Langchain mongodb agent. prompts import PromptTemplate from langchain_core.

Langchain mongodb agent agent_toolkit. May 29, 2024 · In this tutorial we will outline a method to prefilter data using metadata extraction with MongoDB vector search and LangChain Agent, ensuring more precise retrieval of documents. Learn how semantic search and embeddings revolutionize data retrieval. `agent_executor = AgentExecutor(agent=agent, tools=tools, handle_parsing_errors=True, verbose=True)` (or something like that) Reply reply overflow74 Source code for langchain_mongodb. py. Store your operational data, metadata, and vector embeddings in oue VectorStore, MongoDBAtlasVectorSearch. Sep 18, 2024 · Discover the integration of MongoDB Atlas Vector Search with LangChain, in Python. Redirecting to /resources/solutions/use-cases/webinar-future-of-llm-applications-with-lang-chain-and-mongodb Sep 18, 2024 · MongoDB has streamlined the process for developers to integrate AI into their applications by teaming up with LangChain for the introduction of LangChain Templates. Use of this repository/software is at your own risk. QueryMongoDBCheckerTool Apr 24, 2025 · Chatbot systems enhance the user experience by providing quick and intelligent responses, making interactions more efficient. agent_toolkit. format ( top_k = 5 ) # Create agent agent_executor = create_react_agent ( llm , toolkit . Note that the agent executes multiple queries until it has the information it needs: List available tables; Retrieves the schema for three tables; Queries multiple of the tables via a join operation. To learn more about the built-in generic agent types as well as how to build custom agents, head to the Agents Modules. Jun 24, 2024 · import ast import re from langchain. llm: BaseLanguageModel The language model (for use with QueryMongoDBCheckerTool) Instantiate:. Your contribution. From what I understand, you are seeking guidance on connecting a LangChain agent to a MongoDB database and have received several comments from other users offering potential solutions. com/ronidas39/LLMtutorial/tree/main/tutorial57TELEGRAM: https://t. Our AI agent will be powered by LangChain, MongoDB, and the smarts of OpenAI's GPT-4. Setup: Install ``langchain-mongodb`` code-block:: bash pip install -U langchain-mongodb Key init args: db: MongoDBDatabase The MongoDB database. This collaboration has produced a retrieval-augmented generation template that capitalizes on the strengths of MongoDB Atlas Vector Search along with OpenAI's technologies. session_id (str) – arbitrary key that is used to store the messages of a single chat session. The MongoDB LangGraph integration enables the following capabilities: Sep 18, 2024 · This tutorial was extremely helpful and well-structured. Agents are systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. This repository/software is provided "AS IS", without warranty of any kind. This component stores each entity as a document with relationship fields that reference other documents in your collection. prebuilt import create_react_agent from langchain_mongodb. It is intended for educational and experimental purposes only and should not be considered as a product of MongoDB or associated with MongoDB in any official capacity. MongoDB is developed by MongoDB Inc. , runs the tool), and receives an observation. 6 or newer; pymongo for chatting with MongoDB Jun 22, 2023 · LangChain and MongoDB Atlas. As these applications get more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent. - Wikipedia. Found. toolkit import MongoDBDatabaseToolkit from Sep 21, 2023 · Please note that the SQLDatabaseToolkit is not mentioned in the provided context, so it's unclear how it interacts with the ConversationBufferMemory class. In the walkthrough, we'll demo the SelfQueryRetriever with a MongoDB Atlas vector store. . Han HELOIR, Ph. This includes not only the conversational buffer, but the ability to extract entities and related information about… MongoDB is developed by MongoDB Inc. ListMongoDBDatabaseTool. You switched accounts on another tab or window. Feb 20, 2025 · Join MongoDB Staff Developer Advocate Jesse Hall and LangChain Founding Software Engineer Jacob Lee on the 20th of February at 11am ET for an exclusive webinar that highlights the integration of LangGraph. get Sep 12, 2024 · Since we announced integration with LangChain last year, MongoDB has been building out tooling to help developers create advanced AI applications with LangChain. """Wrapper around a MongoDB database. and licensed under the Server Side Public License (SSPL). Base tool for interacting with a MongoDB database. I will use OpenAI’s GPT-4, which has powerful natural language processing capabilities, along Jul 2, 2023 · Within my organization, a significant portion of our data is stored in MongoDB. It now has support for native Vector Search on the MongoDB document data. Graphs are the core components of LangGraph, representing the workflow of your agent. It’s shown how these technologies combine to create a sophisticated agent capable of assisting researchers by effectively managing and retrieving information from an extensive database of research papers. May 15, 2025 · This document explains the MongoDB Agent Tools provided by the LangChain MongoDB integration. This Python project demonstrates semantic search using MongoDB and two different LLM frameworks: LangChain and LlamaIndex. MongoDB is a NoSQL , document-oriented database that supports JSON-like documents with a dynamic schema. """ from __future__ import annotations import json from datetime import date, datetime from importlib. database_name (str) – name of the database to use. It contains the following packages. About. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. You can use the LangChain MongoDB integration to run natural language MongoDB queries. This library enables the use of MongoDB Atlas as a backend for various LangChain components, leveraging Aug 16, 2024 · Vertex AI's Reasoning Engine agent goes beyond just decision-making tools, transforming LangChain agents into versatile AI assistants that can handle data, connect to systems, and make complex decisions, all while understanding and responding to text. tools import Sep 12, 2023 · MongoDB and Langchain Magic: Your Beginner’s Guide to Setting Up a Generative AI app with Your Own Data. LangChain真是好起来了。24年中的时候用LangChain V2差点把我气死，现在V3用起来开始真香了~ 像 ChatGPT、Gemini 和 Claude 这样的大模型已成为企业必不可少的工具。 Chat message history that stores history in MongoDB. metadata import version from typing import Any, Dict, Iterable, List, Optional, Union from bson import ObjectId from bson. MongoDB. Overview The MongoDB Document Loader returns a list of Langchain Documents from a MongoDB database. """ from __future__ import annotations from typing import Any, Dict, Optional, Type from langchain_core. It includes integrations between MongoDB, Atlas, LangChain, and LangGraph. In this tutorial, you build a basic AI agent that converts natural language to MQL by using the ReAct Agent framework and the MongoDB Agent Toolkit. callbacks import (CallbackManagerForToolRun,) from langchain_core. If it is indeed possible, I am willing to work on implementing this feature. For an in depth explanation, please check out this conceptual guide. tool. MongoDB Atlas. Python 3. In Chains, a sequence of actions is hardcoded. To use MongoDB Atlas vector stores, you’ll need to configure a MongoDB Atlas cluster and install the @langchain/mongodb integration package. language_models import BaseLanguageModel from langchain_core. Concepts There are several key concepts to understand when building agents: Agents, AgentExecutor, Tools, Toolkits. json Dec 20, 2024 · Large language models (LLMs) such as ChatGPT, Gemini, and Claude have become an essential tool for Tagged with mongodb, python, langchain, openai. I particularly appreciate the step-by-step explanations and practical code samples. You Feb 14, 2024 · Grab the connection string MongoDB Atlas gives you. code-block:: python from langchain_mongodb. The schemas for the agents themselves are defined in langchain. Agent Types There are many different types of agents to use. Args schema should be either: A subclass of pydantic. It supports native Vector Search, full text search (BM25), and hybrid search on your MongoDB document data. Crafting the AI Agent 🤖. database. toolkit import MongoDBDatabaseToolkit from Explore how MongoDB, Fireworks, and Langchain are transforming smart agent architectures for advanced, AI-powered applications ANNOUNCEMENT Voyage AI joins MongoDB to power more accurate and trustworthy AI applications on Atlas. Voyage AI joins MongoDB to power more accurate and trustworthy AI applications on Atlas. Dec 30, 2024 · LangChain真是好起来了。24年中的时候用LangChain V2差点把我气死，现在V3用起来开始真香了~像 ChatGPT、Gemini 和 Claude 这样的大模型已成为企业必不可少的工具。如今，几乎每家公司都希望根据自己的需求或客户群体，开发一款定制化的AI Agent。 May 12, 2025 · langchain-mongodb Installation pip install -U langchain-mongodb Usage. agents #. Using MongoDBAtlasVectorSearch The agent executes the action (e. If you do not have a MongoDB URI, see the Setup Mongo section at the bottom for instructions on how to do so. This will let you tailor them to specific tasks like choosing the right tool for the job. After executing actions, the results can be fed back into the LLM to determine whether more actions are needed, or whether it is okay to finish. Nov 17, 2023 · LangChain provides a number of options for managing state via the Memory module. May 5, 2024 · This article explores the implementation of a LangChain Agent using Azure Cosmos DB for MongoDB vCore to handle traveler inquiries and bookings. Apr 23, 2023 · Hi, @nikhilkotiya I'm helping the LangChain team manage their backlog and am marking this issue as stale. BaseModel if accessing v1 namespace in pydantic 2 or - a Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls. Installation and Setup See detail configuration instructions. MongoDB Atlas is a document database that can be used as a vector database. 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. The param args_schema: Type [BaseModel] = <class 'langchain_mongodb. agent_toolkit import MONGODB_AGENT_SYSTEM_PROMPT # Pull prompt (or define your own) system_message = MONGODB_AGENT_SYSTEM_PROMPT . 1k次，点赞28次，收藏13次。LangChain真是好起来了。24年中的时候用LangChain V2差点把我气死，现在V3用起来开始真香了~像 ChatGPT、Gemini 和 Claude 这样的大模型已成为企业必不可少的工具。 For longer-term persistence across chat sessions, you can swap out the default in-memory chatHistory that backs chat memory classes like BufferMemory for a MongoDB instance. g. The project provides detailed instructions for setting up the environment and loading travel data, aiming to empower developers to integrate similar agents into their solutions. Agent is a class that uses an LLM to choose a sequence of actions to take. Parameters: connection_string (str) – connection string to connect to MongoDB. prompts import PromptTemplate from langchain_core. 2# Integrate your operational database and vector search in a single, unified, fully managed platform with full vector database capabilities on MongoDB Atlas. These tools enable LangChain agents to interact with MongoDB databases through a set of standardized inter Use within an agent: from langchain import hub from langgraph. D. Tool for getting collection names. Initial Cluster Configuration To create a MongoDB Atlas cluster, navigate to the MongoDB Atlas website and create an account if you don’t already have one. Source code for langchain_mongodb. Feb 19, 2025 · A big use case for LangChain is creating agents. v1. Tool for getting metadata about a MongoDB database. Dec 30, 2024 · 文章浏览阅读1. In this tutorial, we’ll walk through the process of building a chatbot using Langchain4j and MongoDB Atlas. With recent releases, MongoDB has made it easier to develop agentic AI applications (with a LangGraph integration), perform hybrid search by combining Atlas Search and Atlas Vector Search, and ingest large-scale documents more effectively. The built-in AgentExecutor runs a simple Agent action -> Tool call Feb 6, 2025 · LangChain真是好起来了。24年中的时候用LangChain V2差点把我气死，现在V3用起来开始真香了~ 像 ChatGPT、Gemini 和 Claude 这样的大模型已成为企业必不可少的工具。如今，几乎每家公司都希望根据自己的需求或客户群体，开发一款定制化的AI Agent。这篇文章将重点介绍如何创建一个个性化的助手，这个助手 Oct 31, 2024 · In this guide, I’ll walk you through building a RAG chatbot using MongoDB as the database, Google Cloud Platform (GCP) for deployment, and Langchain to streamline retrieval and generation. BaseModel. This notebook goes over how to use the MongoDBChatMessageHistory class to store chat message history in a Mongodb database. I intend to utilize this agent to establish connections with our databases and develop a practical application around it. The agent is then able to use the result of the final query to generate an answer to the original question. The best way to do this is with LangSmith. Sep 23, 2024 · You'll need a vector database to store the embeddings, and lucky for you MongoDB fits that bill. agent_toolkits import create_retriever_tool # Function to execute a query and parse the results into a list of elements def query_as_list(db, 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. It's going to be a data-wrangling wizard! What You Need. _QueryMongoDBDatabaseToolInput'> # Pydantic model class to validate and parse the tool’s input arguments. Creating a MongoDB Atlas vectorstore First we'll want to create a MongoDB Atlas VectorStore and seed it with some data. When the agent reaches a stopping