# Redis Ingestion Pipeline Pack
This LlamaPack creates an [ingestion pipeline](https://docs.llamaindex.ai/en/stable/module_guides/loading/ingestion_pipeline/root.html), with both a cache and vector store backed by Redis.
## CLI Usage
You can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:
```bash
llamaindex-cli download-llamapack RedisIngestionPipelinePack --download-dir ./redis_ingestion_pack
```
You can then inspect the files at `./redis_ingestion_pack` and use them as a template for your own project!
## Code Usage
You can download the pack to a `./redis_ingestion_pack` directory:
```python
from llama_index.core.llama_pack import download_llama_pack
# download and install dependencies
RedisIngestionPipelinePack = download_llama_pack(
"RedisIngestionPipelinePack", "./redis_ingestion_pack"
)
```
From here, you can use the pack, or inspect and modify the pack in `./redis_ingestion_pack`.
Then, you can set up the pack like so:
```python
from llama_index.core.text_splitter import SentenceSplitter
from llama_index.core.embeddings import OpenAIEmbedding
transformations = [SentenceSplitter(), OpenAIEmbedding()]
# create the pack
ingest_pack = RedisIngestionPipelinePack(
transformations,
hostname="localhost",
port=6379,
cache_collection_name="ingest_cache",
vector_collection_name="vector_store",
)
```
The `run()` function is a light wrapper around `pipeline.run()`.
You can use this to ingest data and then create an index from the vector store.
```python
pipeline.run(documents)
index = VectorStoreIndex.from_vector_store(inget_pack.vector_store)
```
You can learn more about the ingestion pipeline at the [LlamaIndex documentation](https://docs.llamaindex.ai/en/stable/module_guides/loading/ingestion_pipeline/root.html).
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"description": "# Redis Ingestion Pipeline Pack\n\nThis LlamaPack creates an [ingestion pipeline](https://docs.llamaindex.ai/en/stable/module_guides/loading/ingestion_pipeline/root.html), with both a cache and vector store backed by Redis.\n\n## CLI Usage\n\nYou can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:\n\n```bash\nllamaindex-cli download-llamapack RedisIngestionPipelinePack --download-dir ./redis_ingestion_pack\n```\n\nYou can then inspect the files at `./redis_ingestion_pack` and use them as a template for your own project!\n\n## Code Usage\n\nYou can download the pack to a `./redis_ingestion_pack` directory:\n\n```python\nfrom llama_index.core.llama_pack import download_llama_pack\n\n# download and install dependencies\nRedisIngestionPipelinePack = download_llama_pack(\n \"RedisIngestionPipelinePack\", \"./redis_ingestion_pack\"\n)\n```\n\nFrom here, you can use the pack, or inspect and modify the pack in `./redis_ingestion_pack`.\n\nThen, you can set up the pack like so:\n\n```python\nfrom llama_index.core.text_splitter import SentenceSplitter\nfrom llama_index.core.embeddings import OpenAIEmbedding\n\ntransformations = [SentenceSplitter(), OpenAIEmbedding()]\n\n# create the pack\ningest_pack = RedisIngestionPipelinePack(\n transformations,\n hostname=\"localhost\",\n port=6379,\n cache_collection_name=\"ingest_cache\",\n vector_collection_name=\"vector_store\",\n)\n```\n\nThe `run()` function is a light wrapper around `pipeline.run()`.\n\nYou can use this to ingest data and then create an index from the vector store.\n\n```python\npipeline.run(documents)\n\nindex = VectorStoreIndex.from_vector_store(inget_pack.vector_store)\n```\n\nYou can learn more about the ingestion pipeline at the [LlamaIndex documentation](https://docs.llamaindex.ai/en/stable/module_guides/loading/ingestion_pipeline/root.html).\n",
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