From aa4d5c54b874eabd3477cb28a9bcda013f86648f Mon Sep 17 00:00:00 2001 From: geekyrakshit <19soumik.rakshit96@gmail.com> Date: Fri, 27 Sep 2024 14:21:31 +0000 Subject: [PATCH] update: readme --- .gitignore | 1 + finance_multi_modal_rag/README.md | 18 +- finance_multi_modal_rag/assets/trace_1.png | Bin 0 -> 420490 bytes finance_multi_modal_rag/assets/trace_2.png | Bin 0 -> 304890 bytes finance_multi_modal_rag/assets/trace_3.png | Bin 0 -> 444040 bytes finance_multi_modal_rag/test.py | 24 + finance_multi_modal_rag/uv.lock | 2181 ++++++++++++++++++++ 7 files changed, 2223 insertions(+), 1 deletion(-) create mode 100644 finance_multi_modal_rag/assets/trace_1.png create mode 100644 finance_multi_modal_rag/assets/trace_2.png create mode 100644 finance_multi_modal_rag/assets/trace_3.png create mode 100644 finance_multi_modal_rag/test.py create mode 100644 finance_multi_modal_rag/uv.lock diff --git a/.gitignore b/.gitignore index 4751d56..790fd08 100644 --- a/.gitignore +++ b/.gitignore @@ -161,6 +161,7 @@ cython_debug/ # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ +artifacts/ attachments/ index/ wandb/ \ No newline at end of file diff --git a/finance_multi_modal_rag/README.md b/finance_multi_modal_rag/README.md index 72f82bc..6cb4b34 100644 --- a/finance_multi_modal_rag/README.md +++ b/finance_multi_modal_rag/README.md @@ -1,5 +1,9 @@ # Multi-modal RAG for Finance +A simple RAG (Retrieval-Augmented Generation) system for question-answering on [Tesla's](https://www.tesla.com/) financial filing gathered from the [SEC-EDGAR Database](https://www.sec.gov/edgar) by the [US Securities and Exchange Comission](https://www.sec.gov/). This codebase is closely inspired by the materials from the free course [RAG++ : From POC to Production](https://www.wandb.courses/courses/rag-in-production). + +The codebase demonstrates how to build multi-modal RAG pipelines for question-answering systems and other downstream tasks in the finance domain using [Llama3.2 Vision](https://huggingface.co/meta-llama/Llama-3.2-90B-Vision). + ## Installation Install dependencies using the following commands: @@ -21,7 +25,19 @@ Finally, you need to get a Cohere API key (depending on which model you use). ## Usage -First, you need to fetch the 10-Q filings from [Edgar database](https://www.sec.gov/edgar) and generate image descriptions using [meta-llama/Llama-3.2-90B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-90B-Vision-Instruct). +First, you need to create a corpus which acts as a source of truth for the RAG system. We do this by fetching the `10-Q` and the `DEF 14A` filings from the [Edgar database](https://www.sec.gov/edgar). Besides collecting the text, we also fetch all the images associated with the filings. In order to make it easy for chunking and indexing, we generate a comprehensive description corresponding to each image and extract all the text and tabular information using [meta-llama/Llama-3.2-90B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-90B-Vision-Instruct). + +We generate the image descriptions using a 2-stage prompting strategy: + +First, we ask Llama-3.2 to summarize the text content in the filing document and extract some important keywords and observations. + +![](./assets/trace_1.png) + +Next, we pass this summary and set of extracted keyworkds from the text as context along with the images to Llama-90B-Vision-Instruct and ask it to generate descriptions of the image and extract all the text and tabular data in markdown format. + +![](./assets/trace_3.png) + +You can use the following code to create your corpus by fetching filings from the [Edgar database](https://www.sec.gov/edgar) and generating the image descriptions: ```python import weave diff --git 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