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# 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 copy import deepcopy
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 (fallback/legacy)
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
# Hybrid Search imports
from sentence_transformers import SentenceTransformer
from rank_bm25 import BM25Okapi
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.embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
self.kb = {}
self.test_suite = []
self.dense_vectors = {}
# Load the Real World Datasets
import json
import os
seed_path = os.path.join(os.path.dirname(__file__), "kb_seed.json")
with open(seed_path, "r") as f:
self.seed_data = json.load(f)
self._setup_task("easy")
def _setup_task(self, task_id: str):
if task_id not in ["easy", "medium", "hard"]:
task_id = "easy"
# Clone the fresh dataset from seed so the agent can destroy it
self.kb = deepcopy(self.seed_data[task_id])
if task_id == "easy":
self.test_suite = [
{"query": "How do I run the server in production with concurrency?", "target_concept": "uvicorn main:app --workers 4"},
{"query": "Which version of Pydantic does FastAPI use by default?", "target_concept": "defaults to Pydantic v2"}
]
elif task_id == "medium":
self.kb = deepcopy(self.seed_data["hard"])
self.test_suite = [
{"query": "Which entity bankruptcy was the climax of the disaster?", "target_concept": "bankruptcy of Lehman Brothers"},
{"query": "What types of collateralized assets lost their worth?", "target_concept": "Mortgage-backed securities"},
{"query": "Who did predatory lenders primarily go after?", "target_concept": "low-income homebuyers"}
]
elif task_id == "hard":
self.kb = {
# v1 — legacy, wrong routing syntax, poisons retrieval rank for v3
"fastapi_routing_v1": {
"text": "FastAPI routing uses @app.route() decorator similar to Flask. Define routes with methods=['GET','POST']. Pydantic v1 handles all schema validation.",
"metadata": {"version": "legacy"}
},
# v2 — also legacy, still wrong syntax, further dilutes correct doc
"fastapi_routing_v2": {
"text": "FastAPI routes are defined using @app.route() with type hints added. Validation is done through Pydantic v1 models attached to each endpoint.",
"metadata": {"version": "legacy"}
},
# v3 — CORRECT current doc, must survive and rank #1 after purge
"fastapi_routing_v3": {
"text": "FastAPI uses @app.get(), @app.post() and other HTTP method decorators for routing. It defaults to Pydantic v2 for request and response validation.",
"metadata": {"version": "current"}
},
}
# 15 generic distractors — low semantic overlap, provide ranking noise
for i in range(15):
self.kb[f"doc_distractor_{i}"] = {
"text": f"General web framework note {i}: always use async handlers for better throughput in high-traffic services.",
"metadata": {}
}
self.test_suite = [
# v1 and v2 both contain @app.route() which competes semantically with v3
# Deleting v1 and v2 pushes v3 to rank 1 → each delete gives a visible reward jump
{"query": "How are routes defined in FastAPI?",
"target_concept": "@app.get(), @app.post() decorators"},
{"query": "Which Pydantic version does FastAPI v2 use by default?",
"target_concept": "defaults to Pydantic v2"},
]
self._rebuild_cache()
def _rebuild_cache(self):
"""Called whenever KB documents are added, removed, or updated."""
if not self.kb:
self.dense_vectors = {}
return
doc_ids = list(self.kb.keys())
# Append metadata to text for embedding
doc_texts = [(self.kb[d]["text"] + " " + " ".join(self.kb[d]["metadata"].values())).strip() for d in doc_ids]
vectors = self.embedding_model.encode(doc_texts, convert_to_tensor=False)
self.dense_vectors = {doc_id: vectors[i] for i, doc_id in enumerate(doc_ids)}
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, **kwargs) -> RagOptimizerObservation:
self._state = State(episode_id=str(uuid4()), step_count=0)
task_id = kwargs.get("task_id") or kwargs.get("task") or "easy"
self._setup_task(task_id)
return RagOptimizerObservation(
message=f"RagOptimizerEnv Initialized for task: {task_id}. Resolve conflicts, append metadata, or splinter chunks to win.",
current_docs=self._get_kb_summary(),
done=False,
reward=self._evaluate_kb()
)
def _evaluate_kb(self) -> float:
"""The Grader: Evaluates the current KB using Hybrid RRF (BM25 + Semantic MRR)."""
if not self.kb or not self.test_suite:
return 0.01
doc_ids = list(self.kb.keys())
doc_texts = [(self.kb[d]["text"] + " " + " ".join(self.kb[d]["metadata"].values())).strip() for d in doc_ids]
# 1. BM25 Corpus Preparation
tokenized_corpus = [doc.lower().split() for doc in doc_texts]
bm25 = BM25Okapi(tokenized_corpus)
# 2. Dense Matrix
doc_vectors = np.array([self.dense_vectors[d] for d in doc_ids])
mrr_sum = 0.0
for case in self.test_suite:
# BM25 Search
tokenized_query = case["query"].lower().split()
bm25_scores = bm25.get_scores(tokenized_query)
bm25_ranks = bm25_scores.argsort()[::-1]
# Dense Search
query_vec = self.embedding_model.encode(case["query"])
dense_scores = cosine_similarity([query_vec], doc_vectors)[0]
dense_ranks = dense_scores.argsort()[::-1]
# Reciprocal Rank Fusion (RRF)
rrf_scores = {d: 0.0 for d in doc_ids}
k = 60
for rank, idx in enumerate(bm25_ranks):
rrf_scores[doc_ids[idx]] += 1.0 / (k + rank + 1)
for rank, idx in enumerate(dense_ranks):
rrf_scores[doc_ids[idx]] += 1.0 / (k + rank + 1)
# Grade Top-K fused list using MRR
ranked_doc_ids = sorted(rrf_scores.keys(), key=lambda x: rrf_scores[x], reverse=True)
req_meta = case.get("required_metadata_key")
req_meta_val = case.get("required_metadata_value")
case_mrr = 0.0
for i, doc_id in enumerate(ranked_doc_ids):
# Is this the true doc?
doc_text = self.kb[doc_id]["text"].lower()
if case["target_concept"].lower() in doc_text:
valid = True
# Medium Task Semantic Check
if req_meta and req_meta_val:
if self.kb[doc_id]["metadata"].get(req_meta) != req_meta_val:
valid = False
if valid:
case_mrr = 1.0 / (i + 1) # MRR formula starts at rank 1
break
mrr_sum += case_mrr
base_reward = float(mrr_sum / len(self.test_suite))
# Step Cost Penalty calculation (-0.01 per step)
cost_penalty = self._state.step_count * 0.01
return max(0.01, min(0.99, base_reward - cost_penalty))
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]
self._rebuild_cache()
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
self._rebuild_cache()
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
self._rebuild_cache()
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 |