# Vectara


What is Vectara?

**Vectara Overview:**
- Vectara is developer-first API platform for building GenAI applications
- To use Vectara - first [sign up](https://console.vectara.com/signup) and create an account. Then create a corpus and an API key for indexing and searching.
- You can use Vectara's [indexing API](https://docs.vectara.com/docs/indexing-apis/indexing) to add documents into Vectara's index
- You can use Vectara's [Search API](https://docs.vectara.com/docs/search-apis/search) to query Vectara's index (which also supports Hybrid search implicitly).
- You can use Vectara's integration with LangChain as a Vector store or using the Retriever abstraction.

## Installation and Setup
To use Vectara with LangChain no special installation steps are required. You just have to provide your customer_id, corpus ID, and an API key created within the Vectara console to enable indexing and searching.

Alternatively these can be provided as environment variables
- export `VECTARA_CUSTOMER_ID`="your_customer_id"
- export `VECTARA_CORPUS_ID`="your_corpus_id"
- export `VECTARA_API_KEY`="your-vectara-api-key"

## Usage

### VectorStore

There exists a wrapper around the Vectara platform, allowing you to use it as a vectorstore, whether for semantic search or example selection.

To import this vectorstore:
```python
from langchain.vectorstores import Vectara
```

To create an instance of the Vectara vectorstore:
```python
vectara = Vectara(
    vectara_customer_id=customer_id, 
    vectara_corpus_id=corpus_id, 
    vectara_api_key=api_key
)
```
The customer_id, corpus_id and api_key are optional, and if they are not supplied will be read from the environment variables `VECTARA_CUSTOMER_ID`, `VECTARA_CORPUS_ID` and `VECTARA_API_KEY`, respectively.

To query the vectorstore, you can use the `similarity_search` method (or `similarity_search_with_score`), which takes a query string and returns a list of results:
```python
results = vectara.similarity_score("what is LangChain?")
```

`similarity_search_with_score` also supports the following additional arguments:
- `k`: number of results to return (defaults to 5)
- `lambda_val`: the [lexical matching](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching) factor for hybrid search (defaults to 0.025)
- `filter`: a [filter](https://docs.vectara.com/docs/common-use-cases/filtering-by-metadata/filter-overview) to apply to the results (default None)
- `n_sentence_context`: number of sentences to include before/after the actual matching segment when returning results. This defaults to 0 so as to return the exact text segment that matches, but can be used with other values e.g. 2 or 3 to return adjacent text segments.

The results are returned as a list of relevant documents, and a relevance score of each document.


For a more detailed examples of using the Vectara wrapper, see one of these two sample notebooks:
* [Chat Over Documents with Vectara](./vectara/vectara_chat.html)
* [Vectara Text Generation](./vectara/vectara_text_generation.html)