Forem: Nitin Srivastava

How We Cut API Response Time from 2.3s to 180ms Using Redis + Smart Caching

Nitin Srivastava — Thu, 23 Apr 2026 09:14:31 +0000

p95 latency dropped from 2.3 seconds to 180 milliseconds. Same hardware, same database, same traffic. The only thing that changed was how we cached — and I don't mean slapping @lru_cache on a function.

I'm writing this because every Redis caching tutorial I read before this project showed me the same 15-line example: redis.get(key) or fetch_from_db(). That code works in a notebook. It will absolutely wreck you in production the first time real traffic hits it.

This is the layered strategy that actually survived. FastAPI + Python on the server, Redis 7 for caching, Postgres behind it. Everything below is from a real project we shipped for a B2B client earlier this year — roughly 800 requests per minute on the hot endpoints, with read-heavy traffic around product catalog and pricing.

The endpoint that was killing us

The problematic endpoint returned a pricing quote for a product variant, filtered by region, customer tier, and active promotions. Three joins, a couple of window functions for volume-based discounts, and a call to a legacy PHP service for tax lookup. Most requests landed somewhere between 1.8 and 2.6 seconds. p95 sat at 2.3s. We had complaints.

Here's the naive caching attempt we rolled out first. Guess how long it lasted in production.

# app/pricing.py
import json
import redis
from fastapi import APIRouter

r = redis.Redis(host="localhost", port=6379, decode_responses=True)
router = APIRouter()

@router.get("/quote/{product_id}")
def get_quote(product_id: int, region: str, tier: str):
    key = f"quote:{product_id}:{region}:{tier}"
    cached = r.get(key)
    if cached:
        return json.loads(cached)

    quote = compute_quote(product_id, region, tier)  # slow path
    r.setex(key, 300, json.dumps(quote))  # 5 min TTL
    return quote

Three problems showed up within the first hour of production traffic. I want to walk through each, because these are the things the tutorials don't tell you.

Gotcha 1: The thundering herd

The first alert fired at 2:14 AM. Our compute_quote function was being called 400+ times per second for the same key when the cache expired. Postgres spiked, the legacy tax service timed out, everything melted.

This is called cache stampede (or thundering herd). When a popular key expires, every in-flight request misses the cache simultaneously and hammers the origin. The fix is request coalescing — only one request actually does the work, the rest wait for it.

# app/cache.py
import json
import time
import redis

r = redis.Redis(host="localhost", port=6379, decode_responses=True)

def cached_with_lock(key: str, ttl: int, loader):
    """
    Get value for `key` from Redis. If missing, acquire a short lock,
    call `loader()` to compute, write the value, release the lock.
    Other waiters poll until the value appears.
    """
    cached = r.get(key)
    if cached is not None:
        return json.loads(cached)

    lock_key = f"lock:{key}"
    acquired = r.set(lock_key, "1", nx=True, ex=10)

    if acquired:
        try:
            value = loader()
            r.setex(key, ttl, json.dumps(value))
            return value
        finally:
            r.delete(lock_key)

    # Someone else is computing. Poll briefly.
    for _ in range(50):
        time.sleep(0.05)
        cached = r.get(key)
        if cached is not None:
            return json.loads(cached)

    # Lock holder died or is too slow. Fall through and compute ourselves.
    return loader()

Two details matter. First, the lock TTL (10 seconds) must be longer than your worst-case loader time. If loader takes 15 seconds, the lock expires, and now you have two processes computing. Second, the polling fallback at the end prevents permanent deadlock if the lock holder crashes mid-compute.

You can get fancier with pub/sub notifications instead of polling, but the 50ms poll interval is cheap enough that it rarely matters. We tried the pub/sub version. It was 40 lines more code and saved us maybe 15ms on cache misses. Not worth it.

Gotcha 2: Cache key design is 80% of the game

Our first cache key was quote:{product_id}:{region}:{tier}. Looked reasonable. It was wrong in at least three ways.

