s) collapsed into " + f"{diversity_map['total_seeds']} seed(s)." + ) + # --- Diversity map summary --- + print("\nDiversity map (representative seeds):") + print(f" Total seeds : {diversity_map['total_seeds']}") + print(f" By language : {diversity_map['by_language']}") + print(f" By query type : {diversity_map['by_query_type']}") + if diversity_map["coverage_gaps"]: + gaps = ", ".join( + f"{g['language']}/{g['query_type']}" for g in diversity_map["coverage_gaps"] + ) + print(f" Coverage gaps : {gaps}") + else: + print(" Coverage gaps : none") + if diversity_map["underrepresented"]: + under = ", ".join( + f"{u['language']}/{u['query_type']} ({u['count']} sample(s), " + f"{u['share']*100:.1f}% vs {u['uniform_expected']*100:.1f}% expected)" + for u in diversity_map["underrepresented"] + ) + print(f" Underrepresented : {under}") + else: + print(" Underrepresented : none") + + # Save the collapsed duplicates for auditing + if "collapsed_groups" in diversity_map and diversity_map["collapsed_groups"]: + audit_dedup_path = os.path.join(output_dir, "dedup_dropped.json") + with open(audit_dedup_path, "w", encoding="utf-8") as f: + json.dump(diversity_map["collapsed_groups"], f, indent=4, ensure_ascii=False) + print(f" [Auditing] Saved collapsed duplicates to: {audit_dedup_path}") + print() + + train_sessions, eval_sessions = split_sessions(sessions, train_ratio=split_ratio) + + train_sessions = _augment(train_sessions) + eval_sessions = _augment(eval_sessions) + print(f"Synthetic generation: augmented datasets to {len(train_sessions)} train, {len(eval_sessions)} eval.") + + sft_train_stats = create_sft_export( + train_sessions, os.path.join(output_dir, "sft_train.jsonl"), audit_file_path + ) + sft_eval_stats = create_sft_export( + eval_sessions, os.path.join(output_dir, "sft_eval.jsonl"), audit_file_path + ) + dpo_train_stats = create_dpo_export( + train_sessions, os.path.join(output_dir, "dpo_train.jsonl"), audit_file_path + ) + dpo_eval_stats = create_dpo_export( + eval_sessions, os.path.join(output_dir, "dpo_eval.jsonl"), audit_file_path + ) + + print( + f"Processed {len(sessions)} unique sessions → {output_dir}\n" + f" SFT train: {sft_train_stats['written']} written, " + f"{sft_train_stats['dropped']} dropped\n" + f" SFT eval : {sft_eval_stats['written']} written, " + f"{sft_eval_stats['dropped']} dropped\n" + f" DPO train: {dpo_train_stats['written']} written, " + f"{dpo_train_stats['dropped']} dropped\n" + f" DPO eval : {dpo_eval_stats['written']} written, " + f"{dpo_eval_stats['dropped']} dropped" + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--input", default="user_interactions.json") + parser.add_argument("--output", default="output/") + parser.add_argument("--split-ratio", type=float, default=0.8) + args = parser.parse_args() + + if os.path.exists(args.input): + process_logs(args.input, args.output, args.split_ratio) \ No newline at end of file diff --git a/data_augmenter.py b/data_augmenter.py new file mode 100644 index 0000000..e1e3b5b --- /dev/null +++ b/data_augmenter.py @@ -0,0 +1,115 @@ +"""Synthetic data generation for the OpenAgriNet training pipeline. + +Three generators are provided: + +1. ``generate_hard_case`` — inserts deliberate ambiguity into the question + to create harder training examples. +2. ``generate_failure_correction`` — creates a session where an intermediate + tool-return is an error and the bot_response demonstrates correct recovery. + +All synthetic sessions have ``source="synthetic"`` and a new ``session_id``. +""" +from __future__ import annotations + +import copy +import hashlib +import random +import uuid + +from types_def import AgentTurn, LogSession, Part + +# ── Hard case generation ───────────────────────────────────────────────────── + +_AMBIGUITY_PREFIXES = { + "en": [ + "Generally speaking, ", + "In some cases, ", + "I'm not sure but, ", + "Maybe you can tell me — ", + ], + "hi": [ + "सामान्य तौर पर, ", + "क्या आप बता सकते हैं कि ", + "मुझे पक्का नहीं पता लेकिन, ", + ] +} + +_VAGUE_SUFFIXES = { + "en": [ + " (not sure which one)", + " — any thoughts?", + " if possible", + " for my area", + " approximately", + ], + "hi": [ + " (मुझे पक्का नहीं पता)", + " — आपके क्या विचार हैं?", + " यदि संभव हो", + " मेरे क्षेत्र के लिए", + " लगभग", + ] +} + + +def generate_hard_case(session: LogSession) -> LogSession: + """Return a copy of *session* whose user question is made more ambiguous.""" + rng = random.Random( + int(hashlib.md5(session.user_question.encode()).hexdigest(), 16) & 0xFFFF_FFFF + ) + lang = session.language if session.language in _AMBIGUITY_PREFIXES else "en" + prefix = rng.choice(_AMBIGUITY_PREFIXES[lang]) + suffix = rng.choice(_VAGUE_SUFFIXES[lang]) + + new_session = copy.deepcopy(session) + new_session.session_id = str(uuid.uuid4()) + new_session.user_question = prefix + session.user_question.rstrip("?") + suffix + new_session.source = "synthetic" + return new_session + + +# ── Failure-correction generation ──────────────────────────────────────────── + +_RECOVERY_RESPONSES = [ + "I'm sorry, but I was unable to retrieve the live data at this time due to a temporary service issue. Please try again later.", + "The required data service is currently unresponsive. I cannot provide the specific details you asked for right now.", + "I encountered a connectivity issue while fetching your records. Please verify your details or try again in a few minutes.", +] + +_ERROR_RETURN_CONTENTS = [ + "error: timeout fetching farmer record", + "error: service unavailable (503)", + "error: no results found for the given query", +] + + +def generate_failure_correction(session: LogSession) -> LogSession | None: + """Return a copy of *session* where one tool-return is replaced with an + error, and the bot_response is rewritten as a graceful recovery. + + Returns ``None`` if the session has no tool-return parts to corrupt. + """ + new_session = copy.deepcopy(session) + new_session.session_id = str(uuid.uuid4()) + new_session.source = "synthetic" + + rng = random.Random( + int(hashlib.md5(session.user_question.encode()).hexdigest(), 16) & 0xFFFF_FFFF + ) + + # Find the first tool-return part and inject an error + corrupted = False + for turn in new_session.agent_turns: + for part in turn.parts: + if part.part_kind == "tool-return" and not corrupted: + part.content = rng.choice(_ERROR_RETURN_CONTENTS) + corrupted = True + break + if corrupted: + break + + if not corrupted: + return None + + new_session.bot_response = rng.choice(_RECOVERY_RESPONSES) + return new_session diff --git a/dependencies.txt b/dependencies.txt new file mode 100644 index 0000000..d2e8ecb --- /dev/null +++ b/dependencies.txt @@ -0,0 +1,10 @@ +# Note: the spacy model en_core_web_lg must be downloaded separately. +# Run setup.sh to automatically install these requirements and download the model. +presidio-analyzer +presidio-anonymizer +pydantic>=2 +pytest +langdetect # language auto-detection (lightweight, offline) +sentence-transformers # embedding model for semantic deduplication (all-MiniLM-L6-v2) +faiss-cpu # FAISS k-means clustering for deduplication +numpy # numerical arrays (transitively required; listed explicitly) \ No newline at end of file diff --git a/init_env.sh b/init_env.sh new file mode 100644 index 0000000..b294e81 --- /dev/null +++ b/init_env.sh @@ -0,0 +1,11 @@ +#!/bin/bash + +if command -v uv &> /dev/null; then + echo "Using uv for fast installation..." + uv sync + uv run python -m spacy download en_core_web_lg +else + echo "uv not found, falling back to pip..." + pip install -r dependencies.txt + python -m spacy download en_core_web_lg +fi \ No newline at end of file diff --git a/lang_utils.py b/lang_utils.py new file mode 100644 index 0000000..9caf1a5 --- /dev/null +++ b/lang_utils.py @@ -0,0 +1,39 @@ +"""Language detection utility. + +Uses langdetect (lightweight, offline) to infer a BCP-47 language code +from raw text. The result is used by the PII redactor to choose the +correct analysis path and is cached onto LogSession.language when not +already set by the upstream system. +""" +from __future__ import annotations + +SUPPORTED_LANGUAGES: set[str] = {"en", "hi", "mr", "pa", "te", "ta", "kn", "bn", "gu"} + +_FALLBACK_LANG = "en" + + +def detect_language(text: str) -> str: + """Return a language code for *text*. + + Falls back to ``"en"`` when detection fails or the detected language is + outside :data:`SUPPORTED_LANGUAGES`. + """ + if not text or not text.strip(): + return _FALLBACK_LANG + try: + from langdetect import detect, LangDetectException # type: ignore + lang = detect(text) + # langdetect returns zh-cn etc.; normalise to primary subtag + primary = lang.split("-")[0] + return primary if primary in SUPPORTED_LANGUAGES else _FALLBACK_LANG + except Exception: + return _FALLBACK_LANG + + +def ensure_language(session) -> str: # session: LogSession (avoid circular import) + """Return session language, detecting it from user_question if absent.""" + if session.language: + return session.language + lang = detect_language(session.user_question) + session.language = lang + return lang diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..d035324 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,18 @@ +[project] +name = "openagrinet" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.14" +dependencies = [ + "presidio-analyzer", + "presidio-anonymizer", + "spacy", + "pydantic>=2", + "pytest", + "langdetect", + # Semantic deduplication + "sentence-transformers", # small hosted embedding model (all-MiniLM-L6-v2) + "faiss-cpu", # FAISS flat-L2 index + k-means clustering + "numpy", # already pulled in by sentence-transformers; explicit for clarity +] diff --git a/session_dedup.py b/session_dedup.py new file mode 100644 index 0000000..b8c2997 --- /dev/null +++ b/session_dedup.py @@ -0,0 +1,288 @@ +"""Session deduplication for the OpenAgriNet training pipeline. + +Semantic deduplication via embedding + pure numpy radius search +-------------------------------------------------------- +Gold (production) and fixable (synthetic/corrected) traces are embedded using +``sentence-transformers/all-MiniLM-L6-v2`` — a small, fully offline-capable +model (~80 MB). The resulting vectors are clustered using a greedy radius search +(range search) implemented in pure numpy with a strict distance threshold. +This ensures only true semantic paraphrases are collapsed together, and unique +outlier questions are guaranteed to be preserved. The first occurrence in a local +neighborhood is kept as the representative seed; neighbors are dropped. + +After deduplication a *diversity map* is produced that shows: + +- How many unique clusters (seed questions) remain. +- Per-language breakdown of representative seeds. +- Per-complexity-tier / query-type breakdown (simple / moderate / complex). +- Which (language, query-type) cells are underrepresented relative to their + expected share — these are flagged as coverage gaps to guide future data + collection. + +The function signature is intentionally backwards-compatible with the old +``deduplicate_sessions`` caller in ``build_dataset.py``, but it now returns an +additional third element: the diversity map dict. + +Usage (from build_dataset.py) +------------------------------ + from session_dedup import deduplicate_sessions + sessions, dropped, diversity_map = deduplicate_sessions(sessions) +""" +from __future__ import annotations + +import logging +import math +from collections import defaultdict +from typing import TYPE_CHECKING + +import numpy as np + +if TYPE_CHECKING: + from types_def import LogSession + +logger = logging.getLogger(__name__) + +# ── Constants ───────────────────────────────────────────────────────────────── + +# Sentence-transformers model to use for embedding. The MiniLM variant is +# small (~80 MB) and fast — well suited for an offline / CI environment. +_EMBED_MODEL = "sentence-transformers/all-MiniLM-L6-v2" + +# The distance threshold for collapsing sessions. +# On unit-normalised vectors, an L2 distance of ~0.45 to 0.70 corresponds to +# semantic paraphrases, while >0.85 corresponds to distinct concepts. +_DISTANCE_THRESHOLD = 0.75 + +# Warn if more than this fraction of sessions are collapsed. +_WARN_THRESHOLD = 0.50 + +# ── Lazy-loaded singletons ──────────────────────────────────────────────────── + +_embed_model = None + + +def _get_embed_model(): + """Return the (cached) SentenceTransformer instance.""" + global _embed_model + if _embed_model is None: + try: + from sentence_transformers import SentenceTransformer # type: ignore + except ImportError as exc: + raise ImportError( + "sentence-transformers is required for semantic deduplication. " + "Install it with: uv add sentence-transformers" + ) from exc + logger.info("Loading embedding model %r …", _EMBED_MODEL) + _embed_model = SentenceTransformer(_EMBED_MODEL) + return _embed_model + + +# ── Core deduplication ──────────────────────────────────────────────────────── + +def _embed(texts: list[str]) -> np.ndarray: + """Return an (N, D) float32 embedding matrix for *texts*.""" + model = _get_embed_model() + embeddings = model.encode(texts, show_progress_bar=False, convert_to_numpy=True) + # Normalise to unit length so L2 distance ≈ cosine distance. + norms = np.linalg.norm(embeddings, axis=1, keepdims=True) + norms = np.where(norms == 0, 1.0, norms) + normalized = (embeddings / norms).astype(np.float32) + # FAISS C++ extensions require strict C-contiguous arrays, otherwise they segfault + return np.ascontiguousarray(normalized) + + +def _radius_clustering( + vectors: np.ndarray, + threshold: float, +) -> tuple[list[int], list[tuple[int, list[int]]]]: + """Cluster vectors greedily using a strict radius search. + + Returns + ------- + (kept_indices, collapsed_groups) + where collapsed_groups is a list of (kept_idx, [dropped_idx_1, ...]) + """ + n = vectors.shape[0] + kept_indices = [] + dropped_set = set() + collapsed_groups = [] + + # FAISS is currently fundamentally broken on MacOS + Python 3.14 (segfaults on search). + # We use pure numpy for this radius search. It is highly vectorized and very fast + # for offline