docs: Restructure navigation, refactor all component documentation, among many other things (#9115)
* reorg pt 1 * nav reorg pt 2 * update sidebar ad * resolve comments and combine app pages * playground and voice mode rewrite * fix link * add separate bundle pages * add new pages to sidebar * working on bundles * moving content to new bundle pages * move some sidebar items * fix build * nav labels * small edits * Working on helpers * core components work * wrapping up some more agent duplication * aligning file management * webhooks and file management * data components * address vector store and some legacy components * finish logic params * some work on processors * remove unneeded pages and tidy some llm info * progress on bundles pt 1 * bundles pt 2 * bundles pt 3 * finish looking at integrations * it is done * fix errors * coderabbit and typos * coderabbit pt 2 * resolving mcs pt 1 * separate agents and mcp * still working on some memory stuff * finish message history alignment * incorporate PR 9138 * missed a link * file management ui * align w ui pr * Apply suggestions from code review * memory edits after discussion
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---
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title: Vector stores
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title: Vector Stores
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slug: /components-vector-stores
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---
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import Icon from "@site/src/components/icon";
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# Vector store components in Langflow
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Langflow's vector store components connect to your vector databases or create in-memory vector stores for storing and retrieving vector data in flows.
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Vector databases store vector data, which backs AI workloads like chatbots and Retrieval Augmented Generation.
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Vector databases and vector store components are specifically designed for storing and retrieving vector data, such as embeddings generated by language models. They are used to perform similarity searches, enabling applications like chatbots to retrieve relevant context from large datasets.
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Vector database components establish connections to existing vector databases or create in-memory vector stores for storing and retrieving vector data.
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Vector database components are distinct from [memory components](/components-memories), which are built specifically for storing and retrieving chat messages from internal Langflow memory or external databases. For more information, see [Memory management options](/memory).
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Other types of storage, like traditional structured databases and chat memory, are handled through other components like the [**SQL Database** component](/components-data#sql-database) or the [**Message History** component](/components-helpers#message-history).
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## Use a vector store component in a flow
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:::tip
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For examples of vector store components in flows, see [Create a vector RAG chatbot](/chat-with-rag) and [Embedding Model components](/components-embedding-models).
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:::
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This example uses the **Chroma DB** vector store component. Your vector store component's parameters and authentication may be different, but the document ingestion workflow is the same. A document is loaded from a local machine and chunked. The vector store component generates embeddings with the connected [model](/components-models) component, and stores them in the connected vector database.
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This vector data can then be retrieved for workloads like Retrieval Augmented Generation.
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@ -22,16 +24,24 @@ This vector data can then be retrieved for workloads like Retrieval Augmented Ge
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The user's chat input is embedded and compared to the vectors embedded during document ingestion for a similarity search.
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The results are output from the vector database component as a [Data](/data-types#data) object and parsed into text.
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This text fills the `{context}` variable in the **Prompt** component, which informs the **Open AI model** component's responses.
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The results are output from the vector database component as a [`Data`](/data-types#data) object and parsed into text.
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This text fills the `{context}` variable in the **Prompt Template** component, which informs the **OpenAI model** component's responses.
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## Astra DB Vector Store
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### Configure vector store parameters
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This component implements a Vector Store using Astra DB with search capabilities.
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Most vector store components have the same utility within a flow, but each provider can offer different parameters and functionality.
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Inspect a component's parameters to learn more about the inputs it accepts and how to configure it.
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For more information, see the [DataStax documentation](https://docs.datastax.com/en/astra-db-serverless/databases/create-database.html).
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Many input parameters for vector store components are hidden by default in the visual editor.
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You can toggle parameters through the <Icon name="SlidersHorizontal" aria-hidden="true"/> **Controls** in each [component's header menu](/concepts-components#component-menus).
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For details about a specific provider's parameters, see the provider's documentation.
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## Astra DB
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This component implements an [Astra DB Serverless vector store](https://docs.datastax.com/en/astra-db-serverless/databases/create-database.html) with search capabilities.
