Langchain mongodb vector search example. This is a user-friendly interface that: Embeds documents.

Langchain mongodb vector search example It works well. test collection by using the LangChain helper method or the PyMongo driver method. In the below example, embedding is the name of the field that contains the embedding vector. py. Implementing the RAG Application Application Overview. From there, make sure you select Atlas Vector Search - JSON Editor, then select the appropriate database and collection and paste the following into the textbox: Create Vector Search Index Now, let's create a vector search index on your cluster. Afterwards, choose the JSON Editor to declare the index parameters as well as the database and collection where the Atlas Vector Search will be established (langchain. In the Atlas UI, choose Search and then Create Search. This notebook covers how to MongoDB Atlas vector search in LangChain, using the langchain-mongodb package. Store custom data on Atlas. While vector-based RAG finds documents that are semantically similar to the query, GraphRAG finds connected entities to the query and traverses the relationships in the graph to retrieve relevant information. After configuring your cluster, you’ll need to create an index on the collection field you want to search over. See MongoDBAtlasVectorSearch for kwargs and further description. Set index name as Aug 22, 2023 · Hello, I created an Vector Search Index in my Atlas cluster, on the “embedding” field of a “embeddings” collection. Jun 4, 2025 · langchain4j-mongodb-atlas. Switch to the Atlas Search tab and click Create Search Index. Even luckier for you, the folks at LangChain have a MongoDB Atlas module that will do all the heavy lifting for you! Don't forget to add your MongoDB Atlas connection string to params. Example. This is a user-friendly interface that: Embeds documents. The index definition specifies indexing the following fields: Install and import from the "@langchain/mongodb" integration package instead. It supports native Vector Search, full text search (BM25), and hybrid search on your MongoDB document data. When Atlas Vector Search runs on search nodes, Atlas Vector Search parallelizes query execution across segments of data. The lower the penalty, the higher the full-text search score. MongoDB Atlas is a fully-managed cloud database available in AWS, Azure, and GCP. Create an Atlas Vector Search index on your data. Adds support for using MongoDB Atlas as the vector store and retrieval database. My code: from langchain GraphRAG is an alternative approach to traditional RAG that structures data as a knowledge graph of entities and their relationships instead of as vector embeddings. Parameters Enabling semantic search on user-specific data is a multi-step process that includes loading, transforming, embedding and storing data before it can be queried. The chatbot leverages Retrieval-Augmented Generation (RAG) using the following . Now I want to filter the results to only retrieve entries for a specific “project”. Dec 9, 2024 · Construct a MongoDB Atlas Vector Search vector store from raw documents. Note that the high-CPU systems might provide more performance improvement. In the documentation it says I can add the filter, as explained here. Specifically, you perform the following actions: Set up the environment. You might see improved query performance on the dedicated Search Nodes. Parameters We recommend dedicated Search Nodes to isolate vector search query processing. Enables storing and querying documents using metadata and embeddings through Atlas Vector Search. Run the following vector search queries: Sep 23, 2024 · You'll need a vector database to store the embeddings, and lucky for you MongoDB fits that bill. Please refer to the documentation to get more details on how to define an Atlas Vector Search index. vectorSearch). {COLLECTION_NAME} . I use LangChain, and the MongoDBAtlasVectorSearch as a retriever. For the project you going Jun 4, 2025 · langchain4j-mongodb-atlas. We will do this through the Atlas UI. Run the following code in your notebook for your preferred method. More detailed steps can be found at Create Vector Search Index for LangChain section. Adds the documents to a provided MongoDB Atlas Vector Search index (Lucene) This is intended to be a quick way to get started. You can name the index {COLLECTION_NAME} and create the index on the namespace {DB_NAME}. The chatbot leverages Retrieval-Augmented Generation (RAG) using the following To enable vector search queries on your vector store, create an Atlas Vector Search index on the langchain_db. Run the following vector search queries: Sep 18, 2024 · Now, it's time to initialize Atlas Vector Search. vector_penalty: The penalty for vector search. Class that is a wrapper around MongoDB Atlas Vector Search. Please refer to the documentation to get more details This tutorial demonstrates how to start using Atlas Vector Search with LangChain to perform semantic search on your data and build a RAG implementation. The retriever returns a list of documents sorted by the sum of the full-text search score and the vector search score. It is used to store embeddings in MongoDB documents, create a vector search index, and perform K-Nearest Neighbors (KNN) search with an approximate nearest neighbor algorithm. That graphic is from the team over at LangChain, whose goal is to provide a set of utilities to greatly simplify this process. The lower the penalty, the higher the vector search score. GraphRAG is an alternative approach to traditional RAG that structures data as a knowledge graph of entities and their relationships instead of as vector embeddings. This tutorial demonstrates how to start using Atlas Vector Search with LangChain to perform semantic search on your data and build a RAG implementation. 5. In this Oct 23, 2024 · Let’s build a simple and blazing fast vector search to use on your Gen AI app, powered by Typescript, NestJS, LangChain, MongoDB with mongoose, and OpenAI Embeddings. vgwe kwqat zvfvmdn ponjg ouue egpi wmuo xjdofiu ozz shzhj