Knowledge Graphs and LlamaIndex

Introduction to Knowledge Graphs and LlamaIndex

In today’s data-driven world, efficiently organizing and leveraging information is crucial for businesses and researchers. Two powerful tools that facilitate this are Knowledge Graphs and LlamaIndex.

Knowledge Graphs

A knowledge graph is an advanced method for structuring and connecting information. In a knowledge graph, data is represented in a graph format where nodes denote entities (such as people, places, or concepts) and edges represent the relationships between these entities. This structure enables efficient data storage, retrieval, and analysis, making it a vital component in various applications. Knowledge graphs underpin the functionality of advanced search engines, recommendation systems, and AI-driven decision-making processes. By offering a more connected and insightful understanding of data, they help address complex challenges across diverse sectors such as e-commerce, healthcare, and media, ultimately supporting more informed, data-driven decisions.

LlamaIndex

LlamaIndex is a state-of-the-art toolkit that enhances large language models (LLMs) with custom, private data through in-context learning. It achieves this by selecting the appropriate context from extensive knowledge bases using connectors or loaders from LlamaHub, and by employing various data structures (indices) to efficiently provide pre-processed data as Documents. Each type of index stores documents in different ways, such as through embeddings for vector searches, or as lists, graphs, or tree structures. These indices act as query interfaces to the LLM, embedding the relevant context transparently.

LlamaIndex not only improves the quality of responses generated by LLMs but also returns the documents used in constructing the answers. It supports complex querying capabilities such as chain of thought reasoning, compare/contrast queries, and natural language querying of databases, making it a powerful tool for extracting and synthesizing information from large datasets.

Implementation Overview

The implementation of LlamaIndex involves several key steps:

1. Setting Up the Environment: Configure the environment to use the LlamaIndex library with OpenAI or another suitable model.
2. Loading and Reading Documents: Use LlamaIndex to load documents and construct an index. For each document, fetch up to five triplets, and include semantic embeddings to facilitate semantically-driven queries.
3. Creating Retriever and Response Synthesizer: Create a retriever and a response synthesizer for querying the documents. Utilize a hybrid retriever mode for combined vector and keyword-based search and a tree summarize response mode for concise summaries.
4. Visualizing the Graph: Generate a visualization of the knowledge graph, which can be viewed in a browser by running the generated HTML file.

Example Implementation

Below is an example of how to implement LlamaIndex to construct and query a knowledge graph:

# Step 1: Set up the environment and import necessary components
from llamaindex import SimpleGraphStore, LlamaIndex, OpenAIModel

# Initialize the OpenAI model
model = OpenAIModel(api_key='your-api-key')

# Step 2: Load and read documents
documents = load_documents_from_source()  # Implement this function to load your documents
index = LlamaIndex(documents=documents, include_embeddings=True)

# Step 3: Create retriever and response synthesizer
retriever = index.create_retriever(retriever_mode='hybrid')
response_synthesizer = index.create_response_synthesizer(response_mode='tree_summarize')

# Example query
query = "What are the key insights from the documents?"
response = index.query(query, retriever=retriever, response_synthesizer=response_synthesizer)
print(response)

# Step 4: Visualize the graph
graph_store = SimpleGraphStore()
index.visualize_graph(graph_store, output_file='example.html')        

import os
from dotenv import load_dotenv
load_dotenv()
os.environ["OPENAI_API_KEY"] = os.getenv('OPENAI_API_KEY')

import logging
import sys
from llama_index import (
)
KnowledgeGraphIndex,
ServiceContext,
SimpleDirectoryReader,
from llama_index.storage.storage_context import StorageContext
from llama_index.graph_stores import SimpleGraphStore
from llama_index.query_engine import RetrieverQueryEngine
from llama_index import get_response_synthesizer
from llama_index.llms import OpenAI
from llama_index.llms import OpenAI
logging.basicConfig(
)
stream-sys.stdout, level=logging.INFO
llm = OpenAI (temperature=0, model="gpt-4")
service_context = ServiceContext.from_defaults (llm=llm, chunk_size=1024, chunl_overlap = 200)


documents = SimpleDirectoryReader(
    "text/"
).load_data()
print(f"Loaded {len(documents)} docs")


graph_store = SimpleGraphStore()
storage_context = StorageContext.from_defaults(graph_store=graph_store)




kg_index = KnowledgeGraphIndex.from_documents(
documents,
storage_context=storage_context,
max_triplets_per_chunk=5,
service_context-service_context,
include_embeddings=True,
)

kg_retriever = KGTableRetriever(
    similarity_top_k = 5,
    index=kg_index, retriever_mode="hybrid", include_text=False
)

response_synthesizer = get_response_synthesizer(
    service_context=service_context,
    response_mode="tree_summarize",
)

kg_query_engine = RetrieverQueryEngine(
    retriever=kg_retriever,
    response_synthesizer=response_synthesizer,
)



g = kg_index.get_networkx_graph()
net = Network(notebook=True, cdn_resources="in_line", directed=True)
net.from_nx(g)
net.show("example.html")

        

Conclusion

Implementing LlamaIndex to construct knowledge graphs significantly enhances data organization and represents a leap forward in accessing, understanding, and utilizing information. This innovative approach offers a structured and interconnected way to navigate vast data landscapes, providing valuable insights and transforming industries by deepening our comprehension of the world.