mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-11 19:56:03 +00:00
updated with vllm based values
rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED
This commit is contained in:
Antony Sallas
parent
a701f68bd7
commit
17e74251e2
11 changed files with 551 additions and 102 deletions
18
compose.yaml
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18
compose.yaml
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services:
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# Jaeger - OpenTelemetry collector and UI
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jaeger:
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image: jaegertracing/jaeger:2.1.0
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container_name: llama-stack-jaeger
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ports:
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- "16686:16686" # Jaeger UI
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- "4318:4318" # OTLP HTTP receiver
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- "4317:4317" # OTLP gRPC receiver (optional)
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environment:
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- COLLECTOR_OTLP_ENABLED=true
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restart: unless-stopped
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networks:
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- llama-stack-network
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networks:
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llama-stack-network:
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driver: bridge
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40
examples/agent.py
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40
examples/agent.py
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@ -0,0 +1,40 @@
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from llama_stack_client import LlamaStackClient
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from llama_stack_client import Agent, AgentEventLogger
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from rich.pretty import pprint
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import uuid
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client = LlamaStackClient(base_url=f"http://localhost:8321",
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default_headers={
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"X-Telemetry-Service": "llama-stack-agent",
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"X-Telemetry-Version": "1.0.0",
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})
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models = client.models.list()
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llm = next(m for m in models if m.model_type == "llm" and m.provider_id == "vllm")
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model_id = llm.identifier
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agent = Agent(client, model=model_id, instructions="You are a helpful assistant.")
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s_id = agent.create_session(session_name=f"s{uuid.uuid4().hex}")
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print("Non-streaming ...")
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response = agent.create_turn(
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messages=[{"role": "user", "content": "Who are you?"}],
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session_id=s_id,
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stream=False,
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)
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print("agent>", response.output_message.content)
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print("Streaming ...")
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stream = agent.create_turn(
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messages=[{"role": "user", "content": "Who are you?"}], session_id=s_id, stream=True
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)
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for event in stream:
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pprint(event)
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print("Streaming with print helper...")
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stream = agent.create_turn(
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messages=[{"role": "user", "content": "Who are you?"}], session_id=s_id, stream=True
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)
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for event in AgentEventLogger().log(stream):
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event.print()
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103
examples/demo_script.py
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103
examples/demo_script.py
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@ -0,0 +1,103 @@
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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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from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient
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# vector_db_id = "my_demo_vector_db"
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vector_db_name = "my_demo_vector_db"
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# Initialize client with telemetry headers
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# All API calls will automatically generate traces sent to Jaeger
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client = LlamaStackClient(
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base_url="http://localhost:8321",
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default_headers={
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"X-Telemetry-Service": "llama-stack-rag-demo",
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"X-Telemetry-Version": "1.0.0",
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}
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)
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print("=" * 80)
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print("🔭 Telemetry enabled: Traces will be sent to Jaeger")
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print(" View traces at: http://localhost:16686")
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print(" Service name: llama-stack-rag-demo")
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print("=" * 80)
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print()
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models = client.models.list()
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# Select the first LLM from vLLM provider and first embedding model
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model_id = next(m for m in models if m.model_type == "llm" and m.provider_id == "vllm").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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# ✅ FIXED: Use vector_stores.create instead of vector_dbs.register
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vector_store = client.vector_stores.create(
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name=vector_db_name,
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extra_body={
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"embedding_model": embedding_model_id,
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},
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)
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vector_db_id = vector_store.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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)
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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()
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print("=" * 80)
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print("✅ Demo completed!")
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print("🔭 View telemetry traces in Jaeger UI: http://localhost:16686")
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print(" - Service: llama-stack-rag-demo")
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print(" - Look for traces showing RAG operations, inference calls, and tool execution")
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print("=" * 80)
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26
examples/inference.py
Normal file
26
examples/inference.py
Normal file
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@ -0,0 +1,26 @@
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from llama_stack_client import LlamaStackClient
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client = LlamaStackClient(base_url="http://localhost:8321",
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default_headers={
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"X-Telemetry-Service": "llama-stack-inference",
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"X-Telemetry-Version": "1.0.0",
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}
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)
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# List available models
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models = client.models.list()
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# Select the first LLM
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llm = next(m for m in models if m.model_type == "llm" and m.provider_id == "vllm")
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model_id = llm.identifier
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print("Model:", model_id)
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response = client.chat.completions.create(
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model=model_id,
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Write a haiku about coding"},
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],
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)
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print(response)
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80
examples/rag_agent.py
Normal file
80
examples/rag_agent.py
Normal file
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@ -0,0 +1,80 @@
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from llama_stack_client import LlamaStackClient
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from llama_stack_client import Agent, AgentEventLogger
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from llama_stack_client.types import Document
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import uuid
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client = LlamaStackClient(base_url="http://localhost:8321",
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default_headers={
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"X-Telemetry-Service": "llama-stack-rag-agent",
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"X-Telemetry-Version": "1.0.0",
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})
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# Create a vector database instance
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embed_lm = next(m for m in client.models.list() if m.model_type == "embedding")
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embedding_model = embed_lm.identifier
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vector_db_name = f"v{uuid.uuid4().hex}"
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# The VectorDB API is deprecated; the server now returns its own authoritative ID.
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# We capture the correct ID from the response's .identifier attribute.
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vector_store = client.vector_stores.create(
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name=vector_db_name,
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embedding_model=embedding_model,
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)
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vector_db_id = vector_store.id
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# Create Documents
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urls = [
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"memory_optimizations.rst",
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"chat.rst",
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"llama3.rst",
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"qat_finetune.rst",
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"lora_finetune.rst",
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]
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documents = [
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Document(
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document_id=f"num-{i}",
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content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
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mime_type="text/plain",
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metadata={},
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)
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for i, url in enumerate(urls)
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]
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# Insert documents
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client.tool_runtime.rag_tool.insert(
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documents=documents,
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vector_db_id=vector_db_id,
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chunk_size_in_tokens=512,
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)
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# Get the model being served
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llm = next(
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m
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for m in client.models.list()
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if m.model_type == "llm" and m.provider_id == "vllm"
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)
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model = llm.identifier
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# Create the RAG agent
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rag_agent = Agent(
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client,
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model=model,
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instructions="You are a helpful assistant. Use the RAG tool to answer questions as needed.",
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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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)
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session_id = rag_agent.create_session(session_name=f"s{uuid.uuid4().hex}")
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turns = ["what is torchtune", "tell me about lora"]
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for t in turns:
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print("user>", t)
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stream = rag_agent.create_turn(
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messages=[{"role": "user", "content": t}], session_id=session_id, stream=True
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)
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for event in AgentEventLogger().log(stream):
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event.print()
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@ -41,7 +41,7 @@ def available_providers() -> list[ProviderSpec]:
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provider_type="inline::sentence-transformers",
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# CrossEncoder depends on torchao.quantization
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pip_packages=[
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"torch torchvision torchao>=0.12.0 --extra-index-url https://download.pytorch.org/whl/cpu",
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"torch torchvision torchao>=0.12.0 --extra-index-url https://download.pytorch.org/whl/cu130",
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"sentence-transformers --no-deps",
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# required by some SentenceTransformers architectures for tensor rearrange/merge ops
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"einops",
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|
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@ -32,7 +32,7 @@ def available_providers() -> list[ProviderSpec]:
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"provider_type": "inline::torchtune-cpu",
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"pip_packages": (
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cast(list[str], torchtune_def["pip_packages"])
