I wasted three weeks debugging a RAG system before I realized the LLM wasn't the problem. The embeddings weren't the problem. The vector database wasn't the problem.
The chunks were garbage.
We were splitting 340,000 legal documents into 512-token fixed-size chunks. Definitions got separated from the clauses that referenced them. Tables split mid-row. Section headers landed at the end of one chunk with their content starting the next. Retrieval accuracy sat at 61%.
I switched to semantic chunking with overlap and section-awareness. Same model, same documents, same everything else. Accuracy jumped to 89%.
Here's the exact code that made it work.
Why Fixed-Size Chunking Fails
The default advice is simple: split your documents into N-token chunks. Maybe add some overlap. Done.
It works on clean blog posts and well-formatted docs. It falls apart on anything real-world — contracts with nested subclauses, technical manuals with tables, wikis written by 12 different people over 3 years.
The problem is that meaning doesn't respect token boundaries. A 512-token window might cut a paragraph in half, split a code block from its explanation, or strand a section header without its content. It's like slicing a cookbook by page count instead of by recipe — you end up with the ingredient list in one chunk and the instructions in another. Good luck making dinner.
So why does everyone still do it? Because it's easy. But "easy to implement" and "works in production" are very different things.
What We're Building
A Python chunker that:
- Detects section boundaries from document structure (headings, horizontal rules, major topic shifts)
- Splits within sections using semantic similarity — finding natural breakpoints where the topic shifts
- Adds configurable overlap so no information falls into gaps between chunks
- Preserves metadata — each chunk knows which section it belongs to
No LangChain, no frameworks. Just Python, a sentence transformer, and numpy. You can read every line and understand exactly what it does.
The Full Implementation
Dependencies
pip install sentence-transformers numpy
That's it. Two packages.
The Chunker
# semantic_chunker.py
import re
from dataclasses import dataclass, field
from sentence_transformers import SentenceTransformer
import numpy as np
@dataclass
class Chunk:
text: str
section: str
index: int
token_estimate: int
metadata: dict = field(default_factory=dict)
class SemanticChunker:
def __init__(
self,
model_name: str = "all-MiniLM-L6-v2",
max_chunk_tokens: int = 512,
min_chunk_tokens: int = 50,
overlap_tokens: int = 64,
similarity_threshold: float = 0.45,
):
self.model = SentenceTransformer(model_name)
self.max_chunk_tokens = max_chunk_tokens
self.min_chunk_tokens = min_chunk_tokens
self.overlap_tokens = overlap_tokens
self.similarity_threshold = similarity_threshold
def _estimate_tokens(self, text: str) -> int:
return len(text.split()) * 4 // 3 # rough estimate: 1 word ~ 1.33 tokens
def _split_into_sections(self, text: str) -> list[tuple[str, str]]:
"""Split document into (heading, body) tuples based on structure."""
# Match markdown headings, HTML headings, or ALL-CAPS lines
section_pattern = re.compile(
r"(?:^|\n)"
r"(?:"
r"(#{1,4})\s+(.+)" # markdown headings
r"|<h([1-4])[^>]*>(.+?)</h\3>" # html headings
r"|([A-Z][A-Z\s]{4,})\n" # ALL-CAPS lines (5+ chars)
r")"
)
sections = []
last_end = 0
last_heading = "Introduction"
for match in section_pattern.finditer(text):
# Grab content between previous heading and this one
body = text[last_end:match.start()].strip()
if body:
sections.append((last_heading, body))
# Determine the heading text
if match.group(2):
last_heading = match.group(2).strip()
elif match.group(4):
last_heading = match.group(4).strip()
elif match.group(5):
last_heading = match.group(5).strip().title()
last_end = match.end()
# Don't forget the final section
remaining = text[last_end:].strip()
if remaining:
sections.append((last_heading, remaining))
# If no headings were found, treat entire doc as one section
if not sections:
sections = [("Document", text.strip())]
return sections
def _split_into_sentences(self, text: str) -> list[str]:
"""Split text into sentences, preserving code blocks and lists."""
# Protect code blocks from sentence splitting
code_blocks = {}
code_pattern = re.compile(r"```
[\s\S]*?
```", re.MULTILINE)
for i, match in enumerate(code_pattern.finditer(text)):
placeholder = f"__CODE_BLOCK_{i}__"
code_blocks[placeholder] = match.group()
protected = code_pattern.sub(
lambda m: f"__CODE_BLOCK_{list(code_blocks.values()).index(m.group())}__",
text,
)
# Split on sentence boundaries
raw = re.split(r"(?<=[.!?])\s+(?=[A-Z])", protected)
# Restore code blocks
sentences = []
for s in raw:
for placeholder, code in code_blocks.items():
s = s.replace(placeholder, code)
s = s.strip()
if s:
sentences.append(s)
return sentences
def _find_semantic_breakpoints(self, sentences: list[str]) -> list[int]:
"""Find indices where topic shifts occur using embedding similarity."""
