Workflow: This is the workflow we’ll follow in this project:
1.Saving Documents on Deep Lake: We will begin by learning how to save documents on Deep Lake, which serves as our knowledge repository. Deep Lake provides information that our agents can leverage for analysis and report generation
2.Creating a Document Retrieval Tool: Next, we will develop a tool that enables our agent to retrieve the most relevant documents from Deep Lake based on a given query.
3.Using the Plan and Execute Agent: The core of our project involves employing a "Plan and Execute" agent to devise a plan for answering a specific query about creating an overview of a topic. Our specific objective is to generate a comprehensive outline of recent events related to Artificial Intelligence regulations by governments, but the final agent could also work for other similar objectives as well. To accomplish this, we will feed the query into the planner component of the agent, which will utilize a language model's reasoning ability to plan out the steps required. The planner will consider various factors, including the complexity of the query and instructions for the tool used to generate a step-by-step plan or lower-level queries.
The plan will then be passed to the executor component, which will determine the appropriate tools or actions required to execute each step of the plan. The executor, initially implemented as an Action Agent, will make use of the tools we developed earlier, such as the document retrieval tool, to gather relevant information and execute the plan.
By employing the "Plan and Execute" agent framework, we can achieve more accurate and reliable analysis reports while handling complex long-term planning scenarios.
So let's dive in and explore the potential for generating insightful analysis reports!
Plan and Execute Plan and Execute agents are a new type of agent executor offering a different approach than the traditional agents supported in LangChain. These agents are heavily inspired by the BabyAGI framework and the recent Plan-and-Solve paper. The primary goal of "Plan and Execute" agents is to enable more complex long-term planning, even at the cost of making more calls to the language model.
The planner in the "Plan-and-Execute" framework typically utilizes a language model's reasoning ability to plan out steps and handle ambiguity and edge cases.
The executor, initially an Action Agent, takes the planner's high-level objectives (steps) and determines the tools or actions required to accomplish each step.
This separation of planning and execution allows for improved reliability and flexibility. It also facilitates the possibility of replacing these components with smaller, fine-tuned models in the future.
We will explore the implementation of the "Plan and Execute" agent and how to integrate it with Deep Lake for document retrieval and see the agent in action as it generates an analysis report based on the given query.