# RAGatouille Retriever Pack
RAGatouille is a [cool library](https://github.com/bclavie/RAGatouille) that lets you use e.g. ColBERT and other SOTA retrieval models in your RAG pipeline. You can use it to either run inference on ColBERT, or use it to train/fine-tune models.
This LlamaPack shows you an easy way to bundle RAGatouille into your RAG pipeline. We use RAGatouille to index a corpus of documents (by default using colbertv2.0), and then we combine it with LlamaIndex query modules to synthesize an answer with an LLM.
A full notebook guide can be found [here](https://github.com/run-llama/llama-hub/blob/main/llama_hub/llama_packs/ragatouille_retriever/ragatouille_retriever.ipynb).
## CLI Usage
You can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:
```bash
llamaindex-cli download-llamapack RAGatouilleRetrieverPack --download-dir ./ragatouille_pack
```
You can then inspect the files at `./` and use them as a template for your own project!
## Code Usage
You can download the pack to a `./ragatouille_pack` directory:
```python
from llama_index.core.llama_pack import download_llama_pack
# download and install dependencies
RAGatouilleRetrieverPack = download_llama_pack(
"RAGatouilleRetrieverPack", "./ragatouille_pack"
)
```
From here, you can use the pack, or inspect and modify the pack in `./ragatouille_pack`.
Then, you can set up the pack like so:
```python
# create the pack
ragatouille_pack = RAGatouilleRetrieverPack(
docs, # List[Document]
llm=OpenAI(model="gpt-3.5-turbo"),
index_name="my_index",
top_k=5,
)
```
The `run()` function is a light wrapper around `query_engine.query`.
```python
response = ragatouille_pack.run("How does ColBERTv2 compare to BERT")
```
You can also use modules individually.
```python
from llama_index.core.response.notebook_utils import display_source_node
retriever = ragatouille_pack.get_modules()["retriever"]
nodes = retriever.retrieve("How does ColBERTv2 compare with BERT?")
for node in nodes:
display_source_node(node)
# try out the RAG module directly
RAG = ragatouille_pack.get_modules()["RAG"]
results = RAG.search(
"How does ColBERTv2 compare with BERT?", index_name=index_name, k=4
)
results
```
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"description": "# RAGatouille Retriever Pack\n\nRAGatouille is a [cool library](https://github.com/bclavie/RAGatouille) that lets you use e.g. ColBERT and other SOTA retrieval models in your RAG pipeline. You can use it to either run inference on ColBERT, or use it to train/fine-tune models.\n\nThis LlamaPack shows you an easy way to bundle RAGatouille into your RAG pipeline. We use RAGatouille to index a corpus of documents (by default using colbertv2.0), and then we combine it with LlamaIndex query modules to synthesize an answer with an LLM.\n\nA full notebook guide can be found [here](https://github.com/run-llama/llama-hub/blob/main/llama_hub/llama_packs/ragatouille_retriever/ragatouille_retriever.ipynb).\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 RAGatouilleRetrieverPack --download-dir ./ragatouille_pack\n```\n\nYou can then inspect the files at `./` and use them as a template for your own project!\n\n## Code Usage\n\nYou can download the pack to a `./ragatouille_pack` directory:\n\n```python\nfrom llama_index.core.llama_pack import download_llama_pack\n\n# download and install dependencies\nRAGatouilleRetrieverPack = download_llama_pack(\n \"RAGatouilleRetrieverPack\", \"./ragatouille_pack\"\n)\n```\n\nFrom here, you can use the pack, or inspect and modify the pack in `./ragatouille_pack`.\n\nThen, you can set up the pack like so:\n\n```python\n# create the pack\nragatouille_pack = RAGatouilleRetrieverPack(\n docs, # List[Document]\n llm=OpenAI(model=\"gpt-3.5-turbo\"),\n index_name=\"my_index\",\n top_k=5,\n)\n```\n\nThe `run()` function is a light wrapper around `query_engine.query`.\n\n```python\nresponse = ragatouille_pack.run(\"How does ColBERTv2 compare to BERT\")\n```\n\nYou can also use modules individually.\n\n```python\nfrom llama_index.core.response.notebook_utils import display_source_node\n\nretriever = ragatouille_pack.get_modules()[\"retriever\"]\nnodes = retriever.retrieve(\"How does ColBERTv2 compare with BERT?\")\n\nfor node in nodes:\n display_source_node(node)\n\n# try out the RAG module directly\nRAG = ragatouille_pack.get_modules()[\"RAG\"]\nresults = RAG.search(\n \"How does ColBERTv2 compare with BERT?\", index_name=index_name, k=4\n)\nresults\n```\n",
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