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In this article, you will learn how to add a lightweight temporal reasoning layer to a Graph-RAG system so that it can distinguish fresh facts from stale ones. Topics we will cover include: How to extend standard subject-predicate-object triples into time-stamped quadruples stored in a simple temporal graph. How to calculate recency weights with exponential decay and use them to rank conflicting facts as of a given query date. How to tune the half-life parameter and integrate the temporal graph into a deterministic 3-tiered Graph-RAG retrieval pipeline. Introduction and Motivation In a previous article, Building a Deterministic 3-Tiered Graph-RAG System, we addressed the challenge of handling conflicting information in RAG (Retrieval-Augmented Generation) architectures. In particular, we built a hierarchy to manage conflicting information by giving “fresh” facts top priority over less-fresh ones. Throughout that journey, a key question arose: how does our graph-based RAG system know exactly what is fresh? Standard knowledge graphs treat facts as “timeless,” context-independent SPO (subject-predicate-object) triples, such as (Company, HAS_CEO, Alice). This approach doesn’t quite fit the real world we live in, where things are messy and change constantly: what if Alice switched jobs and is no longer CEO? Feeding these facts to an LLM without temporal context is the perfect recipe for hallucinations, resulting in confusing or factually incorrect responses. To tackle this challenge, this article shows the key steps to build a dedicated, lightweight temporal reasoning engine for Graph-RAG through a few simple Python functions. We also discuss its integration with the 3-tiered Graph-RAG system built previously. The key idea consists of upgrading standard triples into time-stamped quadruples and calculating recency weights that indicate degrees of “fact freshness.” Ready? Let’s go! A New, Temporal Journey, Step by Step The first step is to extend standard SPO triples into “temporal quads,” where the fourth dimension introduces time, concretely a timestamp: (Subject, Predicate, Object, Timestamp). The following Python class is defined to hold our new, extended knowledge, also called a temporal graph. If you are working in a notebook environment, simply paste this code into your first code cell: import datetime import math class TemporalGraph: def __init__(self): # Data will be stored in this knowledge base as: Subject -> Predicate -> List of (Object, Date) self.knowledge_base = {} def add_fact(self, subject, predicate, obj, date_string): “””Adds a time-stamped fact to the graph.””” # Converting string to a comparable date object fact_date = datetime.datetime.strptime(date_string, “%Y-%m-%d”).date() if subject not in self.knowledge_base: self.knowledge_base[subject] = {} if predicate not in self.knowledge_base[subject]: self.knowledge_base[subject][predicate] = [] self.knowledge_base[subject][predicate].append((obj, fact_date)) print(f”Added: {subject} {predicate} {obj} (as of {date_string})”) # Initializing our graph tg = TemporalGraph() 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 import datetime import math   class TemporalGraph:     def __init__(self):         # Data will be stored in this knowledge base as: Subject -> Predicate -> List of (Object, Date)         self.knowledge_base = {}       def add_fact(self, subject, predicate, obj, date_string):         “”“Adds a time-stamped fact to the graph.”“”         # Converting string to a comparable date object         fact_date = datetime.datetime.strptime(date_string, “%Y-%m-%d”).date()                  if subject not in self.knowledge_base:             self.knowledge_base[subject] = {}         if predicate not in self.knowledge_base[subject]:             self.knowledge_base[subject][predicate] = []                      self.knowledge_base[subject][predicate].append((obj, fact_date))         print(f“Added: {subject} {predicate} {obj} (as of {date_string})”)   # Initializing our graph tg = TemporalGraph() Now it’s time to populate our newly created temporal graph, tg, following a real-world scenario where facts change at light speed … well, maybe not that fast, but still rapidly! If we were tracking the leadership roles in a tech company during a chaotic week full of changes, we could have something like: # A timeline of shifting facts tg.add_fact(“TechCorp”, “HAS_CEO”, “Alice”, “2021-01-15”) tg.add_fact(“TechCorp”, “HAS_CEO”, “Bob”, “2023-11-17”) tg.add_fact(“TechCorp”, “HAS_CEO”, “Charlie”, “2023-11-19”) tg.add_fact(“TechCorp”, “HAS_CEO”, “Bob”, “2023-11-21”) # Bob came back! # Adding also a static fact for a bit of contrast tg.add_fact(“TechCorp”, “FOUNDED_IN”, “San Francisco”, “2010-05-01”) # A timeline of shifting facts tg.add_fact(“TechCorp”, “HAS_CEO”, “Alice”, “2021-01-15”) tg.add_fact(“TechCorp”, “HAS_CEO”, “Bob”, “2023-11-17”) tg.add_fact(“TechCorp”, “HAS_CEO”, “Charlie”, “2023-11-19”) tg.add_fact(“TechCorp”, “HAS_CEO”, “Bob”, “2023-11-21”) # Bob came back!   # Adding also a static fact for a bit of contrast tg.add_fact(“TechCorp”, “FOUNDED_IN”, “San Francisco”, “2010-05-01”) Output: Added: TechCorp HAS_CEO Alice (as of 2021-01-15) Added: TechCorp HAS_CEO Bob (as of 2023-11-17) Added: TechCorp HAS_CEO Charlie (as of 2023-11-19) Added: TechCorp HAS_CEO Bob (as of 2023-11-21) Added: TechCorp FOUNDED_IN San Francisco (as of 2010-05-01) Added: TechCorp HAS_CEO Alice (as of 2021–01–15) Added: TechCorp HAS_CEO Bob (as of 2023–11–17) Added: TechCorp HAS_CEO Charlie (as of 2023–11–19) Added: TechCorp HAS_CEO Bob (as of 2023–11–21) Added: TechCorp FOUNDED_IN San Francisco (as of 2010–05–01) Bear in mind that in a standard RAG system, a search like “Who acts as the CEO of TechCorp?” would likely retrieve Alice, Bob, and Charlie, all at once! Thus, we need a mechanism to assign truthfulness weights to facts, and it’s simpler than you might think. Calculating recency weights is the key to resolving possible conflicts mathematically. We just want a mechanism that says: “hey, this fact is newer than that one, so it’s more likely to constitute today’s truth.” A smart approach to do this is based on exponential decay, which consists of assigning a half-life time window to facts. For instance, if the half-life is set to one year (365 days), then a fact that is one year old will carry a weight of 0.5. Meanwhile, a fact asserted today would carry a weight of 1.0. These two functions are designed to introduce the aforementioned weight scoring logic to our temporal graph: def calculate_recency_weight(fact_date, query_date, half_life_days=365): “”” Calculates a score between 0 and 1 based on how old the fact is. Using exponential decay: weight = (0.5) ^ (age_in_days / half_life) “”” age_in_days = (query_date – fact_date).days # If the fact is from the future relative to our query, it is capped at 1.0 if age_in_days < 0: return 1.0 weight = 0.5 ** (age_in_days / half_life_days) return round(weight, 4) def query_temporal_graph(graph, subject, predicate, as_of_date_str, half_life_days=365): “””Queries the graph and ranks

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In this article, you will learn what embedding drift is, why it matters for production large language models, and how to implement two practical techniques to detect it. Topics we will cover include: The key approaches for detecting embedding drift in production machine learning systems, including model-based detection, centroid distance, and dimensionality reduction combined with statistical tests. How to implement a domain classifier and a centroid distance method using scikit-learn on simulated 384-dimensional embeddings. How to apply these same drift detection techniques to real text embeddings generated with a SentenceTransformer model via Scikit-LLM. Introduction When a large language model (LLM) hits production, the story is far from over. User behavior inevitably evolves in the real world, and so does the data consumed by the model, typically encoded into numerical text representations called embeddings for its internal processing. Therefore, it is crucial to track so-called embedding drifts to ascertain when a deployed model needs an update. However, traditional drift detection metrics designed for tabular data often fail when applied to high-dimensional embeddings. This article starts by providing a brief outline of top techniques for detecting embedding drift, followed by an illustrative implementation of two of them, both simulation-based and in conjunction with the Scikit-LLM library for embedding generation. Techniques for Effective Embedding Drift Detection Below we list three key approaches for accurately identifying embedding drift that have been remarkably put into practice in production LLMs: Model-based detection: This consists of training a domain-specific classifier, usually a binary classifier that has learned to distinguish between baseline data and new (drifted) production data. A model capable of easily telling them apart will be able to signal drifts when they occur. Centroid distance: Following classical anomaly detection algorithms, this strategy boils down to calculating the distance (often cosine for embedding data) between the center of mass of your baseline embedding vectors and that of new, incoming embedding vectors. Combining dimensionality reduction and statistical tests: This method entails compressing the embeddings to a lower dimension using UMAP or PCA, after which we apply standard drift