## Vectara

> [Vectara](https://vectara.com/) is the trusted GenAI platform that provides an easy-to-use API for document indexing and querying.

Vectara provides an end-to-end managed service for Retrieval Augmented Generation or [RAG](https://vectara.com/grounded-generation/), which includes:

1.  A way to extract text from document files and chunk them into sentences.
    
2.  The state-of-the-art [Boomerang](https://vectara.com/how-boomerang-takes-retrieval-augmented-generation-to-the-next-level-via-grounded-generation/) embeddings model. Each text chunk is encoded into a vector embedding using Boomerang, and stored in the Vectara internal knowledge (vector+text) store
    
3.  A query service that automatically encodes the query into embedding, and retrieves the most relevant text segments (including support for [Hybrid Search](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching) and [MMR](https://vectara.com/get-diverse-results-and-comprehensive-summaries-with-vectaras-mmr-reranker/))
    
4.  An option to create [generative summary](https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview), based on the retrieved documents, including citations.
    

See the [Vectara API documentation](https://docs.vectara.com/docs/) for more information on how to use the API.

This notebook shows how to use functionality related to the `Vectara`’s integration with langchain. Specificaly we will demonstrate how to use chaining with [LangChain’s Expression Language](https://python.langchain.com/docs/expression_language/) and using Vectara’s integrated summarization capability.

## Setup

You will need a Vectara account to use Vectara with LangChain. To get started, use the following steps:

1.  [Sign up](https://www.vectara.com/integrations/langchain) for a Vectara account if you don’t already have one. Once you have completed your sign up you will have a Vectara customer ID. You can find your customer ID by clicking on your name, on the top-right of the Vectara console window.
    
2.  Within your account you can create one or more corpora. Each corpus represents an area that stores text data upon ingest from input documents. To create a corpus, use the **“Create Corpus”** button. You then provide a name to your corpus as well as a description. Optionally you can define filtering attributes and apply some advanced options. If you click on your created corpus, you can see its name and corpus ID right on the top.
    
3.  Next you’ll need to create API keys to access the corpus. Click on the **“Authorization”** tab in the corpus view and then the **“Create API Key”** button. Give your key a name, and choose whether you want query only or query+index for your key. Click “Create” and you now have an active API key. Keep this key confidential.
    

To use LangChain with Vectara, you’ll need to have these three values: customer ID, corpus ID and api\_key. You can provide those to LangChain in two ways:

1.  Include in your environment these three variables: `VECTARA_CUSTOMER_ID`, `VECTARA_CORPUS_ID` and `VECTARA_API_KEY`.

> For example, you can set these variables using os.environ and getpass as follows:

```
import osimport getpassos.environ["VECTARA_CUSTOMER_ID"] = getpass.getpass("Vectara Customer ID:")os.environ["VECTARA_CORPUS_ID"] = getpass.getpass("Vectara Corpus ID:")os.environ["VECTARA_API_KEY"] = getpass.getpass("Vectara API Key:")
```

1.  Add them to the Vectara vectorstore constructor:

```
vectorstore = Vectara(                vectara_customer_id=vectara_customer_id,                vectara_corpus_id=vectara_corpus_id,                vectara_api_key=vectara_api_key            )
```

```
from langchain_community.embeddings import FakeEmbeddingsfrom langchain_community.vectorstores import Vectarafrom langchain_core.output_parsers import StrOutputParserfrom langchain_core.prompts import ChatPromptTemplatefrom langchain_core.runnables import RunnableLambda, RunnablePassthrough
```

First we load the state-of-the-union text into Vectara. Note that we use the `from_files` interface which does not require any local processing or chunking - Vectara receives the file content and performs all the necessary pre-processing, chunking and embedding of the file into its knowledge store.

```
vectara = Vectara.from_files(["state_of_the_union.txt"])
```

We now create a Vectara retriever and specify that: \* It should return only the 3 top Document matches \* For summary, it should use the top 5 results and respond in English

```
summary_config = {"is_enabled": True, "max_results": 5, "response_lang": "eng"}retriever = vectara.as_retriever(    search_kwargs={"k": 3, "summary_config": summary_config})
```

When using summarization with Vectara, the retriever responds with a list of `Document` objects: 1. The first `k` documents are the ones that match the query (as we are used to with a standard vector store) 2. With summary enabled, an additional `Document` object is apended, which includes the summary text. This Document has the metadata field `summary` set as True.

