Update README.md

README.md

@@ -13,6 +13,16 @@ license: apache-2.0
 # Chat with Lithuanian Law Documents
 This is a README file for a Streamlit application that allows users to chat with a virtual assistant based on Lithuanian law documents, leveraging local processing power and a compact language model.
 
+
+## Important Disclaimer
+
+This application utilizes a lightweight large language model (LLM) called Qwen2-0.5B-Chat_SFT_DPO.Q8_gguf to ensure smooth local processing on your device. While this model offers efficiency benefits, it comes with some limitations:
+
+####  Potential for Hallucination: Due to its size and training data, the model might occasionally generate responses that are not entirely consistent with the provided documents or factual accuracy.
+####  Character Misinterpretations: In rare instances, the model may introduce nonsensical characters, including those from the Chinese alphabet, during response generation.
+We recommend keeping these limitations in mind when using the application and interpreting the provided responses.
+
+
 ## Features
 
 Users can choose the information retrieval type (similarity or maximum marginal relevance search).
@@ -23,11 +33,16 @@ Technical Details
 
 ####  Sentence Similarity: 
 The application utilizes the Alibaba-NLP/gte-base-en-v1.5 model for efficient sentence embedding, allowing for semantic similarity comparisons between user queries and the legal documents.
-Local Vector Store: chroma acts as a local vector store, efficiently storing and managing the document embeddings for fast retrieval.
-RAG Chain with Quantized LLM: A Retrieval-Augmented Generation (RAG) chain is implemented to process user queries. This chain integrates two key components:
-Lightweight LLM: To ensure local operation, the application employs a compact LLM, specifically JCHAVEROT_Qwen2-0.5B-Chat_SFT_DPO.Q8_gguf, with only 0.5 billion parameters. This LLM is specifically designed for question answering tasks.
-Quantization: This Qwen2 model leverages a technique called quantization, which reduces the model size without sacrificing significant accuracy. This quantization process makes the model more efficient to run on local hardware, contributing to a more environmentally friendly solution.
-CPU-based Processing: The entire application is currently implemented to function entirely on your CPU. While utilizing a GPU could significantly improve processing speed, this CPU-based approach allows the application to run effectively on a wider range of devices.
+####  Local Vector Store: 
+chroma acts as a local vector store, efficiently storing and managing the document embeddings for fast retrieval.
+####  RAG Chain with Quantized LLM: 
+A Retrieval-Augmented Generation (RAG) chain is implemented to process user queries. This chain integrates two key components:
+####  Lightweight LLM:
+To ensure local operation, the application employs a compact LLM, specifically JCHAVEROT_Qwen2-0.5B-Chat_SFT_DPO.Q8_gguf, with only 0.5 billion parameters. This LLM is specifically designed for question answering tasks.
+####  Quantization: 
+This Qwen2 model leverages a technique called quantization, which reduces the model size without sacrificing significant accuracy. This quantization process makes the model more efficient to run on local hardware, contributing to a more environmentally friendly solution.
+####  CPU-based Processing: 
+The entire application is currently implemented to function entirely on your CPU. While utilizing a GPU could significantly improve processing speed, this CPU-based approach allows the application to run effectively on a wider range of devices.
 Benefits of Compact Design
 
 ####  Local Processing: