# Fusion Retriever Packs
## Hybrid Fusion Pack
This LlamaPack provides an example of our hybrid fusion retriever method.
This specific template fuses results from our vector retriever and bm25 retriever; of course, you can provide any template you want.
Check out the [notebook here](https://github.com/run-llama/llama-hub/blob/main/llama_hub/llama_packs/fusion_retriever/hybrid_fusion/hybrid_fusion.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 HybridFusionRetrieverPack --download-dir ./hybrid_fusion_pack
```
You can then inspect the files at `./hybrid_fusion_pack` and use them as a template for your own project.
### Code Usage
You can download the pack to a the `./hybrid_fusion_pack` directory:
```python
from llama_index.core.llama_pack import download_llama_pack
# download and install dependencies
HybridFusionRetrieverPack = download_llama_pack(
"HybridFusionRetrieverPack", "./hybrid_fusion_pack"
)
```
From here, you can use the pack, or inspect and modify the pack in `./hybrid_fusion_pack`.
Then, you can set up the pack like so:
```python
# create the pack
hybrid_fusion_pack = HybridFusionRetrieverPack(
nodes, chunk_size=256, vector_similarity_top_k=2, bm25_similarity_top_k=2
)
```
The `run()` function is a light wrapper around `query_engine.query()`.
```python
response = hybrid_fusion_pack.run("Tell me about a Music celebritiy.")
```
You can also use modules individually.
```python
# use the fusion retriever
nodes = hybrid_fusion_pack.fusion_retriever.retrieve("query_str")
# use the vector retriever
nodes = hybrid_fusion_pack.vector_retriever.retrieve("query_str")
# use the bm25 retriever
nodes = hybrid_fusion_pack.bm25_retriever.retrieve("query_str")
# get the query engine
query_engine = hybrid_fusion_pack.query_engine
```
## Query Rewriting Retriever Pack
This LlamaPack provides an example of query rewriting through our fusion retriever.
This specific template takes in a single retriever, and generates multiple queries against the retriever, and then fuses the results together.
Check out the [notebook here](https://github.com/run-llama/llama-hub/blob/main/llama_hub/llama_packs/fusion_retriever/query_rewrite/query_rewrite.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 QueryRewritingRetrieverPack --download-dir ./query_rewriting_pack
```
You can then inspect the files at `./query_rewriting_pack` and use them as a template for your own project.
### Code Usage
You can download the pack to a the `./query_rewriting_pack` directory:
```python
from llama_index.core.llama_pack import download_llama_pack
# download and install dependencies
QueryRewritingRetrieverPack = download_llama_pack(
"QueryRewritingRetrieverPack", "./query_rewriting_pack"
)
```
From here, you can use the pack, or inspect and modify the pack in `./query_rewriting_pack`.
Then, you can set up the pack like so:
```python
# create the pack
query_rewriting_pack = QueryRewritingRetrieverPack(
nodes,
chunk_size=256,
vector_similarity_top_k=2,
)
```
The `run()` function is a light wrapper around `query_engine.query()`.
```python
response = query_rewriting_pack.run("Tell me a bout a Music celebritiy.")
```
You can also use modules individually.
```python
# use the fusion retriever
nodes = query_rewriting_pack.fusion_retriever.retrieve("query_str")
# use the vector retriever
nodes = query_rewriting_pack.vector_retriever.retrieve("query_str")
# get the query engine
query_engine = query_rewriting_pack.query_engine
```
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"description": "# Fusion Retriever Packs\n\n## Hybrid Fusion Pack\n\nThis LlamaPack provides an example of our hybrid fusion retriever method.\n\nThis specific template fuses results from our vector retriever and bm25 retriever; of course, you can provide any template you want.\n\nCheck out the [notebook here](https://github.com/run-llama/llama-hub/blob/main/llama_hub/llama_packs/fusion_retriever/hybrid_fusion/hybrid_fusion.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 HybridFusionRetrieverPack --download-dir ./hybrid_fusion_pack\n```\n\nYou can then inspect the files at `./hybrid_fusion_pack` and use them as a template for your own project.\n\n### Code Usage\n\nYou can download the pack to a the `./hybrid_fusion_pack` directory:\n\n```python\nfrom llama_index.core.llama_pack import download_llama_pack\n\n# download and install dependencies\nHybridFusionRetrieverPack = download_llama_pack(\n \"HybridFusionRetrieverPack\", \"./hybrid_fusion_pack\"\n)\n```\n\nFrom here, you can use the pack, or inspect and modify the pack in `./hybrid_fusion_pack`.\n\nThen, you can set up the pack like so:\n\n```python\n# create the pack\nhybrid_fusion_pack = HybridFusionRetrieverPack(\n nodes, chunk_size=256, vector_similarity_top_k=2, bm25_similarity_top_k=2\n)\n```\n\nThe `run()` function is a light wrapper around `query_engine.query()`.\n\n```python\nresponse = hybrid_fusion_pack.run(\"Tell me about a Music celebritiy.\")\n```\n\nYou can also use modules individually.\n\n```python\n# use the fusion retriever\nnodes = hybrid_fusion_pack.fusion_retriever.retrieve(\"query_str\")\n\n# use the vector retriever\nnodes = hybrid_fusion_pack.vector_retriever.retrieve(\"query_str\")\n# use the bm25 retriever\nnodes = hybrid_fusion_pack.bm25_retriever.retrieve(\"query_str\")\n\n# get the query engine\nquery_engine = hybrid_fusion_pack.query_engine\n```\n\n## Query Rewriting Retriever Pack\n\nThis LlamaPack provides an example of query rewriting through our fusion retriever.\n\nThis specific template takes in a single retriever, and generates multiple queries against the retriever, and then fuses the results together.\n\nCheck out the [notebook here](https://github.com/run-llama/llama-hub/blob/main/llama_hub/llama_packs/fusion_retriever/query_rewrite/query_rewrite.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 QueryRewritingRetrieverPack --download-dir ./query_rewriting_pack\n```\n\nYou can then inspect the files at `./query_rewriting_pack` and use them as a template for your own project.\n\n### Code Usage\n\nYou can download the pack to a the `./query_rewriting_pack` directory:\n\n```python\nfrom llama_index.core.llama_pack import download_llama_pack\n\n# download and install dependencies\nQueryRewritingRetrieverPack = download_llama_pack(\n \"QueryRewritingRetrieverPack\", \"./query_rewriting_pack\"\n)\n```\n\nFrom here, you can use the pack, or inspect and modify the pack in `./query_rewriting_pack`.\n\nThen, you can set up the pack like so:\n\n```python\n# create the pack\nquery_rewriting_pack = QueryRewritingRetrieverPack(\n nodes,\n chunk_size=256,\n vector_similarity_top_k=2,\n)\n```\n\nThe `run()` function is a light wrapper around `query_engine.query()`.\n\n```python\nresponse = query_rewriting_pack.run(\"Tell me a bout a Music celebritiy.\")\n```\n\nYou can also use modules individually.\n\n```python\n# use the fusion retriever\nnodes = query_rewriting_pack.fusion_retriever.retrieve(\"query_str\")\n\n# use the vector retriever\nnodes = query_rewriting_pack.vector_retriever.retrieve(\"query_str\")\n\n# get the query engine\nquery_engine = query_rewriting_pack.query_engine\n```\n",
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