Let’s define two utility functions to split those out:

```
def get_sources(documents):    return documents[:-1]def get_summary(documents):    return documents[-1].page_contentquery_str = "what did Biden say?"
```

Now we can try a summary response for the query:

```
(retriever | get_summary).invoke(query_str)
```

```
'The returned results did not contain sufficient information to be summarized into a useful answer for your query. Please try a different search or restate your query differently.'
```

And if we would like to see the sources retrieved from Vectara that were used in this summary (the citations):

```
(retriever | get_sources).invoke(query_str)
```

```
[Document(page_content='When they came home, many of the world’s fittest and best trained warriors were never the same. Dizziness. \n\nA cancer that would put them in a flag-draped coffin. I know. \n\nOne of those soldiers was my son Major Beau Biden. We don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. But I’m committed to finding out everything we can.', metadata={'lang': 'eng', 'section': '1', 'offset': '34652', 'len': '60', 'X-TIKA:Parsed-By': 'org.apache.tika.parser.csv.TextAndCSVParser', 'Content-Encoding': 'UTF-8', 'Content-Type': 'text/plain; charset=UTF-8', 'source': 'vectara'}), Document(page_content='The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs. We are joining with our European allies to find and seize your yachts your luxury apartments your private jets. We are coming for your ill-begotten gains. And tonight I am announcing that we will join our allies in closing off American air space to all Russian flights – further isolating Russia – and adding an additional squeeze –on their economy. The Ruble has lost 30% of its value.', metadata={'lang': 'eng', 'section': '1', 'offset': '3807', 'len': '42', 'X-TIKA:Parsed-By': 'org.apache.tika.parser.csv.TextAndCSVParser', 'Content-Encoding': 'UTF-8', 'Content-Type': 'text/plain; charset=UTF-8', 'source': 'vectara'}), Document(page_content='He rejected repeated efforts at diplomacy. He thought the West and NATO wouldn’t respond. And he thought he could divide us at home. We were ready.  Here is what we did. We prepared extensively and carefully.', metadata={'lang': 'eng', 'section': '1', 'offset': '2100', 'len': '42', 'X-TIKA:Parsed-By': 'org.apache.tika.parser.csv.TextAndCSVParser', 'Content-Encoding': 'UTF-8', 'Content-Type': 'text/plain; charset=UTF-8', 'source': 'vectara'})]
```

Vectara’s “RAG as a service” does a lot of the heavy lifting in creating question answering or chatbot chains. The integration with LangChain provides the option to use additional capabilities such as query pre-processing like `SelfQueryRetriever` or `MultiQueryRetriever`. Let’s look at an example of using the [MultiQueryRetriever](https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever/).

