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https://github.com/meta-llama/llama-stack.git
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f50ce11a3b
Renames `inference_recorder.py` to `api_recorder.py` and extends it to support recording/replaying tool invocations in addition to inference calls. This allows us to record web-search, etc. tool calls and thereafter apply recordings for `tests/integration/responses` ## Test Plan ``` export OPENAI_API_KEY=... export TAVILY_SEARCH_API_KEY=... ./scripts/integration-tests.sh --stack-config ci-tests \ --suite responses --inference-mode record-if-missing ```
78 lines
2.9 KiB
Python
78 lines
2.9 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import time
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def new_vector_store(openai_client, name, embedding_model, embedding_dimension):
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"""Create a new vector store, cleaning up any existing one with the same name."""
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# Ensure we don't reuse an existing vector store
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vector_stores = openai_client.vector_stores.list()
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for vector_store in vector_stores:
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if vector_store.name == name:
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openai_client.vector_stores.delete(vector_store_id=vector_store.id)
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# Create a new vector store
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# OpenAI SDK client uses extra_body for non-standard parameters
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from openai import OpenAI
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if isinstance(openai_client, OpenAI):
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# OpenAI SDK client - use extra_body
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vector_store = openai_client.vector_stores.create(
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name=name,
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extra_body={"embedding_model": embedding_model, "embedding_dimension": embedding_dimension},
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)
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else:
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# LlamaStack client - direct parameter
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vector_store = openai_client.vector_stores.create(
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name=name, embedding_model=embedding_model, embedding_dimension=embedding_dimension
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)
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return vector_store
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def upload_file(openai_client, name, file_path):
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"""Upload a file, cleaning up any existing file with the same name."""
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# Ensure we don't reuse an existing file
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files = openai_client.files.list()
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for file in files:
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if file.filename == name:
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openai_client.files.delete(file_id=file.id)
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# Upload a text file with our document content
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return openai_client.files.create(file=open(file_path, "rb"), purpose="assistants")
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def wait_for_file_attachment(compat_client, vector_store_id, file_id):
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"""Wait for a file to be attached to a vector store."""
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file_attach_response = compat_client.vector_stores.files.retrieve(
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vector_store_id=vector_store_id,
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file_id=file_id,
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)
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while file_attach_response.status == "in_progress":
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time.sleep(0.1)
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file_attach_response = compat_client.vector_stores.files.retrieve(
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vector_store_id=vector_store_id,
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file_id=file_id,
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)
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assert file_attach_response.status == "completed", f"Expected file to be attached, got {file_attach_response}"
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assert not file_attach_response.last_error
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return file_attach_response
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def setup_mcp_tools(tools, mcp_server_info):
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"""Replace placeholder MCP server URLs with actual server info."""
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# Create a deep copy to avoid modifying the original test case
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import copy
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tools_copy = copy.deepcopy(tools)
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for tool in tools_copy:
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if tool["type"] == "mcp" and tool["server_url"] == "<FILLED_BY_TEST_RUNNER>":
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tool["server_url"] = mcp_server_info["server_url"]
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return tools_copy
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