FloTorch Glossary

Answer relevancy is critical for ensuring that the model generates answers that directly address the user's needs. It evaluates how closely the generated output matches the intended context, ensuring that irrelevant or off-topic responses are minimized.

An aspect critic is used to provide a detailed critique of generated responses, often breaking them down into individual components to assess how well each part addresses specific facets of the query, such as content accuracy, tone, or conciseness.

When chunking large datasets, overlapping chunks can help maintain continuity between segments, allowing models to retrieve context from adjacent chunks that might otherwise be lost. Overlap ensures that queries can still be effectively answered even if the data context is divided.

Chunk size directly impacts retrieval efficiency and context preservation. A smaller chunk size might result in finer granularity but could miss broader context, while larger chunks may capture more context but become inefficient to search through.

Chunking is crucial for handling large volumes of data, as it allows smaller pieces of the data to be indexed and retrieved more effectively. The chunking strategy defines how data is divided, including considerations for size and overlap, to preserve context and relevance.

Context precision ensures that the retrieved data or generated answers are directly relevant to the user's question or intent. High context precision minimizes irrelevant information, focusing on what is directly applicable.

The data strategy defines how the available data is handled from preprocessing to indexing. A solid data strategy ensures the effective use of data in training and evaluating models, ultimately determining the model's performance.

Embedding models like BERT or GPT convert data into dense vector spaces, allowing for easier manipulation and similarity comparison. These embeddings capture semantic information, helping to retrieve more relevant results in a search or query.

Faithfulness is crucial when the model is generating answers or responses, ensuring that the generated content is not misleading or factually incorrect. A model with high faithfulness generates outputs that stay true to the source material or factual data.

Indexing algorithms like HNSW (Hierarchical Navigable Small World) are used to build structures that make it easy to find similar data points in high-dimensional spaces. HNSW is particularly effective for approximate nearest neighbor (ANN) searches, ensuring fast retrieval even in large datasets.

In FloTorch, an indexing strategy ensures that the data is optimized for quick and accurate searches. Various indexing methods like hashing, vector space indexing, and inverted indices can be used depending on the use case.

In FloTorch, inferencing LLM refers to running a pre-trained language model to generate outputs such as text or answers based on the context provided by the input query or data.

Higher temperatures make the model's output more diverse and creative by allowing more randomness in the prediction. Lower temperatures produce more deterministic and conservative results, ensuring that outputs are more predictable and reliable.

Explanation: In FloTorch, KNN is often used as a retrieval strategy, where the system looks for the k nearest neighbors of a query in the indexed data to determine relevance. It's a simple and effective method for similarity-based tasks.

N-shot learning helps the model understand the task by showing a few examples (e.g., 3-shot, 5-shot). This is especially useful for tasks where the model needs to adapt to a specific context or perform few-shot learning without requiring a full retraining.

Retrieval strategies can vary based on the algorithm used, such as KNN (k-Nearest Neighbors) or semantic search. The goal is to return the most relevant chunks of data based on the query, which may involve using similarity measures like cosine distance.

In machine learning and natural language processing (NLP), data is often transformed into vectors (numerical representations) for easier manipulation. The vector dimension determines how much information each vector contains, and a higher dimension may capture more nuanced relationships in the data.