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<details>
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<summary>Parameters</summary>
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@ -69,39 +79,37 @@ For more information, see the [DataStax documentation](https://docs.datastax.com
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### Generate embeddings
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The **Astra DB Vector Store** component offers two methods for generating embeddings.
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The **Astra DB** component offers two methods for generating embeddings.
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1. **Embedding Model**: Use your own embedding model by connecting an [Embeddings](/components-embedding-models) component in Langflow.
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2. **Astra Vectorize**: Use Astra DB's built-in embedding generation service. When creating a new collection, choose the embeddings provider and models, including NVIDIA's `NV-Embed-QA` model hosted by Datastax.
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:::important
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The embedding model selection is made when creating a new collection and cannot be changed later.
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:::
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For an example of using the **Astra DB Vector Store** component with an embedding model, see the [Vector Store RAG starter project](/vector-store-rag).
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* ***Embedding Model**: Use your own embedding model by connecting an [**Embedding Model** component](/components-embedding-models) in Langflow.
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* **Astra Vectorize**: Use Astra DB's built-in embedding generation service. When creating a new collection, choose the embeddings provider and models, including NVIDIA's `NV-Embed-QA` model hosted by DataStax.
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For more information, see the [Astra DB Serverless documentation](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html).
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:::important
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With vectorize, the embedding model you choose when you create a collection cannot be changed later.
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:::
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For an example of using the **Astra DB** component with an embedding model, see the [**Vector Store RAG** template](/vector-store-rag).
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### Hybrid search
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The **Astra DB** component includes **hybrid search**, which is enabled by default.
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The **Astra DB** component includes Astra DB's [hybrid search](https://docs.datastax.com/en/astra-db-serverless/databases/hybrid-search.html) feature through the Astra DB Data API.
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The component fields related to hybrid search are **Search Query**, **Lexical Terms**, and **Reranker**.
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Hybrid search performs a vector similarity search and a lexical search, compares the results of both searches, and then returns the most relevant results overall.
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* **Search Query** finds results by vector similarity.
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* **Lexical Terms** is a comma-separated string of keywords, like `features, data, attributes, characteristics`.
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* **Reranker** is the re-ranker model used in the hybrid search.
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The re-ranker model is `nvidia/llama-3.2-nv.reranker`.
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To use hybrid search through the **Astra DB** component, you must [create a collection with that supports hybrid search](https://docs.datastax.com/en/astra-db-serverless/api-reference/collection-methods/create-collection.html#example-hybrid).
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[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.
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The following **Astra DB** component parameters are used for hybrid search:
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:::important
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To use hybrid search, your collection must be created with vector, lexical, and rerank capabilities enabled. These capabilities are enabled by default when you create a collection in a database in the AWS us-east-2 region.
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For more information, see the [DataStax documentation](https://docs.datastax.com/en/astra-db-serverless/api-reference/collection-methods/create-collection.html#example-hybrid).
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:::
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* **Search Query**: The query to use for vector search.
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* **Lexical Terms**: A comma-separated string of keywords, like `features, data, attributes, characteristics`.
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* **Reranker**: The re-ranker model to use for hybrid search, such as `nvidia/llama-3.2-nv.reranker`.
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To use **Hybrid search** in the **Astra DB** component, do the following:
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<details>
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<summary>Hybrid search example</summary>
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To use hybrid search through the **Astra DB** component, do the following:
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1. Click **New Flow** > **RAG** > **Hybrid Search RAG**.
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2. In the **OpenAI** model component, add your **OpenAI API key**.
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@ -109,22 +117,28 @@ To use **Hybrid search** in the **Astra DB** component, do the following:
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4. In the **Database** field, select your database.
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5. In the **Collection** field, select or create a collection with hybrid search capabilities enabled.
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6. In the **Playground**, enter a question about your data, such as `What are the features of my data?`
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Your query is sent to two components: an **OpenAI** model component and the **Astra DB** vector database component.
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The **OpenAI** component contains a prompt for creating the lexical query from your input:
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```text
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You are a database query planner that takes a user's requests, and then converts to a search against the subject matter in question.
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You should convert the query into:
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1. A list of keywords to use against a Lucene text analyzer index, no more than 4. Strictly unigrams.
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2. A question to use as the basis for a QA embedding engine.
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Avoid common keywords associated with the user's subject matter.
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```
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Your query is sent to two components: an **OpenAI** model component and the **Astra DB** vector database component.
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The **OpenAI** component contains a prompt for creating the lexical query from your input:
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```text
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You are a database query planner that takes a user's requests, and then converts to a search against the subject matter in question.
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You should convert the query into:
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1. A list of keywords to use against a Lucene text analyzer index, no more than 4. Strictly unigrams.
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2. A question to use as the basis for a QA embedding engine.
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Avoid common keywords associated with the user's subject matter.
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```
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7. To view the keywords and questions the **OpenAI** component generates from your collection, in the **OpenAI** component, click <Icon name="TextSearch" aria-hidden="true"/> **Inspect output**.
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```
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1. Keywords: features, data, attributes, characteristics
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2. Question: What characteristics can be identified in my data?
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```
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```
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1. Keywords: features, data, attributes, characteristics
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2. Question: What characteristics can be identified in my data?
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```
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8. To view the [DataFrame](/data-types#dataframe) generated from the **OpenAI** component's response, in the **Structured Output** component, click <Icon name="TextSearch" aria-hidden="true"/> **Inspect output**.
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The DataFrame is passed to a **Parser** component, which parses the contents of the **Keywords** column into a string.
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The DataFrame is passed to a **Parser** component, which parses the contents of the **Keywords** column into a string.
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This string of comma-separated words is passed to the **Lexical Terms** port of the **Astra DB** component.
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Note that the **Search Query** port of the Astra DB port is connected to the **Chat Input** component from step 6.
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@ -133,11 +147,11 @@ The DataFrame is passed to a **Parser** component, which parses the contents of
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The reranker compares the vector search results against the string of terms from the lexical search.
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The highest-ranked results of your hybrid search are returned to the **Playground**.
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For more information, see the [DataStax documentation](https://docs.datastax.com/en/astra-db-serverless/databases/hybrid-search.html).
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</details>
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## AstraDB Graph vector store
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## Astra DB Graph
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This component implements a Vector Store using AstraDB with graph capabilities.
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This component implements a vector store using Astra DB with graph capabilities.
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For more information, see the [Astra DB Serverless documentation](https://docs.datastax.com/en/astra-db-serverless/tutorials/graph-rag.html).
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<details>
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@ -147,12 +161,12 @@ For more information, see the [Astra DB Serverless documentation](https://docs.d
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| Name | Display Name | Info |
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|------|--------------|------|
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| collection_name | Collection Name | The name of the collection within AstraDB where the vectors are stored. Required. |
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| token | Astra DB Application Token | Authentication token for accessing AstraDB. Required. |
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| api_endpoint | API Endpoint | API endpoint URL for the AstraDB service. Required. |
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| collection_name | Collection Name | The name of the collection within Astra DB where the vectors are stored. Required. |
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| token | Astra DB Application Token | Authentication token for accessing Astra DB. Required. |
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| api_endpoint | API Endpoint | API endpoint URL for the Astra DB service. Required. |
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| search_input | Search Input | Query string for similarity search. |
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| ingest_data | Ingest Data | Data to be ingested into the vector store. |
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| namespace | Namespace | Optional namespace within AstraDB to use for the collection. |
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| keyspace | Keyspace | Optional keyspace within Astra DB to use for the collection. |
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| embedding | Embedding Model | Embedding model to use. |
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| metric | Metric | Distance metric for vector comparisons. The options are "cosine", "euclidean", "dot_product". |
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| setup_mode | Setup Mode | Configuration mode for setting up the vector store. The options are "Sync", "Async", "Off". |
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## Cassandra
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This component creates a Cassandra Vector Store with search capabilities.
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This component creates a Cassandra vector store with search capabilities.
