Principle:Eventual Inc Daft AI Text Embedding

Knowledge Sources	Daft Daft Docs
Domains	Data_Engineering, Machine_Learning
Last Updated	2026-02-08 00:00 GMT

Overview

Technique for computing dense vector embeddings of text data within a distributed dataframe.

Description

Text embedding converts natural language text into fixed-dimensional numerical vectors that capture semantic meaning. In Daft, this is implemented as an expression-level operation that can be applied to any string column, producing a fixed-size list of floating point values (an embedding vector) for each row.

Key capabilities include:

• Multi-provider support: Embeddings can be computed using local models (via the transformers provider) or remote API services (via the openai provider or other OpenAI-compatible APIs).
• Configurable dimensions: For models and providers that support it, the output embedding dimensionality can be explicitly specified via the dimensions parameter.
• Batch processing: Embeddings are computed in batches for efficiency, with configurable batch sizes determined by the provider's UDF options.
• Async/sync execution: The embedding function automatically selects between synchronous and asynchronous execution based on the underlying provider (e.g., async for API-based providers, sync for local model inference).
• GPU support: For local model inference (e.g., via transformers), GPU resources can be allocated to the UDF workers.

Usage

Use this technique when you need to generate vector representations of text for:

• Semantic search and similarity matching
• Clustering text documents by topic or meaning
• Building recommendation systems based on content similarity
• Creating feature vectors for downstream ML models

Theoretical Basis

Text embedding is based on dense vector representation learning where semantically similar texts have proximate vector representations in the embedding space:

1. Contextual encoding: Modern embedding models (BERT, sentence-transformers, etc.) encode the full context of a text passage into a single vector, capturing nuances of meaning beyond individual words.
2. Metric space properties: The resulting embedding space typically preserves semantic similarity through distance metrics (cosine similarity, Euclidean distance), enabling nearest-neighbor search for related content.
3. Fixed-dimensional output: Regardless of input text length, the output vector has a fixed number of dimensions (e.g., 384, 768, 1536), enabling uniform storage and efficient computation.
4. Transfer learning: Pre-trained embedding models encode general language understanding and can be applied to domain-specific text without fine-tuning.

Pseudocode:
1. Resolve provider (explicit -> session -> environment -> default "transformers")
2. Load text embedder descriptor from provider
3. Determine output dtype: FixedSizeList[Float32; dimensions]
4. Create class-based UDF with concurrency and GPU config
5. For each batch of text in partition:
   a. If async provider: send batch to API, await responses
   b. If sync provider: run local model inference on batch
   c. Return embedding vectors as FixedSizeList column

Related Pages

Implemented By

• Implementation:Eventual_Inc_Daft_AI_Embed_Text