[](https://githubtocolab.com/snexus/llm-search/blob/main/notebooks/llmsearch_google_colab_demo.ipynb)
# pyLLMSearch - Advanced RAG
[Documentation](https://llm-search.readthedocs.io/en/latest/)
The purpose of this package is to offer a convenient question-answering (RAG) 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 basic LLM-based RAGs** - better document parsing, hybrid search, HyDE enabled search, chat history, 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 document 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:
* List of formats see [here](https://unstructured-io.github.io/unstructured/core/partition.html).
* Allows interaction with embedded documents, internally supporting the following models and methods (including locally hosted):
* OpenAI models (ChatGPT 3.5/4 and Azure OpenAI).
* HuggingFace models.
* Llama cpp supported models - for full list see [here](https://github.com/ggerganov/llama.cpp#description).
* Interoperability with LiteLLM + Ollama via OpenAI API, supporting hundreds of different models (see [Model configuration for LiteLLM](sample_templates/llm/litellm.yaml))
* Generates dense embeddings from a folder of documents and stores them in a vector database ([ChromaDB](https://github.com/chroma-core/chroma)).
* The following embedding models are supported:
* Hugging Face embeddings.
* Sentence-transformers-based models, e.g., `multilingual-e5-base`.
* Instructor-based models, e.g., `instructor-large`.
* OpenAI embeddings.
* Generates sparse embeddings using SPLADE (https://github.com/naver/splade) to enable hybrid search (sparse + dense).
* An ability to update the embeddings incrementally, without a need to re-index the entire document base.
* Support for table parsing via open-source gmft (https://github.com/conjuncts/gmft) or Azure Document Intelligence.
* Optional support for image parsing using Gemini API.
* Supports the "Retrieve and Re-rank" strategy for semantic search, see [here](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) - see [here](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.
* From my own experiments, 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
* Other features
* Simple CLI and web interfaces.
* 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
## Demo
## Documentation
[Browse Documentation](https://llm-search.readthedocs.io/en/latest/)
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