启用身份的RAG使用PebbloRetrievalQA

PebbloRetrievalQA是一个具有身份和语义强制的检索链，用于问答针对向量数据库。

本笔记本涵盖如何使用身份和语义强制（拒绝主题/实体）检索文档。有关Pebblo及其SafeRetriever功能的更多详细信息，请访问Pebblo文档

步骤：

加载文档： 我们将带有授权和语义元数据的文档加载到内存中的Qdrant向量存储中。此向量存储将作为PebbloRetrievalQA中的检索器使用。

注意： 建议使用PebbloSafeLoader作为加载带有身份验证和语义元数据的文档的对应工具。PebbloSafeLoader保证安全高效地加载文档，同时保持元数据的完整性。

测试强制机制： 我们将分别测试身份和语义强制。对于每个用例，我们将定义一个特定的“请求”函数，包含所需的上下文（auth_context 和 semantic_context），然后提出我们的问题。

设置

依赖项

在本次演示中，我们将使用 OpenAI 大型语言模型、OpenAI 嵌入和 Qdrant 向量存储。

%pip install --upgrade --quiet langchain langchain_core langchain-community langchain-openai qdrant_client

身份感知数据摄取

在这里，我们使用 Qdrant 作为向量数据库；然而，您可以使用任何支持的向量数据库。

PebbloRetrievalQA 链支持以下向量数据库：

Qdrant
Pinecone
Postgres（利用 pgvector 扩展）

使用授权和语义信息加载向量数据库：

在此步骤中，我们将源文档的授权和语义信息捕获到 VectorDB 条目的元数据中的 authorized_identities、pebblo_semantic_topics 和 pebblo_semantic_entities 字段中，每个块都有一个。

注意：要使用 PebbloRetrievalQA 链，您必须始终将授权和语义元数据放置在指定字段中。这些字段必须包含字符串列表。

<!--IMPORTS:[{"imported": "Qdrant", "source": "langchain_community.vectorstores.qdrant", "docs": "https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.qdrant.Qdrant.html", "title": "Identity-enabled RAG using PebbloRetrievalQA"}, {"imported": "Document", "source": "langchain_core.documents", "docs": "https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html", "title": "Identity-enabled RAG using PebbloRetrievalQA"}, {"imported": "OpenAIEmbeddings", "source": "langchain_openai.embeddings", "docs": "https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html", "title": "Identity-enabled RAG using PebbloRetrievalQA"}, {"imported": "OpenAI", "source": "langchain_openai.llms", "docs": "https://python.langchain.com/api_reference/openai/llms/langchain_openai.llms.base.OpenAI.html", "title": "Identity-enabled RAG using PebbloRetrievalQA"}]-->
from langchain_community.vectorstores.qdrant import Qdrant
from langchain_core.documents import Document
from langchain_openai.embeddings import OpenAIEmbeddings
from langchain_openai.llms import OpenAI

llm = OpenAI()
embeddings = OpenAIEmbeddings()
collection_name = "pebblo-identity-and-semantic-rag"

page_content = """
**ACME Corp Financial Report**

**Overview:**
ACME Corp, a leading player in the merger and acquisition industry, presents its financial report for the fiscal year ending December 31, 2020. 
Despite a challenging economic landscape, ACME Corp demonstrated robust performance and strategic growth.

**Financial Highlights:**
Revenue soared to $50 million, marking a 15% increase from the previous year, driven by successful deal closures and expansion into new markets. 
Net profit reached $12 million, showcasing a healthy margin of 24%.

**Key Metrics:**
Total assets surged to $80 million, reflecting a 20% growth, highlighting ACME Corp's strong financial position and asset base. 
Additionally, the company maintained a conservative debt-to-equity ratio of 0.5, ensuring sustainable financial stability.

**Future Outlook:**
ACME Corp remains optimistic about the future, with plans to capitalize on emerging opportunities in the global M&A landscape. 
The company is committed to delivering value to shareholders while maintaining ethical business practices.

