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https://github.com/meta-llama/llama-stack.git
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Merge acb41dd6cd into sapling-pr-archive-ehhuang
This commit is contained in:
ehhuang
GitHub
commit
4c16f2c46a
46 changed files with 49861 additions and 251 deletions
2
.github/workflows/integration-tests.yml
vendored
2
.github/workflows/integration-tests.yml
vendored
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@ -47,7 +47,7 @@ jobs:
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strategy:
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fail-fast: false
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matrix:
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client-type: [library, server, docker]
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client-type: [library, docker]
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# Use Python 3.13 only on nightly schedule (daily latest client test), otherwise use 3.12
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python-version: ${{ github.event.schedule == '0 0 * * *' && fromJSON('["3.12", "3.13"]') || fromJSON('["3.12"]') }}
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client-version: ${{ (github.event.schedule == '0 0 * * *' || github.event.inputs.test-all-client-versions == 'true') && fromJSON('["published", "latest"]') || fromJSON('["latest"]') }}
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|
|
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@ -45,7 +45,7 @@ RUN set -eux; \
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exit 1; \
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fi
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RUN pip install --no-cache-dir uv
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RUN pip install --no-cache uv
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ENV UV_SYSTEM_PYTHON=1
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ENV INSTALL_MODE=${INSTALL_MODE}
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@ -68,7 +68,7 @@ RUN set -eux; \
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echo "LLAMA_STACK_CLIENT_DIR is set but $LLAMA_STACK_CLIENT_DIR does not exist" >&2; \
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exit 1; \
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fi; \
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uv pip install --no-cache-dir -e "$LLAMA_STACK_CLIENT_DIR"; \
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uv pip install --no-cache -e "$LLAMA_STACK_CLIENT_DIR"; \
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fi;
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# Install llama-stack
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@ -78,19 +78,19 @@ RUN set -eux; \
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echo "INSTALL_MODE=editable requires LLAMA_STACK_DIR to point to a directory inside the build context" >&2; \
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exit 1; \
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fi; \
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uv pip install --no-cache-dir -e "$LLAMA_STACK_DIR"; \
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uv pip install --no-cache -e "$LLAMA_STACK_DIR"; \
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elif [ "$INSTALL_MODE" = "test-pypi" ]; then \
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uv pip install --no-cache-dir fastapi libcst; \
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uv pip install --no-cache fastapi libcst; \
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if [ -n "$TEST_PYPI_VERSION" ]; then \
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uv pip install --no-cache-dir --extra-index-url https://test.pypi.org/simple/ --index-strategy unsafe-best-match "llama-stack==$TEST_PYPI_VERSION"; \
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uv pip install --no-cache --extra-index-url https://test.pypi.org/simple/ --index-strategy unsafe-best-match "llama-stack==$TEST_PYPI_VERSION"; \
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else \
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uv pip install --no-cache-dir --extra-index-url https://test.pypi.org/simple/ --index-strategy unsafe-best-match llama-stack; \
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uv pip install --no-cache --extra-index-url https://test.pypi.org/simple/ --index-strategy unsafe-best-match llama-stack; \
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fi; \
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else \
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if [ -n "$PYPI_VERSION" ]; then \
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uv pip install --no-cache-dir "llama-stack==$PYPI_VERSION"; \
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uv pip install --no-cache "llama-stack==$PYPI_VERSION"; \
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else \
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uv pip install --no-cache-dir llama-stack; \
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uv pip install --no-cache llama-stack; \
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fi; \
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fi;
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@ -102,7 +102,7 @@ RUN set -eux; \
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fi; \
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deps="$(llama stack list-deps "$DISTRO_NAME")"; \
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if [ -n "$deps" ]; then \
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printf '%s\n' "$deps" | xargs -L1 uv pip install --no-cache-dir; \
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printf '%s\n' "$deps" | xargs -L1 uv pip install --no-cache; \
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fi
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# Cleanup
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|
|
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@ -19,6 +19,7 @@ Browse that folder to understand available providers and copy a distribution to
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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<Tabs>
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<TabItem value="container" label="Building a container">
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|
|
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|
|
@ -4,65 +4,24 @@
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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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from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient
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vector_db_id = "my_demo_vector_db"
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client = LlamaStackClient(base_url="http://localhost:8321")
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import io, requests
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from openai import OpenAI
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models = client.models.list()
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url="https://www.paulgraham.com/greatwork.html"
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client = OpenAI(base_url="http://localhost:8321/v1/", api_key="none")
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# Select the first LLM and first embedding models
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model_id = next(m for m in models if m.model_type == "llm").identifier
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embedding_model_id = (
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em := next(m for m in models if m.model_type == "embedding")
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).identifier
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embedding_dimension = em.metadata["embedding_dimension"]
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vs = client.vector_stores.create()
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response = requests.get(url)
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pseudo_file = io.BytesIO(str(response.content).encode('utf-8'))
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uploaded_file = client.files.create(file=(url, pseudo_file, "text/html"), purpose="assistants")
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client.vector_stores.files.create(vector_store_id=vs.id, file_id=uploaded_file.id)
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vector_db = client.vector_dbs.register(
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vector_db_id=vector_db_id,
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embedding_model=embedding_model_id,
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embedding_dimension=embedding_dimension,
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provider_id="faiss",
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)
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vector_db_id = vector_db.identifier
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source = "https://www.paulgraham.com/greatwork.html"
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print("rag_tool> Ingesting document:", source)
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document = RAGDocument(
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document_id="document_1",
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content=source,
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mime_type="text/html",
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metadata={},
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)
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client.tool_runtime.rag_tool.insert(
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documents=[document],
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vector_db_id=vector_db_id,
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chunk_size_in_tokens=100,
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)
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agent = Agent(
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client,
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model=model_id,
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instructions="You are a helpful assistant",
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tools=[
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{
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"name": "builtin::rag/knowledge_search",
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"args": {"vector_db_ids": [vector_db_id]},
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}
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],
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resp = client.responses.create(
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model="openai/gpt-4o",
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input="How do you do great work? Use the existing knowledge_search tool.",
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tools=[{"type": "file_search", "vector_store_ids": [vs.id]}],
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include=["file_search_call.results"],
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)
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prompt = "How do you do great work?"
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print("prompt>", prompt)
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use_stream = True
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response = agent.create_turn(
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messages=[{"role": "user", "content": prompt}],
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session_id=agent.create_session("rag_session"),
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stream=use_stream,
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)
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# Only call `AgentEventLogger().log(response)` for streaming responses.
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if use_stream:
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for log in AgentEventLogger().log(response):
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log.print()
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else:
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print(response)
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print(resp)
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|
|
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|
|
@ -35,103 +35,51 @@ OLLAMA_URL=http://localhost:11434 uv run --with llama-stack llama stack run star
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#### Step 3: Run the demo
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Now open up a new terminal and copy the following script into a file named `demo_script.py`.
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```python title="demo_script.py"
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# 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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```python
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import io, requests
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from openai import OpenAI
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from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient
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url="https://www.paulgraham.com/greatwork.html"
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client = OpenAI(base_url="http://localhost:8321/v1/", api_key="none")
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|
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vector_db_id = "my_demo_vector_db"
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client = LlamaStackClient(base_url="http://localhost:8321")
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vs = client.vector_stores.create()
|
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response = requests.get(url)
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pseudo_file = io.BytesIO(str(response.content).encode('utf-8'))
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uploaded_file = client.files.create(file=(url, pseudo_file, "text/html"), purpose="assistants")
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client.vector_stores.files.create(vector_store_id=vs.id, file_id=uploaded_file.id)
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models = client.models.list()
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# Select the first LLM and first embedding models
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model_id = next(m for m in models if m.model_type == "llm").identifier
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embedding_model_id = (
|
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em := next(m for m in models if m.model_type == "embedding")
|
||||
).identifier
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embedding_dimension = em.metadata["embedding_dimension"]
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|
||||
vector_db = client.vector_dbs.register(
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vector_db_id=vector_db_id,
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||||
embedding_model=embedding_model_id,
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embedding_dimension=embedding_dimension,
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||||
provider_id="faiss",
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||||
)
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vector_db_id = vector_db.identifier
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||||
source = "https://www.paulgraham.com/greatwork.html"
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print("rag_tool> Ingesting document:", source)
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document = RAGDocument(
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document_id="document_1",
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content=source,
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mime_type="text/html",
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metadata={},
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||||
)
|
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client.tool_runtime.rag_tool.insert(
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||||
documents=[document],
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||||
vector_db_id=vector_db_id,
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||||
chunk_size_in_tokens=100,
|
||||
)
|
||||
agent = Agent(
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||||
client,
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||||
model=model_id,
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||||
instructions="You are a helpful assistant",
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||||
tools=[
|
||||
{
|
||||
"name": "builtin::rag/knowledge_search",
|
||||
"args": {"vector_db_ids": [vector_db_id]},
|
||||
}
|
||||
],
|
||||
resp = client.responses.create(
|
||||
model="openai/gpt-4o",
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||||
input="How do you do great work? Use the existing knowledge_search tool.",
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||||
tools=[{"type": "file_search", "vector_store_ids": [vs.id]}],
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include=["file_search_call.results"],
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||||
)
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prompt = "How do you do great work?"
