python代码实现es文章内容向量化并搜索
前置条件：
- docker环境
- vscode编辑器
bash
# 拉取镜像
docker pull elasticsearch:7.17.7
# 创建网络
docker network create es-net-7.17
# 启动命令
# 路径需要根据实际情况修改！！！
docker run --name es7.17 -p 9200:9200 -p 9300:9300 \
-e "discovery.type=single-node" \
-e ES_JAVA_OPTS="-Xms512m -Xmx512m" \
-v "D:\docker\es7.17\config\elasticsearch.yml":/usr/share/elasticsearch/config/elasticsearch.yml \
-v "D:\docker\es7.17\data":/usr/share/elasticsearch/data \
-v "D:\docker\es7.17\plugins":/usr/share/elasticsearch/plugins \
--network es-net-7.17 \
-d elasticsearch:7.17.0
# 设置密码
docker exec -it es7.17 /bin/bash
./bin/elasticsearch-setup-passwords auto
# 设置密码返回内容
Changed password for user apm_system
PASSWORD apm_system = ySeDmRr68wtWbv2wyueu
Changed password for user kibana_system
PASSWORD kibana_system = OSFamv1KYzDcZVb885ka
Changed password for user kibana
PASSWORD kibana = OSFamv1KYzDcZVb885ka
Changed password for user logstash_system
PASSWORD logstash_system = UkrEiVXFcyvvXkPE4nkV
Changed password for user beats_system
PASSWORD beats_system = dCoAoLvnsRC0kycFdJcX
Changed password for user remote_monitoring_user
PASSWORD remote_monitoring_user = qZm3chw66jmo1UNPWpwn
Changed password for user elastic
# 记住这是es的密码
PASSWORD elastic = NBh5ObTk1lq2YZ4JQ2Ng文章内容json文件，esData.json
bash
{"id": "01146c1cac8a28009068c089d4d83f537f", "cont": "尽管本赛季多支球队通过调整阵容提升了整体实力，但从表现来看，雷霆队仍然遥遥领先，极有可能打破自2018年勇士以来无球队实现卫冕的魔咒。除了雷霆队有望再度问鼎总冠军外，亚历山大更是有机会蝉联常规赛MVP，并且很可能是一骑绝尘。\n评选常规赛MVP的首要标准就是球队的战绩，亚历山大小可不必操心这一点。在赛季前13场比赛中，雷霆以12胜1负的佳绩开局，虽然他们的八连胜被终结，但依照当前的势头，球队有望展开更长时间的连胜记录。放眼整个联盟，雷霆几乎没有对手，只要他们获得联盟前二的战绩，亚历山大当选MVP便毫无压力。\n此外，亚历山大的个人数据同样令他在竞争中立于不败之地。在雷霆本赛季的一些比赛中，他们以大比分击败对手，亚历山大至今已经有7场比赛实现三节打卡的成就。到目前为止，他的场均数据达到32.5分、5.2个篮板、6.6次助攻、1.2次抢断和1次封盖，且已连续85场比赛得分超过20分，名列历史第三，这正是他争夺MVP的强有力依据。\n不仅如此，亚历山大的个人形象也相当出色。虽然早期联盟倾向于宣传莫兰特的明星潜力，但由于其个性问题和场外行为失控，联盟自然不会把这样的球员推向前台。相比之下，亚历山大则显得沉稳，几乎没有负面新闻，再加上球队的优秀战绩，自然成为了受到重点关注的对象。\n在许多MVP的角逐者中，本赛季的迹象显示，亚历山大几乎没有对手可言。塔图姆因伤缺席，显著影响了凯尔特人的战绩，自己也退出了MVP的竞争。而约基奇虽然实力出众，但关注度有所减退。字母哥和东契奇的数据虽足以与亚历山大一较高下，但他们的球队战绩并不理想，加之在常规赛中均败给雷霆，使得他们的竞争力下降。\n纵观NBA最近30年的MVP榜单，后卫球员一直是主流。自乔丹时代起，后卫通常是MVP评选中的常客，仅有奥尼尔这样的统治级中锋才对他们构成威胁。现如今，联盟亟需培养优质后卫，以应对时代变迁，而亚历山大正是最合适的人选。\n尽管亚历山大的起点并不高，但凭借着出色的表现，他已深得认可，甚至带领雷霆团队重整旗鼓，从而夺得总冠军。综合实力和外部因素，亚历山大有望成功卫冕常规赛MVP，并开启属于自己的新时代。", "cont_vector": [0.4523310661315918, 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{"id": "019693495e04a3d5d6e85ae5ee0de8c823", "cont": "常规赛中，快船在客场以118-121惜败于凯尔特人，遭遇了令人遗憾的失败。此前快船已经经历了六连败，虽然他们刚刚在一场激烈的双加时大战中击败了独行侠，但面对东部倒数第五的凯尔特人，球队再次吃瘪，并且输得相当彻底。从比赛开始到结束，快船始终未曾领先，分差最多一度拉开到24分。\n尽管如此，哈登的表现堪称本场最大亮点，他全场出场38分钟，投篮22中9，三分11中5，罚球15中14，豪取37分7篮板8助攻的数据。值得注意的是，哈登上半场状态并不理想，投篮命中率只有1中7，仅得到5分；但进入下半场后则如同换了个人，单枪匹马砍下了32分，尤其是第四节狂飙18分，几乎一人挑起了快船反扑的大旗。\n比赛第四节还剩5分钟时，快船仍以98-108落后10分，随后哈登带领球队开启疯狂追分模式：连续多次上篮、三分外线命中、两罚全中，搭配科林斯的扣篮，短短五分钟内哈登就贡献了球队最后20分中的18分。在最后不到30秒的时间里，哈登更是连续拿到9分，将差距缩小到仅差1分，似乎胜利就在眼前。\n遗憾的是，关键时刻哈登的绝平三分偏出。如果那一球能进，不仅快船将完成惊天大逆转，这段22秒连得12分的神奇表现还可能载入NBA史册，与麦迪35秒13分的传奇时刻齐名。然而如今只能叹息"差一点"，布置这次进攻的教练显然不是泰伦卢，整体战术安排也引发球迷讨论。\n有评论指出，哈登最后几分钟的拼搏恰似他整个职业生涯的写照：无比神勇却总是差一点点就能创造历史。今年哈登确实付出了巨大努力，最近四场比赛的数据显示他的火爆状态：\n- 对阵老鹰：35分10篮板11助攻\n- 对阵掘金：23分8篮板5助攻\n- 对阵独行侠：41分14篮板11助攻\n- 对阵凯尔特人：37分7篮板8助攻\n四场比赛平均上场39分钟，场均34分9.8篮板8.8助攻接近三双的豪华数据，但快船只拿下一场胜利。而且，本赛季快船开局艰难，受困于多个伤病与状态下滑------比尔早早报销，伦纳德伤缺，小保罗和"大洛"状态低迷，祖巴茨也无法延续去年表现。更雪上加霜的是，备受期待的小琼斯本场负伤退出，第四节已拄拐离场，后场阵容捉襟见肘。\n哈登状态虽接近巅峰，但单打独斗绝非长久之计。快船必须尽快找到破解方案，为哈登分担压力，补强阵容深度。否则，今年快船连季后赛门票都难保，不免成为联盟的笑柄。希望球队管理层能警觉起来，不负哈登的全力以赴。", "cont_vector": [0.34697583317756653, 0.022833378985524178, -0.3224949240684509, 0.18710270524024963, -0.2006617784500122, 0.16572287678718567, -0.05492241680622101, -0.42402806878089905, -0.5780504941940308, -0.011707579717040062, 0.22610926628112793, -0.3112683594226837, 0.060010943561792374, 0.10769341140985489, 0.6409831047058105, 0.6394708156585693, -0.2248380035161972, -0.39919424057006836, 0.3614487946033478, 0.11376171559095383, 0.017768414691090584, 0.42112088203430176, 0.5222823023796082, -0.18153122067451477, -0.1691824495792389, -0.03321228548884392, 0.015711788088083267, 0.08341602981090546, 0.29281875491142273, 0.11923649907112122, 0.23919551074504852, -0.045968540012836456, 0.18990826606750488, 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-0.14228376746177673, -0.008141127415001392, -0.003158475970849395, -0.2257232964038849, -0.4020027816295624]}新建kibana.yml,配置上面得到的kibana_system密码
bash
server.host: "0.0.0.0"
# 这里es7.17是docker启动es的容器名字，kibana就可以连接es了
elasticsearch.hosts: ["http://es7.17:9200"]
# 固定值
elasticsearch.username: "kibana_system"
# 初始化es密码得到的众多密码中的kibana_system密码
elasticsearch.password: "OSFamv1KYzDcZVb885ka"
# 开启账号密码
xpack.security.enabled: truekibana docker启动命令 -v 内容，根据实际路径自己修改！！
bash
docker run --name kibana7.17 \
-p 5601:5601 \
-e TZ="Asia/Shanghai" \
-v "D:\docker\kibana7.17\config\kibana.yml":/usr/share/kibana/config/kibana.yml \
--network es-net-7.17 \
-d kibana:7.17.0打开http://localhost:5601, 输入账号密码登录，在devtool中新建索引
bash
PUT /article-my
{
"mappings": {
"properties": {
"cont": {
"type": "text"
},
"cont_vector": {
"type": "dense_vector",
"dims": 1024
}
}
}
}项目依赖requirement.txt
bash
anyio==4.11.0
certifi==2025.11.12
