Create README.md

README.md

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+# FinGPT Compliance Agent with RAG for XBRL Specifications
+
+This project demonstrates a specialized compliance agent built using a Retrieval-Augmented Generation (RAG) framework. The core Large Language Model (LLM) is **TheFinAI/Fin-o1-8B**, which is augmented with a custom-built knowledge base of XBRL (eXtensible Business Reporting Language) specifications.
+
+The agent can handle two types of queries:
+1.  **General Financial Questions**: Answered directly by the Fin-o1-8B model use code like:
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "TheFinAI/Fin-o1-8B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+input_text = "What is the results of 3-5?"
+inputs = tokenizer(input_text, return_tensors="pt")
+
+output = model.generate(**inputs, max_new_tokens=200)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+
+2.  **XBRL-Specific Compliance Questions**: Answered using the RAG pipeline, which retrieves relevant context from a local knowledge base (`xbrl_results_2_spec_filtered_reindexed.json`) before generating a response. This ensures that answers related to XBRL are accurate, detailed, and grounded in official documentation.
+
+## 
+Project Structure
+
+To use this framework correctly, please organize your project files as follows. All project files, except for `inference.py` and the JSON knowledge base, should be placed inside a directory named after the model, `Fin-o1-8B`.
+
+- **`Fin-o1-8B/`**: This directory should contain the downloaded model artifacts for `TheFinAI/Fin-o1-8B`. The `transformers` library will automatically cache the model here if you specify it as the save directory.
+- **`inference.py`**: The main script for running the RAG-powered XBRL compliance agent.
+- **`xbrl_results_2_spec_filtered_reindexed.json`**: The pre-built knowledge base containing crawled data from XBRL specification websites.