unstructured/docs/source/introduction/key_concepts.rst

Key Concepts
------------

Natural Language Processing (NLP) encompasses a broad spectrum of tasks and methodologies. This section introduces some fundamental concepts crucial for most NLP projects.

Data Ingestion
^^^^^^^^^^^^^^^

The initial step in any NLP task involves ingesting data from varied sources. This might include reading texts from files, scraping websites, listening to speech, or tapping into databases. Efficient data ingestion is vital to ensure that data is accessible and usable for downstream tasks.

Data Preprocessing
^^^^^^^^^^^^^^^^^^^

Before the core analysis, raw data often requires significant preprocessing:

- **Partitioning**: Segregating data into smaller, manageable segments or partitions.
  
- **Cleaning**: Removing anomalies, filling missing values, and eliminating any irrelevant or erroneous information.

Preprocessing ensures data integrity and can significantly influence the outcomes of subsequent tasks.

Chunking Text for Vector Databases
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Vector databases often require data to be in smaller, consistent chunks for efficient storage and retrieval. Chunking involves dividing lengthy texts into smaller segments or chunks, ensuring that each piece retains enough context to be meaningful.

Embeddings
^^^^^^^^^^^

Embeddings convert textual data into fixed-size vectors, preserving semantic context. These vector representations can then be used for a myriad of tasks, including similarity searches, clustering, and classification. Different embeddings might prioritize different aspects of the text, from semantic meaning to sentence structure.

Vector Databases
^^^^^^^^^^^^^^^^^

Vector databases store embeddings in a manner optimized for high-speed similarity searches. Given a query embedding, these databases can quickly retrieve the most similar vectors, facilitating tasks like recommendation, anomaly detection, and clustering.

These foundational concepts provide the groundwork for more advanced NLP methodologies and pipelines. Proper understanding and implementation can vastly improve the outcomes of NLP projects.

Tokens
^^^^^^^

Tokenization decomposes texts into smaller units, called tokens. A token might represent a word, part of a word, or even a single character. This process helps in analyzing and processing the text, making it digestible for models and algorithms.

Large Language Models (LLMs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

LLMs, like GPT, are trained on vast amounts of data and have the capacity to comprehend and generate human-like text. They have achieved state-of-the-art results across a multitude of NLP tasks and can be fine-tuned to cater to specific domains or requirements.