Introduction - What are LLM Chatbots and Why They Matter
In the rapidly evolving landscape of artificial intelligence, Large Language Model (LLM) chatbots have emerged as one of the most transformative technologies of our time. These sophisticated systems can understand human language, engage in meaningful conversations, and provide intelligent responses across a vast array of topics. However, building truly intelligent chatbots that can provide accurate, up-to-date, and contextually relevant information requires more than just a basic LLM implementation.
The challenge lies in the fact that while LLMs are incredibly powerful at understanding and generating human-like text, they have significant limitations when it comes to accessing current information or domain-specific knowledge that wasn't part of their training data. This is where Retrieval-Augmented Generation (RAG) and its advanced cousin, GraphRAG, come into play. These technologies bridge the gap between the general knowledge of LLMs and the specific, current information your chatbot needs to be truly useful.
Understanding Large Language Models (LLMs) - The Foundation
Before diving into RAG and GraphRAG, it's crucial to understand what Large Language Models are and how they function. An LLM is essentially a sophisticated neural network that has been trained on vast amounts of text data from the internet, books, articles, and other written sources. During this training process, the model learns patterns in language, understanding not just grammar and syntax, but also context, meaning, and even some level of reasoning.
Think of an LLM as an incredibly well-read individual who has absorbed information from millions of documents but can only recall what they learned during their "education" period. The model doesn't have the ability to look up new information or access real-time data. It operates purely on the patterns and knowledge it acquired during training, which typically has a knowledge cutoff date.
The power of LLMs lies in their ability to generate coherent, contextually appropriate responses based on the patterns they've learned. They can engage in conversations, answer questions, write creative content, and even perform complex reasoning tasks. However, this same characteristic also represents their primary limitation in practical applications.
The Problem with Basic LLMs - Limitations and Challenges
When you deploy a basic LLM as a chatbot, you quickly encounter several significant limitations that can severely impact its usefulness in real-world scenarios. The most prominent issue is the knowledge cutoff problem. Since LLMs are trained on data up to a specific point in time, they cannot provide information about events, developments, or changes that occurred after their training period. This means your chatbot might provide outdated information or simply state that it doesn't know about recent developments.
Another critical limitation is the lack of domain-specific knowledge. While LLMs have broad general knowledge, they often lack the deep, specialized information required for specific industries, companies, or technical domains. For instance, if you're building a chatbot for a software company, the LLM might not know about your company's specific products, internal processes, or proprietary technologies.
The hallucination problem represents another significant challenge. LLMs sometimes generate information that sounds plausible but is actually incorrect or entirely fabricated. This occurs because the model is designed to generate coherent responses even when it doesn't have accurate information about the topic at hand. In a business context, providing incorrect information can have serious consequences.
Additionally, basic LLMs cannot access external databases, APIs, or real-time information sources. They operate in isolation, unable to retrieve current stock prices, weather information, or any other dynamic data that might be crucial for your chatbot's functionality.
Introduction to RAG (Retrieval-Augmented Generation)
Retrieval-Augmented Generation, commonly known as RAG, represents a paradigm shift in how we approach LLM-based applications. RAG addresses the fundamental limitations of basic LLMs by combining the language generation capabilities of LLMs with the ability to retrieve relevant information from external knowledge sources in real-time.
The core concept behind RAG is elegantly simple yet powerful. Instead of relying solely on the LLM's pre-trained knowledge, RAG systems first search through external knowledge bases, documents, or databases to find information relevant to the user's query. This retrieved information is then provided to the LLM as additional context, enabling it to generate more accurate, current, and contextually appropriate responses.
Think of RAG as giving your LLM chatbot the ability to consult a vast library of up-to-date documents before answering any question. Just as a knowledgeable librarian would first research a topic before providing an answer, a RAG system retrieves relevant information and then uses that information to formulate a comprehensive response.
The beauty of RAG lies in its ability to keep the LLM's knowledge current without requiring expensive retraining. You can continuously update your knowledge base with new documents, and the RAG system will automatically incorporate this new information into its responses. This makes RAG particularly valuable for applications that require access to frequently changing information or domain-specific knowledge.
How RAG Works - The Technical Deep Dive
Understanding the technical mechanics of RAG is essential for building effective systems. The RAG process involves several distinct steps that work together to create an intelligent information retrieval and generation pipeline.
The process begins with document ingestion and preprocessing. Your knowledge base, which might consist of PDFs, web pages, databases, or any other text-based sources, needs to be processed and prepared for efficient retrieval. This involves breaking down large documents into smaller, manageable chunks. The chunking strategy is crucial because it determines how information is segmented and retrieved later.
Once documents are chunked, each piece of text is converted into a mathematical representation called an embedding vector. These embeddings capture the semantic meaning of the text in a high-dimensional space, allowing the system to understand conceptual similarity between different pieces of text. This process is performed using specialized embedding models that have been trained to create meaningful representations of text.
The embedding vectors are then stored in a vector database, which is optimized for similarity searches. When a user submits a query, the system converts the query into an embedding vector using the same embedding model used for the documents. The vector database then performs a similarity search to find the most relevant document chunks based on the mathematical distance between the query embedding and the document embeddings.
The retrieved relevant chunks are then combined with the original user query and fed into the LLM as context. The LLM uses this additional context to generate a response that is informed by the retrieved information, resulting in more accurate and relevant answers.
Building Your First RAG System - Step by Step Implementation
Let's walk through building a practical RAG system to demonstrate these concepts in action. This implementation will help you understand how all the components work together to create an intelligent chatbot.
The first step involves setting up the necessary dependencies and libraries. We'll use popular Python libraries that provide the core functionality needed for a RAG system. The following code example demonstrates the initial setup and imports required for our implementation.
import os
from langchain.document_loaders import PyPDFLoader, TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.schema import Document
This code imports the essential components we need for our RAG system. The document loaders allow us to read various file formats, the text splitter helps us chunk documents appropriately, embeddings convert text to vectors, the vector store manages our searchable knowledge base, and the retrieval chain orchestrates the entire RAG process.
Next, we need to implement the document loading and chunking functionality. This step is critical because how we split our documents directly impacts the quality of information retrieval. The following code demonstrates how to load documents and split them into appropriate chunks.
def load_and_split_documents(file_paths):
documents = []
for file_path in file_paths:
if file_path.endswith('.pdf'):
loader = PyPDFLoader(file_path)
elif file_path.endswith('.txt'):
loader = TextLoader(file_path)
else:
continue
docs = loader.load()
documents.extend(docs)
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
length_function=len,
separators=["\n\n", "\n", " ", ""]
)
split_documents = text_splitter.split_documents(documents)
return split_documents
This function handles the loading of multiple document types and splits them into chunks of approximately 1000 characters with a 200-character overlap between chunks. The overlap ensures that context isn't lost at chunk boundaries, which is important for maintaining coherent information retrieval.
The next crucial step involves creating embeddings and setting up the vector database. This is where we convert our text chunks into mathematical representations that can be efficiently searched. The following code shows how to create and populate a vector database.
def create_vector_database(documents, persist_directory="./chroma_db"):
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(
documents=documents,
embedding=embeddings,
persist_directory=persist_directory
)
vectorstore.persist()
return vectorstore
This function creates embeddings for all document chunks and stores them in a Chroma vector database. The persist_directory parameter ensures that our vector database is saved to disk, allowing us to reuse it without reprocessing all documents each time we run our application.
Now we can implement the core RAG functionality by creating a retrieval-based question-answering system. This component ties together the vector database with the LLM to create our intelligent chatbot.
def create_rag_chain(vectorstore):
llm = OpenAI(temperature=0.7)
retrieval_qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vectorstore.as_retriever(search_kwargs={"k": 3}),
return_source_documents=True
)
return retrieval_qa
This function creates a RetrievalQA chain that retrieves the top 3 most relevant document chunks for each query and uses them as context for the LLM to generate responses. The "stuff" chain type means all retrieved documents are included in a single prompt to the LLM.
Finally, we can put everything together to create a functional RAG chatbot. The following code demonstrates how to use all the components we've built to create an interactive system.
def main():
# Load and process documents
file_paths = ["document1.pdf", "document2.txt", "document3.pdf"]
documents = load_and_split_documents(file_paths)
# Create vector database
vectorstore = create_vector_database(documents)
# Create RAG chain
rag_chain = create_rag_chain(vectorstore)
# Interactive chat loop
print("RAG Chatbot is ready! Type 'quit' to exit.")
while True:
user_query = input("\nYou: ")
if user_query.lower() == 'quit':
break
result = rag_chain({"query": user_query})
print(f"\nBot: {result['result']}")
# Optionally show source documents
print("\nSources:")
for i, doc in enumerate(result['source_documents']):
print(f"{i+1}. {doc.page_content[:100]}...")
if __name__ == "__main__":
main()
This main function orchestrates the entire RAG system, creating an interactive chatbot that can answer questions based on the provided documents. The system retrieves relevant information and provides source citations, making it transparent about where the information comes from.
Understanding GraphRAG - The Next Evolution
While traditional RAG systems represent a significant improvement over basic LLMs, they still have limitations that GraphRAG addresses. GraphRAG, or Graph-based Retrieval-Augmented Generation, takes the concept of RAG further by incorporating graph-based knowledge representation and reasoning capabilities.
The fundamental difference between RAG and GraphRAG lies in how information is structured and retrieved. Traditional RAG treats documents as independent chunks of text and relies on semantic similarity for retrieval. GraphRAG, on the other hand, understands the relationships between different pieces of information and can perform more sophisticated reasoning across connected concepts.
In a GraphRAG system, information is represented as a knowledge graph where entities (people, places, concepts, etc.) are nodes, and relationships between them are edges. This graph structure allows the system to understand not just what information is relevant to a query, but how different pieces of information relate to each other and what insights can be derived from these relationships.
Consider a scenario where a user asks about the impact of a particular technology on multiple industries. A traditional RAG system might retrieve separate documents about the technology and each industry, but it might miss the nuanced connections between them. GraphRAG, however, can traverse the knowledge graph to understand how the technology relates to different industries and provide a more comprehensive, interconnected response.
GraphRAG also excels at handling complex queries that require multi-hop reasoning. For instance, if someone asks "What are the potential risks of implementing AI in healthcare given the recent regulatory changes in Europe?", GraphRAG can connect information about AI technologies, healthcare applications, regulatory frameworks, and European policy changes to provide a comprehensive answer that considers all these interconnected factors.
How GraphRAG Differs from Traditional RAG
The architectural differences between RAG and GraphRAG are substantial and impact every aspect of how these systems operate. Understanding these differences is crucial for determining which approach is most suitable for your specific use case.
In traditional RAG systems, the knowledge base consists of text chunks stored as embedding vectors in a vector database. Retrieval is based primarily on semantic similarity between the query and document chunks. While this approach is effective for many scenarios, it treats each piece of information in isolation and doesn't capture the complex relationships that exist between different concepts.
GraphRAG systems, in contrast, maintain a rich knowledge graph that explicitly represents entities and their relationships. This graph is typically constructed through entity extraction and relationship identification processes that analyze the source documents to identify key entities and how they relate to each other. The resulting graph becomes a structured representation of knowledge that can be queried and reasoned over.
The retrieval process in GraphRAG is fundamentally different. Instead of simply finding semantically similar text chunks, GraphRAG can perform graph traversals to find relevant information through relationship paths. This enables more sophisticated query understanding and can surface relevant information that might not be semantically similar to the query but is connected through the knowledge graph.
GraphRAG systems also typically employ more advanced reasoning capabilities. They can perform graph-based reasoning to infer new information from existing relationships, identify patterns across the knowledge graph, and provide explanations for their conclusions based on the relationship paths they traverse.
The generation phase in GraphRAG is also enhanced. The LLM receives not just relevant text chunks, but also structured information about entities, relationships, and the reasoning paths that led to the retrieved information. This richer context enables more nuanced and comprehensive responses.
Building a GraphRAG System - Implementation Guide
Implementing a GraphRAG system requires additional components and considerations compared to traditional RAG. The process involves entity extraction, relationship identification, graph construction, and graph-aware retrieval mechanisms.
The first step in building a GraphRAG system involves extracting entities and relationships from your documents. This process requires natural language processing techniques to identify important entities and understand how they relate to each other. The following code demonstrates a basic approach to entity extraction using spaCy.
import spacy
import networkx as nx
from collections import defaultdict
def extract_entities_and_relationships(documents):
nlp = spacy.load("en_core_web_sm")
entities = set()
relationships = []
for doc in documents:
nlp_doc = nlp(doc.page_content)
# Extract named entities
doc_entities = []
for ent in nlp_doc.ents:
if ent.label_ in ["PERSON", "ORG", "GPE", "PRODUCT", "EVENT"]:
entities.add((ent.text, ent.label_))
doc_entities.append(ent.text)
# Create co-occurrence relationships
for i, ent1 in enumerate(doc_entities):
for ent2 in doc_entities[i+1:]:
relationships.append((ent1, "CO_OCCURS", ent2, doc.page_content))
return entities, relationships
This function uses spaCy's named entity recognition to identify important entities in documents and creates co-occurrence relationships between entities that appear in the same document. While this is a simplified approach, it demonstrates the basic concept of extracting structured information from unstructured text.
