Identity-enabled RAG using PebbloRetrievalQA

PebbloRetrievalQA is a Retrieval chain with Identity & Semantic Enforcement for question-answering against a vector database.

This notebook covers how to retrieve documents using Identity & Semantic Enforcement (Deny Topics/Entities). For more details on Pebblo and its SafeRetriever feature visit Pebblo documentation

Steps:

Loading Documents: We will load documents with authorization and semantic metadata into an in-memory Qdrant vectorstore. This vectorstore will be used as a retriever in PebbloRetrievalQA.

Note: It is recommended to use PebbloSafeLoader as the counterpart for loading documents with authentication and semantic metadata on the ingestion side. PebbloSafeLoader guarantees the secure and efficient loading of documents while maintaining the integrity of the metadata.

Testing Enforcement Mechanisms: We will test Identity and Semantic Enforcement separately. For each use case, we will define a specific "ask" function with the required contexts (auth_context and semantic_context) and then pose our questions.

Setup

Dependencies

We'll use an OpenAI LLM, OpenAI embeddings and a Qdrant vector store in this walkthrough.

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

Identity-aware Data Ingestion

Here we are using Qdrant as a vector database; however, you can use any of the supported vector databases.

PebbloRetrievalQA chain supports the following vector databases:

Qdrant
Pinecone
Postgres(utilizing the pgvector extension)

Load vector database with authorization and semantic information in metadata:

In this step, we capture the authorization and semantic information of the source document into the authorized_identities, pebblo_semantic_topics, and pebblo_semantic_entities fields within the metadata of the VectorDB entry for each chunk.

NOTE: To use the PebbloRetrievalQA chain, you must always place authorization and semantic metadata in the specified fields. These fields must contain a list of strings.

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 Reference:Qdrant | Document | OpenAIEmbeddings | OpenAI

Vectordb loaded.

Retrieval with Identity Enforcement

PebbloRetrievalQA chain uses a SafeRetrieval to enforce that the snippets used for in-context are retrieved only from the documents authorized for the user. To achieve this, the Gen-AI application needs to provide an authorization context for this retrieval chain. This auth_context should be filled with the identity and authorization groups of the user accessing the Gen-AI app.

Here is the sample code for the PebbloRetrievalQA with user_auth(List of user authorizations, which may include their User ID and the groups they are part of) from the user accessing the RAG application, passed in auth_context.

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 Reference:PebbloRetrievalQA | AuthContext | ChainInput

1. Questions by Authorized User

We ingested data for authorized identities ["finance-team", "exec-leadership"], so a user with the authorized identity/group finance-team should receive the correct answer.

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. Questions by Unauthorized User

Since the user's authorized identity/group eng-support is not included in the authorized identities ["finance-team", "exec-leadership"], we should not receive an answer.

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. Using PromptTemplate to provide additional instructions

You can use PromptTemplate to provide additional instructions to the LLM for generating a custom response.

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 Reference:PromptTemplate

3.1 Questions by Authorized User

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 Questions by Unauthorized Users

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.

Retrieval with Semantic Enforcement

The PebbloRetrievalQA chain uses SafeRetrieval to ensure that the snippets used in context are retrieved only from documents that comply with the provided semantic context. To achieve this, the Gen-AI application must provide a semantic context for this retrieval chain. This semantic_context should include the topics and entities that should be denied for the user accessing the Gen-AI app.

Below is a sample code for PebbloRetrievalQA with topics_to_deny and entities_to_deny. These are passed in semantic_context to the chain input.

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 Reference:PebbloRetrievalQA | ChainInput | SemanticContext

1. Without semantic enforcement

Since no semantic enforcement is applied, the system should return the answer without excluding any context due to the semantic labels associated with the context.

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. Deny financial-report topic

Data has been ingested with the topics: ["financial-report"]. Therefore, an app that denies the financial-report topic should not receive an answer.

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. Deny us-bank-account-number entity

Since the entity us-bank-account-number is denied, the system should not return the answer.

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.

Steps:​

Setup​

Dependencies​

Identity-aware Data Ingestion​

Retrieval with Identity Enforcement​

1. Questions by Authorized User​

2. Questions by Unauthorized User​

3. Using PromptTemplate to provide additional instructions​

3.1 Questions by Authorized User​

3.2 Questions by Unauthorized Users​

Retrieval with Semantic Enforcement​

1. Without semantic enforcement​

2. Deny financial-report topic​

3. Deny us-bank-account-number entity​

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