Many developers find LangChain, an open-source library, can be particularly useful in chaining together LLMs, embedding models and knowledge bases. NVIDIA uses LangChain in its reference architecture for retrieval-augmented generation. The LangChain community provides its owndescription of a RAG proces...
conceptual tasks and components used in the indexing and retrieval processes. Its primary goal is to highlight the different phases data go through and the shared components used by both processes. Data Scientists generally design such a system with the help of frameworks such asLangchainorLlama ...
Chunk size is an important hyperparameter for the RAG system. When chunks are too large, the data points can become too general and fail to correspond directly to potential user queries. But if chunks are too small, the data points can lose semantic coherency. The retriever Vectorizing the d...
As for thecalculateMaxTokensfunction in thecount_tokens.tsfile of the langchainjs repository, it is used to calculate the maximum number of tokens that can be used for a given model, after considering the number of tokens in the prompt. The function takes an object as an argument, which ...
LangChain is a very helpful tool that can analyze code repositories on GitHub. It brings together three important parts: VectorStores, Conversational RetrieverChain, and an LLM (Language Model) to assist you with understanding code, answering questions about it in context, and even generating new ...
If you’re interested in learning more about vector search, we recommend the following articles: What is a Vector Database? and What are Vector Embeddings?. RAG architecture At its core, a RAG architecture includes the retriever and the generator. Let's start by understanding what each of ...
The advent of Retrieval-Augmented Generation (RAG) models has been a significant milestone in the field of Natural Language Processing (NLP). These models combine the power of information retrieval w...
In the snippet above, the VectorIndexRetriever, RetrieverQueryEngine, and SimilarityPostprocessor are utilized to construct a customized query engine. This example demonstrates a more controlled query process. Parsing the Response Post query, a Response object is returned which contains the response tex...
Theretriever: An AI model that searches the knowledge base for relevant data. Theintegration layer: The portion of the RAG architecture that coordinates its overall functioning. Thegenerator: A generative AI model that creates an output based on the user query and retrieved data. ...
In addition, developers and IT teams can try the free, hands-onNVIDIA LaunchPad labfor building AI chatbots with RAG, enabling fast and accurate responses from enterprise data. All of these resources useNVIDIA NeMo Retriever, which provides leading, large-scale retrieval accuracy andNVIDIA NIMmicr...