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SambaNova

SambaNovaSambastudio 是一个运行您自己的开源模型的平台

本示例介绍如何使用 LangChain 与 SambaNova 嵌入模型进行交互

SambaStudio

SambaStudio 允许您训练、运行批量推理任务,并部署在线推理端点,以运行您自己微调的开源模型。

部署模型需要一个 SambaStudio 环境。获取更多信息请访问 sambanova.ai/products/enterprise-ai-platform-sambanova-suite

注册您的环境变量:

import os

sambastudio_base_url = "<Your SambaStudio environment URL>"
sambastudio_base_uri = "<Your SambaStudio environment URI>"
sambastudio_project_id = "<Your SambaStudio project id>"
sambastudio_endpoint_id = "<Your SambaStudio endpoint id>"
sambastudio_api_key = "<Your SambaStudio endpoint API key>"

# Set the environment variables
os.environ["SAMBASTUDIO_EMBEDDINGS_BASE_URL"] = sambastudio_base_url
os.environ["SAMBASTUDIO_EMBEDDINGS_BASE_URI"] = sambastudio_base_uri
os.environ["SAMBASTUDIO_EMBEDDINGS_PROJECT_ID"] = sambastudio_project_id
os.environ["SAMBASTUDIO_EMBEDDINGS_ENDPOINT_ID"] = sambastudio_endpoint_id
os.environ["SAMBASTUDIO_EMBEDDINGS_API_KEY"] = sambastudio_api_key

直接从LangChain调用SambaStudio托管的嵌入!

<!--IMPORTS:[{"imported": "SambaStudioEmbeddings", "source": "langchain_community.embeddings.sambanova", "docs": "https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.sambanova.SambaStudioEmbeddings.html", "title": "SambaNova"}]-->
from langchain_community.embeddings.sambanova import SambaStudioEmbeddings

embeddings = SambaStudioEmbeddings()

text = "Hello, this is a test"
result = embeddings.embed_query(text)
print(result)

texts = ["Hello, this is a test", "Hello, this is another test"]
results = embeddings.embed_documents(texts)
print(results)

您可以手动传递端点参数,并手动设置在SambaStudio嵌入端点中的批量大小

embeddings = SambaStudioEmbeddings(
sambastudio_embeddings_base_url=sambastudio_base_url,
sambastudio_embeddings_base_uri=sambastudio_base_uri,
sambastudio_embeddings_project_id=sambastudio_project_id,
sambastudio_embeddings_endpoint_id=sambastudio_endpoint_id,
sambastudio_embeddings_api_key=sambastudio_api_key,
batch_size=32, # set depending on the deployed endpoint configuration
)

或者您可以使用部署的CoE中包含的嵌入模型专家

embeddings = SambaStudioEmbeddings(
batch_size=1,
model_kwargs={
"select_expert": "e5-mistral-7b-instruct",
},
)

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