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bd468ee 374834e bd468ee 374834e bd468ee 374834e bd468ee 374834e bd468ee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""
Rag Optimizer Environment Implementation.
The agent acts as a Data Engineer to un-block a broken RAG pipeline.
"""
from uuid import uuid4
from typing import Dict, Any, List
from openenv.core.env_server.interfaces import Environment
from openenv.core.env_server.types import State
# Import scikit-learn for our Grader
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
try:
from models import RagOptimizerAction, RagOptimizerObservation
except ImportError:
from models import RagOptimizerAction, RagOptimizerObservation
class RagOptimizerEnvironment(Environment):
"""
RAG Optimizer Engine.
Maintains a simulated Knowledge Base and grades it using TF-IDF.
"""
SUPPORTS_CONCURRENT_SESSIONS: bool = True
def __init__(self):
self._state = State(episode_id=str(uuid4()), step_count=0)
self.kb = self._get_initial_kb()
# Hidden test suite for the grader
self.test_suite = [
{
"query": "What is the current 2024 price for standard?",
"target_concept": "750/mo"
},
{
"query": "What is the refund policy for enterprise?",
"target_concept": "Refunds are not permitted"
},
{
"query": "UI issues frontend CSS missing button",
"target_concept": "frontend team fixed the button"
},
{
"query": "How long does shipping take to branch offices?",
"target_concept": "5-7 business days"
},
{
"query": "What months do parking passes need to be renewed?",
"target_concept": "March"
},
{
"query": "What holidays are we off in 2024?",
"target_concept": "July 4"
}
]
def _get_initial_kb(self) -> Dict[str, Dict]:
"""Returns a fresh copy of the initial messy knowledge base."""
return {
"doc_pricing_legacy": {
"text": "Pricing for 2021: Enterprise tier is $1000/mo. Standard is $500/mo. All plans include 10 users.",
"metadata": {"type": "pricing"}
},
"doc_pricing_current_v2": {
"text": "Current Pricing 2024: Enterprise is $1500/mo. Standard is $750/mo. Refunds are not permitted on the enterprise tier.",
"metadata": {}
},
"doc_shipping_policy": {
"text": "All internal shipments to remote branch offices take 5-7 business days. Overnight shipping is only available for C-suite.",
"metadata": {"department": "logistics"}
},
"doc_messy_support_ticket_1": {
"text": "User complained the button disappeared on the frontend. Another user said the database latency was high. The frontend team fixed the button by updating CSS.",
"metadata": {}
},
"doc_messy_support_ticket_2": {
"text": "Email integration is failing with error 401 Unauthorized. The API key was rotated on Tuesday.",
"metadata": {}
},
"doc_monolithic_onboarding": {
"text": "Welcome to the company! Here are some rules. 1) VPN access requires DUO. 2) The cafetaria opens at 8 AM. 3) For HR issues, email hr@company.com. 4) The 2024 holiday schedule includes Dec 25, Jan 1, and July 4. 5) Parking passes must be renewed annually in March.",
"metadata": {}
},
# Add distractor files
**{f"doc_distractor_hr_{i}": {"text": f"This is an old HR policy document regarding {['pto', 'sick leave', 'travel', 'expenses'][i%4]} from 201{i%10}.", "metadata":{}} for i in range(10)},
**{f"doc_distractor_eng_{i}": {"text": f"Engineering architecture decision record {i}. We decided to use {['React', 'Postgres', 'Redis', 'Kafka'][i%4]} because of scaling concerns.", "metadata":{}} for i in range(10)},
**{f"doc_distractor_random_{i}": {"text": f"Weekly team update notes. Nothing important here, just discussed the weather and the upcoming launch {i}.", "metadata":{}} for i in range(10)},
}
def _get_kb_summary(self) -> Dict[str, Dict]:
"""Returns a summary of the KB for the observation."""
summary = {}
for k, v in self.kb.items():
summary[k] = {"metadata": v.get("metadata", {}), "length": len(v.get("text", ""))}
return summary
def reset(self) -> RagOptimizerObservation:
self._state = State(episode_id=str(uuid4()), step_count=0)
self.kb = self._get_initial_kb()
return RagOptimizerObservation(
message="RagOptimizerEnv Initialized. You have messy chunks in the KB. Resolve conflicts, add metadata tags to short tickets, and splinter monolithic files to win.",
current_docs=self._get_kb_summary(),
done=False,
reward=self._evaluate_kb()
)
def _evaluate_kb(self) -> float:
"""The Grader: Evaluates the agent's current KB using TF-IDF."""
if not self.kb:
return 0.01
doc_texts = [doc["text"] for doc in self.kb.values()]
vectorizer = TfidfVectorizer(stop_words='english')
try:
doc_vectors = vectorizer.fit_transform(doc_texts)
except ValueError:
return 0.01
score = 0.0
for case in self.test_suite:
query_vec = vectorizer.transform([case["query"]])
similarities = cosine_similarity(query_vec, doc_vectors)[0]
# Get top 3
top_k_indices = similarities.argsort()[-3:][::-1]
found = False
for idx in top_k_indices:
if similarities[idx] > 0.01:
if case["target_concept"].lower() in doc_texts[idx].lower():
found = True
break
if found:
score += 1.0
return max(0.01, min(0.99, float(score / len(self.test_suite))))
def step(self, action: RagOptimizerAction) -> RagOptimizerObservation: # type: ignore[override]
self._state.step_count += 1
msg = ""
done = False
reward = 0.01
try:
if action.action_type == "read_document":
if action.doc_id in self.kb:
msg = f"Content of {action.doc_id}: {self.kb[action.doc_id]['text']}"
else:
msg = f"Error: doc_id {action.doc_id} not found."
elif action.action_type == "delete_document":
if action.doc_id in self.kb:
del self.kb[action.doc_id]
msg = f"Deleted {action.doc_id}."
else:
msg = f"Error: doc_id {action.doc_id} not found."
elif action.action_type == "update_document":
if not action.doc_id or not action.text:
msg = "Error: doc_id and text required for update_document."
else:
if action.doc_id not in self.kb:
self.kb[action.doc_id] = {"text": "", "metadata": {}}
self.kb[action.doc_id]["text"] = action.text
msg = f"Updated text for {action.doc_id}."
elif action.action_type == "add_metadata":
if not action.doc_id or not action.metadata_key or not action.metadata_value:
msg = "Error: doc_id, metadata_key, and metadata_value required."
else:
if action.doc_id not in self.kb:
msg = f"Error: doc_id {action.doc_id} not found."
else:
self.kb[action.doc_id]["metadata"][action.metadata_key] = action.metadata_value
msg = f"Added metadata to {action.doc_id}."
elif action.action_type == "submit":
done = True
reward = self._evaluate_kb()
msg = f"Evaluation complete. Final reward: {reward:.2f}"
except Exception as e:
msg = f"Action failed: {str(e)}"
if not done:
reward = self._evaluate_kb()
return RagOptimizerObservation(
message=msg,
current_docs=self._get_kb_summary(),
done=done,
reward=reward,
)
@property
def state(self) -> State:
return self._state
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