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Semantic Search Engine over a knowledge-base

Implementing Vector Search over a knowledge-base.

Implementing Retrieval-Augmented Generation (RAG) for Semantic Search over a Knowledge Base

Introduction

Retrieval-Augmented Generation (RAG) is a powerful solution that enhances the capabilities of Large Language Models (LLMs) by providing them with external data sources to improve context. This technique is particularly useful for tasks such as question answering over knowledge bases. By integrating a semantic search layer, RAG allows users to query a large corpus of documents using natural language, retrieving relevant documents based on the meaning of their queries, and generating precise responses through an LLM.

In this post, I'll walk through the implementation of an AI-powered search feature that utilizes RAG to improve user interactions with a knowledge base.

What is RAG?

RAG works by combining the strengths of two main components:

  1. Semantic Search: Finds relevant documents based on the meaning of the user's query rather than exact keyword matches.
  2. LLMs: Once relevant documents are retrieved, the LLM generates answers based on those documents.

Here’s how the flow works:

  • A user asks a question.
  • A semantic search is performed over a document corpus to find relevant materials.
  • The top results are then passed to the LLM.
  • The LLM generates an answer, incorporating the relevant documents into the context.

Key Components

1. Semantic Search

Traditional search engines use keyword matching, which has limitations when users don’t use exact keywords. Semantic search solves this problem by relying on the meaning of the text. It does this through vector embeddings, where textual data is converted into numerical vectors.

For example:

  • Text: "A cat jumps over a hedge."
  • Embedding: [1.5, -0.4, 7.2, 19.6, 20.2]

These embeddings allow for more nuanced matching between the query and the documents.

2. Vector Search

In vector search, the similarity between the query and the documents is calculated based on the distance between their vector representations. Algorithms like k-nearest neighbors (k-NN) help retrieve the most similar documents by ranking their similarity scores.

You can use tools like ElasticSearch, which supports vector search starting from version 8.0, to integrate this into existing infrastructures.

3. Large Language Models (LLMs)

LLMs, like GPT-4, are used to generate answers by processing the documents retrieved by the semantic search. LangChain is a useful framework that helps connect LLMs with the retrieved documents to provide answers with relevant document references.

System Design Overview

Our RAG system design involves the following steps:

  1. Search Query: User inputs a query.
  2. Semantic Search: The query is used to perform a semantic search on the knowledge base, retrieving the top documents.
  3. Embedding Lookup: The search engine compares the query embedding with document embeddings.
  4. LLM Query: The retrieved documents are sent to the LLM, along with the original question, to generate a natural language answer.
  5. Answer Generation: The LLM provides an answer, potentially with source links to the documents for further reading.

Implementation

1. Creating a Vector Index for Documents

We use ElasticSearch to store vector embeddings. The document embeddings are generated using a model like OpenAI's text-embedding-ada-002, which provides embeddings of size 1536.

When a document is created or updated, we generate the vector embeddings for the document content.

# Generate embeddings for documents using LangChain
from langchain.embeddings import OpenAIEmbeddings
 
# Assuming we have a list of document texts
documents = ["doc1 text", "doc2 text", "doc3 text"]
embeddings = OpenAIEmbeddings().embed_documents(documents)

2. Running Semantic Search

Once the vector index is set up, we can run a search query and return the top k most relevant documents using a cosine similarity search.

# ElasticVectorSearch client from LangChain
from langchain.vectorstores import ElasticVectorSearch
 
search_client = ElasticVectorSearch()
relevant_docs, scores = search_client.similarity_search_with_score(query="How do I implement RAG?", k=3)

3. Integrating with LLM for Q/A

Once we retrieve the relevant documents, we query an LLM like GPT-4 to generate a response. LangChain simplifies this with its pre-built chains for Q/A.

from langchain.chains.question_answering import load_qa_chain
 
# Initialize the chain with the chosen LLM
qa_chain = load_qa_chain(llm="gpt-4")
 
# Generate an answer
answer = qa_chain.run(question="How do I implement RAG?", documents=relevant_docs)

4. Caching Results

To optimize costs and improve speed, we implement semantic caching. This ensures that frequent or similar queries don't repeatedly hit the LLM but instead retrieve cached results.

Tools like GPTCache can be integrated with vector databases like Milvus for this purpose.

Cost Considerations

When using LLMs for RAG, costs primarily come from two sources: LLM requests and embedding generation. Here's a rough estimate:

LLM Request Costs

  • GPT-4: ~$0.06 per 1000 tokens
  • GPT-3.5: ~$0.002 per 1000 tokens Embedding Costs Using OpenAI’s text-embedding-ada-002, the cost is ~$0.0004 per 1000 tokens.

Conclusion

By implementing a Retrieval-Augmented Generation (RAG) system, we can significantly improve the efficiency and accuracy of search systems over large knowledge bases. Using tools like ElasticSearch for vector search and LangChain for integrating LLMs, we can build a robust AI-powered question/answering system.

Through this method, users can query complex datasets using natural language and receive contextually accurate answers powered by state-of-the-art AI models.