How to Build a Teammate ? What are the components
Teammates: Workforce feature autonomous chat models that are meticulously crafted to deliver optimized outputs. These models are pre-built, enabling them to handle specific tasks and workflows with remarkable efficiency.
Team Workflows: Workforce utilize a sequence of calls to LLMs or other utilities to facilitate complex operations. They come with a standardized interface that allows for seamless integration with various tools and supports the creation of comprehensive end-to-end applications.
Models: These models provide a structured API that builds upon the capabilities of LLMs, ensuring conversational context is effectively managed. They are designed to accept a history of chat messages, enabling them to generate coherent and contextually relevant responses.
Knowledge Base: Workforce can interface with external data sources, significantly enhancing the text generation process. This capability is particularly useful for tasks such as summarizing documents or generating responses that are informed by specific data sets.
Embeddings: The embedding models within Workforce transform text into numerical representations, enabling advanced analytical tasks. These tasks include document retrieval, clustering, and the comparison of similarities between texts, thus improving the AI's comprehension and organizational abilities.
Instructions: In Workforce, instructions serve as text inputs that direct the AI to produce the desired response. These instructions can take the form of questions, information snippets, or specific instructions tailored to elicit a particular outcome.
Output Format: Additionally to Instructions user can provide what format of output should be used by Teammate during generating final answers.
Text Splitters: This functionality within Workforce breaks down provided text into smaller, semantically meaningful segments. This process is often employed to separate sentences, enhancing the semantic clarity and aiding in the AI's processing capabilities.
Tools: Workforce is distinguished by its tools, which are interfaces that enable agents to interact with the world. Users are also able to create their own tools using My Tools functionality. This key differentiator allows Workforce to be applied in real-world applications, extending their utility beyond conventional AI interactions.
Vector Stores: A critical component of Workforce is the vector stores, which manage and facilitate the search of unstructured data through embedding vectors. When a query is made, the system retrieves embedding vectors that most closely match the embedded query, ensuring relevant and accurate results
Team Workflows: Workforce utilize a sequence of calls to LLMs or other utilities to facilitate complex operations. They come with a standardized interface that allows for seamless integration with various tools and supports the creation of comprehensive end-to-end applications.
Models: These models provide a structured API that builds upon the capabilities of LLMs, ensuring conversational context is effectively managed. They are designed to accept a history of chat messages, enabling them to generate coherent and contextually relevant responses.
Knowledge Base: Workforce can interface with external data sources, significantly enhancing the text generation process. This capability is particularly useful for tasks such as summarizing documents or generating responses that are informed by specific data sets.
Embeddings: The embedding models within Workforce transform text into numerical representations, enabling advanced analytical tasks. These tasks include document retrieval, clustering, and the comparison of similarities between texts, thus improving the AI's comprehension and organizational abilities.
Instructions: In Workforce, instructions serve as text inputs that direct the AI to produce the desired response. These instructions can take the form of questions, information snippets, or specific instructions tailored to elicit a particular outcome.
Output Format: Additionally to Instructions user can provide what format of output should be used by Teammate during generating final answers.
Text Splitters: This functionality within Workforce breaks down provided text into smaller, semantically meaningful segments. This process is often employed to separate sentences, enhancing the semantic clarity and aiding in the AI's processing capabilities.
Tools: Workforce is distinguished by its tools, which are interfaces that enable agents to interact with the world. Users are also able to create their own tools using My Tools functionality. This key differentiator allows Workforce to be applied in real-world applications, extending their utility beyond conventional AI interactions.
Vector Stores: A critical component of Workforce is the vector stores, which manage and facilitate the search of unstructured data through embedding vectors. When a query is made, the system retrieves embedding vectors that most closely match the embedded query, ensuring relevant and accurate results
Updated on: 06/02/2025
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