From b0c83a1e0cea8bcc71082d1a3ff4e084a39d59e2 Mon Sep 17 00:00:00 2001 From: Karan Saxena <64839518+Karan0310@users.noreply.github.com> Date: Wed, 20 Nov 2024 11:34:35 -0500 Subject: [PATCH] Hal9_Swarm Article (#452) * Update authors.yml * 2024-11-19-Hal9_Swarm * Delete website/blog/2024-11-19-Hal9_Swarm * Create index.html * Add files via upload * Add files via upload * Delete website/blog/2024-11-19-Hal9_Swarm/Hal9 Swarm.md * Add files via upload * Delete website/blog/2024-11-19-Hal9_Swarm/Hal9 Swarm.md * Delete website/blog/2024-11-19-Hal9_Swarm/index.html * Update Hal9_Swarm.md --- .../blog/2024-11-19-Hal9_Swarm/Hal9_Swarm.md | 112 ++++++++++++++++++ .../movie recommendations.jpg | Bin 0 -> 102979 bytes website/blog/authors.yml | 5 + 3 files changed, 117 insertions(+) create mode 100644 website/blog/2024-11-19-Hal9_Swarm/Hal9_Swarm.md create mode 100644 website/blog/2024-11-19-Hal9_Swarm/movie recommendations.jpg diff --git a/website/blog/2024-11-19-Hal9_Swarm/Hal9_Swarm.md b/website/blog/2024-11-19-Hal9_Swarm/Hal9_Swarm.md new file mode 100644 index 00000000..18f9a2ef --- /dev/null +++ b/website/blog/2024-11-19-Hal9_Swarm/Hal9_Swarm.md @@ -0,0 +1,112 @@ +--- +title: "Using OpenAI Swarm with Hal9: Unlocking New Potential for Enterprise AI" +authors: "Karan Saxena" +tags: + - "AI" + - "Swarm Intelligence" +description: "Explore the integration of OpenAI Swarm with Hal9 to +unlock innovative AI capabilities" +--- + +## Using OpenAI Swarm with Hal9: Unlocking New Potential for EnterpriseAI + +As enterprises seek innovative ways to harness the power of AI, +combining the best tools for flexibility, speed, and collaborative +insights becomes essential. OpenAI Swarm is a promising approach that +leverages multiple instances of AI models working together in a +coordinated manner, effectively mimicking a \"swarm intelligence\" +system. When paired with Hal9---a platform optimized for customizing and +deploying generative AI---OpenAI Swarm opens new horizons for +enterprise-level solutions. + +In this post, we'll dive into what OpenAI Swarm is, why it's a valuable +asset for enterprise use, and how integrating it with Hal9 can transform +your AI-driven applications. We\'ll also walk through some example code +and provide visuals generated with this integration. + +### What is OpenAI Swarm? + +OpenAI Swarm is a system designed to run multiple AI models in parallel, +each contributing to a collective response based on the input provided. +By orchestrating a "swarm" of AI models that analyze the same prompt +from various perspectives, OpenAI Swarm generates richer, more nuanced +responses. This can be incredibly useful for applications requiring deep +analytical insights, creative brainstorming, or decision-making in +complex scenarios. + +### Why Use OpenAI Swarm with Hal9? + +Hal9 makes it simple for businesses to customize and deploy AI models at +scale. By integrating OpenAI Swarm with Hal9, enterprises can harness +the combined power of multiple AI models tailored specifically for their +unique needs. This setup is especially advantageous in scenarios where a +single model may not capture the full spectrum of insights required. + +### Example Code + +```python +import json import hal9 as h9 from dotenv import +load_dotenv from swarm import Swarm, Agent, repl from recommendations +import book_recommendation, comic_recommendation, movie_recommendation + +load_dotenv() + +Define transfer functions def transfer_to_receptionist(): return receptionist + +def transfer_to_book_expert(): return book_expert + +def transfer_to_comic_expert(): return comic_expert + +def transfer_to_movie_expert(): return movie_expert + +#Define expert agents book_expert = Agent( name="Book Expert", instructions="You are a classic books expert. Provide bookrecommendations. +#If the conversation drifts away, return to the receptionist.", functions=[book_recommendation, transfer_to_receptionist], ) + +comic_expert = Agent( name=\"Comic Expert\", instructions=\"You are an +expert in comics. Provide comic recommendations. If the conversation +drifts away, return to the receptionist.\", +functions=\[comic_recommendation, transfer_to_receptionist\], ) + +movie_expert = Agent( name=\"Movie Expert\", instructions=\"You are an +expert in movies. Provide movie recommendations. If the conversation +drifts away, return to the receptionist.\", +functions=\[movie_recommendation, transfer_to_receptionist\], ) + +receptionist = Agent( name=\"Receptionist\", instructions=\"You are a +receptionist. Direct users to a book, comic, or movie expert based on +their input.\", functions=\[transfer_to_book_expert, +transfer_to_comic_expert, transfer_to_movie_expert\], ) + +client = Swarm() messages = h9.load(\'messages\', \[\]) agents = +{\'Receptionist\': receptionist, \'Comic Expert\': comic_expert, \'Movie +Expert\': movie_expert} agent = agents\[h9.load(\'last_agent\', +\'Receptionist\')\] + +\# Handle user input user_input = input() messages.append({\"role\": +\"user\", \"content\": user_input}) + +response = client.run(agent=agent, messages=messages) + +for message in response.messages: if message\[\"role\"\] == +\"assistant\": print(f\"{message\[\'sender\'\]}: +{message\[\'content\'\]}\") + +messages.extend(response.messages) agent = response.agent + +h9.save(\'messages\', messages, hidden=True) h9.save(\'last_agent\', +agent.name, hidden=True) +``` + +## Code Explanation + +This code is a recommendation system using a "swarm" of AI agents on the Hal9 platform, each specializing in a category: books, comics, or movies. + +#### • Agents: There are four agents: receptionist, book_expert, comic_expert, and movie_expert. The receptionist determines the user’s interest and directs them to the appropriate expert. +#### • Functionality: Each expert agent provides recommendations in its domain, and if the conversation shifts away, it returns control to the receptionist. +#### • Session Management: The code maintains conversation history and the last active agent, enabling smooth, continuous interactions. +#### • Execution: The system takes user input, selects the relevant agent, generates a response, and logs any tool (function) calls made for recommendations. +This setup allows for interactive, specialized recommendations that adapt to user preferences in real-time. + + +## Sample Conversation +![movie recommendations](https://github.com/user-attachments/assets/bdb64b5d-b3df-4fc6-815e-9eae4277c29d) diff --git a/website/blog/2024-11-19-Hal9_Swarm/movie recommendations.jpg b/website/blog/2024-11-19-Hal9_Swarm/movie recommendations.jpg new file mode 100644 index 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