根据搜索提示查找电影

一旦电影节点具有编码其标题和情节的嵌入，并且数据库中存在这些嵌入的向量索引，您就可以根据模糊的描述（一个搜索提示）检索电影，这有点像您查询搜索引擎以根据几个关键词查找相关网页。

本页中的示例展示了如何检索与提示一个罪犯因爱而改变相关的电影。您可以想象这就像去电影院问：“您会向我推荐哪些电影？今天我想看一部罪犯因爱而改变的电影。” 主要区别在于，在电影院对话以自然语言进行，而在数据库中搜索时，提示首先被转换为嵌入。然后，向量索引可以使用提示嵌入来检索与搜索提示最相似的节点。

始终使用相同的模型生成嵌入：选择一个模型，并用它为数据集和搜索提示生成嵌入。尝试混合使用不同向量大小的模型将导致错误。具有相同向量大小的模型将起作用，但它们之间不太可能很好地交互，因为它们的训练方式很可能不同。

使用开源库生成的嵌入相似度

此示例使用SentenceTransformers来检索与描述一个罪犯因爱而改变相关的节点。

from sentence_transformers import SentenceTransformer
import neo4j


URI = '<URI for Neo4j database>'
AUTH = ('<Username>', '<Password>')
DB_NAME = '<Database name>'  # examples: 'recommendations-50', 'neo4j'

driver = neo4j.GraphDatabase.driver(URI, auth=AUTH)
driver.verify_connectivity()

model = SentenceTransformer('all-MiniLM-L6-v2')  (1)

query_prompt = 'a criminal is changed through love'  (2)
query_embedding = model.encode(query_prompt)

related_movies, _, _ = driver.execute_query('''  (3)
    CALL db.index.vector.queryNodes('moviePlots', 5, $queryEmbedding)
    YIELD node, score
    RETURN node.title AS title, node.plot AS plot, score
    ''', queryEmbedding=query_embedding,
    database_=DB_NAME)
print(f'Movies whose plot and title relate to `{query_prompt}`:')
for record in related_movies:
    print(record)
1 重要的是，为搜索提示生成嵌入所使用的模型，必须与用于生成您搜索的嵌入的模型相同。本教程使用all-MiniLM-L6-V2生成嵌入，这里也再次使用了该模型。
2 查询提示包含对要检索的电影的模糊描述。然后将其编码为嵌入，以便可以用于查询相似节点。
3 要查询向量索引，请使用 Cypher 过程 db.index.vector.queryNodes。数据库将从向量索引moviePlots中返回与query_embedding最相似的5个节点，以及它们与查询嵌入的匹配程度分数。 
Movies whose plot and title relate to `a criminal is changed through love`:
<Record title='I Love You Phillip Morris' plot="A cop turns con man once he comes out of the closet. Once imprisoned, he meets the second love of his life, whom he'll stop at nothing to be with." score=0.792834997177124>
<Record title='Laura' plot='A police detective falls in love with the woman whose murder he is investigating.' score=0.7741715908050537>
<Record title='Despicable Me' plot='When a criminal mastermind uses a trio of orphan girls as pawns for a grand scheme, he finds their love is profoundly changing him for the better.' score=0.772994875907898>
<Record title='Laws of Attraction' plot='Amidst a sea of litigation, two New York City divorce lawyers find love.' score=0.7727792263031006>
<Record title='Love the Hard Way' plot='The story of a petty thief who meets an innocent young woman and brings her into his world of crime while she teaches him the lessons of enjoying life and being loved.' score=0.7681001424789429>

使用 OpenAI 和其他云服务生成的嵌入相似度

此示例使用 OpenAI 来检索与描述一个罪犯因爱而改变相关的节点。

import neo4j


URI = '<URI for Neo4j database>'
AUTH = ('<Username>', '<Password>')
DB_NAME = '<Database name>'  # examples: 'recommendations-50', 'neo4j'
driver = neo4j.GraphDatabase.driver(URI, auth=AUTH)
driver.verify_connectivity()

openAI_token = '<OpenAI API token>'  (1)

search_prompt = 'a criminal is changed through love'  (2)

