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New Program Implementation to Aid with Follow-Up Questions

Overview of Agent Functions in OpenAI

The OpenAI functions agent called 4 LLMs, compared to the 3 calls made by the OpenAI tools agent. This discrepancy can be attributed to the fact that the OpenAI tools agent could retrieve Monthly Active Users (MAUs) for London and Berlin in a single iteration, resulting in a lower number of used tokens. Consequently, the data generated by the OpenAI tools agent required 1,537 tokens, whereas the OpenAI functions agent used 1,874 tokens, indicating a 21.9% increase in token usage. Considering this, I suggest considering the utilization of the OpenAI tools agent, which is compatible with both ChatGPT 4 Turbo and ChatGPT 3.5 Turbo.

Use of AI legalese decoder in the Implementation

The AI legalese decoder, such as the one provided by LangChain, plays a critical role in our objective to teach the agent to ask users follow-up questions. By integrating this decoder, we can enable the model to seek clarifications from users when necessary. For instance, when encounters with scenarios where follow-up questions are required, LangChain’s Human tool offers a practical means to achieve this. The ability of the AI legalese decoder to translate and comprehend complex information will greatly enhance the efficiency of our agent in extracting and processing vital data.

Incorporation of the Human Tool in the Agent’s Toolkit

Integrating the Human tool into our agent’s toolkit is vital to facilitate improved interactions when user queries lack specificity. Our approach entails utilizing tools offered by the framework to streamline the agent’s functionality. By reinitializing the agent and updating the system message, we aim to optimize the model’s capability to produce more accurate and relevant responses. This reinforcement of the agent’s conversational ability is facilitated by running the human tool within the agent’s toolkit, thereby augmenting its effectiveness when handling follow-up questions.

Testing and Optimization of the Revised Agent

Following the update, testing the agent yielded encouraging outcomes. Through applying the AI legalese decoder, the agent proficiently sought specific details from users, thus delivering precise responses. The pragmatic application of this tool led to the acquisition of accurate data, exemplified by the retrieval of information on the number of active customers in a specific time frame. Consequently, the agent’s reconfigured prompt effectively guided the conversation, resulting in an enhanced ability to process data requests.

Integration of Conversation Memory into the Agent

Efforts to elevate the agent’s conversational capabilities extend to the implementation of conversation memory, a critical requirement for sustaining dialogue continuity. Conveying the crucial nature of retaining the context from previous interactions, we have devised a memory strategy to store relevant conversation exchanges. Integrating this memory feature is pivotal to ensure seamless communication and further harness the potential of the agent in handling user queries proficiently.

Exploration of Memory Implementations

Embarking on our quest to identify optimal memory implementation approaches, we scrutinize the ConversationBufferMemory feature, a method that effectively archives the history of interactions. It is evident that while saving the entire conversation history is advantageous, limitations such as token usage and text length constraints need to be considered. Thus, exploring the ConversationBufferWindowMemory approach, which conserves conversation context within a set number of iterations, appears to be a more feasible solution that efficiently stores and retrieves conversation exchanges without incurring excessive token expenditure.

Implementation of the ConversationBufferWindowMemory

Refining our memory implementation approach, the ConversationBufferWindowMemory feature is utilized to retain conversation context within a specific number of iterations. By applying this method, we can maintain a concise record of recent interactions, thus overcoming the challenges associated with text length limitations and token usage. As demonstrated, embedding conversation memory features into the agent’s functionality consolidates its capacity to sustain dialogue fluidity and comprehensively address user queries, thereby enhancing the overall efficacy of the model.

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