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Fix typos (#1319)
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pages/advanced-algorithms/available-algorithms/biconnected_components.mdx

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@@ -95,7 +95,7 @@ MERGE (a:Node {id: 8}) MERGE (b:Node {id: 9}) CREATE (a)-[:RELATION]->(b);
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MERGE (a:Node {id: 10}) MERGE (b:Node {id: 11}) CREATE (a)-[:RELATION]->(b);
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```
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{<h3 className="custom-header"> Find biconnected componentes </h3>}
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{<h3 className="custom-header"> Find biconnected components </h3>}
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Get the values using the following query:
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pages/advanced-algorithms/available-algorithms/elasticsearch_synchronization.mdx

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@@ -142,10 +142,10 @@ If subgraph is not specified, the algorithm is computed on the entire graph by d
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- `edge_index: string` -> The name of the **edge index**. Can be used for both **streaming and parallel bulk**.
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- `thread_count: int` -> **Size of the threadpool** to use for the bulk requests.
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- `chunk_size: int (default=500)` -> The number of docs sent to Elasticsearch in one chunk.
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- `max_chunk_bytes: int (default=104857600)` -> The maximum size of the request in bytes. The default equalls to 100 MB.
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- `max_chunk_bytes: int (default=104857600)` -> The maximum size of the request in bytes. The default equals to 100 MB.
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- `raise_on_error: bool (default=True)` -> Raise `BulkIndexError` containing errors (as .errors) from the execution of the last chunk when some occur.
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- `raise_on_exception: bool` -> If `False` then don’t propagate exceptions from call to bulk and just report the items that failed as failed.
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- `max_retries: int (default=0)` -> Maximum number of times a document will be retried when a 429 is received. The 0 equalls to no retries on 429.
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- `max_retries: int (default=0)` -> Maximum number of times a document will be retried when a 429 is received. The 0 equals to no retries on 429.
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- `initial_backoff: float` -> The number of seconds Elasticsearch should wait before the first retry. Any subsequent retries will be powers of `initial_backoff * 2**retry_number`
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- `max_backoff: float` -> The maximum number of seconds a retry will wait.
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- `yield_ok: float` -> If set to `False` will skip successful documents in the output.
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- `edge_index: string` -> The name of the **edge index**. Can be used for both **streaming and parallel bulk**.
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- `thread_count: int` -> **Size of the threadpool** to use for the bulk requests.
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- `chunk_size: int (default=500)` -> The number of docs sent to Elasticsearch in one chunk.
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- `max_chunk_bytes: int (default=104857600)` -> The maximum size of the request in bytes. The default equalls to 100 MB.
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- `max_chunk_bytes: int (default=104857600)` -> The maximum size of the request in bytes. The default equals to 100 MB.
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- `raise_on_error: bool (default=True)` -> Raise `BulkIndexError` containing errors (as .errors) from the execution of the last chunk when some occur.
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- `raise_on_exception: bool` -> If `False` then don’t propagate exceptions from call to bulk and just report the items that failed as failed.
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- `max_retries: int (default=0)` -> Maximum number of times a document will be retried when a 429 is received. The 0 equalls to no retries on 429.
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- `max_retries: int (default=0)` -> Maximum number of times a document will be retried when a 429 is received. The 0 equals to no retries on 429.
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- `initial_backoff: float` -> The number of seconds Elastichsearch should wait before the first retry. Any subsequent retries will be powers of `initial_backoff * 2**retry_number`
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- `max_backoff: float` -> The maximum number of seconds a retry will wait.
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- `yield_ok: float` -> If set to `False` will skip successful documents in the output.

pages/advanced-algorithms/available-algorithms/gnn_link_prediction.mdx

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@@ -145,7 +145,7 @@ networks** to compute **node embeddings**.
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All other parameters are optional.
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When setting the model paramaters, if you specify the `split_ratio 1.0`, the
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When setting the model parameters, if you specify the `split_ratio 1.0`, the
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model will train normally on a whole dataset without validating its performance
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on a validation set. However, if the `split_ratio` is a value between 0.0 and
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1.0 but the graph is too small to have such a split, an exception will be

pages/advanced-algorithms/available-algorithms/igraphalg.mdx

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{<h4 className="custom-header"> Usage: </h4>}
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Use the following query to find the community strucutre:
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Use the following query to find the community structure:
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```cypher
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CALL igraphalg.community_leiden()

pages/advanced-algorithms/available-algorithms/node2vec_online.mdx

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### `update()`
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Use the `udpate()` procedure to (re)calculate embeddings.
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Use the `update()` procedure to (re)calculate embeddings.
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{<h4 className="custom-header"> Input: </h4>}
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MERGE (n:Node {id: 4}) MERGE (m:Node {id: 6}) CREATE (n)-[:RELATION]->(m);
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```
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{<h3 className="custom-header"> Set the paramaters </h3>}
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{<h3 className="custom-header"> Set the parameters </h3>}
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To set the parameters for the updater and the learner, use the following query:
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pages/advanced-algorithms/available-algorithms/nxalg.mdx

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{<h4 className="custom-header"> Output: </h4>}
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- `paths: List[Vertex]` ➡ List of ndoes for a certain path.
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- `paths: List[Vertex]` ➡ List of nodes for a certain path.
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{<h4 className="custom-header"> Usage: </h4>}
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{<h4 className="custom-header"> Usage: </h4>}
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To get nodes in a stronly connected components, run the following query:
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To get nodes in strongly connected components, run the following query:
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```cypher
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CALL nxalg.strongly_connected_components()

pages/advanced-algorithms/available-algorithms/tgn.mdx

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{<h4 className="custom-header"> Usage: </h4>}
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To get resuts, use the following query:
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To get results, use the following query:
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```cypher
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CALL tgn.get_results()

pages/advanced-algorithms/available-algorithms/weakly_connected_components.mdx

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### `get()`
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Use the procedure to get disconnected componentes.
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Use the procedure to get disconnected components.
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{<h4 className="custom-header"> Input: </h4>}
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pages/advanced-algorithms/available-algorithms/xml_module.mdx

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{<h4 className="custom-header"> Usage: </h4>}
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This section shows the usage of the procedure on the folllowing [**XML
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This section shows the usage of the procedure on the following [**XML
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file**](https://www.w3schools.com/xml/note.xml)
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**Parse an XML file from URL**

pages/ai-ecosystem/integrations.mdx

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@@ -218,7 +218,7 @@ paths and in LlamaIndex, several node retrieval methods at once can be combined.
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If no sub-retrievers are provided, the defaults are
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[LLMSynonymRetriever](https://docs.llamaindex.ai/en/latest/module_guides/indexing/lpg_index_guide/#default-llmsynonymretriever)
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and
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[VectorContexRetriever](https://docs.llamaindex.ai/en/latest/module_guides/indexing/lpg_index_guide/#default-if-supported-vectorcontextretriever),
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[VectorContextRetriever](https://docs.llamaindex.ai/en/latest/module_guides/indexing/lpg_index_guide/#default-if-supported-vectorcontextretriever),
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if supported.
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From the latest update, LlamaIndex utilizes Memgraph's **[vector search](/querying/vector-search)** feature in
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Don't forget to install `langgraph`, as it is a prerequisite to use Memgraph
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toolkit.
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{<h4 className="custom-header">Enviroment setup </h4>}
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{<h4 className="custom-header">Environment setup </h4>}
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Make sure you have [Memgraph](/getting-started) running
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in the background.

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