Harnessing the Power of Haystack, Weaviate , LLM, RAG for Advanced Neural Search & Question-Answering /Invoice Data processing

The digital landscape is vast, ever-expanding, and brimming with information. But, with this abundance, comes the challenge of retrieving relevant information swiftly. Today, I'm thrilled to shed light on a system that promises not just quick retrievals but intelligent responses - all thanks to the Haystack framework.

?? 1. Setting the Stage with Language Models

Before diving deep, let's familiarize ourselves with the core of this setup – the integration of a language model. The system primarily employs the PromptModel to generate textual outputs based on prompts, leveraging the LlamaCPPInvocationLayer for possibly invoking the underlying model, hinting at a sophisticated mechanism. Vital configurations, ensuring modularity and easy adjustments, are housed in a separate 'config.yml' file.

?? 2. The Nuances of Prompt Design

Our second building block is the prompt template, which is meticulously structured to guide the system in interpreting the input and generating precise answers. The template's formatting sensitivity accentuates the intricacy involved, ensuring optimal system functioning.

?? 3. Assembling the Pipeline

The most exhilarating part is arguably the construction of a robust pipeline for the question-answering system:

- Document Storage: Weaviate, an innovative knowledge graph-based data store, is at the helm, safeguarding and retrieving documents.

- Document Retrieval: The EmbeddingRetriever takes center stage, fetching relevant documents using advanced embeddings, bridging the gap between raw data and the answer-generation mechanism.

- Answer Generation: The PromptNode, equipped with the earlier discussed prompt template, navigates the model to generate responses based on the retrieved documents.

?? 4. Ingestion, Embeddings, and Beyond

At the foundation lies the data ingestion process. By pulling files, especially PDFs, and converting them into a digestible format, the system prepares for the advanced embeddings generation. The PyPDFToDocument converter transforms files, while the preprocessor refines them, ensuring they're in optimal states. Subsequently, embeddings are computed and stored, enabling swift, semantically-aware retrievals.

?? 5. Interacting with the System

A well-designed interface awaits the user, accepting queries and producing intelligent responses. By running a simple command, the system kicks into action, leveraging the Retrieval-Augmented Generation (RAG) pipeline to process the query and provide the answer. Performance metrics, such as response time, offer a glimpse into the system's efficiency.

For instance, a query like,

python main.py "What is the invoice client name, address, and tax ID?"        

swiftly returns:

```

Answer: Invoice client name: Rodriguez-Stevens
Address: 2280 Angela Plain, Hortonshire, MS 93248
Tax ID: 939-98-8477
Time to retrieve answer: 124.1885514879832        

```

?? Conclusion

In this era of information overload, systems like these, which blend the capabilities of advanced embeddings-based retrieval with modern language models, are not just desirable – they are essential. The versatility, efficiency, and intelligence showcased promise a brighter future for information retrieval and processing.

Github Link : https://github.com/kndeepak/LLM-RAG-invoice-Local_CPU