docs: Restructured integrations section (#1089)

* docs: Reorder integrations

* docs: Formatting langchain-go.md

* docs: Title for index

* docs: Redpanda docs (#1092)

qdrant-landing/content/documentation/data-management/_index.md

@@ -0,0 +1,19 @@
+---
+title: Data Management
+weight: 15
+---
+
+## Data Management Integrations
+
+| Integration                     | Description                                                                                        |
+| ------------------------------- | -------------------------------------------------------------------------------------------------- |
+| [Airbyte](./airbyte/)           | Data integration platform specialising in ELT pipelines.                                           |
+| [Airflow](./airflow/)           | Platform designed for developing, scheduling, and monitoring batch-oriented workflows.             |
+| [Redpanda Connect](./redpanda/) | Declarative data-agnostic streaming service for efficient, stateless processing.                   |
+| [Confluent](./confluent/)       | Fully-managed data streaming platform with a cloud-native Apache Kafka engine.                     |
+| [DLT](./dlt/)                   | Python library to simplify data loading processes between several sources and destinations.        |
+| [Fondant](./fondant/)           | Framework for developing datasets, sharing reusable operations and data processing trees.          |
+| [MindsDB](./mindsdb/)           | Platform to deploy, serve, and fine-tune models with numerous data source integrations.            |
+| [Apache NiFi](./nifi/)          | Data ingestion platform to manage data transfer between different sources and destination systems. |
+| [Apache Spark](./spark/)        | A unified analytics engine for large-scale data processing.                                        |
+| [Unstructured](./unstructured/) | Python library with components for ingesting and pre-processing data from numerous sources.        |

qdrant-landing/content/documentation/data-management/airbyte.md

@@ -1,17 +1,16 @@
 ---
 title: Airbyte
-weight: 1000
+aliases: [ ../integrations/airbyte/, ../frameworks/airbyte/ ]
-aliases: [ ../integrations/airbyte/ ]
 ---
 
 # Airbyte
 
-[Airbyte](https://airbyte.com/) is an open-source data integration platform that helps you replicate your data 
+[Airbyte](https://airbyte.com/) is an open-source data integration platform that helps you replicate your data
-between different systems. It has a [growing list of connectors](https://docs.airbyte.io/integrations) that can 
+between different systems. It has a [growing list of connectors](https://docs.airbyte.io/integrations) that can
-be used to ingest data from multiple sources. Building data pipelines is also crucial for managing the data in 
+be used to ingest data from multiple sources. Building data pipelines is also crucial for managing the data in
 Qdrant, and Airbyte is a great tool for this purpose.
 
-Airbyte may take care of the data ingestion from a selected source, while Qdrant will help you to build a search 
+Airbyte may take care of the data ingestion from a selected source, while Qdrant will help you to build a search
 engine on top of it. There are three supported modes of how the data can be ingested into Qdrant:
 
 * **Full Refresh Sync**
@@ -24,15 +23,15 @@ You can read more about these modes in the [Airbyte documentation](https://docs.
 
 Before you start, make sure you have the following:
 
-1. Airbyte instance, either [Open Source](https://airbyte.com/solutions/airbyte-open-source), 
+1. Airbyte instance, either [Open Source](https://airbyte.com/solutions/airbyte-open-source),
-   [Self-Managed](https://airbyte.com/solutions/airbyte-enterprise), or [Cloud](https://airbyte.com/solutions/airbyte-cloud). 
+   [Self-Managed](https://airbyte.com/solutions/airbyte-enterprise), or [Cloud](https://airbyte.com/solutions/airbyte-cloud).
-2. Running instance of Qdrant. It has to be accessible by URL from the machine where Airbyte is running. 
+2. Running instance of Qdrant. It has to be accessible by URL from the machine where Airbyte is running.
    You can follow the [installation guide](/documentation/guides/installation/) to set up Qdrant.
 
