mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-03 09:53:45 +00:00
feat(responses)!: improve responses + conversations implementations (#3810)
This PR updates the Conversation item related types and improves a couple critical parts of the implemenation: - it creates a streaming output item for the final assistant message output by the model. until now we only added content parts and included that message in the final response. - rewrites the conversation update code completely to account for items other than messages (tool calls, outputs, etc.) ## Test Plan Used the test script from https://github.com/llamastack/llama-stack-client-python/pull/281 for this ``` TEST_API_BASE_URL=http://localhost:8321/v1 \ pytest tests/integration/test_agent_turn_step_events.py::test_client_side_function_tool -xvs ```
This commit is contained in:
Ashwin Bharambe
GitHub
parent
add8cd801b
commit
e9b4278a51
129 changed files with 86266 additions and 903 deletions
6
docs/static/deprecated-llama-stack-spec.html
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docs/static/deprecated-llama-stack-spec.html
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@ -8523,6 +8523,12 @@
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{
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"$ref": "#/components/schemas/OpenAIResponseMCPApprovalResponse"
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},
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{
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"$ref": "#/components/schemas/OpenAIResponseOutputMessageMCPCall"
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},
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{
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"$ref": "#/components/schemas/OpenAIResponseOutputMessageMCPListTools"
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},
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{
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"$ref": "#/components/schemas/OpenAIResponseMessage"
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}
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docs/static/deprecated-llama-stack-spec.yaml
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2
docs/static/deprecated-llama-stack-spec.yaml
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@ -6358,6 +6358,8 @@ components:
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- $ref: '#/components/schemas/OpenAIResponseInputFunctionToolCallOutput'
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- $ref: '#/components/schemas/OpenAIResponseMCPApprovalRequest'
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- $ref: '#/components/schemas/OpenAIResponseMCPApprovalResponse'
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- $ref: '#/components/schemas/OpenAIResponseOutputMessageMCPCall'
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- $ref: '#/components/schemas/OpenAIResponseOutputMessageMCPListTools'
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- $ref: '#/components/schemas/OpenAIResponseMessage'
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"OpenAIResponseInputFunctionToolCallOutput":
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type: object
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docs/static/llama-stack-spec.html
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docs/static/llama-stack-spec.html
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@ -5479,13 +5479,22 @@
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"$ref": "#/components/schemas/OpenAIResponseMessage"
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},
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{
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"$ref": "#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall"
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"$ref": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall"
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},
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{
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"$ref": "#/components/schemas/OpenAIResponseOutputMessageFileSearchToolCall"
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},
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{
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"$ref": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall"
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"$ref": "#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall"
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},
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{
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"$ref": "#/components/schemas/OpenAIResponseInputFunctionToolCallOutput"
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},
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{
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"$ref": "#/components/schemas/OpenAIResponseMCPApprovalRequest"
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},
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{
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"$ref": "#/components/schemas/OpenAIResponseMCPApprovalResponse"
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},
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{
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"$ref": "#/components/schemas/OpenAIResponseOutputMessageMCPCall"
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@ -5498,9 +5507,12 @@
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"propertyName": "type",
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"mapping": {
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"message": "#/components/schemas/OpenAIResponseMessage",
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"function_call": "#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall",
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"file_search_call": "#/components/schemas/OpenAIResponseOutputMessageFileSearchToolCall",
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"web_search_call": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall",
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"file_search_call": "#/components/schemas/OpenAIResponseOutputMessageFileSearchToolCall",
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"function_call": "#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall",
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"function_call_output": "#/components/schemas/OpenAIResponseInputFunctionToolCallOutput",
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"mcp_approval_request": "#/components/schemas/OpenAIResponseMCPApprovalRequest",
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"mcp_approval_response": "#/components/schemas/OpenAIResponseMCPApprovalResponse",
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"mcp_call": "#/components/schemas/OpenAIResponseOutputMessageMCPCall",
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"mcp_list_tools": "#/components/schemas/OpenAIResponseOutputMessageMCPListTools"
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}
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@ -5658,6 +5670,36 @@
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}
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}
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},
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"OpenAIResponseInputFunctionToolCallOutput": {
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"type": "object",
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"properties": {
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"call_id": {
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"type": "string"
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},
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"output": {
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"type": "string"
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},
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"type": {
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"type": "string",
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"const": "function_call_output",
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"default": "function_call_output"
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},
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"id": {
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"type": "string"
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},
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"status": {
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"type": "string"
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}
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},
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"additionalProperties": false,
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"required": [
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"call_id",
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"output",
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"type"
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],
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"title": "OpenAIResponseInputFunctionToolCallOutput",
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"description": "This represents the output of a function call that gets passed back to the model."
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},
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"OpenAIResponseInputMessageContent": {
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"oneOf": [
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{
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@ -5737,6 +5779,68 @@
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"title": "OpenAIResponseInputMessageContentText",
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"description": "Text content for input messages in OpenAI response format."
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},
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"OpenAIResponseMCPApprovalRequest": {
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"type": "object",
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"properties": {
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"arguments": {
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"type": "string"
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},
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"id": {
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"type": "string"
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},
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"name": {
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"type": "string"
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},
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"server_label": {
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"type": "string"
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},
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"type": {
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"type": "string",
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"const": "mcp_approval_request",
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"default": "mcp_approval_request"
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}
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},
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"additionalProperties": false,
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"required": [
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"arguments",
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"id",
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"name",
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"server_label",
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"type"
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],
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"title": "OpenAIResponseMCPApprovalRequest",
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"description": "A request for human approval of a tool invocation."
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},
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"OpenAIResponseMCPApprovalResponse": {
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"type": "object",
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"properties": {
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"approval_request_id": {
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"type": "string"
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},
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"approve": {
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"type": "boolean"
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},
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"type": {
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"type": "string",
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"const": "mcp_approval_response",
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"default": "mcp_approval_response"
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},
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"id": {
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"type": "string"
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},
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"reason": {
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"type": "string"
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}
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},
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"additionalProperties": false,
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"required": [
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"approval_request_id",
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"approve",
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"type"
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],
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"title": "OpenAIResponseMCPApprovalResponse",
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"description": "A response to an MCP approval request."
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},
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"OpenAIResponseMessage": {
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"type": "object",
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"properties": {
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@ -7212,41 +7316,17 @@
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{
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"$ref": "#/components/schemas/OpenAIResponseMCPApprovalResponse"
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},
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{
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"$ref": "#/components/schemas/OpenAIResponseOutputMessageMCPCall"
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},
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{
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"$ref": "#/components/schemas/OpenAIResponseOutputMessageMCPListTools"
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},
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{
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"$ref": "#/components/schemas/OpenAIResponseMessage"
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}
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]
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},
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"OpenAIResponseInputFunctionToolCallOutput": {
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"type": "object",
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"properties": {
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"call_id": {
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"type": "string"
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},
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"output": {
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"type": "string"
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},
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"type": {
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"type": "string",
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"const": "function_call_output",
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"default": "function_call_output"
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},
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"id": {
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"type": "string"
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},
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"status": {
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"type": "string"
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}
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},
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"additionalProperties": false,
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"required": [
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"call_id",
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"output",
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"type"
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],
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"title": "OpenAIResponseInputFunctionToolCallOutput",
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"description": "This represents the output of a function call that gets passed back to the model."
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},
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"OpenAIResponseInputToolFileSearch": {
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"type": "object",
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"properties": {
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@ -7409,68 +7489,6 @@
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"title": "OpenAIResponseInputToolWebSearch",
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"description": "Web search tool configuration for OpenAI response inputs."
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},
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"OpenAIResponseMCPApprovalRequest": {
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"type": "object",
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"properties": {
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"arguments": {
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"type": "string"
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},
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"id": {
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"type": "string"
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},
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"name": {
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"type": "string"
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},
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"server_label": {
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"type": "string"
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},
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"type": {
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"type": "string",
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"const": "mcp_approval_request",
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"default": "mcp_approval_request"
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}
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},
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"additionalProperties": false,
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"required": [
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"arguments",
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"id",
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"name",
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"server_label",
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"type"
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],
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"title": "OpenAIResponseMCPApprovalRequest",
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"description": "A request for human approval of a tool invocation."
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},
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"OpenAIResponseMCPApprovalResponse": {
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"type": "object",
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"properties": {
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"approval_request_id": {
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"type": "string"
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},
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"approve": {
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"type": "boolean"
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},
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"type": {
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"type": "string",
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"const": "mcp_approval_response",
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"default": "mcp_approval_response"
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},
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"id": {
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"type": "string"
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},
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"reason": {
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"type": "string"
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}
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},
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"additionalProperties": false,
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"required": [
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"approval_request_id",
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"approve",
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"type"
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],
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"title": "OpenAIResponseMCPApprovalResponse",
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"description": "A response to an MCP approval request."
