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Feature request : LLM Integration for Knowledge Graph Enhancement #741
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Implementing Direct Embedding Association in TxtAI: Feature: Direct Embedding Association
import networkx as nx
from txtai.embeddings import Embeddings
class EnhancedGraph(nx.Graph):
def __init__(self):
super().__init__()
self.embeddings = Embeddings()
def add_node(self, node_for_adding, **attr):
super().add_node(node_for_adding, **attr)
if 'text' in attr:
embedding = self.embeddings.transform(attr['text'])
self.nodes[node_for_adding]['embedding'] = embedding
def get_node_embedding(self, node):
return self.nodes[node].get('embedding', None)
def update_node_content(self, node, new_text):
self.nodes[node]['text'] = new_text
new_embedding = self.embeddings.transform(new_text)
self.nodes[node]['embedding'] = new_embedding
def update_affected_nodes(self, changed_node):
for neighbor in self.neighbors(changed_node):
neighbor_text = self.nodes[neighbor]['text']
context = f"{self.nodes[changed_node]['text']} {neighbor_text}"
new_embedding = self.embeddings.transform(context)
self.nodes[neighbor]['embedding'] = new_embedding Integration with TxtAI ecosystem: Usage example: graph = EnhancedGraph()
graph.add_node(1, text="Example node content")
embedding = graph.get_node_embedding(1)
graph.update_node_content(1, "Updated node content")
graph.update_affected_nodes(1) This feature enhances the "LLM Integration for Knowledge Graph Enhancement" part of the roadmap by providing a direct and efficient way to associate embeddings with graph nodes. It allows for quick retrieval and update of embeddings, which is crucial for real-time graph updates and queries. The implementation is simple, well-integrated with TxtAI's existing components, and uses NetworkX as the underlying graph library. This approach ensures that the new feature fits seamlessly into the TxtAI ecosystem while providing the necessary functionality for direct embedding association and efficient updates. Citations: |
Proposal for implementing Indexing Optimization with HNSW and hybrid indexing: Feature: Advanced Indexing Optimization
import hnswlib
from txtai.graph import Graph
class HNSWGraph(Graph):
def __init__(self, dim, max_elements, ef_construction=200, M=16):
super().__init__()
self.index = hnswlib.Index(space='cosine', dim=dim)
self.index.init_index(max_elements=max_elements, ef_construction=ef_construction, M=M)
self.node_map = {}
def add_node(self, node_id, embedding, **attr):
super().add_node(node_id, **attr)
index = len(self.node_map)
self.node_map[node_id] = index
self.index.add_items(embedding.reshape(1, -1), [index])
def nearest_neighbors(self, query_embedding, k=10):
labels, distances = self.index.knn_query(query_embedding.reshape(1, -1), k=k)
return [list(self.node_map.keys())[label] for label in labels[0]]
import networkx as nx
from txtai.embeddings import Embeddings
class HybridGraph(HNSWGraph):
def __init__(self, dim, max_elements, ef_construction=200, M=16):
super().__init__(dim, max_elements, ef_construction, M)
self.graph = nx.Graph()
self.embeddings = Embeddings()
def add_node(self, node_id, text, **attr):
embedding = self.embeddings.transform(text)
super().add_node(node_id, embedding, **attr)
self.graph.add_node(node_id, text=text, **attr)
def add_edge(self, u, v, **attr):
self.graph.add_edge(u, v, **attr)
def search(self, query, k=10):
query_embedding = self.embeddings.transform(query)
nn_nodes = self.nearest_neighbors(query_embedding, k)
subgraph = self.graph.subgraph(nn_nodes)
pagerank = nx.pagerank(subgraph)
return sorted(pagerank.items(), key=lambda x: x[1], reverse=True) This implementation integrates HNSW for fast nearest neighbor search and combines it with NetworkX for graph structure analysis. It relates to the "LLM Integration for Knowledge Graph Enhancement" feature in the roadmap, as it provides an efficient way to search and analyze the knowledge graph created from LLM outputs. The This approach is well-integrated with TxtAI's existing ecosystem, utilizing its To use this new feature: graph = HybridGraph(dim=768, max_elements=100000)
graph.add_node("1", "This is a sample text")
graph.add_node("2", "Another example")
graph.add_edge("1", "2")
results = graph.search("sample query", k=5) This implementation provides a solid foundation for advanced indexing optimization in TxtAI, combining the speed of HNSW with the structural analysis capabilities of graph algorithms. Citations: |
Proposal for implementing Query Optimization in TxtAI: Feature: Advanced Query Optimization
import networkx as nx
from txtai.embeddings import Embeddings
from txtai.graph import Graph
class SemanticQueryPlanner:
def __init__(self, graph: Graph, embeddings: Embeddings):
self.graph = graph
self.embeddings = embeddings
def plan_query(self, query: str):
# Get semantic embedding of the query
query_embedding = self.embeddings.transform(query)
# Find semantically similar nodes
similar_nodes = self.find_similar_nodes(query_embedding)
# Use NetworkX to find optimal paths in the graph
subgraph = self.graph.graph.subgraph(similar_nodes)
