Exploring Rag and MongoDB: Enhancing AI Applications through Contextual Data

Unlocking the Power of MongoDB: Leveraging Document Model, Vector Search, and AI Integrations to Transform Your Applications

1. Speaker is excited to talk about the use of Rag (Retrieval Augmented Generation) with MongoDB's document model and Atlas platform.
2. Rag is used to give context to a generic AI or model, such as a large language model (LLM), by augmenting it with specific data at the time of prompting.
3. This allows for the creation of transformative, accurate, and consistent AI-powered applications that can answer questions related to the use cases they are being used for.
4. A typical Rag setup involves a user entering a prompt, which is then embedded and used to search a vector database (such as MongoDB Atlas Vector Search) for similar documents.
5. These documents, along with the original prompt, are then input into the LLM to generate an answer, which is returned to the user.
6. MongoDB's document model is unique because it allows for the storage of objects that applications interact with directly in the database, rather than having to stitch together different tables in a
7. Documents are also flexible and can store JSON, tabular data, key-value stores, geospatial data, graphs, and more.
8. This results in a more efficient, productive, and scalable system for developers building systems.
9. MongoDB Atlas has added hnsw indexes to allow for approximate nearest neighbor Vector search over data stored in the database.
10. Embeddings can be added directly into documents and stored in the database, allowing for vectors of up to 4,096 dimensions to be searched.
11. An index definition must be created to specify the type of index, type of field being indexed, path, number of dimensions, and similarity function.
12. The vector index is immediately built and kept in sync with data as it is updated in the database.
13. A dollar Vector search aggregation stage can be used to compute an approximate nearest neighbor search.
14. A filter can be used to pre-filter documents as the graph is traversed, allowing for irrelevant documents to be filtered out.
15. MongoDB has also introduced search nodes to decouple the approach to scaling for transactional databases and Vector search workloads.
16. This allows for the amount of resources brought to bear to be tuned perfectly for specific workloads.
17. MongoDB is integrated with several popular AI frameworks, including Llama Index, Lang chain, Microsoft Semantic Kernel, AWS Bedrock, and Haya2k.
18. These integrations support various primitives, such as Vector store, chat message history abstraction, and more.
19. Rag can be taken to the next level by combining transactional data inside of a database with Vector search to augment prompts.
20. Examples of this include semantic caching and chat history.
21. Semantic caching can reduce the number of calls made to a large language model by first sending a question to a retriever to determine the prompt and additional augmented data.
22. If the semantic cache is a hit based on semantic similarity, the cached answer is returned instead of hitting the LLM again.
23. Chat history can be used to create an experience similar to chatGPT, where the history of chats is continuously fetched and put back into the prompt to maintain continuity in the conversation with
24. MongoDB Atlas provides comprehensive security controls, privacy, uptime, automation, and optimal performance for AI-powered applications.

Source: AI Engineer via YouTube

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