tests such as Kolmogorov-Smirnov. Interested in exploring further how they work? Let’s examine how to implement the core logic behind two of these techniques based on an open-source stack. Illustrating Drift Detection on Simulated Embeddings Let’s build a mathematical foundation for two of the listed techniques using standard scikit-learn and simulated embeddings first. We generate an initial, random set of embeddings, after which we create another synthetic set — this time containing “production embeddings” that shift from the original embeddings’ mean to simulate the existence of data drift. import numpy as np # Simulating 384-dimensional embeddings (e.g. standard sentence-transformers output) n_samples = 500 n_features = 384 # 1. Referencing Embeddings (Baseline / Training Data) # Imagine this is the data your LLM/Vector DB was originally populated with np.random.seed(42) X_reference = np.random.normal(loc=0.0, scale=1.0, size=(n_samples, n_features)) # 2. Production Embeddings (New Data) # The original mean is shifted to loc=0.3 to simulate data drift (e.g. new topic emerging) X_production = np.random.normal(loc=0.3, scale=1.0, size=(n_samples, n_features)) import numpy as np   # Simulating 384-dimensional embeddings (e.g. standard sentence-transformers output) n_samples = 500 n_features = 384   # 1. Referencing Embeddings (Baseline / Training Data) # Imagine this is the data your LLM/Vector DB was originally populated with np.random.seed(42) X_reference = np.random.normal(loc=0.0, scale=1.0, size=(n_samples, n_features))   # 2. Production Embeddings (New Data) # The original mean is shifted to loc=0.3 to simulate data drift (e.g. new topic emerging) X_production = np.random.normal(loc=0.3, scale=1.0, size=(n_samples, n_features)) Next, we train a domain classifier based on random forests to separate baseline data (labeled 0) from new, production data (labeled 1). If the accuracy metric — for instance, ROC-AUC — signals a high value, e.g. above 0.65, the classifier will trigger a drift alert. from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import roc_auc_score # 1. Assigning labels: 0 for reference, baseline embeddings; 1 for production embeddings y_reference = np.zeros(n_samples) y_production = np.ones(n_samples) # 2. Combining into a single dataset X_combined = np.vstack((X_reference, X_production)) y_combined = np.hstack((y_reference, y_production)) # 3. Randomly splitting into train and test sets for the drift detector X_train, X_test, y_train, y_test = train_test_split( X_combined, y_combined, test_size=0.3, random_state=42 ) # 4. Training a lightweight Random Forest classifier drift_classifier = RandomForestClassifier(n_estimators=50, max_depth=5, random_state=42) drift_classifier.fit(X_train, y_train) # 5. Evaluating the classifier using ROC-AUC y_pred_proba = drift_classifier.predict_proba(X_test)[:, 1] roc_auc = roc_auc_score(y_test, y_pred_proba) print(f”Domain Classifier ROC-AUC Score: {roc_auc:.3f}”) # 6. Alerting Logic # If the metric score is around 0.5 it means the model can’t tell the datasets apart (no drift detected). # Meanwhile, a score closer to 1.0 means they are easily distinguishable (high drift). if roc_auc > 0.65: print(“ALERT: Significant embedding drift detected! Trigger retraining/review pipeline.”) else: print(“System stable: Distributions are sufficiently similar.”) 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 from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import roc_auc_score   # 1. Assigning labels: 0 for reference, baseline embeddings; 1 for production embeddings y_reference = np.zeros(n_samples) y_production = np.ones(n_samples)   # 2. Combining into a single dataset X_combined = np.vstack((X_reference, X_production)) y_combined = np.hstack((y_reference, y_production))   # 3. Randomly splitting into train and test sets for the drift detector X_train, X_test, y_train, y_test = train_test_split(     X_combined, y_combined, test_size=0.3, random_state=42 )   # 4. Training a lightweight Random Forest classifier drift_classifier = RandomForestClassifier(n_estimators=50, max_depth=5, random_state=42) drift_classifier.fit(X_train, y_train)   # 5. Evaluating the classifier using ROC-AUC y_pred_proba = drift_classifier.predict_proba(X_test)[:, 1] roc_auc = roc_auc_score(y_test, y_pred_proba)   print(f“Domain Classifier ROC-AUC Score: {roc_auc:.3f}”)   # 6. Alerting Logic # If the metric score is around 0.5 it means the model can’t tell the datasets apart (no drift detected). # Meanwhile, a score closer to 1.0 means they are easily distinguishable (high drift). if roc_auc > 0.65:     print(“ALERT: Significant embedding drift detected! Trigger retraining/review pipeline.”) else:     print(“System stable: Distributions are sufficiently similar.”)