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fix: changes to update pipecat version to 0.0.100 (#122)
* feat: add stt evals * add smart turn as provider * chore: remove deprecations * chore: format files * fix: remove deprecated UserIdleProcessor * fix: remove deprecated TranscriptProcessor * chore: update pipecat submodule * feat: add evals visualisation * fix: trigger llm generation on client connected and pipeline started * chore: update pipecat * chore: update pipecat submodule * Add tests * fix: slow loading of workflow page * chore: update pipecat submodule * Show version after release * Fixes #99 * fix: provider check for websocket connection * Fixes #107 * Fix #96 * chore: fix documentation * fix: cloudonix campaign call error --------- Co-authored-by: Sabiha Khan <sabihak89@gmail.com>
This commit is contained in:
Abhishek
GitHub
parent
a4367bd83b
commit
911c5ed416
104 changed files with 16919 additions and 597 deletions
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@ -42,7 +42,6 @@ from pipecat.processors.aggregators.llm_response_universal import (
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)
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from pipecat.tests import MockLLMService, MockTTSService
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# Define prompts for test nodes
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START_NODE_PROMPT = "Start Node System Prompt"
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AGENT_NODE_PROMPT = "Agent Node System Prompt"
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@ -143,14 +142,20 @@ class ContextCapturingMockLLM(MockLLMService):
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msg_copy = dict(msg)
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# Copy content to avoid reference issues
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if "content" in msg_copy:
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msg_copy["content"] = str(msg_copy["content"]) if msg_copy["content"] else None
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msg_copy["content"] = (
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str(msg_copy["content"]) if msg_copy["content"] else None
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)
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messages_snapshot.append(msg_copy)
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self.captured_contexts.append({
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"step": self._current_step,
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"messages": messages_snapshot,
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"system_prompt": messages_snapshot[0]["content"] if messages_snapshot else None,
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})
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self.captured_contexts.append(
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{
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"step": self._current_step,
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"messages": messages_snapshot,
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"system_prompt": messages_snapshot[0]["content"]
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if messages_snapshot
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else None,
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}
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)
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# Call parent implementation to stream the mock chunks
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return await super()._stream_chat_completions_universal_context(context)
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@ -306,14 +311,26 @@ class TestContextUpdateBeforeNextCompletion:
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transition completes. The test verifies the context is still correctly updated.
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"""
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# Step 0 (Start node): call collect_info to transition to agent
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step_0_chunks = MockLLMService.create_multiple_function_call_chunks([
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{"name": "collect_info", "arguments": {}, "tool_call_id": "call_transition_1"},
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])
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step_0_chunks = MockLLMService.create_multiple_function_call_chunks(
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[
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{
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"name": "collect_info",
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"arguments": {},
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"tool_call_id": "call_transition_1",
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},
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]
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)
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# Step 1 (Agent node): call end_call to transition to end
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step_1_chunks = MockLLMService.create_multiple_function_call_chunks([
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{"name": "end_call", "arguments": {}, "tool_call_id": "call_transition_2"},
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])
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step_1_chunks = MockLLMService.create_multiple_function_call_chunks(
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[
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{
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"name": "end_call",
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"arguments": {},
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"tool_call_id": "call_transition_2",
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},
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]
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)
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# Step 2 (End node): text response (end node has no outgoing edges)
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step_2_chunks = MockLLMService.create_text_chunks("Goodbye!")
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@ -327,7 +344,7 @@ class TestContextUpdateBeforeNextCompletion:
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)
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# Should have been called 3 times: start node, agent node, end node
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assert llm.get_current_step() == 2, (
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assert llm.get_current_step() == 3, (
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f"Expected 3 LLM generations (start, agent, end), got {llm.get_current_step()}"
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)
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@ -376,14 +393,26 @@ class TestContextUpdateBeforeNextCompletion:
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is handled correctly.
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"""
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# Step 0 (Start node): call collect_info to transition to agent
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step_0_chunks = MockLLMService.create_multiple_function_call_chunks([
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{"name": "collect_info", "arguments": {}, "tool_call_id": "call_transition_1"},
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])
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step_0_chunks = MockLLMService.create_multiple_function_call_chunks(
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[
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{
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"name": "collect_info",
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"arguments": {},
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"tool_call_id": "call_transition_1",
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},
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]
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)
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# Step 1 (Agent node): call end_call to transition to end
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step_1_chunks = MockLLMService.create_multiple_function_call_chunks([
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{"name": "end_call", "arguments": {}, "tool_call_id": "call_transition_2"},
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])
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step_1_chunks = MockLLMService.create_multiple_function_call_chunks(
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[
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{
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"name": "end_call",
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"arguments": {},
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"tool_call_id": "call_transition_2",
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},
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]
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)
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# Step 2 (End node): text response
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step_2_chunks = MockLLMService.create_text_chunks("Goodbye!")