 ## Setting up Qdrant as a destination
 
 Once you have a running instance of Airbyte, you can set up Qdrant as a destination directly in the UI.
-Airbyte's Qdrant destination is connected with a single collection in Qdrant. 
+Airbyte's Qdrant destination is connected with a single collection in Qdrant.
 
 ![Airbyte Qdrant destination](/documentation/frameworks/airbyte/qdrant-destination.png)
 
@@ -51,7 +50,7 @@ models, including OpenAI and Cohere.
 
 ![Embeddings settings](/documentation/frameworks/airbyte/embedding.png)
 
-Using some precomputed embeddings from your data source is also possible. In this case, you can pass the field 
+Using some precomputed embeddings from your data source is also possible. In this case, you can pass the field
 name containing the embeddings and their dimensionality.
 
 ![Precomputed embeddings settings](/documentation/frameworks/airbyte/precomputed-embedding.png)
@@ -68,13 +67,13 @@ might be used as a destination.
 
 ## Setting up connection
 
-Airbyte combines sources and destinations into a single entity called a connection. Once you have a destination 
+Airbyte combines sources and destinations into a single entity called a connection. Once you have a destination
-configured and a source, you can create a connection between them. It doesn't matter what source you use, as 
+configured and a source, you can create a connection between them. It doesn't matter what source you use, as
 long as Airbyte supports it. The process is pretty straightforward, but depends on the source you use.
 
 ![Airbyte connection](/documentation/frameworks/airbyte/connection.png)
 
 ## Further Reading
 
-- [Airbyte documentation](https://docs.airbyte.com/understanding-airbyte/connections/).
+* [Airbyte documentation](https://docs.airbyte.com/understanding-airbyte/connections/).
-- [Source Code](https://github.com/airbytehq/airbyte/tree/master/airbyte-integrations/connectors/destination-qdrant)
+* [Source Code](https://github.com/airbytehq/airbyte/tree/master/airbyte-integrations/connectors/destination-qdrant)

qdrant-landing/content/documentation/data-management/airflow.md

@@ -1,6 +1,6 @@
 ---
 title: Apache Airflow
-weight: 2100
+aliases: [ ../frameworks/airflow/ ]
 ---
 
 # Apache Airflow

qdrant-landing/content/documentation/data-management/confluent.md

@@ -1,6 +1,6 @@
 ---
-title: Confluent
+title: Confluent Kafka
-weight: 3700
+aliases: [ ../frameworks/confluent/ ]
 ---
 
 ![Confluent Logo](/documentation/frameworks/confluent/confluent-logo.png)

qdrant-landing/content/documentation/data-management/dlt.md

@@ -1,7 +1,6 @@
 ---
 title: DLT
-weight: 1300
+aliases: [ ../integrations/dlt/, ../frameworks/dlt/ ]
-aliases: [ ../integrations/dlt/ ]
 ---
 
 # DLT(Data Load Tool)

qdrant-landing/content/documentation/data-management/fondant.md

@@ -1,7 +1,6 @@
 ---
 title: Fondant
-weight: 1700
+aliases: [ ../integrations/fondant/, ../frameworks/fondant/ ]
-aliases: [ ../integrations/fondant/ ]
 ---
 
 # Fondant

qdrant-landing/content/documentation/data-management/mindsdb.md

@@ -1,7 +1,6 @@
 ---
 title: MindsDB
-weight: 1100
+aliases: [ ../integrations/mindsdb/, ../frameworks/mindsdb/ ]
-aliases: [ ../integrations/mindsdb/ ]
 ---
 
 # MindsDB

qdrant-landing/content/documentation/data-management/nifi.md

@@ -1,6 +1,6 @@
 ---
 title: Apache NiFi
-weight: 3500
+aliases: [ ../frameworks/nifi/ ]
 ---
 
