Upload 3 files

app.py

@@ -0,0 +1,55 @@
+import gradio as gr
+import pinecone
+from pinecone import PineconeGRPC, GRPCClientConfig
+
+# Initialize Pinecone connection
+pinecone.init(api_key="your-pinecone-api-key", environment="us-west1-gcp")
+pc = PineconeGRPC(api_key="your-pinecone-api-key")
+index = pc.Index(host="localhost:5081", grpc_config=GRPCClientConfig(secure=False))
+
+# Function to upsert a vector to Pinecone
+def upsert_vector(vector_id, vector_values, genre):
+    index.upsert(
+        vectors=[
+            {
+                "id": vector_id, 
+                "values": vector_values,
+                "metadata": {"genre": genre}
+            }
+        ],
+        namespace="example-namespace"
+    )
+    return f"Vector {vector_id} upserted with genre {genre}"
+
+# Function to query vectors based on genre
+def query_vector(query_vector, genre_filter):
+    query = index.query(
+        vector=query_vector,
+        filter={"genre": {"$eq": genre_filter}},
+        top_k=1,
+        include_values=True,
+        include_metadata=True,
+        namespace="example-namespace"
+    )
+    return query
+
+# Create Gradio Interface
+with gr.Blocks() as app:
+    gr.Markdown("### Pinecone Vector Search with Gradio")
+    
+    with gr.Row():
+        vector_id = gr.Textbox(label="Vector ID")
+        vector_values = gr.Textbox(label="Vector Values (comma separated)", placeholder="e.g. 1.0, 1.5")
+        genre = gr.Textbox(label="Genre")
+        
+        submit_button = gr.Button("Upsert Vector")
+        submit_button.click(upsert_vector, inputs=[vector_id, vector_values, genre], outputs="status")
+
+    with gr.Row():
+        query_vector = gr.Textbox(label="Query Vector (comma separated)", placeholder="e.g. 1.0, 1.5")
+        genre_filter = gr.Textbox(label="Filter Genre")
+        
+        search_button = gr.Button("Search")
+        search_button.click(query_vector, inputs=[query_vector, genre_filter], outputs="query_result")
+
+app.launch()

import pytest.py

@@ -0,0 +1,29 @@
+import pytest
+import pinecone
+
+def test_upsert_vector():
+    # Initialize Pinecone connection
+    pinecone.init(api_key="your-pinecone-api-key", environment="us-west1-gcp")
+    index = pinecone.Index("example-index")
+
+    # Upsert a vector
+    response = index.upsert(
+        vectors=[{"id": "test1", "values": [1.0, 2.0], "metadata": {"genre": "comedy"}}],
+        namespace="example-namespace"
+    )
+
+    assert response["status"] == "success"
+
+def test_query_vector():
+    # Initialize Pinecone connection
+    pinecone.init(api_key="your-pinecone-api-key", environment="us-west1-gcp")
+    index = pinecone.Index("example-index")
+
+    # Query a vector
+    query_result = index.query(
+        vector=[1.0, 2.0],
+        top_k=1,
+        namespace="example-namespace"
+    )
+
+    assert len(query_result["matches"]) > 0

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+pinecone-client
+pytest