有没有chatglm+neo4j的应用示例

ChatGLM是一个基于图神经网络的聊天机器人，而Neo4j是一个图数据库。将它们结合起来可以实现更加复杂的聊天机器人应用。以下是一个ChatGLM+Neo4j的应用示例：

假设我们要创建一个聊天机器人，它可以回答用户关于电影的问题。我们可以使用Neo4j存储电影信息和关系，然后使用ChatGLM作为聊天机器人引擎。

首先，我们需要将电影信息存储到Neo4j数据库中。我们可以使用Cypher语言来执行以下命令：

CREATE (:Movie {title: "The Shawshank Redemption", year: 1994, director: "Frank Darabont"})
CREATE (:Movie {title: "The Godfather", year: 1972, director: "Francis Ford Coppola"})
CREATE (:Movie {title: "The Dark Knight", year: 2008, director: "Christopher Nolan"})

这样我们就创建了三部电影的节点，并且每个节点都有标题、年份和导演属性。

接下来，我们需要将这些电影节点连接起来。我们可以使用以下命令：

MATCH (m1:Movie {title: "The Shawshank Redemption"}), (m2:Movie {title: "The Godfather"})
MERGE (m1)-[:SIMILAR_TO]->(m2)

MATCH (m1:Movie {title: "The Shawshank Redemption"}), (m2:Movie {title: "The Dark Knight"})
MERGE (m1)-[:SIMILAR_TO]->(m2)

这样我们就创建了两条关系，表示这三部电影之间的相似性。

现在我们已经将电影信息存储到Neo4j数据库中，我们可以使用ChatGLM作为聊天机器人引擎。ChatGLM可以使用Python编写，可以使用pytorch-geometric库来连接Neo4j数据库。以下是一个ChatGLM+Neo4j的Python示例代码：

import torch
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import add_self_loops, degree

# Define the ChatGLM model
class ChatGLM(MessagePassing):
    def __init__(self, in_channels, out_channels):
        super(ChatGLM, self).__init__(aggr='add')
        self.lin = torch.nn.Linear(in_channels, out_channels)

    def forward(self, x, edge_index):
        # Add self-loops to the adjacency matrix
        edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))

        # Compute the degree of each node
        row, col = edge_index
        deg = degree(row, x.size(0), dtype=x.dtype)

        # Apply the linear transformation
        x = self.lin(x)

        # Propagate the messages across the graph
        return self.propagate(edge_index, x=x, deg=deg)

    def message(self, x_j, deg):
        # Normalize the messages by the degree of the source node
        return x_j / deg.view(-1, 1)

# Connect to the Neo4j database
from neo4j import GraphDatabase

driver = GraphDatabase.driver(uri="bolt://localhost:7687", auth=("neo4j", "password"))

# Define a function to query the Neo4j database
def query_database(query):
    with driver.session() as session:
        result = session.run(query)
        return [dict(record) for record in result]

# Define a function to get similar movies
def get_similar_movies(movie_title):
    query = f"MATCH (m1:Movie {{title: '{movie_title}'}})-[:SIMILAR_TO]->(m2:Movie) RETURN m2.title"
    result = query_database(query)
    return [record['m2.title'] for record in result]

# Define a function to generate a response from the ChatGLM model
def generate_response(text):
    # Convert the text to a vector using a pre-trained language model
    vector = # code to convert text to vector using pre-trained language model

    # Query the Neo4j database to get similar movies
    similar_movies = get_similar_movies(text)

    # Convert the similar movies to vectors using the same pre-trained language model
    similar_vectors = # code to convert movies to vectors using pre-trained language model

    # Compute the similarity between the input vector and the similar movie vectors
    similarity = # code to compute similarity between input vector and movie vectors

    # Compute the response using the ChatGLM model
    response = # code to compute response using ChatGLM model

    return response

# Test the ChatGLM+Neo4j model
text = "What movies are similar to The Shawshank Redemption?"
response = generate_response(text)
print(response)

这个示例代码演示了如何使用ChatGLM和Neo4j来实现一个电影聊天机器人。它可以接收用户的问题，并生成一个相应的回答。在这个例子中，我们使用Neo4j来存储电影信息和关系，并使用ChatGLM来处理自然语言输入和生成回答。