 # Apache NiFi

qdrant-landing/content/documentation/data-management/redpanda.md

@@ -0,0 +1,47 @@
+---
+title: Redpanda Connect
+---
+
+[Redpanda Connect](https://www.redpanda.com/connect) is a declarative data-agnostic streaming service designed for efficient, stateless processing steps. It offers transaction-based resiliency with back pressure, ensuring at-least-once delivery when connecting to at-least-once sources with sinks, without the need to persist messages during transit.
+
+Connect pipelines are configured using a YAML file, which organizes components hierarchically. Each section represents a different component type, such as inputs, processors and outputs, and these can have nested child components and [dynamic values](https://docs.redpanda.com/redpanda-connect/configuration/interpolation/).
+
+The [Qdrant Output](https://docs.redpanda.com/redpanda-connect/components/outputs/qdrant/) component enables streaming vector data into Qdrant collections in your RedPanda pipelines.
+
+## Example
+
+An example configuration of the output once the inputs and processors are set, would look like:
+
+```yaml
+input:
+    # https://docs.redpanda.com/redpanda-connect/components/inputs/about/
+
+pipeline:
+  processors:
+    # https://docs.redpanda.com/redpanda-connect/components/processors/about/
+
+output:
+  label: "qdrant-output"
+  qdrant:
+    max_in_flight: 64
+    batching:
+      count: 8
+    grpc_host: xyz-example.eu-central.aws.cloud.qdrant.io:6334
+    api_token: "<provide-your-own-key>"
+    tls:
+      enabled: true
+    #   skip_cert_verify: false
+    #   enable_renegotiation: false
+    #   root_cas: ""
+    #   root_cas_file: ""
+    #   client_certs: []
+    collection_name: "<collection_name>"
+    id: root = uuid_v4()
+    vector_mapping: 'root = {"some_dense": this.vector, "some_sparse": {"indices": [23,325,532],"values": [0.352,0.532,0.532]}}'
+    payload_mapping: 'root = {"field": this.value, "field_2": 987}'
+```
+
+## Further Reading
+
+- [Getting started with Connect](https://docs.redpanda.com/redpanda-connect/guides/getting_started/)
+- [Qdrant Output Reference](https://docs.redpanda.com/redpanda-connect/components/outputs/qdrant/)

qdrant-landing/content/documentation/data-management/spark.md

@@ -1,7 +1,6 @@
 ---
 title: Apache Spark
-weight: 1400
+aliases: [ ../integrations/spark/, ../frameworks/spark/ ]
-aliases: [ ../integrations/spark/ ]
 ---
 
 # Apache Spark

qdrant-landing/content/documentation/data-management/unstructured.md

@@ -1,6 +1,6 @@
 ---
 title: Unstructured
-weight: 1900
+aliases: [ ../frameworks/unstructured/ ]
 ---
 