paths = nx.all_pairs_shortest_path(subgraph)
# Combine semantic similarity and graph structure for planning
plan = self.combine_semantic_and_structure(paths, query_embedding)
return plan
def find_similar_nodes(self, query_embedding, top_k=10):
# Find nodes with similar embeddings
similar = self.embeddings.search(query_embedding, top_k)
return [node for node, _ in similar]
def combine_semantic_and_structure(self, paths, query_embedding):
# Implement logic to combine path information and semantic similarity
# This is a placeholder for more sophisticated combination logic
plan = []
for start, end_dict in paths:
for end, path in end_dict.items():
plan.append((start, end, path))
return plan
from functools import lru_cache
import numpy as np
class SemanticCache:
def __init__(self, embeddings: Embeddings, similarity_threshold=0.9):
self.embeddings = embeddings
self.similarity_threshold = similarity_threshold
self.cache = {}
@lru_cache(maxsize=1000)
def get(self, query: str):
query_embedding = self.embeddings.transform(query)
for cached_query, (cached_embedding, result) in self.cache.items():
similarity = np.dot(query_embedding, cached_embedding)
if similarity > self.similarity_threshold:
return result
return None
def set(self, query: str, result):
query_embedding = self.embeddings.transform(query)
self.cache[query] = (query_embedding, result)
class CostBasedOptimizer:
def __init__(self, graph: Graph):
self.graph = graph
def optimize(self, query_plan):
# Implement cost estimation for different query operations
estimated_costs = self.estimate_costs(query_plan)
# Use NetworkX's optimization algorithms to find the best plan
G = nx.DiGraph()
for i, step in enumerate(query_plan):
G.add_node(i, cost=estimated_costs[i])
if i > 0:
G.add_edge(i-1, i)
optimal_path = nx.dag_longest_path(G)
return [query_plan[i] for i in optimal_path]
def estimate_costs(self, query_plan):
# Placeholder for cost estimation logic
# This should be replaced with more sophisticated cost models
return [len(step) for step in query_plan] Integration with TxtAI: This implementation leverages TxtAI's existing Usage example: graph = Graph()
embeddings = Embeddings()
planner = SemanticQueryPlanner(graph, embeddings)
cache = SemanticCache(embeddings)
optimizer = CostBasedOptimizer(graph)
query = "Find connections between AI and healthcare"
initial_plan = planner.plan_query(query)
if cached_result := cache.get(query):
print("Using cached result")
result = cached_result
else:
optimized_plan = optimizer.optimize(initial_plan)
result = execute_plan(optimized_plan) # This function needs to be implemented
cache.set(query, result)
print(result) This feature enhances the "LLM Integration for Knowledge Graph Enhancement" part of the roadmap by providing advanced query optimization capabilities. It combines semantic understanding from embeddings with graph structure analysis to create more efficient query plans. The semantic caching mechanism helps in reducing redundant computations for similar queries, while the cost-based optimizer ensures that complex graph queries are executed in the most efficient manner possible. The implementation is designed to be simple and well-integrated with TxtAI's existing components, using NetworkX for graph algorithms and building upon TxtAI's Graph and Embeddings classes. This approach ensures that the new feature fits seamlessly into the TxtAI ecosystem while providing powerful query optimization capabilities. Citations: |
Based on the requirements and the existing TxtAI ecosystem, here's a proposed approach to develop LLM Integration for Knowledge Graph Enhancement:
This implementation:
TextToGraph
pipeline for converting LLM outputs to graph structures.Embeddings
for similarity checks in the validation process.LLM
pipeline for generating new knowledge.To use this enhanced graph system:
This approach provides a simple, integrated solution for enhancing knowledge graphs with LLM outputs within the TxtAI ecosystem, while also incorporating feedback mechanisms for continuous improvement.
Citations:
[1] https://github.com/dylanhogg/llmgraph
[2] https://neo4j.com/developer-blog/construct-knowledge-graphs-unstructured-text/
[3] https://www.visual-design.net/post/llm-prompt-engineering-techniques-for-knowledge-graph
[4] https://datavid.com/blog/merging-large-language-models-and-knowledge-graphs-integration
[5] https://arxiv.org/pdf/2405.15436.pdf
[6] https://medium.com/neo4j/a-tale-of-llms-and-graphs-the-inaugural-genai-graph-gathering-c880119e43fe
[7] https://www.linkedin.com/pulse/transforming-llm-reliability-graphster-20-wisecubes-hallucination-j8adf
[8] https://ragaboutit.com/building-a-graph-rag-system-enhancing-llms-with-knowledge-graphs/
[9] https://arxiv.org/html/2312.11282v2
[10] https://blog.langchain.dev/enhancing-rag-based-applications-accuracy-by-constructing-and-leveraging-knowledge-graphs/
[11] https://github.com/XiaoxinHe/Awesome-Graph-LLM
[12] https://www.linkedin.com/pulse/optimizing-llm-precision-knowledge-graph-based-natural-language-lyere
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