all the best fixes

app.py

@@ -90,9 +90,9 @@ User Query:
 {query}
 
 Context:
-{context}<|eot_id|>
+{context}
 
-<|start_header_id|>assistant<|end_header_id|>
+<|eot_id|><|start_header_id|>assistant<|end_header_id|>
 """
 
 ### 2. CREATE PROMPT TEMPLATE

chainlit.md

@@ -1 +1,36 @@
-# FILL OUT YOUR CHAINLIT MD HERE WITH A DESCRIPTION OF YOUR APPLICATION
+### SF Sentinel: The Cutting-Edge AI Experience
+
+Welcome to **SF Sentinel**, your gateway to the future of intelligent information retrieval and augmented generation, inspired by the innovative spirit of San Francisco. Here’s why SF Sentinel is not just an app but a technological marvel:
+
+---
+
+#### **Powered by State-of-the-Art Models**
+
+1. **LLaMA 3: The Next Generation Language Model**
+   - **NousResearch/Meta-Llama-3-8B-Instruct**: At the heart of SF Sentinel is the LLaMA 3, a powerful language model designed to understand and generate human-like text. With 8 billion parameters, this model brings unparalleled accuracy and fluency to natural language processing, ensuring that every response is as insightful as a conversation with a San Francisco sage.
+
+2. **Arctic Embed: Precision Embeddings for Context-Aware Insights**
+   - **Snowflake/snowflake-arctic-embed-m**: Our embedding model, Arctic Embed, excels at capturing the essence of complex texts. By transforming textual data into high-dimensional vectors, it allows SF Sentinel to understand the nuanced relationships between different pieces of information, delivering precise and context-aware insights every time.
+
+---
+
+#### **Leveraging Hugging Face Inference Endpoints**
+
+- **Hugging Face Inference Endpoints**: The backbone of SF Sentinel's real-time processing capabilities, these endpoints enable seamless integration and deployment of cutting-edge models. By utilizing Hugging Face's robust infrastructure, we ensure that SF Sentinel can handle intensive computations with speed and reliability, providing instant responses to your queries.
+
+---
+
+#### **Frameworks That Empower**
+
+1. **LangChain: The Ultimate Chain of Intelligence**
+   - **LangChain**: This powerful framework orchestrates the seamless interaction between different AI components. LangChain enables SF Sentinel to combine the strengths of LLaMA 3 and Arctic Embed, ensuring that data flows smoothly and insights are generated efficiently.
+
+2. **FAISS: High-Speed Similarity Search**
+   - **Facebook AI Similarity Search (FAISS)**: A critical component for managing and querying large-scale vector data, FAISS ensures that SF Sentinel can perform rapid and accurate similarity searches. This means you get the most relevant information faster than ever before.
+
+3. **Chainlit: Interactive AI Conversations**
+   - **Chainlit**: Our conversational framework, Chainlit, transforms SF Sentinel into an interactive assistant. With Chainlit, you can engage in dynamic, back-and-forth conversations, making the experience not just informative but also engaging and intuitive.
+
+---
+
+Embrace the future. Experience **SF Sentinel**.

solution_app.py

@@ -83,9 +83,9 @@ User Query:
 {query}
 
 Context:
-{context}<|eot_id|>
+{context}
 
-<|start_header_id|>assistant<|end_header_id|>
+<|eot_id|><|start_header_id|>assistant<|end_header_id|>
 """
 
 rag_prompt = PromptTemplate.from_template(RAG_PROMPT_TEMPLATE)