From fb1ff413d4de8b608b44ef09198ee7de38dae97f Mon Sep 17 00:00:00 2001
From: Rishiraj Acharya <44090649+rishiraj@users.noreply.github.com>
Date: Mon, 29 Apr 2024 04:55:16 +0530
Subject: [PATCH] Update README.md

---
 README.md | 20 ++++++++++++++------
 1 file changed, 14 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index c05ae31..53834ef 100644
--- a/README.md
+++ b/README.md
@@ -16,13 +16,21 @@ vector_db.add_texts(texts)
 ```
 This will encode the texts into embeddings and store them in the database.
 
-3. Search for similar texts:
+3. Search for similar texts or images:
 ```python
-query = "we play football"
-top_results = vector_db.search(query, top_k=3)
-print(top_results)
+text_query = "we play football"
+text_results = vector_db.search(text_query, top_k=2, type='text')
+print("Text search results:")
+for text, similarity in text_results:
+    print(f"Text: {text}, Similarity: {similarity}")
+
+image_url = "https://example.com/image.jpg"
+image_results = vector_db.search(image_url, top_k=2, type='image')
+print("\nImage search results:")
+for text, similarity in image_results:
+    print(f"Text: {text}, Similarity: {similarity}")
 ```
-This will retrieve the top-3 most similar texts to the query based on cosine similarity. The `search` method returns a list of tuples, where each tuple contains the text and its similarity score.
+This will retrieve the top-2 most similar texts or images to the query based on cosine similarity. The `search` method returns a list of tuples, where each tuple contains the text and its similarity score. You can specify the search type using the `type` parameter (`'text'` for text search and `'image'` for image search).
 
 4. Delete a text from the database:
 ```python
@@ -118,4 +126,4 @@ df = loaded_vector_db.to_df()
 print(df.head())
 ```
 
-This example demonstrates how to create a 🍑👋 `VectorDB` instance, add texts, search for similar texts, update and delete texts, and iterate over the stored texts.
\ No newline at end of file
+This example demonstrates how to create a 🍑👋 `VectorDB` instance, add texts, search for similar texts, update and delete texts, and iterate over the stored texts.