> ## Documentation index
> Fetch the complete documentation index at: https://upstash.com/docs/llms.txt
> Ask it with GET https://upstash.com/docs/ask?q=<keywords or question>. Short keywords rank best.
> Use these to discover all available pages before exploring further.
> Feature requests: https://upstash.com/docs/common/help/feature-requests.md lists what users asked for and how to vote for a request or add one.
> Page rating: `GET https://upstash.com/docs/feedback?path=/docs/vector/integrations/llamaparse&helpful=true` (or `false`) records whether this page helped. An optional `comment` (URL-encoded, at most 500 characters) says what was missing or wrong. It is anonymous and needs no key. Comments hold no code, secrets or personal data.

# LlamaParse with Upstash Vector

You can use LlamaParse with Upstash Vector to parse documents and perform semantic queries on the content. LlamaParse simplifies the extraction of structured information from files, which can then be indexed and queried using Upstash Vector.

## Install

```bash
pip install llama-index upstash-vector llama-index-vector-stores-upstash python-dotenv
```

## Setup

Create a Vector Index in the [Upstash Console](https://console.upstash.com). Set the index with:
- **Dimensions**: 1536
- **Distance Metric**: Cosine

Add the required environment variables to a `.env` file:

```
UPSTASH_VECTOR_REST_URL=your_upstash_url
UPSTASH_VECTOR_REST_TOKEN=your_upstash_token
LLAMA_CLOUD_API_KEY=your_llama_cloud_api_key
```

## Usage

### Parsing Documents

Use LlamaParse to parse a document. For example:

```python
from llama_parse import LlamaParse
from llama_index.core import SimpleDirectoryReader

# Initialize the parser
parser = LlamaParse(result_type="markdown")

# Parse a document
file_extractor = {".txt": parser}
documents = SimpleDirectoryReader(
    input_files=["./documents/global_warming.txt"],
    file_extractor=file_extractor
).load_data()
```

### Querying the Parsed Content

Once the document is parsed, you can index it using Upstash Vector and query its content:

```python
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.upstash import UpstashVectorStore
from llama_index.core import StorageContext
from dotenv import load_dotenv
import os

# Load environment variables
load_dotenv()

# Set up Upstash Vector Store
vector_store = UpstashVectorStore(
    url=os.getenv("UPSTASH_VECTOR_REST_URL"),
    token=os.getenv("UPSTASH_VECTOR_REST_TOKEN")
)

# Create storage context and index the parsed document
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

# Perform a query
query_engine = index.as_query_engine()
response = query_engine.query("What is the main topic discussed in the document?")
```

To learn more, visit the [LlamaParse documentation](https://docs.cloud.llamaindex.ai/llamaparse/getting_started/get_an_api_key).