Step Guide by the action to create an automated graphic pipe using Langgraph and Networkx
In this lesson, we show how we can build an automated information graph (kg) Pipeline using Langgraph and Networkx. Pipeline is imitating the sequence of clever agencies such as data gathering, organizational, and graph confirmation, and Graph Excellence, and Graph Excellence, and Graph Exhibitions, and Graph Excellence. Since the user-given article, such as “artificial intelligence,” the system that removes relevant organizations and relationships, resolves multiplication, and includes information on the component. In view of the final information graph, engineers and data scientists get a clear understanding of the complexity between ideas, making the most beneficial for Semantic analysis, environmental processing, and information management.
!pip install langgraph langchain_coreWe include two important Python information libraries: Langgraph, used to create and food from agent-based Combion operations, and Langchain Core, providing access to basinies and powerful language programs. These libraries enable the Focused Agency integration to smart data pipes.
import re
import networkx as nx
import matplotlib.pyplot as plt
from typing import TypedDict, List, Tuple, Dict, Any
from langchain_core.messages import HumanMessage, AIMessage
from langgraph.graph import StateGraph, ENDWe import important libraries to create an automatic graphic pipe. Includes the processing of the general statements, networkx and matplotlib in creation of graphs and visualization, Langgraph and typing for formal data management, and Langchaph and Langchain_Core in the order of AII within the work of Agents.
class KGState(TypedDict):
topic: str
raw_text: str
entities: List[str]
relations: List[Tuple[str, str, str]]
resolved_relations: List[Tuple[str, str, str]]
graph: Any
validation: Dict[str, Any]
messages: List[Any]
current_agent: strIt describes a formal type of data, KGSTate, is using Python's typing. We put the SCHEMA to control the country in different steps of the information graph of information. Including information such as the selected title, recorded text, frameworks and relationships, resolved duplicate, synthesis, collaborative messages, and tracking the current agent.
def data_gatherer(state: KGState) -> KGState:
topic = state["topic"]
print(f"📚 Data Gatherer: Searching for information about '{topic}'")
collected_text = f"{topic} is an important concept. It relates to various entities like EntityA, EntityB, and EntityC. EntityA influences EntityB. EntityC is a type of EntityB."
state["messages"].append(AIMessage(content=f"Collected raw text about {topic}"))
state["raw_text"] = collected_text
state["current_agent"] = "entity_extractor"
return stateThis work, data_garer, works as a first step in the pipe. Imitating to collect green text data regarding the title provided (saved in the Province[“topic”]). Keeps this information made in a situation[“raw_text”]Adds a message that reflects the completion of data collection, and updates the Pipeline status by setting the following agent (business_extractor) as it works.
def entity_extractor(state: KGState) -> KGState:
print("🔍 Entity Extractor: Identifying entities in the text")
text = state["raw_text"]
entities = re.findall(r"Entity[A-Z]", text)
entities = [state["topic"]] + entities
state["entities"] = list(set(entities))
state["messages"].append(AIMessage(content=f"Extracted entities: {state['entities']}"))
print(f" Found entities: {state['entities']}")
state["current_agent"] = "relation_extractor"
return stateEmity_Extractor is identifying organizations from the green text using a simple standard pattern of standard relating to the terms such as “Entiitge”, etc. It includes a major topic as a business and ensuring variants by turning a set of set. Released organizations are kept in government, AI message involves the result, as well as a pipeline developed in the relation agent.
def relation_extractor(state: KGState) -> KGState:
print("🔗 Relation Extractor: Identifying relationships between entities")
text = state["raw_text"]
entities = state["entities"]
relations = []
relation_patterns = [
(r"([A-Za-z]+) relates to ([A-Za-z]+)", "relates_to"),
(r"([A-Za-z]+) influences ([A-Za-z]+)", "influences"),
(r"([A-Za-z]+) is a type of ([A-Za-z]+)", "is_type_of")
]
for e1 in entities:
for e2 in entities:
if e1 != e2:
for pattern, rel_type in relation_patterns:
if re.search(f"{e1}.*{rel_type}.*{e2}", text.replace("_", " "), re.IGNORECASE) or
re.search(f"{e1}.*{e2}", text, re.IGNORECASE):
relations.append((e1, rel_type, e2))
state["relations"] = relations
state["messages"].append(AIMessage(content=f"Extracted relations: {relations}"))
print(f" Found relations: {relations}")
state["current_agent"] = "entity_resolver"
return stateRelationships_the user sees the Semantintic relationship between unripe text structures. It uses regex patterns described described in order to identify phrases such as “influences” or “type” between pairs. When the game is found, you add a compatible relationship like Triple (title, predictor, item) in the previous list. This integrated integrated government, the message is logged in to accompany the agent, and regulate the next agent: Entity_Resolver.
def entity_resolver(state: KGState) -> KGState:
print("🔄 Entity Resolver: Resolving duplicate entities")
entity_map = {}
for entity in state["entities"]:
canonical_name = entity.lower().replace(" ", "_")
entity_map[entity] = canonical_name
resolved_relations = []
for s, p, o in state["relations"]:
s_resolved = entity_map.get(s, s)
o_resolved = entity_map.get(o, o)
resolved_relations.append((s_resolved, p, o_resolved))
state["resolved_relations"] = resolved_relations
state["messages"].append(AIMessage(content=f"Resolved relations: {resolved_relations}"))
state["current_agent"] = "graph_integrator"
return stateEmity_rerolver work marked Entity and to avoid duplicate and inconsistency. It creates a map (business_map) by converting each entity into vacancies with spaces with underscores. After that, this Mapment is used in all subjects and things in relationships released to produce solved relationships. These common agencies are added to the government, the verification message is added, and control is transmitted to Graph_Nigglator agent.
