Parsing and implementation complete, not tested or working

trustgraph-flow/trustgraph/agent/react/README.md

@@ -2,8 +2,8 @@
 agent-manager-react \
     -p pulsar://localhost:6650 \
     --tool-type \
-        shuttle=knowledge-base:query \
-        cats=knowledge-base:query \
+        shuttle=knowledge-query:query \
+        cats=knowledge-query:query \
         compute=text-completion:computation \
     --tool-description \
         shuttle="Query a knowledge base with information about the space shuttle.  The query should be a simple natural language question" \

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

@@ -22,9 +22,9 @@ from ... clients.prompt_client import PromptClient
 from ... clients.llm_client import LlmClient
 from ... clients.graph_rag_client import GraphRagClient
 
-from . tools import CatsKb, ShuttleKb, Compute
+from . tools import KnowledgeQueryImpl, TextCompletionImpl
 from . agent_manager import AgentManager
-from . types import Final, Action
+from . types import Final, Action, Tool
 
 module = ".".join(__name__.split(".")[1:-1])
 
@@ -49,32 +49,50 @@ class Processor(ConsumerProducer):
                         f"Tool-type string not well-formed: {t}"
                     )
                 ttoks = toks[1].split(":", 1)
-                if len(ttoks) == 1:
-                    tool_base[toks[0]] = {
-                        "type": ttoks[0],
-                        "input-arg": "query",
-                        "arguments": {},
-                    }
-                else:
-                    tool_base[toks[0]] = {
-                        "type": ttoks[0],
-                        "input-arg": ttoks[1],
-                        "arguments": {},
-                    }
+                if len(ttoks) < 1:
+                    raise RuntimeError(
+                        f"Tool-type string not well-formed: {t}"
+                    )
 
-        # Parsing the prompt information to the prompt configuration
+                if ttoks[0] == "knowledge-query":
+                    impl = KnowledgeQueryImpl
+                elif ttoks[0] == "text-completion":
+                    impl = TextCompletionImpl
+                else:
+                    raise RuntimeError(
+                        f"Tool-kind {ttoks[0]} not known"
+                    )
+
+                if len(ttoks) == 1:
+                    tool_base[toks[0]] = Tool(
+                        name = ttoks[0],
+                        description = "",
+                        implementation = impl,
+                        config = { "input": "query" },
+                        arguments = {},
+                    )
+                else:
+                    tool_base[toks[0]] = Tool(
+                        name = ttoks[0],
+                        description = "",
+                        implementation = impl,
+                        config = { "input": ttoks[1] },
+                        arguments = {},
+                    )
+
+        # parsing the prompt information to the prompt configuration
         # structure
         tool_desc_arg = params.get("tool_description", [])
         if tool_desc_arg:
             for t in tool_desc_arg:
                 toks = t.split("=", 1)
                 if len(toks) < 2:
-                    raise RuntimeError(
-                        f"Tool-type string not well-formed: {t}"
+                    raise runtimeerror(
+                        f"tool-type string not well-formed: {t}"
                     )
                 if toks[0] not in tool_base:
-                    raise RuntimeError(f"Description, tool {toks[0]} not known")
-                tool_base[toks[0]]["description"] = toks[1]
+                    raise runtimeerror(f"description, tool {toks[0]} not known")
+                tool_base[toks[0]].description = toks[1]
 
         # Parsing the prompt information to the prompt configuration
         # structure
@@ -93,13 +111,13 @@ class Processor(ConsumerProducer):
                     )
                 if toks[0] not in tool_base:
                     raise RuntimeError(f"Description, tool {toks[0]} not known")
-                tool_base[toks[0]]["arguments"][ttoks[0]] = {
+                tool_base[toks[0]].arguments[ttoks[0]] = {
                     "name": ttoks[0],
                     "type": ttoks[1],
                     "description": ttoks[2],
                 }
 
-        print(json.dumps(tool_base, indent=4))
+        print(json.dumps({k: str(v) for k, v in tool_base.items()}, indent=4))
 
         sys.exit(0)
 
@@ -169,7 +187,7 @@ class Processor(ConsumerProducer):
         )
 
         tools = [
-            CatsKb(self), ShuttleKb(self), Compute(self),
+#            CatsKb(self), ShuttleKb(self), Compute(self),
         ]
 
         self.agent = AgentManager(self, tools)
@@ -347,7 +365,7 @@ class Processor(ConsumerProducer):
             '--tool-type', nargs='*',
             help=f'''Specifies the type of an agent tool.  Takes the form
 <id>=<specifier>.  <id> is the name of the tool.  <specifier> is one of
-knowledge-base, text-completion.  Additional parameters are specified
+knowledge-query, text-completion.  Additional parameters are specified
 for different tools which are tool-specific. e.g. knowledge-query:<arg>
 which specifies the name of the arg whose content is fed into the knowledge
 query as a question.  text-completion:<arg> specifies the name of the arg

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

@@ -1,52 +1,67 @@
 
-from . types import Tool, Argument
+# from . types import Tool
 
-class CatsKb:
-    tool = Tool(
-        name = "cats-kb",
-        description = "Query a knowledge base with information about Mark's cats.  The query should be a simple natural language question",
-        arguments = [
-            Argument(
-                name = "query",
-                type = "string",
-                description = "The search query string"
-            )
-        ]
-    )
+# class CatsKb:
+#     tool = Tool(
+#         name = "cats-kb",
+#         description = "Query a knowledge base with information about Mark's cats.  The query should be a simple natural language question",
+#         arguments = [
+#             Argument(
+#                 name = "query",
+#                 type = "string",
+#                 description = "The search query string"
+#             )
+#         ]
+#     )
+#     def __init__(self, context):
+#         self.context = context
+#     def invoke(self, **arguments):
+#         return self.context.graph_rag.request(arguments.get("query"))
+
+# class ShuttleKb:
+#     tool = Tool(
+#         name = "shuttle-kb",
+#         description = "Query a knowledge base with information about the space shuttle.  The query should be a simple natural language question",
+#         arguments = [
+#             Argument(
+#                 name = "query",
+#                 type = "string",
+#                 description = "The search query string"
+#             )
+#         ]
+#     )
+#     def __init__(self, context):
+#         self.context = context
+#     def invoke(self, **arguments):
+#         return self.context.graph_rag.request(arguments.get("query"))
+
+# class Compute:
+#     tool = Tool(
+#         name = "compute",
+#         description = "A computation engine which can answer questions about maths and computation",
+#         arguments = [
+#             Argument(
+#                 name = "computation",
+#                 type = "string",
+#                 description = "The computation to solve"
+#             )
+#         ]
+#     )
+#     def __init__(self, context):
+#         self.context = context
+#     def invoke(self, **arguments):
+#         return self.context.prompt.request(
+#             "question", { "question": arguments.get("computation") }
+#         )
+
+
+class KnowledgeQueryImpl:
     def __init__(self, context):
         self.context = context
     def invoke(self, **arguments):
         return self.context.graph_rag.request(arguments.get("query"))
 
-class ShuttleKb:
-    tool = Tool(
-        name = "shuttle-kb",
-        description = "Query a knowledge base with information about the space shuttle.  The query should be a simple natural language question",
-        arguments = [
-            Argument(
-                name = "query",
-                type = "string",
-                description = "The search query string"
-            )
-        ]
-    )
-    def __init__(self, context):
-        self.context = context
-    def invoke(self, **arguments):
-        return self.context.graph_rag.request(arguments.get("query"))
-
-class Compute:
-    tool = Tool(
-        name = "compute",
-        description = "A computation engine which can answer questions about maths and computation",
-        arguments = [
-            Argument(
-                name = "computation",
-                type = "string",
-                description = "The computation to solve"
-            )
-        ]
-    )
+class TextCompletionImpl:
     def __init__(self, context):
         self.context = context
     def invoke(self, **arguments):

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

@@ -1,5 +1,6 @@
 
 import dataclasses
+from typing import Any, Dict
 
 @dataclasses.dataclass
 class Argument:
@@ -12,6 +13,8 @@ class Tool:
     name : str
     description : str
     arguments : list[Argument]
+    implementation : Any
+    config : Dict[str, str]
     
 @dataclasses.dataclass
 class Action: