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Release/v1.2 (#457)

Browse source 
* 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
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49
docs/apis/api-flow.md
View file

@ -210,6 +210,51 @@ Request schema:
Response schema:
`trustgraph.schema.FlowResponse`
## Flow Service Methods
Flow instances provide access to various TrustGraph services through flow-specific endpoints:
### MCP Tool Service - Invoke MCP Tools
The `mcp_tool` method allows invoking MCP (Model Control Protocol) tools within a flow context.
Request:
```json
{
"name": "file-reader",
"parameters": {
"path": "/path/to/file.txt"
}
}
```
Response:
```json
{
"object": {"content": "file contents here", "size": 1024}
}
```
Or for text responses:
```json
{
"text": "plain text response"
}
```
### Other Service Methods
Flow instances also provide access to:
- `text_completion` - LLM text completion
- `agent` - Agent question answering
- `graph_rag` - Graph-based RAG queries
- `document_rag` - Document-based RAG queries
- `embeddings` - Text embeddings
- `prompt` - Prompt template processing
- `triples_query` - Knowledge graph queries
- `load_document` - Document loading
- `load_text` - Text loading
## Python SDK
The Python SDK provides convenient access to the Flow API:
@ -233,6 +278,10 @@ flows = await client.list_flows()
# Stop a flow instance
await client.stop_flow("flow-123")
# Use flow instance services
flow = client.id("flow-123")
result = await flow.mcp_tool("file-reader", {"path": "/path/to/file.txt"})
```
## Features

57
docs/apis/api-librarian.md
View file

@ -12,6 +12,17 @@ The request contains the following fields:
- `operation`: The operation to perform (see operations below)
- `document_id`: Document identifier (for document operations)
- `document_metadata`: Document metadata object (for add/update operations)
- `id`: Document identifier (required)
- `time`: Unix timestamp in seconds as a float (required for add operations)
- `kind`: MIME type of document (required, e.g., "text/plain", "application/pdf")
- `title`: Document title (optional)
- `comments`: Document comments (optional)
- `user`: Document owner (required)
- `tags`: Array of tags (optional)
- `metadata`: Array of RDF triples (optional) - each triple has:
- `s`: Subject with `v` (value) and `e` (is_uri boolean)
- `p`: Predicate with `v` (value) and `e` (is_uri boolean)
- `o`: Object with `v` (value) and `e` (is_uri boolean)
- `content`: Document content as base64-encoded bytes (for add operations)
- `processing_id`: Processing job identifier (for processing operations)
- `processing_metadata`: Processing metadata object (for add-processing)
@ -38,7 +49,7 @@ Request:
"operation": "add-document",
"document_metadata": {
"id": "doc-123",
"time": 1640995200000,
"time": 1640995200.0,
"kind": "application/pdf",
"title": "Research Paper",
"comments": "Important research findings",
@ -46,9 +57,18 @@ Request:
"tags": ["research", "ai", "machine-learning"],
"metadata": [
{
"subject": "doc-123",
"predicate": "dc:creator",
"object": "Dr. Smith"
"s": {
"v": "http://example.com/doc-123",
"e": true
},
"p": {
"v": "http://purl.org/dc/elements/1.1/creator",
"e": true
},
"o": {
"v": "Dr. Smith",
"e": false
}
}
]
},
@ -77,7 +97,7 @@ Response:
{
"document_metadata": {
"id": "doc-123",
"time": 1640995200000,
"time": 1640995200.0,
"kind": "application/pdf",
"title": "Research Paper",
"comments": "Important research findings",
@ -85,9 +105,18 @@ Response:
"tags": ["research", "ai", "machine-learning"],
"metadata": [
{
"subject": "doc-123",
"predicate": "dc:creator",
"object": "Dr. Smith"
"s": {
"v": "http://example.com/doc-123",
"e": true
},
"p": {
"v": "http://purl.org/dc/elements/1.1/creator",
"e": true
},
"o": {
"v": "Dr. Smith",
"e": false
}
}
]
}
@ -129,7 +158,7 @@ Response:
"document_metadatas": [
{
"id": "doc-123",
"time": 1640995200000,
"time": 1640995200.0,
"kind": "application/pdf",
"title": "Research Paper",
"comments": "Important research findings",
@ -138,7 +167,7 @@ Response:
},
{
"id": "doc-124",
"time": 1640995300000,
"time": 1640995300.0,
"kind": "text/plain",
"title": "Meeting Notes",
"comments": "Team meeting discussion",
@ -157,10 +186,12 @@ Request:
"operation": "update-document",
"document_metadata": {
"id": "doc-123",
"time": 1640995500.0,
"title": "Updated Research Paper",
"comments": "Updated findings and conclusions",
"user": "alice",
"tags": ["research", "ai", "machine-learning", "updated"]
"tags": ["research", "ai", "machine-learning", "updated"],
"metadata": []
}
}
```
@ -197,7 +228,7 @@ Request:
"processing_metadata": {
"id": "proc-456",
"document_id": "doc-123",
"time": 1640995400000,
"time": 1640995400.0,
"flow": "pdf-extraction",
"user": "alice",
"collection": "research",
@ -229,7 +260,7 @@ Response:
{
"id": "proc-456",
"document_id": "doc-123",
"time": 1640995400000,
"time": 1640995400.0,
"flow": "pdf-extraction",
"user": "alice",
"collection": "research",

137
docs/apis/api-mcp-tool.md Normal file
View file

@ -0,0 +1,137 @@
# TrustGraph MCP Tool API
This is a higher-level interface to the MCP (Model Control Protocol) tool service. The input
specifies an MCP tool by name and parameters to pass to the tool.
## Request/response
### Request
The request contains the following fields:
- `name`: The MCP tool name
- `parameters`: A set of key/values describing the tool parameters
### Response
The response contains either of these fields:
- `text`: A plain text response
- `object`: A structured object response
## REST service
The REST service accepts `name` and `parameters` fields, with parameters
encoded as a JSON object.
e.g.
In this example, the MCP tool takes parameters and returns a
structured response in the `object` field.
Request:
```
{
"name": "file-reader",
"parameters": {
"path": "/path/to/file.txt"
}
}
```
Response:
```
{
"object": {"content": "file contents here", "size": 1024}
}
```
## Websocket
Requests have `name` and `parameters` fields.
e.g.
Request:
```
{
"id": "akshfkiehfkseffh-142",
"service": "mcp-tool",
"flow": "default",
"request": {
"name": "file-reader",
"parameters": {
"path": "/path/to/file.txt"
}
}
}
```
Responses:
```
{
"id": "akshfkiehfkseffh-142",
"response": {
"object": {"content": "file contents here", "size": 1024}
},
"complete": true
}
```
e.g.
An example which returns plain text
Request:
```
{
"id": "akshfkiehfkseffh-141",
"service": "mcp-tool",
"request": {
"name": "calculator",
"parameters": {
"expression": "2 + 2"
}
}
}
```
Response:
```
{
"id": "akshfkiehfkseffh-141",
"response": {
"text": "4"
},
"complete": true
}
```
## Pulsar
The Pulsar schema for the MCP Tool API is defined in Python code here:
https://github.com/trustgraph-ai/trustgraph/blob/master/trustgraph-base/trustgraph/schema/mcp_tool.py
Default request queue:
`non-persistent://tg/request/mcp-tool`
Default response queue:
`non-persistent://tg/response/mcp-tool`
Request schema:
`trustgraph.schema.McpToolRequest`
Response schema:
`trustgraph.schema.McpToolResponse`
## Pulsar Python client
The client class is
`trustgraph.clients.McpToolClient`
https://github.com/trustgraph-ai/trustgraph/blob/master/trustgraph-base/trustgraph/clients/mcp_tool_client.py