prompt orchestration system
The emerging category of software you’re referring to is often called prompt engineering platforms or prompt orchestration systems. These tools focus on chaining prompts and managing interactions with large language models to create seamless, dynamic, and context-aware chatbots or applications.
Key Terms and Related Concepts
- Prompt Chaining: Structuring prompts in sequences to achieve more complex tasks by breaking them into smaller, manageable parts.
- LLM Orchestration: Coordinating interactions with large language models to generate contextually relevant responses.
- Agent Frameworks: Some tools like LangChain, OpenAI Functions, or Semantic Kernel allow developers to build "agents" that use prompt chaining and external tool integration.
- Conversational AI Workflows: Platforms for designing workflows that involve multiple steps of dialogue and computation.
Examples of Prompt Orchestration Systems
Here’s a list of 10 examples of prompt orchestration systems and related tools/platforms that facilitate advanced prompt engineering and chaining:
-
LangChain
– A framework for developing applications using large language models (LLMs) through prompt chaining, memory, and external tool integration.
Semantic Kernel
– A lightweight framework by Microsoft for creating AI-powered workflows with prompt orchestration and contextual data integration.
OpenAI Functions
– Enables developers to structure prompts and connect them with function calls for more complex workflows.
PromptLayer
– A system for managing and optimizing prompts, allowing developers to track changes and improve prompt effectiveness.
Haystack
– A tool for building pipelines that combine LLMs with knowledge bases, search engines, and other components.
LlamaIndex (formerly GPT Index)
– A framework for connecting LLMs with structured and unstructured data, enabling prompt chaining with database queries and knowledge graphs.
Cohere RAG (Retrieval-Augmented Generation)
– Combines prompt orchestration with external document retrieval to provide accurate, context-rich responses.
Hugging Face Transformers Pipelines
– Allows for chaining multiple models and tools to build robust workflows for natural language understanding and generation.
ChatGPT Plugins
– Plugins extend ChatGPT’s capabilities by enabling dynamic prompts, external API calls, and task-specific workflows.
Airflow with LLM Integration
– Apache Airflow can be configured to orchestrate LLM-based tasks as part of larger data workflows, allowing for prompt sequencing and chaining.
Design of SQL Schema for an Prompt Orchestration Systems
The purpose of the SQL schema is to provide a structured framework for managing and orchestrating prompts within a dynamic system. It enables the storage of individual prompts, their relationships in sequential chains, and any dynamic variables that may be involved in the prompt execution process.
By maintaining tables for tracking prompt results, execution logs, and external tool integrations, the schema ensures a comprehensive view of the entire orchestration flow.
Additionally, it manages user or agent-specific contexts, allowing for context-aware prompt execution and personalized interactions.
This design ensures scalability, traceability, and flexibility for building complex prompt-based workflows, supporting both simple and highly dynamic applications.
The purpose of the SQL schema is to provide a structured framework for managing and orchestrating prompts within a dynamic system. It enables the storage of individual prompts, their relationships in sequential chains, and any dynamic variables that may be involved in the prompt execution process.
By maintaining tables for tracking prompt results, execution logs, and external tool integrations, the schema ensures a comprehensive view of the entire orchestration flow.
Additionally, it manages user or agent-specific contexts, allowing for context-aware prompt execution and personalized interactions. This design ensures scalability, traceability, and flexibility for building complex prompt-based workflows, supporting both simple and highly dynamic applications.
-- Table for storing prompts
CREATE TABLE prompts (
id SERIAL PRIMARY KEY,
content TEXT NOT NULL, -- The actual prompt text
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);
-- Table for storing prompt chains (sequence of prompts)
CREATE TABLE prompt_chains (
id SERIAL PRIMARY KEY,
name VARCHAR(255) NOT NULL, -- A name or description for the chain
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);
-- Many-to-many relationship between prompts and prompt chains
CREATE TABLE prompt_chain_prompts (
prompt_chain_id INTEGER REFERENCES prompt_chains(id) ON DELETE CASCADE,
prompt_id INTEGER REFERENCES prompts(id) ON DELETE CASCADE,
sequence_order INTEGER, -- The order in which the prompt appears in the chain
PRIMARY KEY (prompt_chain_id, prompt_id)
);
-- Table for storing prompt variables (dynamic parts within prompts)
CREATE TABLE prompt_variables (
id SERIAL PRIMARY KEY,
prompt_id INTEGER REFERENCES prompts(id) ON DELETE CASCADE,
variable_name VARCHAR(255) NOT NULL, -- The name of the variable (e.g., {{user_name}})
variable_value TEXT, -- The value of the variable
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);
-- Table for storing external tools or integrations used in the orchestration
CREATE TABLE external_tools (
id SERIAL PRIMARY KEY,
name VARCHAR(255) NOT NULL, -- The name of the external tool (e.g., API, service)
description TEXT, -- Optional description of the tool
endpoint VARCHAR(255), -- API endpoint or location of the tool
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);
-- Table for storing the results of each prompt execution
CREATE TABLE prompt_results (
id SERIAL PRIMARY KEY,
prompt_id INTEGER REFERENCES prompts(id) ON DELETE CASCADE,
result_content TEXT, -- The result of executing the prompt
success BOOLEAN, -- Whether the execution was successful or not
execution_timestamp TIMESTAMP DEFAULT NOW(),
metadata JSONB -- Any additional metadata related to the result (e.g., execution time)
);
-- Table for storing execution logs or history
CREATE TABLE prompt_execution_logs (
id SERIAL PRIMARY KEY,
prompt_chain_id INTEGER REFERENCES prompt_chains(id) ON DELETE CASCADE,
user_agent_id INTEGER, -- Reference to the user/agent executing the prompts
status VARCHAR(50), -- Status of the execution (e.g., "success", "error")
error_message TEXT, -- Error message if there was an issue
execution_timestamp TIMESTAMP DEFAULT NOW(),
log_details JSONB -- Any detailed logs (can include execution traces)
);
-- Table for managing user or agent context (e.g., a chatbot's state)
CREATE TABLE user_agent_context (
id SERIAL PRIMARY KEY,
user_agent_name VARCHAR(255) NOT NULL, -- The name or identifier of the user or agent
context_data JSONB, -- Contextual data (user profile, session data, etc.)
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);