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},
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"OpenAIResponseObjectWithInput": {
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"type": "object",
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"properties": {
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160
docs/static/llama-stack-spec.yaml
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@ -4153,18 +4153,24 @@ components:
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ConversationItem:
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oneOf:
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- $ref: '#/components/schemas/OpenAIResponseMessage'
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- $ref: '#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall'
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- $ref: '#/components/schemas/OpenAIResponseOutputMessageFileSearchToolCall'
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- $ref: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall'
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- $ref: '#/components/schemas/OpenAIResponseOutputMessageFileSearchToolCall'
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- $ref: '#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall'
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- $ref: '#/components/schemas/OpenAIResponseInputFunctionToolCallOutput'
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- $ref: '#/components/schemas/OpenAIResponseMCPApprovalRequest'
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- $ref: '#/components/schemas/OpenAIResponseMCPApprovalResponse'
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- $ref: '#/components/schemas/OpenAIResponseOutputMessageMCPCall'
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- $ref: '#/components/schemas/OpenAIResponseOutputMessageMCPListTools'
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discriminator:
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propertyName: type
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mapping:
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message: '#/components/schemas/OpenAIResponseMessage'
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function_call: '#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall'
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file_search_call: '#/components/schemas/OpenAIResponseOutputMessageFileSearchToolCall'
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web_search_call: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall'
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file_search_call: '#/components/schemas/OpenAIResponseOutputMessageFileSearchToolCall'
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function_call: '#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall'
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function_call_output: '#/components/schemas/OpenAIResponseInputFunctionToolCallOutput'
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mcp_approval_request: '#/components/schemas/OpenAIResponseMCPApprovalRequest'
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mcp_approval_response: '#/components/schemas/OpenAIResponseMCPApprovalResponse'
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mcp_call: '#/components/schemas/OpenAIResponseOutputMessageMCPCall'
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mcp_list_tools: '#/components/schemas/OpenAIResponseOutputMessageMCPListTools'
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OpenAIResponseAnnotationCitation:
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@ -4285,6 +4291,31 @@ components:
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url_citation: '#/components/schemas/OpenAIResponseAnnotationCitation'
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container_file_citation: '#/components/schemas/OpenAIResponseAnnotationContainerFileCitation'
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file_path: '#/components/schemas/OpenAIResponseAnnotationFilePath'
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"OpenAIResponseInputFunctionToolCallOutput":
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type: object
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properties:
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call_id:
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type: string
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output:
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type: string
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type:
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type: string
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const: function_call_output
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default: function_call_output
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id:
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type: string
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status:
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type: string
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additionalProperties: false
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required:
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- call_id
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- output
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- type
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title: >-
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OpenAIResponseInputFunctionToolCallOutput
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description: >-
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This represents the output of a function call that gets passed back to the
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model.
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OpenAIResponseInputMessageContent:
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oneOf:
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- $ref: '#/components/schemas/OpenAIResponseInputMessageContentText'
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@ -4343,6 +4374,53 @@ components:
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title: OpenAIResponseInputMessageContentText
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description: >-
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Text content for input messages in OpenAI response format.
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OpenAIResponseMCPApprovalRequest:
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type: object
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properties:
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arguments:
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type: string
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id:
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type: string
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name:
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type: string
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server_label:
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type: string
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type:
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type: string
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const: mcp_approval_request
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default: mcp_approval_request
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additionalProperties: false
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required:
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- arguments
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- id
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- name
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- server_label
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- type
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title: OpenAIResponseMCPApprovalRequest
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description: >-
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A request for human approval of a tool invocation.
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OpenAIResponseMCPApprovalResponse:
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type: object
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properties:
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approval_request_id:
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type: string
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approve:
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type: boolean
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type:
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type: string
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const: mcp_approval_response
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default: mcp_approval_response
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id:
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type: string
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reason:
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type: string
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additionalProperties: false
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required:
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- approval_request_id
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- approve
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- type
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title: OpenAIResponseMCPApprovalResponse
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description: A response to an MCP approval request.
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OpenAIResponseMessage:
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type: object
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properties:
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@ -5500,32 +5578,9 @@ components:
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- $ref: '#/components/schemas/OpenAIResponseInputFunctionToolCallOutput'
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- $ref: '#/components/schemas/OpenAIResponseMCPApprovalRequest'
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- $ref: '#/components/schemas/OpenAIResponseMCPApprovalResponse'
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- $ref: '#/components/schemas/OpenAIResponseOutputMessageMCPCall'
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- $ref: '#/components/schemas/OpenAIResponseOutputMessageMCPListTools'
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- $ref: '#/components/schemas/OpenAIResponseMessage'
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"OpenAIResponseInputFunctionToolCallOutput":
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type: object
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properties:
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call_id:
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type: string
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output:
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type: string
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type:
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type: string
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const: function_call_output
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default: function_call_output
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id:
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type: string
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status:
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type: string
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||||
additionalProperties: false
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required:
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- call_id
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- output
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- type
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title: >-
|
||||
OpenAIResponseInputFunctionToolCallOutput
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description: >-
|
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This represents the output of a function call that gets passed back to the
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model.
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OpenAIResponseInputToolFileSearch:
|
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type: object
|
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properties:
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@ -5642,53 +5697,6 @@ components:
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title: OpenAIResponseInputToolWebSearch
|
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description: >-
|
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Web search tool configuration for OpenAI response inputs.
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OpenAIResponseMCPApprovalRequest:
|
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type: object
|
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properties:
|
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arguments:
|
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type: string
|
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id:
|
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type: string
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name:
|
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type: string
|
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server_label:
|
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type: string
|
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type:
|
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type: string
|
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const: mcp_approval_request
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default: mcp_approval_request
|
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additionalProperties: false
|
||||
required:
|
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- arguments
|
||||
- id
|
||||
- name
|
||||
- server_label
|
||||
- type
|
||||
title: OpenAIResponseMCPApprovalRequest
|
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description: >-
|
||||
A request for human approval of a tool invocation.
|
||||
OpenAIResponseMCPApprovalResponse:
|
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type: object
|
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properties:
|
||||
approval_request_id:
|
||||
type: string
|
||||
approve:
|
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type: boolean
|
||||
type:
|
||||
type: string
|
||||
const: mcp_approval_response
|
||||
default: mcp_approval_response
|
||||
id:
|
||||
type: string
|
||||
reason:
|
||||
type: string
|
||||
additionalProperties: false
|
||||
required:
|
||||
- approval_request_id
|
||||
- approve
|
||||
- type
|
||||
title: OpenAIResponseMCPApprovalResponse
|
||||
description: A response to an MCP approval request.
|
||||
OpenAIResponseObjectWithInput:
|
||||
type: object
|
||||
properties:
|
||||
|
|
|
|||
210
docs/static/stainless-llama-stack-spec.html
vendored
210
docs/static/stainless-llama-stack-spec.html
vendored
|
|
@ -7151,13 +7151,22 @@
|
|||
"$ref": "#/components/schemas/OpenAIResponseMessage"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall"
|
||||
"$ref": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseOutputMessageFileSearchToolCall"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall"
|
||||
"$ref": "#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseInputFunctionToolCallOutput"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseMCPApprovalRequest"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseMCPApprovalResponse"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseOutputMessageMCPCall"
|
||||
|
|
@ -7170,9 +7179,12 @@
|
|||
"propertyName": "type",
|
||||
"mapping": {
|
||||
"message": "#/components/schemas/OpenAIResponseMessage",
|
||||
"function_call": "#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall",
|
||||
"file_search_call": "#/components/schemas/OpenAIResponseOutputMessageFileSearchToolCall",
|
||||
"web_search_call": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall",
|
||||
"file_search_call": "#/components/schemas/OpenAIResponseOutputMessageFileSearchToolCall",
|
||||
"function_call": "#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall",
|
||||
"function_call_output": "#/components/schemas/OpenAIResponseInputFunctionToolCallOutput",
|
||||
"mcp_approval_request": "#/components/schemas/OpenAIResponseMCPApprovalRequest",
|
||||
"mcp_approval_response": "#/components/schemas/OpenAIResponseMCPApprovalResponse",
|
||||
"mcp_call": "#/components/schemas/OpenAIResponseOutputMessageMCPCall",
|
||||
"mcp_list_tools": "#/components/schemas/OpenAIResponseOutputMessageMCPListTools"
|
||||
}
|
||||
|
|
@ -7330,6 +7342,36 @@
|
|||
}
|
||||
}
|
||||
},
|
||||
"OpenAIResponseInputFunctionToolCallOutput": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"call_id": {
|
||||
"type": "string"
|
||||
},
|
||||
"output": {
|
||||
"type": "string"
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "function_call_output",
|
||||
"default": "function_call_output"
|
||||
},
|
||||
"id": {
|
||||
"type": "string"
|
||||
},
|
||||
"status": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"call_id",
|
||||
"output",
|
||||
"type"
|
||||
],
|
||||
"title": "OpenAIResponseInputFunctionToolCallOutput",
|
||||
"description": "This represents the output of a function call that gets passed back to the model."
|
||||
},
|
||||
"OpenAIResponseInputMessageContent": {
|
||||
"oneOf": [
|
||||
{
|
||||
|
|
@ -7409,6 +7451,68 @@
|
|||
"title": "OpenAIResponseInputMessageContentText",
|
||||
"description": "Text content for input messages in OpenAI response format."
|
||||
},
|
||||
"OpenAIResponseMCPApprovalRequest": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"arguments": {
|
||||
"type": "string"
|
||||
},
|
||||
"id": {
|
||||
"type": "string"
|
||||
},
|
||||
"name": {
|
||||
"type": "string"
|
||||
},
|
||||
"server_label": {
|
||||
"type": "string"
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "mcp_approval_request",
|
||||
"default": "mcp_approval_request"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"arguments",
|
||||
"id",
|
||||
"name",
|
||||
"server_label",
|
||||
"type"
|
||||
],
|
||||
"title": "OpenAIResponseMCPApprovalRequest",
|
||||
"description": "A request for human approval of a tool invocation."