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For more information, see the [Cassandra documentation](https://cassandra.apache.org/doc/latest/cassandra/vector-search/overview.html).
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<details>
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@ -184,11 +198,11 @@ For more information, see the [Cassandra documentation](https://cassandra.apache
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| Name | Type | Description |
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|------|------|-------------|
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| database_ref | String | Contact points for the database or AstraDB database ID. |
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| username | String | Username for the database (leave empty for AstraDB). |
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| token | SecretString | User password for the database or AstraDB token. |
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| keyspace | String | Table Keyspace or AstraDB namespace. |
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| table_name | String | Name of the table or AstraDB collection. |
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| database_ref | String | Contact points for the database or Astra DB database ID. |
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| username | String | Username for the database (leave empty for Astra DB). |
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| token | SecretString | User password for the database or Astra DB token. |
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| keyspace | String | Table or keyspace. |
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| table_name | String | Name of the table or Astra DB collection. |
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| ttl_seconds | Integer | Time-to-live for added texts. |
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| batch_size | Integer | Number of data to process in a single batch. |
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| setup_mode | String | Configuration mode for setting up the Cassandra table. |
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</details>
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## Cassandra Graph Vector Store
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## Cassandra Graph
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This component implements a Cassandra Graph Vector Store with search capabilities.
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This component implements a Cassandra Graph vector store with search capabilities.
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<details>
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<summary>Parameters</summary>
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@ -223,11 +237,11 @@ This component implements a Cassandra Graph Vector Store with search capabilitie
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| Name | Display Name | Info |
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|------|--------------|------|
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| database_ref | Contact Points / Astra Database ID | The contact points for the database or AstraDB database ID. Required. |
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| username | Username | The username for the database. Leave this field empty for AstraDB. |
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| token | Password / AstraDB Token | The user password for the database or AstraDB token. Required. |
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| keyspace | Keyspace | The table Keyspace or AstraDB namespace. Required. |
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| table_name | Table Name | The name of the table or AstraDB collection where vectors are stored. Required. |
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| database_ref | Contact Points / Astra Database ID | The contact points for the database or Astra DB database ID. Required. |
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| username | Username | The username for the database. Leave this field empty for Astra DB. |
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| token | Password / Astra DB Token | The user password for the database or Astra DB token. Required. |
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| keyspace | Keyspace | The table or keyspace. Required. |
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| table_name | Table Name | The name of the table or Astra DB collection where vectors are stored. Required. |
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| setup_mode | Setup Mode | The configuration mode for setting up the Cassandra table. The options are "Sync" or "Off". Default: "Sync". |
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| cluster_kwargs | Cluster arguments | An optional dictionary of additional keyword arguments for the Cassandra cluster. |
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| search_query | Search Query | The query string for similarity search. |
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## Chroma DB
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This component creates a Chroma Vector Store with search capabilities.
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The **Chroma DB** component creates an ephemeral, Chroma vector database with search capabilities that you can use for experimentation and vector storage.
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For more information, see the [Chroma documentation](https://docs.trychroma.com/).
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The Chroma DB component creates an ephemeral vector database for experimentation and vector storage.
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<details>
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<summary>Chroma DB sample flow</summary>
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1. To use this component in a flow, connect it to a component that outputs **Data** or **DataFrame**.
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This example splits text from a [URL](/components-data#url) component, and computes embeddings with the connected **OpenAI Embeddings** component. Chroma DB computes embeddings by default, but you can connect your own embeddings model, as seen in this example.
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@ -270,7 +286,7 @@ When loading duplicate documents, enable the **Allow Duplicates** option in Chro
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6. To query your loaded data, open the **Playground** and query your database.
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Your input is converted to vector data and compared to the stored vectors in a vector similarity search.
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For more information, see the [Chroma documentation](https://docs.trychroma.com/).
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</details>
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<details>
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<summary>Parameters</summary>
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@ -305,7 +321,7 @@ For more information, see the [Chroma documentation](https://docs.trychroma.com/
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## Clickhouse
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This component implements a Clickhouse Vector Store with search capabilities.
