Welcome to pyLLMSearch’s documentation!

The purpose of this package is to offer a convenient question-answering system with a simple YAML-based configuration that enables interaction with multiple collections of local documents. Special attention is given to improvements in various components of the system in addition to LLMs - better document parsing, hybrid search, HyDE enabled search, deep linking, re-ranking, the ability to customize embeddings, and more. The package is designed to work with custom Large Language Models (LLMs) – whether from OpenAI or installed locally.

Features

  • Supported formats
    • Build-in parsers:

      • .md - Divides files based on logical components such as headings, subheadings, and code blocks. Supports additional features like cleaning image links, adding custom metadata, and more.

      • .pdf - MuPDF-based parser.

      • .docx - custom parser, supports nested tables.

    • Other common formats are supported by Unstructured pre-processor:

  • Supports multiple collection of documents, and filtering the results by a collection.

  • An ability to update the embeddings incrementally, without a need to re-index the entire document base.

  • Generates dense embeddings from a folder of documents and stores them in a vector database (ChromaDB).

    • The following embedding models are supported:

      • Huggingface embeddings.

      • Sentence-transformers-based models, e.g., multilingual-e5-base.

      • Instructor-based models, e.g., instructor-large.

  • Generates sparse embeddings using SPLADE (https://github.com/naver/splade) to enable hybrid search (sparse + dense).

  • Supports the “Retrieve and Re-rank” strategy for semantic search, see - https://www.sbert.net/examples/applications/retrieve_rerank/README.html.

    • Besides the originally ms-marco-MiniLM cross-encoder, more modern bge-reranker is supported.

  • Supports HyDE (Hypothetical Document Embeddings) - https://arxiv.org/pdf/2212.10496.pdf

    • WARNING: Enabling HyDE (via config OR webapp) can significantly alter the quality of the results. Please make sure to read the paper before enabling.

    • Based on empirical observations, enabling HyDE significantly boosts quality of the output on a topics where user can’t formulate the quesiton using domain specific language of the topic - e.g. when learning new topics.

  • Support for multi-querying, inspired by RAG Fusion - https://towardsdatascience.com/forget-rag-the-future-is-rag-fusion-1147298d8ad1

    • When multi-querying is turned on (either config or webapp), the original query will be replaced by 3 variants of the same query, allowing to bridge the gap in the terminology and “offer different angles or perspectives” according to the article.

  • Supprts optional chat history with question contextualization

  • Allows interaction with embedded documents, internally supporting the following models and methods (including locally hosted):

  • Interoperability with LiteLLM + Ollama via OpenAI API, supporting hundreds of different models (see [Model configuration for LiteLLM](sample_templates/llm/litellm.yaml))

  • Other features

    • Simple web interface.

    • Deep linking into document sections - jump to an individual PDF page or a header in a markdown file.

    • Ability to save responses to an offline database for future analysis.

    • Experimental API

Installation

Configuration

llmsearch requires two YAML configuration files:

  • Documents and embeddings configuration

  • LLM model configuration

Usage

Indices and tables