What are effective document chunking strategies when indexing documents into ChromaDB for RAG?

# pip install langchain-text-splitters
from langchain_text_splitters import (
    RecursiveCharacterTextSplitter,
    TokenTextSplitter,
)
import chromadb

# RecursiveCharacterTextSplitter — tries to split at natural boundaries
# (paragraphs → sentences → words → characters)
splitter = RecursiveCharacterTextSplitter(
    chunk_size=500,      # characters per chunk (aim for ~200–400 tokens)
    chunk_overlap=50,    # overlap prevents losing context at chunk boundaries
    separators=["\n\n", "\n", ". ", " ", ""],
)

long_document = """ChromaDB is an open-source vector database.
It supports multiple embedding functions including OpenAI and HuggingFace.
ChromaDB uses HNSW for approximate nearest-neighbour search.
You can filter results using metadata fields.
Persistent storage uses SQLite under the hood.
""" * 20  # repeat to make it long

chunks = splitter.split_text(long_document)
print(f"Split into {len(chunks)} chunks")
print(f"First chunk length: {len(chunks[0])} chars")

# Add chunks to ChromaDB with source metadata
client = chromadb.Client()
col = client.create_collection("chunked_docs")

col.add(
    documents=chunks,
    metadatas=[{"source": "chroma_guide.txt", "chunk_idx": i}
               for i in range(len(chunks))],
    ids=[f"chunk-{i}" for i in range(len(chunks))],
)