* Added Anthropic support for VertexAI
* Update tests to match code
* Fixed private.json usage with Anthropic (I think).
* Fixed test
---------
Co-authored-by: Cyber MacGeddon <cybermaggedon@gmail.com>
* 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
- Changed GoogleAIStudio LLM code to match latest documentation
- Very minor tweak to vertexai LLM code - just matching what's in SDK docs
no actual change to implementation.
- Tweaked VertexAI container build to speed up in dev
- Comments in LLM code to mention which docs it was built from. Google
SDKs are confusing ATM.
- Keeps processing in different flows separate so that data can go to different stores / collections etc.
- Potentially supports different processing flows
- Tidies the processing API with common base-classes for e.g. LLMs, and automatic configuration of 'clients' to use the right queue names in a flow
* - Refactored retry for rate limits into the base class
- ConsumerProducer is derived from Consumer to simplify code
- Added rate_limit_count metrics for rate limit events
* Add rate limit events to VertexAI and Google AI Studio
* Added Grafana rate limit dashboard
* Add rate limit handling to all LLMs
- Change templates to interpolate environment variables in docker compose
- Change templates to invoke secrets for environment variable credentials in K8s configuration
- Update LLMs to pull in credentials from environment variables if not specified
* - Locked 0.11 packages to 0.11 deps
- Added 'trustgraph' uber-package which installs the rest
- Added dependency to set package versions before building packages
* Bump version
