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99141c29b1
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feat: Add responses and safety impl extra_body (#3781)
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# What does this PR do? Have closed the previous PR due to merge conflicts with multiple PRs Addressed all comments from https://github.com/llamastack/llama-stack/pull/3768 (sorry for carrying over to this one) ## Test Plan Added UTs and integration tests |
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ef0736527d
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feat(tools)!: substantial clean up of "Tool" related datatypes (#3627)
This is a sweeping change to clean up some gunk around our "Tool" definitions. First, we had two types `Tool` and `ToolDef`. The first of these was a "Resource" type for the registry but we had stopped registering tools inside the Registry long back (and only registered ToolGroups.) The latter was for specifying tools for the Agents API. This PR removes the former and adds an optional `toolgroup_id` field to the latter. Secondly, as pointed out by @bbrowning in https://github.com/llamastack/llama-stack/pull/3003#issuecomment-3245270132, we were doing a lossy conversion from a full JSON schema from the MCP tool specification into our ToolDefinition to send it to the model. There is no necessity to do this -- we ourselves aren't doing any execution at all but merely passing it to the chat completions API which supports this. By doing this (and by doing it poorly), we encountered limitations like not supporting array items, or not resolving $refs, etc. To fix this, we replaced the `parameters` field by `{ input_schema, output_schema }` which can be full blown JSON schemas. Finally, there were some types in our llama-related chat format conversion which needed some cleanup. We are taking this opportunity to clean those up. This PR is a substantial breaking change to the API. However, given our window for introducing breaking changes, this suits us just fine. I will be landing a concurrent `llama-stack-client` change as well since API shapes are changing. |
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6cce553c93
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fix: mcp tool with array type should include items (#3602)
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# What does this PR do? Fixes error: ``` [ERROR] Error executing endpoint route='/v1/openai/v1/responses' method='post': Error code: 400 - {'error': {'message': "Invalid schema for function 'pods_exec': In context=('properties', 'command'), array schema missing items.", 'type': 'invalid_request_error', 'param': 'tools[7].function.parameters', 'code': 'invalid_function_parameters'}} ``` From script: ``` #!/usr/bin/env python3 """ Script to test Responses API with kubernetes-mcp-server. This script: 1. Connects to the llama stack server 2. Uses the Responses API with MCP tools 3. Asks for the list of Kubernetes namespaces using the kubernetes-mcp-server """ import json from openai import OpenAI # Connect to the llama stack server base_url = "http://localhost:8321/v1/openai/v1" client = OpenAI(base_url=base_url, api_key="fake") # Define the MCP tool pointing to the kubernetes-mcp-server # The kubernetes-mcp-server is running on port 3000 with SSE endpoint at /sse mcp_server_url = "http://localhost:3000/sse" tools = [ { "type": "mcp", "server_label": "k8s", "server_url": mcp_server_url, } ] # Create a response request asking for k8s namespaces print("Sending request to list Kubernetes namespaces...") print(f"Using MCP server at: {mcp_server_url}") print("Available tools will be listed automatically by the MCP server.") print() response = client.responses.create( # model="meta-llama/Llama-3.2-3B-Instruct", # Using the vllm model model="openai/gpt-4o", input="what are all the Kubernetes namespaces? Use tool call to `namespaces_list`. make sure to adhere to the tool calling format.", tools=tools, stream=False, ) print("\n" + "=" * 80) print("RESPONSE OUTPUT:") print("=" * 80) # Print the output for i, output in enumerate(response.output): print(f"\n[Output {i + 1}] Type: {output.type}") if output.type == "mcp_list_tools": print(f" Server: {output.server_label}") print(f" Tools available: {[t.name for t in output.tools]}") elif output.type == "mcp_call": print(f" Tool called: {output.name}") print(f" Arguments: {output.arguments}") print(f" Result: {output.output}") if output.error: print(f" Error: {output.error}") elif output.type == "message": print(f" Role: {output.role}") print(f" Content: {output.content}") print("\n" + "=" * 80) print("FINAL RESPONSE TEXT:") print("=" * 80) print(response.output_text) ``` ## Test Plan new unit tests script now runs successfully |