docs: small README improvements

README.md

@@ -8,13 +8,13 @@ RAGLite is a Python package for Retrieval-Augmented Generation (RAG) with Postgr
 
 1. ❤️ Only lightweight and permissive open source dependencies (e.g., no [PyTorch](https://github.com/pytorch/pytorch), [LangChain](https://github.com/langchain-ai/langchain), or [PyMuPDF](https://github.com/pymupdf/PyMuPDF))
 2. 🧠 Choose any LLM provider with [LiteLLM](https://github.com/BerriAI/litellm), including local [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) models
-3. 💾 [PostgreSQL](https://github.com/postgres/postgres) or [SQLite](https://github.com/sqlite/sqlite) as keyword & vector search database
+3. 💾 Either [PostgreSQL](https://github.com/postgres/postgres) or [SQLite](https://github.com/sqlite/sqlite) as a keyword & vector search database
 4. 🚀 Acceleration with Metal on macOS, and CUDA on Linux and Windows
 5. 📖 PDF to Markdown conversion on top of [pdftext](https://github.com/VikParuchuri/pdftext) and [pypdfium2](https://github.com/pypdfium2-team/pypdfium2)
-6. ✂️ Optimal [level 4 semantic chunking](https://medium.com/@anuragmishra_27746/five-levels-of-chunking-strategies-in-rag-notes-from-gregs-video-7b735895694d) by solving a [binary integer programming problem](https://en.wikipedia.org/wiki/Integer_programming)
-7. 🧬 Multi-vector chunk embedding with [late chunking](https://weaviate.io/blog/late-chunking) and [contextual chunk headings](https://d-star.ai/solving-the-out-of-context-chunk-problem-for-rag)
-8.  🌀 Optimal [closed-form linear query adapter](src/raglite/_query_adapter.py) by solving an [orthogonal Procrustes problem](https://en.wikipedia.org/wiki/Orthogonal_Procrustes_problem)
-9.  🔍 [Hybrid search](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) that combines the database's built-in keyword search ([tsvector](https://www.postgresql.org/docs/current/datatype-textsearch.html) in PostgreSQL, [FTS5](https://www.sqlite.org/fts5.html) in SQLite) with their native vector search extensions ([pgvector](https://github.com/pgvector/pgvector) in PostgreSQL, [sqlite-vec](https://github.com/asg017/sqlite-vec) in SQLite)
+6. 🧬 Multi-vector chunk embedding with [late chunking](https://weaviate.io/blog/late-chunking) and [contextual chunk headings](https://d-star.ai/solving-the-out-of-context-chunk-problem-for-rag)
+7. ✂️ Optimal [level 4 semantic chunking](https://medium.com/@anuragmishra_27746/five-levels-of-chunking-strategies-in-rag-notes-from-gregs-video-7b735895694d) by solving a [binary integer programming problem](https://en.wikipedia.org/wiki/Integer_programming)
+8. 🌀 Optimal [closed-form linear query adapter](src/raglite/_query_adapter.py) by solving an [orthogonal Procrustes problem](https://en.wikipedia.org/wiki/Orthogonal_Procrustes_problem)
+9. 🔍 [Hybrid search](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) that combines the database's built-in keyword search ([tsvector](https://www.postgresql.org/docs/current/datatype-textsearch.html) in PostgreSQL, [FTS5](https://www.sqlite.org/fts5.html) in SQLite) with their native vector search extensions ([pgvector](https://github.com/pgvector/pgvector) in PostgreSQL, [sqlite-vec](https://github.com/asg017/sqlite-vec) in SQLite)
 10. ✍️ Optional: conversion of any input document to Markdown with [Pandoc](https://github.com/jgm/pandoc)
 11. ✅ Optional: evaluation of retrieval and generation performance with [Ragas](https://github.com/explodinggradients/ragas)
 
@@ -57,7 +57,7 @@ pip install raglite[ragas]
 ### 1. Configuring RAGLite
 
 > [!TIP]
-> RAGLite extends [LiteLLM](https://github.com/BerriAI/litellm) with support for [llama.cpp](https://github.com/ggerganov/llama.cpp) models using [llama-cpp-python](https://github.com/abetlen/llama-cpp-python). To select a llama.cpp model (e.g., from [bartowski's collection](https://huggingface.co/collections/bartowski/recent-highlights-65cf8e08f8ab7fc669d7b5bd)), use a model identifier of the form `"llama-cpp-python/<hugging_face_repo_id>/<filename>@<n_ctx>"`, where `n_ctx` is an optional parameter that specifies the context size of the model.
+> 🧠 RAGLite extends [LiteLLM](https://github.com/BerriAI/litellm) with support for [llama.cpp](https://github.com/ggerganov/llama.cpp) models using [llama-cpp-python](https://github.com/abetlen/llama-cpp-python). To select a llama.cpp model (e.g., from [bartowski's collection](https://huggingface.co/collections/bartowski/recent-highlights-65cf8e08f8ab7fc669d7b5bd)), use a model identifier of the form `"llama-cpp-python/<hugging_face_repo_id>/<filename>@<n_ctx>"`, where `n_ctx` is an optional parameter that specifies the context size of the model.
 
 First, configure RAGLite with your preferred PostgreSQL or SQLite database and [any LLM supported by LiteLLM](https://docs.litellm.ai/docs/providers/openai):
 
@@ -82,7 +82,10 @@ my_config = RAGLiteConfig(
 ### 2. Inserting documents
 
 > [!TIP]
-> To insert documents other than PDF, make sure to install the `pandoc` extra with `pip install raglite[pandoc]`.
+> ✍️ To insert documents other than PDF, make sure to install the `pandoc` extra with `pip install raglite[pandoc]`.
+
+> [!TIP]
+> 💾 You can create a PostgreSQL database for free in a few clicks at [neon.tech](https://neon.tech) (not sponsored).
 
 Next, insert some documents into the database. RAGLite will take care of the [conversion to Markdown](src/raglite/_markdown.py), [optimal level 4 semantic chunking](src/raglite/_split_chunks.py), and [multi-vector embedding with late chunking](src/raglite/_embed.py):