Using LlamaIndex

LlamaIndex is a powerful tool for data indexing and retrieval that integrates seamlessly with MonsterAPI to implement Retrieval-Augmented Generation (RAG). This section outlines how to set up and utilize LlamaIndex for indexing data and our serverless LLM APIs.

Follow these steps to use LLM endpoints with LlamaIndex:

Step 1: Data Indexing with LlamaIndex

1. Prepare Your Data: Collect and clean your data sources (e.g., PDFs, HTML, Word documents).

2. Preprocess the Data: Convert the data into plain text and chunk it into manageable segments that fit within the model's context limits.

3. Create Vectors: Use LlamaIndex’s embedding functions to convert text chunks into vectors.

4. Build the Index: Create an index linking each chunk to its vector representation.

   Python

   import os
   from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
   from llama_index.core.node_parser import SentenceSplitter
   
   # Load and prepare data
   documents = SimpleDirectoryReader('path_to_your_data').load_data()
   nodes = SentenceSplitter().split(documents)
   
   # Create an index
   index = VectorStoreIndex.from_documents(nodes)
   

Step 2: Setting Up Retrieval with LlamaIndex

1. Encode the Query: Use the same embedding model to encode the user’s query.

2. Retrieve Relevant Chunks: Calculate similarity between the query vector and document vectors, and retrieve the top K relevant chunks.

   Python

   from llama_index.llms.monsterapi import MonsterLLM
   
   # Initialize the MonsterLLM
   llm = MonsterLLM(model_name="your_model_name_here")
   
   # Perform a query
   query_text = "Explain Retrieval-Augmented Generation"
   response = llm.query(query_text)
   print(response)
   

Step 3: Generating Responses with MonsterLLM

1. Combine Query and Context: Use the retrieved chunks to create a comprehensive prompt.

2. Generate Response: Feed the prompt into MonsterLLM to generate a relevant response.

   Python

   # Text Generation Example
   prompt = "Explain the concept of Retrieval-Augmented Generation."
   response = llm.complete(prompt)
   print(response)
   

By following these steps, you can efficiently utilize LlamaIndex with our platform to build a powerful RAG system. This setup allows for accurate and contextually enriched responses by leveraging indexed data and advanced generative models.