Next, we need to construct a knowledge graph from the extracted entities and relationships. NetworkX provides an excellent framework for creating and manipulating graphs in Python.
def build_knowledge_graph(entities, relationships):
graph = nx.Graph()
# Add entities as nodes
for entity, entity_type in entities:
graph.add_node(entity, type=entity_type)
# Add relationships as edges
for ent1, relation, ent2, context in relationships:
if graph.has_node(ent1) and graph.has_node(ent2):
if graph.has_edge(ent1, ent2):
# Add context to existing edge
graph[ent1][ent2]['contexts'].append(context)
else:
graph.add_edge(ent1, ent2,
relation=relation,
contexts=[context])
return graph
This function creates a NetworkX graph where entities are nodes and relationships are edges. Each edge can store multiple contexts where the relationship was observed, providing rich information for later retrieval.
The retrieval mechanism in GraphRAG needs to be more sophisticated than simple similarity search. We need to implement graph-based retrieval that can find relevant information through relationship traversals.
def graph_retrieval(graph, query, max_hops=2, top_k=5):
nlp = spacy.load("en_core_web_sm")
query_doc = nlp(query)
# Extract entities from query
query_entities = [ent.text for ent in query_doc.ents]
relevant_contexts = []
visited_nodes = set()
# Start from query entities and explore the graph
for entity in query_entities:
if entity in graph.nodes():
# Get direct neighbors
neighbors = list(graph.neighbors(entity))
visited_nodes.add(entity)
# Add contexts from direct connections
for neighbor in neighbors:
if graph.has_edge(entity, neighbor):
contexts = graph[entity][neighbor].get('contexts', [])
relevant_contexts.extend(contexts)
visited_nodes.add(neighbor)
# Explore second-hop neighbors if max_hops > 1
if max_hops > 1:
for neighbor in neighbors:
second_neighbors = list(graph.neighbors(neighbor))
for second_neighbor in second_neighbors:
if second_neighbor not in visited_nodes:
if graph.has_edge(neighbor, second_neighbor):
contexts = graph[neighbor][second_neighbor].get('contexts', [])
relevant_contexts.extend(contexts)
# Remove duplicates and return top-k
unique_contexts = list(set(relevant_contexts))
return unique_contexts[:top_k]
This retrieval function starts from entities mentioned in the user's query and explores the knowledge graph to find relevant information through relationship paths. It can perform multi-hop traversals to find information that might not be directly connected to the query entities.
Finally, we can integrate the graph-based retrieval with an LLM to create a complete GraphRAG system.
def create_graphrag_system(graph, llm):
def answer_query(query):
# Retrieve relevant contexts using graph traversal
relevant_contexts = graph_retrieval(graph, query)
# Prepare context for the LLM
context = "\n\n".join(relevant_contexts)
# Create prompt with retrieved context
prompt = f"""
Based on the following information from a knowledge graph:
{context}
Please answer the following question: {query}
Provide a comprehensive answer that considers the relationships and connections
between different pieces of information.
"""
# Generate response using LLM
response = llm(prompt)
return {
"answer": response,
"contexts": relevant_contexts,
"reasoning_path": "Graph traversal from query entities"
}
return answer_query
This function creates a GraphRAG system that uses graph-based retrieval to find relevant information and then uses an LLM to generate comprehensive answers based on the retrieved contexts and their relationships.
Using Local LLMs Instead of Remote APIs
While the examples we've shown so far use OpenAI's API, many organizations prefer to use local LLMs for various reasons including data privacy, cost control, reduced latency, and independence from external services. Running LLMs locally has become increasingly practical with the availability of efficient models and improved hardware capabilities.
Local LLMs offer several significant advantages over remote API-based solutions. Privacy and security represent the most compelling reasons for many organizations. When you use a remote API, your data is sent to external servers, which may not be acceptable for sensitive or proprietary information. Local LLMs keep all data processing within your infrastructure, providing complete control over data handling and security.
Cost considerations also favor local deployment in many scenarios. While remote APIs charge per token or request, local LLMs have a fixed infrastructure cost regardless of usage volume. For applications with high query volumes, local deployment can result in substantial cost savings over time.
Latency is another important factor. Local LLMs eliminate network round-trip time, which can be particularly important for interactive applications where response time directly impacts user experience. Additionally, local deployment provides independence from external service availability and rate limits.
However, local LLM deployment also comes with challenges. You need sufficient computational resources, typically requiring GPUs with adequate memory. Model management, updates, and optimization become your responsibility. You also need to handle scaling and load balancing if your application has varying demand patterns.
Several excellent open-source LLMs are available for local deployment. Models like Llama 2, Mistral, Code Llama, and Vicuna provide strong performance across various tasks. The choice of model depends on your specific requirements regarding model size, performance, and computational resources.
Let's explore how to modify our RAG implementation to use a local LLM. We'll use the Ollama framework, which provides an easy way to run local LLMs with a simple API interface.
from langchain.llms import Ollama
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
def create_local_llm(model_name="llama2"):
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
llm = Ollama(
model=model_name,
callback_manager=callback_manager,
temperature=0.7,
top_p=0.9,
num_ctx=4096 # Context window size
)
return llm
This function creates a local LLM instance using Ollama. The callback manager enables streaming output, which provides better user experience by showing responses as they're generated rather than waiting for the complete response.
You can also use Hugging Face transformers directly for more control over the model configuration. This approach gives you access to a wider range of models and more fine-grained control over model parameters.
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
def create_local_hf_llm(model_name="microsoft/DialoGPT-medium"):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="auto"
)
text_generator = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
device=device,
max_length=2048,
temperature=0.7,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
return text_generator
This implementation uses Hugging Face transformers to load and run a local model. The code automatically detects GPU availability and configures the model accordingly. The pipeline interface provides a simple way to generate text while handling tokenization and decoding automatically.
When using local LLMs, you need to be more careful about prompt engineering and context management. Local models often have smaller context windows than commercial APIs, so you may need to implement more sophisticated context management strategies.
def create_local_rag_chain(vectorstore, local_llm):
def local_rag_query(query):
# Retrieve relevant documents
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
relevant_docs = retriever.get_relevant_documents(query)
# Prepare context with length management
context_parts = []
total_length = 0
max_context_length = 2000 # Adjust based on your model's context window
for doc in relevant_docs:
if total_length + len(doc.page_content) < max_context_length:
context_parts.append(doc.page_content)
total_length += len(doc.page_content)
else:
# Truncate the last document to fit
remaining_length = max_context_length - total_length
if remaining_length > 100: # Only add if meaningful content can fit
context_parts.append(doc.page_content[:remaining_length])
break
context = "\n\n".join(context_parts)
# Create prompt for local LLM
prompt = f"""Context: {context}
Question: {query}
Answer based on the provided context:"""
# Generate response
if hasattr(local_llm, 'invoke'): # LangChain LLM
response = local_llm.invoke(prompt)
else: # Hugging Face pipeline
response = local_llm(prompt, max_new_tokens=512)[0]['generated_text']
# Extract only the new generated text
response = response[len(prompt):].strip()
return {
"answer": response,
"source_documents": relevant_docs,
"context_used": context
}
return local_rag_query
This implementation creates a RAG system specifically designed for local LLMs. It includes context length management to ensure the prompt fits within the model's context window and handles different types of local LLM interfaces.
