Added dynamic graph creation
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Vasilije
parent
8d1038ae98
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b7cf9a69b2
1 changed files with 145 additions and 62 deletions
207
level_4/main.py
207
level_4/main.py
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@ -43,7 +43,7 @@ OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
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txt_path = "dune.txt"
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graph = Neo4jGraph(url="bolt://localhost:7687", username="neo4j", password="pleaseletmein")
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import openai
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import instructor
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@ -61,20 +61,6 @@ class Node(BaseModel):
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color: str ="blue"
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memory_type: str
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#
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# class EntityNode(BaseModel):
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# id: int
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# description: str
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#
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#
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# class TimeContextNode(BaseModel):
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# id: int
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# description: str
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#
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#
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# class ActionNode(BaseModel):
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# id: int
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# description: str
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class Edge(BaseModel):
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@ -129,28 +115,10 @@ def generate_graph(input) -> KnowledgeGraph:
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# async def memory_route(self, memory_type: str):
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# @ai_classifier
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# class MemoryRoute(Enum):
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# """Represents classifer for type of memories"""
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#
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# semantic_memory = "semantic_memory"
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# episodic_memory = "episodic_memory"
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#
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#
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# namespace = MemoryRoute(str(memory_type))
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#
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# return namespace
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#
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# graph = generate_graph("I went to a walk in the forest in the afternoon and got information from a book.")
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# # print("got here")
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# #
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# print(graph)
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def execute_cypher_query(query: str):
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graph.query(query)
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graph_ = Neo4jGraph(url="bolt://localhost:7687", username="neo4j", password="pleaseletmein")
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graph_.query(query)
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# This is a placeholder for the logic that will execute the Cypher query
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# You would replace this with the actual logic to run the query in your Neo4j database
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print(query)
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@ -202,29 +170,8 @@ def execute_cypher_query(query: str):
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# )
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def create_cypher_queries_from_graph(graph:str, user_id: str):
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# Create nodes
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# Create the user and memory components if they don't exist
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user_memory_cypher = f"""
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MERGE (user:User {{userId: '{user_id}'}})
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MERGE (semantic:SemanticMemory {{userId: '{user_id}'}})
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MERGE (episodic:EpisodicMemory {{userId: '{user_id}'}})
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MERGE (buffer:Buffer {{userId: '{user_id}'}})
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MERGE (user)-[:HAS_SEMANTIC_MEMORY]->(semantic)
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MERGE (user)-[:HAS_EPISODIC_MEMORY]->(episodic)
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MERGE (user)-[:HAS_BUFFER]->(buffer)
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"""
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# Combine all Cypher queries
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combined_cypher_query = f"""
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{user_memory_cypher}
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{graph}
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"""
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return combined_cypher_query
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from graphviz import Digraph
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@ -256,24 +203,160 @@ def visualize_knowledge_graph(kg: KnowledgeGraph):
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dot.render("knowledge_graph.gv", view=True)
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import uuid
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def create_base_queries_from_user( user_id: str):
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# Create the user and memory components if they don't exist
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user_memory_cypher = f"""
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MERGE (user:User {{userId: '{user_id}'}})
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MERGE (semantic:SemanticMemory {{userId: '{user_id}'}})
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MERGE (episodic:EpisodicMemory {{userId: '{user_id}'}})
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MERGE (buffer:Buffer {{userId: '{user_id}'}})
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MERGE (user)-[:HAS_SEMANTIC_MEMORY]->(semantic)
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MERGE (user)-[:HAS_EPISODIC_MEMORY]->(episodic)
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MERGE (user)-[:HAS_BUFFER]->(buffer)
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"""
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return user_memory_cypher
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# Function to append a UUID4 to the variable names to ensure uniqueness
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def append_uuid_to_variable_names(variable_mapping):
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unique_variable_mapping = {}
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for original_name in variable_mapping.values():
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unique_name = f"{original_name}_{uuid.uuid4().hex}"
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unique_variable_mapping[original_name] = unique_name
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return unique_variable_mapping
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# Update the functions to use the unique variable names
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def create_node_variable_mapping(nodes):
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mapping = {}
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for node in nodes:
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variable_name = f"{node['category']}{node['id']}".lower()
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mapping[node['id']] = variable_name
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return mapping
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def create_edge_variable_mapping(edges):
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mapping = {}
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for edge in edges:
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# Construct a unique identifier for the edge
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variable_name = f"edge{edge['source']}to{edge['target']}".lower()
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mapping[(edge['source'], edge['target'])] = variable_name
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return mapping
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# Update the function to generate Cypher CREATE statements for nodes with unique variable names
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def format_dict(d):
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# Initialize an empty list to store formatted items
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formatted_items = []
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# Iterate through all key-value pairs