condition, it returns a final return value. InfoMongoDBDatabaseTool. decimal128 import Decimal128 from bson. Classes. Wrapper around a MongoDB database. Under the hood, create_sql_agent is just passing in SQL tools to more generic agent constructors. May 15, 2025 · The langchain-mongodb library is a comprehensive integration package that connects the LangChain ecosystem with MongoDB, providing robust tooling for vector search, document storage, message history persistence, caching, and agent tools. You signed out in another tab or window. collection_name (str) – name of the collection to use Jun 18, 2024 · Optimize LLM applications by implementing prompt compression techniques using LLMLingua and LangChain, reducing token count and operational costs without compromising response quality. ☕️ So, you want to build the next super-smart AI agent. We need to install langchain-mongodb python package. agent. Aug 12, 2024 · This tutorial has guided you through building an AI research assistant agent, leveraging tools such as MongoDB, Fireworks AI, and LangChain. Reload to refresh your session. Setup The integration lives in the langchain-mongodb package, so we need to install that. Aug 12, 2024 · Leverage the capabilities of Fireworks AI, MongoDB, and LangChain to construct an AI agent. BaseMongoDBDatabaseTool. This covers basics like initializing an agent, creating tools, and adding memory. "google-cloud-aiplatform[langchain,agent_engines]" requests datasets pymongo langchain langchain-community langchain-mongodb langchain-google-vertexai google-cloud-aiplatform langchain_google_genai requests beautifulsoup4 You signed in with another tab or window. 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. GITHUB: https://github. Environment Setup You should export two environment variables, one being your MongoDB URI, the other being your OpenAI API KEY. or - A subclass of pydantic. langchain-mongodb ; langgraph-checkpoint-mongodb ; Note: This repository replaces all MongoDB integrations currently present in the langchain-community package langchain-mongodb: 0. This tutorial covers the definition, implementation, and benefits of prompt compression in LLM applications, RAG pipelines, and AI agents. me/ttyoutubediscussion #ChatwithMongoDB#ai #llm #langchain #openai # Setup: Install ``langchain-mongodb`` code-block:: bash pip install -U langchain-mongodb Key init args: db: MongoDBDatabase The MongoDB database. Integrate Atlas Vector Search with LangChain for a walkthrough on using your first LangChain implementation with MongoDB Atlas. In addition to now supporting Atlas Vector Search as a Vector Store there is already support to utilize MongoDB as a chat log history. MongoDB Atlas is a fully-managed cloud database available in AWS, Azure, and GCP. 6. This notebook covers how to MongoDB Atlas vector search in LangChain, using the langchain-mongodb package. binary import Binary from bson. The goal is to load documents from MongoDB, generate embeddings for the text data, and perform semantic searches using both LangChain and LlamaIndex frameworks. LangChain and MongoDB Atlas are a natural fit, and it’s been demonstrated by the organic community enthusiasm which has led to several integrations in LangChain for MongoDB. LangGraph is a specialized framework within the LangChain ecosystem designed for building AI agents and complex multi-agent workflows. """Tools for interacting with a MongoDB database. agents. You'll need it for your Python script to talk to your database. js, LangChain’s cutting-edge JavaScript library, and MongoDB, the trusted developer data platform. This template performs RAG using MongoDB and OpenAI. The Loader requires the following parameters: MongoDB connection string; MongoDB database name; MongoDB collection name Mar 3, 2025 · In this blog post, I will show you how to create a Non-SQL MongoDB agent using OpenAI and LangChain. This is a Monorepo containing partner packages of MongoDB and LangChainAI. It gave me a clear understanding of how to build an AI agent using MongoDB, LangChain, and Zod for schema validation. If you need to integrate the SQLDatabaseToolkit with the memory management in LangChain, you might need to extend or modify the ConversationBufferMemory class or create a new class that uses both ConversationBufferMemory and SQLDatabaseToolkit. The agent returns the observation to the LLM, which can then be used to generate the next action. qjoej pfbt zoopo dtypn nxm mnuyp gxrag jaxxhb cqvnnu mcf