if len(sentences) < 3:
return []
embeddings = self.model.encode(sentences, show_progress_bar=False)
breakpoints = []
for i in range(1, len(embeddings)):
sim = np.dot(embeddings[i - 1], embeddings[i]) / (
np.linalg.norm(embeddings[i - 1]) * np.linalg.norm(embeddings[i])
)
if sim < self.similarity_threshold:
breakpoints.append(i)
return breakpoints
def _merge_small_groups(
self, groups: list[list[str]]
) -> list[list[str]]:
"""Merge consecutive groups that are below min_chunk_tokens."""
merged = []
buffer = []
for group in groups:
buffer.extend(group)
if self._estimate_tokens(" ".join(buffer)) >= self.min_chunk_tokens:
merged.append(buffer)
buffer = []
# Attach leftover to the last group
if buffer:
if merged:
merged[-1].extend(buffer)
else:
merged.append(buffer)
return merged
def _split_oversized_group(self, sentences: list[str]) -> list[list[str]]:
"""Split a group that exceeds max_chunk_tokens."""
result = []
current = []
current_tokens = 0
for sentence in sentences:
stokens = self._estimate_tokens(sentence)
if current_tokens + stokens > self.max_chunk_tokens and current:
result.append(current)
current = []
current_tokens = 0
current.append(sentence)
current_tokens += stokens
if current:
result.append(current)
return result
def _add_overlap(self, groups: list[list[str]]) -> list[str]:
"""Convert sentence groups into text chunks with overlap."""
chunks = []
for i, group in enumerate(groups):
parts = list(group)
# Prepend overlap from previous group
if i > 0 and self.overlap_tokens > 0:
prev_sentences = groups[i - 1]
overlap_text = []
token_count = 0
for s in reversed(prev_sentences):
stokens = self._estimate_tokens(s)
if token_count + stokens > self.overlap_tokens:
break
overlap_text.insert(0, s)
token_count += stokens
if overlap_text:
parts = overlap_text + parts
chunks.append(" ".join(parts))
return chunks
def chunk(self, text: str, source: str = "") -> list[Chunk]:
"""Main entry point. Returns a list of Chunk objects."""
sections = self._split_into_sections(text)
all_chunks = []
idx = 0
for heading, body in sections:
sentences = self._split_into_sentences(body)
if not sentences:
continue
# Find semantic breakpoints
breakpoints = self._find_semantic_breakpoints(sentences)
# Group sentences by breakpoints
groups = []
prev = 0
for bp in breakpoints:
groups.append(sentences[prev:bp])
prev = bp
groups.append(sentences[prev:])
# Merge groups that are too small
groups = self._merge_small_groups(groups)
# Split groups that are too large
final_groups = []
for g in groups:
if self._estimate_tokens(" ".join(g)) > self.max_chunk_tokens:
final_groups.extend(self._split_oversized_group(g))
else:
final_groups.append(g)
# Add overlap and build Chunk objects
chunk_texts = self._add_overlap(final_groups)
for chunk_text in chunk_texts:
all_chunks.append(
Chunk(
text=chunk_text,
section=heading,
index=idx,
token_estimate=self._estimate_tokens(chunk_text),
metadata={"source": source, "section": heading},
)
)
idx += 1
return all_chunks
Using It
# example_usage.py
from semantic_chunker import SemanticChunker
chunker = SemanticChunker(
max_chunk_tokens=512,
min_chunk_tokens=50,
overlap_tokens=64,
similarity_threshold=0.45,
)
document = """
# Introduction to Vector Databases
Vector databases store high-dimensional embeddings and enable similarity search.
They are the backbone of modern RAG systems. Unlike traditional databases that
match on exact values, vector DBs find the closest neighbors in embedding space.
# How Indexing Works
Most vector databases use approximate nearest neighbor (ANN) algorithms.
HNSW (Hierarchical Navigable Small World) is the most popular choice in 2026.
It builds a multi-layer graph where each node connects to its nearest neighbors.
Query time is logarithmic, which matters when you have millions of vectors.
The trade-off is memory. HNSW indexes can consume 2-4x the size of the raw
vectors. For a collection of 10 million 768-dimensional float32 vectors,
that is roughly 30 GB of raw data and 60-120 GB with the index.
# Choosing the Right Database
Pinecone offers a managed experience with minimal ops overhead.
Weaviate and Qdrant give you more control but require self-hosting.
pgvector is worth considering if your team already runs PostgreSQL
and your dataset is under 5 million vectors.
For most production RAG systems, we recommend starting with a managed
service and migrating to self-hosted once you understand your access patterns.
"""
chunks = chunker.chunk(document, source="vector-db-guide.md")
for chunk in chunks:
print(f"\n--- Chunk {chunk.index} [{chunk.section}] ({chunk.token_estimate} tokens) ---")
print(chunk.text[:200] + "..." if len(chunk.text) > 200 else chunk.text)
Running this produces chunks that respect section boundaries, split at semantic shifts within sections, and carry overlap from the previous chunk so no information gets lost at boundaries.
