AI-powered Solutions
Data Analysis via API
Leverage our expertise in data analysis via API to gain valuable insights and drive informed business decisions. Our tailored solutions ensure seamless integration and actionable results for your organization.
Example of a Simple Retrieval-Augmented Generation based Chatbot to query your Private Knowledge Base
Store the knowledge of your internal documents in a format that is suitable for querying, using an Embedding Model:
Split text corpus of the entire knowledge base into chunks - a chunk will represent a single piece of context available to be queried. Data of interest can be from multiple sources, e.g. Documentation in MS Sharepoint supplemented by PDF reports.
Use the Embedding Model to transform each of the chunks into a vector embedding.
Store all vector embeddings in a Vector Database.
Save text that represents each of the embeddings separately together with the pointer to the embedding.
Constructing the answer to a question/query of interest:
Embed a question/query you want to ask using the same Embedding Model that was used to embed the knowledge base itself.
Use the resulting Vector Embedding to run a query against the index in the Vector Database.
Vector DB performs an Approximate Nearest Neighbour (ANN) search for the provided vector embedding against the index and returns previously chosen amount of context vectors. The procedure returns vectors that are most similar in a given Embedding/Latent space.
Map the returned Vector Embeddings to the text chunks that represent them.
Pass a question together with the retrieved context text chunks to the LLM via prompt. Instruct the LLM to only use the provided context to answer the given question.
As described, the system is really just a naive RAG that is usually not fit for production applications. Instead, check out our Advanced RAG Systems!
AI Multi-Agent Systems
Multi-Agent System (MAS) is a group of Agents that coomunicate and interact with each other and the environment to achieve a certain goal.
The agents may have different levels of autonomy, decision-making capabilities, and communication methods.
Fine-Tuning and Advanced Prompting
Large Language Models (LLMs) represent a significant advancement in AI technology, capable of understanding, generating, and interacting with human language in a highly sophisticated manner.