charset-normalizer==3.4.4
colorama==0.4.6
elastic-transport==9.2.0
elasticsearch==7.17.12
filelock==3.19.1
fsspec==2025.10.0
huggingface-hub==0.36.0
idna==3.11
Jinja2==3.1.6
joblib==1.5.2
MarkupSafe==3.0.3
mpmath==1.3.0
networkx==3.2.1
numpy==2.0.2
packaging==25.0
pandas==2.3.3
pillow==11.3.0
python-dateutil==2.9.0.post0
pytz==2025.2
PyYAML==6.0.3
regex==2025.11.3
requests==2.32.5
safetensors==0.6.2
scikit-learn==1.6.1
scipy==1.13.1
sentence-transformers==5.1.2
six==1.17.0
sniffio==1.3.1
sympy==1.14.0
threadpoolctl==3.6.0
tokenizers==0.22.1
torch==2.8.0
tqdm==4.67.1
transformers==4.57.1
typing_extensions==4.15.0
tzdata==2025.2
urllib3==1.26.20导入esData.json文件内容到es，python代码
python
from elasticsearch import Elasticsearch
import json
# ======================
# 1. 配置 Elasticsearch 连接
# ======================
# 替换为你的 ES 地址，比如单机版、Docker、云服务地址等
es_host = "http://elastic:NBh5ObTk1lq2YZ4JQ2Ng@localhost:9200" # 例如：http://your-es-server:9200
es = Elasticsearch([es_host])
# 检查 ES 是否可连接（可选）
if not es.ping():
raise ValueError("❌ 无法连接到 Elasticsearch，请检查 ES 地址是否正确或服务是否启动")
else:
print("✅ 已连接到 Elasticsearch")
# ======================
# 2. 配置索引名称
# ======================
index_name = "article-my" # 你要写入的 ES 索引名，可自定义（如：users, products, logs）
# 如果索引不存在，ES 会在第一次插入时自动创建（根据数据结构推断 mapping）
# 你也可以提前手动创建索引和 mapping（推荐用于生产环境）
# ======================
# 3. 读取 data.json 文件并逐行写入 ES
# ======================
# jsonl_file_path = "exported_data.json" # 你的 jsonl 文件路径
jsonl_file_path = "esData.json" # 你的 jsonl 文件路径
def import_jsonlines_to_es():
success_count = 0
error_count = 0
with open(jsonl_file_path, "r", encoding="utf-8") as f:
for line_number, line in enumerate(f, 1):
line = line.strip() # 去掉首尾空白字符
if not line:
print(f"⚠️ 第 {line_number} 行为空，跳过")
continue
try:
# 解析 JSON
doc = json.loads(line)
except json.JSONDecodeError as e:
print(f"❌ 第 {line_number} 行 JSON 解析失败：{e}")
error_count += 1
continue
try:
# 写入到 Elasticsearch
# 如果不指定 _id，ES 会自动生成
id = doc.get("id") # 如果 JSON 中有 _id 字段，可以用它作为文档 ID
response = es.index(index=index_name, id=id, document=doc)
# 或者，如果你想指定文档 _id，可以这样：
# response = es.index(index=index_name, id=doc.get("id"), document=doc)
success_count += 1
if line_number % 10 == 0:
print(f"🟢 已导入第 {line_number} 行，当前成功：{success_count}，失败：{error_count}")
except Exception as e:
print(f"❌ 第 {line_number} 行写入 ES 失败：{e}")
error_count += 1
print(f"🎉 导入完成！成功：{success_count} 条，失败：{error_count} 条")
# ======================
# 4. 执行导入
# ======================
if __name__ == "__main__":
import_jsonlines_to_es()因为esData.json中数据导入之后，向量字段有值，需要置空,devtool中执行
