We will talk about building semantic search pipelines.
A semantic search pipeline is an end to end system designed to retrieve information based on meaning,
not just exact keyword matches.
Unlike traditional search engines that rely on literal text overlap, semantic search interprets user
intent and finds conceptually relevant content even when the wording differs, as illustrated on page
two of the deck.
This pipeline is composed of three tightly connected stages text chunking, embedding generation, and
similarity based retrieval.
Each stage plays a critical role, and failure in any one of them degrades the entire system.
This architecture powers modern use cases such as enterprise knowledge bases, internal documentation,
search and retrieval.
Augmented generation systems.
When a user asks a question, the system doesn't simply scan for matching words.
Instead, it embeds the query, compares it against embedded document chunks, and retrieves the most
semantically similar results.
The most important takeaway from this slide is that semantic search is not a single API call.
It is a system architecture that must be designed holistically.
Strong results come from thoughtful integration of all components, not from model choice alone.
Large language models operate within fixed context windows, as highlighted on page three of the deck.
These limits mean that long documents such as manuals, research papers, or code bases cannot be processed
in a single pass.
Text chunking is the solution to this constraint.
However, chunking is not just about fitting text into token limits.
The way you split documents directly affects retrieval quality and system reliability.
Poor chunking strategies lead to lost context, irrelevant retrieval and increased hallucinations in
downstream models when important information is split across chunk boundaries.
Semantic relationships are broken.
This causes the embedding model to receive incomplete or confusing input, leading to poor similarity
matching in retrieval.
Augmented systems.
This lack of context forces the generation model to fill gaps with fabricated information.
The engineering goal, as emphasized in the slide, is to create chunks that preserve both semantic
meaning and narrative coherence.
Each chunk should represent a complete, self-contained unit of information.
Effective chunking is one of the highest leverage improvements you can make in any semantic search or
Rag pipeline.
Fixed size chunking is the simplest approach to splitting text, as described on page four of the deck.
This method divides documents using predetermined boundaries based on token count or character count.
For example, a system might split text every 512 tokens or every 2000 characters.
The primary advantage of fixed size chunking is predictability.
Chunk sizes are consistent, processing is fast, and system design becomes straightforward.
This approach works well for uniform documents such as product catalogs, standardized reports, or
highly structured data where content patterns are consistent.
However, fixed size chunking has significant limitations because it ignores semantic boundaries, it
often splits text mid-sentence or mid idea.
This disrupts meaning and breaks logical flow, which negatively impacts embedding quality and retrieval
accuracy for complex documents such as technical guides or research papers.
Fixed size chunking can lead to poor search results and increased hallucinations downstream.
While attractive for its simplicity, this approach should be used carefully and only when document
structure supports it.
Semantic chunking addresses the shortcomings of fixed size approaches by respecting the natural structure
of documents, as shown on page five of the deck.
This method splits text at meaningful boundaries such as paragraphs, section headings, or complete
sentences.
By preserving semantic units, semantic chunking maintains the integrity of ideas and improves retrieval
accuracy.
However, even well-chosen boundaries can still cut off important context at the edges.
This is where overlapping chunking becomes essential.
Overlapping chunking introduces redundancy by including a small portion of adjacent text, typically
10 to 20% from neighboring chunks.
This ensures that important information near boundaries is preserved in multiple chunks.
The benefit is significantly improved recall and relevance, especially for complex queries.
The trade off is increased storage and compute cost, usually around 20 to 30% more embeddings in production
systems.
This trade off is almost always worth it.
Better chunking leads to better retrieval, which directly translates into better user experience and
more reliable AI behavior.
Once documents are chunked, the next step is embedding generation as detailed on page six of the deck.
Each chunk is transformed into a high dimensional vector, typically between 768 and 1436 dimensions.
That represents its semantic meaning.
Choosing the right embedding model is critical.
Dedicated embedding models, such as OpenAI's text embedding series, Coheres embedding models, or
open source sentence transformers define the structure of your vector space.