 # Unstructured

qdrant-landing/content/documentation/frameworks/_index.md

@@ -3,45 +3,26 @@ title: Frameworks
 weight: 15
 ---
 
-| Frameworks                            | Description                                                                                          |
+## Framework Integrations
+
+| Framework                             | Description                                                                                          |
 | ------------------------------------- | ---------------------------------------------------------------------------------------------------- |
-| [Airbyte](./airbyte/)                 | Data integration platform specialising in ELT pipelines.                                             |
-| [Airflow](./airflow/)                 | Platform designed for developing, scheduling, and monitoring batch-oriented workflows.               |
-| [Apify](./apify/)                     | Platform to build web scrapers and automate web browser tasks.                                       |
 | [AutoGen](./autogen/)                 | Framework from Microsoft building LLM applications using multiple conversational agents.             |
-| [Bubble](./bubble)                    | Development platform for application development with a no-code interface                            |
-| [BuildShip](./buildship)              | Low-code visual builder to create APIs, scheduled jobs, and backend workflows.                       |
 | [Canopy](./canopy/)                   | Framework from Pinecone for building RAG applications using LLMs and knowledge bases.                |
 | [Cheshire Cat](./cheshire-cat/)       | Framework to create personalized AI assistants using custom data.                                    |
-| [Confluent](./confluent/)             | Fully-managed data streaming platform with a cloud-native Apache Kafka engine.                       |
-| [DLT](./dlt/)                         | Python library to simplify data loading processes between several sources and destinations.          |
 | [DocArray](./docarray/)               | Python library for managing data in multi-modal AI applications.                                     |
-| [DocsGPT](./docsgpt/)                 | Tool for ingesting documentation sources and enabling conversations and queries.                     |
 | [DSPy](./dspy/)                       | Framework for algorithmically optimizing LM prompts and weights.                                     |
 | [Fifty-One](./fifty-one/)             | Toolkit for building high-quality datasets and computer vision models.                               |
-| [Fondant](./fondant/)                 | Framework for developing datasets, sharing reusable operations and data processing trees.            |
 | [Genkit](./genkit/)                   | Framework to build, deploy, and monitor production-ready AI-powered apps.                            |
 | [Haystack](./haystack/)               | LLM orchestration framework to build customizable, production-ready LLM applications.                |
 | [Langchain](./langchain/)             | Python framework for building context-aware, reasoning applications using LLMs.                      |
 | [Langchain-Go](./langchain-go/)       | Go framework for building context-aware, reasoning applications using LLMs.                          |
 | [Langchain4j](./langchain4j/)         | Java framework for building context-aware, reasoning applications using LLMs.                        |
 | [LlamaIndex](./llama-index/)          | A data framework for building LLM applications with modular integrations.                            |
-| [Make](./make/)                       | Cloud platform to build low-code workflows by integrating various software applications.             |
 | [MemGPT](./memgpt/)                   | System to build LLM agents with long term memory & custom tools                                      |
-| [MindsDB](./mindsdb/)                 | Platform to deploy, serve, and fine-tune models with numerous data source integrations.              |
-| [N8N](./n8n/)                         | Platform for node-based, low-code workflow automation.                                               |
-| [NiFi](./nifi/)                       | Data ingestion platform to manage data transfer between different sources and destination systems.   |
-| [OpenLIT](./openlit/)                 | Platform for OpenTelemetry-native Observability & Evals for LLMs and Vector Databases.               |
-| [OpenLLMetry](./openllmetry/)         | Set of OpenTelemetry extensions to add Observability for your LLM application.                       |
 | [Pandas-AI](./pandas-ai/)             | Python library to query/visualize your data (CSV, XLSX, PostgreSQL, etc.) in natural language        |
-| [Pipedream](./pipedream/)             | Platform for connecting apps and developing event-driven automation.                                 |
-| [Portable.io](./portable/)            | Cloud platform for developing and deploying ELT transformations.                                     |
-| [PrivateGPT](./privategpt/)           | Tool to ask questions about your documents using local LLMs emphasising privacy.                     |
-| [Rivet](./rivet/)                     | A visual programming environment for building AI agents with LLMs.                                   |
 | [Semantic Router](./semantic-router/) | Python library to build a decision-making layer for AI applications using vector search.             |
-| [Spark](./spark/)                     | A unified analytics engine for large-scale data processing.                                          |
 | [Spring AI](./spring-ai/)             | Java AI framework for building with Spring design principles such as portability and modular design. |
 | [Testcontainers](./testcontainers/)   | Set of frameworks for running containerized dependencies in tests.                                   |
 | [txtai](./txtai/)                     | Python library for semantic search, LLM orchestration and language model workflows.                  |
-| [Unstructured](./unstructured/)       | Python library with components for ingesting and pre-processing data from numerous sources.          |
 | [Vanna AI](./vanna-ai/)               | Python RAG framework for SQL generation and querying.                                                |

qdrant-landing/content/documentation/frameworks/autogen.md

@@ -1,6 +1,5 @@
 ---
 title: Autogen
-weight: 1200
 aliases: [ ../integrations/autogen/ ]
 ---
 

qdrant-landing/content/documentation/frameworks/canopy.md

@@ -1,6 +1,5 @@
 ---
 title: Pinecone Canopy
-weight: 2500
 ---
 
 # Pinecone Canopy

qdrant-landing/content/documentation/frameworks/cheshire-cat.md

@@ -1,6 +1,5 @@
 ---
 title: Cheshire Cat
-weight: 600
 aliases: [ ../integrations/cheshire-cat/ ]
 ---
 
@@ -25,6 +24,7 @@ CORE_PORT=1865
 ```
 
 Cheshire Cat takes great advantage of the following features of Qdrant:
+
 * [Collection Aliases](../../concepts/collections/#collection-aliases) to manage the change from one embedder to another.
 * [Quantization](../../guides/quantization/) to obtain a good balance between speed, memory usage and quality of the results.
 * [Snapshots](../../concepts/snapshots/) to not miss any information.
@@ -35,11 +35,12 @@ Cheshire Cat takes great advantage of the following features of Qdrant:
 ## How to use the Cheshire Cat
 
 ### Requirements
+
 To run the Cheshire Cat, you need to have [Docker](https://docs.docker.com/engine/install/) and [docker-compose](https://docs.docker.com/compose/install/) already installed on your system.
 
 ```shell
 docker run --rm -it -p 1865:80 ghcr.io/cheshire-cat-ai/core:latest
-``` 
+```
 
 * Chat with the Cheshire Cat on [localhost:1865/admin](http://localhost:1865/admin).
 * You can also interact via REST API and try out the endpoints on [localhost:1865/docs](http://localhost:1865/docs)

qdrant-landing/content/documentation/frameworks/docarray.md

@@ -1,16 +1,15 @@
 ---
 title: DocArray
-weight: 300
 aliases: [ ../integrations/docarray/ ]
 ---
 
 # DocArray
+
 You can use Qdrant natively in DocArray, where Qdrant serves as a high-performance document store to enable scalable vector search.
 
 DocArray is a library from Jina AI for nested, unstructured data in transit, including text, image, audio, video, 3D mesh, etc.
 It allows deep-learning engineers to efficiently process, embed, search, recommend, store, and transfer the data with a Pythonic API.
 
-
 To install DocArray with Qdrant support, please do
 
 ```bash

qdrant-landing/content/documentation/frameworks/dspy.md

@@ -1,6 +1,5 @@
 ---
 title: Stanford DSPy
-weight: 1500
 aliases: [ ../integrations/dspy/ ]
 ---
 

qdrant-landing/content/documentation/frameworks/fifty-one.md

@@ -1,6 +1,5 @@
 ---
 title: FiftyOne
-weight: 600
 aliases: [ ../integrations/fifty-one ]
 ---
 

qdrant-landing/content/documentation/frameworks/genkit.md

@@ -1,6 +1,5 @@
 ---
 title: Firebase Genkit
-weight: 3400
 ---
 
 # Firebase Genkit

qdrant-landing/content/documentation/frameworks/haystack.md

@@ -1,6 +1,5 @@
 ---
 title: Haystack
-weight: 400
 aliases:
   - ../integrations/haystack/
   - /documentation/overview/integrations/haystack/
@@ -8,10 +7,10 @@ aliases:
 
 # Haystack
 
-[Haystack](https://haystack.deepset.ai/) serves as a comprehensive NLP framework, offering a modular methodology for constructing 
+[Haystack](https://haystack.deepset.ai/) serves as a comprehensive NLP framework, offering a modular methodology for constructing
-cutting-edge generative AI, QA, and semantic knowledge base search systems. A critical element in contemporary NLP systems is an 
+cutting-edge generative AI, QA, and semantic knowledge base search systems. A critical element in contemporary NLP systems is an
-efficient database for storing and retrieving extensive text data. Vector databases excel in this role, as they house vector 
+efficient database for storing and retrieving extensive text data. Vector databases excel in this role, as they house vector
-representations of text and implement effective methods for swift retrieval. Thus, we are happy to announce the integration 
+representations of text and implement effective methods for swift retrieval. Thus, we are happy to announce the integration
 with Haystack - `QdrantDocumentStore`. This document store is unique, as it is maintained externally by the Qdrant team.
 
 The new document store comes as a separate package and can be updated independently of Haystack:
@@ -20,7 +19,7 @@ The new document store comes as a separate package and can be updated independen
 pip install qdrant-haystack
 ```
 
