fix: broken links, images (#475)

* fix: broken links, images * fix: aliases
2026-10-02 09:28:30 +02:00 · 2023-12-17 23:25:07 +00:00
parent b99735dbb9
commit 64e6bb66e2
20 changed files with 29 additions and 29 deletions
@@ -57,7 +57,7 @@ Both tools are easy to combine, so you can start working with semantic search in
 And what if your needs are so specific that you need to fine-tune a general usage model? Co.embed API goes beyond 
 pre-trained encoders and allows providing some custom datasets to 
-[customize the embedding model with your own data](https://docs.cohere.ai/docs/training-a-representation-model). 
+[customize the embedding model with your own data](https://docs.cohere.com/docs/finetuning). 
 As a result, you get the quality of domain-specific models, but without worrying about infrastructure.
 ## System architecture overview
@@ -1,7 +1,7 @@
 ---
 title: Aleph Alpha
 weight: 900
-aliases: [ /integrations/aleph-alpha/ ]
+aliases: [ ../integrations/aleph-alpha/ ]
 ---
 Aleph Alpha is a multimodal and multilingual embeddings' provider. Their API allows creating the embeddings for text and images, both 
@@ -1,7 +1,7 @@
 ---
 title: Cohere
 weight: 700
-aliases: [ /integrations/cohere/ ]
+aliases: [ ../integrations/cohere/ ]
 ---
 # Cohere
@@ -1,7 +1,7 @@
 ---
 title: Jina Embeddings
 weight: 800
-aliases: [ /integrations/jina-embeddings/ ]
+aliases: [ ../integrations/jina-embeddings/ ]
 ---
 # Jina Embeddings
@@ -1,7 +1,7 @@
 ---
 title: OpenAI
 weight: 800
-aliases: [ /integrations/openai/ ]
+aliases: [ ../integrations/openai/ ]
 ---
 # OpenAI
@@ -1,7 +1,7 @@
 ---
 title: Airbyte
 weight: 1000
-aliases: [ /integrations/airbyte/ ]
+aliases: [ ../integrations/airbyte/ ]
 ---
 # Airbyte
@@ -34,7 +34,7 @@ Before you start, make sure you have the following:
 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 Qdrant destination](/documentation/integrations/airbyte/qdrant-destination.png)
+![Airbyte Qdrant destination](/documentation/frameworks/airbyte/qdrant-destination.png)
 ### Text processing
@@ -42,26 +42,26 @@ Airbyte has some built-in mechanisms to transform your texts into embeddings. Yo
 chunk your fields into pieces before calculating the embeddings, but also which fields should be used to
 create the point payload.
-![Processing settings](/documentation/integrations/airbyte/processing.png)
+![Processing settings](/documentation/frameworks/airbyte/processing.png)
 ### Embeddings
 You can choose the model that will be used to calculate the embeddings. Currently, Airbyte supports multiple
 models, including OpenAI and Cohere.
-![Embeddings settings](/documentation/integrations/airbyte/embedding.png)
+![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 
 name containing the embeddings and their dimensionality.
-![Precomputed embeddings settings](/documentation/integrations/airbyte/precomputed-embedding.png)
+![Precomputed embeddings settings](/documentation/frameworks/airbyte/precomputed-embedding.png)
 ### Qdrant connection details
 Finally, we can configure the target Qdrant instance and collection. In case you use the built-in authentication
 mechanism, here is where you can pass the token.
-![Qdrant connection details](/documentation/integrations/airbyte/qdrant-config.png)
+![Qdrant connection details](/documentation/frameworks/airbyte/qdrant-config.png)
 Once you confirm creating the destination, Airbyte will test if a specified Qdrant cluster is accessible and
 might be used as a destination.
@@ -72,7 +72,7 @@ Airbyte combines sources and destinations into a single entity called a connecti
 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/integrations/airbyte/connection.png)
+![Airbyte connection](/documentation/frameworks/airbyte/connection.png)
 More information about creating connections can be found in the 
 [Airbyte documentation](https://docs.airbyte.com/understanding-airbyte/connections/).
@@ -1,7 +1,7 @@
 ---
 title: Autogen
 weight: 1200
-aliases: [ /integrations/autogen/ ]
+aliases: [ ../integrations/autogen/ ]
 ---
 # Microsoft Autogen
@@ -100,4 +100,4 @@ ragproxyagent.initiate_chat(assistant, problem=code_problem)
 ## Next steps
-Check out more Autogen [examples](https://microsoft.github.io/autogen/docs/Examples/AgentChat). You can find detailed documentation about AutoGen [here](https://microsoft.github.io/autogen/).
+Check out more Autogen [examples](https://microsoft.github.io/autogen/docs/Examples). You can find detailed documentation about AutoGen [here](https://microsoft.github.io/autogen/).
@@ -1,14 +1,14 @@
 ---
 title: Cheshire Cat
 weight: 600
-aliases: [ /integrations/cheshire-cat/ ]
+aliases: [ ../integrations/cheshire-cat/ ]
 ---
 # Cheshire Cat
 [Cheshire Cat](https://cheshirecat.ai/) is an open-source framework that allows you to develop intelligent agents on top of many Large Language Models (LLM). You can develop your custom AI architecture to assist you in a wide range of tasks.
-![Cheshire cat](/documentation/integrations/cheshire-cat/cat.jpg)
+![Cheshire cat](/documentation/frameworks/cheshire-cat/cat.jpg)
 ## Cheshire Cat and Qdrant
@@ -30,7 +30,7 @@ Cheshire Cat takes great advantage of the following features of Qdrant:
 * [Snapshots](../../concepts/snapshots/) to not miss any information.
 * [Community](https://discord.com/invite/tdtYvXjC4h)
-![RAG Pipeline](/documentation/integrations/cheshire-cat/stregatto.jpg)
+![RAG Pipeline](/documentation/frameworks/cheshire-cat/stregatto.jpg)
 ## How to use the Cheshire Cat
@@ -1,7 +1,7 @@
 ---
 title: DLT
 weight: 1300
-aliases: [ /integrations/dlt/ ]
+aliases: [ ../integrations/dlt/ ]
 ---
 # DLT(Data Load Tool)
@@ -1,7 +1,7 @@
 ---
 title: DocArray
 weight: 300
-aliases: [ /integrations/docarray/ ]
+aliases: [ ../integrations/docarray/ ]
 ---
 # DocArray
@@ -1,7 +1,7 @@
 ---
 title: Stanford DSPy
 weight: 1500
-aliases: [ /integrations/dspy/ ]
+aliases: [ ../integrations/dspy/ ]
 ---
 # Stanford DSPy
@@ -1,7 +1,7 @@
 ---
 title: FiftyOne
 weight: 600
-aliases: [ /integrations/fifty-one/ ]
+aliases: [ ../integrations/fifty-one ]
 ---
 # FiftyOne
@@ -1,7 +1,7 @@
 ---
 title: ML6 Fondant
 weight: 1700
-aliases: [ /integrations/fondant/ ]
+aliases: [ ../integrations/fondant/ ]
 ---
 # ML6 Fondant
@@ -1,7 +1,7 @@
 ---
 title: Haystack
 weight: 400
-aliases: [ /integrations/haystack/ ]
+aliases: [ ../integrations/haystack/ ]
 ---
 # Haystack
@@ -1,7 +1,7 @@
 ---
 title: LangChain
 weight: 100
-aliases: [ /integrations/langchain/ ]
+aliases: [ ../integrations/langchain/ ]
 ---
 # LangChain
@@ -1,7 +1,7 @@
 ---
 title: LlamaIndex
 weight: 200
-aliases: [ /integrations/llama-index/ ]
+aliases: [ ../integrations/llama-index/ ]
 ---
 # LlamaIndex (GPT Index)
@@ -1,7 +1,7 @@
 ---
 title: MindsDB
 weight: 1100
-aliases: [ /integrations/mindsdb/ ]
+aliases: [ ../integrations/mindsdb/ ]
 ---
 # MindsDB
@@ -1,7 +1,7 @@
 ---
 title: PrivateGPT
 weight: 1600
-aliases: [ /integrations/privategpt/ ]
+aliases: [ ../integrations/privategpt/ ]
 ---
 # PrivateGPT
@@ -1,7 +1,7 @@
 ---
 title: Apache Spark
 weight: 1400
-aliases: [ /integrations/spark/ ]
+aliases: [ ../integrations/spark/ ]
 ---
 # Apache Spark
@@ -1,7 +1,7 @@
 ---
 title: txtai
 weight: 500
-aliases: [ /integrations/txtai/ ]
+aliases: [ ../integrations/txtai/ ]
 ---
 # txtai