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print("prompt>", prompt)
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||||
|
||||
use_stream = True
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response = agent.create_turn(
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messages=[{"role": "user", "content": prompt}],
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session_id=agent.create_session("rag_session"),
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stream=use_stream,
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||||
)
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||||
|
||||
# Only call `AgentEventLogger().log(response)` for streaming responses.
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||||
if use_stream:
|
||||
for log in AgentEventLogger().log(response):
|
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log.print()
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||||
else:
|
||||
print(response)
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||||
```
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||||
We will use `uv` to run the script
|
||||
```
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uv run --with llama-stack-client,fire,requests demo_script.py
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||||
```
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And you should see output like below.
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```python
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>print(resp.output[1].content[0].text)
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To do great work, consider the following principles:
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|
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1. **Follow Your Interests**: Engage in work that genuinely excites you. If you find an area intriguing, pursue it without being overly concerned about external pressures or norms. You should create things that you would want for yourself, as this often aligns with what others in your circle might want too.
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|
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2. **Work Hard on Ambitious Projects**: Ambition is vital, but it should be tempered by genuine interest. Instead of detailed planning for the future, focus on exciting projects that keep your options open. This approach, known as "staying upwind," allows for adaptability and can lead to unforeseen achievements.
|
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|
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3. **Choose Quality Colleagues**: Collaborating with talented colleagues can significantly affect your own work. Seek out individuals who offer surprising insights and whom you admire. The presence of good colleagues can elevate the quality of your work and inspire you.
|
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|
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4. **Maintain High Morale**: Your attitude towards work and life affects your performance. Cultivating optimism and viewing yourself as lucky rather than victimized can boost your productivity. It’s essential to care for your physical health as well since it directly impacts your mental faculties and morale.
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|
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5. **Be Consistent**: Great work often comes from cumulative effort. Daily progress, even in small amounts, can result in substantial achievements over time. Emphasize consistency and make the work engaging, as this reduces the perceived burden of hard labor.
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|
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6. **Embrace Curiosity**: Curiosity is a driving force that can guide you in selecting fields of interest, pushing you to explore uncharted territories. Allow it to shape your work and continually seek knowledge and insights.
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By focusing on these aspects, you can create an environment conducive to great work and personal fulfillment.
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```
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rag_tool> Ingesting document: https://www.paulgraham.com/greatwork.html
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|
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prompt> How do you do great work?
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inference> [knowledge_search(query="What is the key to doing great work")]
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|
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tool_execution> Tool:knowledge_search Args:{'query': 'What is the key to doing great work'}
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|
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tool_execution> Tool:knowledge_search Response:[TextContentItem(text='knowledge_search tool found 5 chunks:\nBEGIN of knowledge_search tool results.\n', type='text'), TextContentItem(text="Result 1:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 2:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 3:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 4:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 5:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text='END of knowledge_search tool results.\n', type='text')]
|
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|
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inference> Based on the search results, it seems that doing great work means doing something important so well that you expand people's ideas of what's possible. However, there is no clear threshold for importance, and it can be difficult to judge at the time.