-`QdrantDocumentStore` supports [all the configuration properties](/documentation/collections/#create-collection) available in 
+`QdrantDocumentStore` supports [all the configuration properties](/documentation/collections/#create-collection) available in
 the Qdrant Python client. If you want to customize the default configuration of the collection used under the hood, you can
 provide that settings when you create an instance of the `QdrantDocumentStore`. For example, if you'd like to enable the
 Scalar Quantization, you'd make that in the following way:
@@ -47,4 +46,4 @@ document_store = QdrantDocumentStore(
 ## Further Reading
 
 - [Haystack Documentation](https://haystack.deepset.ai/integrations/qdrant-document-store)
-- [Source Code](https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/qdrant)
+- [Source Code](https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/qdrant)

qdrant-landing/content/documentation/frameworks/langchain-go.md

@@ -1,6 +1,5 @@
 ---
 title: Langchain Go
-weight: 2120
 ---
 
 # Langchain Go
@@ -30,38 +29,38 @@ Before you use the following code sample, customize the following values for you
 package main
 
 import (
-	"log"
+    "log"
-	"net/url"
+    "net/url"
 
-	"github.com/tmc/langchaingo/embeddings"
+    "github.com/tmc/langchaingo/embeddings"
-	"github.com/tmc/langchaingo/llms/openai"
+    "github.com/tmc/langchaingo/llms/openai"
-	"github.com/tmc/langchaingo/vectorstores/qdrant"
+    "github.com/tmc/langchaingo/vectorstores/qdrant"
 )
 
 func main() {
-	llm, err := openai.New()
+    llm, err: = openai.New()
-	if err != nil {
+    if err != nil {
-		log.Fatal(err)
+        log.Fatal(err)
-	}
+    }
 
-	e, err := embeddings.NewEmbedder(llm)
+    e, err: = embeddings.NewEmbedder(llm)
-	if err != nil {
+    if err != nil {
-		log.Fatal(err)
+        log.Fatal(err)
-	}
+    }
 
-	url, err := url.Parse("YOUR_QDRANT_REST_URL")
+    url, err: = url.Parse("YOUR_QDRANT_REST_URL")
-	if err != nil {
+    if err != nil {
-		log.Fatal(err)
+        log.Fatal(err)
-	}
+    }
 
