Langchain mongodb query. schema import AttributeInfo from langchain.

Langchain mongodb query MongoDB Atlas. """ from __future__ import annotations from typing import Any, Dict, Optional, Type from langchain_core. Here's one recommended approach from MongoDB. 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. schema import AttributeInfo from langchain. Jun 24, 2024 · import os import getpass from operator import itemgetter from langchain_openai import ChatOpenAI from langchain_core. MongoDB Atlas is a document database that can be used as a vector database. retrievers. MongoDB 是一个 NoSQL 文档型数据库，支持类似 JSON 的文档和动态模式。 概述 . 304 In the notebook we will demonstrate how to perform Retrieval Augmented Generation (RAG) using MongoDB Atlas, OpenAI and Langchain. 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. """ from typing import Dict, Tuple, Union from langchain_core. This component stores each entity as a document with relationship fields that reference other documents in your collection. query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0. langchain-mongodb: 0. arxiv : Python library to download papers from the arXiv repository. Next, NumDimensions represents the About. IN, Comparator. base import SelfQueryRetriever from langchain_core. Creating a MongoDB Atlas vectorstore First we'll want to create a MongoDB Atlas VectorStore and seed it with some data. 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 . We'll then use libraries from LangChain to Load, Transform, Embed and Store: langchain-mongodb: 0. "); Apr 24, 2025 · Chatbot systems enhance the user experience by providing quick and intelligent responses, making interactions more efficient. collection (Collection[Dict[str, Any]]) – MongoDB collection to add the texts to. In this tutorial, we’ll walk through the process of building a chatbot using Langchain4j and MongoDB Atlas. Usando o MongoDB Atlas e a página da AT&T na Wikipedia como caso de sucesso, demonstramos como usar efetivamente as bibliotecas May 15, 2025 · These tools enable LangChain agents to interact with MongoDB databases through a set of standardized interfaces, allowing them to query data, inspect schemas, and analyze information stored in MongoDB collections. You can use the LangChain MongoDB integration to run natural language MongoDB queries. Setup langchain. 定义 LangChain 提示模板，指示 LLM 使用检索到的文档作为查询的上下文。 LangChain 将这些文档传递给 输入变量，并将您的查询传递给{context} {query}变量。 构建一条 链 指定以下内容： 您定义的用于检索相关文档的混合搜索检索器。 您定义的提示模板。 Sep 18, 2024 · Learn about Vector Search with MongoDB, LLMs, and OpenAI with the Python programming language. MongoDB Atlas is a document database that can be. 📄️ Neo4j. Installation and Setup See detail configuration instructions. 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. This vector representation could be used to search through vector data stored in MongoDB Atlas using its vector search feature. Below is the script for a tool that generates vectors for the query string Query analysis So far, we are executing the retrieval using the raw input query. 6. 📄️ MongoDB Atlas. So, we'll define embedding for Path. Constructs a chain that specifies the following: Atlas Vector Search as the retriever to search for documents to use as context. MongoDB 连接字符串; MongoDB 数据库名称; MongoDB 集合名称 (可选) 内容过滤器字典 (可选) 输出中包含的 使用适用于MongoDB的 LangChain 集成，通过Atlas Vector Search实现RAG。 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 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. This repository/software is provided "AS IS", without warranty of any kind. """Tools for interacting with a MongoDB database. 📄️ OpenSearch This project provides a Streamlit web application that allows users to upload CSV files, generate MongoDB queries using LLM (Language Learning Model), and save query results. Aug 16, 2024 · LangChain acts as the bridge between your generative model and MongoDB Atlas, allowing it to query vectors. It contains the following packages. MongoDB Atlas. structured_query import (Comparator, Comparison, Operation, Operator, StructuredQuery, Visitor,) MULTIPLE_ARITY_COMPARATORS = [Comparator. g. tool. Sep 23, 2024 · In this tutorial, we'll walk through each of these steps, using MongoDB Atlas as our Store. ; Dynamic Database and Collection Switching: The set_db_and_collection method allows you to switch databases and collections dynamically. Este guia passo a passo simplifica o complexo processo de carregar, transformar, incorporar e armazenar dados para recursos de pesquisa aprimorados. This collaboration has produced a retrieval-augmented generation template that capitalizes on the strengths of MongoDB Atlas Vector Search along with OpenAI's technologies. ├── data Documentation for LangChain. Execute SQL query: Execute the query. 5, ** kwargs: Any,) → list [Document] # Async return docs selected using the maximal marginal relevance. View the GitHub repo for the implementation code. Just like we have SQLDatabaseChain in LangChain to connect with a SQL database such as Postgres, do we have something similar to connect with a NoSQL database like Mongo? I looked at the documentation and didn't find any alternative for NoSQL. Setup: Install ``langchain-mongodb`` code-block:: bash pip install -U langchain-mongodb Key init args: db: MongoDBDatabase The MongoDB database. query_constructors. runnables import Runnable from langchain_core. text_key (str) – MongoDB field that will contain the text for each document Defines a LangChain prompt template to instruct the LLM to use these documents as context for your query. js. Answer the question: Model responds to user input using the query results. This Python project demonstrates semantic search using MongoDB and two different LLM frameworks: LangChain and LlamaIndex. The Loader requires the following parameters: MongoDB connection string; MongoDB database name; MongoDB collection name You can use LangChain's built-in retrievers or the following MongoDB retrievers to query and retrieve data from Atlas. The MongoDB. , "Find documents since the year 2020. This step-by-step guide will show you how to create AI-driven applications capable of remembering conversations, accessing databases, and delivering smart responses. Actor: This component acts as the trigger, initiating the retrieval and generation process based on the user query. Users utilizing earlier versions of MongoDB Atlas need to pin their LangChain version to <=0. Mar 3, 2025 · In this blog post, I will show you how to create a Non-SQL MongoDB agent using OpenAI and LangChain. 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. We need to install langchain-mongodb python package. Constructs a chain that uses OpenAI's chat model to generate context-aware responses based on your prompt. MongoDB Atlas is a fully-managed cloud database available in AWS, Azure, and GCP. Specifically, we'll use the AT&T and Bank of America Wikipedia pages as our data source. Initialization: The MongoDBManager class is initialized with the MongoDB connection string. It instructs LangChain and OpenAI to work together to retrieve relevant information and formulate a response. In addition to now supporting Atlas Vector Search as a Vector Store there is already support to utilize MongoDB as a chat log history. Neo4j is a graph database that stores nodes and relationships, that also supports native vector search. Sample code can be found here. May 22, 2024 · Explanation. MongoDBAtlasTranslator¶ class langchain. Query Prompt: This signifies the user's question or input that the chatbot needs to respond to. Query embedding: Designed to generate user query embeddings. MongoDB 文档加载器从 MongoDB 数据库返回 Langchain 文档列表。 加载器需要以下参数. If you do not have a MongoDB URI, see the Setup Mongo section at the bottom for instructions on how to do so. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. self_query. 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. I will use OpenAI’s GPT-4, which has powerful natural language processing capabilities, along Oct 6, 2024 · The variable Path refers to the name that holds the embedding, and in Langchain, it is set to "embedding" by default. Constructs a chain that specifies the following: The hybrid search retriever you defined to retrieve relevant documents. Defines a LangChain prompt template to instruct the LLM to use these documents as context for your query. MongoDBAtlasTranslator [source] ¶ Translate Mongo internal query language elements to valid filters. prompts import PromptTemplate from langchain_core. configurable_alternatives (ConfigurableField (id = "llm"), default_key = "anthropic", openai = ChatOpenAI ()) # uses the default model Mar 15, 2024 · There are different approaches to achieving your goal. See our how-to guide on question-answering over CSV data for more detail. callbacks import (CallbackManagerForToolRun,) from langchain_core. It supports native Vector Search, full text search (BM25), and hybrid search on your MongoDB document data. 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. LangChain passes these documents to the {context} input variable and your query to the {question} variable. In this section, we'll set up the database. The goal is to load documents from MongoDB, generate embeddings for the text data, and perform semantic searches using both LangChain and LlamaIndex frameworks. This article from MongoDB describes a high-level approach for storing vector embeddings with each document in the existing collection and creating a separate vector search index. Aug 12, 2024 · langchain-mongodb: Python package to use MongoDB as a vector store, semantic cache, chat history store, etc. structured_query import MongoDB is a NoSQL, document-oriented Golden Query; Google Books; from langchain_mongodb. Dec 9, 2024 · Parameters. Parameters: query (str) – Text to look up documents similar to. You can access your database in SQL and also from here, LangChain. However, there are some advantages to allowing a model to generate the query for retrieval purposes. This notebook covers how to MongoDB Atlas vector search in LangChain, using the langchain-mongodb package. MongoDB is a NoSQL , document-oriented database that supports JSON-like documents with a dynamic schema. chat_message_histories import MongoDBChatMessageHistory. Use of this repository/software is at your own risk. Using MongoDBAtlasVectorSearch LangChain과 Atlas Vector Search를 통합하여 생성형 인공지능과 RAG 애플리케이션을 구축할 수 있습니다. prompts import PromptTemplate from langchain. langchain-mongodb ; langgraph-checkpoint-mongodb ; Note: This repository replaces all MongoDB integrations currently present in the langchain-community package MongoDB. language_models import BaseLanguageModel from langchain_core. Attributes 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. Note that querying data in CSVs can follow a similar approach. . Convert question to SQL query: Model converts user input to a SQL query. In the walkthrough, we'll demo the SelfQueryRetriever with a MongoDB Atlas vector store. Store your operational data, metadata, and vector embeddings in oue VectorStore, MongoDBAtlasVectorSearch. 