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|
||||
To further clarify, I would suggest that doing great work involves:
|
||||
|
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* Completing tasks with high quality and attention to detail
|
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* Expanding on existing knowledge or ideas
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* Making a positive impact on others through your work
|
||||
* Striving for excellence and continuous improvement
|
||||
|
||||
Ultimately, great work is about making a meaningful contribution and leaving a lasting impression.
|
||||
```
|
||||
Congratulations! You've successfully built your first RAG application using Llama Stack! 🎉🥳
|
||||
|
||||
:::tip HuggingFace access
|
||||
|
|
|
|||
|
|
@ -236,6 +236,7 @@ def setup_logging(category_levels: dict[str, int] | None = None, log_file: str |
|
|||
}
|
||||
},
|
||||
"loggers": {
|
||||
**{
|
||||
category: {
|
||||
"handlers": list(handlers.keys()), # Apply all handlers
|
||||
"level": category_levels.get(category, DEFAULT_LOG_LEVEL),
|
||||
|
|
@ -243,6 +244,23 @@ def setup_logging(category_levels: dict[str, int] | None = None, log_file: str |
|
|||
}
|
||||
for category in CATEGORIES
|
||||
},
|
||||
# Explicitly configure uvicorn loggers to preserve their INFO level
|
||||
"uvicorn": {
|
||||
"handlers": list(handlers.keys()),
|
||||
"level": logging.INFO,
|
||||
"propagate": False,
|
||||
},
|
||||
"uvicorn.error": {
|
||||
"handlers": list(handlers.keys()),
|
||||
"level": logging.INFO,
|
||||
"propagate": False,
|
||||
},
|
||||
"uvicorn.access": {
|
||||
"handlers": list(handlers.keys()),
|
||||
"level": logging.INFO,
|
||||
"propagate": False,
|
||||
},
|
||||
},
|
||||
"root": {
|
||||
"handlers": list(handlers.keys()),
|
||||
"level": root_level, # Set root logger's level dynamically
|
||||
|
|
@ -250,9 +268,13 @@ def setup_logging(category_levels: dict[str, int] | None = None, log_file: str |
|
|||
}
|
||||
dictConfig(logging_config)
|
||||
|
||||
# Ensure third-party libraries follow the root log level
|
||||
for _, logger in logging.root.manager.loggerDict.items():
|
||||
# Ensure third-party libraries follow the root log level, but preserve
|
||||
# already-configured loggers (e.g., uvicorn) and our own llama_stack loggers
|
||||
for name, logger in logging.root.manager.loggerDict.items():
|
||||
if isinstance(logger, logging.Logger):
|
||||
# Skip infrastructure loggers (uvicorn, fastapi) and our own loggers
|
||||
if name.startswith(("uvicorn", "fastapi", "llama_stack")):
|
||||
continue
|
||||
logger.setLevel(root_level)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -6,9 +6,12 @@
|
|||
|
||||
import asyncio
|
||||
import base64
|
||||
import platform
|
||||
import struct
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
import torch
|
||||
|
||||
from llama_stack.log import get_logger
|
||||
|
||||
if TYPE_CHECKING:
|
||||
|
|
@ -24,6 +27,8 @@ from llama_stack.apis.inference import (
|
|||
|
||||
EMBEDDING_MODELS = {}
|
||||
|
||||
DARWIN = "Darwin"
|
||||
|
||||
|
||||
log = get_logger(name=__name__, category="providers::utils")
|
||||
|
||||
|
|
@ -83,6 +88,13 @@ class SentenceTransformerEmbeddingMixin:
|
|||
def _load_model():
|
||||
from sentence_transformers import SentenceTransformer
|
||||
|
||||
platform_name = platform.system()
|
||||
if platform_name == DARWIN:
|
||||
# PyTorch's OpenMP kernels can segfault on macOS when spawned from background
|
||||
# threads with the default parallel settings, so force a single-threaded CPU run.
|
||||
log.debug(f"Constraining torch threads on {platform_name} to a single worker")
|
||||
torch.set_num_threads(1)
|
||||
|
||||
return SentenceTransformer(model, trust_remote_code=True)
|
||||
|
||||
loaded_model = await asyncio.to_thread(_load_model)
|
||||
|
|
|
|||
|
|
@ -370,16 +370,6 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
logger.debug(
|
||||
f"Using embedding config from metadata (takes precedence over extra_body): model='{embedding_model}', dimension={embedding_dimension}"
|
||||
)
|
||||
|
||||
# Check for conflicts with extra_body
|
||||
if extra_body.get("embedding_model") and extra_body["embedding_model"] != embedding_model:
|
||||
raise ValueError(
|
||||
f"Embedding model inconsistent between metadata ('{embedding_model}') and extra_body ('{extra_body['embedding_model']}')"
|
||||
)
|
||||
if extra_body.get("embedding_dimension") and extra_body["embedding_dimension"] != embedding_dimension:
|
||||
raise ValueError(
|
||||
f"Embedding dimension inconsistent between metadata ({embedding_dimension}) and extra_body ({extra_body['embedding_dimension']})"
|
||||
)
|
||||
else:
|
||||
embedding_model = extra_body.get("embedding_model")
|
||||
embedding_dimension = extra_body.get("embedding_dimension", EMBEDDING_DIMENSION)
|
||||
|
|
|
|||
33
llama_stack/ui/package-lock.json
generated
33
llama_stack/ui/package-lock.json
generated
|
|
@ -18,7 +18,7 @@
|
|||
"class-variance-authority": "^0.7.1",
|
||||
"clsx": "^2.1.1",
|
||||
"framer-motion": "^12.23.24",
|
||||
"llama-stack-client": "^0.2.23",
|
||||
"llama-stack-client": "^0.3.0",
|
||||
"lucide-react": "^0.545.0",
|
||||
"next": "15.5.4",
|
||||
"next-auth": "^4.24.11",
|
||||
|
|
@ -75,20 +75,6 @@
|
|||
"url": "https://github.com/sponsors/sindresorhus"
|
||||
}
|
||||
},
|
||||
"node_modules/@ampproject/remapping": {
|
||||
"version": "2.3.0",
|
||||
"resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.3.0.tgz",
|
||||
"integrity": "sha512-30iZtAPgz+LTIYoeivqYo853f02jBYSd5uGnGpkFV0M3xOt9aN73erkgYAmZU43x4VfqcnLxW9Kpg3R5LC4YYw==",
|
||||
"dev": true,
|
||||
"license": "Apache-2.0",
|
||||
"dependencies": {
|
||||
"@jridgewell/gen-mapping": "^0.3.5",
|
||||
"@jridgewell/trace-mapping": "^0.3.24"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=6.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@asamuzakjp/css-color": {
|
||||
"version": "3.2.0",
|
||||
"resolved": "https://registry.npmjs.org/@asamuzakjp/css-color/-/css-color-3.2.0.tgz",
|
||||
|
|
@ -3647,17 +3633,6 @@
|
|||
"dev": true,
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/@tybys/wasm-util": {
|
||||
"version": "0.9.0",
|
||||
"resolved": "https://registry.npmjs.org/@tybys/wasm-util/-/wasm-util-0.9.0.tgz",
|
||||
"integrity": "sha512-6+7nlbMVX/PVDCwaIQ8nTOPveOcFLSt8GcXdx8hD0bt39uWxYT88uXzqTd4fTvqta7oeUJqudepapKNt2DYJFw==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"dependencies": {
|
||||
"tslib": "^2.4.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@types/aria-query": {
|
||||
"version": "5.0.4",
|
||||
"resolved": "https://registry.npmjs.org/@types/aria-query/-/aria-query-5.0.4.tgz",
|
||||
|
|
@ -9660,9 +9635,9 @@
|
|||
"license": "MIT"
|
||||
},
|
||||
"node_modules/llama-stack-client": {
|
||||
"version": "0.2.23",
|
||||
"resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.23.tgz",
|
||||
"integrity": "sha512-J3YFH1HW2K70capejQxGlCyTgKdfx+sQf8Ab+HFi1j2Q00KtpHXB79RxejvBxjWC3X2E++P9iU57KdU2Tp/rIQ==",
|
||||
"version": "0.3.0",
|
||||
"resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.3.0.tgz",
|
||||
"integrity": "sha512-76K/t1doaGmlBbDxCADaral9Vccvys9P8pqAMIhwBhMAqWudCEORrMMhUSg+pjhamWmEKj3wa++d4zeOGbfN/w==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"@types/node": "^18.11.18",
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@
|
|||
"class-variance-authority": "^0.7.1",
|
||||
"clsx": "^2.1.1",
|
||||
"framer-motion": "^12.23.24",
|
||||
"llama-stack-client": "^0.2.23",
|
||||
"llama-stack-client": "^0.3.0",
|
||||
"lucide-react": "^0.545.0",
|
||||
"next": "15.5.4",
|
||||
"next-auth": "^4.24.11",
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ required-version = ">=0.7.0"
|
|||
|
||||
[project]
|
||||
name = "llama_stack"
|
||||
version = "0.2.23"
|
||||
version = "0.3.0"
|
||||
authors = [{ name = "Meta Llama", email = "llama-oss@meta.com" }]
|
||||
description = "Llama Stack"
|
||||
readme = "README.md"
|
||||
|
|
@ -30,7 +30,7 @@ dependencies = [
|
|||
"httpx",
|
||||
"jinja2>=3.1.6",
|
||||
"jsonschema",
|
||||
"llama-stack-client>=0.2.23",
|
||||
"llama-stack-client>=0.3.0",
|
||||
"openai>=1.107", # for expires_after support
|
||||
"prompt-toolkit",
|
||||
"python-dotenv",
|
||||
|
|
@ -55,7 +55,7 @@ dependencies = [
|
|||
ui = [
|
||||
"streamlit",
|
||||
"pandas",
|
||||
"llama-stack-client>=0.2.23",
|
||||
"llama-stack-client>=0.3.0",
|
||||
"streamlit-option-menu",
|
||||
]