First, active promotions affected the price but weren't in the key. So a customer would fetch a quote, the promo would end, and they'd keep getting the stale promoted price for 5 minutes. Support tickets rolled in.

Second, we had currency as an optional query param that defaulted to USD. When a user explicitly passed currency=EUR, we'd cache it under the EUR key. When they then hit the endpoint without the param (defaulting to USD), we'd correctly hit a different key. Fine so far. But internal services that always sent currency=USD and external clients that omitted it were filling the cache with duplicates for the same logical value.

Third, we were caching per-user-tier, but three tiers (silver, gold, platinum) had identical pricing for 70% of products. We were storing three copies of the same thing.

Here's the redesigned key structure:

# app/keys.py
import hashlib
import json

def quote_cache_key(product_id: int, region: str, tier: str,
                    currency: str, active_promo_ids: list[int]) -> str:
    """
    Stable cache key that accounts for every input that can change the output.
    """
    normalized = {
        "product_id": product_id,
        "region": region.upper(),
        "tier": tier.lower(),
        "currency": (currency or "USD").upper(),
        "promos": sorted(active_promo_ids or []),
    }
    payload = json.dumps(normalized, separators=(",", ":"), sort_keys=True)
    digest = hashlib.sha1(payload.encode()).hexdigest()[:16]
    return f"quote:v2:{digest}"

Three changes from the original. We normalize inputs before keying (uppercase region, lowercase tier, default currency). We include every input that can change the output, including promotion IDs. And we hash the whole thing with a version prefix so we can invalidate the entire cache namespace by bumping v2 to v3.

That version prefix has saved us twice since then. When we changed the pricing rules in March, we shipped with v3 and every old cached quote became instantly irrelevant — no flush, no downtime.

Gotcha 3: Writes should invalidate, not wait for TTL

TTL-based invalidation is fine for data that's allowed to be slightly stale. It's terrible for anything a user just changed themselves.

The pattern I see everywhere is: cache with TTL, shrug when users report they're seeing old data right after updating a record. "Wait 5 minutes and refresh." That's not acceptable in 2026.

We use a write-through-invalidate pattern. When a write happens, we delete related cache keys explicitly. The hard part is knowing which keys are related — this is where the key design from the previous section pays off.

# app/invalidation.py
import redis

r = redis.Redis(host="localhost", port=6379, decode_responses=True)

def invalidate_product(product_id: int):
    """
    A product's price data changed. Invalidate every cached quote for it.
    Uses SCAN instead of KEYS to avoid blocking Redis on large datasets.
    """
    pattern = f"quote:v2:*"
    cursor = 0
    deleted = 0

    while True:
        cursor, batch = r.scan(cursor=cursor, match=pattern, count=500)
        for key in batch:
            meta_key = f"{key}:meta"
            stored_product_id = r.hget(meta_key, "product_id")
            if stored_product_id and int(stored_product_id) == product_id:
                r.delete(key, meta_key)
                deleted += 1
        if cursor == 0:
            break

    return deleted

Two things to notice. We use SCAN instead of KEYS. KEYS blocks the Redis event loop on large datasets — we learned this when a KEYS quote:* took 800ms during a traffic peak and we queued up a couple thousand waiting commands behind it. Don't use KEYS in production. Ever.

Second, we store a lightweight metadata hash alongside each cached value ({key}:meta) so we can look up what's inside the opaque hashed key. This costs us a tiny bit of memory but makes targeted invalidation possible without re-deriving keys from the invalidation side.

Bringing it together

Here's the actual handler that ships in production, roughly.

# app/pricing.py
from fastapi import APIRouter, Query
from app.cache import cached_with_lock
from app.keys import quote_cache_key
from app.quotes import compute_quote, get_active_promo_ids

router = APIRouter()
QUOTE_TTL_SECONDS = 300  # 5 minutes

@router.get("/quote/{product_id}")
def get_quote(
    product_id: int,
    region: str,
    tier: str,
    currency: str = Query("USD"),
):
    promo_ids = get_active_promo_ids(product_id, region)
    key = quote_cache_key(product_id, region, tier, currency, promo_ids)

    def loader():
        return compute_quote(product_id, region, tier, currency, promo_ids)

    return cached_with_lock(key, QUOTE_TTL_SECONDS, loader)

Six lines of logic on top of the shared cached_with_lock helper. Most of the complexity lives in two places: the key function and the loader. The route itself stays boring, which is what you want. Python development shines when the code reads this plainly — if you're building this kind of API layer and want a second pair of eyes, our team offers Python development services and we've walked through this exact pattern with a few clients.

The numbers, measured

I hate when people claim a perf win without showing the measurement method. Here's how we actually verified it.

# scripts/benchmark.py
import asyncio
import statistics
import time
import httpx

async def hit(client, url):
    start = time.perf_counter()
    r = await client.get(url)
    r.raise_for_status()
    return time.perf_counter() - start

async def main():
    url = "http://api.local/quote/1234?region=EU&tier=gold"
    async with httpx.AsyncClient(timeout=10) as client:
        latencies = await asyncio.gather(*[hit(client, url) for _ in range(500)])

    latencies.sort()
    print(f"count     : {len(latencies)}")
    print(f"p50 (ms)  : {statistics.median(latencies) * 1000:.0f}")
    print(f"p95 (ms)  : {latencies[int(0.95 * len(latencies))] * 1000:.0f}")
    print(f"p99 (ms)  : {latencies[int(0.99 * len(latencies))] * 1000:.0f}")

if __name__ == "__main__":
    asyncio.run(main())

Results over 500 requests against the same endpoint, warmed cache:

Metric	Before	After
p50	1.9s	110ms
p95	2.3s	180ms
p99	3.1s	240ms

The p99 number is the one I care about most. p50 and p95 dropping is expected when cache hit rate goes up. But p99 represents the tail — the cache misses, the unlucky timing windows, the lock-wait fallbacks. If your p99 doesn't drop meaningfully, you've accidentally traded one set of slow requests for another.

What not to cache

Two things we deliberately left uncached, because caching them was making things worse.

User-specific data with low reuse — a user's cart, their last order, their notification state. The cache hit rate was under 4% and Redis memory usage ballooned. We moved these to an in-process LRU cache with a 60-second TTL and dropped Redis involvement entirely.

Anything with a write-read latency requirement under 500ms. If a user writes, then immediately reads, they expect to see their write. Cache-aside patterns will serve them stale data from the cache for a few hundred milliseconds until the invalidation propagates. For those endpoints, we skip the cache entirely. The DB is fast enough, and correctness matters more.

One concrete thing to do today

Go look at your slowest endpoint. Pull its last week of logs, find the keys (query params, path params, user ID) that actually determine the response. Now ask: if I cached this, what's my invalidation story? If the answer is "5-minute TTL, users will deal with it," that's fine for read-mostly data. If the answer is "I don't know," you have work to do — and that work starts with the key design, not the Redis client library.

Caching is like plumbing. Nobody notices it when it works, everyone's angry when it leaks, and the leaks usually trace back to decisions you made on day one. Spend the extra hour on the key design. You'll thank yourself the first time a customer asks why they're seeing last week's price.

If you're building this into a larger system and the architecture choices start to feel heavier — where to put the cache, how to handle multi-region, when to go from Redis to a proper CDN — that's the point where we usually get called in. We've helped a few clients through exactly this scaling inflection point as part of our custom software development work, and the answer is almost never "just add more Redis." It's usually "simplify the thing you're trying to cache."

Semantic Chunking with Overlap and Section-Awareness: The RAG Tutorial Nobody Wrote

Nitin Srivastava — Mon, 20 Apr 2026 12:40:52 +0000

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.