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What is FlockMTL?

Overview​

FlockMTL enhances DuckDB by integrating semantic functions and robust resource management capabilities, enabling advanced analytics and language model operations directly within SQL queries.

Semantic Functions​

FlockMTL offers a suite of semantic functions that allow users to perform various language model operations:

  • Scalar Map Functions:

    • llm_complete: Generates text completions using a specified language model.
    • llm_complete_json: Produces JSON-formatted text completions.
    • llm_filter: Filters data based on language model evaluations, returning boolean values.
    • llm_embedding: Generates embeddings for input text, useful for semantic similarity tasks.

  • Aggregate Reduce Functions:

    • llm_reduce: Aggregates multiple inputs into a single output using a language model.
    • llm_reduce_json: Similar to llm_reduce, but outputs JSON-formatted results.
    • llm_rerank: Reorders query results based on relevance scores from a language model.
    • llm_first: Selects the top-ranked result after reranking.
    • llm_last: Selects the bottom-ranked result after reranking.

This allows users to perform tasks such as text generation, summarization, classification, filtering, fusion, and embedding generation.

Hybrid Search Functions​

FlockMTL also provides functions that support hybrid search. Namely, the following data fusion algorithms to combine scores of various retrievers:

  • fusion_rrf: Implements Reciprocal Rank Fusion (RRF) to combine rankings from multiple scoring systems.
  • fusion_combsum: Sums normalized scores from different scoring systems.
  • fusion_combmnz: Sums normalized scores and multiplies by the hit count.
  • fusion_combmed: Computes the median of normalized scores.
  • fusion_combanz: Calculates the average of normalized scores.

These functions enable users to combine the strengths of different scoring methods, such as BM25 and embedding scores, to produce the best-fit results, and even create end-to-end RAG pipelines.

We believe that relational DBMSs and LLMs are a match made in heaven. We are leaning on the tradition of declarative interfaces to unburden users from lower-level implementation details. Users can query both structured and unstructred datasets while combining analytics and semantic analysis directly within SQL.

Resource Management​

FlockMTL introduces a resource management framework that treats models (MODEL) and prompts (PROMPT) similarly to tables, allowing for organized storage and retrieval.

System Requirements​

FlockMTL is supported by the different operating systems and platforms, such as:

  • Linux
  • macOS
  • Windows

And to ensure stable and reliable performance, it is important to meet only two requirements:

  • DuckDB Setup: Version 1.1.1 or later. FlockMTL is compatible with the latest stable release of DuckDB, which can be installed from the official DuckDB installation guide.
  • Provider API Key: FlockMTL supports multiple providers such as OpenAI, Azure, and Ollama. Configure the provider of your choice to get started.