Release/v1.2 (#457)

* Bump setup.py versions for 1.1

* PoC MCP server (#419)

* Very initial MCP server PoC for TrustGraph

* Put service on port 8000

* Add MCP container and packages to buildout

* Update docs for API/CLI changes in 1.0 (#421)

* Update some API basics for the 0.23/1.0 API change

* Add MCP container push (#425)

* Add command args to the MCP server (#426)

* Host and port parameters

* Added websocket arg

* More docs

* MCP client support (#427)

- MCP client service
- Tool request/response schema
- API gateway support for mcp-tool
- Message translation for tool request & response
- Make mcp-tool using configuration service for information
  about where the MCP services are.

* Feature/react call mcp (#428)

Key Features

  - MCP Tool Integration: Added core MCP tool support with ToolClientSpec and ToolClient classes
  - API Enhancement: New mcp_tool method for flow-specific tool invocation
  - CLI Tooling: New tg-invoke-mcp-tool command for testing MCP integration
  - React Agent Enhancement: Fixed and improved multi-tool invocation capabilities
  - Tool Management: Enhanced CLI for tool configuration and management

Changes

  - Added MCP tool invocation to API with flow-specific integration
  - Implemented ToolClientSpec and ToolClient for tool call handling
  - Updated agent-manager-react to invoke MCP tools with configurable types
  - Enhanced CLI with new commands and improved help text
  - Added comprehensive documentation for new CLI commands
  - Improved tool configuration management

Testing

  - Added tg-invoke-mcp-tool CLI command for isolated MCP integration testing
  - Enhanced agent capability to invoke multiple tools simultaneously

* Test suite executed from CI pipeline (#433)

* Test strategy & test cases

* Unit tests

* Integration tests

* Extending test coverage (#434)

* Contract tests

* Testing embeedings

* Agent unit tests

* Knowledge pipeline tests

* Turn on contract tests

* Increase storage test coverage (#435)

* Fixing storage and adding tests

* PR pipeline only runs quick tests

* Empty configuration is returned as empty list, previously was not in response (#436)

* Update config util to take files as well as command-line text (#437)

* Updated CLI invocation and config model for tools and mcp (#438)

* Updated CLI invocation and config model for tools and mcp

* CLI anomalies

* Tweaked the MCP tool implementation for new model

* Update agent implementation to match the new model

* Fix agent tools, now all tested

* Fixed integration tests

* Fix MCP delete tool params

* Update Python deps to 1.2

* Update to enable knowledge extraction using the agent framework (#439)

* Implement KG extraction agent (kg-extract-agent)

* Using ReAct framework (agent-manager-react)
 
* ReAct manager had an issue when emitting JSON, which conflicts which ReAct manager's own JSON messages, so refactored ReAct manager to use traditional ReAct messages, non-JSON structure.
 
* Minor refactor to take the prompt template client out of prompt-template so it can be more readily used by other modules. kg-extract-agent uses this framework.

* Migrate from setup.py to pyproject.toml (#440)

* Converted setup.py to pyproject.toml

* Modern package infrastructure as recommended by py docs

* Install missing build deps (#441)

* Install missing build deps (#442)

* Implement logging strategy (#444)

* Logging strategy and convert all prints() to logging invocations

* Fix/startup failure (#445)

* Fix loggin startup problems

* Fix logging startup problems (#446)

* Fix logging startup problems (#447)

* Fixed Mistral OCR to use current API (#448)

* Fixed Mistral OCR to use current API

* Added PDF decoder tests

* Fix Mistral OCR ident to be standard pdf-decoder (#450)

* Fix Mistral OCR ident to be standard pdf-decoder

* Correct test

* Schema structure refactor (#451)

* Write schema refactor spec

* Implemented schema refactor spec

* Structure data mvp (#452)

* Structured data tech spec

* Architecture principles

* New schemas

* Updated schemas and specs

* Object extractor

* Add .coveragerc

* New tests

* Cassandra object storage

* Trying to object extraction working, issues exist

* Validate librarian collection (#453)

* Fix token chunker, broken API invocation (#454)

* Fix token chunker, broken API invocation (#455)

* Knowledge load utility CLI (#456)

* Knowledge loader

* More tests

trustgraph-vertexai/trustgraph/model/text_completion/vertexai/llm.py

@@ -19,6 +19,7 @@ Google Cloud.   Input is prompt, output is response.
 from google.oauth2 import service_account
 import google
 import vertexai
+import logging
 
 # Why is preview here?
 from vertexai.generative_models import (
@@ -29,6 +30,9 @@ from vertexai.generative_models import (
 from .... exceptions import TooManyRequests
 from .... base import LlmService, LlmResult
 
+# Module logger
+logger = logging.getLogger(__name__)
+
 default_ident = "text-completion"
 
 default_model = 'gemini-2.0-flash-001'
@@ -91,7 +95,7 @@ class Processor(LlmService):
             ),
         ]
 
-        print("Initialise VertexAI...", flush=True)
+        logger.info("Initializing VertexAI...")
 
         if private_key:
             credentials = (
@@ -113,11 +117,11 @@ class Processor(LlmService):
                 location=region
             )
 
-        print(f"Initialise model {model}", flush=True)
+        logger.info(f"Initializing model {model}")
         self.llm = GenerativeModel(model)
         self.model = model
 
-        print("Initialisation complete", flush=True)
+        logger.info("VertexAI initialization complete")
 
     async def generate_content(self, system, prompt):
 
@@ -137,16 +141,16 @@ class Processor(LlmService):
                 model = self.model
             )
 
-            print(f"Input Tokens: {resp.in_token}", flush=True)
-            print(f"Output Tokens: {resp.out_token}", flush=True)
+            logger.info(f"Input Tokens: {resp.in_token}")
+            logger.info(f"Output Tokens: {resp.out_token}")
 
-            print("Send response...", flush=True)
+            logger.debug("Send response...")
 
             return resp
 
         except google.api_core.exceptions.ResourceExhausted as e:
 
-            print("Hit rate limit:", e, flush=True)
+            logger.warning(f"Hit rate limit: {e}")
 
             # Leave rate limit retries to the base handler
             raise TooManyRequests()
@@ -154,7 +158,7 @@ class Processor(LlmService):
         except Exception as e:
 
             # Apart from rate limits, treat all exceptions as unrecoverable
-            print(f"Exception: {e}")
+            logger.error(f"VertexAI LLM exception: {e}", exc_info=True)
             raise e
 
     @staticmethod