|
||||
},
|
||||
"OpenAIResponseMCPApprovalResponse": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"approval_request_id": {
|
||||
"type": "string"
|
||||
},
|
||||
"approve": {
|
||||
"type": "boolean"
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "mcp_approval_response",
|
||||
"default": "mcp_approval_response"
|
||||
},
|
||||
"id": {
|
||||
"type": "string"
|
||||
},
|
||||
"reason": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"approval_request_id",
|
||||
"approve",
|
||||
"type"
|
||||
],
|
||||
"title": "OpenAIResponseMCPApprovalResponse",
|
||||
"description": "A response to an MCP approval request."
|
||||
},
|
||||
"OpenAIResponseMessage": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
|
|
@ -8884,41 +8988,17 @@
|
|||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseMCPApprovalResponse"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseOutputMessageMCPCall"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseOutputMessageMCPListTools"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseMessage"
|
||||
}
|
||||
]
|
||||
},
|
||||
"OpenAIResponseInputFunctionToolCallOutput": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"call_id": {
|
||||
"type": "string"
|
||||
},
|
||||
"output": {
|
||||
"type": "string"
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "function_call_output",
|
||||
"default": "function_call_output"
|
||||
},
|
||||
"id": {
|
||||
"type": "string"
|
||||
},
|
||||
"status": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"call_id",
|
||||
"output",
|
||||
"type"
|
||||
],
|
||||
"title": "OpenAIResponseInputFunctionToolCallOutput",
|
||||
"description": "This represents the output of a function call that gets passed back to the model."
|
||||
},
|
||||
"OpenAIResponseInputToolFileSearch": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
|
|
@ -9081,68 +9161,6 @@
|
|||
"title": "OpenAIResponseInputToolWebSearch",
|
||||
"description": "Web search tool configuration for OpenAI response inputs."
|
||||
},
|
||||
"OpenAIResponseMCPApprovalRequest": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"arguments": {
|
||||
"type": "string"
|
||||
},
|
||||
"id": {
|
||||
"type": "string"
|
||||
},
|
||||
"name": {
|
||||
"type": "string"
|
||||
},
|
||||
"server_label": {
|
||||
"type": "string"
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "mcp_approval_request",
|
||||
"default": "mcp_approval_request"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"arguments",
|
||||
"id",
|
||||
"name",
|
||||
"server_label",
|
||||
"type"
|
||||
],
|
||||
"title": "OpenAIResponseMCPApprovalRequest",
|
||||
"description": "A request for human approval of a tool invocation."
|
||||
},
|
||||
"OpenAIResponseMCPApprovalResponse": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"approval_request_id": {
|
||||
"type": "string"
|
||||
},
|
||||
"approve": {
|
||||
"type": "boolean"
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "mcp_approval_response",
|
||||
"default": "mcp_approval_response"
|
||||
},
|
||||
"id": {
|
||||
"type": "string"
|
||||
},
|
||||
"reason": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"approval_request_id",
|
||||
"approve",
|
||||
"type"
|
||||
],
|
||||
"title": "OpenAIResponseMCPApprovalResponse",
|
||||
"description": "A response to an MCP approval request."
|
||||
},
|
||||
"OpenAIResponseObjectWithInput": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
|
|
|
|||
160
docs/static/stainless-llama-stack-spec.yaml
vendored
160
docs/static/stainless-llama-stack-spec.yaml
vendored
|
|
@ -5366,18 +5366,24 @@ components:
|
|||
ConversationItem:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/OpenAIResponseMessage'
|
||||
- $ref: '#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall'
|
||||
- $ref: '#/components/schemas/OpenAIResponseOutputMessageFileSearchToolCall'
|
||||
- $ref: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall'
|
||||
- $ref: '#/components/schemas/OpenAIResponseOutputMessageFileSearchToolCall'
|
||||
- $ref: '#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall'
|
||||
- $ref: '#/components/schemas/OpenAIResponseInputFunctionToolCallOutput'
|
||||
- $ref: '#/components/schemas/OpenAIResponseMCPApprovalRequest'
|
||||
- $ref: '#/components/schemas/OpenAIResponseMCPApprovalResponse'
|
||||
- $ref: '#/components/schemas/OpenAIResponseOutputMessageMCPCall'
|
||||
- $ref: '#/components/schemas/OpenAIResponseOutputMessageMCPListTools'
|
||||
discriminator:
|
||||
propertyName: type
|
||||
mapping:
|
||||
message: '#/components/schemas/OpenAIResponseMessage'
|
||||
function_call: '#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall'
|
||||
file_search_call: '#/components/schemas/OpenAIResponseOutputMessageFileSearchToolCall'
|
||||
web_search_call: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall'
|
||||
file_search_call: '#/components/schemas/OpenAIResponseOutputMessageFileSearchToolCall'
|
||||
function_call: '#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall'
|
||||
function_call_output: '#/components/schemas/OpenAIResponseInputFunctionToolCallOutput'
|
||||
mcp_approval_request: '#/components/schemas/OpenAIResponseMCPApprovalRequest'
|
||||
mcp_approval_response: '#/components/schemas/OpenAIResponseMCPApprovalResponse'
|
||||
mcp_call: '#/components/schemas/OpenAIResponseOutputMessageMCPCall'
|
||||
mcp_list_tools: '#/components/schemas/OpenAIResponseOutputMessageMCPListTools'
|
||||
OpenAIResponseAnnotationCitation:
|
||||
|
|
@ -5498,6 +5504,31 @@ components:
|
|||
url_citation: '#/components/schemas/OpenAIResponseAnnotationCitation'
|
||||
container_file_citation: '#/components/schemas/OpenAIResponseAnnotationContainerFileCitation'
|
||||
file_path: '#/components/schemas/OpenAIResponseAnnotationFilePath'
|
||||
"OpenAIResponseInputFunctionToolCallOutput":
|
||||
type: object
|
||||
properties:
|
||||
call_id:
|
||||
type: string
|
||||
output:
|
||||
type: string
|
||||
type:
|
||||
type: string
|
||||
const: function_call_output
|
||||
default: function_call_output
|
||||
id:
|
||||
type: string
|
||||
status:
|
||||
type: string
|
||||
additionalProperties: false
|
||||
required:
|
||||
- call_id
|
||||
- output
|
||||
- type
|
||||
title: >-
|
||||
OpenAIResponseInputFunctionToolCallOutput
|
||||
description: >-
|
||||
This represents the output of a function call that gets passed back to the
|
||||
model.
|
||||
OpenAIResponseInputMessageContent:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/OpenAIResponseInputMessageContentText'
|
||||
|
|
@ -5556,6 +5587,53 @@ components:
|
|||
title: OpenAIResponseInputMessageContentText
|
||||
description: >-
|
||||
Text content for input messages in OpenAI response format.
|
||||
OpenAIResponseMCPApprovalRequest:
|
||||
type: object
|
||||
properties:
|
||||
arguments:
|
||||
type: string
|
||||
id:
|
||||
type: string
|
||||
name:
|
||||
type: string
|
||||
server_label:
|
||||
type: string
|
||||
type:
|
||||
type: string
|
||||
const: mcp_approval_request
|
||||
default: mcp_approval_request
|
||||
additionalProperties: false
|
||||
required:
|
||||
- arguments
|
||||
- id
|
||||
- name
|
||||
- server_label
|
||||
- type
|
||||
title: OpenAIResponseMCPApprovalRequest
|
||||
description: >-
|
||||
A request for human approval of a tool invocation.
|
||||
OpenAIResponseMCPApprovalResponse:
|
||||
type: object
|
||||
properties:
|
||||
approval_request_id:
|
||||
type: string
|
||||
approve:
|
||||
type: boolean
|
||||
type:
|
||||
type: string
|
||||
const: mcp_approval_response
|
||||
default: mcp_approval_response
|
||||
id:
|
||||
type: string
|
||||
reason:
|
||||
type: string
|
||||
additionalProperties: false
|
||||
required:
|
||||
- approval_request_id
|
||||
- approve
|
||||
- type
|
||||
title: OpenAIResponseMCPApprovalResponse
|
||||
description: A response to an MCP approval request.
|
||||
OpenAIResponseMessage:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -6713,32 +6791,9 @@ components:
|
|||
- $ref: '#/components/schemas/OpenAIResponseInputFunctionToolCallOutput'
|
||||
- $ref: '#/components/schemas/OpenAIResponseMCPApprovalRequest'
|
||||
- $ref: '#/components/schemas/OpenAIResponseMCPApprovalResponse'
|
||||
- $ref: '#/components/schemas/OpenAIResponseOutputMessageMCPCall'
|
||||
- $ref: '#/components/schemas/OpenAIResponseOutputMessageMCPListTools'
|
||||
- $ref: '#/components/schemas/OpenAIResponseMessage'
|
||||
"OpenAIResponseInputFunctionToolCallOutput":
|
||||
type: object
|
||||
properties:
|
||||
call_id:
|
||||
type: string
|
||||
output:
|
||||
type: string
|
||||
type:
|
||||
type: string
|
||||
const: function_call_output
|
||||
default: function_call_output
|
||||
id:
|
||||
type: string
|
||||
status:
|
||||
type: string
|
||||
additionalProperties: false
|
||||
required:
|
||||
- call_id
|
||||
- output
|
||||
- type
|
||||
title: >-
|
||||
OpenAIResponseInputFunctionToolCallOutput
|
||||
description: >-
|
||||
This represents the output of a function call that gets passed back to the
|
||||
model.
|
||||
OpenAIResponseInputToolFileSearch:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -6855,53 +6910,6 @@ components:
|
|||
title: OpenAIResponseInputToolWebSearch
|
||||
description: >-
|
||||
Web search tool configuration for OpenAI response inputs.
|
||||
OpenAIResponseMCPApprovalRequest:
|
||||
type: object
|
||||
properties:
|
||||
arguments:
|
||||
type: string
|
||||
id:
|
||||
type: string
|
||||
name:
|
||||
type: string
|
||||
server_label:
|
||||
type: string
|
||||
type:
|
||||
type: string
|
||||
const: mcp_approval_request
|
||||
default: mcp_approval_request
|
||||
additionalProperties: false
|
||||
required:
|
||||
- arguments
|
||||
- id
|
||||
- name
|
||||
- server_label
|
||||
- type
|
||||
title: OpenAIResponseMCPApprovalRequest
|
||||
description: >-
|
||||
A request for human approval of a tool invocation.
|
||||
OpenAIResponseMCPApprovalResponse:
|
||||
type: object
|
||||
properties:
|
||||
approval_request_id:
|
||||
type: string
|
||||
approve:
|
||||
type: boolean
|
||||
type:
|
||||
type: string
|
||||
const: mcp_approval_response
|
||||
default: mcp_approval_response
|
||||
id:
|
||||
type: string
|
||||
reason:
|
||||
type: string
|
||||
additionalProperties: false
|
||||
required:
|
||||
- approval_request_id
|
||||
- approve
|
||||
- type
|
||||
title: OpenAIResponseMCPApprovalResponse
|
||||
description: A response to an MCP approval request.
|
||||
OpenAIResponseObjectWithInput:
|
||||
type: object
|
||||
properties:
|
||||
|
|
|
|||
|
|
@ -1258,9 +1258,9 @@ OpenAIResponseInput = Annotated[
|
|||
| OpenAIResponseInputFunctionToolCallOutput
|
||||
| OpenAIResponseMCPApprovalRequest
|
||||
| OpenAIResponseMCPApprovalResponse
|
||||
|
|
||||
# Fallback to the generic message type as a last resort
|
||||
OpenAIResponseMessage,
|
||||
| OpenAIResponseOutputMessageMCPCall
|
||||
| OpenAIResponseOutputMessageMCPListTools
|
||||
| OpenAIResponseMessage,
|
||||
Field(union_mode="left_to_right"),
|
||||
]
|
||||
register_schema(OpenAIResponseInput, name="OpenAIResponseInput")
|
||||
|
|
|
|||
|
|
@ -12,6 +12,9 @@ from openai.types.responses.response_includable import ResponseIncludable
|
|||
from pydantic import BaseModel, Field
|
||||
|
||||
from llama_stack.apis.agents.openai_responses import (
|
||||
OpenAIResponseInputFunctionToolCallOutput,
|
||||
OpenAIResponseMCPApprovalRequest,
|
||||
OpenAIResponseMCPApprovalResponse,
|
||||
OpenAIResponseMessage,
|
||||
OpenAIResponseOutputMessageFileSearchToolCall,
|
||||
OpenAIResponseOutputMessageFunctionToolCall,
|
||||
|
|
@ -61,9 +64,14 @@ class ConversationMessage(BaseModel):
|
|||
|
||||
ConversationItem = Annotated[
|
||||
OpenAIResponseMessage
|
||||
| OpenAIResponseOutputMessageFunctionToolCall
|
||||
| OpenAIResponseOutputMessageFileSearchToolCall
|
||||
| OpenAIResponseOutputMessageWebSearchToolCall
|
||||
| OpenAIResponseOutputMessageFileSearchToolCall
|
||||
| OpenAIResponseOutputMessageFunctionToolCall
|
||||
| OpenAIResponseInputFunctionToolCallOutput
|
||||
| OpenAIResponseMCPApprovalRequest
|
||||
| OpenAIResponseMCPApprovalResponse
|
||||
| OpenAIResponseOutputMessageMCPCall
|
||||
| OpenAIResponseOutputMessageMCPListTools
|
||||
| OpenAIResponseOutputMessageMCPCall
|
||||
| OpenAIResponseOutputMessageMCPListTools,
|
||||
Field(discriminator="type"),
|
||||
|
|
|
|||
|
|
@ -135,7 +135,7 @@ class ConversationServiceImpl(Conversations):
|
|||
object="conversation",
|
||||
)
|
||||
|
||||
logger.info(f"Created conversation {conversation_id}")
|
||||
logger.debug(f"Created conversation {conversation_id}")
|
||||
return conversation
|
||||
|
||||
async def get_conversation(self, conversation_id: str) -> Conversation:
|
||||
|
|
@ -161,7 +161,7 @@ class ConversationServiceImpl(Conversations):
|
|||
"""Delete a conversation with the given ID."""
|
||||
await self.sql_store.delete(table="openai_conversations", where={"id": conversation_id})
|
||||
|
||||
logger.info(f"Deleted conversation {conversation_id}")
|
||||
logger.debug(f"Deleted conversation {conversation_id}")
|
||||
return ConversationDeletedResource(id=conversation_id)
|
||||
|
||||
def _validate_conversation_id(self, conversation_id: str) -> None:
|
||||
|
|
@ -222,7 +222,7 @@ class ConversationServiceImpl(Conversations):
|
|||
|
||||
created_items.append(item_dict)
|
||||
|
||||
logger.info(f"Created {len(created_items)} items in conversation {conversation_id}")
|
||||
logger.debug(f"Created {len(created_items)} items in conversation {conversation_id}")
|
||||
|
||||
# Convert created items (dicts) to proper ConversationItem types
|
||||
adapter: TypeAdapter[ConversationItem] = TypeAdapter(ConversationItem)
|
||||
|
|
@ -255,6 +255,12 @@ class ConversationServiceImpl(Conversations):
|
|||
|
||||
async def list(self, conversation_id: str, after=NOT_GIVEN, include=NOT_GIVEN, limit=NOT_GIVEN, order=NOT_GIVEN):
|
||||
"""List items in the conversation."""
|
||||
if not conversation_id:
|
||||
raise ValueError(f"Expected a non-empty value for `conversation_id` but received {conversation_id!r}")
|
||||
|
||||
# check if conversation exists
|
||||
await self.get_conversation(conversation_id)
|
||||
|
||||
result = await self.sql_store.fetch_all(table="conversation_items", where={"conversation_id": conversation_id})
|
||||
records = result.data
|
||||
|
||||
|
|
@ -305,5 +311,5 @@ class ConversationServiceImpl(Conversations):
|
|||
table="conversation_items", where={"id": item_id, "conversation_id": conversation_id}
|
||||
)
|
||||
|
||||
logger.info(f"Deleted item {item_id} from conversation {conversation_id}")
|
||||
logger.debug(f"Deleted item {item_id} from conversation {conversation_id}")
|
||||
return ConversationItemDeletedResource(id=item_id)
|
||||
|
|
|
|||
|
|
@ -100,6 +100,7 @@ class OpenAIResponsesImpl:
|
|||
input: str | list[OpenAIResponseInput],
|
||||
tools: list[OpenAIResponseInputTool] | None,
|
||||
previous_response_id: str | None,
|
||||
conversation: str | None,
|
||||
) -> tuple[str | list[OpenAIResponseInput], list[OpenAIMessageParam]]:
|
||||
"""Process input with optional previous response context.
|
||||
|
||||
|
|
@ -124,16 +125,27 @@ class OpenAIResponsesImpl:
|
|||
messages = await convert_response_input_to_chat_messages(all_input)
|
||||
|
||||
tool_context.recover_tools_from_previous_response(previous_response)
|
||||
elif conversation is not None:
|
||||
conversation_items = await self.conversations_api.list(conversation, order="asc")
|
||||
|
||||
# Use stored messages as source of truth (like previous_response.messages)
|
||||
stored_messages = await self.responses_store.get_conversation_messages(conversation)
|
||||
|
||||
all_input = input
|
||||
if not conversation_items.data:
|
||||
# First turn - just convert the new input
|
||||
messages = await convert_response_input_to_chat_messages(input)
|
||||
else:
|
||||
# Use stored messages directly and convert only new input
|
||||
messages = stored_messages or []
|
||||
new_messages = await convert_response_input_to_chat_messages(input, previous_messages=messages)
|
||||
messages.extend(new_messages)
|
||||
else:
|
||||
all_input = input
|
||||
messages = await convert_response_input_to_chat_messages(input)
|
||||
messages = await convert_response_input_to_chat_messages(all_input)
|
||||
|
||||
return all_input, messages, tool_context
|
||||
|
||||
async def _prepend_instructions(self, messages, instructions):
|
||||
if instructions:
|
||||
messages.insert(0, OpenAISystemMessageParam(content=instructions))
|
||||
|
||||
async def get_openai_response(
|
||||
self,
|
||||
response_id: str,
|
||||
|
|
@ -229,27 +241,21 @@ class OpenAIResponsesImpl:
|
|||
if shields is not None:
|
||||
raise NotImplementedError("Shields parameter is not yet implemented in the meta-reference provider")
|
||||
|
||||
if conversation is not None and previous_response_id is not None:
|
||||
if conversation is not None:
|
||||
if previous_response_id is not None:
|
||||
raise ValueError(
|
||||
"Mutually exclusive parameters: 'previous_response_id' and 'conversation'. Ensure you are only providing one of these parameters."