|
||||
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@
|
|||
# the root directory of this source tree.
|
||||
import inspect
|
||||
import itertools
|
||||
import logging # allow-direct-logging
|
||||
import os
|
||||
import tempfile
|
||||
import textwrap
|
||||
|
|
@ -57,6 +58,12 @@ def pytest_sessionstart(session):
|
|||
patch_httpx_for_test_id()
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def suppress_httpx_logs(caplog):
|
||||
"""Suppress httpx INFO logs for all integration tests"""
|
||||
caplog.set_level(logging.WARNING, logger="httpx")
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def _track_test_context(request):
|
||||
"""Automatically track current test context for isolated recordings.
|
||||
|
|
|
|||
|
|
@ -0,0 +1,660 @@
|
|||
{
|
||||
"test_id": "tests/integration/responses/test_tool_responses.py::test_response_non_streaming_file_search[openai_client-txt=openai/gpt-4o:emb=sentence-transformers/nomic-ai/nomic-embed-text-v1.5:dim=768-llama_experts_pdf]",
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "https://api.openai.com/v1/v1/chat/completions",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "gpt-4o",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "How many experts does the Llama 4 Maverick model have?"
|
||||
}
|
||||
],
|
||||
"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__": {
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"id": "rec-00913934356f",
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|
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{
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||||
"index": 0,
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||||
"id": "call_M8gyYiB39MwYdJKc4aHIGbfA",
|
||||
"function": {
|
||||
"arguments": "",
|
||||
"name": "knowledge_search"
|
||||
},
|
||||
"type": "function"
|
||||
}
|
||||
]
|
||||
},
|
||||
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|
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"created": 0,
|
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"model": "gpt-4o-2024-08-06",
|
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"object": "chat.completion.chunk",
|
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"service_tier": "default",
|
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"system_fingerprint": "fp_f64f290af2",
|
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"obfuscation": "8anw"
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|
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{
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"system_fingerprint": "fp_f64f290af2",
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"object": "chat.completion.chunk",
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"service_tier": "default",
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1901
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1091
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1834
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2684
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}
|
||||
2239
tests/integration/responses/recordings/f970cb793e3a68879444e3b218cfbe37829f7ed7ca4706ff0cbb6113f9a3253b.json
generated
Normal file
2239
tests/integration/responses/recordings/f970cb793e3a68879444e3b218cfbe37829f7ed7ca4706ff0cbb6113f9a3253b.json
generated
Normal file
File diff suppressed because it is too large
Load diff
|
|
@ -1079,50 +1079,6 @@ async def test_embedding_config_consistency_check_passes(vector_io_adapter):
|
|||
assert call_args.embedding_dimension == 768
|
||||
|
||||
|
||||
async def test_embedding_config_inconsistency_errors(vector_io_adapter):
|
||||
"""Test that inconsistent embedding config between metadata and extra_body raises errors."""
