Who should use the Similarity Search workflow?
Teams or solo builders working on data tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Data
Practical execution plan for similarity search with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
User receives actionable similarity search results with context.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
User receives actionable similarity search results with context.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Zilliz to clear search goal and data source identified, ready for embedding generation. Then, you pass the output to Voyage AI to all data items are converted to vector embeddings and stored for search. Then, you pass the output to LanceDB to vector index created, enabling sub-second similarity search on large datasets. Then, you pass the output to LanceDB to list of top-k similar items retrieved with similarity scores. Then, you pass the output to Superlinked to filtered and ranked list of similar items, ready for presentation. Finally, Onyx AI (formerly Danswer AI) is used to user receives actionable similarity search results with context.
Define Search Objective and Data Source
Clear search goal and data source identified, ready for embedding generation.
Generate Embeddings for the Corpus
All data items are converted to vector embeddings and stored for search.
Index Embeddings for Efficient Retrieval
Vector index created, enabling sub-second similarity search on large datasets.
Encode Query and Perform Search
List of top-k similar items retrieved with similarity scores.
Post-Process and Rank Results
Filtered and ranked list of similar items, ready for presentation.
Present Results and Gather Feedback
User receives actionable similarity search results with context.
Clarify what you are searching for (e.g., similar documents, images, or product descriptions) and identify the dataset. This step ensures alignment between the query type and the data format, preventing wasted effort on incompatible embeddings.
Why Zilliz: Zilliz is a vector database purpose-built for similarity search, providing direct data source connection and indexing for vector search workflows.
Convert all items in the dataset into vector embeddings using a suitable model (e.g., sentence-transformers for text, CLIP for images). This step creates the numerical representation needed for similarity computation.
Why Voyage AI: Voyage AI is specifically designed for creating vector embeddings from text, directly matching the embedding generation need.
Build an index (e.g., FAISS, Annoy, or HNSW) to enable fast approximate nearest neighbor search. This step is critical for scaling to large datasets where brute-force comparison is too slow.
Why LanceDB: LanceDB is a vector database designed for storing and querying embeddings with efficient indexing for retrieval.
Convert the user's query into an embedding using the same model as the corpus, then run the search against the index to retrieve the top-k most similar items. This yields the raw similarity results.
Why LanceDB: LanceDB directly supports semantic similarity search and querying embeddings, fulfilling the encode and search step.
Refine the raw results by applying filters (e.g., metadata constraints), re-ranking with a more accurate model, or deduplicating. This step improves relevance and usability for the end user.
Why Superlinked: Superlinked can classify documents by topic or intent, which aligns with re-ranking and post-processing search results.
Display the top results to the user in a clear format (e.g., ranked list with similarity scores and metadata). Optionally collect implicit or explicit feedback to improve future searches.
Why Onyx AI (formerly Danswer AI): Onyx AI offers enterprise knowledge search and AI-powered Q&A, providing a UI for presenting results and gathering feedback.
§ Before you start
Teams or solo builders working on data tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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