Update README.md

README.md

@@ -10,10 +10,10 @@ pinned: false
 license: apache-2.0
 ---
 
-Chat with Lithuanian Law Documents
+# 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.
 
-Features
+## Features
 
 Users can choose the information retrieval type (similarity or maximum marginal relevance search).
 Users can specify the number of documents to retrieve.
@@ -21,7 +21,7 @@ Users can ask questions about the provided documents.
 The virtual assistant provides answers based on the retrieved documents and a powerful, yet environmentally friendly, large language model (LLM).
 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.
+####  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.
@@ -29,25 +29,25 @@ Quantization: This Qwen2 model leverages a technique called quantization, which
 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: The compact size of the LLM and the application itself enable local processing on your device, reducing reliance on cloud-based resources and associated environmental impact.
+####  Local Processing: The compact size of the LLM and the application itself enable local processing on your device, reducing reliance on cloud-based resources and associated environmental impact.
 Mobile Potential: Due to its small footprint, this application has the potential to be adapted for mobile devices, bringing legal information access to a wider audience.
 Adaptability of Qwen2 0.5B
 
-Fine-tuning: While the Qwen2 0.5B model is powerful for its size, it can be further enhanced through fine-tuning on specific legal datasets or domains, potentially improving its understanding of Lithuanian legal terminology and nuances.
+####  Fine-tuning: While the Qwen2 0.5B model is powerful for its size, it can be further enhanced through fine-tuning on specific legal datasets or domains, potentially improving its understanding of Lithuanian legal terminology and nuances.
 Conversation Style: Depending on user needs and desired conversation style, alternative pre-trained models could be explored, potentially offering a trade-off between model size and specific capabilities.
-Requirements
+####  Requirements
 
 Streamlit
 langchain
 langchain-community
-utills
+chromadb
 transformers
 Running the application
 
 Install the required libraries.
 Set the environment variable lang_api_key with your Langchain API key (if applicable).
 Run streamlit run main.py.
-Code Structure
+####  Code Structure
 
 create_retriever_from_chroma: Creates a document retriever using Chroma and the Alibaba-NLP/gte-base-en-v1.5 model for sentence similarity.
 main: Defines the Streamlit application layout and functionalities.
@@ -55,7 +55,7 @@ handle_userinput: Processes user input, retrieves relevant documents, and genera
 create_conversational_rag_chain: Creates a RAG chain for processing user questions with the compressed LLM retriever.
 Additional Notes
 
-This application uses pre-trained document files. You can modify the data path to use your own documents.
+
 The Lithuanian law documents might not be the latest versions.