fixing links and toc scroll (deepset-ai#130)

* fixing links and toc scroll

* limiting the menu on the right to hot fix

* limiting the menu on the right to hot fix

components/Toc.tsx

@@ -52,8 +52,10 @@ export default function Toc({ stars, editOnGitHubLink, toc }: TocProps) {
  </a>
  </Link>
  )}
+ {toc && (
  <ul className="border-l-4 pl-4">
  {toc?.map((c) => (
+ c.level < 3 && (
  <li
  key={c.text}
  className={`mb-3 text-gray-400 hover:text-gray-700 ${
@@ -62,8 +64,10 @@ export default function Toc({ stars, editOnGitHubLink, toc }: TocProps) {
  >
  <a href={`#${c.link}`}>{c.text}</a>
  </li>
+ )
  ))}
  </ul>
+ )}
  </div>
  );
 }

docs/v0.5.0/usage/document_store.mdx

@@ -52,7 +52,7 @@ See API documentation for more info.
 
 DocumentStores expect Documents in dictionary form, like that below.
 They are loaded using the `DocumentStore.write_documents()` method.
-See [Preprocessing](/docs/v0.5.0/preprocessingmd) for more information on how to best prepare your data.
+See [Preprocessing](/usage/v0.5.0/preprocessing) for more information on how to best prepare your data.
 
 [//]: # "Add link to preprocessing section"
 

docs/v0.5.0/usage/languages.mdx

@@ -30,7 +30,7 @@ The default model that is loaded in the DensePassageRetriever is for English.
 We are currently working on training a German DensePassageRetriever model and know other teams who work on further languages.
 If you have a language model and a question answering dataset in your own language, you can also train a DPR model using Haystack!
 Below is a simplified example.
-See the [API reference](/docs/v0.5.0/apiretrievermd#train) for `DensePassageRetriever.train()` for more details.
+See the [API reference](/reference/v0.5.0/retriever#train) for `DensePassageRetriever.train()` for more details.
 
 ```python
 dense_passage_retriever.train(self,

docs/v0.5.0/usage/preprocessing.mdx

@@ -28,7 +28,7 @@ docs = [
 There are a range of different file converters in Haystack that
 can extract text from files and cast them into the unified dictionary format shown above.
 Haystack features support for txt, pdf and docx files and there is even a converter that leverages Apache Tika.
-Please refer to [the API docs](/docs/v0.5.0/file_convertersmd) to see which converter best suits you.
+Please refer to [the API docs](/reference/v0.5.0/file-converters) to see which converter best suits you.
 
 <Tabs
  options={[

docs/v0.9.0/overview/faq.mdx

@@ -32,9 +32,9 @@ You can reduce the work load on the Reader by instructing the Retriever to pass
 This is done by setting the `top_k_retriever` parameter to a lower value.
 
 Making sure that your documents are shorter can also increase the speed of your system. You can split
-your documents into smaller chunks by using the `PreProcessor` (see [tutorial](https://haystack.deepset.ai/docs/v0.9.0/tutorial11md)).
+your documents into smaller chunks by using the `PreProcessor` (see [tutorial](https://haystack.deepset.ai/tutorials/pipelines)).
 
-For more optimization suggestions, have a look at our [optimization page](https://haystack.deepset.ai/docs/v0.9.0/optimizationmd)
+For more optimization suggestions, have a look at our [optimization page](https://haystack.deepset.ai/usage/optimization)
 and also our [blogs](https://medium.com/deepset-ai)
 
 <div style={{ marginBottom: "3rem" }} />
@@ -43,16 +43,16 @@ and also our [blogs](https://medium.com/deepset-ai)
 
 The components in Haystack, such as the `Retriever` or the `Reader`, are designed in a language agnostic way. However you may
 have to set certain parameters or load models pretrained for your language in order to get good performance out of Haystack.
-See our [languages page](https://haystack.deepset.ai/docs/v0.9.0/languagesmd) for more details.
+See our [languages page](https://haystack.deepset.ai/usage/languages) for more details.
 
 <div style={{ marginBottom: "3rem" }} />
 
 ## How can I add metadata to my documents so that I can apply filters?
 
-When providing your documents in the input format (see [here](https://haystack.deepset.ai/docs/v0.9.0/documentstoremd#Input-Format))
+When providing your documents in the input format (see [here](https://haystack.deepset.aihttps://haystack.deepset.ai/usage/document-store#input-format))
 you can provide metadata information as a dictionary under the `meta` key. At query time, you can provide a `filters` argument
 (most likely through `Pipelines.run()`) that specifies the accepted values for a certain metadata field
-(for an example of what a `filters` dictionary might look like, please refer to [this example](https://haystack.deepset.ai/docs/v0.9.0/apiretrievermd#__init__))
+(for an example of what a `filters` dictionary might look like, please refer to [this example](https://haystack.deepset.ai/reference/retriever#__init__))
 
 <div style={{ marginBottom: "3rem" }} />
 
@@ -77,7 +77,7 @@ The confidence scores are in the range of 0 and 1 and reflect how confident the
 Having a confidence score is particularly useful in cases where you need Haystack to work with a certain accuracy threshold.
 Many of our users have built systems where predictions below a certain confidence value are routed on to a fallback system.
 
-For more information on model confidence and how to tune it, please refer to [this section](https://haystack.deepset.ai/docs/v0.9.0/readermd#Confidence-Scores).
+For more information on model confidence and how to tune it, please refer to [this section](https://haystack.deepset.ai/usage/reader#confidence-scores).
 
 <div style={{ marginBottom: "3rem" }} />
 
@@ -95,5 +95,5 @@ Note that this also applies at evaluation where labels are written into their ow
 In short, the FARMReader using a QA pipeline implementation that comes from our own
 [FARM framework](https://github.com/deepset-ai/FARM) that we can more easily update and also optimize for performance.
 By contrast, the TransformersReader uses a QA pipeline implementation that comes from HuggingFace's [Transformers](https://github.com/huggingface/transformers).
-See [this section](https://haystack.deepset.ai/docs/v0.9.0/readermd#Deeper-Dive-FARM-vs-Transformers)
+See [this section](https://haystack.deepset.ai/usage/reader#deeper-dive-farm-vs-transformers)
 for a more details about their differences!

docs/v0.9.0/usage/document_store.mdx

@@ -116,7 +116,7 @@ See API documentation for more info.
 
 DocumentStores expect Documents in dictionary form, like that below.
 They are loaded using the `DocumentStore.write_documents()` method.
-See [Preprocessing](/docs/v0.9.0/preprocessingmd) for more information on the cleaning and splitting steps that will help you maximize Haystack's performance.
+See [Preprocessing](/usage/preprocessing) for more information on the cleaning and splitting steps that will help you maximize Haystack's performance.
 
 [//]: # "Add link to preprocessing section"
 

docs/v0.9.0/usage/generator.mdx

@@ -12,8 +12,8 @@ retriever and generator can be trained concurrently from the one loss signal.
 
 **Tutorial**
 
-Checkout our tutorial notebooks for a guide on how to build your own generative QA system with RAG ([here](/docs/v0.9.0/tutorial7md))
-or with LFQA ([here](/docs/v0.9.0/tutorial12md)).
+Checkout our tutorial notebooks for a guide on how to build your own generative QA system with RAG ([here](/tutorials/retrieval-augmented-generation))
+or with LFQA ([here](/tutorials/pipelines)).
 
 </div>
 

docs/v0.9.0/usage/languages.mdx

@@ -49,7 +49,7 @@ The default model that is loaded in the DensePassageRetriever is for English.
 We have created a [German DensePassageRetriever model](https://deepset.ai/germanquad) and know other teams who work on further languages.
 If you have a language model and a question answering dataset in your own language, you can also train a DPR model using Haystack!
 Below is a simplified example.
-See [our tutorial](/docs/v0.9.0/tutorial9md) and also the [API reference](/docs/v0.9.0/apiretrievermd#train) for `DensePassageRetriever.train()` for more details.
+See [our tutorial](/tutorials/train-dpr) and also the [API reference](/reference/retriever#train) for `DensePassageRetriever.train()` for more details.
 
 ```python
 from haystack.retriever import DensePassageRetriever

docs/v0.9.0/usage/optimization.mdx

@@ -86,7 +86,7 @@ retrieved_docs = retriever.retrieve(top_k=10)
 
 ## Metadata Filtering
 
-Metadata can be attached to the documents which you index into your DocumentStore (see the input data format [here](/docs/v0.9.0/retrievermd)).
+Metadata can be attached to the documents which you index into your DocumentStore (see the input data format [here](/usage/retriever)).
 At query time, you can apply filters based on this metadata to limit the scope of your search and ensure your answers
 come from a specific slice of your data.
 
@@ -96,7 +96,7 @@ This can reduce the work load of the retriever and also ensure that you get more
 
 Filters are applied via the `filters` argument of the `Retriever` class. In practice, this argument will probably
 be passed into the `Pipeline.run()` call, which will then route it on to the `Retriever` class
-(see our the Arguments on the [Pipelines page](/docs/v0.9.0/pipelinesmd) for an explanation).
+(see our the Arguments on the [Pipelines page](/usage/pipelines) for an explanation).
 
 ```python
 pipeline.run(

docs/v0.9.0/usage/pipelines.mdx

@@ -88,7 +88,7 @@ To load, simply call:
 pipeline.load_from_yaml(Path("sample.yaml"))
 ```
 
-For another example YAML config, check out [this file](https://github.com/deepset-ai/haystack/blob/master/rest_api/pipelines.yaml).
+For another example YAML config, check out [this file](https://github.com/deepset-ai/haystack/blob/master/rest_api/pipeline/pipelines.yaml).
 
 <div style={{ marginBottom: "3rem" }} />
 
@@ -153,7 +153,7 @@ Or you can add decision nodes where only one "branch" is executed afterwards. Th
 ### Evaluation nodes
 
 There are nodes in Haystack that are used to evaluate the performance of readers, retrievers and combine systems.
-To get hands on with this kind of node, have a look at the [evaluation tutorial](/docs/v0.9.0/tutorial5md).
+To get hands on with this kind of node, have a look at the [evaluation tutorial](/tutorials/evaluation).
 
 <div style={{ marginBottom: "3rem" }} />
 
@@ -183,6 +183,6 @@ res = doc_pipe.run(query="How can I change my address?", top_k_retriever=3)
 
 ```
 
-See also the [Pipelines API documentation](/docs/v0.9.0/apipipelinesmd) for more details.
+See also the [Pipelines API documentation](/reference/pipelines) for more details.
 
 We plan many more features around the new pipelines incl. parallelized execution, distributed execution, dry runs - so stay tuned ...

docs/v0.9.0/usage/preprocessing.mdx

@@ -6,7 +6,7 @@ Haystack includes a suite of tools to:
 - normalize white space
 - split text into smaller pieces to optimize retrieval
 
-Check out our [preprocessing tutorial](/docs/v0.9.0/tutorial8md) if you'd like to start working with code examples already!
+Check out our [preprocessing tutorial](/tutorials/train-dpr) if you'd like to start working with code examples already!
 
 These data preprocessing steps can have a big impact on the systems performance
 and effective handling of data is key to getting the most out of Haystack.
@@ -30,7 +30,7 @@ docs = [
 There are a range of different file converters in Haystack that
 can extract text from files and cast them into the unified dictionary format shown above.
 Haystack features support for txt, pdf and docx files and there is even a converter that leverages Apache Tika.
-Please refer to [the API docs](/docs/v0.9.0/file_convertersmd) to see which converter best suits you.
+Please refer to [the API docs](/reference/file-converters) to see which converter best suits you.
 
 <Tabs
  options={[
@@ -86,13 +86,13 @@ Please refer to [the API docs](/docs/v0.9.0/file_convertersmd) to see which conv
 ## Web Crawler
 
 In Haystack, you will find a web crawler that will help you scrape text from websites and save it to file.
-See the [API documentation](https://haystack.deepset.ai/docs/v0.9.0/apicrawlermd) for more details.
+See the [API documentation](https://haystack.deepset.ai/reference/crawler) for more details.
 
 ```python
 from haystack.connector import Crawler
 
 crawler = Crawler()
-docs = crawler.crawl(urls=["https://haystack.deepset.ai/docs/v0.9.0/get_startedmd"],
+docs = crawler.crawl(urls=["https://haystack.deepset.ai/overview/get-started"],
  output_dir="crawled_files",
  filter_urls= ["haystack\.deepset\.ai\/docs\/"])
 ```
@@ -106,7 +106,7 @@ it is recommended that they are further processed in order to ensure optimal Ret
 The `PreProcessor` takes one of the documents created by the converter as input,
 performs various cleaning steps and splits them into multiple smaller documents.
 
-For suggestions on how best to split your documents, see [Optimization](/docs/v0.9.0/optimizationmd)
+For suggestions on how best to split your documents, see [Optimization](/usage/optimization)
 
 ```python
 from haystack.preprocessor import PreProcessor

docs/v0.9.0/usage/ranker.mdx

@@ -1,7 +1,7 @@
 # Ranker
 
 There are pure "semantic document search" use cases that do not need question answering functionality but only document ranking.
-While the [Retriever](/docs/v0.9.0/retrievermd) is a perfect fit for document retrieval, we can further improve its results with the Ranker.
+While the [Retriever](/usage/retriever) is a perfect fit for document retrieval, we can further improve its results with the Ranker.
 For example, BM25 (sparse retriever) does not take into account semantics of the documents and the query but only their keywords.
 The Ranker can re-rank the results of the retriever step by taking semantics into account.
 Similar to the Reader, it is based on the latest language models.

docs/v0.9.0/usage/retriever.mdx

@@ -26,7 +26,7 @@ Here are the combinations which are supported:
 | Embedding | Y | Y | N | Y | Y |
 | DPR | Y | Y | N | Y | Y |
 
-See [Optimization](/docs/v0.9.0/optimizationmd) for suggestions on how to choose top-k values.
+See [Optimization](/usage/optimization) for suggestions on how to choose top-k values.
 
 <div style={{ marginBottom: "3rem" }} />
 
@@ -131,7 +131,7 @@ There are two design decisions that have made DPR particularly performant.
 - Training with ‘In-batch negatives’ (gold labels are treated as negative examples for other samples in same batch) is highly efficient
 
 In Haystack, you can simply download the pretrained encoders needed to start using DPR.
-If you’d like to learn how to set up a DPR based system, have a look at the [tutorial](/docs/v0.9.0/tutorial6md)!
+If you’d like to learn how to set up a DPR based system, have a look at the [tutorial](/tutorials/dense-passage-retrieval)!
 
 <div style={{ marginBottom: "3rem" }} />
 
@@ -165,7 +165,7 @@ finder = ExtractiveQAPipeline(reader, retriever)
 
 <div className="max-w-xl bg-yellow-light-theme border-l-8 border-yellow-dark-theme px-6 pt-6 pb-4 my-4 rounded-md dark:bg-yellow-900">
 
-**Training DPR:** Haystack supports training of your own DPR model! Check out the [tutorial](/docs/v0.9.0/tutorial9md) to see how this is done!
+**Training DPR:** Haystack supports training of your own DPR model! Check out the [tutorial](/tutorials/train-dpr) to see how this is done!
 
 </div>