touchups

pgml-cms/docs/SUMMARY.md

@@ -85,7 +85,7 @@
   * [Documents](resources/data-storage-and-retrieval/documents.md)
   * [Partitioning](resources/data-storage-and-retrieval/partitioning.md)
   * [LLM based pipelines with PostgresML and dbt (data build tool)](resources/data-storage-and-retrieval/llm-based-pipelines-with-postgresml-and-dbt-data-build-tool.md)
-* [Benchmarks](resources/benchmarks/README.md)
+* [Benchmarks](resources/benchmarks/postgresml-is-8-40x-faster-than-python-http-microservices.md)
   * [PostgresML is 8-40x faster than Python HTTP microservices](resources/benchmarks/postgresml-is-8-40x-faster-than-python-http-microservices.md)
   * [Scaling to 1 Million Requests per Second](resources/benchmarks/million-requests-per-second.md)
   * [MindsDB vs PostgresML](resources/benchmarks/mindsdb-vs-postgresml.md)

pgml-cms/docs/api/apis.md

@@ -22,6 +22,7 @@ The following functions are implemented and maintained by the PostgresML extensi
 |------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | [pgml.embed()](sql-extension/pgml.embed)     | Generate embeddings inside the database using open source embedding models from Hugging Face.                                                                                                |
 | [pgml.transform()](sql-extension/pgml.transform/) | Download and run latest Hugging Face transformer models, like Llama, Mixtral, and many more to perform various NLP tasks like text generation, summarization, sentiment analysis and more. |
+| pgml.transform_stream() | Streaming version of [pgml.transform()](sql-extension/pgml.transform/). Retrieve tokens as they are generated by the LLM, decreasing time to first token. |
 | [pgml.train()](sql-extension/pgml.train/)     | Train a machine learning model on data from a Postgres table or view. Supports XGBoost, LightGBM, Catboost and all Scikit-learn algorithms.       |
 | [pgml.deploy()](sql-extension/pgml.deploy)    | Deploy a version of the model created with pgml.train(). |
 | [pgml.predict()](sql-extension/pgml.predict/) | Perform real time inference using a model trained with pgml.train() on live application data. |
@@ -33,7 +34,7 @@ Together with standard database functionality provided by PostgreSQL, these func
 
 The client SDK implements best practices and common use cases, using the PostgresML SQL functions and standard PostgreSQL features to do it. The SDK core is written in Rust, which manages creating and running queries, connection pooling, and error handling.
 
-For each additional language we support (current JavaScript and Python), we create and publish language-native bindings. This architecture ensures all programming languages we support have identical APIs and similar performance when interacting with PostgresML.
+For each additional language we support (currently JavaScript and Python), we create and publish language-native bindings. This architecture ensures all programming languages we support have identical APIs and similar performance when interacting with PostgresML.
 
 ### Use cases
 

pgml-cms/docs/api/sql-extension/README.md

@@ -19,7 +19,7 @@ PostgresML defines two SQL functions which use [🤗 Hugging Face](https://huggi
 |---------------|-------------|
 | [pgml.embed()](pgml.embed) | Generate embeddings using latest sentence transformers from Hugging Face. |
 | [pgml.transform()](pgml.transform/) | Text generation using LLMs like Llama, Mixtral, and many more, with models downloaded from Hugging Face. |
-| pgml.transform_stream() | Streaming version of [pgml.transform()](pgml.transform/), which enables to fetch partial responses as they are being generated by the model. |
+| pgml.transform_stream() | Streaming version of [pgml.transform()](pgml.transform/), which fetches partial responses as they are being generated by the model, substantially decreasing time to first token. |
 | [pgml.tune()](pgml.tune) | Perform fine tuning tasks on Hugging Face models, using data stored in the database. | 
 
 ### Example
@@ -48,11 +48,11 @@ SELECT pgml.embed(
 {% endtab %}
 {% endtabs %}
 
-Using the `pgml` SQL functions inside regular queries, it's possible to add embeddings and LLM-generated text inside any query, without the data ever leaving the database and the cost of a remote network call.
+Using the `pgml` SQL functions inside regular queries, it's possible to add embeddings and LLM-generated text inside any query, without the data ever leaving the database, removing the cost of a remote network call.
 
 ## Classical machine learning
 
-PostgresML defines three SQL functions which allow training regression, classification, and clustering models on tabular data:
+PostgresML defines four SQL functions which allow training regression, classification, and clustering models on tabular data:
 
 | Function | Description |
 |---------------|-------------|