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Row embeddings APIs exposed (#646)
* Added row embeddings API and CLI support * Updated protocol specs * Row embeddings agent tool * Add new agent tool to CLI
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cybermaggedon
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25 changed files with 1090 additions and 29 deletions
126
trustgraph-cli/trustgraph/cli/invoke_row_embeddings.py
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126
trustgraph-cli/trustgraph/cli/invoke_row_embeddings.py
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@ -0,0 +1,126 @@
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"""
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Queries row data by text similarity using vector embeddings on indexed fields.
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Returns matching rows with their index values and similarity scores.
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"""
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import argparse
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import os
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from trustgraph.api import Api
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default_url = os.getenv("TRUSTGRAPH_URL", 'http://localhost:8088/')
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default_token = os.getenv("TRUSTGRAPH_TOKEN", None)
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def query(url, flow_id, query_text, schema_name, user, collection, index_name, limit, token=None):
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# Create API client
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api = Api(url=url, token=token)
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socket = api.socket()
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flow = socket.flow(flow_id)
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try:
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# Call row embeddings query service
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result = flow.row_embeddings_query(
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text=query_text,
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schema_name=schema_name,
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user=user,
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collection=collection,
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index_name=index_name,
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limit=limit
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)
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matches = result.get("matches", [])
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for match in matches:
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print(f"Index: {match['index_name']}")
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print(f" Values: {match['index_value']}")
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print(f" Text: {match['text']}")
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print(f" Score: {match['score']:.4f}")
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print()
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finally:
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# Clean up socket connection
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socket.close()
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def main():
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parser = argparse.ArgumentParser(
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prog='tg-invoke-row-embeddings',
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description=__doc__,
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)
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parser.add_argument(
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'-u', '--url',
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default=default_url,
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help=f'API URL (default: {default_url})',
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)
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parser.add_argument(
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'-t', '--token',
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default=default_token,
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help='Authentication token (default: $TRUSTGRAPH_TOKEN)',
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)
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parser.add_argument(
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'-f', '--flow-id',
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default="default",
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help=f'Flow ID (default: default)'
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)
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parser.add_argument(
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'-U', '--user',
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default="trustgraph",
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help='User/keyspace (default: trustgraph)',
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)
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parser.add_argument(
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'-c', '--collection',
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default="default",
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help='Collection (default: default)',
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)
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parser.add_argument(
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'-s', '--schema-name',
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required=True,
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help='Schema name to search within (required)',
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)
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parser.add_argument(
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'-i', '--index-name',
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default=None,
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help='Index name to filter search (optional)',
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)
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parser.add_argument(
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'-l', '--limit',
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type=int,
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default=10,
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help='Maximum number of results (default: 10)',
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)
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parser.add_argument(
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'query',
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nargs=1,
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help='Query text to search for similar row index values',
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)
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args = parser.parse_args()
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try:
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query(
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url=args.url,
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flow_id=args.flow_id,
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query_text=args.query[0],
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schema_name=args.schema_name,
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user=args.user,
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collection=args.collection,
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index_name=args.index_name,
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limit=args.limit,
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token=args.token,
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)
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except Exception as e:
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print("Exception:", e, flush=True)
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if __name__ == "__main__":
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main()
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@ -2,8 +2,9 @@
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Configures and registers tools in the TrustGraph system.
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This script allows you to define agent tools with various types including:
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- knowledge-query: Query knowledge bases
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- knowledge-query: Query knowledge bases
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- structured-query: Query structured data using natural language
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- row-embeddings-query: Semantic search on structured data indexes
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- text-completion: Text generation
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- mcp-tool: Reference to MCP (Model Context Protocol) tools
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- prompt: Prompt template execution
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@ -64,6 +65,9 @@ def set_tool(
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mcp_tool : str,
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collection : str,
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template : str,
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schema_name : str,
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index_name : str,
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limit : int,
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arguments : List[Argument],
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group : List[str],
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state : str,
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@ -89,6 +93,12 @@ def set_tool(
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if template: object["template"] = template
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if schema_name: object["schema-name"] = schema_name
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if index_name: object["index-name"] = index_name
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if limit: object["limit"] = limit
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if arguments:
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object["arguments"] = [
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{
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@ -120,30 +130,37 @@ def main():
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description=__doc__,
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epilog=textwrap.dedent('''
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Valid tool types:
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knowledge-query - Query knowledge bases (fixed args)
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structured-query - Query structured data using natural language (fixed args)
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text-completion - Text completion/generation (fixed args)
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mcp-tool - Model Control Protocol tool (configurable args)
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prompt - Prompt template query (configurable args)
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Note: Tools marked "(fixed args)" have predefined arguments and don't need
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knowledge-query - Query knowledge bases (fixed args)
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structured-query - Query structured data using natural language (fixed args)
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row-embeddings-query - Semantic search on structured data indexes (fixed args)
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text-completion - Text completion/generation (fixed args)
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mcp-tool - Model Control Protocol tool (configurable args)
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prompt - Prompt template query (configurable args)
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Note: Tools marked "(fixed args)" have predefined arguments and don't need
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--argument specified. Tools marked "(configurable args)" require --argument.
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Valid argument types:
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string - String/text parameter
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string - String/text parameter
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number - Numeric parameter
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Examples:
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%(prog)s --id weather_tool --name get_weather \\
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--type knowledge-query \\
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--description "Get weather information for a location" \\
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--collection weather_data
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%(prog)s --id data_query_tool --name query_data \\
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--type structured-query \\
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--description "Query structured data using natural language" \\
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--collection sales_data
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%(prog)s --id customer_search --name find_customer \\
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--type row-embeddings-query \\
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--description "Find customers by name using semantic search" \\
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--schema-name customers --collection sales \\
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--index-name full_name --limit 20
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%(prog)s --id calc_tool --name calculate --type mcp-tool \\
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--description "Perform mathematical calculations" \\
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--mcp-tool calculator \\
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@ -181,7 +198,7 @@ def main():
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parser.add_argument(
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'--type',
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help=f'Tool type, one of: knowledge-query, structured-query, text-completion, mcp-tool, prompt',
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help=f'Tool type, one of: knowledge-query, structured-query, row-embeddings-query, text-completion, mcp-tool, prompt',
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)
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parser.add_argument(
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parser.add_argument(
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'--collection',
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help=f'For knowledge-query and structured-query types: collection to query',
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help=f'For knowledge-query, structured-query, and row-embeddings-query types: collection to query',
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)
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parser.add_argument(
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'--schema-name',
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help=f'For row-embeddings-query type: schema name to search within (required)',
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)
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parser.add_argument(
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'--index-name',
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help=f'For row-embeddings-query type: specific index to filter search (optional)',
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)
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parser.add_argument(
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'--limit',
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type=int,
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help=f'For row-embeddings-query type: maximum results to return (default: 10)',
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)
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parser.add_argument(
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try:
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valid_types = [
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"knowledge-query", "structured-query", "text-completion", "mcp-tool", "prompt"
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"knowledge-query", "structured-query", "row-embeddings-query",
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"text-completion", "mcp-tool", "prompt"
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]
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if args.id is None:
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@ -261,6 +295,9 @@ def main():
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mcp_tool=mcp_tool,
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collection=args.collection,
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template=args.template,
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schema_name=args.schema_name,
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index_name=args.index_name,
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limit=args.limit,
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arguments=arguments,
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group=args.group,
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state=args.state,
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@ -4,8 +4,9 @@ Displays the current agent tool configurations
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Shows all configured tools including their types:
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- knowledge-query: Tools that query knowledge bases
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- structured-query: Tools that query structured data using natural language
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- row-embeddings-query: Tools for semantic search on structured data indexes
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- text-completion: Tools for text generation
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- mcp-tool: References to MCP (Model Context Protocol) tools
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- mcp-tool: References to MCP (Model Context Protocol) tools
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- prompt: Tools that execute prompt templates
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"""
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@ -41,11 +42,19 @@ def show_config(url, token=None):
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if tp == "mcp-tool":
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table.append(("mcp-tool", data["mcp-tool"]))
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if tp == "knowledge-query" or tp == "structured-query":
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if tp in ("knowledge-query", "structured-query", "row-embeddings-query"):
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if "collection" in data:
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table.append(("collection", data["collection"]))
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if tp == "row-embeddings-query":
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if "schema-name" in data:
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table.append(("schema-name", data["schema-name"]))
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if "index-name" in data:
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table.append(("index-name", data["index-name"]))
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if "limit" in data:
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table.append(("limit", data["limit"]))
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if tp == "prompt":
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table.append(("template", data["template"]))
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for n, arg in enumerate(data["arguments"]):
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