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+ ["torch torchtune>=0.5.0 torchao>=0.12.0 --extra-index-url https://download.pytorch.org/whl/cpu"]
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+ ["torch torchtune>=0.5.0 torchao>=0.12.0 --extra-index-url https://download.pytorch.org/whl/cu130"]
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),
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},
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),
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|
|
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@ -98,7 +98,7 @@ class VLLMInferenceAdapter(OpenAIMixin):
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params = params.model_copy()
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# Apply vLLM-specific defaults
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if params.max_tokens is None and self.config.max_tokens:
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if (params.max_tokens is None or params.max_tokens == 0) and self.config.max_tokens:
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params.max_tokens = self.config.max_tokens
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# This is to be consistent with OpenAI API and support vLLM <= v0.6.3
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|
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26
llama_stack/ui/package-lock.json
generated
26
llama_stack/ui/package-lock.json
generated
|
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@ -141,6 +141,7 @@
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"integrity": "sha512-IaaGWsQqfsQWVLqMn9OB92MNN7zukfVA4s7KKAI0KfrrDsZ0yhi5uV4baBuLuN7n3vsZpwP8asPPcVwApxvjBQ==",
|
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"dev": true,
|
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"license": "MIT",
|
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"peer": true,
|
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"dependencies": {
|
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"@ampproject/remapping": "^2.2.0",
|
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"@babel/code-frame": "^7.27.1",
|
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|
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@ -760,6 +761,7 @@
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}
|
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],
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"license": "MIT",
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"peer": true,
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"engines": {
|
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"node": ">=18"
|
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},
|
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|
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@ -783,6 +785,7 @@
|
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}
|
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],
|
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"license": "MIT",
|
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"peer": true,
|
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"engines": {
|
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"node": ">=18"
|
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}
|
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|
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@ -3964,6 +3967,7 @@
|
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"resolved": "https://registry.npmjs.org/@types/node/-/node-24.3.0.tgz",
|
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"integrity": "sha512-aPTXCrfwnDLj4VvXrm+UUCQjNEvJgNA8s5F1cvwQU+3KNltTOkBm1j30uNLyqqPNe7gE3KFzImYoZEfLhp4Yow==",
|
||||
"license": "MIT",
|
||||
"peer": true,
|
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"dependencies": {
|
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"undici-types": "~7.10.0"
|
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}
|
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|
|
@ -3983,6 +3987,7 @@
|
|||
"resolved": "https://registry.npmjs.org/@types/react/-/react-19.2.2.tgz",
|
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"integrity": "sha512-6mDvHUFSjyT2B2yeNx2nUgMxh9LtOWvkhIU3uePn2I2oyNymUAX1NIsdgviM4CH+JSrp2D2hsMvJOkxY+0wNRA==",
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"dependencies": {
|
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"csstype": "^3.0.2"
|
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}
|
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|
|
@ -3993,6 +3998,7 @@
|
|||
"integrity": "sha512-/EEvYBdT3BflCWvTMO7YkYBHVE9Ci6XdqZciZANQgKpaiDRGOLIlRo91jbTNRQjgPFWVaRxcYc0luVNFitz57A==",
|
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"devOptional": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"peerDependencies": {
|
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"@types/react": "^19.2.0"
|
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}
|
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|
|
@ -4080,6 +4086,7 @@
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"integrity": "sha512-jCNyAuXx8dr5KJMkecGmZ8KI61KBUhkCob+SD+C+I5+Y1FWI2Y3QmY4/cxMCC5WAsZqoEtEETVhUiUMIGCf6Bw==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"@typescript-eslint/scope-manager": "8.40.0",
|
||||
"@typescript-eslint/types": "8.40.0",
|
||||
|
|
@ -4587,6 +4594,7 @@
|
|||
"integrity": "sha512-NZyJarBfL7nWwIq+FDL6Zp/yHEhePMNnnJ0y3qfieCrmNvYct8uvtiV41UvlSe6apAfk0fY1FbWx+NwfmpvtTg==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"bin": {
|
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"acorn": "bin/acorn"
|
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},
|
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|
|
@ -5109,9 +5117,9 @@
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"license": "MIT"
|
||||
},
|
||||
"node_modules/brace-expansion": {
|
||||
"version": "1.1.11",
|
||||
"resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.11.tgz",
|
||||
"integrity": "sha512-iCuPHDFgrHX7H2vEI/5xpz07zSHB00TpugqhmYtVmMO6518mCuRMoOYFldEBl0g187ufozdaHgWKcYFb61qGiA==",
|
||||
"version": "1.1.12",
|
||||
"resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.12.tgz",
|
||||
"integrity": "sha512-9T9UjW3r0UW5c1Q7GTwllptXwhvYmEzFhzMfZ9H7FQWt+uZePjZPjBP/W1ZEyZ1twGWom5/56TF4lPcqjnDHcg==",
|
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"dev": true,
|
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"license": "MIT",
|
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"dependencies": {
|
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|
|
@ -5152,6 +5160,7 @@
|
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}
|
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],
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"caniuse-lite": "^1.0.30001716",
|
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"electron-to-chromium": "^1.5.149",
|
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|
|
@ -6200,6 +6209,7 @@
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"integrity": "sha512-XyLmROnACWqSxiGYArdef1fItQd47weqB7iwtfr9JHwRrqIXZdcFMvvEcL9xHCmL0SNsOvF0c42lWyM1U5dgig==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"@eslint-community/eslint-utils": "^4.8.0",
|
||||
"@eslint-community/regexpp": "^4.12.1",
|
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|
|
@ -6289,6 +6299,7 @@
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"integrity": "sha512-82GZUjRS0p/jganf6q1rEO25VSoHH0hKPCTrgillPjdI/3bgBhAE1QzHrHTizjpRvy6pGAvKjDJtk2pF9NDq8w==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"bin": {
|
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"eslint-config-prettier": "bin/cli.js"
|
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},
|
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|
|
@ -6390,6 +6401,7 @@
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"integrity": "sha512-ixmkI62Rbc2/w8Vfxyh1jQRTdRTF52VxwRVHl/ykPAmqG+Nb7/kNn+byLP0LxPgI7zWA16Jt82SybJInmMia3A==",
|
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"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"@rtsao/scc": "^1.1.0",
|
||||
"array-includes": "^3.1.8",
|
||||
|
|
@ -9404,6 +9416,7 @@
|
|||
"integrity": "sha512-Cvc9WUhxSMEo4McES3P7oK3QaXldCfNWp7pl2NNeiIFlCoLr3kfq9kb1fxftiwk1FLV7CvpvDfonxtzUDeSOPg==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"cssstyle": "^4.2.1",
|
||||
"data-urls": "^5.0.0",
|
||||
|
|
@ -11805,6 +11818,7 @@
|
|||
"resolved": "https://registry.npmjs.org/preact/-/preact-10.26.9.tgz",
|
||||
"integrity": "sha512-SSjF9vcnF27mJK1XyFMNJzFd5u3pQiATFqoaDy03XuN00u4ziveVVEGt5RKJrDR8MHE/wJo9Nnad56RLzS2RMA==",
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"funding": {
|
||||
"type": "opencollective",
|
||||
"url": "https://opencollective.com/preact"
|
||||
|
|
@ -11844,6 +11858,7 @@
|
|||
"integrity": "sha512-I7AIg5boAr5R0FFtJ6rCfD+LFsWHp81dolrFD8S79U9tb8Az2nGrJncnMSnys+bpQJfRUzqs9hnA81OAA3hCuQ==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"bin": {
|
||||
"prettier": "bin/prettier.cjs"
|
||||
},
|
||||
|
|
@ -11991,6 +12006,7 @@
|
|||
"resolved": "https://registry.npmjs.org/react/-/react-19.2.0.tgz",
|
||||
"integrity": "sha512-tmbWg6W31tQLeB5cdIBOicJDJRR2KzXsV7uSK9iNfLWQ5bIZfxuPEHp7M8wiHyHnn0DD1i7w3Zmin0FtkrwoCQ==",
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"engines": {
|
||||
"node": ">=0.10.0"
|
||||
}
|
||||
|
|
@ -12000,6 +12016,7 @@
|
|||
"resolved": "https://registry.npmjs.org/react-dom/-/react-dom-19.2.0.tgz",
|
||||
"integrity": "sha512-UlbRu4cAiGaIewkPyiRGJk0imDN2T3JjieT6spoL2UeSf5od4n5LB/mQ4ejmxhCFT1tYe8IvaFulzynWovsEFQ==",
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"scheduler": "^0.27.0"
|
||||
},
|
||||
|
|
@ -13237,6 +13254,7 @@
|
|||
"integrity": "sha512-M7BAV6Rlcy5u+m6oPhAPFgJTzAioX/6B0DxyvDlo9l8+T3nLKbrczg2WLUyzd45L8RqfUMyGPzekbMvX2Ldkwg==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"engines": {
|
||||
"node": ">=12"
|
||||
},
|
||||
|
|
@ -13342,6 +13360,7 @@
|
|||
"integrity": "sha512-f0FFpIdcHgn8zcPSbf1dRevwt047YMnaiJM3u2w2RewrB+fob/zePZcrOyQoLMMO7aBIddLcQIEK5dYjkLnGrQ==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"@cspotcode/source-map-support": "^0.8.0",
|
||||
"@tsconfig/node10": "^1.0.7",
|
||||
|
|
@ -13529,6 +13548,7 @@
|
|||
"integrity": "sha512-CWBzXQrc/qOkhidw1OzBTQuYRbfyxDXJMVJ1XNwUHGROVmuaeiEm3OslpZ1RV96d7SKKjZKrSJu3+t/xlw3R9A==",
|
||||
"dev": true,
|
||||
"license": "Apache-2.0",
|
||||
"peer": true,
|
||||
"bin": {
|
||||
"tsc": "bin/tsc",
|
||||
"tsserver": "bin/tsserver"
|
||||
|
|
|
|||
|
|
@ -49,6 +49,8 @@ dependencies = [
|
|||
"aiosqlite>=0.21.0", # server - for metadata store
|
||||
"asyncpg", # for metadata store
|
||||
"sqlalchemy[asyncio]>=2.0.41", # server - for conversations
|
||||
"mcp>=1.12.2",
|
||||
"faiss-cpu>=1.11.0.post1",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
|
|
@ -154,13 +156,13 @@ where = ["."]