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. pymupdf : Enables allowing for the extraction of text, images, and metadata from PDF files. 이 페이지에서는 MongoDB LangChain Python 통합과 애플리케이션에서 사용할 수 있는 다양한 구성 요소에 대한 개요를 제공합니다. It now has support for native Vector Search on the MongoDB document data. MongoDB Atlas query execution: Facilitates the execution of queries, combining filters and vector embeddings for precise data retrieval. Environment Setup You should export two environment variables, one being your MongoDB URI, the other being your OpenAI API KEY. RAG com Atlas Vector Search, LangChain e OpenAI. in LangChain. js supports MongoDB Atlas as a vector store, and supports both standard similarity search and maximal marginal relevance search, which takes a combination of documents are most similar to langchain_community. MongoDBAtlasTranslator¶ class langchain_community. Insert into a Chain via a Vector, FullText, or Hybrid This is a Monorepo containing partner packages of MongoDB and LangChainAI. RAG avançado: recuperação de documento principal. MongoDBAtlasTranslator Source code for langchain_mongodb. Mar 14, 2024 · I want to query my mongo collection using LangChain. LangChain. Perfect for JavaScript developers looking to integrate AI into their web apps. 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. Store Vector Embeddings with Document. Source code for langchain. 📄️ MyScale. MyScale is an integrated vector database. runnables. This repository demonstrates how to use LangChain to interact with both MySQL and MongoDB databases. chains import create_sql_query Apr 12, 2024 · Hello, I am Neeraj Mahapatra, In this tutorial, we dive into the world of NoSQL databases, specifically MongoDB, and exp Jul 2, 2023 · I was skimming through the repository for the MongoDB Agent and I discovered that it does not exist. from typing import Any, Dict, Sequence, Tuple, Union from langchain. NIN] May 12, 2025 · langchain-mongodb Installation pip install -U langchain-mongodb Usage. llm: BaseLanguageModel The language model (for use with QueryMongoDBCheckerTool) Instantiate:. 2# Integrate your operational database and vector search in a single, unified, fully managed platform with full vector database capabilities on MongoDB Atlas. Integrate Atlas Vector Search with LangChain for a walkthrough on using your first LangChain implementation with MongoDB Atlas. embedding – Text embedding model to use. This is a sample database that LangChain レトリーバー はベクトル ストアから関連するドキュメントを取得するために使用するコンポーネントです。LangChain の組み込み検索ツールまたは次の MongoDB 検索システムを使用して、Atlas からデータをクエリして検索できます。 全文検索システム Jul 3, 2024 · Descubra o poder da pesquisa semântica com nosso tutorial abrangente sobre integração de LangChain e MongoDB. 0. agent_toolkit. LangChain passes these documents to the {context} input variable and your query to the {query} variable. chains. code-block:: python from langchain_mongodb. O MongoDB também fornece os seguintes recursos para desenvolvedores: Introdução ao LangChain e MongoDB Atlas pesquisa vetorial. toolkit import MongoDBDatabaseToolkit from To use MongoDB Atlas vector stores, you’ll need to configure a MongoDB Atlas cluster and install the @langchain/mongodb integration package. 1. js and MongoDB. query_constructor. Sep 18, 2024 · Learn how to build a powerful AI agent using LangGraph. """Logic for converting internal query language to a valid MongoDB Atlas query. Usar a Atlas Vector Search do MongoDB com LangChain MongoDB Atlas. Overview The MongoDB Document Loader returns a list of Langchain Documents from a MongoDB database. The integration allows seamless query generation and retrieval of data from these databases using natural language inputs. For example: In addition to semantic search, we can build in structured filters (e. Insert into a Chain via a Vector, FullText, or Hybrid 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. The application uses Google's Gemini API for query generation and MongoDB for data storage. from langchain_anthropic import ChatAnthropic from langchain_core. It takes a "query" as input, transforms it into embeddings using Google's embedding models, and retrieves the most semantically near data from MongoDB Atlas. mongodb_atlas. Then, it will pass this context along with the query to . Is it feasible to develop a MongoDB agent that establishes a connection with MongoDB, generates MongoDB queries based on given questions, and retrieves the corresponding data? Within my organization Jun 22, 2023 · LangChain and MongoDB Atlas. 8# Integrate your operational database and vector search in a single, unified, fully managed platform with full vector database capabilities on MongoDB Atlas. Class that is a wrapper around MongoDB Atlas Vector Search. 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. This template performs RAG using MongoDB and OpenAI. Defines a LangChain prompt template to instruct the LLM to use the retrieved documents as context for your query. 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. tools import RAG básico com MongoDB e OpenAI. utils import ConfigurableField from langchain_openai import ChatOpenAI model = ChatAnthropic (model_name = "claude-3-sonnet-20240229"). It includes integrations between MongoDB, Atlas, LangChain, and LangGraph. ziey vyqleh jrqcmex lzvr dgat ovwp uzvlx iwfhrx mgabd mikjr