|
||||
|
||||
# Mock register_vector_store to avoid actual registration
|
||||
vector_io_adapter.register_vector_store = AsyncMock()
|
||||
# Set provider_id attribute for the adapter
|
||||
vector_io_adapter.__provider_id__ = "test_provider"
|
||||
|
||||
# Test with inconsistent embedding model
|
||||
params = OpenAICreateVectorStoreRequestWithExtraBody(
|
||||
name="test_store",
|
||||
metadata={
|
||||
"embedding_model": "metadata-model",
|
||||
"embedding_dimension": "768",
|
||||
},
|
||||
**{
|
||||
"embedding_model": "extra-body-model",
|
||||
"embedding_dimension": 768,
|
||||
},
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match="Embedding model inconsistent between metadata"):
|
||||
await vector_io_adapter.openai_create_vector_store(params)
|
||||
|
||||
# Reset mock for second test
|
||||
vector_io_adapter.register_vector_store.reset_mock()
|
||||
|
||||
# Test with inconsistent embedding dimension
|
||||
params = OpenAICreateVectorStoreRequestWithExtraBody(
|
||||
name="test_store",
|
||||
metadata={
|
||||
"embedding_model": "same-model",
|
||||
"embedding_dimension": "512",
|
||||
},
|
||||
**{
|
||||
"embedding_model": "same-model",
|
||||
"embedding_dimension": 1024,
|
||||
},
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match="Embedding dimension inconsistent between metadata"):
|
||||
await vector_io_adapter.openai_create_vector_store(params)
|
||||
|
||||
|
||||
async def test_embedding_config_defaults_when_missing(vector_io_adapter):
|
||||
"""Test that embedding dimension defaults to 768 when not provided."""
|
||||
|
||||
|
|
|
|||
12
uv.lock
generated
12
uv.lock
generated
|
|
@ -1756,7 +1756,7 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "llama-stack"
|
||||
version = "0.2.23"
|
||||
version = "0.3.0"
|
||||
source = { editable = "." }
|
||||
dependencies = [
|
||||
{ name = "aiohttp" },
|
||||
|
|
@ -1889,8 +1889,8 @@ requires-dist = [
|
|||
{ name = "httpx" },
|
||||
{ name = "jinja2", specifier = ">=3.1.6" },
|
||||
{ name = "jsonschema" },
|
||||
{ name = "llama-stack-client", specifier = ">=0.2.23" },
|
||||
{ name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.23" },
|
||||
{ name = "llama-stack-client", specifier = ">=0.3.0" },
|
||||
{ name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.3.0" },
|
||||
{ name = "openai", specifier = ">=1.107" },
|
||||
{ name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.30.0" },
|
||||
{ name = "opentelemetry-sdk", specifier = ">=1.30.0" },
|
||||
|
|
@ -1996,7 +1996,7 @@ unit = [
|
|||
|
||||
[[package]]
|
||||
name = "llama-stack-client"
|
||||
version = "0.2.23"
|
||||
version = "0.3.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "anyio" },
|
||||
|
|
@ -2015,9 +2015,9 @@ dependencies = [
|
|||
{ name = "tqdm" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/9f/8f/306d5fcf2f97b3a6251219b03c194836a2ff4e0fcc8146c9970e50a72cd3/llama_stack_client-0.2.23.tar.gz", hash = "sha256:68f34e8ac8eea6a73ed9d4977d849992b2d8bd835804d770a11843431cd5bf74", size = 322288, upload-time = "2025-09-26T21:11:08.342Z" }
|
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sdist = { url = "https://files.pythonhosted.org/packages/1d/d9/3c720f420fc80ce51de1a0ad90c53edc613617b68980137dcf716a86198a/llama_stack_client-0.3.0.tar.gz", hash = "sha256:1e974a74d0da285e18ba7df30b9a324e250782b130253bcef3e695830c5bb03d", size = 340443, upload-time = "2025-10-21T23:58:25.855Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/fa/75/3eb58e092a681804013dbec7b7f549d18f55acf6fd6e6b27de7e249766d8/llama_stack_client-0.2.23-py3-none-any.whl", hash = "sha256:eee42c74eee8f218f9455e5a06d5d4be43f8a8c82a7937ef51ce367f916df847", size = 379809, upload-time = "2025-09-26T21:11:06.856Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/96/27/1c65035ce58100be22409c98e4d65b1cdaeff7811ea968f9f844641330d7/llama_stack_client-0.3.0-py3-none-any.whl", hash = "sha256:9f85d84d508ef7da44b96ca8555d7783da717cfc9135bab6a5530fe8c852690d", size = 425234, upload-time = "2025-10-21T23:58:24.246Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue