Langchain mongodb vector search example. from_connection_string(connection .

Langchain mongodb vector search example from_documents (documents, embedding, **kwargs) Return VectorStore initialized from documents and embeddings. Specifically, you perform the following actions: Set up the environment. vector_penalty: The penalty for vector search. See MongoDBAtlasVectorSearch for kwargs and further description. This notebook covers how to MongoDB Atlas vector search in LangChain, using the langchain-mongodb package. To create an Atlas Vector Search index for a collection using the MongoDB C# driver v3. This tutorial walks you through how to create an Atlas Vector Search index programmatically with a supported MongoDB Driver or using the Atlas CLI. My code: from langchain May 15, 2025 · This document explains the vector search and retrieval capabilities in the langchain-mongodb library. pipelines ¶ Aggregation pipeline components used in Atlas Full-Text, Vector, and Hybrid Search. The RAG system extracts and processes this data to Feb 14, 2024 · Here is a quick tutorial on how to use MongoDB’s Atlas vector search with RAG architecture to build your Q&A app. We are excited Construct a MongoDB Atlas Vector Search vector store from raw documents. MongoDB Operators. max_marginal_relevance_search (query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0. {COLLECTION_NAME} . vectorstores import MongoDBAtlasVectorSearch from langchain_community. Azure Cosmos DB for MongoDB vCore makes it easy to create a database with full native MongoDB support. In the below example, embedding is the name of the field that contains the embedding vector. openai import OpenAIEmbeddings from May 23, 2025 · Vector Search: Employ MongoDB's vector search functionality to find similar items, locations, and preferences based on semantic meaning, enhancing the relevance of travel recommendations. "Write May 29, 2024 · In this article we’ve implemented examples for using metadata filters using MongoDB, enhancing vector search accuracy and has minimal overhead compared to an unfiltered vector search. To use MongoDB Atlas vector stores, you’ll need to configure a MongoDB Atlas cluster and install the @langchain/mongodb integration package. \\n1. ", "What are the subgoals for achieving XYZ?", (2) by using task-specific instructions; e. These code snippets provide instructions and examples for setting up LangChain. You can leverage Atlas Vector Search's support for aNN queries to find results analogous to a specific product, conduct image searches, and more. These components enable semantic searching of document collections stored in MongoDB Atlas using v Sep 18, 2024 · (Spoiler: It’s a game-changer!) 02:35 - MongoDB + LangChain setup: Chunking strategies & metadata tips 10:06 - Async processing: Ingest 25K docs WITHOUT crashing your system 15:04 - Vector search indexes: Optimize for speed & accuracy 20:12 - AI Agent demo: Answer complex questions with context expansion 25:56 - Pro tips: Avoid “tool loops Sep 18, 2024 · Learn about Vector Search with MongoDB, LLMs, and OpenAI with the Python programming language. However, a number of vector store implementations (Astra DB, ElasticSearch, Neo4J, AzureSearch, Qdrant) also support more advanced search combining vector similarity search and other search techniques (full-text, BM25, and so on). You can apply your MongoDB experience and continue to use your favorite MongoDB drivers, SDKs, and tools by pointing your application to the API for MongoDB vCore account’s connection string. Parameters: texts (List[str]) – embedding – Learn how to deploy MongoDB Atlas Vector Search, Atlas Search, and Search Nodes using the Atlas Kubernetes Operator. \nTask decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ. Atlas Vector Search utilizes the Hierarchical Navigable Small Worlds algorithm to execute semantic searches. Functions¶ Construct a MongoDB Atlas Vector Search vector store from a MongoDB connection URI. embeddings. I was looking at Run a Hybrid Search Query and i’ve seen that the retrieved scores in the provided example are really low, eg: Search score: 0. 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. from_texts (texts, embedding[, metadatas, ]) Construct a MongoDB Atlas Vector Search vector store from raw documents. Implementing the RAG Application Application Overview. The chatbot leverages Retrieval-Augmented Generation (RAG) using the following Oct 6, 2024 · In this Blog i want to show you how you can set up the Hybrid Search with MongoDBAtlas and Langchain. Installation and Setup See detail configuration instructions. Parameters. Bases: BaseRetriever Hybrid Search class MongoDBAtlasVectorSearch (VectorStore): """MongoDB Atlas vector store integration. This is a user-friendly interface that: Embeds documents. In the documentation it says I can add the filter, as explained here. Run the following vector search queries: Create Vector Search Index Now, let's create a vector search index on your cluster. Please refer to the documentation to get more details Aug 22, 2023 · Hello, I created an Vector Search Index in my Atlas cluster, on the “embedding” field of a “embeddings” collection. Vector Search. 019230769230769232 Vector Search score: 0. About. 2. 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. For detailed documentation of all MongoDBAtlasVectorSearch features and configurations head to the API reference. 