Update llm

bindings/ceylon/ceylon/llm/llm_agent.py

@@ -1,4 +1,6 @@
+import asyncio
 import pickle
+import random
 from collections import deque
 from typing import List
 
@@ -21,6 +23,7 @@ class LLMAgentResponse:
 class LLMAgent(AgentCore, MessageHandler, Processor):
     tools: list[StructuredTool]
     network_graph: nx.DiGraph
+    network_graph_original: nx.DiGraph
     queue: deque
     original_goal = None
 
@@ -53,7 +56,7 @@ async def on_message(self, agent_id, data, time):
 
         next_agent = self.get_next_agent()
         if next_agent == definition.name:
-            dependencies = list(self.network_graph.predecessors(next_agent))
+            dependencies = list(self.network_graph_original.predecessors(next_agent))
             print("Dependencies are:", dependencies, "for", next_agent)
 
             only_dependencies = {dt.agent_name: dt for dt in self.agent_replies if dt.agent_name in dependencies}
@@ -79,10 +82,13 @@ async def run(self, inputs):
     def _initialize_graph(self, network):
         # Add nodes and edges based on the agents and their dependencies
         for agent, dependencies in network.items():
+            print(agent)
             self.network_graph.add_node(agent)
             for dependency in dependencies:
                 self.network_graph.add_edge(dependency, agent)
 
+        self.network_graph_original = self.network_graph.copy()
+
         # Initialize the queue with nodes that have no dependencies (indegree 0)
         self.queue.extend([node for node in self.network_graph if self.network_graph.in_degree(node) == 0])
 
@@ -101,13 +107,16 @@ async def execute(self, input):
             result = process_agent_request(self.llm, input, definition, tools=self.tools)
             # result = f"{definition.name} executed successfully"
             response = LLMAgentResponse(agent_id=definition.id, agent_name=definition.name, response=result)
+            await asyncio.sleep(random.randint(1, 10))
 
             await self.broadcast(pickle.dumps(response))
 
             await self.update_status(next_agent)
 
             next_agent = self.get_next_agent()
             print("Next agent will be:", next_agent)
+        else:
+            print("Not executing", definition.name, "as it is not the next agent in the queue.")
 
     async def update_status(self, agent):
         if agent not in self.queue:

bindings/ceylon/examples/blog_writer/editor_panel.py

@@ -0,0 +1,91 @@
+import asyncio
+
+from duckduckgo_search import DDGS
+from langchain_community.chat_models import ChatOllama, ChatOpenAI
+from langchain_core.tools import StructuredTool
+
+from ceylon import AgentRunner
+from ceylon.llm.llm_agent import LLMAgent
+from ceylon.tools.file_publisher_tool import FilePublisherTool
+from ceylon.tools.search_tool import SearchTool
+
+
+async def main():
+    runner = AgentRunner(workspace_name="ceylon-ai")
+    # llm_lib = ChatOllama(model="phi3:instruct")
+    llm_lib = ChatOllama(model="gemma2:latest")
+    # llm_lib = ChatOpenAI(model="gpt-4o")
+    runner.register_agent(LLMAgent(
+        name="name_chooser",
+        position="Select File name",
+        llm=llm_lib,
+        responsibilities=["Create high-quality, SEO friendly file name."],
+        instructions=[
+            "Easy to read and understand."
+        ]
+    ))
+
+    runner.register_agent(LLMAgent(
+        name="researcher",
+        position="Content Researcher",
+        llm=llm_lib,
+        responsibilities=[
+            "Conducting thorough and accurate research to support content creation.",
+
+        ],
+        instructions=[
+            "Must only Find the most relevant 2 or 3 sources."
+            "Find credible sources, verify information, and provide comprehensive and relevant "
+            "data while ensuring ethical "
+            "standards and privacy are maintained.",
+            "Must  summarize output without source references."
+        ],
+        tools=[
+            SearchTool()
+        ]
+    ))
+    #
+
+    runner.register_agent(LLMAgent(
+        name="writer",
+        position="Assistant Writer",
+        llm=llm_lib,
+        responsibilities=["Create high-quality, original content that matches the audience's tone and style."],
+        instructions=[
+            "Ensure clarity, accuracy, and proper formatting while respecting ethical guidelines and privacy."]
+    ))
+    #
+    runner.register_agent(LLMAgent(
+        name="publisher",
+        position="Content Publisher",
+        llm=llm_lib,
+        responsibilities=[
+            "Publish the finalized content using the publishing tools and platforms specified by the writer.",
+        ],
+        instructions=[
+            "Publish it as finalized and polished content",
+        ],
+        tools=[
+            FilePublisherTool()
+        ]
+    ))
+
+    await runner.run(
+        {
+            "request": "I want to create a blog post",
+            "title": "How to use AI for Machine Learning",
+            "tone": "informal",
+            "length": "large",
+            "style": "creative"
+        },
+        network={
+            "name_chooser": [],
+            "researcher": [],
+            "writer": ["researcher"],
+            "publisher": ["writer", "name_chooser"]
+        }
+    )
+
+
+if __name__ == '__main__':
+    asyncio.run(main())

bindings/ceylon/tests/llm_agen_test.py

@@ -12,8 +12,8 @@
 
 async def main():
     runner = AgentRunner(workspace_name="ceylon-ai")
-    # llm_lib = ChatOllama(model="llama3:instruct")
-    llm_lib = ChatOpenAI(model="gpt-4o")
+    llm_lib = ChatOllama(model="llama3:instruct")
+    # llm_lib = ChatOpenAI(model="gpt-4o")
     runner.register_agent(LLMAgent(
         name="writer",
         position="Assistant Writer",
@@ -38,14 +38,15 @@ async def main():
         llm=llm_lib,
         responsibilities=[
             "Conducting thorough and accurate research to support content creation.",
+            "summarize with source references"
 
         ],
         instructions=[
             "Must only Find the most relevant 2 or 3 sources."
             "Find credible sources, verify information, and provide comprehensive and relevant "
             "data while ensuring ethical "
             "standards and privacy are maintained.",
-            "Must  summarize output without source references."
+            "Must  summarize output with source references."
         ],
         tools=[
             SearchTool()
@@ -57,7 +58,12 @@ async def main():
         position="Content Editor",
         llm=llm_lib,
         responsibilities=[
-            "Review and refine content to ensure it meets quality standards and aligns with the editorial guidelines."],
+            "Review and refine content to ensure it meets quality standards and aligns with the editorial guidelines.",
+            "Write content in a clear and concise manner.",
+            "Ensure the content is appropriate for the target audience.",
+            "Ensure the content is engaging and maintains the intended tone and style.",
+            "Include source references when appropriate."
+        ],
         instructions=[
             "Check for grammatical errors, clarity, and coherence.",
             "Ensure the content is engaging and maintains the intended tone and style.",
@@ -85,7 +91,7 @@ async def main():
             "request": "I want to create a blog post",
             "title": "How to use AI for Machine Learning",
             "tone": "informal",
-            "length": "short",
+            "length": "large",
             "style": "creative"
         },
         network={