Mongodb langchain example pdf chains import create_retrieval_chain from langchain. MongoDB. 4. To load the sample data, run the following code snippet. Uses a text splitter to split the data into smaller Dec 8, 2023 · The first package is langchain (the package for the framework we are using to integrate language model capabilities), pypdf (a library for working with PDF documents in Python), pymongo (the official MongoDB driver for Python so we can interact with our database from our application), openai (so we can use OpenAI’s language models), python Sample code can be found here. Oct 6, 2024 · We’ll use a straightforward setup with a single PDF and Langchain. See the integration docs for more information about using Unstructured with LangChain. 2. By delving into these frameworks, we aim to understand their respective syntax, and showcasing how they stack up with MongoDB vector search. The first step is to initialize Langchain’s Recursive Character Splitter, which splits the text into smaller chunks. This vector representation could be used to search through vector data stored in MongoDB Atlas using its vector search feature. Add the following code to the asynchronous function that you defined in your get-started. Horizontal Scalability: As documents grows, whether it’s product manuals, legal documents, or research articles, MongoDB scales effortlessly through sharding and distributed clusters. MongoDB launched a MongoDB University course focused on building AI applications with MongoDB and AWS. The RAG system extracts and processes this data to pip3 install langchain langchain_community langchain_openai pymongo pypdf python-dotenv . Jun 4, 2025 · MongoDB’s schema-less design enables you to update or extend documents without painful migrations. Mar 20, 2024 · Check out the “PDFtoChat” app to see langchain-mongodb JS in action. MongoDB is a NoSQL , document-oriented database that supports JSON-like documents with a dynamic schema. test collection. Overview The MongoDB Document Loader returns a list of Langchain Documents from a MongoDB database. 2 Example Use in RAG For this tutorial, you use a publicly accessible PDF document about a recent MongoDB earnings report as the data source for your vector store. prompts import ChatPromptTemplate system_prompt = ("You are an assistant for question-answering tasks. , "fast" or "hi-res") API or local processing. These are applications that can answer questions about specific source information. python To enable vector search queries on your vector store, create an Atlas Vector Search index on the langchain_db. g. This component stores each entity as a document with relationship fields that reference other documents in your collection. It allows you to have a conversation with your proprietary PDFs using AI and is built with MongoDB Atlas, LangChain. This notebook covers how to MongoDB Atlas vector search in LangChain, using the langchain-mongodb package. Sep 18, 2024 · The deployment and management of infrastructure and database resources required for data replication and distribution are taken care of by MongoDB Atlas. The following code initializes a MongoDB collection and loads a PDF file, then splits the PDF content into chunks of 50 characters with no overlap using a recursive character text splitter. combine_documents import create_stuff_documents_chain from langchain_core. We'll look at LangChain, LlamaIndex, and PyMongo, showing you step-by-step how to use their methods for semantic search. One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. Jun 6, 2024 · In this article, we will see the basics of vector search in simple terms. It does the following: Retrieves the PDF from the specified URL and loads the raw text data. The Loader requires the following parameters: MongoDB connection string; MongoDB database name; MongoDB collection name Sep 18, 2024 · For example, a developer could use LangChain to create an application where a user's query is processed by a large language model, which then generates a vector representation of the query. 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. MongoDB Atlas is a fully-managed cloud database available in AWS, Azure, and GCP. The instructions offer a practical roadmap for harnessing the capabilities of MongoDB Atlas and Fireworks LLM in crafting agent-driven applications. Usage recursive_splitter. It supports native Vector Search, full text search (BM25), and hybrid search on your MongoDB document data. . from langchain. Oct 31, 2024 · Start by creating a MongoDB Atlas database deployment and using Langchain with the Vertex AI Text embeddings API to transform the PDF documents into vector embeddings. This architecture depicts a Retrieval-Augmented Generation (RAG) chatbot system built with LangChain, OpenAI, and MongoDB Atlas Vector Search. Under the hood it uses the langchain-unstructured library. You will need an API key to use the API. MongoDB Atlas. 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. 3. py file. ""Use the following pieces of retrieved context to answer ""the question. It’s an end-to-end SaaS-in-a-box app and includes user authentication, saving PDFs, and saving chats per PDF. js file. chains. js, and TogetherAI. MongoDB announced new technology integrations for AI, data analytics, and automating database deployments across various environments. Unstructured supports multiple parameters for PDF parsing: strategy (e. The full cookbook to run the agents example with MongoDB can be found here. MongoDB obtained the AWS Modernization Competency designation. These applications use a technique known as Retrieval Augmented Generation, or RAG. \n\nFor example, if a user has an accounts collection that they want to distribute among their three regions of business, Atlas Global Cluster ensures that the data is written to and read from Jan 29, 2025 · 特に、PDFデータを外部情報源として扱う具体的な方法を取り上げ、「データ検索と回答生成の流れ」 を順を追って説明します。 本記事の目的は、次の3点です。 RAGの基本概念・メリットを理解する; LangChainを使ったPDFデータの登録・検索・回答生成を実装する 1. abktv yuf nidpygzm fqka lbgleh cks vwgiyrx tnp aocy fhr