#Embeddings

Articles about Embeddings — exploring patterns, best practices, and real-world implementations in production systems.

11 posts tagged with embeddings. ← All posts

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Pratik Dhanave · ·12 min read

Embeddings and Reranking with watsonx

Building RAG's retrieval core with watsonx.ai from Python — turning a corpus into vectors with IBM's slate embedding models, scoring a query against them, and then sharpening the shortlist with a reranking model so the LLM gets the right passages, not just plausible ones.

Use watsonx.ai's slate embedding models and reranking from Python to build RAG's retrieval core: embed_documents vs embed_query, numpy cosine scoring, and a two-stage retrieve-then-rerank pipeline — plus the langchain-ibm WatsonxEmbeddings/WatsonxRerank path.

#IBM watsonx#RAG#Embeddings#Python#AI Engineering#Software Engineering
Pratik Dhanave · ·13 min read

Embeddings and Reranking with NeMo Retriever

Building RAG's retrieval core in Python — turning a corpus and a query into vectors with NeMo Retriever embedding NIMs, scoring by cosine similarity, then sharpening the shortlist with a cross-encoder reranker NIM.

Use NeMo Retriever from Python to build RAG's retrieval core: NVIDIAEmbeddings (embed_documents vs embed_query for the asymmetric passage/query distinction) with cosine scoring, and NVIDIARerank.compress_documents for a two-stage retrieve-then-rerank pipeline.

#NVIDIA#NeMo Retriever#RAG#Python#AI Engineering#Software Engineering#Embeddings
Pratik Dhanave · ·13 min read

Evaluating AI Systems

How to know whether an LLM system actually works — building an eval dataset, the four metric families (deterministic checks, text overlap, embedding similarity, LLM-as-judge) in Go, task-specific eval for RAG and classification, and wiring a scored regression gate into CI so you measure instead of vibe.

How to know whether an LLM system works when outputs are non-deterministic: build an eval dataset, score with deterministic checks, embedding similarity, and LLM-as-judge (with its biases), evaluate RAG and classification, and gate regressions in CI.

#AI Engineering#Evaluation#Go#LLMs#Backend Engineering#RAG#Embeddings
Pratik Dhanave · ·15 min read

Agent Memory and Planning

Give the hand-rolled Go agent from post 11 a memory it can carry between turns and a plan it can follow across many steps — a compacting conversation buffer, retrieval over the post-8 vector store, and a plan-then-execute-then-reflect loop, all built from scratch.

Give the agent memory and planning in Go: a compacting short-term conversation buffer, long-term memory as timestamped embeddings in the vector store, and planning — plan-then-execute, reflection and re-planning when observations contradict the plan, and task decomposition.

#AI Engineering#AI Agents#Go#Memory#Backend Engineering#Embeddings#Vector Databases
Pratik Dhanave · ·13 min read

Retrieval-Augmented Generation

Wire the embedding client, vector store, and chat client from the last five posts into one working RAG pipeline in Go — ingest and chunk documents, retrieve the top matches for a question, inject them as grounded context, and generate a cited answer, all from scratch.

Assemble embeddings and vector search into a working RAG pipeline in Go: chunk documents, embed and store them, retrieve the top-k for a query, augment the prompt with grounded context (and cite sources), then generate — a baseline end-to-end Answer() built from scratch.

#AI Engineering#RAG#Go#Vector Search#Backend Engineering#Embeddings#Vector Databases#Prompt Engineering
Pratik Dhanave · ·16 min read

Vector Search from Scratch

Build a working in-memory vector store and exact k-nearest-neighbor search in Go by hand — no vector database — then understand precisely what HNSW, FAISS, and pgvector optimize when brute force finally runs out of road.

Build an in-memory vector store and exact k-NN search in Go by hand: a VectorStore with Add and Search, top-k selection with container/heap, normalize-on-insert, an honest look at when brute force is right, and when ANN (HNSW, FAISS, pgvector) earns its keep.

#AI Engineering#Vector Search#Go#Embeddings#Backend Engineering#Vector Databases
Pratik Dhanave · ·13 min read

Embeddings

Turn text into a `[]float32` that places meaning in space — what an embedding is, cosine similarity implemented by hand in Go, calling an OpenAI-compatible /embeddings endpoint with net/http, and a worked pairwise-similarity example that scores related sentences higher.

Turn text into a []float32 that places meaning in space — what an embedding is, cosine similarity implemented by hand in Go, calling an OpenAI-compatible /embeddings endpoint with net/http, and a worked pairwise-similarity example that scores related sentences higher.

#AI Engineering#Embeddings#Go#Vector Search#Backend Engineering#Vector Databases
Pratik Dhanave · ·13 min read

Structs, Embedding, and Tags

How Go builds aggregate types from value semantics up — why a struct is a copy, when it stops being comparable, what embedding actually promotes (and what it deliberately doesn't), and how a backtick string in a field definition ends up steering `encoding/json`.

How Go builds aggregate types from value semantics up — why a struct is a copy, when it stops being comparable, what embedding actually promotes (and what it deliberately doesn't), and how a backtick string...

#Go#Structs#Data Structures#Backend Engineering#Embeddings

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.