Ailixir is an application that utilises LLMs and custom user input to generate AI agent prototypes specialised in fields such as health, economics, physics etc. The prototypes enable the user, which is an entrepreneur-developer, to compare the results produced by different LLMs.
Description: As part of our multiple LLM strategy, this task involves researching/selecting and implementing the fifth LLM that will be implemented as additional LLM to the previously implemented LLMs. This enhance our application's capabilities by leveraging the strengths of multiple LLMs, ensuring better performance, flexibility, and robustness in handling various tasks involved in the langchain framework.
I want to select and implement a new LLM into the Langchain framework to support our multiple LLM strategy,
so that the capacity of our application is improved by leveraging the strengths of multiple LLMs.
Acceptance Criteria
[ ] Identify potential LLM candidates based on criteria such as performance, compatibility, and licensing, then selecting the fifth LLM for integration.
[ ] install dependencies and libraries for the selected LLM, and implementing it to Langchain framework
[ ] Ensure the new LLM can be easily switched with other LLMs in the framework as part of the multiple LLM strategy
[ ] Perform thorough testing to ensure the new LLM interacts correctly with existing components.
Definition of Done
[ ] The feature has been fully implemented.
[ ] The feature has been manually tested and works as expected without critical bugs.
[ ] The feature code is documented with clear explanations of its functionality and usage.
[ ] The feature code has been reviewed and approved by at least one team member.
[ ] The feature branches have been merged into the main branch and closed.
[ ] The feature utility, function and usage have been documented in the respective project wiki on github.
Item type: data pipeline
Description: As part of our multiple LLM strategy, this task involves researching/selecting and implementing the fifth LLM that will be implemented as additional LLM to the previously implemented LLMs. This enhance our application's capabilities by leveraging the strengths of multiple LLMs, ensuring better performance, flexibility, and robustness in handling various tasks involved in the langchain framework.
This langchain provided list of LLMs can serve as base for comparison and research https://python.langchain.com/v0.2/docs/integrations/llms/
User Story
Acceptance Criteria
Definition of Done