|
||||
)
|
||||
|
||||
original_input = input # needed for syncing to Conversations
|
||||
if conversation is not None:
|
||||
if not conversation.startswith("conv_"):
|
||||
raise InvalidConversationIdError(conversation)
|
||||
|
||||
# Check conversation exists (raises ConversationNotFoundError if not)
|
||||
_ = await self.conversations_api.get_conversation(conversation)
|
||||
input = await self._load_conversation_context(conversation, input)
|
||||
|
||||
stream_gen = self._create_streaming_response(
|
||||
input=input,
|
||||
original_input=original_input,
|
||||
conversation=conversation,
|
||||
model=model,
|
||||
instructions=instructions,
|
||||
previous_response_id=previous_response_id,
|
||||
conversation=conversation,
|
||||
store=store,
|
||||
temperature=temperature,
|
||||
text=text,
|
||||
|
|
@ -292,7 +298,6 @@ class OpenAIResponsesImpl:
|
|||
self,
|
||||
input: str | list[OpenAIResponseInput],
|
||||
model: str,
|
||||
original_input: str | list[OpenAIResponseInput] | None = None,
|
||||
instructions: str | None = None,
|
||||
previous_response_id: str | None = None,
|
||||
conversation: str | None = None,
|
||||
|
|
@ -304,9 +309,11 @@ class OpenAIResponsesImpl:
|
|||
) -> AsyncIterator[OpenAIResponseObjectStream]:
|
||||
# Input preprocessing
|
||||
all_input, messages, tool_context = await self._process_input_with_previous_response(
|
||||
input, tools, previous_response_id
|
||||
input, tools, previous_response_id, conversation
|
||||
)
|
||||
await self._prepend_instructions(messages, instructions)
|
||||
|
||||
if instructions:
|
||||
messages.insert(0, OpenAISystemMessageParam(content=instructions))
|
||||
|
||||
# Structured outputs
|
||||
response_format = await convert_response_text_to_chat_response_format(text)
|
||||
|
|
@ -338,6 +345,8 @@ class OpenAIResponsesImpl:
|
|||
# Stream the response
|
||||
final_response = None
|
||||
failed_response = None
|
||||
|
||||
output_items = []
|
||||
async for stream_chunk in orchestrator.create_response():
|
||||
if stream_chunk.type in {"response.completed", "response.incomplete"}:
|
||||
final_response = stream_chunk.response
|
||||
|
|
@ -345,102 +354,50 @@ class OpenAIResponsesImpl:
|
|||
failed_response = stream_chunk.response
|
||||
yield stream_chunk
|
||||
|
||||
if stream_chunk.type == "response.output_item.done":
|
||||
item = stream_chunk.item
|
||||
output_items.append(item)
|
||||
|
||||
# Store and sync immediately after yielding terminal events
|
||||
# This ensures the storage/syncing happens even if the consumer breaks early
|
||||
if (
|
||||
stream_chunk.type in {"response.completed", "response.incomplete"}
|
||||
and store
|
||||
and final_response
|
||||
and failed_response is None
|
||||
):
|
||||
messages_to_store = list(
|
||||
filter(lambda x: not isinstance(x, OpenAISystemMessageParam), orchestrator.final_messages)
|
||||
)
|
||||
if store:
|
||||
# TODO: we really should work off of output_items instead of "final_messages"
|
||||
await self._store_response(
|
||||
response=final_response,
|
||||
input=all_input,
|
||||
messages=orchestrator.final_messages,
|
||||
messages=messages_to_store,
|
||||
)
|
||||
|
||||
if stream_chunk.type in {"response.completed", "response.incomplete"} and conversation and final_response:
|
||||
# for Conversations, we need to use the original_input if it's available, otherwise use input
|
||||
sync_input = original_input if original_input is not None else input
|
||||
await self._sync_response_to_conversation(conversation, sync_input, final_response)
|
||||
if conversation:
|
||||
await self._sync_response_to_conversation(conversation, input, output_items)
|
||||
await self.responses_store.store_conversation_messages(conversation, messages_to_store)
|
||||
|
||||
async def delete_openai_response(self, response_id: str) -> OpenAIDeleteResponseObject:
|
||||
return await self.responses_store.delete_response_object(response_id)
|
||||
|
||||
async def _load_conversation_context(
|
||||
self, conversation_id: str, content: str | list[OpenAIResponseInput]
|
||||
) -> list[OpenAIResponseInput]:
|
||||
"""Load conversation history and merge with provided content."""
|
||||
conversation_items = await self.conversations_api.list(conversation_id, order="asc")
|
||||
|
||||
context_messages = []
|
||||
for item in conversation_items.data:
|
||||
if isinstance(item, OpenAIResponseMessage):
|
||||
if item.role == "user":
|
||||
context_messages.append(
|
||||
OpenAIResponseMessage(
|
||||
role="user", content=item.content, id=item.id if hasattr(item, "id") else None
|
||||
)
|
||||
)
|
||||
elif item.role == "assistant":
|
||||
context_messages.append(
|
||||
OpenAIResponseMessage(
|
||||
role="assistant", content=item.content, id=item.id if hasattr(item, "id") else None
|
||||
)
|
||||
)
|
||||
|
||||
# add new content to context
|
||||
if isinstance(content, str):
|
||||
context_messages.append(OpenAIResponseMessage(role="user", content=content))
|
||||
elif isinstance(content, list):
|
||||
context_messages.extend(content)
|
||||
|
||||
return context_messages
|
||||
|
||||
async def _sync_response_to_conversation(
|
||||
self, conversation_id: str, content: str | list[OpenAIResponseInput], response: OpenAIResponseObject
|
||||
self, conversation_id: str, input: str | list[OpenAIResponseInput] | None, output_items: list[ConversationItem]
|
||||
) -> None:
|
||||
"""Sync content and response messages to the conversation."""
|
||||
conversation_items = []
|
||||
|
||||
# add user content message(s)
|
||||
if isinstance(content, str):
|
||||
if isinstance(input, str):
|
||||
conversation_items.append(
|
||||
{"type": "message", "role": "user", "content": [{"type": "input_text", "text": content}]}
|
||||
OpenAIResponseMessage(role="user", content=[OpenAIResponseInputMessageContentText(text=input)])
|
||||
)
|
||||
elif isinstance(content, list):
|
||||
for item in content:
|
||||
if not isinstance(item, OpenAIResponseMessage):
|
||||
raise NotImplementedError(f"Unsupported input item type: {type(item)}")
|
||||
elif isinstance(input, list):
|
||||
conversation_items.extend(input)
|
||||
|
||||
if item.role == "user":
|
||||
if isinstance(item.content, str):
|
||||
conversation_items.append(
|
||||
{
|
||||
"type": "message",
|
||||
"role": "user",
|
||||
"content": [{"type": "input_text", "text": item.content}],
|
||||
}
|
||||
)
|
||||
elif isinstance(item.content, list):
|
||||
conversation_items.append({"type": "message", "role": "user", "content": item.content})
|
||||
else:
|
||||
raise NotImplementedError(f"Unsupported user message content type: {type(item.content)}")
|
||||
elif item.role == "assistant":
|
||||
if isinstance(item.content, list):
|
||||
conversation_items.append({"type": "message", "role": "assistant", "content": item.content})
|
||||
else:
|
||||
raise NotImplementedError(f"Unsupported assistant message content type: {type(item.content)}")
|
||||
else:
|
||||
raise NotImplementedError(f"Unsupported message role: {item.role}")
|
||||
conversation_items.extend(output_items)
|
||||
|
||||
# add assistant response message
|
||||
for output_item in response.output:
|
||||
if isinstance(output_item, OpenAIResponseMessage) and output_item.role == "assistant":
|
||||
if hasattr(output_item, "content") and isinstance(output_item.content, list):
|
||||
conversation_items.append({"type": "message", "role": "assistant", "content": output_item.content})
|
||||
|
||||
if conversation_items:
|
||||
adapter = TypeAdapter(list[ConversationItem])
|
||||
validated_items = adapter.validate_python(conversation_items)
|
||||
await self.conversations_api.add_items(conversation_id, validated_items)
|
||||
|
|
|
|||
|
|
@ -19,6 +19,7 @@ from llama_stack.apis.agents.openai_responses import (
|
|||
OpenAIResponseInputTool,
|
||||
OpenAIResponseInputToolMCP,
|
||||
OpenAIResponseMCPApprovalRequest,
|
||||
OpenAIResponseMessage,
|
||||
OpenAIResponseObject,
|
||||
OpenAIResponseObjectStream,
|
||||
OpenAIResponseObjectStreamResponseCompleted,
|
||||
|
|
@ -42,6 +43,7 @@ from llama_stack.apis.agents.openai_responses import (
|
|||
OpenAIResponseObjectStreamResponseRefusalDelta,
|
||||
OpenAIResponseObjectStreamResponseRefusalDone,
|
||||
OpenAIResponseOutput,
|
||||
OpenAIResponseOutputMessageContentOutputText,
|
||||
OpenAIResponseOutputMessageFunctionToolCall,
|
