Introduction: Automating Security Document Management
Managing security documents can be daunting, especially when the volume of data is large and complex. Traditional keyword-based searches often need to catch up, making it difficult to find relevant information quickly. By leveraging AI, particularly natural language processing (NLP) and vector databases, we can create an intelligent Q&A system that simplifies document management and enhances search capabilities. In this post, we’ll make an AI-powered Q&A system using LangChain, GPT, and VectorDB on AWS Serverless.
Solution Overview: Simplifying Complex Searches
The solution we’re building automates and simplifies searching through security documents. By using semantic search with a vector database, we can enable users to find relevant information based on the meaning of their queries rather than relying on exact keyword matches. The system is built on AWS’s serverless architecture, ensuring scalability and cost-effectiveness.
Step 1: Building the Vector Database with Faiss
Understanding Vector Databases and Semantic Similarity
Vector databases store data as high-dimensional vectors, making them ideal for semantic similarity searches. These vectors represent the meaning of documents and queries, allowing for more accurate information retrieval than traditional search methods.
Installing Necessary Libraries and Reading Documents
First, you must install the necessary libraries to build the vector database. This includes Faiss for vector search, OpenAI for generating embeddings, and LangChain for integrating the Q&A logic.
pip install faiss-cpu openai langchain
Once the libraries are installed, read the security documents that must be indexed. These documents will be converted into vector embeddings.
Creating the Vector Database with OpenAI Embeddings
Convert the documents into vector format using OpenAI’s embeddings. Faiss will then index these vectors, allowing fast and efficient similarity searches.
import faiss
from openai.embeddings_utils import get_embedding
# Example of creating vectors
documents = [“Document 1 content”, “Document 2 content”]
embeddings = [get_embedding(doc) for doc in documents]
# Create Faiss index
index = faiss.IndexFlatL2(len(embeddings[0]))
index.add(embeddings)
Step 2: Develop the Q&A Code with LangChain
Implementing Chatbot Logic with RetrievalQA
LangChain provides an easy way to integrate your vector database with a Q&A system. RetrievalQA lets you fetch the most relevant documents from the vector database and generate answers using GPT.
from langchain.retrievers import FaissRetriever
from langchain.chains import RetrievalQA
retriever = FaissRetriever(index=index)
qa_chain = RetrievalQA(retriever=retriever)
Using Prompt Templates to Optimize Responses
To optimize the responses, you can use prompt templates that structure the query passed to GPT. This ensures that the responses are relevant and well-formatted.
from langchain.prompts import PromptTemplate
template = PromptTemplate(
input_variables=[“question”],
template=”Given the following question: {question}, provide a concise and accurate answer.”
)
qa_chain.template = template
Step 3: Configuring the AWS Lambda Function
Installing Libraries and Using Lambda Layers
AWS Lambda is used to host the Q&A code. Since Lambda limits the size of deployment packages, you can use Lambda Layers to manage your dependencies.
# Example command to create a Lambda Layer
zip -r9 layer.zip python/
aws lambda publish-layer-version –layer-name my-layer –zip-file fileb://layer.zip
Managing Environment Variables and Authentication Tokens
To keep your API keys and environment variables secure, use AWS Secrets Manager or Lambda’s environment variable feature to manage them.
Step 4: Setting Up the AWS Infrastructure
Using EFS (Elastic File System) for Database Storage
Since Faiss requires a persistent file system, use EFS to store your vector database. EFS can be mounted directly to Lambda functions, ensuring your index is always accessible.
Configuring VPC (Virtual Private Cloud) and NAT Gateway
Set up a VPC and NAT Gateway to securely connect your Lambda function to the internet, allowing it to access external APIs like OpenAI.
Connecting EFS to Lambda Function and Uploading the Database
Mount the EFS to your Lambda function and upload the vector database files, ensuring they are available during execution.
Creating the API Gateway and Final Testing
Use AWS API Gateway to expose your Lambda function as a REST API. This will allow external applications to interact with your Q&A system.
aws apigateway create-rest-api –name “Q&A API”
Configuring API Gateway for External Access
Configure API Gateway with the necessary security settings, such as API keys or OAuth, to control access to your Q&A system.
Testing Lambda Integration and API with HTTP Calls
Finally, test your setup by making HTTP calls to the API Gateway endpoint to ensure everything works as expected.
Conclusion: An Intelligent Q&A for Security Documents
Summary of Steps and Key Components
In this guide, we’ve built an AI-powered Q&A system for security documents using LangChain, GPT, and Faiss on AWS serverless infrastructure. The key components included building a vector database, developing the Q&A logic, configuring AWS Lambda, and setting up the necessary AWS infrastructure.
Potential Improvements and Further Applications
Improvements include expanding the vector database to cover more document types or integrating additional AI models for even better accuracy. This solution can also be adapted for other industries that require complex document management and search capabilities.
References
Use LangChain and vector search on Amazon DocumentDB to build a generative AI chatbot.