Comparing RAG vs GraphRAG - When to Use What
Choosing between RAG and GraphRAG depends on several factors related to your specific use case, data characteristics, and performance requirements. Understanding these factors will help you make an informed decision about which approach to implement.
Traditional RAG systems excel in scenarios where you have large volumes of relatively independent documents and your queries are primarily focused on finding specific information within those documents. RAG is particularly effective for knowledge bases consisting of articles, manuals, reports, or other documents where the primary goal is to find and present relevant information based on semantic similarity.
RAG systems are also more straightforward to implement and maintain. The pipeline is relatively simple, with well-established tools and libraries available for each component. The computational requirements are generally lower than GraphRAG, making RAG a good choice when you need to deploy quickly or have limited computational resources.
GraphRAG becomes more valuable when your domain involves complex relationships between entities and when users frequently ask questions that require understanding these relationships. GraphRAG excels in scenarios involving research databases, scientific literature, business intelligence, or any domain where understanding connections between different concepts is crucial.
GraphRAG is particularly powerful for exploratory queries where users might not know exactly what they're looking for but want to understand how different concepts relate to each other. It's also superior for queries that require multi-step reasoning or when you need to provide explanations for why certain information is relevant.
However, GraphRAG systems are more complex to build and maintain. They require sophisticated entity extraction and relationship identification processes, and the graph construction and maintenance can be computationally expensive. GraphRAG also requires more careful consideration of data quality, as errors in entity extraction or relationship identification can propagate through the graph and affect retrieval quality.
In terms of performance characteristics, RAG systems typically provide faster response times for simple queries, while GraphRAG may be slower due to the graph traversal operations but can provide more comprehensive and nuanced answers for complex queries.
Best Practices and Common Pitfalls
Building effective RAG and GraphRAG systems requires attention to several critical factors that can significantly impact system performance and user satisfaction. Understanding these best practices and common pitfalls will help you avoid costly mistakes and build more robust systems.
One of the most critical aspects of any RAG system is the chunking strategy. The way you split your documents into chunks directly affects retrieval quality. Chunks that are too small may lack sufficient context, while chunks that are too large may contain too much irrelevant information. The optimal chunk size depends on your specific use case, but generally, chunks of 500-1500 characters work well for most applications. It's also important to ensure that chunks overlap slightly to prevent important information from being split across chunk boundaries.
The quality of your embedding model significantly impacts retrieval performance. Different embedding models are optimized for different types of content and use cases. For general-purpose applications, models like OpenAI's text-embedding-ada-002 or sentence-transformers work well. However, for domain-specific applications, you might benefit from fine-tuning embeddings on your specific domain data.
Evaluation and monitoring are crucial but often overlooked aspects of RAG systems. You need to establish metrics for measuring retrieval quality, response accuracy, and user satisfaction. This might involve creating test datasets with known correct answers, implementing user feedback mechanisms, or using automated evaluation techniques.
For GraphRAG systems, the quality of entity extraction and relationship identification is paramount. Poor entity extraction will result in an incomplete or inaccurate knowledge graph, which will degrade the entire system's performance. It's important to validate your entity extraction results and consider using multiple extraction methods or manual validation for critical applications.
Another common pitfall is neglecting to handle edge cases and error conditions. Your system should gracefully handle queries that don't match any content in your knowledge base, malformed inputs, or system failures.
Providing clear feedback to users about what went wrong and how they might reformulate their queries is essential for a good user experience.
Security and privacy considerations are also crucial, especially when dealing with sensitive or proprietary information. You need to ensure that your RAG system doesn't inadvertently expose sensitive information and that access controls are properly implemented.
Conclusion and Next Steps
Building intelligent LLM chatbots with RAG and GraphRAG represents a significant advancement in creating AI systems that can provide accurate, current, and contextually relevant information. These technologies bridge the gap between the general knowledge capabilities of LLMs and the specific, up-to-date information requirements of real-world applications.
RAG systems provide an excellent starting point for most applications, offering a good balance of implementation complexity and performance benefits. They're particularly well-suited for scenarios involving large document collections where semantic similarity-based retrieval is sufficient for user needs.
GraphRAG represents the next evolution in this space, offering sophisticated relationship-aware retrieval and reasoning capabilities. While more complex to implement, GraphRAG systems can provide superior performance for applications requiring deep understanding of entity relationships and complex reasoning capabilities.
The choice between RAG and GraphRAG should be based on your specific requirements, available resources, and the complexity of relationships in your domain. Many successful applications start with RAG and evolve to GraphRAG as their requirements become more sophisticated.
As you embark on building your own intelligent chatbot systems, remember that success depends not just on choosing the right technology, but also on careful attention to data quality, system design, evaluation, and user experience. The field is rapidly evolving, with new techniques and improvements being developed regularly, so staying current with the latest developments will help you build increasingly effective systems.
The future of intelligent chatbots lies in systems that can seamlessly combine the language understanding capabilities of LLMs with sophisticated knowledge retrieval and reasoning mechanisms. By mastering RAG and GraphRAG technologies, you'll be well-positioned to build the next generation of AI-powered applications that can truly understand and respond to complex human information needs.
