Akcio: A New Era in ChatBot Technology
Akcio is a cutting-edge project designed to enhance chatbot technology by integrating it with sophisticated AI tools. This platform is aimed at tackling the limitations faced by traditional chatbots like ChatGPT, which often provide inaccurate answers due to their restricted knowledge bases. Akcio introduces an innovative stack combining ChatGPT, a vector database, and a feature called "prompt-as-code," collectively known as the CVP Stack.
Overview
Akcio empowers developers to build intelligent, knowledge-enhanced chatbots. Unlike standard chat systems that rely solely on preprogrammed responses, Akcio enhances its responses with pertinent, contextually relevant information retrieved from an extensive database. This sophisticated retrieval process gives the Large Language Model (LLM) the necessary data to generate answers that are both accurate and contextually appropriate.
This project provides a choice between two AI platforms—Towhee and LangChain—each offering unique features and integration capabilities with various LLM services and databases.
Integrations and Options
Towhee Option
Towhee simplifies the process by providing pre-defined pipelines, which require minimal coding, thus making system creation more streamlined. These pipelines handle the entire process, from inserting documents into the database to crafting responses using the LLM.
- Insert Pipeline: Builds a knowledge base by inputting documents into databases.
- Search Pipeline: Enhances Q&A functionality with semantic search, LLM services, and keyword matching.
- Prompt Operator: Prepares messages by assembling system information, user query, and chat history.
LangChain Option
The LangChain option leverages agents to enable the LLM to utilize specific tools, enhancing its comprehension and decision-making abilities. This option demands more from the LLM, offering advanced customization and tool use.
- ChatAgent: Integrates all modules to create a comprehensive question-answering system.
- VectorStore and MemoryStore: Store document chunks and chat history to aid in meaningful conversations.
Deployment Process
Deploying Akcio involves a few straightforward steps:
- Clone the Akcio repository from GitHub.
- Install necessary dependencies using a package manager.
- Configure modules such as LLM, Embedding, and Store to suit your needs.
- Start the service via Towhee or LangChain, depending on your preferred setup.
Once the service is up and running, users can access its features through a web interface on a local browser, offering various API endpoints for status checks and data management.
Data Loading
Akcio provides two methods for loading project data. Offline loading is preferred for handling large datasets and allows more control over document processing. Online data loading, on the other hand, is ideal for smaller datasets and can be performed via a simple POST request.
Licensing
Akcio is shared under the Server Side Public License (SSPL) v1, promoting collaboration and expansion of this innovative technology.
By integrating Akcio's advanced features into a chatbot platform, developers are equipped to create more accurate, intelligent, and context-aware conversational agents, pushing the boundaries of what AI-driven chatbots can achieve.