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Contextual AI

Contextual AI 提供最先进的 RAG 组件，专门为准确可靠的企业级 AI 应用设计。我们的 LangChain 集成提供了针对我们专用模型的独立 API 端点。

  • 接地语言模型 (GLM)：世界上最可靠的语言模型，旨在通过优先确保对检索到的知识的忠实度来最小化幻觉。GLM 以内联归因的方式提供卓越的事实准确性，使其成为可靠性至关重要的企业级 RAG 和智能体应用的理想选择。

  • 指令遵循重排器：第一个遵循自定义指令的重排器，可根据新近度、来源或文档类型等特定标准智能地优先排序文档。我们的重排器在行业基准测试中表现优于竞争对手，解决了企业知识库中信息冲突的挑战。

Contextual AI 由 RAG 技术的发明者创立，其专业组件可帮助创新团队加速开发可投入生产的 RAG 智能体，从而提供卓越准确性的回复。

接地语言模型 (GLM)​

接地语言模型 (GLM) 专门设计用于最小化企业级 RAG 和智能体应用中的幻觉。GLM 提供：

  • 在 FACTS 基准测试中表现强劲，事实准确率达到 88% (查看基准测试结果)
  • 严格基于所提供知识来源的回复，并带有内联归因 (阅读产品详情)
  • 精确的来源引用直接集成到生成的回复中
  • 优先考虑检索到的上下文而非参数化知识 (查看技术概览)
  • 当信息不可用时，明确承认不确定性

GLM 可作为 RAG 管道中通用 LLM 的直接替代品，显著提高关键企业应用的可靠性。

指令遵循重排器​

世界上第一个指令遵循重排器以史无前例的控制和准确性革新了文档排序。主要功能包括：

  • 自然语言指令，可根据新近度、来源、元数据等优先排序文档 (查看工作原理)
  • 在 BEIR 基准测试中表现卓越，得分为 61.2，显著超越竞争对手 (查看基准数据)
  • 智能解决来自多个知识源的冲突信息
  • 无缝集成，可直接替代现有重排器
  • 通过自然语言命令动态控制文档排序

该重排器擅长处理可能包含矛盾信息的企业知识库，允许您准确指定在各种场景下哪些来源应优先。

将Contextual AI与LangChain结合使用​

详情请点击这里。

此集成允许您轻松将 Contextual AI 的 GLM 和指令遵循重排器集成到您的 LangChain 工作流程中。GLM 确保您的应用程序提供严格可靠的回复，而重排器则通过智能地优先排序最相关的文档来显著提高检索质量。

无论您是为受监管行业还是注重安全的环境构建应用程序，Contextual AI 都能提供您的企业用例所需的准确性、控制和可靠性。

立即开始免费试用，体验针对企业级 AI 应用最可靠的语言模型和指令遵循重排器。

接地语言模型​

# Integrating the Grounded Language Model
import getpass
import os

from langchain_contextual import ChatContextual

# Set credentials
if not os.getenv("CONTEXTUAL_AI_API_KEY"):
    os.environ["CONTEXTUAL_AI_API_KEY"] = getpass.getpass(
        "Enter your Contextual API key: "
    )

# initialize Contextual llm
llm = ChatContextual(
    model="v1",
    api_key="",
)
# include a system prompt (optional)
system_prompt = "You are a helpful assistant that uses all of the provided knowledge to answer the user's query to the best of your ability."

# provide your own knowledge from your knowledge-base here in an array of string
knowledge = [
    "There are 2 types of dogs in the world: good dogs and best dogs.",
    "There are 2 types of cats in the world: good cats and best cats.",
]

# create your message
messages = [
    ("human", "What type of cats are there in the world and what are the types?"),
]

# invoke the GLM by providing the knowledge strings, optional system prompt
# if you want to turn off the GLM's commentary, pass True to the `avoid_commentary` argument
ai_msg = llm.invoke(
    messages, knowledge=knowledge, system_prompt=system_prompt, avoid_commentary=True
)

print(ai_msg.content)
According to the information available, there are two types of cats in the world:

1. Good cats
2. Best cats

指令遵循重排器​

import getpass
import os

from langchain_contextual import ContextualRerank

if not os.getenv("CONTEXTUAL_AI_API_KEY"):
    os.environ["CONTEXTUAL_AI_API_KEY"] = getpass.getpass(
        "Enter your Contextual API key: "
    )


api_key = ""
model = "ctxl-rerank-en-v1-instruct"

compressor = ContextualRerank(
    model=model,
    api_key=api_key,
)

from langchain_core.documents import Document

query = "What is the current enterprise pricing for the RTX 5090 GPU for bulk orders?"
instruction = "Prioritize internal sales documents over market analysis reports. More recent documents should be weighted higher. Enterprise portal content supersedes distributor communications."

document_contents = [
    "Following detailed cost analysis and market research, we have implemented the following changes: AI training clusters will see a 15% uplift in raw compute performance, enterprise support packages are being restructured, and bulk procurement programs (100+ units) for the RTX 5090 Enterprise series will operate on a $2,899 baseline.",
    "Enterprise pricing for the RTX 5090 GPU bulk orders (100+ units) is currently set at $3,100-$3,300 per unit. This pricing for RTX 5090 enterprise bulk orders has been confirmed across all major distribution channels.",
    "RTX 5090 Enterprise GPU requires 450W TDP and 20% cooling overhead.",
]

metadata = [
    {
        "Date": "January 15, 2025",
        "Source": "NVIDIA Enterprise Sales Portal",
        "Classification": "Internal Use Only",
    },
    {"Date": "11/30/2023", "Source": "TechAnalytics Research Group"},
    {
        "Date": "January 25, 2025",
        "Source": "NVIDIA Enterprise Sales Portal",
        "Classification": "Internal Use Only",
    },
]

documents = [
    Document(page_content=content, metadata=metadata[i])
    for i, content in enumerate(document_contents)
]
reranked_documents = compressor.compress_documents(
    query=query,
    instruction=instruction,
    documents=documents,
)
API 参考：Document