EXAMPLE - SOPHISTICATED ASTROPHYSICS CHATBOT WITH RAG AND GRAPHRAG
Now, let me provide a complete implementation of a sophisticated chatbot that incorporates both RAG and GraphRAG specifically designed for answering astrophysics questions.
import os
import json
import numpy as np
import networkx as nx
import spacy
from typing import List, Dict, Any, Tuple
from dataclasses import dataclass
from collections import defaultdict
import sqlite3
import requests
from datetime import datetime
# Core libraries
from langchain.document_loaders import PyPDFLoader, TextLoader, WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
from langchain.llms import Ollama
from langchain.schema import Document
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
# Scientific libraries
import astropy.units as u
from astropy.coordinates import SkyCoord
from astropy.time import Time
import astroquery
from astroquery.simbad import Simbad
from astroquery.nasa_ads import ADS
@dataclass
class AstrophysicsEntity:
"""Represents an astrophysical entity with its properties"""
name: str
entity_type: str # star, galaxy, planet, phenomenon, etc.
coordinates: str = None
magnitude: float = None
distance: str = None
spectral_type: str = None
properties: Dict[str, Any] = None
class AstrophysicsKnowledgeGraph:
"""Advanced knowledge graph specifically designed for astrophysics"""
def __init__(self):
self.graph = nx.MultiDiGraph()
self.nlp = spacy.load("en_core_web_sm")
self.entity_types = {
'CELESTIAL_BODY': ['star', 'planet', 'galaxy', 'nebula', 'asteroid', 'comet'],
'PHENOMENON': ['supernova', 'black hole', 'pulsar', 'quasar', 'gamma-ray burst'],
'INSTRUMENT': ['telescope', 'spectrometer', 'detector', 'satellite'],
'THEORY': ['relativity', 'quantum mechanics', 'dark matter', 'dark energy'],
'MEASUREMENT': ['redshift', 'luminosity', 'magnitude', 'parallax']
}
def extract_astrophysics_entities(self, documents: List[Document]) -> Tuple[List[AstrophysicsEntity], List[Tuple]]:
"""Extract astrophysics-specific entities and relationships"""
entities = []
relationships = []
for doc in documents:
text = doc.page_content
nlp_doc = self.nlp(text)
# Extract named entities
doc_entities = []
for ent in nlp_doc.ents:
entity_type = self._classify_astrophysics_entity(ent.text, ent.label_)
if entity_type:
astro_entity = AstrophysicsEntity(
name=ent.text,
entity_type=entity_type,
properties=self._extract_entity_properties(text, ent.text)
)
entities.append(astro_entity)
doc_entities.append(astro_entity)
# Extract relationships using dependency parsing
relationships.extend(self._extract_relationships(nlp_doc, doc_entities, text))
# Extract numerical relationships (distances, magnitudes, etc.)
relationships.extend(self._extract_numerical_relationships(text, doc_entities))
return entities, relationships
def _classify_astrophysics_entity(self, entity_text: str, spacy_label: str) -> str:
"""Classify entities into astrophysics-specific categories"""
entity_lower = entity_text.lower()
# Check against known astrophysics terms
for category, terms in self.entity_types.items():
if any(term in entity_lower for term in terms):
return category
# Use spaCy labels as fallback
if spacy_label in ['PERSON', 'ORG']:
return 'SCIENTIST_OR_INSTITUTION'
elif spacy_label in ['GPE']:
return 'LOCATION'
return None
def _extract_entity_properties(self, text: str, entity_name: str) -> Dict[str, Any]:
"""Extract numerical and categorical properties for entities"""
properties = {}
# Look for common astrophysical measurements near the entity
patterns = {
'magnitude': r'magnitude\s*[:\-]?\s*([\d\.\-]+)',
'distance': r'distance\s*[:\-]?\s*([\d\.\-]+)\s*(ly|pc|kpc|Mpc|km|AU)',
'mass': r'mass\s*[:\-]?\s*([\d\.\-]+)\s*(solar|M☉|kg)',
'temperature': r'temperature\s*[:\-]?\s*([\d\.\-]+)\s*K',
'redshift': r'redshift\s*[:\-]?\s*([\d\.\-]+)'
}
import re
entity_context = self._get_entity_context(text, entity_name, window=200)
for prop, pattern in patterns.items():
matches = re.findall(pattern, entity_context, re.IGNORECASE)
if matches:
properties[prop] = matches[0] if isinstance(matches[0], str) else matches[0][0]
return properties
def _get_entity_context(self, text: str, entity_name: str, window: int = 200) -> str:
"""Get text context around an entity mention"""
entity_pos = text.lower().find(entity_name.lower())
if entity_pos == -1:
return ""
start = max(0, entity_pos - window)
end = min(len(text), entity_pos + len(entity_name) + window)
return text[start:end]
def _extract_relationships(self, nlp_doc, entities: List[AstrophysicsEntity], text: str) -> List[Tuple]:
"""Extract relationships using dependency parsing and domain knowledge"""
relationships = []
# Define astrophysics-specific relationship patterns
relationship_patterns = {
'ORBITS': ['orbits', 'revolves around', 'circles'],
'CONTAINS': ['contains', 'hosts', 'harbors'],
'PART_OF': ['part of', 'member of', 'in', 'within'],
'DISCOVERED_BY': ['discovered by', 'found by', 'observed by'],
'LOCATED_IN': ['located in', 'found in', 'in the constellation'],
'EMITS': ['emits', 'radiates', 'produces'],
'FORMED_FROM': ['formed from', 'created by', 'result of']
}
for i, ent1 in enumerate(entities):
for j, ent2 in enumerate(entities[i+1:], i+1):
# Check if entities co-occur in the same sentence
for sent in nlp_doc.sents:
if ent1.name.lower() in sent.text.lower() and ent2.name.lower() in sent.text.lower():
# Look for relationship patterns
for rel_type, patterns in relationship_patterns.items():
for pattern in patterns:
if pattern in sent.text.lower():
relationships.append((
ent1.name, rel_type, ent2.name, sent.text, 'pattern_based'
))
break
# Add co-occurrence relationship as fallback
relationships.append((
ent1.name, 'CO_OCCURS', ent2.name, sent.text, 'co_occurrence'
))
return relationships
def _extract_numerical_relationships(self, text: str, entities: List[AstrophysicsEntity]) -> List[Tuple]:
"""Extract numerical relationships like distances, magnitudes, etc."""