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This component implements a Clickhouse vector store with search capabilities.
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For more information, see the [Clickhouse Documentation](https://clickhouse.com/docs/en/intro).
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<details>
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@ -343,7 +359,7 @@ For more information, see the [Clickhouse Documentation](https://clickhouse.com/
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## Couchbase
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This component creates a Couchbase Vector Store with search capabilities.
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This component creates a Couchbase vector store with search capabilities.
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For more information, see the [Couchbase documentation](https://docs.couchbase.com/home/index.html).
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<details>
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@ -373,52 +389,9 @@ For more information, see the [Couchbase documentation](https://docs.couchbase.c
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</details>
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## Local DB
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The **Local DB** component is Langflow's enhanced version of Chroma DB.
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The component adds a user-friendly interface with two modes (Ingest and Retrieve), automatic collection management, and built-in persistence in Langflow's cache directory.
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Local DB includes **Ingest** and **Retrieve** modes.
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The **Ingest** mode works similarly to [ChromaDB](#chroma-db), and persists your database to the Langflow cache directory. The Langflow cache directory location is specified in `LANGFLOW_CONFIG_DIR`. For more information, see [Environment variables](/environment-variables).
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The **Retrieve** mode can query your **Chroma DB** collections.
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For more information, see the [Chroma documentation](https://docs.trychroma.com/).
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|
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<details>
|
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<summary>Parameters</summary>
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**Inputs**
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| Name | Type | Description |
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|------|------|-------------|
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| collection_name | String | The name of the Chroma collection. Default: "langflow". |
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| persist_directory | String | Custom base directory to save the vector store. Collections are stored under `$DIRECTORY/vector_stores/$COLLECTION_NAME`. If not specified, it uses your system's cache folder. |
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| existing_collections | String | Select a previously created collection to search through its stored data. |
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| embedding | Embeddings | The embedding function to use for the vector store. |
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| allow_duplicates | Boolean | If false, will not add documents that are already in the Vector Store. |
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| search_type | String | Type of search to perform: "Similarity" or "MMR". |
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| ingest_data | Data/DataFrame | Data to store. It is embedded and indexed for semantic search. |
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| search_query | String | Enter text to search for similar content in the selected collection. |
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| number_of_results | Integer | Number of results to return. Default: 10. |
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| limit | Integer | Limit the number of records to compare when Allow Duplicates is False. |
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**Outputs**
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| Name | Type | Description |
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|------|------|-------------|
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| vector_store | Chroma | A local Chroma vector store instance configured with the specified parameters. |
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| search_results | List[Data](/data-types#data) | The results of the similarity search as a list of [Data](/data-types#data) objects. |
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</details>
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## Elasticsearch
|
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This component creates an Elasticsearch Vector Store with search capabilities.
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This component creates an Elasticsearch vector store with search capabilities.
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For more information, see the [Elasticsearch documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html).
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||||
|
||||
<details>
|
||||
|
|
@ -450,7 +423,7 @@ For more information, see the [Elasticsearch documentation](https://www.elastic.
|
|||
|
||||
## FAISS
|
||||
|
||||
This component creates a FAISS Vector Store with search capabilities.
|
||||
This component creates a FAISS vector store with search capabilities.
|
||||
For more information, see the [FAISS documentation](https://faiss.ai/index.html).
|
||||
|
||||
<details>
|
||||
|
|
@ -477,12 +450,13 @@ For more information, see the [FAISS documentation](https://faiss.ai/index.html)
|
|||
|
||||
</details>
|
||||
|
||||
|
||||
## Graph RAG
|
||||
|
||||
This component performs Graph RAG (Retrieval Augmented Generation) traversal in a vector store, enabling graph-based document retrieval.
|
||||
This component performs Graph RAG traversal in a vector store, enabling graph-based document retrieval.
|
||||
For more information, see the [Graph RAG documentation](https://datastax.github.io/graph-rag/).
|
||||
|
||||
For an example flow, see the **Graph RAG** template.
|
||||
For an example flow, see the **Graph RAG** template in Langflow.
|
||||
|
||||
<details>
|
||||
<summary>Parameters</summary>
|
||||
|
|
@ -508,14 +482,14 @@ For an example flow, see the **Graph RAG** template.
|
|||
|
||||
## Hyper-Converged Database (HCD)
|
||||
|
||||
This component implements a Vector Store using HCD.
|
||||
This component implements a vector store using HCD.
|
||||
|
||||
To use the HCD vector store, add your deployment's collection name, username, password, and HCD Data API endpoint.
|
||||
The endpoint must be formatted like `http[s]://**DOMAIN_NAME** or **IP_ADDRESS**[:port]`, for example, `http://192.0.2.250:8181`.
|
||||
|
||||
Replace **DOMAIN_NAME** or **IP_ADDRESS** with the domain name or IP address of your HCD Data API connection.
|
||||
|
||||
To use the HCD vector store for embeddings ingestion, connect it to an embeddings model and a file loader:
|
||||
To use the HCD vector store for embeddings ingestion, connect it to an embeddings model and a file loader.
|
||||
|
||||
|
||||
|
||||
|
|
@ -559,9 +533,58 @@ To use the HCD vector store for embeddings ingestion, connect it to an embedding
|
|||
|
||||
</details>
|
||||
|
||||
## Local DB
|
||||
|
||||
The **Local DB** component is Langflow's enhanced version of Chroma DB.
|
||||
|
||||
The component adds a user-friendly interface with two modes (Ingest and Retrieve), automatic collection management, and built-in persistence in Langflow's cache directory.
|
||||
|
||||
Local DB includes **Ingest** and **Retrieve** modes.
|
||||
|
||||
The **Ingest** mode works similarly to [ChromaDB](#chroma-db), and persists your database to the Langflow cache directory. The Langflow cache directory location is specified in `LANGFLOW_CONFIG_DIR`. For more information, see [Environment variables](/environment-variables).
|
||||
|
||||
The **Retrieve** mode can query your **Chroma DB** collections.
|
||||
|
||||
|
||||
|
||||
For more information, see the [Chroma documentation](https://docs.trychroma.com/).
|
||||
|
||||
<details>
|
||||
<summary>Parameters</summary>
|
||||
|
||||
**Inputs**
|
||||
|
||||
| Name | Type | Description |
|
||||
|------|------|-------------|
|
||||
| collection_name | String | The name of the Chroma collection. Default: "langflow". |
|
||||
| persist_directory | String | Custom base directory to save the vector store. Collections are stored under `$DIRECTORY/vector_stores/$COLLECTION_NAME`. If not specified, it uses your system's cache folder. |
|
||||
| existing_collections | String | Select a previously created collection to search through its stored data. |
|
||||
| embedding | Embeddings | The embedding function to use for the vector store. |
|
||||
| allow_duplicates | Boolean | If false, will not add documents that are already in the vector store. |
|
||||
| search_type | String | Type of search to perform: "Similarity" or "MMR". |
|
||||
| ingest_data | Data/DataFrame | Data to store. It is embedded and indexed for semantic search. |
|
||||
| search_query | String | Enter text to search for similar content in the selected collection. |
|
||||
| number_of_results | Integer | Number of results to return. Default: 10. |
|
||||
| limit | Integer | Limit the number of records to compare when Allow Duplicates is False. |
|
||||
|
||||
**Outputs**
|
||||
|
||||
| Name | Type | Description |
|
||||
|------|------|-------------|
|
||||
| vector_store | Chroma | A local Chroma vector store instance configured with the specified parameters. |
|
||||
| search_results | List[Data](/data-types#data) | The results of the similarity search as a list of [Data](/data-types#data) objects. |
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## Milvus
|
||||
|
||||
This component creates a Milvus Vector Store with search capabilities.
|
||||
This component creates a Milvus vector store with search capabilities.
|
||||
For more information, see the [Milvus documentation](https://milvus.io/docs).