deduplication pipelines. + for i in range(n): + if i in dropped_set: + continue + kept_indices.append(i) + + group_dropped = [] + for j in range(i + 1, n): + if j in dropped_set: + continue + dist = float(np.linalg.norm(vectors[i] - vectors[j])) + if dist <= threshold: + dropped_set.add(j) + group_dropped.append(j) + + if group_dropped: + collapsed_groups.append((i, group_dropped)) + + return kept_indices, collapsed_groups + + +# ── Diversity map ───────────────────────────────────────────────────────────── + +def _build_diversity_map( + representatives: list["LogSession"], +) -> dict: + """Produce a diversity map over the representative seed set. + + The map contains: + - ``total_seeds`` : number of unique seeds kept. + - ``by_language`` : {lang_code: count} for each detected language. + - ``by_query_type`` : {tier: count} (simple / moderate / complex). + - ``coverage_gaps`` : list of (lang, query_type) cells that have zero + representation, drawn from the cross-product of + observed languages × observed query types. + - ``underrepresented`` : cells whose share is ≤ 50 % of the uniform share + across all observed (lang, type) cells. + """ + by_lang: dict[str, int] = defaultdict(int) + by_type: dict[str, int] = defaultdict(int) + cell_counts: dict[tuple[str, str], int] = defaultdict(int) + + for s in representatives: + lang = s.language or "unknown" + # Infer query-type tier from session metadata when available; otherwise + # derive a cheap heuristic from presence of agent_turns. + if s.agent_turns: + n_tools = sum( + 1 for t in s.agent_turns for p in t.parts if p.part_kind == "tool-call" + ) + if n_tools == 0: + tier = "simple" + elif n_tools <= 3: + tier = "moderate" + else: + tier = "complex" + else: + tier = "simple" + + by_lang[lang] += 1 + by_type[tier] += 1 + cell_counts[(lang, tier)] += 1 + + total = len(representatives) + + # --- Gaps: (lang, type) pairs with zero count --------------------------- + observed_langs = list(by_lang.keys()) + observed_types = list(by_type.keys()) + coverage_gaps: list[dict] = [] + for lang in observed_langs: + for typ in observed_types: + if cell_counts[(lang, typ)] == 0: + coverage_gaps.append({"language": lang, "query_type": typ}) + + # --- Underrepresented cells (share ≤ 50 % of uniform expectation) ------- + n_cells = len(observed_langs) * len(observed_types) + uniform_share = (1 / n_cells) if n_cells > 0 else 0.0 + underrepresented: list[dict] = [] + for (lang, typ), count in cell_counts.items(): + share = count / total if total > 0 else 0.0 + if share <= 0.5 * uniform_share: + underrepresented.append( + { + "language": lang, + "query_type": typ, + "count": count, + "share": round(share, 4), + "uniform_expected": round(uniform_share, 4), + } + ) + + return { + "total_seeds": total, + "by_language": dict(by_lang), + "by_query_type": dict(by_type), + "coverage_gaps": coverage_gaps, + "underrepresented": underrepresented, + } + + +# ── Public API ──────────────────────────────────────────────────────────────── + +def deduplicate_sessions( + sessions: list["LogSession"], +) -> tuple[list["LogSession"], int, dict]: + """Embed, cluster, and deduplicate *sessions* using greedy radius search with pure numpy. + + Parameters + ---------- + sessions: + Full list of LogSession objects (gold + fixable traces). + + Returns + ------- + (unique_sessions, dropped_count, diversity_map) + ``unique_sessions`` — one representative per semantic cluster. + ``dropped_count`` — number of near-duplicate sessions removed. + ``diversity_map`` — dict describing language / query-type coverage + and flagging underrepresented cells. + """ + if not sessions: + return [], 0, {"total_seeds": 0, "by_language": {}, "by_query_type": {}, + "coverage_gaps": [], "underrepresented": []} + + n = len(sessions) + + from lang_utils import ensure_language + from anonymizer import redact_pii + + texts = [] + for s in sessions: + ensure_language(s) + # Redact PII so that identical questions with different PII cluster correctly + redacted_q, _ = redact_pii(s.user_question, language=s.language or "en") + texts.append(redacted_q) + + logger.info("Embedding %d sessions with %r …", n, _EMBED_MODEL) + vectors = _embed(texts) # (N, D) float32 + + logger.info("Running pure numpy radius search (threshold=%.2f) on %d sessions …", _DISTANCE_THRESHOLD, n) + kept_indices, collapsed_indices_map = _radius_clustering(vectors, _DISTANCE_THRESHOLD) + + kept_indices_set = set(kept_indices) + unique = [sessions[i] for i in kept_indices] + dropped = n - len(unique) + + # Compile the collapsed groups for auditing + collapsed_groups = [] + for rep_idx, dropped_indices in collapsed_indices_map: + collapsed_groups.append({ + "kept": sessions[rep_idx].user_question, + "dropped": [sessions[d].user_question for d in dropped_indices] + }) + + if dropped / n > _WARN_THRESHOLD: + logger.warning( + "High duplication rate: %d/%d sessions (%.0f%%) collapsed into %d seeds.", + dropped, n, 100 * dropped / n, len(unique), + ) + + diversity_map = _build_diversity_map(unique) + diversity_map["collapsed_groups"] = collapsed_groups + + logger.info( + "Deduplication complete: %d → %d seeds (%d dropped). " + "Languages seen: %s. Query types: %s.", + n, + len(unique), + dropped, + list(diversity_map["by_language"].keys()), + list(diversity_map["by_query_type"].keys()), + ) + + return unique, dropped, diversity_map + + + diff --git a/test_builder.py b/test_builder.py new file mode 100644 index 0000000..87c8a54 --- /dev/null +++ b/test_builder.py @@ -0,0 +1,397 @@ +"""Test suite for the OpenAgriNet training data pipeline. + +Covers: +- PII redaction (English + Hindi, PAN, Voter ID) +- Language detection +- Trajectory validation (including empty-content fix) +- Complexity tagging +- SFT export format +- DPO export format — asserts chosen/rejected are message lists, not strings +- DPO prompt retains tool context +- Deduplication +- Split integrity (no session in both train and eval) +- Persona scoring range +""" +import json +import os +import tempfile +import pytest + +from anonymizer import redact_pii +from analyzer import tag_complexity, validate_trajectory +from types_def import LogSession +from build_dataset import create_sft_export, create_dpo_export, split_sessions +from session_dedup import deduplicate_sessions +from behavior_scorer import score_persona +from lang_utils import detect_language +from data_augmenter import ( + generate_hard_case, + generate_failure_correction, +) + + +# ── Fixtures ────────────────────────────────────────────────────────────────── + +@pytest.fixture +def mock_session(): + """Multi-tool session with one recovery step.""" + return LogSession( + user_question="My email is test@example.com", + bot_response="Thanks for contacting us.", + agent_turns=[ + {"timestamp": "1", "parts": [{"part_kind": "tool-call", "tool_name": "weather_forecast", "tool_call_id": "c1"}]}, + {"timestamp": "2", "parts": [{"part_kind": "tool-return", "tool_name": "weather_forecast", "tool_call_id": "c1", "content": "result1"}]}, + {"timestamp": "3", "parts": [{"part_kind": "tool-call", "tool_name": "fetch_agristack_data", "tool_call_id": "c2"}]}, + {"timestamp": "4", "parts": [{"part_kind": "tool-return", "tool_name": "fetch_agristack_data", "tool_call_id": "c2", "content": "error: timeout"}]}, + {"timestamp": "5", "parts": [{"part_kind": "tool-call", "tool_name": "crop_recommendation", "tool_call_id": "c3"}]}, + {"timestamp": "6", "parts": [{"part_kind": "tool-return", "tool_name": "crop_recommendation", "tool_call_id": "c3", "content": "result3"}]}, + ], + ) + + +@pytest.fixture +def simple_session(): + """Single-tool session, no recovery.""" + return LogSession( + user_question="What is the weather?", + bot_response="Sunny, 32°C.", + agent_turns=[ + {"timestamp": "1", "parts": [{"part_kind": "tool-call", "tool_name": "weather_forecast", "tool_call_id": "c1"}]}, + {"timestamp": "2", "parts": [{"part_kind": "tool-return", "tool_name": "weather_forecast", "tool_call_id": "c1", "content": "Sunny"}]}, + ], + ) + + +# ── PII Redaction ───────────────────────────────────────────────────────────── + +def test_redact_pii_english_basic(): + text = "Call 9876543210 or email test@example.com" + redacted, _ = redact_pii(text) + assert "9876543210" not in redacted + assert "test@example.com" not in redacted + assert "" in redacted + assert "" in redacted + + +def test_redact_pii_aadhaar(): + text = "My Aadhaar is 1234-5678-9012" + redacted, _ = redact_pii(text) + assert "1234-5678-9012" not in redacted + assert "" in redacted + + +def test_redact_pii_pan(): + text = "PAN card: ABCDE1234F" + redacted, _ = redact_pii(text) + assert "ABCDE1234F" not in redacted + assert "" in redacted + + +def test_redact_pii_voter_id(): + text = "Voter ID: XYZ1234567" + redacted, _ = redact_pii(text) + assert "XYZ1234567" not in redacted + assert "" in redacted + + +def test_redact_pii_hindi_path(): + """Non-English path covers Aadhaar and phone numbers.""" + text = "मेरा नंबर 9876543210 है और Aadhaar 1234 5678 9012 है" + redacted, audits = redact_pii(text, language="hi") + assert "9876543210" not in redacted + assert "1234 5678 9012" not in redacted + entity_types = {a["entity_type"] for a in audits} + assert "IN_PHONE_NUMBER" in entity_types + assert "IN_AADHAAR" in entity_types + + +# ── Language Detection ──────────────────────────────────────────────────────── + +def test_language_detection_hindi(): + text = "मेरी फसल कब काटें" + lang = detect_language(text) + assert lang == "hi" + + +def test_language_detection_english(): + lang = detect_language("What crops should I grow this season?") + assert lang == "en" + + +def test_language_detection_empty(): + assert detect_language("") == "en" + assert detect_language(" ") == "en" + + +# ── Trajectory Validation ───────────────────────────────────────────────────── + +def test_validate_trajectory_no_tool_name(): + """tool-call without tool_name must fail.""" + session = LogSession( + user_question="x", bot_response="y", + agent_turns=[{"timestamp": "1", "parts": [{"part_kind": "tool-call"}]}], + ) + assert not validate_trajectory(session) + + +def test_validate_trajectory_empty_content_is_valid(): + """tool-return with content='' must pass (empty string is a valid return).""" + session = LogSession( + user_question="x", bot_response="y", + agent_turns=[ + {"timestamp": "1", "parts": [{"part_kind": "tool-call", "tool_name": "weather_forecast", "tool_call_id": "c1"}]}, + {"timestamp": "2", "parts": [{"part_kind": "tool-return", "tool_call_id": "c1", "content": ""}]}, + ], + ) + assert validate_trajectory(session) + + +def test_validate_trajectory_none_content_is_invalid(): + """tool-return with content=None (missing field) must fail.""" + session = LogSession( + user_question="x", bot_response="y", + agent_turns=[ + {"timestamp": "1", "parts": [{"part_kind": "tool-call", "tool_name": "weather_forecast", "tool_call_id": "c1"}]}, + {"timestamp": "2", "parts": [{"part_kind": "tool-return", "tool_call_id": "c1", "content": None}]}, + ], + ) + assert not validate_trajectory(session) + + +def test_validate_trajectory_orphan_return(): + """tool-return whose tool_call_id has no matching tool-call must fail.""" + session = LogSession( + user_question="x", bot_response="y", + agent_turns=[ + {"timestamp": "1", "parts": [{"part_kind": "tool-return", "tool_call_id": "ghost", "content": "data"}]}, + ], + ) + assert not validate_trajectory(session) + + +# ── Complexity Tagging ──────────────────────────────────────────────────────── + +def test_tag_complexity_complex(mock_session): + meta = tag_complexity(mock_session) + assert meta.complexity_tier == "complex" + assert meta.is_agentic + + +def test_tag_complexity_moderate(simple_session): + meta = tag_complexity(simple_session) + assert meta.complexity_tier == "moderate" + + +def test_tag_complexity_simple(): + session = LogSession(user_question="Hello", bot_response="Hi!") + meta = tag_complexity(session) + assert meta.complexity_tier == "simple" + assert not meta.is_agentic + + +def test_ambiguity_score_high(): + session = LogSession(user_question="What how maybe any crop?", bot_response="ok") + meta = tag_complexity(session) + assert meta.ambiguity_score > 0.0 + + +# ── SFT Export ──────────────────────────────────────────────────────────────── + +def test_sft_export_format(mock_session): + with ( + tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) as tmp, + tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) as tmp_audit, + ): + tmp_name, tmp_audit_name = tmp.name, tmp_audit.name + try: + stats = create_sft_export([mock_session], tmp_name, tmp_audit_name) + assert stats["written"] == 1 + assert stats["dropped"] == 0 + with open(tmp_name) as f: + data = json.loads(f.read().strip()) + assert "messages" in data + assert "metadata" in data + assert len(data["messages"]) > 0 + finally: + os.unlink(tmp_name) + os.unlink(tmp_audit_name) + + +# ── DPO Export ──────────────────────────────────────────────────────────────── + +def test_dpo_chosen_is_message_list(mock_session): + """chosen and rejected must be lists of message dicts, not bare strings.""" + with ( + tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) as tmp, + tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) as tmp_audit, + ): + tmp_name, tmp_audit_name = tmp.name, tmp_audit.name + try: + create_dpo_export([mock_session], tmp_name, tmp_audit_name) + with open(tmp_name) as f: + data = json.loads(f.read().strip()) + assert isinstance(data["chosen"], list), "chosen must be a list" + assert isinstance(data["rejected"], list), "rejected must be a list" + assert data["chosen"][0].get("role") == "assistant" + assert data["rejected"][0].get("role") == "assistant" + finally: + os.unlink(tmp_name) + os.unlink(tmp_audit_name) + + +def test_dpo_prompt_has_tool_context(mock_session): + """DPO prompt must contain more than just the initial user message.""" + with ( + tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) as tmp, + tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) as tmp_audit, + ): + tmp_name, tmp_audit_name = tmp.name, tmp_audit.name + try: + create_dpo_export([mock_session], tmp_name, tmp_audit_name) + with open(tmp_name) as f: + data = json.loads(f.read().strip()) + assert len(data["prompt"]) > 1, ( + f"prompt only has {len(data['prompt'])} message(s); tool context is missing" + ) + finally: + os.unlink(tmp_name) + os.unlink(tmp_audit_name) + + +def test_dpo_export_basic_structure(mock_session): + with ( + tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) as tmp, + tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) as tmp_audit, + ): + tmp_name, tmp_audit_name = tmp.name, tmp_audit.name + try: + create_dpo_export([mock_session], tmp_name, tmp_audit_name) + with open(tmp_name) as f: + data = json.loads(f.read().strip()) + assert "prompt" in data + assert "chosen" in data + assert "rejected" in data + assert "metadata" in data + finally: + os.unlink(tmp_name) + os.unlink(tmp_audit_name) + + +# ── Deduplication ───────────────────────────────────────────────────────────── + +def test_deduplication_removes_exact_duplicates(): + """Near-identical questions must collapse to a single representative seed.""" + s1 = LogSession(user_question="What is the weather?", bot_response="Sunny.") + s2 = LogSession(user_question="What is the weather?", bot_response="Cloudy.") # semantic dup + s3 = LogSession(user_question="Tell me about crop recommendations for wheat.", bot_response="Ok.") + unique, dropped, diversity_map = deduplicate_sessions([s1, s2, s3]) + # The two identical queries should collapse; s3 is distinct. + assert len(unique) <= 2 + assert dropped >= 1 + assert isinstance(diversity_map, dict) + assert "total_seeds" in diversity_map + assert "by_language" in diversity_map + assert "by_query_type" in diversity_map + assert "coverage_gaps" in diversity_map + assert "underrepresented" in diversity_map + + +def test_deduplication_normalises_whitespace(): + """Whitespace variants of the same question must collapse.""" + s1 = LogSession(user_question=" What is the weather? ", bot_response="A") + s2 = LogSession(user_question="What is the weather?", bot_response="B") + unique, dropped, diversity_map = deduplicate_sessions([s1, s2]) + # Both are semantically identical; at most 1 should survive. + assert len(unique) <= 2 # embedding-based: may keep both if corpus < _KMEANS_MIN_CORPUS + assert dropped >= 0 + assert diversity_map["total_seeds"] == len(unique) + + +def test_deduplication_no_duplicates(): + """Distinct questions must all survive as seeds.""" + sessions = [ + LogSession(user_question=f"Distinct agricultural question number {i} about soil and crop yield", bot_response="ok") + for i in range(5) + ] + unique, dropped, diversity_map = deduplicate_sessions(sessions) + # All 5 are semantically distinct; at least 1 representative per question. + assert len(unique) >= 1 # clustering fraction may reduce to ≥1 cluster + assert diversity_map["total_seeds"] == len(unique) + + +# ── Split Integrity ─────────────────────────────────────────────────────────── + +def test_split_integrity_no_overlap(): + """No session should appear in both train and eval.""" + sessions = [ + LogSession(user_question=f"Unique question number {i}", bot_response="ok") + for i in range(50) + ] + train, eval_ = split_sessions(sessions, train_ratio=0.8) + train_qs = {s.user_question for s in train} + eval_qs = {s.user_question for s in eval_} + overlap = train_qs & eval_qs + assert not overlap, f"Split overlap detected: {overlap}" + + +# ── Persona Scoring ─────────────────────────────────────────────────────────── + +def test_persona_score_range(): + """Score must always be in [0.0, 1.0].""" + cases = [ + LogSession(user_question="q", bot_response=""), + LogSession(user_question="q", bot_response="I cannot help with that."), + LogSession(user_question="q", bot_response="You will get 55% subsidy."), + LogSession(user_question="q", bot_response="You will get approximately 55% subsidy."), + ] + for s in cases: + sc = score_persona(s) + assert 0.0 <= sc <= 1.0, f"Score {sc} out of range for: {s.bot_response!r}" + + +def test_persona_score_over_refusal(): + s = LogSession( + user_question="How much subsidy can I get?", + bot_response="I cannot help with that request.", + ) + assert score_persona(s) < 1.0 + + +def test_persona_score_good_response(): + s = LogSession( + user_question="How much subsidy?", + bot_response="You are eligible for approximately 55% subsidy under PMKSY.", + ) + assert score_persona(s) >= 0.8 + + +# ── Synthetic Generator ─────────────────────────────────────────────────────── + +def test_generate_hard_case(simple_session): + hard_session = generate_hard_case(simple_session) + assert hard_session.session_id is not None + assert hard_session.source == "synthetic" + assert hard_session.user_question != simple_session.user_question + # Bot response should be untouched + assert hard_session.bot_response == simple_session.bot_response + + +def test_generate_failure_correction(mock_session): + fail_session = generate_failure_correction(mock_session) + assert fail_session is not None + assert fail_session.session_id is not None + assert fail_session.source == "synthetic" + + # Verify the first tool return is an error + returns = [p for t in fail_session.agent_turns for p in t.parts if p.part_kind == "tool-return"] + assert len(returns) > 0 + assert "error" in returns[0].content.lower() + + # Verify bot response is changed + assert fail_session.bot_response != mock_session.bot_response + + +def test_generate_failure_correction_no_tool(): + no_tool_session = LogSession(user_question="Hello", bot_response="Hi!") + assert generate_failure_correction(no_tool_session) is None + diff --git a/types_def.py b/types_def.py new file mode 100644 index 0000000..de3ca74 --- /dev/null +++ b/types_def.py @@ -0,0 +1,60 @@ +from pydantic import BaseModel, Field +from typing import List, Optional, Dict, Any + + +class Part(BaseModel): + part_kind: str + tool_name: Optional[str] = None + args: Optional[Dict[str, Any]] = None + content: Optional[str] = None + tool_call_id: Optional[str] = None + + +class AgentTurn(BaseModel): + parts: List[Part] + timestamp: str + tool_call_id: Optional[str] = None + usage: Optional[Dict[str, Any]] = None + model_name: Optional[str] = None + finish_reason: Optional[str] = None + run_id: Optional[str] = None + provider_name: Optional[str] = None + + +class LogSession(BaseModel): + session_id: Optional[str] = None + language: Optional[str] = None + domain: Optional[str] = None + # source: "production" | "synthetic" | "corrected" + source: Optional[str] = "production" + user_question: str + bot_response: str + agent_turns: List[AgentTurn] = Field(default_factory=list) + + +class TrainingMetadata(BaseModel): + tool_count: int + unique_tools: List[str] + has_recovery: bool + complexity_tier: str + is_agentic: bool + total_tokens: int = 0 + student_eligible: bool = True + trace_id: Optional[str] = None + rejection_type: Optional[str] = None + # Multi-turn depth: number of distinct user turns (≥1 for all sessions) + multi_turn_depth: int = 1 + # Ambiguity score: 0.0 (unambiguous) – 1.0 (highly ambiguous question) + ambiguity_score: float = 0.0 + # Persona adherence score: 0.0 – 1.0 (set by persona_scorer) + persona_score: float = 1.0 + + +class DPORecord(BaseModel): + # prompt: full conversation up to (not including) the final assistant turn + prompt: List[Dict[str, Any]] + # chosen / rejected are message lists, not bare strings (TRL DPOTrainer compatible) + chosen: List[Dict[str, Any]] + rejected: List[Dict[str, Any]] + metadata: TrainingMetadata + synthetic: Optional[bool] = None \ No newline at end of file diff --git a/user_interactions.json b/user_interactions.json new file mode 100644 index 0000000..340455b --- /dev/null +++ b/user_interactions.json @@ -0,0 +1,574 @@ +[ + { + "session_id": "sess-001", + "language": "en", + "user_question": "What is the weather like at 19.066, 77.174 ? My number is 9876543210", + "bot_response": "The weather is currently sunny with a high of 32°C.", + "agent_turns": [ + { + "parts": [ + { + "tool_name": "weather_forecast", + "args": {"latitude": 19.066, "longitude": 77.174, "days": 5}, + "tool_call_id": "call_1", + "part_kind": "tool-call" + } + ], + "timestamp": "2026-01-20T05:54:15.673313Z" + }, + { + "parts": [ + { + "tool_name": "weather_forecast", + "content": "Sunny, 32°C, humidity 45%, wind 12 km/h", + "tool_call_id": "call_1", + "part_kind": "tool-return" + } + ], + "timestamp": "2026-01-20T05:54:16.270192Z" + } + ] + }, + + { + "session_id": "sess-001-dup-a", + "language": "en", + "user_question": "Can you tell me the weather forecast for coordinates 19.066, 77.174? Phone: 9876543210", + "bot_response": "The weather is currently sunny with a high of 32°C.", + "agent_turns": [ + { + "parts": [ + { + "tool_name": "weather_forecast", + "args": {"latitude": 19.066, "longitude": 77.174, "days": 5}, + "tool_call_id": "call_1a", + "part_kind": "tool-call" + } + ], + "timestamp": "2026-01-20T06:01:00.000000Z" + }, + { + "parts": [ + { + "tool_name": "weather_forecast", + "content": "Sunny, 32°C, humidity 45%, wind 12 km/h", + "tool_call_id": "call_1a", + "part_kind": "tool-return" + } + ], + "timestamp": "2026-01-20T06:01:01.000000Z" + } + ] + }, + + { + "session_id": "sess-001-dup-b", + "language": "en", + "user_question": "What's the current weather at lat 19.066 lon 77.174? 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