Merge pull request #1481 from jinhonglin-ryan/master

attu tutorial

bootcamp/tutorials/quickstart/quickstart_with_attu.md

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+# Quick Start with Attu Desktop
+
+## 1. Introduction
+[Attu](https://github.com/zilliztech/attu) is an all-in-one, open-source administration tool for Milvus. It features an intuitive graphical user interface (GUI), allowing you to easily interact with your databases. With just a few clicks, you can visualize your cluster status, manage metadata, perform data queries, and much more.
+
+
+
+---
+
+## 2. Install Desktop Application
+Download the desktop version of Attu by visiting the [Attu GitHub Releases page](https://github.com/zilliztech/attu/releases). Select the appropriate version for your operating system and follow the installation steps.
+
+### Note for macOS (M series chip):
+If you encounter the error:
+```
+attu.app is damaged and cannot be opened.
+```
+Run the following command in terminal to bypass this issue:
+```
+sudo xattr -rd com.apple.quarantine /Applications/attu.app
+```
+
+---
+
+## 3. Connect to Zilliz Cloud
+To use [Zilliz Cloud](https://zilliz.com/cloud), the fully managed cloud service for Milvus:
+
+1. Create a collection in Zilliz Cloud.
+2. Find the corresponding **Public Endpoint** and **Token** in the Zilliz Cloud console.
+3. Open Attu and enter these credentials in the connection interface:
+   - Milvus Address: `<ZILLIZ_CLOUD_URI>`
+   - Token: `<ZILLIZ_CLOUD_TOKEN>`
+4. Click **Connect** to access your database.
+
+<p align="center">
+  <img src="../../../images/attu_login_page.png" alt="Attu Login Page" width="80%">
+</p>
+
+---
+
+## 4. Prepare Data, Create Collection, and Insert Data
+
+### 4.1 Prepare the Data
+We use the FAQ pages from the [Milvus Documentation 2.4.x](https://github.com/milvus-io/milvus-docs/releases/download/v2.4.6-preview/milvus_docs_2.4.x_en.zip) as the dataset for this example.
+
+#### Download and Extract Data:
+```bash
+wget https://github.com/milvus-io/milvus-docs/releases/download/v2.4.6-preview/milvus_docs_2.4.x_en.zip
+unzip -q milvus_docs_2.4.x_en.zip -d milvus_docs
+```
+
+#### Process Markdown Files:
+```python
+from glob import glob
+
+text_lines = []
+for file_path in glob("milvus_docs/en/faq/*.md", recursive=True):
+    with open(file_path, "r") as file:
+        file_text = file.read()
+    text_lines += file_text.split("# ")
+```
+
+---
+
+### 4.2 Generate Embeddings
+Define a embedding model to generate text embeddings using the `milvus_model`. We use the `DefaultEmbeddingFunction` model as an example, which is a pre-trained and lightweight embedding model.
+```python
+from pymilvus import model as milvus_model
+
+embedding_model = milvus_model.DefaultEmbeddingFunction()
+
+# Generate test embedding
+test_embedding = embedding_model.encode_queries(["This is a test"])[0]
+embedding_dim = len(test_embedding)
+print(embedding_dim)
+print(test_embedding[:10])
+```
+#### Output:
+```
+768
+[-0.04836066  0.07163023 -0.01130064 -0.03789345 -0.03320649 -0.01318448
+ -0.03041712 -0.02269499 -0.02317863 -0.00426028]
+```
+---
+
+### 4.3 Create Collection
+Connect to Zilliz Cloud and create a collection:
+```python
+from pymilvus import MilvusClient
+
+# Connect to Zilliz Cloud
+client = MilvusClient(uri="ZILLIZ_CLOUD_URI", token="ZILLIZ_CLOUD_TOKEN")
+
+collection_name = "attu_tutorial"
+
+# Drop collection if it exists
+if client.has_collection(collection_name):
+    client.drop_collection(collection_name)
+
+# Create a new collection
+client.create_collection(
+    collection_name=collection_name,
+    dimension=embedding_dim,
+    metric_type="IP",  # Inner product distance
+    consistency_level="Strong"
+)
+```
+
+---
+
+### 4.4 Insert Data
+Iterate through the text lines, create embeddings, and insert the data into Milvus:
+```python
+from tqdm import tqdm
+
+data = []
+doc_embeddings = embedding_model.encode_documents(text_lines)
+
+for i, line in enumerate(tqdm(text_lines, desc="Creating embeddings")):
+    data.append({"id": i, "vector": doc_embeddings[i], "text": line})
+
+client.insert(collection_name=collection_name, data=data)
+```
+---
+
+### 4.5 Visualize Data and Schema
+
+Now we can visualize the data schema and inserted entities using Attu's interface. The schema displays defined fields, including an `id` field of type `Int64` and a `vector` field of type `FloatVector(768)` with an `Inner Product (IP)` metric. The collection is loaded with **72 entities**. 
+
+Additionally, we can view the inserted data, including ID, vector embeddings, and dynamic fields storing metadata such as text content. The interface supports filtering and querying based on specified conditions or dynamic fields.
+<p align="center">
+  <img src="../../../images/attu_after_data_insertion_1.png" alt="Schema View" width="45%" />
+  <img src="../../../images/attu_after_data_insertion_2.png" alt="Data View" width="45%" />
+</p>
+
+
+## 5. Visualizing Search Results and Relationships
+
+Attu provides a powerful interface for visualizing and exploring data relationships. To examine the inserted data points and their similarity relationships, follow these steps:
+
+### 5.1 **Perform a Search**  
+Navigate to the **Vector Search** tab in Attu.  
+1. Click on the **Generate Random Data** button to create test queries.  
+2. Click **Search** to retrieve results based on the generated data.  
+
+The results are displayed in a table, showing IDs, similarity scores, and dynamic fields for each matching entity.  
+
+<p align="center">
+  <img src="../../../images/attu_searched_table.png" alt="Search Results Table" width="80%">
+</p>
+
+---
+
+### 5.2 **Explore Data Relationships**  
+Click the **Explore** button in the results panel to visualize the relationships between the query vector and the search results in a **knowledge graph-like structure**.  
+
+- The **central node** represents the search vector.  
+- The **connected nodes** represent the search results, clicking them will display the detailed information of the corresponding node.
+
+<p align="center">
+  <img src="../../../images/attu_searched_graph.png" alt="Knowledge Graph Visualization" width="80%">
+</p>
+
+---
+
+### 5.3 **Expand the Graph**  
+Double-click on any result node to expand its connections. This action reveals additional relationships between the selected node and other data points in the collection, creating a **larger, interconnected knowledge graph**.  
+
+This expanded view allows for deeper exploration of how data points are related, based on vector similarity.
+
+<p align="center">
+  <img src="../../../images/attu_expanded_searched_graph.png" alt="Expanded Knowledge Graph" width="80%">
+</p>
+
+---
+
+## 6. Conclusion
+
+Attu simplifies the management and visualization of vector data stored in Milvus. From data insertion to query execution and interactive exploration, it provides an intuitive interface for handling complex vector search tasks. With features like dynamic schema support, graphical search visualizations, and flexible query filters, Attu empowers users to analyze large-scale datasets effectively.
+
+By leveraging Attu’s visual exploration tools, users can better understand their data, identify hidden relationships, and make data-driven decisions. Start exploring your own datasets today with Attu and Milvus!
+
+
+---
+
+