|
||||
|
||||
<details>
|
||||
|
|
@ -598,7 +621,7 @@ For more information, see the [Milvus documentation](https://milvus.io/docs).
|
|||
|
||||
## MongoDB Atlas
|
||||
|
||||
This component creates a MongoDB Atlas Vector Store with search capabilities.
|
||||
This component creates a MongoDB Atlas vector store with search capabilities.
|
||||
For more information, see the [MongoDB Atlas documentation](https://www.mongodb.com/docs/atlas/atlas-vector-search/tutorials/vector-search-quick-start/).
|
||||
|
||||
<details>
|
||||
|
|
@ -631,10 +654,10 @@ For more information, see the [MongoDB Atlas documentation](https://www.mongodb.
|
|||
|
||||
</details>
|
||||
|
||||
## Opensearch
|
||||
## OpenSearch
|
||||
|
||||
This component creates an Opensearch vector store with search capabilities
|
||||
For more information, see [Opensearch documentation](https://opensearch.org/platform/search/vector-database.html).
|
||||
This component creates an OpenSearch vector store with search capabilities
|
||||
For more information, see [OpenSearch documentation](https://opensearch.org/platform/search/vector-database.html).
|
||||
|
||||
<details>
|
||||
<summary>Parameters</summary>
|
||||
|
|
@ -667,7 +690,7 @@ For more information, see [Opensearch documentation](https://opensearch.org/plat
|
|||
|
||||
## PGVector
|
||||
|
||||
This component creates a PGVector Vector Store with search capabilities.
|
||||
This component creates a PGVector vector store with search capabilities.
|
||||
For more information, see the [PGVector documentation](https://github.com/pgvector/pgvector).
|
||||
|
||||
<details>
|
||||
|
|
@ -695,7 +718,7 @@ For more information, see the [PGVector documentation](https://github.com/pgvect
|
|||
|
||||
## Pinecone
|
||||
|
||||
This component creates a Pinecone Vector Store with search capabilities.
|
||||
This component creates a Pinecone vector store with search capabilities.
|
||||
For more information, see the [Pinecone documentation](https://docs.pinecone.io/home).
|
||||
|
||||
<details>
|
||||
|
|
@ -726,7 +749,7 @@ For more information, see the [Pinecone documentation](https://docs.pinecone.io/
|
|||
|
||||
## Qdrant
|
||||
|
||||
This component creates a Qdrant Vector Store with search capabilities.
|
||||
This component creates a Qdrant vector store with search capabilities.
|
||||
For more information, see the [Qdrant documentation](https://qdrant.tech/documentation/).
|
||||
|
||||
<details>
|
||||
|
|
@ -764,7 +787,7 @@ For more information, see the [Qdrant documentation](https://qdrant.tech/documen
|
|||
|
||||
## Redis
|
||||
|
||||
This component creates a Redis Vector Store with search capabilities.
|
||||
This component creates a Redis vector store with search capabilities.
|
||||
For more information, see the [Redis documentation](https://redis.io/docs/latest/develop/interact/search-and-query/advanced-concepts/vectors/).
|
||||
|
||||
<details>
|
||||
|
|
@ -794,7 +817,7 @@ For more information, see the [Redis documentation](https://redis.io/docs/latest
|
|||
|
||||
## Supabase
|
||||
|
||||
This component creates a connection to a Supabase Vector Store with search capabilities.
|
||||
This component creates a connection to a Supabase vector store with search capabilities.
|
||||
For more information, see the [Supabase documentation](https://supabase.com/docs/guides/ai).
|
||||
|
||||
<details>
|
||||
|
|
@ -824,7 +847,7 @@ For more information, see the [Supabase documentation](https://supabase.com/docs
|
|||
|
||||
## Upstash
|
||||
|
||||
This component creates an Upstash Vector Store with search capabilities.
|
||||
This component creates an Upstash vector store with search capabilities.
|
||||
For more information, see the [Upstash documentation](https://upstash.com/docs/introduction).
|
||||
|
||||
<details>
|
||||
|
|
@ -855,7 +878,7 @@ For more information, see the [Upstash documentation](https://upstash.com/docs/i
|
|||
|
||||
## Vectara
|
||||
|
||||
This component creates a Vectara Vector Store with search capabilities.
|
||||
This component creates a Vectara vector store with search capabilities.
|
||||
For more information, see the [Vectara documentation](https://docs.vectara.com/docs/).
|
||||
|
||||
<details>
|
||||
|
|
@ -882,36 +905,14 @@ For more information, see the [Vectara documentation](https://docs.vectara.com/d
|
|||
|
||||
</details>
|
||||
|
||||
## Vectara Search
|
||||
## Vectara RAG
|
||||
|
||||
This component searches a Vectara Vector Store for documents based on the provided input.
|
||||
This component enabled Vectara's full end-to-end RAG capabilities with reranking options.
|
||||
For more information, see the [Vectara documentation](https://docs.vectara.com/docs/).
|
||||
|
||||
<details>
|
||||
<summary>Parameters</summary>
|
||||
|
||||
**Inputs**
|
||||
|
||||
| Name | Type | Description |
|
||||
|---------------------|--------------|-------------------------------------------|
|
||||
| search_type | String | The type of search, such as "Similarity" or "MMR". |
|
||||
| input_value | String | The search query. |
|
||||
| vectara_customer_id | String | The Vectara customer ID. |
|
||||
| vectara_corpus_id | String | The Vectara corpus ID. |
|
||||
| vectara_api_key | SecretString | The Vectara API key. |
|
||||
| files_url | List[String] | Optional URLs for file initialization. |
|
||||
|
||||
**Outputs**
|
||||
|
||||
| Name | Type | Description |
|
||||
|----------------|------------|----------------------------|
|
||||
| search_results | List[Data] | The results of the similarity search as a list of [Data](/data-types#data) objects. |
|
||||
|
||||
</details>
|
||||
|
||||
## Weaviate
|
||||
|
||||
This component facilitates a Weaviate Vector Store setup, optimizing text and document indexing and retrieval.
|
||||
This component facilitates a Weaviate vector store setup, optimizing text and document indexing and retrieval.
|
||||
For more information, see the [Weaviate Documentation](https://weaviate.io/developers/weaviate).
|
||||
|
||||
<details>
|
||||
|
|
@ -936,34 +937,4 @@ For more information, see the [Weaviate Documentation](https://weaviate.io/devel
|
|||
|--------------|------------------|-------------------------------|
|
||||
| vector_store | WeaviateVectorStore | The Weaviate vector store instance. |
|
||||
|
||||
</details>
|
||||
|
||||
## Weaviate Search
|
||||
|
||||
This component searches a Weaviate Vector Store for documents similar to the input.
|
||||
For more information, see the [Weaviate Documentation](https://weaviate.io/developers/weaviate).
|
||||
|
||||
<details>
|
||||
<summary>Parameters</summary>
|
||||
|
||||
**Inputs**
|
||||
|
||||
| Name | Type | Description |
|
||||
|---------------|--------------|-------------------------------------------|
|
||||
| search_type | String | The type of search, such as "Similarity" or "MMR" |
|
||||
| input_value | String | The search query. |
|
||||
| weaviate_url | String | The default instance URL. |
|
||||
| search_by_text| Boolean | A boolean value that indicates whether to search by text. |
|
||||
| api_key | SecretString | The optional API key for authentication. |
|
||||
| index_name | String | The optional index name. |
|
||||
| text_key | String | The default text extraction key. |
|
||||
| embedding | Embeddings | The embeddings model used. |
|
||||
| attributes | List[String] | Optional additional attributes. |
|
||||
|
||||
**Outputs**
|
||||
|
||||
| Name | Type | Description |
|
||||
|----------------|------------|----------------------------|
|
||||
| search_results | List[Data] | The results of the similarity search as a list of [Data](/data-types#data) objects. |
|
||||
|
||||
</details>
|
||||
Loading…
Add table
Add a link
Reference in a new issue