fix: broken stuff in docs

README.md

@@ -168,16 +168,6 @@ Please note that using Ollama requires a large (>4GB) download of the docker ima
     This query selects from the `wiki_embeddings` view, which is created by the vectorizer and joins the embeddings with the original data in the `wiki` table to give us the ability to search using the embeddings but still be able to access (or filter on) all the data in the original table (e.g. the `title` column).
     
     Note the `ai.ollama_embed` function is used to call the `all-minilm` model. This is part of pgai's  [model calling capabilities](#model-calling).
-
-    
-    
-    | title | chunk |
-    |-------|-------|
-    | Albedo |  Water reflects light very differently from typical terrestrial materials. The reflectivity of a water surface is calculated using the Fresnel equations.... |
-
-    | title | chunk |
-    |-------|-------|
-    | Albedo |  Water reflects light very differently from typical terrestrial materials. The reflectivity of a water surface is calculated using the Fresnel equations.... |
     
  1. **Modify your data and have the vectorizer automatically update the embeddings**
  

docs/model_calling/litellm.md

@@ -144,7 +144,7 @@ extracted from the "Target URI" field in the "Endpoint section". The Target URI
 `https://your-resource-name.openai.azure.com/openai/deployments/your-deployment-name/embeddings?api-version=2023-05-15`.
 In this example, the base URL is: `https://your-resource-name.openai.azure.com` and the version is `2023-05-15`.
 
-![Azure AI Foundry console example](./images/azure_openai.png)
+![Azure AI Foundry console example](/docs/images/azure_openai.png)
 
 Obtain embeddings as follows, note that the base URL and version are configured through `extra_options`:
 

docs/security/handling-api-keys.md

@@ -8,12 +8,12 @@ recommendations for which option to use.
 API keys are sensitive values, so we provide several ways to specify them so
 they can be provided securely:
 
-**Recommended ways (most secure) **
+**Recommended ways (most secure)**
 
 1. If you are using Timescale Cloud, we recommend that you [configure an API key in Timescale Cloud](#configure-an-api-key-in-timescale-cloud).
 2. If you are self-hosting, you can [configure an API key through an environment variable available to the PostgresSQL process](#configure-an-api-key-through-an-environment-variable-available-to-the-postgres-process-self-hosted)
 
-** Other ways **
+**Other ways**
 1. You can [configure the api key for an interactive a psql session](#configure-an-api-key-for-an-interactive-psql-session).
 2. You can [provide the api key directly with the `api_key` function parameter](#provide-the-api-key-directly-with-the-api_key-function-parameter).
 
@@ -33,11 +33,11 @@ default is provider-dependent:
 
 1. Navigate to the "AI Model API Keys" tab under "Project settings"
 
-   ![Timescale Cloud project settings](./images/timescale_project_settings.png)
+   ![Timescale Cloud project settings](/docs/images/timescale_project_settings.png)
 
 1. Add a new AI Model API key, providing the name and API key
 
-   ![Timescale Cloud new AI model API key](./images/timescale_new_ai_model_api_key.png)
+   ![Timescale Cloud new AI model API key](/docs/images/timescale_new_ai_model_api_key.png)
 
 1. Use this API key name in calls to pgai functions, like so:
     ```sql

docs/vectorizer/api-reference.md

@@ -361,7 +361,7 @@ extracted from the "Target URI" field in the "Endpoint section". The Target URI
 `https://your-resource-name.openai.azure.com/openai/deployments/your-deployment-name/embeddings?api-version=2023-05-15`.
 In this example, the base URL is: `https://your-resource-name.openai.azure.com` and the version is `2023-05-15`. 
 
-![Azure AI Foundry console example](./images/azure_openai.png)
+![Azure AI Foundry console example](/docs/images/azure_openai.png)
 
 Configure the vectorizer, note that the base URL and version are configured through `extra_options`: