Update README

Specifically regarding embeddability of the known vector DBs

README.md

@@ -294,7 +294,7 @@ ok   github.com/philippgille/chromem-go 28.402s
 
 In December 2023, when I wanted to play around with retrieval augmented generation (RAG) in a Go program, I looked for a vector database that could be embedded in the Go program, just like you would embed SQLite in order to not require any separate DB setup and maintenance. I was surprised when I didn't find any, given the abundance of embedded key-value stores in the Go ecosystem.
 
-At the time most of the popular vector databases like Pinecone, Qdrant, Milvus, Weaviate and others were not embeddable at all. ChromaDB was, but only in Python.
+At the time most of the popular vector databases like Pinecone, Qdrant, Milvus, Chroma, Weaviate and others were not embeddable at all or only in Python or JavaScript/TypeScript.
 
 Then I found [@eliben](https://github.com/eliben)'s [blog post](https://eli.thegreenplace.net/2023/retrieval-augmented-generation-in-go/) and [example code](https://github.com/eliben/code-for-blog/tree/eda87b87dad9ed8bd45d1c8d6395efba3741ed39/2023/go-rag-openai) which showed that with very little Go code you could create a very basic PoC of a vector database.
 
@@ -303,12 +303,12 @@ That's when I decided to build my own vector database, embeddable in Go, inspire
 ## Related projects
 
 - Shoutout to [@eliben](https://github.com/eliben) whose [blog post](https://eli.thegreenplace.net/2023/retrieval-augmented-generation-in-go/) and [example code](https://github.com/eliben/code-for-blog/tree/eda87b87dad9ed8bd45d1c8d6395efba3741ed39/2023/go-rag-openai) inspired me to start this project!
-- [Chroma](https://github.com/chroma-core/chroma): Looking at Pinecone, Qdrant, Milvus, Weaviate and others, Chroma stood out by showing its core API in 4 commands on their README and on the landing page of their website. It was also the only one which could be embedded (in Python).
+- [Chroma](https://github.com/chroma-core/chroma): Looking at Pinecone, Qdrant, Milvus, Weaviate and others, Chroma stood out by showing its core API in 4 commands on their README and on the landing page of their website. It was also putting the most emphasis on its embeddability (in Python).
 - The big, full-fledged client-server-based vector databases for maximum scale and performance:
   - [Pinecone](https://www.pinecone.io/): Closed source
-  - [Qdrant](https://github.com/qdrant/qdrant): Written in Rust
+  - [Qdrant](https://github.com/qdrant/qdrant): Written in Rust, not embeddable in Go
   - [Milvus](https://github.com/milvus-io/milvus): Written in Go and C++, but not embeddable as of December 2023
-  - [Weaviate](https://github.com/weaviate/weaviate): Written in Go, but not embeddable in Go as of March 2024 (only in Python and JavaScript/TypeScript and that's experimental))
+  - [Weaviate](https://github.com/weaviate/weaviate): Written in Go, but not embeddable in Go as of March 2024 (only in Python and JavaScript/TypeScript and that's experimental)
 - Some non-specialized SQL, NoSQL and Key-Value databases added support for storing vectors and (some of them) querying based on similarity:
   - [pgvector](https://github.com/pgvector/pgvector) extension for [PostgreSQL](https://www.postgresql.org/): Client-server model
   - [Redis](https://github.com/redis/redis) ([1](https://redis.io/docs/interact/search-and-query/query/vector-search/), [2](https://redis.io/docs/interact/search-and-query/advanced-concepts/vectors/)): Client-server model