# Vectara

## Quickstart Tutorial

{% embed url="<https://www.youtube.com/watch?v=rBqpvFcD5XY>" %}

## Prerequisite

1. Register an account for [Vectara](https://vectara.com/integrations/flowise)
2. Click **Create Corpus**

<figure><img src="/files/cZ1Rt4jgSxWATLxrLjSb" alt=""><figcaption></figcaption></figure>

Name the corpus to be created and click **Create Corpus** then wait for the corpus to finish setting up.

## Setup

1. Click on the **"Access Control"** tab in the corpus view

<figure><img src="/files/y2hkhxAiFu6Li0HZ6D7Z" alt=""><figcaption></figcaption></figure>

2. Click on the **"Create API Key"** button, choose a name for the API key and pick the **QueryService & IndexService** option

<figure><img src="/files/3zu2geru2yihyH25WLgE" alt=""><figcaption></figcaption></figure>

3. Click **Create** to create the API key
4. Get your **Corpus ID, API Key, and Customer ID** by clicking the down-arrow under "copy" for your new API key:

<figure><img src="/files/oMe07ycYvJH77DU4v6G1" alt=""><figcaption></figcaption></figure>

5. Back to Flowise canvas, and create your chatflow. Click **Create New** from the Credentials dropdown ane enter your Vectara credentials.

<figure><img src="/files/qPjCP8ydiKklRsLCOG8f" alt="" width="500"><figcaption></figcaption></figure>

6. Enjoy!

## Vectara Query Parameters

For finer control over the Vectara query parameters, click on "**Additional Parameters**" and then you can update the following parameters from their default:

* Metadata Filter: Vectara supports meta-data filtering. To use [filtering](https://docs.vectara.com/docs/common-use-cases/filtering-by-metadata/filter-overview), ensure that metadata fields you want to filter by are defined in your Vectara corpus.
* "Sentences before" and "Sentences after": these control how many sentences before/after the matching text are returned as results from the Vectara retrieval engine
* Lambda: defines the behavior of [hybrid search](https://docs.vectara.com/docs/learn/hybrid-search) in Vectara
* Top-K: how many results to return from Vectara for the query
* MMR-K: number of results to use for [MMR](https://docs.vectara.com/docs/api-reference/search-apis/reranking#maximal-marginal-relevance-mmr-reranker) (max marginal relvance)

<figure><img src="/files/0PjJRaV9lRUwYIFjvqv1" alt="" width="500"><figcaption></figcaption></figure>

## Resources

* [LangChain JS Vectara Blog Post](https://blog.langchain.dev/langchain-vectara-better-together/)
* [5 Reasons to Use Vectara's Langchain Integration Blog Post](https://vectara.com/5-reasons-to-use-vectaras-langchain-integration/)
* [Max Marginal Relevance in Vectara](https://vectara.com/blog/get-diverse-results-and-comprehensive-summaries-with-vectaras-mmr-reranker/)
* [Vectara Boomerang embedding model Blog Post](https://vectara.com/introducing-boomerang-vectaras-new-and-improved-retrieval-model/)
* [Detecting Hallucination with Vectara's HHEM](https://vectara.com/blog/cut-the-bull-detecting-hallucinations-in-large-language-models/)


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.flowiseai.com/integrations/langchain/vector-stores/vectara.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.