Langchain mongodb agent example. example_selectors import .

Langchain mongodb agent example The full cookbook to run the agents example with MongoDB can be found here. It now has support for native Vector Search on the MongoDB document data. tool. It contains the following packages. This vector representation could be used to search through vector data stored in MongoDB Atlas using its vector search feature. The instructions offer a practical roadmap for harnessing the capabilities of MongoDB Atlas and Fireworks LLM in crafting agent-driven applications. This notebook goes over how to use the MongoDBChatMessageHistory class to store chat message history in a Mongodb database. LangChain agents are AI components that use language models to choose a sequence of actions to take. This repository/software is provided "AS IS", without warranty of any kind. To create a MongoDB Atlas cluster, navigate to the MongoDB Atlas website and create an account if you don’t already have one. When this FewShotPromptTemplate is formatted, it formats the passed examples using the example_prompt, then and adds them to the final prompt before suffix: Nov 17, 2023 · LangChain provides a number of options for managing state via the Memory module. LangGraph is a specialized framework within the LangChain ecosystem designed for building AI agents and complex multi-agent workflows. Note: The cluster created must be MongoDB 7. vectorstores import FAISS from langchain_core. Be sure that the tables actually exist by calling sql_db_list_tables first! Example Input: table1, table2, table3', db=<langchain_community. Environment Setup You should export two environment variables, one being your MongoDB URI, the other being your OpenAI API KEY. py. The Mar 3, 2025 · In this blog post, I will show you how to create a Non-SQL MongoDB agent using OpenAI and LangChain. utilities. This step-by-step guide will show you how to create AI-driven applications capable of remembering conversations, accessing databases, and delivering smart responses. agent_toolkit import MONGODB_AGENT_SYSTEM_PROMPT # Pull prompt (or define your own) system_message = MONGODB_AGENT_SYSTEM_PROMPT . example_selectors import import ast import re from langchain. RAG combines AI language generation with knowledge retrieval for more informative responses. """ from __future__ import annotations import json from datetime import date, datetime from importlib. Whereas in the latter it is common to generate text that can be searched against a vector database, the approach for structured data is often for the LLM to write and execute queries in a DSL, such as SQL. Jun 6, 2024 · I showed you how to connect your MongoDB database to LangChain and LlamaIndex separately, load the data, create embeddings, store them back to the MongoDB collection, and then execute a semantic search using MongoDB Atlas vector search capabilities. The schemas for the agents themselves are defined in langchain. In Chains, a sequence of actions is hardcoded. 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. db_name (str) – Name of the database to connect to. This tutorial covers the definition, implementation, and benefits of prompt compression in LLM applications, RAG pipelines, and AI agents. prompts import PromptTemplate from langchain_core. This is a Monorepo containing partner packages of MongoDB and LangChainAI. Rather, they have their own independent scratchpads, and then their final responses are appended to a global scratchpad. Python 3. This tutorial utilizes the News Category Dataset from HuffPost, covering news headlines from 2012 May 15, 2025 · This document explains the MongoDB Agent Tools provided by the LangChain MongoDB integration. agent. 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. I will use OpenAI’s GPT-4, which has powerful natural language processing capabilities, along 4 days ago · MongoDB’s schema-less design enables you to update or extend documents without painful migrations. Your engagement will help extend the reach of this langchain-mongodb: 0. Specifically, the agent performs the following steps: The agent receives a user query. prebuilt import create_react_agent from langchain_mongodb. 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. agents #. It's going to be a data-wrangling wizard! What You Need. It includes integrations between MongoDB, Atlas, LangChain, and LangGraph. To learn more about the built-in generic agent types as well as how to build custom agents, head to the Agents Modules. - Wikipedia. 2# Integrate your operational database and vector search in a single, unified, fully managed platform with full vector database capabilities on MongoDB Atlas. In the walkthrough, we'll demo the SelfQueryRetriever with a MongoDB Atlas vector store. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. MongoDB Atlas. Feb 14, 2024 · Grab the connection string MongoDB Atlas gives you. "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 Sep 12, 2023 · Before we start! 🦸🏻‍♀️. MongoDB Atlas is a fully-managed cloud database available in AWS, Azure, and GCP. The best way to do this is with LangSmith. The goal is to load documents from MongoDB, generate embeddings for the text data, and perform semantic searches using both LangChain and LlamaIndex frameworks. Parameters: connection_string (str) – MongoDB connection URI. Debug poor-performing LLM app runs This covers basics like initializing an agent, creating tools, and adding memory. 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. LangChain simplifies building the chatbot logic, while MongoDB Atlas' vector Oct 31, 2024 · RAG_Pattern. 