使用 PebbloRetrievalQA 的身份验证 RAG

PebbloRetrievalQA 是一个检索链，具有身份和语义强制功能，用于针对向量数据库进行问答。

本笔记本介绍了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。此向量存储将用作 PebbloRetrievalQA 中的检索器。

注意：建议使用 PebbloSafeLoader 作为在摄取端加载具有身份验证和语义元数据的文档的对应物。PebbloSafeLoader 保证文档的安全高效加载，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“ask”函数，其中包含所需的上下文（auth_context 和 semantic_context），然后提出我们的问题。

设置

依赖项

在此演练中，我们将使用 OpenAI LLM、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

这里我们使用 Qdrant 作为向量数据库；但是，您可以使用任何受支持的向量数据库。

PebbloRetrievalQA 链支持以下向量数据库

Qdrant
Pinecone
Postgres（利用 pgvector 扩展）

在元数据中加载具有授权和语义信息的向量数据库

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

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

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.")

API 参考：Qdrant | Document | OpenAIEmbeddings | OpenAI

Vectordb loaded.

使用身份强制进行检索

PebbloRetrievalQA 链使用 SafeRetrieval 来强制执行用于上下文学习的代码片段仅从授权用户的文档中检索。为了实现这一点，Gen-AI 应用程序需要为此检索链提供授权上下文。此 auth_context 应填充访问 Gen-AI 应用程序的用户的身份和授权组。

以下是 PebbloRetrievalQA 的示例代码，其中 user_auth（用户授权列表，可能包括其用户 ID 和他们所属的组）从访问 RAG 应用程序的用户处传递到 auth_context 中。

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())

API 参考：PebbloRetrievalQA | AuthContext | ChainInput

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 未包含在授权身份 ["finance-team", "exec-leadership"] 中，因此我们不应收到答案。

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 提供其他说明

您可以使用 PromptTemplate 为 LLM 提供其他说明，以生成自定义响应。

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)

API 参考：PromptTemplate

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.

使用语义强制进行检索

PebbloRetrievalQA 链使用 SafeRetrieval 来确保上下文中使用的代码片段仅从符合提供的语义上下文的文档中检索。为了实现这一点，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 PebbloRetrievalQA 的示例代码，其中包含 topics_to_deny 和 entities_to_deny。这些在 semantic_context 中传递到链输入。

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())

API 参考：PebbloRetrievalQA | ChainInput | SemanticContext

1. 无语义强制

由于未应用语义强制，因此系统应返回答案，而不会因与上下文关联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 financial-report 主题

数据已使用主题 ["financial-report"] 摄取。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 us-bank-account-number 实体

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

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.

步骤:​

设置​

依赖项​

身份感知数据摄取​

使用身份强制进行检索​

1. 授权用户的问题​

2. 未授权用户的问题​

3. 使用 PromptTemplate 提供其他说明​

3.1 授权用户的问题​

3.2 未授权用户的问题​

使用语义强制进行检索​

1. 无语义强制​

2. 拒绝 financial-report 主题​

3. 拒绝 us-bank-account-number 实体​

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步骤:

设置

依赖项