README more descriptive

README.md

@@ -1 +1,15 @@
-# LocalAIAgentWithRAG
+# LocalAIAgentWithRAG
+Find the video [here](https://www.youtube.com/watch?v=E4l91XKQSgw&ab_channel=TechWithTim)
+
+# Steps at a high level
+1. Create virtual env - `python3 -m venv ./venv`
+2. Activate venv - `source venv/bin/activate` on MacOS
+3. Install [Ollama](https://ollama.com/) then get the following models:
+    * ollama pull llama3.2
+    * ollama pull mxbai-embed-large
+    We need both a language model and an embedding model.
+4. Look at main.py - This describes the Llama model and a prompt for it. The line `reviews = retriever.invoke(question)` makes a call to the vector DB in vector.py and gets a response.
+5. Look at vector.py - This uses ChromaDB to perform vector searching. We load the `mxbai-embed-large` model to use with the dataframe created from the CSV provided.
+6. Chroma DB is stored on disk as a file, we provide the path to this as `./chrome_langchain_db`. Every time a retrival request is made the top 5 matching reviews are returned based on a similarity sesarch metric.
+
+

main.py

@@ -5,7 +5,7 @@
 model = OllamaLLM(model="llama3.2")
 
 template = """
-You are an exeprt in answering questions about a pizza restaurant
+You are an expert in answering questions about a pizza restaurant
 
 Here are some relevant reviews: {reviews}