Skip to main content
Open In ColabOpen on GitHub

SambaNova

SambaNova's Sambastudio 是一个用于运行您自己的开源模型的平台

本示例介绍了如何使用LangChain与SambaNova嵌入模型进行交互

SambaStudio​

SambaStudio 允许您训练、运行批量推理作业，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！

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)
API Reference:SambaStudioEmbeddings

您可以手动传递端点参数并手动设置您在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在CoE中的嵌入模型专家

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

这个页面有帮助吗？