This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
https://github.com/NirDiamant/RAG_Techniques
https://github.com/NirDiamant/RAG_Techniques/tree/main/all_rag_techniques
Perplexica is an open-source AI-powered searching tool or an AI-powered search engine that goes deep into the internet to find answers. Inspired by Perplexity AI, it’s an open-source option that not just searches the web but understands your questions. It uses advanced machine learning algorithms like similarity searching and embeddings to refine results and provides clear answers with sources cited.
Using SearxNG to stay current and fully open source, Perplexica ensures you always get the most up-to-date information without compromising your privacy.
RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation
RAG Foundry is a library designed to improve LLMs ability to use external information by fine-tuning models on specially created RAG-augmented datasets. The library helps create the data for training, given a RAG technique, helps easily train models using parameter-efficient finetuning (PEFT), and finally can help users measure the improved performance using various, RAG-specific metrics. The library is modular, workflows are customizable using configuration files.
In deepeval, a metric serves as a standard of measurement for evaluating the performance of an LLM output based on a specific criteria of interest. Essentially, while the metric acts as the ruler, a test case represents the thing you’re trying to measure. deepeval offers a range of default metrics for you to quickly get started with, such as:
deepeval also offers conversational metrics, which are metrics used to evaluate conversations instead of individual, granular LLM interactions. These include:
Fine-tuning an embedding model is a powerful technique for optimizing retrieval augmented generation (RAG) systems in finance. By training a smaller open-source embedding model like BAAI/bge-small-en on a domain-specific dataset, the model learns more meaningful vector representations that capture the nuances and semantics of financial language. This leads to significantly improved retrieval performance compared to using generic pre-trained embeddings.
https://huggingface.co/baconnier/Finance_embedding_large_en-V1.5
Fine-tuned financial embedding models, such as Banker++ RAG, demonstrate superior accuracy on tasks like semantic search, text similarity, and clustering. They enable RAG systems to better understand complex financial jargon and retrieve the most relevant information given a query.
Integrating these specialized embeddings is straightforward using libraries like LlamaIndex or Sentence-Transformers.
As the financial industry increasingly adopts AI, fine-tuned embedding models will play a crucial role in powering domain-specific NLP applications. From analyzing market sentiment to personalizing investment recommendations, these optimized embeddings unlock new possibilities for harnessing unstructured financial data. By combining the power of open-source models with the domain expertise embedded in financial corpora, fine-tuning paves the way for more intelligent and impactful RAG systems in finance.
Banker++ is trained to act like a Senior Banker.
I’m excited to share a resource that could potentially be a valuable addition to your financial toolkit. It’s a Financial LLM Model tailored for corporate entities and financial institutions.
In today’s complex financial landscape, having access to reliable analytical tools is crucial. This model, available at link:
https://huggingface.co/spaces/baconnier/Finance
https://huggingface.co/baconnier/Finance_dolphin-2.9.1-yi-1.5-9b
It offers a framework for assessing various financial scenarios with a level of precision and insight that could benefit your decision-making processes.
However, it’s important to note that this content is strictly for educational purposes and should not be construed as financial advice. Please exercise caution when applying any information provided.
While I’m not suggesting it’s a one-size-fits-all solution or a replacement for professional financial advice, it may offer valuable insights into areas such as risk management, investment strategies, and portfolio optimization.
To illustrate, let’s consider a common question in finance:
Question: What is CDS and how does it compare to a swap?
Answer:
CDS differs from swaps as it focuses specifically on credit risk protection, providing insurance against default events. In contrast, swaps involve the exchange of cash flows, often related to interest rates, currencies, or commodities, without directly addressing credit risk.
Feel free to explore the model and see if it aligns with your organization’s needs and objectives. Remember to approach its use with caution and consider consulting with financial experts when making significant decisions.
As we navigate the complexities of the financial world together, let’s remain humble in our pursuit of knowledge and improvement.
In this post, they implement a GPT from scratch in just 60 lines of numpy. We’ll then load the trained GPT-2 model weights released by OpenAI into our implementation and generate some text.
Note:
Large language models (LLMs) can be used for many tasks, but often have a limited context size that can be smaller than documents you might want to use. To use documents of larger length, you often have to split your text into chunks to fit within this context size.
This crate provides methods for splitting longer pieces of text into smaller chunks, aiming to maximize a desired chunk size, but still splitting at semantically sensible boundaries whenever possible.
Levels Of Text Splitting
Semantic text splitting library
https://github.com/benbrandt/text-splitter
Chunks Vizualizer
Bunkatopics is a package designed for Data Cleaning, Topic Modeling Visualization and Frame Analysis. Its primary goal is to assist developers in gaining insights from unstructured data, potentially facilitating data cleaning and optimizing LLMs through fine-tuning processes. Bunkatopics is constructed using well-known libraries like langchain, chroma, and transformers, enabling seamless integration into various environments.
https://github.com/charlesdedampierre/BunkaTopics?tab=readme-ov-file