|
|||
include = ["llama_stack", "llama_stack.*"]
|
||||
|
||||
[[tool.uv.index]]
|
||||
name = "pytorch-cpu"
|
||||
url = "https://download.pytorch.org/whl/cpu"
|
||||
name = "pytorch-cu130"
|
||||
url = "https://download.pytorch.org/whl/cu130"
|
||||
explicit = true
|
||||
|
||||
[tool.uv.sources]
|
||||
torch = [{ index = "pytorch-cpu" }]
|
||||
torchvision = [{ index = "pytorch-cpu" }]
|
||||
torch = [{ index = "pytorch-cu130" }]
|
||||
torchvision = [{ index = "pytorch-cu130" }]
|
||||
|
||||
[tool.ruff]
|
||||
line-length = 120
|
||||
|
|
|
|||
324
uv.lock
generated
324
uv.lock
generated
|
|
@ -1762,6 +1762,7 @@ dependencies = [
|
|||
{ name = "aiohttp" },
|
||||
{ name = "aiosqlite" },
|
||||
{ name = "asyncpg" },
|
||||
{ name = "faiss-cpu" },
|
||||
{ name = "fastapi" },
|
||||
{ name = "fire" },
|
||||
{ name = "h11" },
|
||||
|
|
@ -1769,6 +1770,7 @@ dependencies = [
|
|||
{ name = "jinja2" },
|
||||
{ name = "jsonschema" },
|
||||
{ name = "llama-stack-client" },
|
||||
{ name = "mcp" },
|
||||
{ name = "openai" },
|
||||
{ name = "opentelemetry-exporter-otlp-proto-http" },
|
||||
{ name = "opentelemetry-sdk" },
|
||||
|
|
@ -1853,10 +1855,9 @@ test = [
|
|||
{ name = "qdrant-client" },
|
||||
{ name = "requests" },
|
||||
{ name = "sqlalchemy", extra = ["asyncio"] },
|
||||
{ name = "torch", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
|
||||
{ name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
|
||||
{ name = "torchvision", version = "0.23.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
|
||||
{ name = "torchvision", version = "0.23.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
|
||||
{ name = "torch" },
|
||||
{ name = "torchvision", version = "0.24.0", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" },
|
||||
{ name = "torchvision", version = "0.24.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "platform_machine != 'aarch64' or sys_platform != 'linux'" },
|
||||
{ name = "transformers" },
|
||||
{ name = "weaviate-client" },
|
||||
]
|
||||
|
|
@ -1883,6 +1884,7 @@ requires-dist = [
|
|||
{ name = "aiohttp" },
|
||||
{ name = "aiosqlite", specifier = ">=0.21.0" },
|
||||
{ name = "asyncpg" },
|
||||
{ name = "faiss-cpu", specifier = ">=1.11.0.post1" },
|
||||
{ name = "fastapi", specifier = ">=0.115.0,<1.0" },
|
||||
{ name = "fire" },
|
||||
{ name = "h11", specifier = ">=0.16.0" },
|
||||
|
|
@ -1891,6 +1893,7 @@ requires-dist = [
|
|||
{ name = "jsonschema" },
|
||||
{ name = "llama-stack-client", specifier = ">=0.2.23" },
|
||||
{ name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.23" },
|
||||
{ name = "mcp", specifier = ">=1.12.2" },
|
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{ name = "openai", specifier = ">=1.107" },
|
||||
{ name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.30.0" },
|
||||
{ name = "opentelemetry-sdk", specifier = ">=1.30.0" },
|
||||
|
|
@ -1970,8 +1973,8 @@ test = [
|
|||
{ name = "requests" },
|
||||
{ name = "sqlalchemy" },
|
||||
{ name = "sqlalchemy", extras = ["asyncio"], specifier = ">=2.0.41" },
|
||||
{ name = "torch", specifier = ">=2.6.0", index = "https://download.pytorch.org/whl/cpu" },
|
||||
{ name = "torchvision", specifier = ">=0.21.0", index = "https://download.pytorch.org/whl/cpu" },
|
||||
{ name = "torch", specifier = ">=2.6.0", index = "https://download.pytorch.org/whl/cu130" },
|
||||
{ name = "torchvision", specifier = ">=0.21.0", index = "https://download.pytorch.org/whl/cu130" },
|
||||
{ name = "transformers" },
|
||||
{ name = "weaviate-client", specifier = ">=4.16.4" },
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|
|
@ -2610,6 +2613,155 @@ wheels = [
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[[package]]
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[[package]]
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name = "typer"
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version = "0.15.3"
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||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue