Master Vector Database with Python for AI & LLM Use Cases

Explore interfacing with vector databases using python, performing vector embedding, storage, and similarity search with sqlite and chroma, and scale toward pinecone for production use.

Learn the fundamentals of a vector database by turning objects into embeddings, measuring Euclidean distance, and performing vector search and indexing with nearest-neighbor methods like k-d trees.

Learn to set up a sqlite3 in-memory sql db, create a connection and cursor, define an employees table, and perform insert and select operations.

Learn how to store and retrieve vectors in SQLite by converting numpy arrays to binary blobs, creating a vectors table, and deserializing back to numpy objects.

Install and initialize Chroma db for in-memory vector similarity search. Create a collection of article documents, generate vector embeddings by default, and insert metadata and IDs.

Explore Chroma db local vectors and the dot add vs dot upsert distinction, perform semantic vector queries, and shift from in-memory to persistent memory with settings and a persistent directory.

Set up a Windows development environment, install Python, upgrade pip, install the Pinecone client with pip, and verify the installation by importing Pinecone.

Learn how to fetch vectors by id using Pinecone in Python, connect to a test index, and retrieve single or multiple vectors with their values and sparse indices.

Learn to delete vectors from a Pinecone vector index by IDs or all, after initializing Pinecone with your API key and environment and selecting the index.

Learn to query vector data with Pinecone by creating a pointer to the index, setting top-k, and using cosine similarity to retrieve relevant vectors.

Learn to delete vectors in Pinecone by ID or by removing all, including initializing Pinecone with API key and environment, inspecting indexes, and confirming vector counts.

Learn how to upsert vectors into a Pinecone index using namespaces in Python, explore subject, body, and other partitions, and map integer IDs to strings for namespace-specific vector storage.

Explore semantic search and how it understands meaning behind terms, uses embeddings and transformers to retrieve relevant results from a vector database, improving accuracy and intent-aware search.

Clean and prepare data for semantic search by removing incomplete rows, combining title and subtitle into a text blob, and structuring category metadata for 81,000 vectors in Pinecone.

Prepare and upsert a 6k dataset into a Pinecone index by embedding titles with a sentence transformer, converting IDs to strings, and packaging metadata (title, subtitle, category) for semantic search.

Explore hands-on named entity recognition in Python using Jupyter Notebook, Transformers, and a pre-trained token classification model from Hugging Face to extract entities like person and organization.

Explore vector embedding models and load data for semantic search and entity recognition, using sentence transformer with 768-d embeddings, create a pinecone index, prepare data, and upsert vectors for querying.

Prepare data for semantic search powered by named entity recognition by dropping missing values, sampling 10,000 rows with seed 45, and creating a text extended field from title and content.

Generate ids from the dataframe index and upsert vectors and metadata into the pinecone index. Drop text fields to fit metadata, process in batches, and monitor vector counts during updates.

Build a retrieval AI agent with LangChain and OpenAI by setting up a notebook, installing libraries, creating embeddings, indexing vectors in Pinecone, and enabling vector querying.

Learn to generate embeddings with OpenAI's text embedding Ada 002 model, set up API key, clean inputs, and parse vectors in Python; note 1436-dimension vectors inform a Pinecone index.

Set up a pinecone vector database for AI and LLM use by initializing pinecone, creating an index named AI agent with 1536 dimensions, using dot product metrics.

Learn how to perform semantic search with OpenAI by embedding queries and documents, using LangChain and Pinecone for a vector store, and executing similarity searches.

Define and invoke a retrieval QA agent for retrieval augmented generation (RAG) using a vector store and conversational memory for context, powered by OpenAI chat models.

Set up the environment for audio similarity search, load libraries and data, perform basic analysis with Librosa, and initialize the audio embedding model and Pinecone for vector indexing and querying.

Initialize the audio embedding model by detecting the device, importing the audio tagging module, and instantiating the model on cuda or cpu; determine 2048-dimensional vectors to prepare the pinecone index.

Index audio vectors in Pinecone through batch processing and upsert, using embeddings, IDs, and metadata. Handle batch sizing, progress tracking with tqdm, and correct metadata formatting for scalable vector databases.

Learn to query your own audio data with a vector database by embedding audio and performing out-of-sample searches, using Librosa, downloading audio from Google APIs, and interpreting similarity results.

Query your own audio data with embeddings and vector search, loading with Librosa and playing results, using metadata to compare out-of-sample sounds like coughing and frogs.