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for key, value in d.items():
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# Format key-value pairs with a colon and space, and adding quotes for string values
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formatted_item = f"{key}: '{value}'" if isinstance(value, str) else f"{key}: {value}"
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formatted_items.append(formatted_item)
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# Join all formatted items with a comma and a space
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formatted_string = ", ".join(formatted_items)
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# Add curly braces to mimic a dictionary
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formatted_string = f"{{{formatted_string}}}"
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return formatted_string
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def generate_create_statements_for_nodes_with_uuid(nodes, unique_mapping):
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create_statements = []
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for node in nodes:
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original_variable_name = node_variable_mapping[node['id']]
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unique_variable_name = unique_mapping[original_variable_name]
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node_label = node['category'].capitalize()
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properties = {k: v for k, v in node.items() if k not in ['id', 'category']}
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try:
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properties = format_dict(properties)
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except:
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pass
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create_statements.append(f"CREATE ({unique_variable_name}:{node_label} {properties})")
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return create_statements
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# Update the function to generate Cypher CREATE statements for edges with unique variable names
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def generate_create_statements_for_edges_with_uuid(edges, unique_mapping):
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create_statements = []
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with_statement = f"WITH {', '.join(unique_mapping.values())}, user, semantic, episodic, buffer"
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create_statements.append(with_statement)
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for edge in edges:
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# print("HERE IS THE EDGE", edge)
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source_variable = unique_mapping[node_variable_mapping[edge['source']]]
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target_variable = unique_mapping[node_variable_mapping[edge['target']]]
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relationship = edge['description'].replace(" ", "_").upper()
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create_statements.append(f"CREATE ({source_variable})-[:{relationship}]->({target_variable})")
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return create_statements
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# Update the function to generate Cypher CREATE statements for memory type relationships with unique variable names
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def generate_memory_type_relationships_with_uuid_and_time_context(nodes, unique_mapping):
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create_statements = []
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with_statement = f"WITH {', '.join(unique_mapping.values())}, user, semantic, episodic, buffer"
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create_statements.append(with_statement)
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# Loop through each node and create relationships based on memory_type
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for node in nodes:
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original_variable_name = node_variable_mapping[node['id']]
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unique_variable_name = unique_mapping[original_variable_name]
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if node['memory_type'] == 'semantic':
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create_statements.append(f"CREATE (semantic)-[:HAS_KNOWLEDGE]->({unique_variable_name})")
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elif node['memory_type'] == 'episodic':
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create_statements.append(f"CREATE (episodic)-[:HAS_EVENT]->({unique_variable_name})")
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if node['category'] == 'time':
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create_statements.append(f"CREATE (buffer)-[:HAS_TIME_CONTEXT]->({unique_variable_name})")
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# Assuming buffer holds all actions and times
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# if node['category'] in ['action', 'time']:
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create_statements.append(f"CREATE (buffer)-[:CURRENTLY_HOLDING]->({unique_variable_name})")
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return create_statements
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# Main execution logic
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if __name__ == "__main__":
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user_id = "User1"
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query_input = "I walked in the forest yesterday and added to my list I need to buy some milk in the store"
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# Generate the knowledge graph from the user input
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# knowledge_graph = generate_graph(query_input)
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knowledge_graph = generate_graph(query_input)
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visualize_knowledge_graph(knowledge_graph)
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# out = knowledge_graph.dict()
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# print(out)
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graph: KnowledgeGraph = generate_graph("I walked in the forest yesterday and added to my list I need to buy some milk in the store")
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print(graph.dict())
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visualize_knowledge_graph(graph)
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#
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# graph: KnowledgeGraph = generate_graph("I walked in the forest yesterday and added to my list I need to buy some milk in the store")
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# graph_dic = graph.dict()
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#
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# node_variable_mapping = create_node_variable_mapping(graph_dic['nodes'])
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# edge_variable_mapping = create_edge_variable_mapping(graph_dic['edges'])
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# # Create unique variable names for each node
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# unique_node_variable_mapping = append_uuid_to_variable_names(node_variable_mapping)
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# unique_edge_variable_mapping = append_uuid_to_variable_names(edge_variable_mapping)
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# create_nodes_statements = generate_create_statements_for_nodes_with_uuid(graph_dic['nodes'], unique_node_variable_mapping)
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# create_edges_statements = generate_create_statements_for_edges_with_uuid(graph_dic['edges'], unique_node_variable_mapping)
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#
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# memory_type_statements_with_uuid_and_time_context = generate_memory_type_relationships_with_uuid_and_time_context(
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# graph_dic['nodes'], unique_node_variable_mapping)
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#
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# # # Combine all statements
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# cypher_statements = [create_base_queries_from_user(user_id)] + create_nodes_statements + create_edges_statements + memory_type_statements_with_uuid_and_time_context
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# cypher_statements_joined = "\n".join(cypher_statements)
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#
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# print(cypher_statements_joined)
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#
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# execute_cypher_query(cypher_statements_joined)
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# Translate the KnowledgeGraph into Cypher queries
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# cypher_query = create_cypher_queries_from_graph(out['graph_query'], user_id)
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# print(cypher_query)
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# #
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