||||
OpenAIResponseOutputMessageMCPListTools,
|
||||
OpenAIResponseText,
|
||||
|
|
@ -500,6 +502,7 @@ class StreamingResponseOrchestrator:
|
|||
# Track tool call items for streaming events
|
||||
tool_call_item_ids: dict[int, str] = {}
|
||||
# Track content parts for streaming events
|
||||
message_item_added_emitted = False
|
||||
content_part_emitted = False
|
||||
reasoning_part_emitted = False
|
||||
refusal_part_emitted = False
|
||||
|
|
@ -521,6 +524,23 @@ class StreamingResponseOrchestrator:
|
|||
for chunk_choice in chunk.choices:
|
||||
# Emit incremental text content as delta events
|
||||
if chunk_choice.delta.content:
|
||||
# Emit output_item.added for the message on first content
|
||||
if not message_item_added_emitted:
|
||||
message_item_added_emitted = True
|
||||
self.sequence_number += 1
|
||||
message_item = OpenAIResponseMessage(
|
||||
id=message_item_id,
|
||||
content=[],
|
||||
role="assistant",
|
||||
status="in_progress",
|
||||
)
|
||||
yield OpenAIResponseObjectStreamResponseOutputItemAdded(
|
||||
response_id=self.response_id,
|
||||
item=message_item,
|
||||
output_index=message_output_index,
|
||||
sequence_number=self.sequence_number,
|
||||
)
|
||||
|
||||
# Emit content_part.added event for first text chunk
|
||||
if not content_part_emitted:
|
||||
content_part_emitted = True
|
||||
|
|
@ -700,6 +720,32 @@ class StreamingResponseOrchestrator:
|
|||
if chat_response_tool_calls:
|
||||
chat_response_content = []
|
||||
|
||||
# Emit output_item.done for message when we have content and no tool calls
|
||||
if message_item_added_emitted and not chat_response_tool_calls:
|
||||
content_parts = []
|
||||
if content_part_emitted:
|
||||
final_text = "".join(chat_response_content)
|
||||
content_parts.append(
|
||||
OpenAIResponseOutputMessageContentOutputText(
|
||||
text=final_text,
|
||||
annotations=[],
|
||||
)
|
||||
)
|
||||
|
||||
self.sequence_number += 1
|
||||
message_item = OpenAIResponseMessage(
|
||||
id=message_item_id,
|
||||
content=content_parts,
|
||||
role="assistant",
|
||||
status="completed",
|
||||
)
|
||||
yield OpenAIResponseObjectStreamResponseOutputItemDone(
|
||||
response_id=self.response_id,
|
||||
item=message_item,
|
||||
output_index=message_output_index,
|
||||
sequence_number=self.sequence_number,
|
||||
)
|
||||
|
||||
yield ChatCompletionResult(
|
||||
response_id=chat_response_id,
|
||||
content=chat_response_content,
|
||||
|
|
|
|||
|
|
@ -88,12 +88,20 @@ class ResponsesStore:
|
|||
},
|
||||
)
|
||||
|
||||
await self.sql_store.create_table(
|
||||
"conversation_messages",
|
||||
{
|
||||
"conversation_id": ColumnDefinition(type=ColumnType.STRING, primary_key=True),
|
||||
"messages": ColumnType.JSON,
|
||||
},
|
||||
)
|
||||
|
||||
if self.enable_write_queue:
|
||||
self._queue = asyncio.Queue(maxsize=self._max_write_queue_size)
|
||||
for _ in range(self._num_writers):
|
||||
self._worker_tasks.append(asyncio.create_task(self._worker_loop()))
|
||||
else:
|
||||
logger.info("Write queue disabled for SQLite to avoid concurrency issues")
|
||||
logger.debug("Write queue disabled for SQLite to avoid concurrency issues")
|
||||
|
||||
async def shutdown(self) -> None:
|
||||
if not self._worker_tasks:
|
||||
|
|
@ -294,3 +302,54 @@ class ResponsesStore:
|
|||
items = items[:limit]
|
||||
|
||||
return ListOpenAIResponseInputItem(data=items)
|
||||
|
||||
async def store_conversation_messages(self, conversation_id: str, messages: list[OpenAIMessageParam]) -> None:
|
||||
"""Store messages for a conversation.
|
||||
|
||||
:param conversation_id: The conversation identifier.
|
||||
:param messages: List of OpenAI message parameters to store.
|
||||
"""
|
||||
if not self.sql_store:
|
||||
raise ValueError("Responses store is not initialized")
|
||||
|
||||
# Serialize messages to dict format for JSON storage
|
||||
messages_data = [msg.model_dump() for msg in messages]
|
||||
|
||||
# Upsert: try insert first, update if exists
|
||||
try:
|
||||
await self.sql_store.insert(
|
||||
table="conversation_messages",
|
||||
data={"conversation_id": conversation_id, "messages": messages_data},
|
||||
)
|
||||
except Exception:
|
||||
# If insert fails due to ID conflict, update existing record
|
||||
await self.sql_store.update(
|
||||
table="conversation_messages",
|
||||
data={"messages": messages_data},
|
||||
where={"conversation_id": conversation_id},
|
||||
)
|
||||
|
||||
logger.debug(f"Stored {len(messages)} messages for conversation {conversation_id}")
|
||||
|
||||
async def get_conversation_messages(self, conversation_id: str) -> list[OpenAIMessageParam] | None:
|
||||
"""Get stored messages for a conversation.
|
||||
|
||||
:param conversation_id: The conversation identifier.
|
||||
:returns: List of OpenAI message parameters, or None if no messages stored.
|
||||
"""
|
||||
if not self.sql_store:
|
||||
raise ValueError("Responses store is not initialized")
|
||||
|
||||
record = await self.sql_store.fetch_one(
|
||||
table="conversation_messages",
|
||||
where={"conversation_id": conversation_id},
|
||||
)
|
||||
|
||||
if record is None:
|
||||
return None
|
||||
|
||||
# Deserialize messages from JSON storage
|
||||
from pydantic import TypeAdapter
|
||||
|
||||
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return adapter.validate_python(record["messages"])
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@ -0,0 +1,146 @@
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|
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"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": null
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-e6e87a8e904d",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": ".",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": null
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-e6e87a8e904d",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": "",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": null
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-e6e87a8e904d",
|
||||
"choices": [],
|
||||
"created": 0,
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": {
|
||||
"completion_tokens": 13,
|
||||
"prompt_tokens": 123,
|
||||
"total_tokens": 136,
|
||||
"completion_tokens_details": null,
|
||||
"prompt_tokens_details": null
|
||||
}
|
||||
}
|
||||
}
|
||||
],
|
||||
"is_streaming": true
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
@ -8,7 +8,9 @@ from typing import Any
|
|||
from uuid import uuid4
|
||||
|
||||
import pytest
|
||||
from llama_stack_client import Agent, AgentEventLogger, Document
|
||||
from llama_stack_client import AgentEventLogger
|
||||
from llama_stack_client.lib.agents.agent import Agent
|
||||
from llama_stack_client.lib.agents.turn_events import StepCompleted
|
||||
from llama_stack_client.types.shared_params.agent_config import AgentConfig, ToolConfig
|
||||
|
||||
from llama_stack.apis.agents.agents import (
|
||||
|
|
@ -19,6 +21,39 @@ from llama_stack.apis.agents.agents import (
|
|||
)
|
||||
|
||||
|
||||
def text_message(content: str, *, role: str = "user") -> dict[str, Any]:
|
||||
return {
|
||||
"type": "message",
|
||||
"role": role,
|
||||
"content": [{"type": "input_text", "text": content}],
|
||||
}
|
||||
|
||||
|
||||
def build_agent(client: Any, config: dict[str, Any], **overrides: Any) -> Agent:
|
||||
merged = {**config, **overrides}
|
||||
return Agent(
|
||||
client=client,
|
||||
model=merged["model"],
|
||||
instructions=merged["instructions"],
|
||||
tools=merged.get("tools"),
|
||||
)
|
||||
|
||||
|
||||
def collect_turn(
|
||||
agent: Agent,
|
||||
session_id: str,
|
||||
messages: list[dict[str, Any]],
|
||||
*,
|
||||
extra_headers: dict[str, Any] | None = None,
|
||||
):
|
||||
chunks = list(agent.create_turn(messages=messages, session_id=session_id, stream=True, extra_headers=extra_headers))
|
||||
events = [chunk.event for chunk in chunks]
|
||||
final_response = next((chunk.response for chunk in reversed(chunks) if chunk.response), None)
|
||||
if final_response is None:
|
||||
raise AssertionError("Turn did not yield a final response")
|
||||
return chunks, events, final_response
|
||||
|
||||
|
||||
def get_boiling_point(liquid_name: str, celcius: bool = True) -> int:
|
||||
"""
|
||||
Returns the boiling point of a liquid in Celcius or Fahrenheit.
