semvec/pypi-packages huggingface dataset:
llama-index-embeddings-octoai 0.3.0
llama-index embeddings octoai integration
License: MIT
Requires Python: <4.0,>=3.9

# LlamaIndex Embeddings Integration: Octoai

Using the [OctoAI](https://octo.ai) Embeddings Integration is a simple as:

```python
from llama_index.embeddings.octoai import OctoAIEmbedding
from os import environ

OCTOAI_API_KEY = environ["OCTOAI_API_KEY"]
embed_model = OctoAIEmbedding(api_key=OCTOAI_API_KEY)
embeddings = embed_model.get_text_embedding("How do I sail to the moon?")
assert len(embeddings) == 1024
```

One can also request a batch of embeddings via:

```python
texts = [
    "How do I sail to the moon?",
    "What is the best way to cook a steak?",
    "How do I apply for a job?",
]

embeddings = embed_model.get_text_embedding_batch(texts)
assert len(embeddings) == 3
```

## API Access

[Here](https://octo.ai/docs/getting-started/how-to-create-an-octoai-access-token) are some instructions on how to get your OctoAI API key.

## Contributing

Follow the good practices of all poetry based projects.

When in VScode, one may want to manually select the Python interpreter, specially to run the example iPython notebook. For this use `ctrl+shift+p`, then type or select: `Python: Select Interpreter`
llama-index-packs-redis-ingestion-pipeline 0.3.0
llama-index packs redis_ingestion_pipeline integration
License: MIT
Requires Python: <4.0,>=3.9
Keywords: index, ingestion, pipeline, redis

# 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.node_parser import SentenceSplitter
from llama_index.embeddings.openai 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).