LLaMA Index and PaLM2

Recent advances in large language models like PaLM, GPT-4 etc have opened up new possibilities for text search and information retrieval. In this post, we will look at how to build a simple query-based summarizer using the open source LLaMA Index library and PaLM model.

Overview

The core idea is:

• Load documents (webpages) into the index
• Use a LLM like PaLM to encode the documents into vectors
• Index these document vectors for fast nearest neighbor search
• Take a user query, encode it with the LLM, and find most relevant documents
• Extract relevant text snippets from these documents to generate a summary

This allows querying the document collection in natural language and getting a contextual summary as response.

Implementation

We will build the summarizer in Python using LLaMA Index and PaLM.

First install the deps:

pip install llama_index google-generativeai

from llama_index.llms.palm import PaLM
from llama_index import ServiceContext
from llama_index import VectorStoreIndex, download_loader
from IPython.display import display


llm = PaLM(api_key="<PaLM_API_KEY>")

SimpleWebPageReader = download_loader("SimpleWebPageReader")

loader = SimpleWebPageReader()
documents = loader.load_data(urls=['https://h3manth.com'])

service_context = ServiceContext.from_defaults(llm=llm, chunk_size=800, chunk_overlap=20)

index = VectorStoreIndex.from_documents(documents,service_context=service_context)

engine = index.as_query_engine()

response = engine.query("Summarize the website in 5 points")
display((f"{response}"))

This will print a 5 point summary extracted from the indexed document!

- Hemanth is a Google Developer Expert for Web and Payments.
- He is a TC39 delegate, working on JavaScript feature proposals.
- He is a DuckDuckGo community leader.
- He is a member of the Node.js Foundation.
- He hosts the TC39er.us podcast.

The key aspects are:

• Using PaLM's vector embedding API to encode text
• Using ServiceContext to set PaLM as the llm rather then OpenAI
• Building nearest neighbor index on document vectors
• Querying the index for relevant snippets
• Extracting and combining summary points

LLaMA Index provides the framework to build such a pipeline efficiently without much code.

Conclusion

In this way, leveraging large language models like PaLM with libraries like LLaMA Index makes it easy to build powerful semantic search and summarization applications. The capabilities of LLMs can be packaged into customizable products and solutions.

There is a lot more potential in combining retrieval with generative abilities for natural language use cases. Exciting times ahead!