Llm to generate mongodb query. This call is taking time .

Llm to generate mongodb query Compass uses AI to generate aggregations based on prompts you provide. As part of Chain Sequence, the prompt and data catalog metadata are passed to the LLM, hosted on Bedrock, to create a MongoDB query. Results: View the query results in a table and download the output as a CSV file if desired. Contribute to selvam85/getting-started-with-llm development by creating an account on GitHub. Learn to efficiently handle LLM queries by storing and retrieving embeddings—numerical vectors representing the semantic meaning of text—reducing the need for repeated API calls. button("Query LLM"): with st. Built using GPT-4, it validates and executes queries against an SQLite database. core. Step 3: Modify or confirm the query and click "Execute Query" to run it against your MongoDB database. By allowing users to input queries in plain language, MongoDB Compass simplifies the querying process, especially for those new to MongoDB or database queries in general. This way, it's possible to retrieve data listings from the database by making requests in natural language, such as: "What are the 5 most Jun 20, 2024 · Large language model (LLM): This is the AI engine powering the agent's tasks, like text processing and generation. Different types of queries can use Step 2: Click "Generate Query" to see the MongoDB query generated by the LLM. The generated text is then parsed to extract the SQL query. GITHUB: https://github. The final operation uses the vector store instance as a Mar 12, 2024 · Then, return to our collections. Configure the ChatLlm The ChatLlm is the interface between the chatbot server and the LLM. A visual representation of the retrieval and generation process is as follows: This project implements an automated data query and retrieval system using Offline LLM, MongoDB, and CSV data processing. Sep 28, 2024 · Hi All I am working on Natural language generation for Mongodb query using OpenAI, Python Langchain. Learn about advanced reasoning, benchmark performance, and practical implementation steps that make this cutting-edge AI solution Jun 24, 2024 · Given a user query, it is first embedded using the same embedding model, and the most relevant chunks are retrieved based on the similarity between the query and chunk vectors. 5 model to generate context-aware responses. Building a retrieval system involves searching for and returning the most relevant documents from your vector database to augment the LLM with. Apr 7, 2025 · Accessing specific information within databases, especially NoSQL systems like MongoDB, often requires specialized query knowledge. Summary. We extract entities using an LLM using LangChain, storing them as documents in a MongoDB collection, which we use as a graph database to find Sep 2, 2024 · Discover how to reduce API costs and improve response times for Large Language Models (LLMs) by implementing semantic caching using MongoDB Atlas and Vector Search. I am passing this to LLM again to convert this json to Natural Language text to the user. 3. Sep 23, 2024 · You'll need a vector database to store the embeddings, and lucky for you MongoDB fits that bill. LangSmith. from_vector_store(vector_store) query_engine = index. com/ronidas39/LLMtutorial/tree/main/tutorial57TELEGRAM: https://t. Chat with an LLM. 5 Turbo. spinner("Querying LLM Dec 9, 2024 · SM3-Text-to-Query is a new Dataset and benchmark that enables the evaluation across four query languages (SQL, MongoDB Query Language, Cypher, and SPARQL) and three data models (relational, graph, document). Use custom function tool to verify that the generated query is accurate. Feb 13, 2024 · Create a MongoDB Atlas cluster with a M0 tier (free-forever). The application uses Google's Gemini API for query generation and MongoDB for data storage. Mar 28, 2024 · from llama_index. Jun 24, 2024 · When provided with a natural language query, it employs a query-constructing LLM chain to create a structured query, which it then uses to search its underlying VectorStore. GraphRAG is an alternative approach to traditional RAG that structures data as a knowledge graph of entities and their relationships instead of as vector embeddings. Introduction. GPT 3. Choosing a different LLM can significantly impact the agent's output based on the underlying LLM’s strengths and weaknesses. 1 def handle_user_query ( query, collection ) : Jul 2, 2023 · Feature request I was skimming through the repository for the MongoDB Agent and I discovered that it does not exist. It improves the experience by presenting the output in a human-friendly format. core import VectorStoreIndex import pprint from llama_index. Dec 13, 2024 · The chunks themselves and any duplicate parent documents are then dropped, and unique parent documents are passed on to the LLM as context to answer the user query. I am getting the results accurately in Mongodb Json format. Jan 13, 2025 · I built a system where the user’s natural language query is converted into an SQL query using an LLM. We now describe our implementation. Is it feasible to develop a MongoDB agent that establishes a connection with MongoDB, generates MongoDB queries based on Oct 2, 2024 · Anthropic’s Claude hosted in Amazon Bedrock glues it together by choosing the best matching tools to perform search on MongoDB Atlas based on the user query. response. Learn how to write complex queries with multiple aggregation stages. You can also specify parameters for your search in the as_query_engine call—in our case, we will set the similarity_top_k to 5 to get the top five most relevant results from our searches, and the vector_store_query_mode to indicate the Jul 1, 2024 · The handle_user_query function performs a vector search on the MongoDB collection based on the user's query and utilizes OpenAI's GPT-3. Apr 12, 2024 · Hello, I am Neeraj Mahapatra, In this tutorial, we dive into the world of NoSQL databases, specifically MongoDB, and exp Apr 18, 2024 · def read_mongodb_query(user_input: str, table_schema: str, schema_description: str, database: str, collectionName: str): """Generates a MongoDB raw aggregation pipeline based on user input and Feb 17, 2025 · import streamlit as st st. I hope you have enjoyed this content. text_input("Enter search query:") MongoDB Setup We connect to MongoDB using pymongo, which allows us to query data and Feb 27, 2025 · MongoDBGraphStore. About this Task Apr 14, 2025 · This information, along with the original query, then goes back to the LLM to generate an accurate final response. . The goal is to load documents from MongoDB, generate embeddings for the text data, and perform semantic searches using both LangChain and LlamaIndex frameworks. About. It also accepts optional parameters to control the generation process, such as num_beams, max_length, repetition_penalty, length_penalty, early_stopping, top_p, top_k, and num_return_sequences. We will mock up the verification step to keep the example simple. MongoDB query with OpenAI function calling. This extension uses an OpenAPI specification and the Cloud Run function you created to map natural language to database operations and query your data in Atlas. The following functions initialize the LLM and add the MongoDB python functions as tools to the agent. Generate complex aggregation queries for real-time analytics. You can use MongoDB Compass to generate queries using natural language. Alternatively, you can enable natural language querying by clicking the Log in to Atlas to enable button within the Use natural language to generate queries and pipelines modal. An LLM then uses the user’s question, prompt, and the retrieved documents to generate an answer to the question. Jan 8, 2024 · MongoDB query with OpenAI. Behavior Jul 28, 2024 · Key Components. Run query directly online through the web or copy-paste and use in your own setup. notebook_utils import display_response index = VectorStoreIndex. It is instructed to generate a response based on the provided context and user query, summarizing the answer while citing the page number and file name. MongoDB Atlas’ integration in LangChain for GraphRAG follows an entity-based graph approach. Compass uses AI to generate queries based on prompts you provide. Embeddings. Querying with natural language can be a helpful starting point and assist you in learning to write MongoDB queries. 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. The generated response might vary. title("Azure OpenAI Mongo Agent Search") query = st. invoke Feb 22, 2024 · In conclusion, MongoDB Compass’s Query with Natural Language feature revolutionizes the way users interact with databases. Learn more about Large Language Models (LLMs) and how MongoDB Atlas Vector Search uses this technology to take your software applications to the next level. MongoDB Query Generation with LLMs: Similarly, I developed a system where the user’s natural language query is translated into a MongoDB query Aug 30, 2024 · This way, given a user query and its embedding, we can retrieve the most relevant source documents from the knowledge base based on how similar their embeddings are to the query embedding. ” You can use MongoDB Compass to generate aggregation queries using natural language. SM3-Text-to-Query Benchmark Construction. To enable natural language querying in Compass, follow the steps below. End to end examples of using LLM. The generate_query method takes a textual query and returns a MongoDB query. The temperature parameter set to 0. Invokes the chain with a sample query. This Node application receives a natural language query and converts it into a pipeline compatible with the aggregate method of a MongoDB database collection. The generated SQL query is then executed on a relational database to fetch the relevant records. me/ttyoutubediscussion #ChatwithMongoDB#ai #llm #langchain #openai # We would like to show you a description here but the site won’t allow us. Workik's MongoDB Query Generator shines across various use cases, such as: 1. It is called LLM Based MongoDB Querying System through which users can search in MongoDB databases just by using simple words of English language rather than using complex language queries. This Python project demonstrates semantic search using MongoDB and two different LLM frameworks: LangChain and LlamaIndex. py. Mar 12, 2025 · The conversation is structured with predefined roles and messages. Use custom function tool to format the output in JSON format. While vector-based RAG finds documents that are semantically similar to the query, GraphRAG finds connected entities to the query and traverses the relationships in the graph to retrieve relevant information. LangChain. Feb 1, 2025 · Discover how DeepSeek-R1—a revolutionary open-source LLM trained with innovative reinforcement learning—challenges commercial giants like GPT-4, while MongoDB’s LLM-agnostic architecture powers a cost-efficient, real-time retrieval-augmented generation system. To execute the generated MongoDB query and retrieve the data, the PyMongo library, a Python-based tool for MongoDB interaction, establishes a connection with the database. Aug 12, 2024 · The data store for the back end of the retriever for this tutorial will be a vector store enabled by the MongoDB database. 5 Pro model. Sample code can be found here. Figure 3. Finally, the retrieved context and user question, along with any other prompts, are passed to an LLM to generate an answer. Learn more about retrieval-augmented generation (RAG) and how MongoDB Atlas Vector Search uses this technology to take your software applications to the next level. MongoDB. as_query_engine(). Ensure that the query solely relies on keys and columns present in the schema. The code snippet below shows the implementation required to initialize a MongoDB vector store using the MongoDB connection string and specifying other arguments. May 22, 2024 · Explanation. Here, you’ll find the “Generate Query” option. # Sep 17, 2024 · SQL Query Generation: The generate_sql_query function takes a natural language input, constructs a prompt, and uses the model to generate the corresponding SQL query. 