Contextual AI

Contextual AI 提供最先进的 RAG 组件，专为准确可靠的企业 AI 应用程序而设计。我们的 LangChain 集成公开了我们专用模型的独立 API 端点

Grounded Language Model (GLM)：世界上最扎实的基础语言模型，通过优先考虑对检索知识的忠实度来最大限度地减少幻觉。 GLM 提供卓越的事实准确性和内联归因，使其成为可靠性至关重要的企业 RAG 和代理应用程序的理想选择。
指令跟随重排序器：第一个遵循自定义指令的重排序器，可以根据特定标准（如新近度、来源或文档类型）智能地对文档进行优先级排序。我们的重排序器在行业基准测试中优于竞争对手，解决了企业知识库中冲突信息带来的挑战。

Contextual AI 的专业组件由 RAG 技术的发明者创立，可帮助创新团队加速开发生产就绪的 RAG 代理，从而提供异常准确的响应。

Grounded Language Model (GLM)

Grounded Language Model (GLM) 专门设计用于最大限度地减少企业 RAG 和代理应用程序中的幻觉。 GLM 提供

在 FACTS 基准测试中表现强劲，事实准确率达 88%（查看基准测试结果）
响应严格基于提供的知识来源，并带有内联归因（阅读产品详情）
精确的来源引文直接集成在生成的响应中
优先考虑检索到的上下文而不是参数知识（查看技术概述）
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，从而显著提高任务关键型企业应用程序的可靠性。

指令跟随重排序器

世界上第一个指令跟随重排序器以前所未有的控制和准确性彻底改变了文档排序。主要功能包括

自然语言指令，用于根据新近度、来源、元数据等对文档进行优先级排序（了解其工作原理）
在 BEIR 基准测试中表现出色，得分为 61.2，显著优于竞争对手（查看基准测试数据）
智能解决来自多个知识来源的冲突信息
作为现有重排序器的直接替代品实现无缝集成
通过自然语言命令动态控制文档排序

该重排序器擅长处理可能包含矛盾信息的企业知识库，使您可以准确指定在各种情况下哪些来源应优先考虑。

将 Contextual AI 与 LangChain 结合使用

请参阅此处了解详情。

此集成使您可以轻松地将 Contextual AI 的 GLM 和指令跟随重排序器集成到您的 LangChain 工作流程中。 GLM 确保您的应用程序提供严格基于事实的响应，而重排序器通过智能地优先考虑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、控制性和可靠性。

立即开始免费试用，体验最扎实的基础语言模型和指令跟随重排序器，用于企业 AI 应用程序。

Grounded Language Model

# 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

指令跟随重排序器

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：文档 (Document)

Grounded Language Model (GLM)​

指令跟随重排序器​

将 Contextual AI 与 LangChain 结合使用​

Grounded Language Model​

指令跟随重排序器​

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Grounded Language Model (GLM)

指令跟随重排序器

将 Contextual AI 与 LangChain 结合使用

Grounded Language Model

指令跟随重排序器