MongoDB Atlas Vector Database: Zero to Advanced with Python

Connect to MongoDB using mongosh and Compass to manage databases and collections. Create and switch databases, add collections, and drop databases as shown.

Explore how MongoDB handles diverse data types in documents, including int, string, boolean, date, null, arrays, and an address document, with practical schema creation and insertion examples.

Use PyMongo's insert_many to add multiple documents into a MongoDB collection, building a list of documents with fields such as name, age, and address. Verify by counting with find.

Master PyMongo's limit, sort, and skip to control query results, using ascending and descending order on fields like age and salary.

Explore how to use PyMongo's find operator for querying and projection, applying comparison operators such as $eq, and shaping results with projection controls.

Master PyMongo logical operators to combine conditions using $and and $or, querying age greater than 30 and salary at least 50,000, with name and salary projection.

Explore PyMongo's element query operators in MongoDB, focusing on $exists and $type to filter documents by field presence and data type, with practical examples.

Explore how to query MongoDB arrays with PyMongo using $elemMatch to match documents where an array’s element meets multiple criteria, and use $size to filter arrays by length.

Learn to use the aggregate stage and the $match operator in PyMongo to filter documents in a pipeline, such as selecting records where sex is female.

demonstrates how aggregate stage $count tallies documents after a match, using a pipeline with tip amount >= 3 and an alias like 'count of documents' to return the total.

Create a tip bucket in MongoDB's aggregation pipeline using the condition operator to implement an if-then-else that marks tips as high or low by value.

Learn the second syntax for the MongoDB $cond operator, using a boolean expression with a true and false case in a list to drive if-then-else logic.

Explore linear interpolation to estimate unknown values between data points using a straight line, the slope formula m = (y1−y0)/(x1−x0), and y = y0 + m(x−x0) in MongoDB.

Learn how last observation carry forward (locf) uses forward fill and backward fill to estimate missing values, with sorting documents and filling values in a MongoDB workflow using PyMongo.

Learn to fill missing average IQ score using forward fill and backward fill with the $fill stage, sorting by a numerical field to carry the last observation forward or backward.

Learn to use MongoDB's aggregate $merge with PyMongo to merge documents into tips copy collection in my save data database, and create a tip bucket field bucketing by tip values.

Demonstrates the merge operator in the aggregation stage, covering when matched and when not matched behaviors, including merge, replace, keep existing, insert, discard, and fail options.

Explore how MongoDB's $text full text search uses NLP techniques like tokenization, normalization, diacritic sensitivity, case sensitivity, stemming, and stop-word removal to score and rank documents with language options.

Demonstrates setting up a MongoDB Atlas vector search with toy movie data, creating eight-dimensional embeddings, building a vector index, and querying with cosine similarity via an aggregate pipeline.

Explore the LangChain chat OpenAI model by building a chat interface with user, assistant, and system roles, and crafting prompts using system and human messages.

Master retrieval augmented generation with Langchain by mastering indexing, retrieving, and pre-processing documents into chunks and embeddings stored in MongoDB for effective llm insights.

Learn how to store document chunks and their embeddings in a MongoDB Atlas vector search collection using long chain's from_documents, configure the embedding model, and verify indexing.

Master vector search on embeddings in MongoDB Atlas by building a two-stage aggregate pipeline, generating a query embedding, and projecting text, page, and similarity scores.