relationships = []
import re
# Pattern for numerical comparisons
comparison_patterns = [
r'(\w+)\s+is\s+(larger|smaller|brighter|dimmer|closer|farther)\s+than\s+(\w+)',
r'(\w+)\s+has\s+a\s+(higher|lower|greater|smaller)\s+(\w+)\s+than\s+(\w+)'
]
for pattern in comparison_patterns:
matches = re.findall(pattern, text, re.IGNORECASE)
for match in matches:
if len(match) >= 3:
entity1, comparison, entity2 = match[0], match[1], match[-1]
rel_type = f"COMPARISON_{comparison.upper()}"
relationships.append((entity1, rel_type, entity2, text, 'numerical'))
return relationships
def build_graph(self, entities: List[AstrophysicsEntity], relationships: List[Tuple]):
"""Build the knowledge graph from extracted entities and relationships"""
# Add entities as nodes
for entity in entities:
self.graph.add_node(
entity.name,
type=entity.entity_type,
coordinates=entity.coordinates,
magnitude=entity.magnitude,
distance=entity.distance,
spectral_type=entity.spectral_type,
properties=entity.properties or {}
)
# Add relationships as edges
for rel in relationships:
if len(rel) >= 4:
ent1, rel_type, ent2, context = rel[:4]
source = rel[4] if len(rel) > 4 else 'unknown'
if self.graph.has_node(ent1) and self.graph.has_node(ent2):
self.graph.add_edge(
ent1, ent2,
relation=rel_type,
context=context,
source=source,
weight=self._calculate_relationship_weight(rel_type, source)
)
def _calculate_relationship_weight(self, rel_type: str, source: str) -> float:
"""Calculate edge weight based on relationship type and source"""
type_weights = {
'ORBITS': 0.9,
'CONTAINS': 0.8,
'PART_OF': 0.8,
'DISCOVERED_BY': 0.7,
'LOCATED_IN': 0.7,
'EMITS': 0.6,
'FORMED_FROM': 0.8,
'CO_OCCURS': 0.3
}
source_weights = {
'pattern_based': 1.0,
'numerical': 0.9,
'co_occurrence': 0.5
}
return type_weights.get(rel_type, 0.5) * source_weights.get(source, 0.5)
class AstrophysicsRAGSystem:
"""Advanced RAG system for astrophysics with external data integration"""
def __init__(self, model_name: str = "llama2"):
self.embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2"
)
self.llm = self._create_local_llm(model_name)
self.vectorstore = None
self.knowledge_graph = AstrophysicsKnowledgeGraph()
self.external_apis = self._setup_external_apis()
def _create_local_llm(self, model_name: str):
"""Create local LLM instance"""
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
return Ollama(
model=model_name,
callback_manager=callback_manager,
temperature=0.3, # Lower temperature for scientific accuracy
top_p=0.9,
num_ctx=4096
)
def _setup_external_apis(self) -> Dict[str, Any]:
"""Setup connections to external astrophysics databases"""
apis = {}
try:
# Configure SIMBAD for stellar data
Simbad.add_votable_fields('flux(V)', 'flux(B)', 'sp', 'parallax', 'distance')
apis['simbad'] = Simbad
except Exception as e:
print(f"Warning: Could not setup SIMBAD API: {e}")
try:
# Configure NASA ADS for literature
apis['ads'] = ADS
except Exception as e:
print(f"Warning: Could not setup ADS API: {e}")
return apis
def load_astrophysics_documents(self, file_paths: List[str], urls: List[str] = None) -> List[Document]:
"""Load astrophysics documents from various sources"""
documents = []
# Load local files
for file_path in file_paths:
try:
if file_path.endswith('.pdf'):
loader = PyPDFLoader(file_path)
elif file_path.endswith('.txt'):
loader = TextLoader(file_path)
else:
continue
docs = loader.load()
documents.extend(docs)
except Exception as e:
print(f"Error loading {file_path}: {e}")
# Load web sources (arXiv, NASA, etc.)
if urls:
for url in urls:
try:
loader = WebBaseLoader(url)
docs = loader.load()
documents.extend(docs)
except Exception as e:
print(f"Error loading {url}: {e}")
return documents
def process_documents(self, documents: List[Document]) -> None:
"""Process documents for both RAG and GraphRAG"""
# Split documents for RAG
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
length_function=len,
separators=["\n\n", "\n", ". ", " ", ""]
)
split_documents = text_splitter.split_documents(documents)
# Create vector database for RAG
self.vectorstore = Chroma.from_documents(
documents=split_documents,
embedding=self.embeddings,
persist_directory="./astrophysics_vectordb"
)
self.vectorstore.persist()
# Build knowledge graph for GraphRAG
entities, relationships = self.knowledge_graph.extract_astrophysics_entities(documents)
self.knowledge_graph.build_graph(entities, relationships)
print(f"Processed {len(split_documents)} document chunks")
print(f"Extracted {len(entities)} entities and {len(relationships)} relationships")
def query_external_databases(self, query: str, entity_name: str = None) -> Dict[str, Any]:
"""Query external astrophysics databases for additional information"""
external_data = {}
if entity_name and 'simbad' in self.external_apis:
try:
# Query SIMBAD for stellar data
result_table = self.external_apis['simbad'].query_object(entity_name)
if result_table:
external_data['simbad'] = {
'coordinates': result_table['RA'][0] + ' ' + result_table['DEC'][0],
'magnitude': result_table['FLUX_V'][0] if 'FLUX_V' in result_table.colnames else None,
'spectral_type': result_table['SP_TYPE'][0] if 'SP_TYPE' in result_table.colnames else None,
'distance': result_table['Distance_distance'][0] if 'Distance_distance' in result_table.colnames else None
}
except Exception as e:
print(f"SIMBAD query failed: {e}")
return external_data
def hybrid_retrieval(self, query: str, k: int = 5) -> Dict[str, Any]:
"""Perform hybrid retrieval using both RAG and GraphRAG"""
# RAG retrieval
rag_results = []
if self.vectorstore:
retriever = self.vectorstore.as_retriever(search_kwargs={"k": k})
rag_docs = retriever.get_relevant_documents(query)
rag_results = [doc.page_content for doc in rag_docs]
# GraphRAG retrieval
graph_results = self._graph_retrieval(query, max_hops=2, top_k=k)
# Extract entities from query for external database lookup
query_entities = self._extract_query_entities(query)
external_data = {}
for entity in query_entities:
external_data.update(self.query_external_databases(query, entity))
return {
'rag_contexts': rag_results,
'graph_contexts': graph_results['contexts'],
'graph_entities': graph_results['entities'],
'graph_relationships': graph_results['relationships'],
'external_data': external_data,
'reasoning_path': graph_results['reasoning_path']
}
def _extract_query_entities(self, query: str) -> List[str]:
"""Extract entities from user query"""
nlp_doc = self.knowledge_graph.nlp(query)
entities = []
for ent in nlp_doc.ents:
if ent.label_ in ['PERSON', 'ORG', 'GPE'] or any(
keyword in ent.text.lower()
for keywords in self.knowledge_graph.entity_types.values()
for keyword in keywords
):
entities.append(ent.text)
return entities
def _graph_retrieval(self, query: str, max_hops: int = 2, top_k: int = 5) -> Dict[str, Any]:
"""Perform graph-based retrieval"""
query_entities = self._extract_query_entities(query)
relevant_contexts = []
relevant_entities = set()
relevant_relationships = []
reasoning_path = []
for entity in query_entities:
if entity in self.knowledge_graph.graph.nodes():
reasoning_path.append(f"Starting from entity: {entity}")
relevant_entities.add(entity)
# Get direct neighbors
neighbors = list(self.knowledge_graph.graph.neighbors(entity))
reasoning_path.append(f"Found {len(neighbors)} direct connections")
for neighbor in neighbors:
if self.knowledge_graph.graph.has_edge(entity, neighbor):
edge_data = self.knowledge_graph.graph[entity][neighbor]
for edge_key, edge_attrs in edge_data.items():
relevant_contexts.append(edge_attrs.get('context', ''))
relevant_relationships.append({
'source': entity,
'target': neighbor,
'relation': edge_attrs.get('relation', ''),
'weight': edge_attrs.get('weight', 0)
})
relevant_entities.add(neighbor)
# Multi-hop traversal
if max_hops > 1:
for neighbor in neighbors:
second_neighbors = list(self.knowledge_graph.graph.neighbors(neighbor))
for second_neighbor in second_neighbors[:3]: # Limit to prevent explosion
if second_neighbor not in relevant_entities:
if self.knowledge_graph.graph.has_edge(neighbor, second_neighbor):
edge_data = self.knowledge_graph.graph[neighbor][second_neighbor]
for edge_key, edge_attrs in edge_data.items():
relevant_contexts.append(edge_attrs.get('context', ''))
relevant_relationships.append({
'source': neighbor,
'target': second_neighbor,
'relation': edge_attrs.get('relation', ''),
'weight': edge_attrs.get('weight', 0)
})
relevant_entities.add(second_neighbor)
# Remove duplicates and sort by relevance
unique_contexts = list(set(filter(None, relevant_contexts)))
# Sort relationships by weight
relevant_relationships.sort(key=lambda x: x['weight'], reverse=True)
return {
'contexts': unique_contexts[:top_k],
'entities': list(relevant_entities),
'relationships': relevant_relationships[:top_k],
'reasoning_path': reasoning_path
}
def generate_response(self, query: str) -> Dict[str, Any]:
"""Generate comprehensive response using hybrid RAG+GraphRAG approach"""
# Perform hybrid retrieval
retrieval_results = self.hybrid_retrieval(query)
# Prepare context for LLM
context_parts = []
# Add RAG contexts
if retrieval_results['rag_contexts']:
context_parts.append("=== Document-based Information ===")
context_parts.extend(retrieval_results['rag_contexts'][:3])
# Add GraphRAG contexts
if retrieval_results['graph_contexts']:
context_parts.append("\n=== Relationship-based Information ===")
context_parts.extend(retrieval_results['graph_contexts'][:3])
# Add external database information
if retrieval_results['external_data']:
context_parts.append("\n=== External Database Information ===")
for source, data in retrieval_results['external_data'].items():
context_parts.append(f"{source.upper()}: {json.dumps(data, indent=2)}")
# Add relationship information
if retrieval_results['graph_relationships']:
context_parts.append("\n=== Key Relationships ===")
for rel in retrieval_results['graph_relationships'][:5]:
context_parts.append(
f"{rel['source']} --{rel['relation']}--> {rel['target']} (confidence: {rel['weight']:.2f})"
)
context = "\n\n".join(context_parts)
# Create specialized prompt for astrophysics
prompt = f"""You are an expert astrophysicist with access to comprehensive scientific literature and databases.
Based on the following information, provide a detailed and accurate answer to the astrophysics question.
Context Information:
{context}
Question: {query}
Instructions:
1. Provide a scientifically accurate and comprehensive answer
2. Include relevant numerical values, measurements, and units when available
3. Explain the physical principles and mechanisms involved
4. Cite relationships between different astrophysical objects or phenomena
5. If the information is incomplete, clearly state what is known and what remains uncertain
6. Use proper scientific terminology while remaining accessible
Answer:"""
# Generate response
try:
response = self.llm.invoke(prompt)
except Exception as e:
response = f"I apologize, but I encountered an error generating the response: {e}"
return {
'answer': response,
'sources': {
'rag_sources': len(retrieval_results['rag_contexts']),
'graph_sources': len(retrieval_results['graph_contexts']),
'external_sources': list(retrieval_results['external_data'].keys()),
'entities_used': retrieval_results['graph_entities'],
'relationships_used': len(retrieval_results['graph_relationships'])
},
'reasoning_path': retrieval_results['reasoning_path'],
'confidence_indicators': self._assess_confidence(retrieval_results)
}
def _assess_confidence(self, retrieval_results: Dict[str, Any]) -> Dict[str, Any]:
"""Assess confidence in the response based on available information"""
confidence = {
'overall_score': 0.0,
'factors': {}
}
# Factor 1: Number of sources
total_sources = (
len(retrieval_results['rag_contexts']) +
len(retrieval_results['graph_contexts']) +
len(retrieval_results['external_data'])
)
source_score = min(total_sources / 5.0, 1.0) # Normalize to max 1.0
confidence['factors']['source_diversity'] = source_score
# Factor 2: External database confirmation
external_score = 1.0 if retrieval_results['external_data'] else 0.5
confidence['factors']['external_confirmation'] = external_score
# Factor 3: Relationship strength
if retrieval_results['graph_relationships']:
avg_weight = np.mean([rel['weight'] for rel in retrieval_results['graph_relationships']])
relationship_score = avg_weight
else:
relationship_score = 0.3
confidence['factors']['relationship_strength'] = relationship_score
# Calculate overall confidence
confidence['overall_score'] = (source_score + external_score + relationship_score) / 3.0
return confidence
class AstrophysicsChatbot:
"""Main chatbot interface for astrophysics Q&A"""
def __init__(self, model_name: str = "llama2"):
self.rag_system = AstrophysicsRAGSystem(model_name)
self.conversation_history = []
self.session_stats = {
'queries_processed': 0,
'entities_discovered': set(),
'topics_covered': set()
}
def initialize_knowledge_base(self, document_paths: List[str], urls: List[str] = None):
"""Initialize the knowledge base with astrophysics documents"""
print("Loading astrophysics documents...")
documents = self.rag_system.load_astrophysics_documents(document_paths, urls)
print("Processing documents and building knowledge structures...")
self.rag_system.process_documents(documents)
print("Knowledge base initialization complete!")
print(f"Graph contains {self.rag_system.knowledge_graph.graph.number_of_nodes()} entities")
print(f"Graph contains {self.rag_system.knowledge_graph.graph.number_of_edges()} relationships")
def chat(self, query: str) -> Dict[str, Any]:
"""Process a user query and return comprehensive response"""
# Update session statistics
self.session_stats['queries_processed'] += 1
# Generate response
response = self.rag_system.generate_response(query)
# Update session tracking
if 'entities_used' in response['sources']:
self.session_stats['entities_discovered'].update(response['sources']['entities_used'])
# Store conversation
self.conversation_history.append({
'query': query,
'response': response['answer'],
'timestamp': datetime.now().isoformat(),
'confidence': response['confidence_indicators']['overall_score']
})
return response
def interactive_session(self):
"""Run an interactive chat session"""
print("\n" + "="*80)
print("ASTROPHYSICS CHATBOT - Advanced RAG + GraphRAG System")
print("="*80)
print("Ask me anything about astrophysics! Type 'quit' to exit.")