search_query = '''
WITH genai.vector.encode($searchPrompt, 'OpenAI', { token: $token }) AS queryVector  (3)
CALL db.index.vector.queryNodes('moviePlots', 5, queryVector)  (4)
YIELD node, score
RETURN node.title as title, node.plot, score
'''
records, summary, _ = driver.execute_query(
    search_query, searchPrompt=search_prompt, token=openAI_token,
    database_=DB_NAME)
print(f'Movies whose plot and title relate to `{search_prompt}`:')
for record in records:
    print(record)
1 您的 OpenAI API 令牌，例如sk-proj-XXXX。
2 查询提示包含对要检索的电影的模糊描述。
3 查询提示通过 Cypher 函数 genai.vector.encode() 编码为嵌入，以便可以用于查询相似节点。
4 要查询向量索引，请使用 Cypher 过程 db.index.vector.queryNodes。数据库将从向量索引moviePlots中返回与queryVector最相似的5个节点，以及它们与查询嵌入的匹配程度分数。 
Movies whose plot and title relate to `a criminal is changed through love`:
<Record title='I Love You Phillip Morris' node.plot="A cop turns con man once he comes out of the closet. Once imprisoned, he meets the second love of his life, whom he'll stop at nothing to be with." score=0.9272396564483643>
<Record title='Love the Hard Way' node.plot='The story of a petty thief who meets an innocent young woman and brings her into his world of crime while she teaches him the lessons of enjoying life and being loved.' score=0.9221653938293457>
<Record title='Laura' node.plot='A police detective falls in love with the woman whose murder he is investigating.' score=0.9215129017829895>
<Record title='Despicable Me' node.plot='When a criminal mastermind uses a trio of orphan girls as pawns for a grand scheme, he finds their love is profoundly changing him for the better.' score=0.9206478595733643>
<Record title='Cook the Thief His Wife & Her Lover, The' node.plot='The wife of an abusive criminal finds solace in the arms of a kind regular guest in her husbands restaurant.' score=0.9205931425094604>

匹配质量

匹配质量完全取决于嵌入模型和数据集，而不是 Neo4j 向量索引。 嵌入始终在 Neo4j 外部生成；数据库仅将其作为属性存储。

考虑使用 SentenceTransformers 检索到的与搜索提示一个罪犯因爱而改变相关的节点（OpenAI 的结果类似）

Movies whose plot and title relate to `a criminal is changed through love`:
<Record title='I Love You Phillip Morris' plot="A cop turns con man once he comes out of the closet. Once imprisoned, he meets the second love of his life, whom he'll stop at nothing to be with." score=0.792834997177124>
<Record title='Laura' plot='A police detective falls in love with the woman whose murder he is investigating.' score=0.7741715908050537>
<Record title='Despicable Me' plot='When a criminal mastermind uses a trio of orphan girls as pawns for a grand scheme, he finds their love is profoundly changing him for the better.' score=0.772994875907898>
<Record title='Laws of Attraction' plot='Amidst a sea of litigation, two New York City divorce lawyers find love.' score=0.7727792263031006>
<Record title='Love the Hard Way' plot='The story of a petty thief who meets an innocent young woman and brings her into his world of crime while she teaches him the lessons of enjoying life and being loved.' score=0.7681001424789429>

此示例显示嵌入按预期工作：Despicable Me 以 77% 的相关性得分排在第三位。与此同时，它也显示了嵌入模型的局限性，因为它检索到的电影与提示并不真正相关

  • Laura 没有“因爱而改变的罪犯”，但它有一个在谋杀背景下坠入爱河的警察侦探（他经常与罪犯打交道）（再次与罪犯相关）。

  • Laws of Attraction 完全没有罪犯，但它有：与爱相关的吸引；通常发生在法院并与罪犯相关的诉讼；经常与罪犯联系在一起的律师；以及爱，尽管是在律师之间。

  • Love the Hard Way 几乎是反过来的：一个无辜的学生爱上了一个低级的罪犯（一个小偷），并陷入了堕落的深渊。

即使这些电影与搜索提示几乎不相关，数据库是正确的：它们是根据嵌入最相关的。为什么嵌入没有按预期方式编码含义，这是一个与向量索引无关，而与外部 AI 模型完全相关的问题。如果您的搜索提示返回的结果不佳，您应该调查嵌入模型及其应用的数据集，而不是调整 Neo4j 的设置。

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