In today’s guest post by PyQuant News partner London Stock Exchange Group, we demonstrate how to build the Bond Copilot: a consulting agent using large language models (LLMs) and the Refinitiv Data (RD) Search API.
The article combines the API, LLMs, and LangChain to make it easy to query Refinitiv’s expansive repository of financial content.
Make a powerful bond research agent with LLMs and Refinitiv
The Bond Copilot lets users navigate over 12 million financial instruments data using natural language.
Using natural language can simplify data manipulation and make it easier for users to get the information they need.
The integration of natural language understanding with LLMs marks an important shift in financial analytics, reshaping the roles of financial analysts, investors, and risk managers.
Like I always say:
AI won’t replace you but someone using AI will.
Integrating Large Language Models
First we integrate LLMs like OpenAI’s GPT, Meta’s Llama2, and Google’s Bard.
These models are trained on extensive internet-based datasets and are really good at converting natural language to queries.
They serve as the engine for interpreting our queries and generating responses.
Implementing LangChain Framework
Then we use LangChain to connect LLMs with external data sources.
LangChain is the glue between the LLMs and the RD Library Search API which lets us use conversational memory to maintain the context for the LLM.
Connecting RD Library API
Next, we use the RD Library Search API provided by LSEG.
The API grants access to extensive fixed income information, including real-time pricing and reference data.
Designing Conversational Interface
Finally, we build the conversational interface to interact with the system using natural language.
To get the full step-by-step walkthrough, check out the full article at the LSEG website here.