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docs: Added providers page for Pebblo and docs for PebbloRetrievalQA (#20746)
- **Description:** Added providers page for Pebblo and docs for PebbloRetrievalQA - **Issue:** NA - **Dependencies:** None - **Unit tests**: NA
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Rajendra Kadam
GitHub
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docs/docs/integrations/providers/pebblo/index.md
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docs/docs/integrations/providers/pebblo/index.md
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# Pebblo
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[Pebblo](https://www.daxa.ai/pebblo) enables developers to safely load and retrieve data to promote their Gen AI app to deployment without
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worrying about the organization’s compliance and security requirements. The Pebblo SafeLoader identifies semantic topics and entities found in the
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loaded data and the Pebblo SafeRetriever enforces identity and semantic controls on the retrieved context. The results are
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summarized on the UI or a PDF report.
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## Pebblo Overview:
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`Pebblo` provides a safe way to load and retrieve data for Gen AI applications.
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It includes:
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1. **Identity-aware Safe Loader** that loads data and identifies semantic topics and entities.
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2. **SafeRetrieval** that enforces identity and semantic controls on the retrieved context.
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3. **User Data Report** that summarizes the data loaded and retrieved.
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## Example Notebooks
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For a more detailed examples of using Pebblo, see the following notebooks:
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* [PebbloSafeLoader](/docs/integrations/document_loaders/pebblo) shows how to use Pebblo loader to safely load data.
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* [PebbloRetrievalQA](/docs/integrations/providers/pebblo/pebblo_retrieval_qa) shows how to use Pebblo retrieval QA chain to safely retrieve data.
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@ -0,0 +1,584 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "3ce451e9-f8f1-4f27-8c6b-4a93a406d504",
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"metadata": {},
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"source": [
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"# Identity-enabled RAG using PebbloRetrievalQA\n",
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"\n",
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"> PebbloRetrievalQA is a Retrieval chain with Identity & Semantic Enforcement for question-answering\n",
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"against a vector database.\n",
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"\n",
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"This notebook covers how to retrieve documents using Identity & Semantic Enforcement (Deny Topics/Entities).\n",
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"For more details on Pebblo and its SafeRetriever feature visit [Pebblo documentation](https://daxa-ai.github.io/pebblo/retrieval_chain/)\n",
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"\n",
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"### Steps:\n",
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"\n",
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"1. **Loading Documents:**\n",
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"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. \n",
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"\n",
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"> **Note:** It is recommended to use [PebbloSafeLoader](https://daxa-ai.github.io/pebblo/rag) 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.\n",
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"\n",
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"\n",
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"\n",
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"2. **Testing Enforcement Mechanisms**:\n",
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" 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.\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4ee16b6b-5dac-4b5c-bb69-3ec87398a33c",
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"metadata": {},
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"source": [
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"## Setup\n",
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"\n",
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"### Dependencies\n",
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"\n",
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"We'll use an OpenAI LLM, OpenAI embeddings and a Qdrant vector store in this walkthrough.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e68494fa-f387-4481-9a6c-58294865d7b7",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet langchain langchain_core langchain-community langchain-openai qdrant_client"
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]
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},
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{
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"cell_type": "markdown",
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"id": "61498d51-0c38-40e2-adcd-19dfdf4d37ef",
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"metadata": {},
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"source": [
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"### Identity-aware Data Ingestion\n",
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"\n",
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"Here we are using Qdrant as a vector database; however, you can use any of the supported vector databases.\n",
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"\n",
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"**PebbloRetrievalQA chain supports the following vector databases:**\n",
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"- Qdrant\n",
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"- Pinecone\n",
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"\n",
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"\n",
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"**Load vector database with authorization and semantic information in metadata:**\n",
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"\n",
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"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. \n",
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"\n",
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"\n",
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"*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.*"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "ae4fcbc1-bdc3-40d2-b2df-8c82cad1f89c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Vectordb loaded.\n"
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]
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}
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],
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"source": [
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"from langchain_community.vectorstores.qdrant import Qdrant\n",
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"from langchain_core.documents import Document\n",
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"from langchain_openai.embeddings import OpenAIEmbeddings\n",
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"from langchain_openai.llms import OpenAI\n",
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"\n",
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"llm = OpenAI()\n",
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"embeddings = OpenAIEmbeddings()\n",
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"collection_name = \"pebblo-identity-and-semantic-rag\"\n",
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"\n",
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"page_content = \"\"\"\n",
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"**ACME Corp Financial Report**\n",
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"\n",
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"**Overview:**\n",
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"ACME Corp, a leading player in the merger and acquisition industry, presents its financial report for the fiscal year ending December 31, 2020. \n",
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"Despite a challenging economic landscape, ACME Corp demonstrated robust performance and strategic growth.\n",
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"\n",
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"**Financial Highlights:**\n",
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"Revenue soared to $50 million, marking a 15% increase from the previous year, driven by successful deal closures and expansion into new markets. \n",
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"Net profit reached $12 million, showcasing a healthy margin of 24%.\n",
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"\n",
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"**Key Metrics:**\n",
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"Total assets surged to $80 million, reflecting a 20% growth, highlighting ACME Corp's strong financial position and asset base. \n",
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"Additionally, the company maintained a conservative debt-to-equity ratio of 0.5, ensuring sustainable financial stability.\n",
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"\n",
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"**Future Outlook:**\n",
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"ACME Corp remains optimistic about the future, with plans to capitalize on emerging opportunities in the global M&A landscape. \n",
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"The company is committed to delivering value to shareholders while maintaining ethical business practices.\n",
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"\n",
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"**Bank Account Details:**\n",
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"For inquiries or transactions, please refer to ACME Corp's US bank account:\n",
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"Account Number: 123456789012\n",
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"Bank Name: Fictitious Bank of America\n",
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"\"\"\"\n",
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"\n",
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"documents = [\n",
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" Document(\n",
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" **{\n",
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" \"page_content\": page_content,\n",
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" \"metadata\": {\n",
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" \"pebblo_semantic_topics\": [\"financial-report\"],\n",
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" \"pebblo_semantic_entities\": [\"us-bank-account-number\"],\n",
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" \"authorized_identities\": [\"finance-team\", \"exec-leadership\"],\n",
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" \"page\": 0,\n",
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" \"source\": \"https://drive.google.com/file/d/xxxxxxxxxxxxx/view\",\n",
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" \"title\": \"ACME Corp Financial Report.pdf\",\n",
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" },\n",
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" }\n",
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" )\n",
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"]\n",
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"\n",
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"vectordb = Qdrant.from_documents(\n",
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" documents,\n",
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" embeddings,\n",
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" location=\":memory:\",\n",
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" collection_name=collection_name,\n",
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")\n",
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"\n",
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"print(\"Vectordb loaded.\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f630bb8b-67ba-41f9-8715-76d006207e75",
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"metadata": {},
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"source": [
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"## Retrieval with Identity Enforcement\n",
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"\n",
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"PebbloRetrievalQA chain uses a SafeRetrieval to enforce that the snippets used for in-context are retrieved only from the documents authorized for the user. \n",
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"To achieve this, the Gen-AI application needs to provide an authorization context for this retrieval chain. \n",
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"This *auth_context* should be filled with the identity and authorization groups of the user accessing the Gen-AI app.\n",
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"\n",
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"\n",
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"Here is the sample code for the `PebbloRetrievalQA` with `user_auth`(List of user authorizations, which may include their User ID and \n",
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" the groups they are part of) from the user accessing the RAG application, passed in `auth_context`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "e978bee6-3a8c-459f-ab82-d380d7499b36",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.chains import PebbloRetrievalQA\n",
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"from langchain_community.chains.pebblo_retrieval.models import AuthContext, ChainInput\n",
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"\n",
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"# Initialize PebbloRetrievalQA chain\n",
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"qa_chain = PebbloRetrievalQA.from_chain_type(\n",
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" llm=llm,\n",
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" retriever=vectordb.as_retriever(),\n",
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" app_name=\"pebblo-identity-rag\",\n",
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" description=\"Identity Enforcement app using PebbloRetrievalQA\",\n",
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" owner=\"ACME Corp\",\n",
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")\n",
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"\n",
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"\n",
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"def ask(question: str, auth_context: dict):\n",
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" \"\"\"\n",
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" Ask a question to the PebbloRetrievalQA chain\n",
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" \"\"\"\n",
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" auth_context_obj = AuthContext(**auth_context) if auth_context else None\n",
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" chain_input_obj = ChainInput(query=question, auth_context=auth_context_obj)\n",
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" return qa_chain.invoke(chain_input_obj.dict())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7a267e96-70cb-468f-b830-83b65e9b7f6f",
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"metadata": {},
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"source": [
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"### 1. Questions by Authorized User\n",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "2688fc18-1eac-45a5-be55-aabbe6b25af5",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Question: Share the financial performance of ACME Corp for the year 2020\n",
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"\n",
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"Answer: \n",
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"Revenue: $50 million (15% increase from previous year)\n",
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"Net profit: $12 million (24% margin)\n",
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"Total assets: $80 million (20% growth)\n",
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"Debt-to-equity ratio: 0.5\n"
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]
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}
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],
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"source": [
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"auth = {\n",
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" \"user_id\": \"finance-user@acme.org\",\n",
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" \"user_auth\": [\n",
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" \"finance-team\",\n",
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" ],\n",
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"}\n",
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"\n",
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"question = \"Share the financial performance of ACME Corp for the year 2020\"\n",
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"resp = ask(question, auth)\n",
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"print(f\"Question: {question}\\n\\nAnswer: {resp['result']}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b4db6566-6562-4a49-b19c-6d99299b374e",
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"metadata": {},
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"source": [
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"### 2. Questions by Unauthorized User\n",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "2d736ce3-6e05-48d3-a5e1-fb4e7cccc1ee",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Question: Share the financial performance of ACME Corp for the year 2020\n",
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"\n",
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"Answer: I don't know.\n"
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]
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}
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],
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"source": [
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"auth = {\n",
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" \"user_id\": \"eng-user@acme.org\",\n",
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" \"user_auth\": [\n",
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" \"eng-support\",\n",
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" ],\n",
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"}\n",
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"\n",
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"question = \"Share the financial performance of ACME Corp for the year 2020\"\n",
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"resp = ask(question, auth)\n",
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"print(f\"Question: {question}\\n\\nAnswer: {resp['result']}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "33a8afe1-3071-4118-9714-a17cba809ee4",
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"metadata": {},
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"source": [
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"### 3. Using PromptTemplate to provide additional instructions\n",
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"You can use PromptTemplate to provide additional instructions to the LLM for generating a custom response."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "59c055ba-fdd1-48c6-9bc9-2793eb47438d",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.prompts import PromptTemplate\n",
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"\n",
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"prompt_template = PromptTemplate.from_template(\n",
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" \"\"\"\n",
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"Answer the question using the provided context. \n",
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"If no context is provided, just say \"I'm sorry, but that information is unavailable, or Access to it is restricted.\".\n",
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"\n",
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"Question: {question}\n",
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"\"\"\"\n",
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")\n",
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"\n",
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"question = \"Share the financial performance of ACME Corp for the year 2020\"\n",
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"prompt = prompt_template.format(question=question)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c4d27c00-73d9-4ce8-bc70-29535deaf0e2",
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"metadata": {},
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"source": [
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"#### 3.1 Questions by Authorized User"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"id": "e68a13a4-b735-421d-9655-2a9a087ba9e5",
|
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"metadata": {},
|
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"outputs": [
|
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{
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"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
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"Question: Share the financial performance of ACME Corp for the year 2020\n",
|
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"\n",
|
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"Answer: \n",
|
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"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.\n"
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]
|
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}
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],
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"source": [
|
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"auth = {\n",
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" \"user_id\": \"finance-user@acme.org\",\n",
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" \"user_auth\": [\n",
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" \"finance-team\",\n",
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" ],\n",
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"}\n",
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"resp = ask(prompt, auth)\n",
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"print(f\"Question: {question}\\n\\nAnswer: {resp['result']}\")"
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]
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},
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{
|
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"cell_type": "markdown",
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||||
"id": "7b97a9ca-bdc6-400a-923d-65a8536658be",
|
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"metadata": {},
|
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"source": [
|
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"#### 3.2 Questions by Unauthorized Users"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"id": "438e48c6-96a2-4d5e-81db-47f8c8f37739",
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
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"Question: Share the financial performance of ACME Corp for the year 2020\n",
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"\n",
|
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"Answer: \n",
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"I'm sorry, but that information is unavailable, or Access to it is restricted.\n"
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]
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}
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],
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"source": [
|
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"auth = {\n",
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" \"user_id\": \"eng-user@acme.org\",\n",
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" \"user_auth\": [\n",
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" \"eng-support\",\n",
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" ],\n",
|
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"}\n",
|
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"resp = ask(prompt, auth)\n",
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"print(f\"Question: {question}\\n\\nAnswer: {resp['result']}\")"
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]
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},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "4306cab3-d070-405f-a23b-5c6011a61c50",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Retrieval with Semantic Enforcement"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1c3757cf-832f-483e-aafe-cb09b5130ec0",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The PebbloRetrievalQA chain uses SafeRetrieval to ensure that the snippets used in context are retrieved only from documents that comply with the\n",
|
||||
"provided semantic context.\n",
|
||||
"To achieve this, the Gen-AI application must provide a semantic context for this retrieval chain.\n",
|
||||
"This `semantic_context` should include the topics and entities that should be denied for the user accessing the Gen-AI app.\n",
|
||||
"\n",
|
||||
"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."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"id": "daf37bf7-9a16-4102-8893-5b698cae1b07",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from typing import List, Optional\n",
|
||||
"\n",
|
||||
"from langchain_community.chains import PebbloRetrievalQA\n",
|
||||
"from langchain_community.chains.pebblo_retrieval.models import (\n",
|
||||
" ChainInput,\n",
|
||||
" SemanticContext,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Initialize PebbloRetrievalQA chain\n",
|
||||
"qa_chain = PebbloRetrievalQA.from_chain_type(\n",
|
||||
" llm=llm,\n",
|
||||
" retriever=vectordb.as_retriever(),\n",
|
||||
" app_name=\"pebblo-semantic-rag\",\n",
|
||||
" description=\"Semantic Enforcement app using PebbloRetrievalQA\",\n",
|
||||
" owner=\"ACME Corp\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def ask(\n",
|
||||
" question: str,\n",
|
||||
" topics_to_deny: Optional[List[str]] = None,\n",
|
||||
" entities_to_deny: Optional[List[str]] = None,\n",
|
||||
"):\n",
|
||||
" \"\"\"\n",
|
||||
" Ask a question to the PebbloRetrievalQA chain\n",
|
||||
" \"\"\"\n",
|
||||
" semantic_context = dict()\n",
|
||||
" if topics_to_deny:\n",
|
||||
" semantic_context[\"pebblo_semantic_topics\"] = {\"deny\": topics_to_deny}\n",
|
||||
" if entities_to_deny:\n",
|
||||
" semantic_context[\"pebblo_semantic_entities\"] = {\"deny\": entities_to_deny}\n",
|
||||
"\n",
|
||||
" semantic_context_obj = (\n",
|
||||
" SemanticContext(**semantic_context) if semantic_context else None\n",
|
||||
" )\n",
|
||||
" chain_input_obj = ChainInput(query=question, semantic_context=semantic_context_obj)\n",
|
||||
" return qa_chain.invoke(chain_input_obj.dict())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "9718819b-f5cd-4212-9947-d18cd507c8b7",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 1. Without semantic enforcement\n",
|
||||
"\n",
|
||||
"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.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"id": "69158be1-f223-4d14-b61f-f4afdf5af526",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Topics to deny: []\n",
|
||||
"Entities to deny: []\n",
|
||||
"Question: Share the financial performance of ACME Corp for the year 2020\n",
|
||||
"Answer: \n",
|
||||
"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.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"topic_to_deny = []\n",
|
||||
"entities_to_deny = []\n",
|
||||
"question = \"Share the financial performance of ACME Corp for the year 2020\"\n",
|
||||
"resp = ask(question, topics_to_deny=topic_to_deny, entities_to_deny=entities_to_deny)\n",
|
||||
"print(\n",
|
||||
" f\"Topics to deny: {topic_to_deny}\\nEntities to deny: {entities_to_deny}\\n\"\n",
|
||||
" f\"Question: {question}\\nAnswer: {resp['result']}\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c8789c58-0d64-404e-bc09-92f6952022ac",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 2. Deny financial-report topic\n",
|
||||
"\n",
|
||||
"Data has been ingested with the topics: `[\"financial-report\"]`.\n",
|
||||
"Therefore, an app that denies the `financial-report` topic should not receive an answer."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"id": "9b17b2fc-eefb-4229-a41e-2f943d2eb48e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Topics to deny: ['financial-report']\n",
|
||||
"Entities to deny: []\n",
|
||||
"Question: Share the financial performance of ACME Corp for the year 2020\n",
|
||||
"Answer: \n",
|
||||
"\n",
|
||||
"Unfortunately, I do not have access to the financial performance of ACME Corp for the year 2020.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"topic_to_deny = [\"financial-report\"]\n",
|
||||
"entities_to_deny = []\n",
|
||||
"question = \"Share the financial performance of ACME Corp for the year 2020\"\n",
|
||||
"resp = ask(question, topics_to_deny=topic_to_deny, entities_to_deny=entities_to_deny)\n",
|
||||
"print(\n",
|
||||
" f\"Topics to deny: {topic_to_deny}\\nEntities to deny: {entities_to_deny}\\n\"\n",
|
||||
" f\"Question: {question}\\nAnswer: {resp['result']}\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "894f21b0-2913-4ef6-b5ed-cbca8f74214d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 3. Deny us-bank-account-number entity\n",
|
||||
"Since the entity `us-bank-account-number` is denied, the system should not return the answer."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"id": "2b8abce3-7af3-437f-8999-2866a4b9beda",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Topics to deny: []\n",
|
||||
"Entities to deny: ['us-bank-account-number']\n",
|
||||
"Question: Share the financial performance of ACME Corp for the year 2020\n",
|
||||
"Answer: I don't have information about ACME Corp's financial performance for 2020.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"topic_to_deny = []\n",
|
||||
"entities_to_deny = [\"us-bank-account-number\"]\n",
|
||||
"question = \"Share the financial performance of ACME Corp for the year 2020\"\n",
|
||||
"resp = ask(question, topics_to_deny=topic_to_deny, entities_to_deny=entities_to_deny)\n",
|
||||
"print(\n",
|
||||
" f\"Topics to deny: {topic_to_deny}\\nEntities to deny: {entities_to_deny}\\n\"\n",
|
||||
" f\"Question: {question}\\nAnswer: {resp['result']}\"\n",
|
||||
")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.18"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user