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@ -397,7 +426,7 @@ class TestContextUpdateBeforeNextCompletion:
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)
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# Verify all three nodes were executed
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assert llm.get_current_step() == 2, (
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assert llm.get_current_step() == 3, (
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f"Expected 3 steps, got {llm.get_current_step()}"
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)
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@ -408,8 +437,7 @@ class TestContextUpdateBeforeNextCompletion:
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assert AGENT_NODE_PROMPT in llm.get_system_prompt_at_step(1)
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# Step 2: End node - should have end prompt
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# FIXME - EndFrame is getting processed before LLMContextFrame
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# assert END_NODE_PROMPT in llm.get_system_prompt_at_step(2)
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assert END_NODE_PROMPT in llm.get_system_prompt_at_step(2)
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# Verify each subsequent step has the previous tool results
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step_1_ctx = llm.get_context_at_step(1)
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@ -423,14 +451,14 @@ class TestContextUpdateBeforeNextCompletion:
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assert step_1_has_tool, "Agent node should see collect_info tool result"
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# Step 2 should have tool results from both transitions
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# FIXME - EndFrame is getting processed before LLMContextFrame
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# step_2_tool_messages = [
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# msg for msg in step_2_ctx["messages"]
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# if msg.get("role") == "tool" or msg.get("tool_call_id")
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# ]
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# assert len(step_2_tool_messages) >= 2, (
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# f"End node should see at least 2 tool results, got {len(step_2_tool_messages)}"
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# )
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step_2_tool_messages = [
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msg
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for msg in step_2_ctx["messages"]
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if msg.get("role") == "tool" or msg.get("tool_call_id")
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]
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assert len(step_2_tool_messages) >= 2, (
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f"End node should see at least 2 tool results, got {len(step_2_tool_messages)}"
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)
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@pytest.mark.asyncio
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async def test_context_messages_preserve_conversation_history(
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@ -444,14 +472,26 @@ class TestContextUpdateBeforeNextCompletion:
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- Tool call messages and results
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"""
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# Step 0 (Start node): call collect_info to transition to agent
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step_0_chunks = MockLLMService.create_multiple_function_call_chunks([
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{"name": "collect_info", "arguments": {}, "tool_call_id": "call_transition_1"},
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])
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step_0_chunks = MockLLMService.create_multiple_function_call_chunks(
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[
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{
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"name": "collect_info",
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"arguments": {},
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"tool_call_id": "call_transition_1",
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},
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]
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)
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# Step 1 (Agent node): call end_call to transition to end
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step_1_chunks = MockLLMService.create_multiple_function_call_chunks([
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{"name": "end_call", "arguments": {}, "tool_call_id": "call_transition_2"},
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])
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step_1_chunks = MockLLMService.create_multiple_function_call_chunks(
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[
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{
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"name": "end_call",
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"arguments": {},
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"tool_call_id": "call_transition_2",
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},
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]
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)
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# Step 2 (End node): text response
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step_2_chunks = MockLLMService.create_text_chunks("Goodbye!")
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@ -472,18 +512,15 @@ class TestContextUpdateBeforeNextCompletion:
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assert len(ctx_1["messages"]) > len(ctx_0["messages"]), (
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"Context at step 1 should have more messages than step 0"
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)
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# FIXME
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# assert len(ctx_2["messages"]) > len(ctx_1["messages"]), (
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# "Context at step 2 should have more messages than step 1"
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# )
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assert len(ctx_2["messages"]) > len(ctx_1["messages"]), (
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"Context at step 2 should have more messages than step 1"
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)
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# Verify assistant messages are accumulated
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# FIXME
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# assistant_messages_at_step_2 = [
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# msg for msg in ctx_2["messages"]
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# if msg.get("role") == "assistant"
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# ]
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# assert len(assistant_messages_at_step_2) >= 2, (
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# "Should have at least 2 assistant messages by step 2"
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# )
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assistant_messages_at_step_2 = [
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msg for msg in ctx_2["messages"] if msg.get("role") == "assistant"
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]
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assert len(assistant_messages_at_step_2) >= 2, (
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"Should have at least 2 assistant messages by step 2"
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)
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