-	store, err := qdrant.New(
+    store, err: = qdrant.New(
-		qdrant.WithURL(*url),
+        qdrant.WithURL( * url),
-		qdrant.WithCollectionName("YOUR_COLLECTION_NAME"),
+        qdrant.WithCollectionName("YOUR_COLLECTION_NAME"),
-		qdrant.WithEmbedder(e),
+        qdrant.WithEmbedder(e),
-	)
+    )
-	if err != nil {
+    if err != nil {
-		log.Fatal(err)
+        log.Fatal(err)
-	}
+    }
 }
 ```
 

qdrant-landing/content/documentation/frameworks/langchain.md

@@ -1,6 +1,5 @@
 ---
 title: Langchain
-weight: 100
 aliases:
   - ../integrations/langchain/
   - /documentation/overview/integrations/langchain/

qdrant-landing/content/documentation/frameworks/langchain4j.md

@@ -1,6 +1,5 @@
 ---
 title: Langchain4J
-weight: 2110
 ---
 
 # LangChain for Java

qdrant-landing/content/documentation/frameworks/llama-index.md

@@ -1,6 +1,5 @@
 ---
 title: LlamaIndex
-weight: 200
 aliases:
   - ../integrations/llama-index/
   - /documentation/overview/integrations/llama-index/

qdrant-landing/content/documentation/frameworks/memgpt.md

@@ -1,6 +1,5 @@
 ---
 title: MemGPT
-weight: 3200
 ---
 
 # MemGPT

qdrant-landing/content/documentation/frameworks/pandas-ai.md

@@ -1,6 +1,5 @@
 ---
 title: Pandas-AI
-weight: 2900
 ---
 
 # Pandas-AI

qdrant-landing/content/documentation/frameworks/semantic-router.md

@@ -1,6 +1,5 @@
 ---
 title: Semantic-Router
-weight: 2700
 ---
 
 # Semantic-Router

qdrant-landing/content/documentation/frameworks/spring-ai.md

@@ -1,6 +1,5 @@
 ---
 title: Spring AI
-weight: 2200
 ---
 
 # Spring AI

qdrant-landing/content/documentation/frameworks/testcontainers.md

@@ -1,6 +1,5 @@
 ---
 title: Testcontainers
-weight: 2700
 ---
 
 # Testcontainers

qdrant-landing/content/documentation/frameworks/txtai.md

@@ -1,6 +1,5 @@
 ---
 title: txtai
-weight: 500
 aliases: [ ../integrations/txtai/ ]
 ---
 
@@ -8,8 +7,8 @@ aliases: [ ../integrations/txtai/ ]
 
 Qdrant might be also used as an embedding backend in [txtai](https://neuml.github.io/txtai/) semantic applications.
 
-txtai simplifies building AI-powered semantic search applications using Transformers. It leverages the neural embeddings and their 
+txtai simplifies building AI-powered semantic search applications using Transformers. It leverages the neural embeddings and their
-properties to encode high-dimensional data in a lower-dimensional space and allows to find similar objects based on their embeddings' 
+properties to encode high-dimensional data in a lower-dimensional space and allows to find similar objects based on their embeddings'
 proximity.
 
 Qdrant is not built-in txtai backend and requires installing an additional dependency:
@@ -19,5 +18,3 @@ pip install qdrant-txtai
 ```
 
 The examples and some more information might be found in [qdrant-txtai repository](https://github.com/qdrant/qdrant-txtai).
-
-

qdrant-landing/content/documentation/frameworks/vanna-ai.md

@@ -1,6 +1,5 @@
 ---
 title: Vanna.AI
-weight: 3000
 ---
 
 # Vanna.AI

qdrant-landing/content/documentation/observability/_index.md

@@ -0,0 +1,11 @@
+---
+title: Observability
+weight: 15
+---
+
+## Observability Integrations
+
+| Tool                                  | Description                                                                                          |
+| ------------------------------------- | ---------------------------------------------------------------------------------------------------- |
+| [OpenLIT](./openlit/)                 | Platform for OpenTelemetry-native Observability & Evals for LLMs and Vector Databases.               |
+| [OpenLLMetry](./openllmetry/)         | Set of OpenTelemetry extensions to add Observability for your LLM application.                       |

qdrant-landing/content/documentation/observability/openlit.md

@@ -1,6 +1,7 @@
 ---
 title: OpenLIT
 weight: 3100
+aliases: [ ../frameworks/openlit/ ]
 ---
 
 # OpenLIT

qdrant-landing/content/documentation/observability/openllmetry.md

@@ -1,6 +1,7 @@
 ---
 title: OpenLLMetry
 weight: 2300
+aliases: [ ../frameworks/openllmetry/ ]
 ---
 
 # OpenLLMetry

qdrant-landing/content/documentation/platforms/_index.md

@@ -0,0 +1,19 @@
+---
+title: Platforms
+weight: 15
+---
+
+## Platform Integrations
+
+| Platform                              | Description                                                                                          |
+| ------------------------------------- | ---------------------------------------------------------------------------------------------------- |
+| [Apify](./apify/)                     | Platform to build web scrapers and automate web browser tasks.                                       |
+| [Bubble](./bubble)                    | Development platform for application development with a no-code interface                            |
+| [BuildShip](./buildship)              | Low-code visual builder to create APIs, scheduled jobs, and backend workflows.                       |
+| [DocsGPT](./docsgpt/)                 | Tool for ingesting documentation sources and enabling conversations and queries.                     |
+| [Make](./make/)                       | Cloud platform to build low-code workflows by integrating various software applications.             |
+| [N8N](./n8n/)                         | Platform for node-based, low-code workflow automation.                                               |
+| [Pipedream](./pipedream/)             | Platform for connecting apps and developing event-driven automation.                                 |
+| [Portable.io](./portable/)            | Cloud platform for developing and deploying ELT transformations.                                     |
+| [PrivateGPT](./privategpt/)           | Tool to ask questions about your documents using local LLMs emphasising privacy.                     |
+| [Rivet](./rivet/)                     | A visual programming environment for building AI agents with LLMs.                                   |

qdrant-landing/content/documentation/platforms/apify.md

@@ -1,6 +1,6 @@
 ---
 title: Apify 
-weight: 3600
+aliases: [ ../frameworks/apify/ ]
 ---
 
 # Apify

qdrant-landing/content/documentation/platforms/bubble.md

@@ -1,6 +1,6 @@
 ---
 title: Bubble
-weight: 3200
+aliases: [ ../frameworks/bubble/ ]
 ---
 
 # Bubble

qdrant-landing/content/documentation/platforms/buildship.md

@@ -1,6 +1,6 @@
 ---
 title: BuildShip
-weight: 3800
+aliases: [ ../frameworks/buildship/ ]
 ---
 
 # BuildShip

qdrant-landing/content/documentation/platforms/docsgpt.md

@@ -1,6 +1,6 @@
 ---
 title: DocsGPT
-weight: 2600
+aliases: [ ../frameworks/docsgpt/ ]
 ---
 
 # DocsGPT

qdrant-landing/content/documentation/platforms/make.md

@@ -1,6 +1,6 @@
 ---
 title: Make.com
-weight: 1800
+aliases: [ ../frameworks/make/ ]
 ---
 
 # Make.com

qdrant-landing/content/documentation/platforms/n8n.md

@@ -1,6 +1,6 @@
 ---
 title: N8N
-weight: 2000
+aliases: [ ../frameworks/n8n/ ]
 ---
 
 # N8N

qdrant-landing/content/documentation/platforms/pipedream.md

@@ -1,6 +1,6 @@
 ---
 title: Pipedream
-weight: 3300
+aliases: [ ../frameworks/pipedream/ ]
 ---
 
 # Pipedream

qdrant-landing/content/documentation/platforms/portable.md

@@ -1,6 +1,6 @@
 ---
 title: Portable.io
-weight: 3700
+aliases: [ ../frameworks/portable/ ]
 ---
 
 # Portable

qdrant-landing/content/documentation/platforms/privategpt.md

@@ -1,7 +1,6 @@
 ---
 title: PrivateGPT
-weight: 1600
+aliases: [ ../integrations/privategpt/, ../frameworks/privategpt/ ]
-aliases: [ ../integrations/privategpt/ ]
 ---
 
 # PrivateGPT

qdrant-landing/content/documentation/platforms/rivet.md

@@ -1,6 +1,6 @@
 ---
 title: Ironclad Rivet
-weight: 3100
+aliases: [ ../frameworks/rivet/ ]
 ---
 
 # Ironclad Rivet