Since MQR uses an LLM we have to set that up - here we choose `ChatOpenAI`:

```
from langchain.retrievers.multi_query import MultiQueryRetrieverfrom langchain_openai import ChatOpenAIllm = ChatOpenAI(temperature=0)mqr = MultiQueryRetriever.from_llm(retriever=retriever, llm=llm)(mqr | get_summary).invoke(query_str)
```

```
"President Biden has made several notable quotes and comments. He expressed a commitment to investigate the potential impact of burn pits on soldiers' health, referencing his son's brain cancer [1]. He emphasized the importance of unity among Americans, urging us to see each other as fellow citizens rather than enemies [2]. Biden also highlighted the need for schools to use funds from the American Rescue Plan to hire teachers and address learning loss, while encouraging community involvement in supporting education [3]."
```

```
(mqr | get_sources).invoke(query_str)
```

```
[Document(page_content='When they came home, many of the world’s fittest and best trained warriors were never the same. Dizziness. \n\nA cancer that would put them in a flag-draped coffin. I know. \n\nOne of those soldiers was my son Major Beau Biden. We don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. But I’m committed to finding out everything we can.', metadata={'lang': 'eng', 'section': '1', 'offset': '34652', 'len': '60', 'X-TIKA:Parsed-By': 'org.apache.tika.parser.csv.TextAndCSVParser', 'Content-Encoding': 'UTF-8', 'Content-Type': 'text/plain; charset=UTF-8', 'source': 'vectara'}), Document(page_content='The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs. We are joining with our European allies to find and seize your yachts your luxury apartments your private jets. We are coming for your ill-begotten gains. And tonight I am announcing that we will join our allies in closing off American air space to all Russian flights – further isolating Russia – and adding an additional squeeze –on their economy. The Ruble has lost 30% of its value.', metadata={'lang': 'eng', 'section': '1', 'offset': '3807', 'len': '42', 'X-TIKA:Parsed-By': 'org.apache.tika.parser.csv.TextAndCSVParser', 'Content-Encoding': 'UTF-8', 'Content-Type': 'text/plain; charset=UTF-8', 'source': 'vectara'}), Document(page_content='And, if Congress provides the funds we need, we’ll have new stockpiles of tests, masks, and pills ready if needed. I cannot promise a new variant won’t come. But I can promise you we’ll do everything within our power to be ready if it does. Third – we can end the shutdown of schools and businesses. We have the tools we need.', metadata={'lang': 'eng', 'section': '1', 'offset': '24753', 'len': '82', 'X-TIKA:Parsed-By': 'org.apache.tika.parser.csv.TextAndCSVParser', 'Content-Encoding': 'UTF-8', 'Content-Type': 'text/plain; charset=UTF-8', 'source': 'vectara'}), Document(page_content='The returned results did not contain sufficient information to be summarized into a useful answer for your query. Please try a different search or restate your query differently.', metadata={'summary': True}), Document(page_content='Danielle says Heath was a fighter to the very end. He didn’t know how to stop fighting, and neither did she. Through her pain she found purpose to demand we do better. Tonight, Danielle—we are. The VA is pioneering new ways of linking toxic exposures to diseases, already helping more veterans get benefits.', metadata={'lang': 'eng', 'section': '1', 'offset': '35502', 'len': '58', 'X-TIKA:Parsed-By': 'org.apache.tika.parser.csv.TextAndCSVParser', 'Content-Encoding': 'UTF-8', 'Content-Type': 'text/plain; charset=UTF-8', 'source': 'vectara'}), Document(page_content='Let’s stop seeing each other as enemies, and start seeing each other for who we really are: Fellow Americans. We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun.', metadata={'lang': 'eng', 'section': '1', 'offset': '26312', 'len': '89', 'X-TIKA:Parsed-By': 'org.apache.tika.parser.csv.TextAndCSVParser', 'Content-Encoding': 'UTF-8', 'Content-Type': 'text/plain; charset=UTF-8', 'source': 'vectara'}), Document(page_content='The American Rescue Plan gave schools money to hire teachers and help students make up for lost learning. I urge every parent to make sure your school does just that. And we can all play a part—sign up to be a tutor or a mentor. Children were also struggling before the pandemic. Bullying, violence, trauma, and the harms of social media.', metadata={'lang': 'eng', 'section': '1', 'offset': '33227', 'len': '61', 'X-TIKA:Parsed-By': 'org.apache.tika.parser.csv.TextAndCSVParser', 'Content-Encoding': 'UTF-8', 'Content-Type': 'text/plain; charset=UTF-8', 'source': 'vectara'})]
```