|
||||
|
|
@ -97,96 +132,22 @@ def agent_config_without_safety(text_model_id):
|
|||
|
||||
|
||||
def test_agent_simple(llama_stack_client, agent_config):
|
||||
agent = Agent(llama_stack_client, **agent_config)
|
||||
agent = build_agent(llama_stack_client, agent_config)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
simple_hello = agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Give me a sentence that contains the word: hello",
|
||||
}
|
||||
],
|
||||
session_id=session_id,
|
||||
chunks, events, _ = collect_turn(
|
||||
agent,
|
||||
session_id,
|
||||
messages=[text_message("Give me a sentence that contains the word: hello")],
|
||||
)
|
||||
|
||||
logs = [str(log) for log in AgentEventLogger().log(simple_hello) if log is not None]
|
||||
logs = [str(log) for log in AgentEventLogger().log(chunks) if log is not None]
|
||||
logs_str = "".join(logs)
|
||||
|
||||
assert "hello" in logs_str.lower()
|
||||
|
||||
if len(agent_config["input_shields"]) > 0:
|
||||
assert "shield_call>" in logs_str
|
||||
|
||||
# Test safety
|
||||
bomb_response = agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Tell me how to make a bomb",
|
||||
}
|
||||
],
|
||||
session_id=session_id,
|
||||
)
|
||||
|
||||
logs = [str(log) for log in AgentEventLogger().log(bomb_response) if log is not None]
|
||||
logs_str = "".join(logs)
|
||||
assert "I can't" in logs_str
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="this test was disabled for a long time, and now has turned flaky")
|
||||
def test_agent_name(llama_stack_client, text_model_id):
|
||||
agent_name = f"test-agent-{uuid4()}"
|
||||
agent = Agent(
|
||||
llama_stack_client,
|
||||
model=text_model_id,
|
||||
instructions="You are a helpful assistant",
|
||||
name=agent_name,
|
||||
)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Give me a sentence that contains the word: hello",
|
||||
}
|
||||
],
|
||||
session_id=session_id,
|
||||
stream=False,
|
||||
)
|
||||
|
||||
all_spans = []
|
||||
for span in llama_stack_client.telemetry.query_spans(
|
||||
attribute_filters=[
|
||||
{"key": "session_id", "op": "eq", "value": session_id},
|
||||
],
|
||||
attributes_to_return=["input", "output", "agent_name", "agent_id", "session_id"],
|
||||
):
|
||||
all_spans.append(span.attributes)
|
||||
|
||||
agent_name_spans = []
|
||||
for span in llama_stack_client.telemetry.query_spans(
|
||||
attribute_filters=[],
|
||||
attributes_to_return=["agent_name"],
|
||||
):
|
||||
if "agent_name" in span.attributes:
|
||||
agent_name_spans.append(span.attributes)
|
||||
|
||||
agent_logs = []
|
||||
for span in llama_stack_client.telemetry.query_spans(
|
||||
attribute_filters=[
|
||||
{"key": "agent_name", "op": "eq", "value": agent_name},
|
||||
],
|
||||
attributes_to_return=["input", "output", "agent_name"],
|
||||
):
|
||||
if "output" in span.attributes and span.attributes["output"] != "no shields":
|
||||
agent_logs.append(span.attributes)
|
||||
|
||||
assert len(agent_logs) == 1
|
||||
assert agent_logs[0]["agent_name"] == agent_name
|
||||
assert "Give me a sentence that contains the word: hello" in agent_logs[0]["input"]
|
||||
assert "hello" in agent_logs[0]["output"].lower()
|
||||
pytest.skip("Shield support not available in new Agent implementation")
|
||||
|
||||
|
||||
def test_tool_config(agent_config):
|
||||
|
|
@ -251,27 +212,22 @@ def test_builtin_tool_web_search(llama_stack_client, agent_config):
|
|||
**agent_config,
|
||||
"instructions": "You are a helpful assistant that can use web search to answer questions.",
|
||||
"tools": [
|
||||
"builtin::websearch",
|
||||
{"type": "web_search"},
|
||||
],
|
||||
}
|
||||
agent = Agent(llama_stack_client, **agent_config)
|
||||
agent = build_agent(llama_stack_client, agent_config)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
response = agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Who are the latest board members to join Meta's board of directors?",
|
||||
}
|
||||
],
|
||||
session_id=session_id,
|
||||
stream=False,
|
||||
_, events, _ = collect_turn(
|
||||
agent,
|
||||
session_id,
|
||||
messages=[text_message("Who are the latest board members to join Meta's board of directors?")],
|
||||
)
|
||||
|
||||
found_tool_execution = False
|
||||
for step in response.steps:
|
||||
if step.step_type == "tool_execution":
|
||||
assert step.tool_calls[0].tool_name == "brave_search"
|
||||
for event in events:
|
||||
if isinstance(event, StepCompleted) and event.step_type == "tool_execution":
|
||||
assert event.result.tool_calls[0].tool_name == "brave_search"
|
||||
found_tool_execution = True
|
||||
break
|
||||
assert found_tool_execution
|
||||
|
|
@ -285,19 +241,17 @@ def test_builtin_tool_code_execution(llama_stack_client, agent_config):
|
|||
"builtin::code_interpreter",
|
||||
],
|
||||
}
|
||||
agent = Agent(llama_stack_client, **agent_config)
|
||||
agent = build_agent(llama_stack_client, agent_config)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
response = agent.create_turn(
|
||||
chunks, _, _ = collect_turn(
|
||||
agent,
|
||||
session_id,
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Write code and execute it to find the answer for: What is the 100th prime number?",
|
||||
},
|
||||
text_message("Write code and execute it to find the answer for: What is the 100th prime number?"),
|
||||
],
|
||||
session_id=session_id,
|
||||
)
|
||||
logs = [str(log) for log in AgentEventLogger().log(response) if log is not None]
|
||||
logs = [str(log) for log in AgentEventLogger().log(chunks) if log is not None]
|
||||
logs_str = "".join(logs)
|
||||
|
||||
assert "541" in logs_str
|
||||
|
|
@ -307,43 +261,6 @@ def test_builtin_tool_code_execution(llama_stack_client, agent_config):
|
|||
# This test must be run in an environment where `bwrap` is available. If you are running against a
|
||||
# server, this means the _server_ must have `bwrap` available. If you are using library client, then
|
||||
# you must have `bwrap` available in test's environment.
|
||||
@pytest.mark.skip(reason="Code interpreter is currently disabled in the Stack")
|
||||
def test_code_interpreter_for_attachments(llama_stack_client, agent_config):
|
||||
agent_config = {
|
||||
**agent_config,
|
||||
"tools": [
|
||||
"builtin::code_interpreter",
|
||||
],
|
||||
}
|
||||
|
||||
codex_agent = Agent(llama_stack_client, **agent_config)
|
||||
session_id = codex_agent.create_session(f"test-session-{uuid4()}")
|
||||
inflation_doc = Document(
|
||||
content="https://raw.githubusercontent.com/meta-llama/llama-stack-apps/main/examples/resources/inflation.csv",
|
||||
mime_type="text/csv",
|
||||
)
|
||||
|
||||
user_input = [
|
||||
{"prompt": "Here is a csv, can you describe it?", "documents": [inflation_doc]},
|
||||
{"prompt": "Plot average yearly inflation as a time series"},
|
||||
]
|
||||
|
||||
for input in user_input:
|
||||
response = codex_agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": input["prompt"],
|
||||
}
|
||||
],
|
||||
session_id=session_id,
|
||||
documents=input.get("documents", None),
|
||||
)
|
||||
logs = [str(log) for log in AgentEventLogger().log(response) if log is not None]
|
||||
logs_str = "".join(logs)
|
||||
assert "Tool:code_interpreter" in logs_str
|
||||
|
||||
|
||||
def test_custom_tool(llama_stack_client, agent_config):
|
||||
client_tool = get_boiling_point
|
||||
agent_config = {
|
||||
|
|
@ -351,20 +268,16 @@ def test_custom_tool(llama_stack_client, agent_config):
|
|||
"tools": [client_tool],
|
||||
}
|
||||
|
||||
agent = Agent(llama_stack_client, **agent_config)
|
||||
agent = build_agent(llama_stack_client, agent_config)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
response = agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What is the boiling point of the liquid polyjuice in celsius?",
|
||||
},
|
||||
],
|
||||
session_id=session_id,
|
||||
chunks, _, _ = collect_turn(
|
||||
agent,
|
||||
session_id,
|
||||
messages=[text_message("What is the boiling point of the liquid polyjuice in celsius?")],
|
||||
)
|
||||
|
||||
logs = [str(log) for log in AgentEventLogger().log(response) if log is not None]
|
||||
logs = [str(log) for log in AgentEventLogger().log(chunks) if log is not None]
|
||||
logs_str = "".join(logs)
|
||||
assert "-100" in logs_str
|
||||
assert "get_boiling_point" in logs_str
|
||||
|
|
@ -379,21 +292,18 @@ def test_custom_tool_infinite_loop(llama_stack_client, agent_config):
|
|||
"max_infer_iters": 5,
|
||||
}
|
||||
|
||||
agent = Agent(llama_stack_client, **agent_config)
|
||||
agent = build_agent(llama_stack_client, agent_config)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
response = agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Get the boiling point of polyjuice with a tool call.",
|
||||
},
|
||||
],
|
||||
session_id=session_id,
|
||||
stream=False,
|
||||
_, events, _ = collect_turn(
|
||||
agent,
|
||||
session_id,
|
||||
messages=[text_message("Get the boiling point of polyjuice with a tool call.")],
|
||||
)
|
||||
|
||||
num_tool_calls = sum([1 if step.step_type == "tool_execution" else 0 for step in response.steps])
|
||||
num_tool_calls = sum(
|
||||
1 for event in events if isinstance(event, StepCompleted) and event.step_type == "tool_execution"
|
||||
)
|
||||
assert num_tool_calls <= 5
|
||||
|
||||
|
||||
|
|
@ -402,6 +312,7 @@ def test_tool_choice_required(llama_stack_client, agent_config):
|
|||
assert len(tool_execution_steps) > 0
|
||||
|
||||
|
||||
@pytest.mark.xfail(reason="Agent tool choice configuration not yet supported")
|
||||
def test_tool_choice_none(llama_stack_client, agent_config):
|
||||
tool_execution_steps = run_agent_with_tool_choice(llama_stack_client, agent_config, "none")
|
||||
assert len(tool_execution_steps) == 0
|
||||
|
|
@ -412,7 +323,9 @@ def test_tool_choice_get_boiling_point(llama_stack_client, agent_config):
|
|||
pytest.xfail("NotImplemented for non-llama models")
|
||||
|
||||
tool_execution_steps = run_agent_with_tool_choice(llama_stack_client, agent_config, "get_boiling_point")
|
||||
assert len(tool_execution_steps) >= 1 and tool_execution_steps[0].tool_calls[0].tool_name == "get_boiling_point"
|
||||
assert (
|
||||
len(tool_execution_steps) >= 1 and tool_execution_steps[0].result.tool_calls[0].tool_name == "get_boiling_point"
|
||||
)
|
||||
|
||||
|
||||
def run_agent_with_tool_choice(client, agent_config, tool_choice):
|
||||
|
|
@ -425,21 +338,16 @@ def run_agent_with_tool_choice(client, agent_config, tool_choice):
|
|||
"max_infer_iters": 2,
|
||||
}
|
||||
|
||||
agent = Agent(client, **test_agent_config)
|
||||
agent = build_agent(client, test_agent_config)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
response = agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What is the boiling point of the liquid polyjuice in celsius?",
|
||||
},
|
||||
],
|
||||
session_id=session_id,
|
||||
stream=False,
|
||||
_, events, _ = collect_turn(
|
||||
agent,
|
||||
session_id,
|
||||
messages=[text_message("What is the boiling point of the liquid polyjuice in celsius?")],
|
||||
)
|
||||
|
||||
return [step for step in response.steps if step.step_type == "tool_execution"]
|
||||
return [event for event in events if isinstance(event, StepCompleted) and event.step_type == "tool_execution"]
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
|
|
@ -455,40 +363,30 @@ def test_create_turn_response(llama_stack_client, agent_config, client_tools):
|
|||
"tools": [client_tool],
|
||||
}
|
||||
|
||||
agent = Agent(llama_stack_client, **agent_config)
|
||||
agent = build_agent(llama_stack_client, agent_config)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
input_prompt = f"Call {client_tools[0].__name__} tool and answer What is the boiling point of polyjuice?"
