Update agent implementation to match the new model

trustgraph-flow/trustgraph/agent/react/service.py

@@ -12,7 +12,7 @@ from ... base import GraphRagClientSpec, ToolClientSpec
 
 from ... schema import AgentRequest, AgentResponse, AgentStep, Error
 
-from . tools import KnowledgeQueryImpl, TextCompletionImpl, McpToolImpl
+from . tools import KnowledgeQueryImpl, TextCompletionImpl, McpToolImpl, PromptImpl
 from . agent_manager import AgentManager
 
 from . types import Final, Action, Tool, Argument
@@ -79,64 +79,76 @@ class Processor(AgentService):
 
         print("Loading configuration version", version)
 
-        if self.config_key not in config:
-            print(f"No key {self.config_key} in config", flush=True)
-            return
-
-        config = config[self.config_key]
-
         try:
 
-            # This is some extra stuff to put in the prompt
-            additional = config.get("additional-context", None)
-
-            ix = json.loads(config["tool-index"])
-
             tools = {}
 
-            for k in ix:
+            # Load tool configurations from the new location
+            if "tool" in config:
+                for tool_id, tool_value in config["tool"].items():
+                    data = json.loads(tool_value)
                     
-                pc = config[f"tool.{k}"]
+                    impl_id = data.get("type")
-                data = json.loads(pc)
+                    name = data.get("name")
                     
-                arguments = {
+                    # Create the appropriate implementation
-                    v.get("name"): Argument(
+                    if impl_id == "knowledge-query":
-                        name = v.get("name"),
+                        impl = functools.partial(
-                        type = v.get("type"),
+                            KnowledgeQueryImpl, 
-                        description = v.get("description")
+                            collection=data.get("collection")
-                    )
+                        )
-                    for v in data["arguments"]
+                        arguments = KnowledgeQueryImpl.get_arguments()
-                }
+                    elif impl_id == "text-completion":
+                        impl = TextCompletionImpl
+                        arguments = TextCompletionImpl.get_arguments()
+                    elif impl_id == "mcp-tool":
+                        impl = functools.partial(
+                            McpToolImpl, 
+                            mcp_tool_id=data.get("mcp-tool")
+                        )
+                        arguments = McpToolImpl.get_arguments()
+                    elif impl_id == "prompt":
+                        # For prompt tools, arguments come from config
+                        config_args = data.get("arguments", [])
+                        arguments = [
+                            Argument(
+                                name=arg.get("name"),
+                                type=arg.get("type"),
+                                description=arg.get("description")
+                            )
+                            for arg in config_args
+                        ]
+                        impl = functools.partial(
+                            PromptImpl,
+                            template_id=data.get("template"),
+                            arguments=arguments
+                        )
+                    else:
+                        raise RuntimeError(
+                            f"Tool type {impl_id} not known"
+                        )
                     
-                impl_id = data.get("type")
+                    tools[name] = Tool(
-
+                        name=name,
-                name = data.get("name")
+                        description=data.get("description"),
-
+                        implementation=impl,
-                if impl_id == "knowledge-query":
+                        config=data,  # Store full config for reference
-                    impl = KnowledgeQueryImpl
+                        arguments=arguments,
-                elif impl_id == "text-completion":
-                    impl = TextCompletionImpl
-                elif impl_id == "mcp-tool":
-                    impl = functools.partial(McpToolImpl, name=k)
-                else:
-                    raise RuntimeError(
-                        f"Tool-kind {impl_id} not known"
                     )
             
-                tools[data.get("name")] = Tool(
+            # Load additional context from agent config if it exists
-                    name = name,
+            additional = None
-                    description = data.get("description"),
+            if self.config_key in config:
-                    implementation = impl,
+                agent_config = config[self.config_key]
-                    config=data.get("config", {}),
+                additional = agent_config.get("additional-context", None)
-                    arguments = arguments,
-                )
             
             self.agent = AgentManager(
                 tools=tools,
                 additional_context=additional
             )
 
-            print("Prompt configuration reloaded.", flush=True)
+            print(f"Loaded {len(tools)} tools", flush=True)
+            print("Tool configuration reloaded.", flush=True)
 
         except Exception as e:
 

trustgraph-flow/trustgraph/agent/react/tools.py

@@ -1,11 +1,24 @@
 
 import json
+from .types import Argument
 
 # This tool implementation knows how to put a question to the graph RAG
 # service
 class KnowledgeQueryImpl:
-    def __init__(self, context):
+    def __init__(self, context, collection=None):
         self.context = context
+        self.collection = collection
+    
+    @staticmethod
+    def get_arguments():
+        return [
+            Argument(
+                name="question",
+                type="string",
+                description="The question to ask the knowledge base"
+            )
+        ]
+    
     async def invoke(self, **arguments):
         client = self.context("graph-rag-request")
         print("Graph RAG question...", flush=True)
@@ -18,6 +31,17 @@ class KnowledgeQueryImpl:
 class TextCompletionImpl:
     def __init__(self, context):
         self.context = context
+    
+    @staticmethod
+    def get_arguments():
+        return [
+            Argument(
+                name="question",
+                type="string",
+                description="The text prompt or question for completion"
+            )
+        ]
+    
     async def invoke(self, **arguments):
         client = self.context("prompt-request")
         print("Prompt question...", flush=True)
@@ -29,18 +53,24 @@ class TextCompletionImpl:
 # the mcp-tool service.
 class McpToolImpl:
 
-    def __init__(self, context, name):
+    def __init__(self, context, mcp_tool_id):
         self.context = context
-        self.name = name
+        self.mcp_tool_id = mcp_tool_id
+    
+    @staticmethod
+    def get_arguments():
+        # MCP tools define their own arguments dynamically
+        # For now, we return empty list and let the MCP service handle validation
+        return []
 
     async def invoke(self, **arguments):
 
         client = self.context("mcp-tool-request")
 
-        print(f"MCP tool invocation: {self.name}...", flush=True)
+        print(f"MCP tool invocation: {self.mcp_tool_id}...", flush=True)
         output = await client.invoke(
-            name = self.name,
+            name = self.mcp_tool_id,
-            parameters = {},
+            parameters = arguments,  # Pass the actual arguments
         )
 
         print(output)
@@ -51,3 +81,21 @@ class McpToolImpl:
             return json.dumps(output)
 
 
+# This tool implementation knows how to execute prompt templates
+class PromptImpl:
+    def __init__(self, context, template_id, arguments=None):
+        self.context = context
+        self.template_id = template_id
+        self.arguments = arguments or []  # These come from config
+    
+    def get_arguments(self):
+        # For prompt tools, arguments are defined in configuration
+        return self.arguments
+    
+    async def invoke(self, **arguments):
+        client = self.context("prompt-request")
+        print(f"Prompt template invocation: {self.template_id}...", flush=True)
+        return await client.template(
+            template_id=self.template_id,
+            variables=arguments
+        )