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. In the meantime, could you please provide additional details about your specific use case, the expected output, and any workarounds you’ve attempted? Vector search for Amazon DocumentDB combines the flexibility and rich querying capability of a JSON-based document database with the power of vector search. Mar 20, 2024 · Earlier, we announced the launch of MongoDB LangChain Templates, which enable the developers to quickly deploy RAG applications, and provided a reference implementation of a basic RAG template using MongoDB Atlas Vector Search and OpenAI and a more advanced Parent-document Retrieval RAG template using MongoDB Atlas Vector Search. 4 days ago · langchain4j-mongodb-atlas. That graphic is from the team over at LangChain, whose goal is to provide a set of utilities to greatly simplify this process. Filter Example. Use vector search in Azure Cosmos DB for MongoDB vCore to seamlessly integrate your AI-based May 15, 2025 · This page documents the various retriever implementations in the `langchain-mongodb` library that provide different strategies for retrieving documents from MongoDB Atlas. MongoDB Atlas is a fully-managed cloud database available in AWS, Azure, and GCP. Aug 12, 2024 · langchain-mongodb: Python package to use MongoDB as a vector store, semantic cache, chat history store, etc. in LangChain. Sep 18, 2024 · It has recently incorporated native vector search capabilities for your MongoDB document data. The Index name should match the one we configured on aggregate function, and the name for that is Construct a MongoDB Atlas Vector Search vector store from raw documents. The lower the penalty, the higher the vector search score. Sep 18, 2024 · In this article, we've explored the synergy of MongoDB Atlas Vector Search with LangChain Templates and the RAG pattern to significantly improve chatbot response quality. Feb 15, 2024 · A comprehensive guide on using LangChain to set up a vector store and perform vector search on Azure Cosmos DB for MongoDB vCore using Python. Parameters: The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote. Select the collection you want to create index for, for our case is vectors collection 5. texts (List[str]) – embedding – The lower the penalty, the higher the full-text search score. Since we announced integration with LangChain last year, MongoDB has been building out tooling to help developers create advanced AI applications with LangChain . 0 or later, perform the following steps: Define the Atlas Vector Search index. You can name the index {COLLECTION_NAME} and create the index on the namespace {DB_NAME}. This tutorial covers step-by-step instructions to integrate advanced search capabilities into Kubernetes clusters, enabling scalable, high-performance workloads with MongoDB Atlas. Store custom data on Atlas. Construct a MongoDB Atlas Vector Search vector store from raw documents. AI models: Integrate machine learning models for natural language processing, sentiment analysis, and predictive modeling to understand user intent In this quick start, you complete the following steps: Create an index definition for the sample_mflix. py. Feb 13, 2024 · Upon receiving a user query, Langchain will use the configured vector search to retrieve the most relevant movie data from MongoDB Atlas. Now I want to filter the results to only retrieve entries for a specific “project”. It now has support for native Vector Search on the MongoDB document data. Refer to OpenAI's FAQs to learn how you can get your OPENAI_API_KEY . get_by_ids (ids, /) Azure Cosmos DB for MongoDB vCore makes it easy to create a database with full native MongoDB support. MongoDBAtlasVectorSearch performs data operations on text, embeddings and arbitrary data. Example. Please refer to the documentation to get more details on how to define an Atlas Vector Search index. Sep 23, 2024 · You'll need a vector database to store the embeddings, and lucky for you MongoDB fits that bill. Dec 9, 2024 · MongoDBAtlasVectorSearch performs data operations on text, embeddings and arbitrary data. Dec 9, 2024 · To use, you should have both: - the ``pymongo`` python package installed - a connection string associated with a MongoDB Atlas Cluster having deployed an Atlas Search index Example:. More detailed steps can be found at Create Vector Search Index for LangChain section. Oct 23, 2024 · 4. MongoDBAtlasHybridSearchRetriever [source] #. Adds the documents to a provided MongoDB Atlas Vector Search index (Lucene) This is intended to be a quick way to get started. I use LangChain, and the MongoDBAtlasVectorSearch as a retriever. retrievers import MongoDBAtlasSelfQueryRetriever from langchain_mongodb import MongoDBAtlasVectorSearch from langchain_ollama. 01818181818181818 Total score: 0. This notebook shows you how to leverage this integrated vector database to store documents in collections, create indicies and perform vector search queries using approximate nearest neighbor algorithms such as COS (cosine distance), L2 (Euclidean distance), and IP (inner product) to locate documents close to the query vectors. js with MongoDB Atlas as a vector store for similarity and maximal marginal relevance (MMR) search. local_rag namespace. For information about the co Example usage:. arxiv : Python library to download papers from the arXiv repository. 1. 