**Bank Account Details:**
For inquiries or transactions, please refer to ACME Corp's US bank account:
Account Number: 123456789012
Bank Name: Fictitious Bank of America
"""

documents = [
    Document(
        **{
            "page_content": page_content,
            "metadata": {
                "pebblo_semantic_topics": ["financial-report"],
                "pebblo_semantic_entities": ["us-bank-account-number"],
                "authorized_identities": ["finance-team", "exec-leadership"],
                "page": 0,
                "source": "https://drive.google.com/file/d/xxxxxxxxxxxxx/view",
                "title": "ACME Corp Financial Report.pdf",
            },
        }
    )
]

vectordb = Qdrant.from_documents(
    documents,
    embeddings,
    location=":memory:",
    collection_name=collection_name,
)

print("Vectordb loaded.")

Vectordb loaded.

具有身份强制的检索

PebbloRetrievalQA链使用SafeRetrieval来确保用于上下文的片段仅从用户授权的文档中检索。为此，Gen-AI应用程序需要为此检索链提供授权上下文。这个auth_context应该填充访问Gen-AI应用程序的用户的身份和授权组。

以下是带有user_auth（用户授权列表，可能包括他们的用户ID和他们所属的组）的PebbloRetrievalQA示例代码，从访问RAG应用程序的用户传入auth_context。

<!--IMPORTS:[{"imported": "PebbloRetrievalQA", "source": "langchain_community.chains", "docs": "https://python.langchain.com/api_reference/community/chains/langchain_community.chains.pebblo_retrieval.base.PebbloRetrievalQA.html", "title": "Identity-enabled RAG using PebbloRetrievalQA"}, {"imported": "AuthContext", "source": "langchain_community.chains.pebblo_retrieval.models", "docs": "https://python.langchain.com/api_reference/community/chains/langchain_community.chains.pebblo_retrieval.models.AuthContext.html", "title": "Identity-enabled RAG using PebbloRetrievalQA"}, {"imported": "ChainInput", "source": "langchain_community.chains.pebblo_retrieval.models", "docs": "https://python.langchain.com/api_reference/community/chains/langchain_community.chains.pebblo_retrieval.models.ChainInput.html", "title": "Identity-enabled RAG using PebbloRetrievalQA"}]-->
from langchain_community.chains import PebbloRetrievalQA
from langchain_community.chains.pebblo_retrieval.models import AuthContext, ChainInput

# Initialize PebbloRetrievalQA chain
qa_chain = PebbloRetrievalQA.from_chain_type(
    llm=llm,
    retriever=vectordb.as_retriever(),
    app_name="pebblo-identity-rag",
    description="Identity Enforcement app using PebbloRetrievalQA",
    owner="ACME Corp",
)


def ask(question: str, auth_context: dict):
    """
    Ask a question to the PebbloRetrievalQA chain
    """
    auth_context_obj = AuthContext(**auth_context) if auth_context else None
    chain_input_obj = ChainInput(query=question, auth_context=auth_context_obj)
    return qa_chain.invoke(chain_input_obj.dict())

1. 授权用户的问题

我们为授权身份["finance-team", "exec-leadership"]摄取了数据，因此具有授权身份/组finance-team的用户应该收到正确的答案。

auth = {
    "user_id": "finance-user@acme.org",
    "user_auth": [
        "finance-team",
    ],
}

question = "Share the financial performance of ACME Corp for the year 2020"
resp = ask(question, auth)
print(f"Question: {question}\n\nAnswer: {resp['result']}")

Question: Share the financial performance of ACME Corp for the year 2020

Answer: 
Revenue: $50 million (15% increase from previous year)
Net profit: $12 million (24% margin)
Total assets: $80 million (20% growth)
Debt-to-equity ratio: 0.5

2. 未授权用户的问题

由于用户的授权身份/组eng-support不包含在授权身份["finance-team", "exec-leadership"]中，我们不应该收到答案。

auth = {
    "user_id": "eng-user@acme.org",
    "user_auth": [
        "eng-support",
    ],
}

question = "Share the financial performance of ACME Corp for the year 2020"
resp = ask(question, auth)
print(f"Question: {question}\n\nAnswer: {resp['result']}")

Question: Share the financial performance of ACME Corp for the year 2020

Answer:  I don't know.

3. 使用PromptTemplate提供额外指令

您可以使用提示词模板为大型语言模型提供额外的指令，以生成自定义响应。

<!--IMPORTS:[{"imported": "PromptTemplate", "source": "langchain_core.prompts", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.prompt.PromptTemplate.html", "title": "Identity-enabled RAG using PebbloRetrievalQA"}]-->
from langchain_core.prompts import PromptTemplate

prompt_template = PromptTemplate.from_template(
    """
Answer the question using the provided context. 
If no context is provided, just say "I'm sorry, but that information is unavailable, or Access to it is restricted.".

Question: {question}
"""
)

question = "Share the financial performance of ACME Corp for the year 2020"
prompt = prompt_template.format(question=question)

3.1 授权用户的问题

auth = {
    "user_id": "finance-user@acme.org",
    "user_auth": [
        "finance-team",
    ],
}
resp = ask(prompt, auth)
print(f"Question: {question}\n\nAnswer: {resp['result']}")

Question: Share the financial performance of ACME Corp for the year 2020

Answer: 
Revenue soared to $50 million, marking a 15% increase from the previous year, and net profit reached $12 million, showcasing a healthy margin of 24%. Total assets also grew by 20% to $80 million, and the company maintained a conservative debt-to-equity ratio of 0.5.

3.2 未授权用户的问题

auth = {
    "user_id": "eng-user@acme.org",
    "user_auth": [
        "eng-support",
    ],
}
resp = ask(prompt, auth)
print(f"Question: {question}\n\nAnswer: {resp['result']}")

Question: Share the financial performance of ACME Corp for the year 2020

Answer: 
I'm sorry, but that information is unavailable, or Access to it is restricted.

语义强制检索

PebbloRetrievalQA链使用SafeRetrieval确保上下文中使用的片段仅从符合提供的语义上下文的文档中检索。提供的语义上下文。为实现这一点，Gen-AI应用程序必须为此检索链提供语义上下文。此semantic_context应包括应拒绝访问Gen-AI应用程序的用户的主题和实体。

以下是带有topics_to_deny和entities_to_deny的PebbloRetrievalQA示例代码。这些被传递到链输入的semantic_context中。

<!--IMPORTS:[{"imported": "PebbloRetrievalQA", "source": "langchain_community.chains", "docs": "https://python.langchain.com/api_reference/community/chains/langchain_community.chains.pebblo_retrieval.base.PebbloRetrievalQA.html", "title": "Identity-enabled RAG using PebbloRetrievalQA"}, {"imported": "ChainInput", "source": "langchain_community.chains.pebblo_retrieval.models", "docs": "https://python.langchain.com/api_reference/community/chains/langchain_community.chains.pebblo_retrieval.models.ChainInput.html", "title": "Identity-enabled RAG using PebbloRetrievalQA"}, {"imported": "SemanticContext", "source": "langchain_community.chains.pebblo_retrieval.models", "docs": "https://python.langchain.com/api_reference/community/chains/langchain_community.chains.pebblo_retrieval.models.SemanticContext.html", "title": "Identity-enabled RAG using PebbloRetrievalQA"}]-->
from typing import List, Optional

from langchain_community.chains import PebbloRetrievalQA
from langchain_community.chains.pebblo_retrieval.models import (
    ChainInput,
    SemanticContext,
)

# Initialize PebbloRetrievalQA chain
qa_chain = PebbloRetrievalQA.from_chain_type(
    llm=llm,
    retriever=vectordb.as_retriever(),
    app_name="pebblo-semantic-rag",
    description="Semantic Enforcement app using PebbloRetrievalQA",
    owner="ACME Corp",
)


def ask(
    question: str,
    topics_to_deny: Optional[List[str]] = None,
    entities_to_deny: Optional[List[str]] = None,
):
    """
    Ask a question to the PebbloRetrievalQA chain
    """
    semantic_context = dict()
    if topics_to_deny:
        semantic_context["pebblo_semantic_topics"] = {"deny": topics_to_deny}
    if entities_to_deny:
        semantic_context["pebblo_semantic_entities"] = {"deny": entities_to_deny}

    semantic_context_obj = (
        SemanticContext(**semantic_context) if semantic_context else None
    )
    chain_input_obj = ChainInput(query=question, semantic_context=semantic_context_obj)
    return qa_chain.invoke(chain_input_obj.dict())

1. 无语义强制

由于没有应用语义强制，系统应返回答案，而不因与上下文相关的语义标签而排除任何上下文。

topic_to_deny = []
entities_to_deny = []
question = "Share the financial performance of ACME Corp for the year 2020"
resp = ask(question, topics_to_deny=topic_to_deny, entities_to_deny=entities_to_deny)
print(
    f"Topics to deny: {topic_to_deny}\nEntities to deny: {entities_to_deny}\n"
    f"Question: {question}\nAnswer: {resp['result']}"
)

Topics to deny: []
Entities to deny: []
Question: Share the financial performance of ACME Corp for the year 2020
Answer: 
Revenue for ACME Corp increased by 15% to $50 million in 2020, with a net profit of $12 million and a strong asset base of $80 million. The company also maintained a conservative debt-to-equity ratio of 0.5.

2. 拒绝财务报告主题

数据已被摄取，主题为：["financial-report"]。因此，拒绝financial-report主题的应用程序不应收到答案。

topic_to_deny = ["financial-report"]
entities_to_deny = []
question = "Share the financial performance of ACME Corp for the year 2020"
resp = ask(question, topics_to_deny=topic_to_deny, entities_to_deny=entities_to_deny)
print(
    f"Topics to deny: {topic_to_deny}\nEntities to deny: {entities_to_deny}\n"
    f"Question: {question}\nAnswer: {resp['result']}"
)

Topics to deny: ['financial-report']
Entities to deny: []
Question: Share the financial performance of ACME Corp for the year 2020
Answer: 

Unfortunately, I do not have access to the financial performance of ACME Corp for the year 2020.

3. 拒绝美国银行账户号码实体

由于实体us-bank-account-number被拒绝，系统不应返回答案。

topic_to_deny = []
entities_to_deny = ["us-bank-account-number"]
question = "Share the financial performance of ACME Corp for the year 2020"
resp = ask(question, topics_to_deny=topic_to_deny, entities_to_deny=entities_to_deny)
print(
    f"Topics to deny: {topic_to_deny}\nEntities to deny: {entities_to_deny}\n"
    f"Question: {question}\nAnswer: {resp['result']}"
)

Topics to deny: []
Entities to deny: ['us-bank-account-number']
Question: Share the financial performance of ACME Corp for the year 2020
Answer:  I don't have information about ACME Corp's financial performance for 2020.

启用身份的RAG使用PebbloRetrievalQA

步骤：

设置

依赖项

身份感知数据摄取

具有身份强制的检索

1. 授权用户的问题

2. 未授权用户的问题

3. 使用PromptTemplate提供额外指令

3.1 授权用户的问题

3.2 未授权用户的问题

语义强制检索

1. 无语义强制

2. 拒绝财务报告主题

3. 拒绝美国银行账户号码实体

Was this page helpful?

You can also leave detailed feedback on GitHub.

步骤：​

设置​

依赖项​

身份感知数据摄取​

具有身份强制的检索​

1. 授权用户的问题​

2. 未授权用户的问题​

3. 使用PromptTemplate提供额外指令​

3.1 授权用户的问题​

3.2 未授权用户的问题​

语义强制检索​

1. 无语义强制​

2. 拒绝财务报告主题​

3. 拒绝美国银行账户号码实体​

Was this page helpful?

You can also leave detailed feedback on GitHub.

步骤：

设置

依赖项

身份感知数据摄取

具有身份强制的检索

1. 授权用户的问题

2. 未授权用户的问题

3. 使用PromptTemplate提供额外指令

3.1 授权用户的问题

3.2 未授权用户的问题

语义强制检索

1. 无语义强制

2. 拒绝财务报告主题

3. 拒绝美国银行账户号码实体