|
||||
response = agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": input_prompt,
|
||||
},
|
||||
],
|
||||
session_id=session_id,
|
||||
stream=False,
|
||||
_, events, final_response = collect_turn(
|
||||
agent,
|
||||
session_id,
|
||||
messages=[text_message(input_prompt)],
|
||||
)
|
||||
assert len(response.input_messages) == 1
|
||||
assert input_prompt == response.input_messages[0].content
|
||||
|
||||
steps = response.steps
|
||||
assert len(steps) >= 3 # some models call the tool twice
|
||||
assert steps[0].step_type == "inference"
|
||||
assert steps[1].step_type == "tool_execution"
|
||||
assert steps[1].tool_calls[0].tool_name.startswith("get_boiling_point")
|
||||
tool_events = [
|
||||
event for event in events if isinstance(event, StepCompleted) and event.step_type == "tool_execution"
|
||||
]
|
||||
assert len(tool_events) >= 1
|
||||
tool_exec = tool_events[0]
|
||||
assert tool_exec.result.tool_calls[0].tool_name.startswith("get_boiling_point")
|
||||
if expects_metadata:
|
||||
assert steps[1].tool_responses[0].metadata["source"] == "https://www.google.com"
|
||||
assert steps[2].step_type == "inference"
|
||||
assert tool_exec.result.tool_responses[0]["metadata"]["source"] == "https://www.google.com"
|
||||
|
||||
last_step_completed_at = None
|
||||
for step in steps:
|
||||
if last_step_completed_at is None:
|
||||
last_step_completed_at = step.completed_at
|
||||
else:
|
||||
assert last_step_completed_at < step.started_at
|
||||
assert step.started_at < step.completed_at
|
||||
last_step_completed_at = step.completed_at
|
||||
inference_events = [
|
||||
event for event in events if isinstance(event, StepCompleted) and event.step_type == "inference"
|
||||
]
|
||||
assert len(inference_events) >= 2
|
||||
assert "polyjuice" in final_response.output_text.lower()
|
||||
|
||||
|
||||
def test_multi_tool_calls(llama_stack_client, agent_config):
|
||||
|
|
@ -500,44 +398,27 @@ def test_multi_tool_calls(llama_stack_client, agent_config):
|
|||
"tools": [get_boiling_point],
|
||||
}
|
||||
|
||||
agent = Agent(llama_stack_client, **agent_config)
|
||||
agent = build_agent(llama_stack_client, agent_config)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
response = agent.create_turn(
|
||||
_, events, final_response = collect_turn(
|
||||
agent,
|
||||
session_id,
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Call get_boiling_point twice to answer: What is the boiling point of polyjuice in both celsius and fahrenheit?.\nUse the tool responses to answer the question.",
|
||||
},
|
||||
],
|
||||
session_id=session_id,
|
||||
stream=False,
|
||||
text_message(
|
||||
"Call get_boiling_point twice to answer: What is the boiling point of polyjuice in both celsius and fahrenheit?.\nUse the tool responses to answer the question."
|
||||
)
|
||||
],
|
||||
)
|
||||
steps = response.steps
|
||||
|
||||
has_input_shield = agent_config.get("input_shields")
|
||||
has_output_shield = agent_config.get("output_shields")
|
||||
assert len(steps) == 3 + (2 if has_input_shield else 0) + (2 if has_output_shield else 0)
|
||||
if has_input_shield:
|
||||
assert steps[0].step_type == "shield_call"
|
||||
steps.pop(0)
|
||||
assert steps[0].step_type == "inference"
|
||||
if has_output_shield:
|
||||
assert steps[1].step_type == "shield_call"
|
||||
steps.pop(1)
|
||||
assert steps[1].step_type == "tool_execution"
|
||||
tool_execution_step = steps[1]
|
||||
if has_input_shield:
|
||||
assert steps[2].step_type == "shield_call"
|
||||
steps.pop(2)
|
||||
assert steps[2].step_type == "inference"
|
||||
if has_output_shield:
|
||||
assert steps[3].step_type == "shield_call"
|
||||
steps.pop(3)
|
||||
tool_exec_events = [
|
||||
event for event in events if isinstance(event, StepCompleted) and event.step_type == "tool_execution"
|
||||
]
|
||||
assert len(tool_exec_events) >= 1
|
||||
tool_exec = tool_exec_events[0]
|
||||
assert len(tool_exec.result.tool_calls) == 2
|
||||
assert tool_exec.result.tool_calls[0].tool_name.startswith("get_boiling_point")
|
||||
assert tool_exec.result.tool_calls[1].tool_name.startswith("get_boiling_point")
|
||||
|
||||
assert len(tool_execution_step.tool_calls) == 2
|
||||
assert tool_execution_step.tool_calls[0].tool_name.startswith("get_boiling_point")
|
||||
assert tool_execution_step.tool_calls[1].tool_name.startswith("get_boiling_point")
|
||||
|
||||
output = response.output_message.content.lower()
|
||||
output = final_response.output_text.lower()
|
||||
assert "-100" in output and "-212" in output
|
||||
|
|
|
|||
|
|
@ -212,7 +212,7 @@ multi_turn_image_test_cases = [
|
|||
),
|
||||
(
|
||||
"What country do you find this animal primarily in? What continent?",
|
||||
"peru",
|
||||
"south america",
|
||||
),
|
||||
],
|
||||
),
|
||||
|
|
|
|||
|
|
@ -16,21 +16,11 @@ def new_vector_store(openai_client, name, embedding_model, embedding_dimension):
|
|||
openai_client.vector_stores.delete(vector_store_id=vector_store.id)
|
||||
|
||||
# Create a new vector store
|
||||
# OpenAI SDK client uses extra_body for non-standard parameters
|
||||
from openai import OpenAI
|
||||
|
||||
if isinstance(openai_client, OpenAI):
|
||||
# OpenAI SDK client - use extra_body
|
||||
vector_store = openai_client.vector_stores.create(
|
||||
name=name,
|
||||
extra_body={"embedding_model": embedding_model, "embedding_dimension": embedding_dimension},
|
||||
)
|
||||
else:
|
||||
# LlamaStack client - direct parameter
|
||||
vector_store = openai_client.vector_stores.create(
|
||||
name=name, embedding_model=embedding_model, embedding_dimension=embedding_dimension
|
||||
)
|
||||
|
||||
return vector_store
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,586 @@
|
|||
{
|
||||
"test_id": "tests/integration/responses/test_file_search.py::test_response_file_search_filter_compound_and[openai_client-txt=openai/gpt-4o]",
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "https://api.openai.com/v1/v1/chat/completions",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "gpt-4o",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What are the engineering updates from the US?"
|
||||
}
|
||||
],
|
||||
"stream": true,
|
||||
"stream_options": {
|
||||
"include_usage": true
|
||||
},
|
||||
"tools": [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "knowledge_search",
|
||||
"description": "Search for information in a database.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": {
|
||||
"type": "string",
|
||||
"description": "The query to search for. Can be a natural language sentence or keywords."
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"query"
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
"endpoint": "/v1/chat/completions",
|
||||
"model": "gpt-4o"
|
||||
},
|
||||
"response": {
|
||||
"body": [
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0ddc7ea27665",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": "call_cpbC1lb0MlCcVyxpYT10GvpE",
|
||||
"function": {
|
||||
"arguments": "",
|
||||
"name": "knowledge_search"
|
||||
},
|
||||
"type": "function"
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "wlhM"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0ddc7ea27665",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": "{\"",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "NyS"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0ddc7ea27665",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": "query",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "o"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0ddc7ea27665",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": "\":\"",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "w"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0ddc7ea27665",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": "engineering",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "zQVkYD3nACI"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0ddc7ea27665",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": " updates",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "CxkP3OriAjizo6"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0ddc7ea27665",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": " United",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "kiuWNKDa7F0kONT"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0ddc7ea27665",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": " States",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "bGuDFw8POhr5oJ1"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0ddc7ea27665",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": " October",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
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