Consistency is essential.
The same embedding model must be used for both document indexing and query embedding.
For efficiency, embeddings should be generated in batches and processed offline during ingestion.
Embedding APIs often support batch sizes of hundreds or thousands of chunks per request, dramatically
reducing latency and cost.
In addition to vectors, embeddings should be stored with rich metadata, including document source,
chunk position timestamps, and domain specific tags.
Finally, versioning is essential.
If you change embedding models, you will need to re-embedded and re-index your entire corpus to maintain
compatibility.
After embeddings are generated, they must be stored in a system optimized for high dimensional similarity
search.
Traditional databases are not designed for this task.
As explained on page seven of the deck, vector databases use approximate nearest neighbor algorithms
to efficiently search millions or even billions of vectors.
Vector databases trade perfect accuracy for massive speed gains, typically achieving 95 to 99% recall
with millisecond level latency.
This performance is essential for production semantic search systems.
Common options include face for open source high performance search.
Pinecone for fully managed vector storage.
Wiviott for flexible schema based search with GraphQL and Milvus for cloud native large scale deployments.
The choice of vector database depends on operational constraints such as scale, latency requirements,
infrastructure expertise, and vendor preferences.
Regardless of the tool, vector databases are a core component of any production semantic search pipeline.
Without them, real time retrieval at scale is not feasible.
Similarity based retrieval is the real time execution phase of the semantic search pipeline, as outlined
on page eight of the deck.
This process mirrors document indexing but operates under strict latency constraints.
First, the user's query is embedded using the same embedding model used for documents.
This consistency is critical.
Mixing embedding models breaks the vector space and invalidates similarity comparisons.
Next, the vector database computes similarity scores between the query vector and all indexed document
vectors.
This step is handled efficiently using an algorithms.
Finally, the system retrieves the top k most similar chunks, typically between 5 and 20.
The quality of retrieval depends on two factors embedding quality and chunk quality.
No amount of tuning can compensate for poor embeddings or badly formed chunks.
Engineers should view retrieval as a mathematical operation whose success is determined upstream by
design choices made during ingestion.
Raw similarity scores provide an initial ranking, but production systems require additional refinement
to deliver high quality results.
Page nine of the deck outlined several critical post retrieval optimization techniques.
Similarity thresholds filter out low confidence matches, preventing irrelevant content from polluting
results.
Metadata filtering further narrows retrieval based on attributes such as document type, date access
permissions, or domain tags.
For higher precision, systems, may apply LLM based reranking to the top k results.
This step improves relevance but adds latency and cost, so it should be used selectively.
Diversity filtering prevents near duplicate chunks from dominating the result set, ensuring broader
coverage across sources.
These techniques transform a raw list of candidates into a curated, high quality retrieval set.
The key principle is iteration.
Retrieval performance must be monitored continuously, and thresholds, filters and chunking strategies
should be adjusted based on real user behavior.
This final slide brings the entire semantic search pipeline together.
Documents are ingested, chunked, embedded, and stored in a vector database at query time.
User input is embedded compared against stored vectors and refined through ranking and filtering before
results are returned.
As emphasized throughout the deck, success depends more on pipeline design than on any individual model.
Choice.
Chunking, strategy, embedding consistency, storage architecture, and retrieval tuning all interact
to determine system quality.
Semantic search pipelines form the backbone of modern Rag systems.
Enterprise search platforms and AI assistants.
When designed correctly, they dramatically reduce hallucinations, improve relevance, and enable grounded,
trustworthy AI responses.
The key takeaway is simple but powerful.
Semantic search is an engineering discipline.
It rewards careful design, measurement, and iteration.
Mastering this pipeline prepares you to build scalable, production grade AI systems that truly understand
user intent, rather than just matching words.
These techniques transform a raw list of candidates into a curated, high quality retrieval set.
The key principle is iteration.
Retrieval performance must be monitored continuously, and thresholds, filters and chunking strategies
should be adjusted based on real user behavior.