2. PostgreSQL. To summarize we were able to utilize Chat Completion API along with function calling capability to generate MongoDB queries and verify them as well. Create an initial query or aggregation pipeline that you can modify to suit your requirements. llm. Jan 31, 2024 · Create an assistant using Assistants API and use it to generate MongoDB queries. Oct 2, 2024 · Converting an existing index into a query engine is as simple as doing <index_name>. The MongoDB Chatbot Server comes with an implementation of the ChatLlm, which uses the OpenAI API MongoDB Atlas query execution: Facilitates the execution of queries, combining filters and vector embeddings for precise data retrieval. About this Task Step 2: Click "Generate Query" to see the MongoDB query generated by the LLM. Minimize the usage of lookup operations wherever feasible to enhance query efficiency. Calls the LLM that you specified when you set up your environment to generate a context-aware response based on the retrieved documents. Craft queries to extract and compile data for periodic reports. Feb 5, 2024 · LangChain requires an LLM to be defined. Click on that: As you can observe, MongoDB will enable a field to enter with queries. I am able to generate the query accurately using OpenAI gpt4 model and I have passed this to Mongodb Aggregate pipeline. 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. The retrieved documents, user query, and any user prompts are then passed as context to an LLM, to generate an answer to the user’s question. Master MongoDB NoSQL Queries and Database with AI Using cutting-edge AI to generate simple or advanced MongoDB queries. Enabling a LLM system to query structured data can be qualitatively different from unstructured text data. All of this is achieved using MongoDB’s rich aggregation framework. ; Dynamic Database and Collection Switching: The set_db_and_collection method allows you to switch databases and collections dynamically. # Query LLM with user input and context data if st. 11226: An Interactive Query Generation Assistant using LLM-based Prompt Modification and User Feedback While search is the predominant method of accessing information, formulating effective queries remains a challenging task, especially for situations where the users are not familiar with a domain, Jul 20, 2023 · In this article, let’s see how we can use MongoDB Atlas Index for Vector Search and LLM for getting curated answer for the question. In this section, you create a Vertex AI Extension that enables natural language querying on your data in Atlas by using the Gemini 1. Create queries based on user input for web services. Prompts the LLM with a sample query about Atlas security recommendations. Returns the LLM 's response and the documents used as context. This can create a significant barrier for non-technical users May 18, 2023 · This is where the the query is parsed, relevant Nodes retrieved through the use of indexes, and provided as an input to a “Large Language Model” (LLM). as_query_engine(similarity_top_k= 3) query = "I want to stay in a place that's warm and friendly, and not too far from resturants, can you This paper present an novel concept to query MongoDB database using NLP and Large Language Model (LLM). We can easily create a UI around it to extend the example and make it more useful! Thanks for reading and happy coding! prompt_template = f"""<s> Task Description: Your task is to create a MongoDB query that accurately fulfills the provided Instruct while strictly adhering to the given MongoDB schema. It leverages the Mistral-7B-Instruct model for natural language query generation and executes predefined test cases to generate query results, which are saved as CSV files. Initialization: The MongoDBManager class is initialized with the MongoDB connection string. First, we establish a connection to the MongoDB server using the pymongo library. The instructions offer a practical roadmap for harnessing the capabilities of MongoDB Atlas and Fireworks LLM in crafting agent-driven applications. This guide contains information on how you can use the MongoDB Chatbot Server to chat with a large language model (LLM). To help improve the ai quality we use OpenAI’s LLM You may want to use natural language to query in Compass to: Ask plain text questions about your data. Nov 30, 2024 · Give a conclusion to the user's question based on the query results Result of the query is as follows: {result} The user had asked the following question: {question} """ response = self. Nov 19, 2023 · Abstract page for arXiv paper 2311. 2 influences the randomness of the output, favoring more deterministic responses. 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. This project is an LLM-powered SQL Query Generator that allows users to generate SQL queries using natural language input. Otherwise, the user query alone is used to retrieve results using vector search. To retrieve relevant documents with Atlas Vector Search, you convert the user's question into vector embeddings and run a vector search query against your data in Atlas to find documents with the most similar embeddings. This guide covers setting up a FastAPI server Contribute to ConniceT/LLM-to-SQL development by creating an account on GitHub. To start, paste the following sentence, click in generate then Find: “Find the countries with the highest population, sorting the results in descending order by population. Cancel Create saved search " Generated MongoDB Query: The gpt-4o chat model from OpenAI to generate a context-aware response. In an era where data-driven decision-making is paramount, the ability to efficiently query and Sep 12, 2024 · If the specified metadata is found, an LLM generates a filter definition that gets applied as a pre-filter during vector search. This connection allows us to access the desired database and collection that contains the data we want to query. This call is taking time Mar 27, 2024 · Given a user query, relevant chunks are retrieved from the knowledge base and passed along with the query and prompt as context for the LLM to generate an answer to the question. daqh yve dannne kjisq wieg cte btyilel elzrzyz aecnkin xhneu