Update README.md

README.md

@@ -10,4 +10,37 @@ pinned: false
 license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+This simple restaurant planner is designed to communicate with MongoDB Atlas Vector Search with the loaded Restaurant data set. 
+
+It uses OpenAI small text embeddings (256 dimesnsions) to query the database for semantic similarity search.
+
+## How to setup your own
+
+1. [Create an Atlas cluter](https://www.mongodb.com/docs/atlas/tutorial/deploy-free-tier-cluster/) (free clusters are available)
+2. Load the [dataset](https://huggingface.co/datasets/AIatMongoDB/whatscooking.restaurants) using the `ingest/ingest.py` with your connection string.
+3. Deploy the relevant [Vector Index](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-tutorial/#create-the-atlas-vector-search-index) on `whatscooking.smart_trips` aggregated collection "name" : `vector_index`.
+ ```
+ {
+  "fields": [
+    {
+      "numDimensions": 256,
+      "path": "embedding",
+      "similarity": "cosine",
+      "type": "vector"
+    },
+    {
+      "path": "searchTrip",
+      "type": "filter"
+    }
+  ]
+}
+```
+     - [Whitelist](https://www.mongodb.com/docs/atlas/security/ip-access-list/#std-label-access-list) access from everywhere (`0.0.0.0/0`)
+     - Locate your [cluster connection](https://www.mongodb.com/docs/atlas/tutorial/connect-to-your-cluster/) URI 
+5. Obtain your Open AI api key
+6. "Duplicate" this space and input
+     - `MONGODB_ATLAS_CLUSTER_URI` - Your Atlas Cluster connection string
+     - `OPENAI_API_KEY`- Open AI API key
+
+Build and use the planner!