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docs: hybrid search feature (#7573)

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* initial-page-and-some-overview

* steps

* remove-file

* feat: enhance Astra DB hybrid search documentation

* numbering

* clarify hybrid search

* dataframe-link

* Apply suggestions from code review

Co-authored-by: KimberlyFields <46325568+KimberlyFields@users.noreply.github.com>
Co-authored-by: April I. Murphy <36110273+aimurphy@users.noreply.github.com>
Co-authored-by: Sarah Edwards <skedwards88@gmail.com>

* code-review

* collection-and-string-not-list

* Apply suggestions from code review

* Apply suggestions from code review

Co-authored-by: April I. Murphy <36110273+aimurphy@users.noreply.github.com>

---------

Co-authored-by: KimberlyFields <46325568+KimberlyFields@users.noreply.github.com>
Co-authored-by: April I. Murphy <36110273+aimurphy@users.noreply.github.com>
Co-authored-by: Sarah Edwards <skedwards88@gmail.com>
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docs/docs/Components/components-vector-stores.md
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@ -3,6 +3,8 @@ title: Vector stores
slug: /components-vector-stores slug: /components-vector-stores
--- ---
import Icon from "@site/src/components/icon";
# Vector store components in Langflow # Vector store components in Langflow
Vector databases store vector data, which backs AI workloads like chatbots and Retrieval Augmented Generation. Vector databases store vector data, which backs AI workloads like chatbots and Retrieval Augmented Generation.
@ -78,6 +80,54 @@ For an example of using the **Astra DB Vector Store** component with an embeddin
For more information, see the [Astra DB Serverless documentation](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html). For more information, see the [Astra DB Serverless documentation](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html).
### Hybrid search
The **Astra DB** component includes **hybrid search**, which is enabled by default.
The component fields related to hybrid search are **Search Query**, **Lexical Terms**, and **Reranker**.
* **Search Query** finds results by vector similarity.
* **Lexical Terms** is a comma-separated string of keywords, like `features, data, attributes, characteristics`.
* **Reranker** is the re-ranker model used in the hybrid search.
The re-ranker model is `nvidia/llama-3.2-nv.reranker`.
[Hybrid search](https://docs.datastax.com/en/astra-db-serverless/databases/hybrid-search.html) performs a vector similarity search and a lexical search, compares the results of both searches, and then returns the most relevant results overall.
To use **Hybrid search** in the **Astra DB** component, do the following:
1. Click **New Flow** > **RAG** > **Hybrid Search RAG**.
2. In the **OpenAI** model component, add your **OpenAI API key**.
3. In the **Astra DB** vector store component, add your **Astra DB Application Token**.
4. In the **Database** field, select your database.
5. In the **Collection** field, select the collection you want to search.
You must enable support for hybrid search when you create the collection.
6. In the **Playground**, enter a question about your data, such as `What are the features of my data?`
Your query is sent to two components: an **OpenAI** model component and the **Astra DB** vector database component.
The **OpenAI** component contains a prompt for creating the lexical query from your input:
```text
You are a database query planner that takes a user's requests, and then converts to a search against the subject matter in question.
You should convert the query into:
1. A list of keywords to use against a Lucene text analyzer index, no more than 4. Strictly unigrams.
2. A question to use as the basis for a QA embedding engine.
Avoid common keywords associated with the user's subject matter.
```
7. To view the keywords and questions the **OpenAI** component generates from your collection, in the **OpenAI** component, click <Icon name="TextSearch" aria-label="Inspect icon" />.
```
1. Keywords: features, data, attributes, characteristics
2. Question: What characteristics can be identified in my data?
```
8. To view the [DataFrame](/concepts-objects#dataframe-object) generated from the **OpenAI** component's response, in the **Structured Output** component, click <Icon name="TextSearch" aria-label="Inspect icon" />.
The DataFrame is passed to a **Parser** component, which parses the contents of the **Keywords** column into a string.
This string of comma-separated words is passed to the **Lexical Terms** port of the **Astra DB** component.
Note that the **Search Query** port of the Astra DB port is connected to the **Chat Input** component from step 6.
This **Search Query** is vectorized, and both the **Search Query** and **Lexical Terms** content are sent to the reranker at the `find_and_rerank` endpoint.
The reranker compares the vector search results against the string of terms from the lexical search.
The highest-ranked results of your hybrid search are returned to the **Playground**.
For more information, see the [DataStax documentation](https://docs.datastax.com/en/astra-db-serverless/databases/hybrid-search.html).
## AstraDB Graph vector store ## AstraDB Graph vector store
This component implements a Vector Store using AstraDB with graph capabilities. This component implements a Vector Store using AstraDB with graph capabilities.