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. kwargs (Any) Returns: The agent executes the action (e. embedding – The text embedding model to use for the vector store. This component stores each entity as a document with relationship fields that reference other documents in your collection. This starter template implements a Retrieval-Augmented Generation (RAG) chatbot using LangChain, MongoDB Atlas, and Render. Store your operational data, metadata, and vector embeddings in oue VectorStore, MongoDBAtlasVectorSearch. 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. """Wrapper around a MongoDB database. Perfect for JavaScript developers looking to integrate AI into their web apps. callbacks import (CallbackManagerForToolRun,) from langchain_core. 2 Example Use in RAG Use within an agent: from langchain import hub from langgraph. agent_toolkits import create Dec 20, 2024 · Nodes are points on graphs and in langgraph nodes are represented with functions. Source code for langchain_mongodb. 6 or newer; pymongo for chatting with MongoDB Initializes the MongoDB loader with necessary database connection details and configurations. This notebook covers how to MongoDB Atlas vector search in LangChain, using the langchain-mongodb package. Setup: Install ``langchain-community`` code-block:: bash pip install -U langchain-community Key init args: db: SQLDatabase The SQL database. MongoDB is developed by MongoDB Inc. Retrieval Augmented Generation (RAG) Part 1 : Build an application that uses your own documents to inform its responses. Horizontal Scalability: As documents grows, whether it’s product manuals, legal documents, or research articles, MongoDB scales effortlessly through sharding and distributed clusters. Concepts There are several key concepts to understand when building agents: Agents, AgentExecutor, Tools, Toolkits. code-block:: python from langchain_community. """ from __future__ import annotations from typing import Any, Dict, Optional, Type from langchain_core. Retrieval Augmented Generation (RAG) Part 2 : Build a RAG application that incorporates a memory of its user interactions and multi-step retrieval. Graphs are the core components of LangGraph, representing the workflow of your agent. metadata import version from typing import Any, Dict, Iterable, List, Optional, Union from bson import ObjectId from bson. Jun 24, 2024 · from langchain_community. This template performs RAG using MongoDB and OpenAI. Jun 22, 2023 · LangChain and MongoDB Atlas. 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. Agent is a class that uses an LLM to choose a sequence of actions to take. Create and name a cluster when prompted, then find it under Database. Installation and Setup See detail configuration instructions. filter_criteria (Optional[Dict]) – MongoDB filter criteria 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. This Python project demonstrates semantic search using MongoDB and two different LLM frameworks: LangChain and LlamaIndex. \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 Sep 18, 2024 · Learn how to build a powerful AI agent using LangGraph. MongoDB. Crafting the AI Agent 🤖. sql_database. format ( top_k = 5 ) # Create agent agent_executor = create_react_agent ( llm , toolkit . 2. SQLDatabase To connect to Databricks SQL or query structured data, see the Databricks structured retriever tool documentation and to create an agent using the above created SQL UDF see Databricks UC Dec 9, 2024 · class SQLDatabaseToolkit (BaseToolkit): """SQLDatabaseToolkit for interacting with SQL databases. MongoDB Atlas is a document database that can be used as a vector database. For an in depth explanation, please check out this conceptual guide. SQLDatabase object at 0x103d5fa60>), In LangChain, async implementations are located in the same classes as their synchronous counterparts, with the asynchronous methods having an "a" prefix. , runs the tool), and receives an observation. 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. Setup The integration lives in the langchain-mongodb package, so we need to install that. As these applications get more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent. llm: BaseLanguageModel The language model (for use with QueryMongoDBCheckerTool) Instantiate:. 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 See MLflow LangChain Integration to learn about the full capabilities of using MLflow with LangChain through extensive code examples and guides. ainvoke sending it the current state of stored messages. Dec 8, 2023 · This allows for the perfect combination where users can query based on meaning rather than by specific words! Apart from MongoDB LangChain Python integration and MongoDB LangChain Javascript integration, MongoDB recently partnered with LangChain on the LangChain templates release to make it easier for developers to build AI-powered apps. js and MongoDB. What are the multiple independent agents? In this case, the independent agents are a LangChain agent. You can use the LangChain MongoDB integration to run natural language MongoDB queries. In order to perform a match query against MongoDB, the Langchain Agent uses the following functions as tools. This includes not only the conversational buffer, but the ability to extract entities and related information about 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. Parameters: connection_string (str) – A valid MongoDB connection URI. Setup: Install ``langchain-mongodb`` code-block:: bash pip install -U langchain-mongodb Key init args: db: MongoDBDatabase The MongoDB database. llm: BaseLanguageModel The language model (for use with QuerySQLCheckerTool) Instantiate:. When the agent reaches a stopping condition, it returns a final return value. Our AI agent will be powered by LangChain, MongoDB, and the smarts of OpenAI's GPT-4. Overview The MongoDB Document Loader returns a list of Langchain Documents from a MongoDB database. binary import Binary from bson. json MongoDB Atlas. tools import Pass the examples and formatter to FewShotPromptTemplate Finally, create a FewShotPromptTemplate object. In addition to now supporting Atlas Vector Search as a Vector Store there is already support to utilize MongoDB as a chat log history. The MongoDB LangGraph integration enables the following capabilities: May 29, 2024 · %pip install --upgrade --quiet langchain langchain-mongodb langchain-openai pymongo The Dataset. database. g. Creating a MongoDB Atlas vectorstore First we'll want to create a MongoDB Atlas VectorStore and seed it with some data. View the GitHub repo for the implementation code. langchain-mongodb ; langgraph-checkpoint-mongodb ; Note: This repository replaces all MongoDB integrations currently present in the langchain-community package About. Under the hood, create_sql_agent is just passing in SQL tools to more generic agent constructors. It supports native Vector Search, full text search (BM25), and hybrid search on your MongoDB document data. 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. 6. The result from the tool are sent back to the agent Sep 18, 2024 · Learn about Vector Search with MongoDB, LLMs, and OpenAI with the Python programming language. In the agent node, the agent processes the query and determines whether to use a tool or to end the conversation. 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. Sample code can be found here. and licensed under the Server Side Public License (SSPL). Use of this repository/software is at your own risk. """Tools for interacting with a MongoDB database. If a tool is needed, the agent routes to the tools node, where it executes the selected tool. Construct a MongoDB Atlas Vector Search vector store from a MongoDB connection URI. If you found value in this article and wish to show your support, please ‘like’ this LinkedIn post. To improve your LLM application development, pair LangChain with: LangSmith - Helpful for agent evals and observability. If you do not have a MongoDB URI, see the Setup Mongo section at the bottom for instructions on how to do so. language_models import BaseLanguageModel from langchain_core. agent_toolkits This comprehensive tutorial takes you through how to integrate LangChain with MongoDB Atlas Vector Search. 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 Azure Cosmos DB for MongoDB vCore makes it easy to create a database with full native MongoDB support. decimal128 import Decimal128 from bson. This will let you tailor them to specific tasks like choosing the right tool for the job. The Loader requires the following parameters: MongoDB connection string; MongoDB database name; MongoDB collection name Setup: Install ``langchain-mongodb`` code-block:: bash pip install -U langchain-mongodb Key init args: db: MongoDBDatabase The MongoDB database. toolkit import MongoDBDatabaseToolkit from Oct 2, 2024 · Let us look at how this application architecture has been implemented for this exercise. The agent returns the observation to the LLM, which can then be used to generate the next action. Feb 19, 2025 · A big use case for LangChain is creating agents. These tools enable LangChain agents to interact with MongoDB databases through a set of standardized inter Sep 23, 2024 · You'll need a vector database to store the embeddings, and lucky for you MongoDB fits that bill. InfoSQLDatabaseTool(description='Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables. . Agents are systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls. This object takes in the few-shot examples and the formatter for the few-shot examples. 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. Create a new function chatbot that calls OpenAI using llm. 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. For example, the synchronous invoke method has an asynchronous counterpart called ainvoke. You'll need it for your Python script to talk to your database. python 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. Aug 12, 2024 · This tutorial has guided you through building an AI research assistant agent, leveraging tools such as MongoDB, Fireworks AI, and LangChain. Select Browse Collections and create either a blank collection or one from the provided sample data. While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications. namespace (str) – A valid MongoDB namespace (database and collection). The built-in AgentExecutor runs a simple Agent action -> Tool call Agents: Build an agent that interacts with external tools. 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. Agent Types There are many different types of agents to use. Use vector search in Azure Cosmos DB for MongoDB vCore to seamlessly integrate your AI-based 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. agents. collection_name (str) – Name of the collection to fetch documents from. agent_toolkit. Classes Jan 23, 2024 · In this example, multiple agents are connected, but compared to above they do NOT share a shared scratchpad. code-block:: python from langchain_mongodb. toolkit import MongoDBDatabaseToolkit from 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. We need to install langchain-mongodb python package. MongoDB is a NoSQL , document-oriented database that supports JSON-like documents with a dynamic schema. 0 Enabling a LLM system to query structured data can be qualitatively different from unstructured text data. get Aug 12, 2024 · Leverage the capabilities of Fireworks AI, MongoDB, and LangChain to construct an AI agent. nrxg ycer hmpgykw qepow dtttd vjye yhx qtsgx vbekjeh jlw