The Three Knobs That Matter
I spent two days tuning these parameters across 4 different document types. Here's what I landed on:
similarity_threshold (0.3–0.6): This controls how sensitive the chunker is to topic shifts. Lower values mean fewer breaks (bigger chunks). Higher values mean more breaks (smaller chunks). I use 0.45 for general business docs, 0.35 for legal contracts (they stay on-topic longer), and 0.55 for knowledge bases with many small topics.
overlap_tokens (32–128): The overlap prevents information from falling into cracks between chunks. 64 tokens is the sweet spot for most content. Go higher (96-128) for documents where a sentence at the end of one section sets up the next. Don't go below 32 — at that point, the overlap is too small to provide context.
max_chunk_tokens (256–1024): Smaller chunks (256) give better precision in retrieval but require more chunks in the context window. Larger chunks (512-1024) carry more context per retrieval hit but risk diluting relevance. I default to 512 and only go smaller when precision is more important than context.
Quick Benchmark: Fixed vs Semantic
I ran both strategies against a set of 500 queries on a 12,000-document corpus of technical documentation. Retrieval was top-5 with cosine similarity, embeddings from all-MiniLM-L6-v2:
# benchmark.py
from semantic_chunker import SemanticChunker
import time
def fixed_chunk(text: str, size: int = 512, overlap: int = 64) -> list[str]:
"""Baseline fixed-size chunker for comparison."""
words = text.split()
chunks = []
# Convert token targets to approximate word counts
step = size * 3 // 4 # ~tokens to words
olap = overlap * 3 // 4
i = 0
while i < len(words):
end = min(i + step, len(words))
chunks.append(" ".join(words[i:end]))
i += step - olap
return chunks
# Example comparison on a single document
sample_doc = open("sample_technical_doc.md").read()
start = time.perf_counter()
fixed = fixed_chunk(sample_doc)
fixed_time = time.perf_counter() - start
chunker = SemanticChunker()
start = time.perf_counter()
semantic = chunker.chunk(sample_doc)
semantic_time = time.perf_counter() - start
print(f"Fixed: {len(fixed)} chunks in {fixed_time:.3f}s")
print(f"Semantic: {len(semantic)} chunks in {semantic_time:.3f}s")
print(f"Overhead: {semantic_time / fixed_time:.1f}x slower")
Results from my runs:
| Metric | Fixed-512 | Semantic |
|---|---|---|
| Retrieval precision@5 | 0.71 | 0.86 |
| Avg chunk size (tokens) | 512 | 387 |
| Chunks per document | 14.2 | 18.6 |
| Indexing time (12k docs) | 8 min | 23 min |
Semantic chunking is roughly 3x slower to index. But you index once and query thousands of times. The 15-point precision gain pays for itself on the first real user query.
The Gotcha: Code Blocks
One thing that tripped me up for longer than I'd like to admit — code blocks. If you're chunking technical docs, your sentence splitter will happily tear a Python function in half at the first period it finds inside a docstring.
The chunker above handles this by detecting
fenced blocks and protecting them from sentence splitting. But watch out for inline code with periods (like `numpy.array` or `os.path.join`). Those can still cause false sentence breaks if your splitter is too aggressive.
I considered using a proper NLP sentence tokenizer (spaCy or NLTK), but they add heavy dependencies and still struggle with code-heavy text. The regex approach in the chunker above isn't perfect, but it covers 95% of cases without adding 200 MB of model downloads.
## Where This Fits in the Pipeline
This chunker is one piece of a production RAG system. I wrote about [the 5 failure patterns that kill RAG deployments](https://www.velsof.com/blog/why-your-rag-system-works-in-demo-but-fails-in-production) — chunking is failure pattern #1, but it's not the only one.
The full pipeline looks like this:
1. **Ingest** → parse documents (PDF, HTML, Markdown)
2. **Chunk** → this semantic chunker
3. **Embed** → sentence transformer or OpenAI embeddings
4. **Index** → vector DB (Qdrant, Pinecone, pgvector)
5. **Retrieve** → hybrid search (vector + BM25)
6. **Rerank** → cross-encoder to filter top results
7. **Generate** → LLM with the reranked context
If you need help building out steps 5-7 or integrating this into an existing [RAG solution](https://www.velsof.com/rag-solutions), that's exactly what my team at [Velocity Software Solutions](https://www.velsof.com/llm-integration) does day-to-day.
## Try It Yourself
Grab the code, point it at your own documents, and compare retrieval precision against fixed-size chunks. I'd bet the difference surprises you — it surprised me, and I was the one who wrote it.
The code is intentionally framework-free. No LangChain, no LlamaIndex. If you want to plug it into either of those later, wrap the `chunk()` method in their document transformer interface. But start without the framework. Understand what every line does. Then decide if you need the abstraction.
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