def graph_integrator(state: KGState) -> KGState:
print("📊 Graph Integrator: Building the knowledge graph")
G = nx.DiGraph()
for s, p, o in state["resolved_relations"]:
if not G.has_node(s):
G.add_node(s)
if not G.has_node(o):
G.add_node(o)
G.add_edge(s, o, relation=p)
state["graph"] = G
state["messages"].append(AIMessage(content=f"Built graph with {len(G.nodes)} nodes and {len(G.edges)} edges"))
state["current_agent"] = "graph_validator"
return stateGrapho_integrate function makes real information graph using networkx.digraph () supports target relationships. It is included on these resolved items (title, predicted, item), ensures that both areas are available, and add the targeted edge and relationship as a metadata. Outgoing graph is stored in the kingdom, a summary message is included, as well as the change in the graph_validator's final confirmation panement.
def graph_validator(state: KGState) -> KGState:
print("✅ Graph Validator: Validating knowledge graph")
G = state["graph"]
validation_report = {
"num_nodes": len(G.nodes),
"num_edges": len(G.edges),
"is_connected": nx.is_weakly_connected(G) if G.nodes else False,
"has_cycles": not nx.is_directed_acyclic_graph(G) if G.nodes else False
}
state["validation"] = validation_report
state["messages"].append(AIMessage(content=f"Validation report: {validation_report}"))
print(f" Validation report: {validation_report}")
state["current_agent"] = END
return stateGraph_validator function performs a basic health check at the designed information graph. It includes a confirmation report containing the number of locations and edges, even if the graph is well linked to (ie, everywhere is neglected), and the graph containing cycles. This report is added to the State and sign in as ai message. When verification has been completed, the pipe is marked as completed with the existing set of advertising – to complete.
def router(state: KGState) -> str:
return state["current_agent"]
def visualize_graph(graph):
plt.figure(figsize=(10, 6))
pos = nx.spring_layout(graph)
nx.draw(graph, pos, with_labels=True, node_color="skyblue", node_size=1500, font_size=10)
edge_labels = nx.get_edge_attributes(graph, 'relation')
nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels)
plt.title("Knowledge Graph")
plt.tight_layout()
plt.show()
The router function directs the pipe in the next agent based on the current field_Eent in the kingdom. At that time, Visual_graph worker uses matplotlib and networkx to indicate the final graph of, indicating areas, edges, and relationships for visual comprehension label.
def build_kg_graph():
workflow = StateGraph(KGState)
workflow.add_node("data_gatherer", data_gatherer)
workflow.add_node("entity_extractor", entity_extractor)
workflow.add_node("relation_extractor", relation_extractor)
workflow.add_node("entity_resolver", entity_resolver)
workflow.add_node("graph_integrator", graph_integrator)
workflow.add_node("graph_validator", graph_validator)
workflow.add_conditional_edges("data_gatherer", router,
{"entity_extractor": "entity_extractor"})
workflow.add_conditional_edges("entity_extractor", router,
{"relation_extractor": "relation_extractor"})
workflow.add_conditional_edges("relation_extractor", router,
{"entity_resolver": "entity_resolver"})
workflow.add_conditional_edges("entity_resolver", router,
{"graph_integrator": "graph_integrator"})
workflow.add_conditional_edges("graph_integrator", router,
{"graph_validator": "graph_validator"})
workflow.add_conditional_edges("graph_validator", router,
{END: END})
workflow.set_entry_point("data_gatherer")
return workflow.compile()Build_grad work describes the full graphic graph using the Langgraph. Includes in order in each agent as a collection area, from data collection to the productivity of the graph, and adorned the conditional changes based on the current agent. The logging point has been placed on the Data_galer, and the graph is included in the effective function of work that is default from the beginning to the end.
def run_knowledge_graph_pipeline(topic):
print(f"🚀 Starting knowledge graph pipeline for: {topic}")
initial_state = {
"topic": topic,
"raw_text": "",
"entities": [],
"relations": [],
"resolved_relations": [],
"graph": None,
"validation": {},
"messages": [HumanMessage(content=f"Build a knowledge graph about {topic}")],
"current_agent": "data_gatherer"
}
kg_app = build_kg_graph()
final_state = kg_app.invoke(initial_state)
print(f"✨ Knowledge graph construction complete for: {topic}")
return final_stateThe work of Run_Nowment_Graph_pipeline starts with a pipe by setting the blank scenario on the provided topic. Creates work flow using buolder_kg_graph (), and run it by installing the mixed graph. Since each agent processs information, the State appears, and the final result contains complete, certified and ready for use.
if __name__ == "__main__":
topic = "Artificial Intelligence"
result = run_knowledge_graph_pipeline(topic)
visualize_graph(result["graph"])Finally, this block works as an entrance to the text. When specifically executed, it causes a pipe of information on the article “artificial intelligence,” running to all agencies, and eventually you can see graph found using visual_agraph () work. Provides the end of the end of the end of the default graph generation.
In conclusion, we learn how we can unite the seam without many special agents in the meeting pipe to meet with the organized way, Levering Langgraph and Networkx. This is the default business eruption of business and processing processes and recognizes a complex relationship, which gives a clear representation and performance of the data collected. By changing and improving the agents of each, such as the use of the visual system inngency or combining actual data sources, these basic data may be measured and desirable for construction projects.
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