SambaNova

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

此示例介绍如何使用 LangChain 与 SambaNova 嵌入模型交互

SambaStudio

SambaStudio 允许您训练、运行批次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 SambaStudio 环境。有关更多信息，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 参考：SambaStudioEmbeddings

您可以手动传递端点参数并手动设置 SambaStudio 嵌入端点中的批次大小

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您的 CoE 中的嵌入模型专家

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

相关

• 嵌入模型 概念指南
• 嵌入模型 操作指南