print("Type 'stats' to see session statistics.")
print("Type 'graph' to see knowledge graph information.")
print("-"*80)
while True:
try:
user_input = input("\n🌟 You: ").strip()
if user_input.lower() == 'quit':
print("\nThank you for exploring the universe with me! 🚀")
break
elif user_input.lower() == 'stats':
self._display_session_stats()
continue
elif user_input.lower() == 'graph':
self._display_graph_info()
continue
elif not user_input:
print("Please enter a question about astrophysics.")
continue
print("\n🤖 Analyzing your question and searching knowledge base...")
response = self.chat(user_input)
print(f"\n🔬 Astrophysics Bot: {response['answer']}")
# Display confidence and sources
confidence = response['confidence_indicators']['overall_score']
print(f"\n📊 Confidence: {confidence:.2f}/1.0")
sources = response['sources']
print(f"📚 Sources: {sources['rag_sources']} documents, "
f"{sources['graph_sources']} relationships, "
f"{len(sources['external_sources'])} external DBs")
if sources['external_sources']:
print(f"🛰️ External databases: {', '.join(sources['external_sources'])}")
if confidence < 0.6:
print("⚠️ Note: This response has moderate confidence. "
"Consider consulting additional sources for critical applications.")
except KeyboardInterrupt:
print("\n\nSession interrupted. Goodbye! 🌌")
break
except Exception as e:
print(f"\n❌ Error processing your question: {e}")
print("Please try rephrasing your question.")
def _display_session_stats(self):
"""Display current session statistics"""
print("\n" + "="*50)
print("SESSION STATISTICS")
print("="*50)
print(f"Queries processed: {self.session_stats['queries_processed']}")
print(f"Unique entities discovered: {len(self.session_stats['entities_discovered'])}")
if self.session_stats['entities_discovered']:
print("Recent entities:", ", ".join(list(self.session_stats['entities_discovered'])[:10]))
if self.conversation_history:
avg_confidence = np.mean([conv['confidence'] for conv in self.conversation_history])
print(f"Average response confidence: {avg_confidence:.2f}")
print("="*50)
def _display_graph_info(self):
"""Display knowledge graph information"""
graph = self.rag_system.knowledge_graph.graph
print("\n" + "="*50)
print("KNOWLEDGE GRAPH INFORMATION")
print("="*50)
print(f"Total entities (nodes): {graph.number_of_nodes()}")
print(f"Total relationships (edges): {graph.number_of_edges()}")
# Show entity type distribution
entity_types = defaultdict(int)
for node, data in graph.nodes(data=True):
entity_types[data.get('type', 'unknown')] += 1
print("\nEntity type distribution:")
for entity_type, count in sorted(entity_types.items(), key=lambda x: x[1], reverse=True):
print(f" {entity_type}: {count}")
# Show most connected entities
if graph.number_of_nodes() > 0:
degrees = dict(graph.degree())
top_entities = sorted(degrees.items(), key=lambda x: x[1], reverse=True)[:5]
print("\nMost connected entities:")
for entity, degree in top_entities:
print(f" {entity}: {degree} connections")
print("="*50)
# Example usage and demonstration
def main():
"""Demonstrate the sophisticated astrophysics chatbot"""
# Initialize the chatbot
print("Initializing Astrophysics Chatbot with local LLM...")
chatbot = AstrophysicsChatbot(model_name="llama2")
# Example astrophysics documents (you would replace these with real paths)
example_documents = [
# "astrophysics_textbook.pdf",
# "stellar_evolution_paper.pdf",
# "galaxy_formation_review.txt",
# Add your astrophysics documents here
]
# Example URLs for web-based astrophysics content
example_urls = [
# "https://arxiv.org/abs/astro-ph/9999999", # Example arXiv paper
# "https://www.nasa.gov/mission_pages/hubble/science/",
# Add relevant astrophysics URLs here
]
# For demonstration, we'll create some sample astrophysics content
sample_content = """
The Andromeda Galaxy (M31) is a spiral galaxy approximately 2.5 million light-years
from Earth and the nearest major galaxy to the Milky Way. It contains approximately
one trillion stars and has a diameter of about 220,000 light-years. The galaxy is
approaching the Milky Way at about 110 km/s and is expected to collide with our
galaxy in approximately 4.5 billion years.
Black holes are regions of spacetime where gravity is so strong that nothing,
not even light, can escape. Stellar-mass black holes form when massive stars
collapse at the end of their lives. Supermassive black holes, found at the
centers of galaxies, can contain millions to billions of solar masses.
The Hubble Space Telescope has observed numerous exoplanets orbiting distant stars.
These observations have revealed that planetary systems are common throughout the
galaxy. The James Webb Space Telescope continues this work with enhanced infrared
capabilities, allowing us to study the atmospheres of these distant worlds.
"""
# Create a temporary document for demonstration
with open("sample_astrophysics.txt", "w") as f:
f.write(sample_content)
try:
# Initialize knowledge base
chatbot.initialize_knowledge_base(["sample_astrophysics.txt"])
# Start interactive session
chatbot.interactive_session()
except Exception as e:
print(f"Error initializing chatbot: {e}")
print("Please ensure you have the required models and dependencies installed.")
finally:
# Clean up temporary file
import os
if os.path.exists("sample_astrophysics.txt"):
os.remove("sample_astrophysics.txt")
if __name__ == "__main__":
main()
This sophisticated astrophysics chatbot implementation incorporates both RAG and GraphRAG technologies with the following advanced features:
Key Features:
- Specialized Entity Extraction: Identifies astrophysical entities like celestial bodies, phenomena, instruments, and theories with domain-specific classification.
- Advanced Knowledge Graph: Builds relationships between astrophysical entities using dependency parsing and domain-specific patterns.
- External Database Integration: Connects to SIMBAD and NASA ADS for real-time astronomical data.
- Hybrid Retrieval: Combines traditional RAG with graph-based retrieval for comprehensive information gathering.
- Local LLM Support: Uses Ollama for privacy and cost-effective deployment.
- Confidence Assessment: Evaluates response reliability based on source diversity and relationship strength.
- Interactive Interface: Provides a user-friendly chat interface with session statistics and graph information.
- Scientific Accuracy: Optimized prompts and lower temperature settings for scientific precision.
The system can answer complex astrophysics questions by leveraging multiple information sources and understanding the relationships between different astronomical concepts, making it a powerful tool for researchers, students, and astronomy enthusiasts.
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