# Vectara RAG Pack
This LlamaPack provides an end-to-end Retrieval Augmented Generation flow using Vectara.
Before you start, if you have not done so already, you would need to follow these steps:
- Create a [free Vectara account](https://vectara.com/integrations/llamaindex).
- Create a [corpus](https://docs.vectara.com/docs/console-ui/creating-a-corpus) to store your data
- Create an [API key](https://docs.vectara.com/docs/common-use-cases/app-authn-authz/api-keys) with QueryService and IndexService access so you can access this corpus
You can configure your `.env` file or provide these arguments directly when creating your VectaraIndex:
```
VECTARA_CUSTOMER_ID=your_customer_id
VECTARA_CORPUS_ID=your_corpus_id
VECTARA_API_KEY=your-vectara-api-key
```
## CLI Usage
You can download the Vectara llamapack directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:
```bash
llamaindex-cli download-llamapack VectaraRagPack --download-dir ./vectara_rag_pack
```
Feel free to inspect the files at `./vectara_rag_pack` and use them as a template for your own project!
## Code Usage
You can download the pack to a `./vectara_rag_pack` directory:
```python
from llama_index.core.llama_pack import download_llama_pack
VectaraRAG = download_llama_pack("VectaraRagPack", "./vectara_rag_pack")
```
Then, you can set up the pack in two ways:
1. If you want to ingest documents into the Vectara Corpus:
```python
nodes = [...]
vectara = VectaraRAG(nodes=nodes)
```
2. If you already indexed data on Vectara, and just want to use the retrieval/query functionality:
```python
vectara = VectaraRAG()
```
Additional optional arguments to VectaraRAG:
- `similarity_top_k`: determines the number of results to return. Defaults to 5.
- `n_sentences_before` and `n_sentences_after`: determine the number of sentences before/after the
matching fact to use with the summarization LLM. defaults to 2.
- `vectara_query_mode`: 'default' or 'mmr'
- `summary_enabled`: whether to generate summaries or not. Defaults to True.
- When summary_enabled is True, you can set the following:
- `summary_response_lang`: language to use (ISO 639-2 code) for summary generation. defaults to "eng".
- `summary_num_results`: number of results to use for summary generation. Defaults to 7.
- `summary_prompt_name`: name of the prompt to use for summary generation.
Defaults to 'vectara-summary-ext-v1.2.0'.
Scale customers can use 'vectara-summary-ext-v1.3.0
- when `vectara_query_mode` is "mmr", you can set the following to control MMR:
- `mmr_k`: number of results to fetch for MMR, defaults to 50
- `mmr_diversity_bias`: number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to minimum diversity and 1 to maximum diversity. Defaults to 0.3.
For example to use maximal diversity with MMR:
```python
vectara = VectaraRAG(
vectara_query_mode="mmr", mmr_k=50, mmr_diversity_bias=1.0
)
```
Or if you want to include more results in the Vectara generated summarization you can try:
```python
vectara = VectaraRAG(summary_num_results=12)
```
Once you have the Vectara RAG object, you can now use it as a retriever:
```python
# use the retriever
nodes = vectara.retrieve("Is light a wave or a particle?")
```
Or as a query engine (with Vectara summarization call):
```python
# use the query engine
response = vectara._query_engine.query(
"Is light a wave or a particle?"
).response
```
Note that the `run()` function is a light wrapper around `query_engine.query()`.
```python
response = vectara.run("Is light a wave or a particle?").response
```
Enjoy your Vectara RAG pack!
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"description": "# Vectara RAG Pack\n\nThis LlamaPack provides an end-to-end Retrieval Augmented Generation flow using Vectara.\n\nBefore you start, if you have not done so already, you would need to follow these steps:\n\n- Create a [free Vectara account](https://vectara.com/integrations/llamaindex).\n- Create a [corpus](https://docs.vectara.com/docs/console-ui/creating-a-corpus) to store your data\n- Create an [API key](https://docs.vectara.com/docs/common-use-cases/app-authn-authz/api-keys) with QueryService and IndexService access so you can access this corpus\n\nYou can configure your `.env` file or provide these arguments directly when creating your VectaraIndex:\n\n```\nVECTARA_CUSTOMER_ID=your_customer_id\nVECTARA_CORPUS_ID=your_corpus_id\nVECTARA_API_KEY=your-vectara-api-key\n```\n\n## CLI Usage\n\nYou can download the Vectara llamapack directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:\n\n```bash\nllamaindex-cli download-llamapack VectaraRagPack --download-dir ./vectara_rag_pack\n```\n\nFeel free to inspect the files at `./vectara_rag_pack` and use them as a template for your own project!\n\n## Code Usage\n\nYou can download the pack to a `./vectara_rag_pack` directory:\n\n```python\nfrom llama_index.core.llama_pack import download_llama_pack\n\nVectaraRAG = download_llama_pack(\"VectaraRagPack\", \"./vectara_rag_pack\")\n```\n\nThen, you can set up the pack in two ways:\n\n1. If you want to ingest documents into the Vectara Corpus:\n\n```python\nnodes = [...]\nvectara = VectaraRAG(nodes=nodes)\n```\n\n2. If you already indexed data on Vectara, and just want to use the retrieval/query functionality:\n\n```python\nvectara = VectaraRAG()\n```\n\nAdditional optional arguments to VectaraRAG:\n\n- `similarity_top_k`: determines the number of results to return. Defaults to 5.\n- `n_sentences_before` and `n_sentences_after`: determine the number of sentences before/after the\n matching fact to use with the summarization LLM. defaults to 2.\n- `vectara_query_mode`: 'default' or 'mmr'\n- `summary_enabled`: whether to generate summaries or not. Defaults to True.\n- When summary_enabled is True, you can set the following:\n - `summary_response_lang`: language to use (ISO 639-2 code) for summary generation. defaults to \"eng\".\n - `summary_num_results`: number of results to use for summary generation. Defaults to 7.\n - `summary_prompt_name`: name of the prompt to use for summary generation.\n Defaults to 'vectara-summary-ext-v1.2.0'.\n Scale customers can use 'vectara-summary-ext-v1.3.0\n- when `vectara_query_mode` is \"mmr\", you can set the following to control MMR:\n - `mmr_k`: number of results to fetch for MMR, defaults to 50\n - `mmr_diversity_bias`: number between 0 and 1 that determines the degree\n of diversity among the results with 0 corresponding\n to minimum diversity and 1 to maximum diversity. Defaults to 0.3.\n\nFor example to use maximal diversity with MMR:\n\n```python\nvectara = VectaraRAG(\n vectara_query_mode=\"mmr\", mmr_k=50, mmr_diversity_bias=1.0\n)\n```\n\nOr if you want to include more results in the Vectara generated summarization you can try:\n\n```python\nvectara = VectaraRAG(summary_num_results=12)\n```\n\nOnce you have the Vectara RAG object, you can now use it as a retriever:\n\n```python\n# use the retriever\nnodes = vectara.retrieve(\"Is light a wave or a particle?\")\n```\n\nOr as a query engine (with Vectara summarization call):\n\n```python\n# use the query engine\nresponse = vectara._query_engine.query(\n \"Is light a wave or a particle?\"\n).response\n```\n\nNote that the `run()` function is a light wrapper around `query_engine.query()`.\n\n```python\nresponse = vectara.run(\"Is light a wave or a particle?\").response\n```\n\nEnjoy your Vectara RAG pack!\n",
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