> ## Documentation Index
> Fetch the complete documentation index at: https://upstash.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Simple Semantic Search

In this tutorial, we'll demonstrate how to use Upstash Vector for semantic search. We will upload several documents and perform a search query to find the most semantically similar documents using embeddings generated automatically by Upstash.

### Installation and Setup

First, we need to create a Vector Index in the [Upstash Console](https://console.upstash.com). Once we have our index, we will copy the `UPSTASH_VECTOR_REST_URL` and `UPSTASH_VECTOR_REST_TOKEN` and paste them to our `.env` file. To learn more about index creation, you can check out [this page](https://docs.upstash.com/vector/overall/getstarted).

Add the following content to your `.env` file (replace with your actual URL and token):

```
UPSTASH_VECTOR_REST_URL=your_upstash_url
UPSTASH_VECTOR_REST_TOKEN=your_upstash_token
```

We now need to install the `upstash-vector` library via PyPI. Additionally, we will install `python-dotenv` to load environment variables from the `.env` file.

```bash theme={"system"}
pip install upstash-vector python-dotenv
```

### Code

Create a Python script (e.g., `main.py`) and add the following code to perform semantic search using Upstash Vector:

```python main.py theme={"system"}
from upstash_vector import Index
from dotenv import load_dotenv
import time

# Load environment variables from a .env file
load_dotenv()

# Initialize the index from environment variables (URL and token)
index = Index.from_env()

# Example documents to be indexed
documents = [
    {"id": "1", "text": "Python is a popular programming language."},
    {"id": "2", "text": "Machine learning enables computers to learn from data."},
    {"id": "3", "text": "Upstash provides low-latency database solutions."},
    {"id": "4", "text": "Semantic search is a technique for understanding the meaning of queries."},
    {"id": "5", "text": "Cloud computing allows for scalable and flexible resource management."}
]

# Reset the index to remove previous data
index.reset()

# Upsert documents into Upstash (embeddings are generated automatically)
for doc in documents:
    index.upsert(
        vectors=[
            (doc["id"], doc["text"], {"text": doc["text"]})
        ]
    )
    print(f"Document {doc['id']} inserted.")

# Wait for the documents to be indexed
time.sleep(1)

# Search for documents similar to the query
query = "What is Python?"
results = index.query(data=query, top_k=3, include_metadata=True)

# Display search results
print("Search Results:")
for result in results:
    print(f"ID: {result.id}")
    print(f"Score: {result.score:.4f}")
    print(f"Metadata: {result.metadata}")
    print("-" * 40)  # Separator line between results
```

### Running the Code

To run the code, execute the following command in your terminal:

```bash theme={"system"}
python main.py
```

Here is an example output for the search query "What is Python?":

```
Document 1 inserted.
Document 2 inserted.
Document 3 inserted.
Document 4 inserted.
Document 5 inserted.
Search Results:
ID: 1
Score: 0.9080
Metadata: {'text': 'Python is a popular programming language.'}
----------------------------------------
ID: 2
Score: 0.7592
Metadata: {'text': 'Machine learning enables computers to learn from data.'}
----------------------------------------
ID: 4
Score: 0.7388
Metadata: {'text': 'Semantic search is a technique for understanding the meaning of queries.'}
----------------------------------------
```

### Code Breakdown

1. **Environment Setup**: We use `python-dotenv` to load our environment variables and use the `Index.from_env()` method to initialize the index client.

2. **Document Insertion**: We define a list of documents, each with a unique ID and text content. The `upsert()` function inserts these documents into our index. These documents are automatically converted into embeddings. To learn more about Upstash Embedding Models, you can check out [this page](https://docs.upstash.com/vector/features/embeddingmodels).

3. **Index Reset**: Before inserting documents, the `reset()` function clears any existing data in the index.

4. **Search Query**: After inserting the documents, we perform semantic search. The `query()` function returns the `top_k` most similar documents to the query along with their metadata if `include_metadata` is set to `True`.