updated

Signed-off-by: Francisco Javier Arceo <[email protected]>

module_4_rag/README.md

@@ -52,3 +52,43 @@ flowchart TD;
     C[Materialize Online] --> D[Retrieval Augmented Generation];
 ```
 
+# Results
+
+The simple demo shows the code below with the retrieved data shown.
+
+```python
+import pandas as pd
+
+from feast import FeatureStore
+from batch_score_documents import run_model, TOKENIZER, MODEL
+from transformers import AutoTokenizer, AutoModel
+
+df = pd.read_parquet("./feature_repo/data/city_wikipedia_summaries_with_embeddings.parquet")
+
+store = FeatureStore(repo_path=".")
+
+# Prepare a query vector
+question = "the most populous city in the U.S. state of Texas?"
+
+tokenizer = AutoTokenizer.from_pretrained(TOKENIZER)
+model = AutoModel.from_pretrained(MODEL)
+query_embedding = run_model(question, tokenizer, model)
+query = query_embedding.detach().cpu().numpy().tolist()[0]
+
+# Retrieve top k documents
+features = store.retrieve_online_documents(
+    feature="city_embeddings:Embeddings",
+    query=query,
+    top_k=3
+)
+```
+And running `features_df` will show:
+
+```
+$features_df
+
+    Embeddings	                                        distance
+0	[0.11749928444623947, -0.04684492573142052, 0....	0.935567
+1	[0.10329511761665344, -0.07897591590881348, 0....	0.939936
+2	[0.11634305864572525, -0.10321836173534393, -0...	0.983343
+```

module_4_rag/module_4.ipynb

@@ -38,8 +38,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import os\n",
     "import pandas as pd\n",
-    "import warnings\n",
+    "from feast import FeatureStore\n",
     "\n",
     "from batch_score_documents import run_model, TOKENIZER, MODEL\n",
     "from transformers import AutoTokenizer, AutoModel"
@@ -164,15 +165,6 @@
     "df.head()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -380,7 +372,6 @@
     }
    ],
    "source": [
-    "from feast import FeatureStore\n",
     "store = FeatureStore(repo_path=\".\")"
    ]
   },