03741258741258741 I’d really like to know the reason for those scores, where can i find an explanation? Nov 21, 2023 · You can refer to the following tutorial: Leveraging OpenAI and MongoDB Atlas for Improved Search Functionality | MongoDB and the Atlas Vector Search Pre-Filter documentation to learn more about it. Dec 9, 2024 · Construct a MongoDB Atlas Vector Search vector store from raw documents. Documentation for LangChain. This architecture depicts a Retrieval-Augmented Generation (RAG) chatbot system built with LangChain, OpenAI, and MongoDB Atlas Vector Search. pymupdf : Enables allowing for the extraction of text, images, and metadata from PDF files. The retriever returns a list of documents sorted by the sum of the full-text search score and the vector search score. This notebook shows you how to use Amazon Document DB Vector Search to store documents in collections, create indicies and perform vector search queries using approximate nearest neighbor 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 integrate Atlas Vector Search with LangChain to build generative AI and RAG applications. We need to install langchain-mongodb python package. Includes instructions on prerequisites, setting up Python, loading data into Cosmos DB, creating a search index, and executing a vector search query. The goal is to load documents from MongoDB, generate embeddings for the text data, and perform semantic searches using both LangChain and LlamaIndex frameworks. In order to use OpenAIEmbeddings , we need to set up our OpenAI API key. Run the following vector search queries: In the below example, embedding is the name of the field that contains the embedding vector. MongoDB Atlas. . This Python project demonstrates semantic search using MongoDB and two different LLM frameworks: LangChain and LlamaIndex. Parameters: texts (List[str]) embedding . MongoDB Vector Search 🍃 Dec 3, 2023 · I created an Vector Search Index in my Atlas cluster, on the “embedding” field of a “embeddings” collection. Sep 16, 2024 · AI Agents, Hybrid Search, and Indexing with LangChain and MongoDB. ) in other applications and understand and utilize recent information. embedded_movies collection that indexes the plot_embedding field as the vector type. Then, it will pass this context along with the query to The following code uses the LangChain integration for Atlas Vector Search to instantiate your local Atlas deployment as a vector database, also called a vector store, using the langchain_db. Let's break down its key players: PDF File: This serves as the knowledge base, containing the information the chatbot draws from to answer questions. Use vector search in Azure Cosmos DB for MongoDB vCore to seamlessly integrate your AI-based The standard search in LangChain is done by vector similarity. Parameters: texts (List[str]) – embedding – May 28, 2025 · Hello guys. hybrid_search. 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. Sep 12, 2024 · MongoDB has added two new custom, purpose-built Retrievers to the langchain-mongodb Python package, giving developers a unified way to perform hybrid search and full-text search with sensible defaults and extensive code annotation. Create an Atlas Vector Search index on your data. It was really complicated a few months ago but now it is easier, but still way more Construct a MongoDB Atlas Vector Search vector store from raw documents. Install and import from the "@langchain/mongodb" integration package instead. js. By implementing these tools, developers can ensure their AI chatbots deliver highly accurate and contextually relevant answers. This is generally referred to as "Hybrid" search. 5. embeddings import OllamaEmbeddings # Start with the standard MongoDB Atlas vector store vectorstore = MongoDBAtlasVectorSearch. from_connection_string(connection MongoDBAtlasHybridSearchRetriever# class langchain_mongodb. Class that is a wrapper around MongoDB Atlas Vector Search. g. In addition to CRUD operations, the VectorStore provides Vector Search based on similarity of embedding vectors following the Hierarchical Navigable Small Worlds (HNSW) algorithm. Jan 9, 2024 · 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 now that graphic is from the team over at Lang chain whose goal is to provide a set of utilities to greatly simplify this process in this tutorial we're going to walk through each of these steps using mongodb Atlas as our Vector store and In this section, you set up Atlas Vector Search to retrieve documents from your vector database. code-block:: python from langchain_community. It supports native Vector Search, full text search (BM25), and hybrid search on your MongoDB document data. This page provides an overview of the LangChain MongoDB Python integration and the different components you can use in your applications. To enable vector search on the sample_airbnb. listingsAndReviews collection, create an Atlas Vector Search index. Langchain Embeddings 🦜. Azure Cosmos DB Mongo vCore. code-block:: python from langchain_mongodb. Jun 6, 2024 · Overall, this code part handles the connections to a MongoDB instance and sets up a vector search system using LangChain, with vector data stored in MongoDB and embeddings generated by OpenAI. 5, pre_filter: Optional [dict] = None, post_filter_pipeline: Optional [List [Dict]] = None, ** kwargs: Any) → create a vector search index using the MongoDB Atlas GUI and; how can we store vector embeddings in MongoDB documents create a vector search index using the MongoDB Atlas GUI Dec 9, 2024 · langchain_mongodb. This comprehensive tutorial takes you through how to integrate LangChain with MongoDB Atlas Vector Search. Adds support for using MongoDB Atlas as the vector store and retrieval database. See the following for more: Full-Text Search. Enables storing and querying documents using metadata and embeddings through Atlas Vector Search. These new classes make it easier than ever to use the full capabilities of MongoDB Vector Search with LangChain. 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. It works well. retrievers. LangChain actually helps facilitate the integration of various LLMs (ChatGPT-3, Hugging Face, etc. metadatas (Optional[List[Dict]]) Dec 8, 2023 · LangChain is a versatile Python library that enables developers to build applications that are powered by large language models (LLMs). fmusetq sjzvod tfqzou sjdwut hantf bqgn nbnig zuxdcy ucwreni cisokf