Langchain mongodb retriever example.
Dec 9, 2024 · langchain_mongodb.
Langchain mongodb retriever example Users utilizing earlier versions of MongoDB Atlas need to pin their LangChain version to <=0. We walked through setting up the embedding model with Ollama, ingesting documents into a vector store backed by MongoDB, and configuring a conversational assistant that retrieves relevant context from internal documents. LangChain passes these documents to the {context} input variable and your query to the {question} variable. We need to install langchain-mongodb python package. LangChain actually helps facilitate the integration of various LLMs (ChatGPT-3, Hugging Face, etc. It now has support for native Vector Search on the MongoDB document data. which is the ID of the paper, for example: 704. embeddings import OllamaEmbeddings # Start with the standard MongoDB Atlas vector store vectorstore = MongoDBAtlasVectorSearch. Pass the examples and formatter to FewShotPromptTemplate Finally, create a FewShotPromptTemplate object. It is more general than a vector store. 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. 5: 6 Args: 7 Defines a LangChain prompt template to instruct the LLM to use these documents as context for your query. This notebook covers how to MongoDB Atlas vector search in LangChain, using the langchain-mongodb package. Vector Search Retriever After instantiating Atlas as a vector store , you can use the vector store instance as a retriever to query your data using Atlas Vector Search . ) in other applications and understand and utilize recent information. This component stores each entity as a document with relationship fields that reference other documents in your collection. Hybrid Search Retriever performs full-text searches using Lucene's standard (BM25) analyzer. full_text_search. MongoDBGraphStore is a component in the LangChain MongoDB integration that allows you to implement GraphRAG by storing entities (nodes) and their relationships (edges) in a MongoDB collection. Creating a MongoDB Atlas vectorstore First we'll want to create a MongoDB Atlas VectorStore and seed it with some data. retrievers ¶ Search Retrievers of various types. These are applications that can answer questions about specific source information. In this article, we explored how to build an AI-powered chatbot in Java using LangChain4j and MongoDB Atlas. . MongoDBAtlasFullTextSearchRetriever. Sep 23, 2024 · You'll need a vector database to store the embeddings, and lucky for you MongoDB fits that bill. In the walkthrough, we'll demo the SelfQueryRetriever with a MongoDB Atlas vector store. This object takes in the few-shot examples and the formatter for the few-shot examples. MongoDB Atlas is a document database that can be used as a vector database. code-block:: python from langchain_mongodb. It supports native Vector Search, full text search (BM25), and hybrid search on your MongoDB document data. You can use LangChain's built-in retrievers or the following MongoDB retrievers to query and retrieve data from Atlas. graphrag Jun 4, 2025 · 6. Conclusion. For information about the co MongoDB. 304 In the notebook we will demonstrate how to perform Retrieval Augmented Generation (RAG) using MongoDB Atlas, OpenAI and Langchain. as_retriever(**) to create MongoDB’s core Vector Search Retriever. Constructs a chain that specifies the following: Atlas Vector Search as the retriever to search for documents to use as context. Dec 8, 2023 · LangChain is a versatile Python library that enables developers to build applications that are powered by large language models (LLMs). MongoDB Atlas. MongoDB Atlas. from_connection_string(connection Dec 9, 2024 · langchain_mongodb. retrievers import MongoDBAtlasSelfQueryRetriever from langchain_mongodb import MongoDBAtlasVectorSearch from langchain_ollama. These applications use a technique known as Retrieval Augmented Generation, or RAG. Retrievers. 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. 0. Overview The MongoDB Document Loader returns a list of Langchain Documents from a MongoDB database. One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. py. MongoDB is a NoSQL , document-oriented database that supports JSON-like documents with a dynamic schema. Aug 12, 2024 · langchain-mongodb: Python package to Step 4: create LangChain retriever with MongoDB. When this FewShotPromptTemplate is formatted, it formats the passed examples using the example_prompt, then and adds them to the final prompt before suffix: Retrievers. Example usage:. Installation and Setup See detail configuration instructions. Retrievers can be created from vector stores, but are also broad enough to include Wikipedia search and Amazon Kendra. 0001. A retriever is an interface that returns documents given an unstructured query. Retriever performs full-text searches using Lucene's standard (BM25) analyzer. MongoDB Atlas is a fully-managed cloud database available in AWS, Azure, and GCP. A retriever does not need to be able to store documents, only to return (or retrieve) them. Use MongoDBAtlasVectorSearch. retrievers. The Loader requires the following parameters: MongoDB connection string; MongoDB database name; MongoDB collection name retrievers. eblplkorjhxhnwnyqrcwtwbodpzyijkwcwrvrgcjeep