bash
## 设置cont_vector字段为空
POST /article-my/_update_by_query
{
"script": {
"source": "ctx._source.remove('cont_vector')",
"lang": "painless"
}
}读取es内容，重新向量化，python代码
python
from elasticsearch import Elasticsearch
import json
from test_tengxun_hunyuan import get_embedding
from es_doc_embeding import get_embedding as get_embedding_local
# 连接到你的旧 Elasticsearch 集群
es = Elasticsearch(["http://elastic:NBh5ObTk1lq2YZ4JQ2Ng@localhost:9200"])
index_name = "article-my"
field_to_export = "cont"
# 查询数据
results = []
def getDocs():
page = es.search(
index=index_name,
_source=[field_to_export],
size=1000,
body={"query": {"bool": {
"must_not": {"exists": {"field": "cont_vector"}
}
}}}
# ,scroll='2m'
)
# print(page)
# scroll_id = page['_scroll_id']
print(f"文档数量：{page['hits']['total']['value']}" )
hits = page['hits']['hits']
return hits
def updateDocVender(doc_id, cont_vector):
es.update(
index=index_name,
id=doc_id,
doc = {
"cont_vector": cont_vector
}
)
def getVenderById(doc_id):
res = es.get(
index=index_name,
id=doc_id
)
if 'cont_vector' not in res['_source']:
return None
return res['_source']['cont_vector']
def calVender(embedding_type='tengxun'):
hits = getDocs()
for hit in hits:
cont_vender_repo = getVenderById(hit['_id'])
if cont_vender_repo:
print('文章id：' + hit['_id'] + ' 已存在向量，跳过')
continue
print('文章id：' + hit['_id'])
print('文章内容去获取向量：' + hit['_source'][field_to_export][:100])
inputStr = hit['_source'][field_to_export][:1000]
if (len(inputStr.strip()) == 0):
print('文章内容为空，跳过')
continue
req_body = {"Input": inputStr}
print('获取向量请求体：' + json.dumps(req_body, ensure_ascii=False))
if embedding_type=='local':
cont_vender = get_embedding_local(inputStr) # 只取前1000字符进行测试
print('获取到的向量：' + str(cont_vender))
updateDocVender(hit['_id'], cont_vender)
print('---------------------')
elif embedding_type=='tengxun':
cont_vender = get_embedding(json.dumps(req_body)) # 只取前1000字符进行测试
print('获取到的向量：' + cont_vender)
cont_vender_obj = json.loads(cont_vender)
updateDocVender(hit['_id'], cont_vender_obj['Response']['Data'][0]['Embedding'])
print('---------------------')
# break # 只处理第一条记录进行测试
if __name__ == "__main__":
calVender('local')es向量查询python代码
python
from elasticsearch import Elasticsearch
from typing import List, Dict
es = Elasticsearch(["http://elastic:NBh5ObTk1lq2YZ4JQ2Ng@localhost:9200"])
index_name = "article"
def getSimilarDocuments(query_vector: List[float], top_k: int = 5) -> List[Dict]:
"""
根据输入的向量查询相似的文档。
:param query_vector: 用于查询的向量。
:param top_k: 返回的相似文档数量。
:return: 包含相似文档信息的列表。
"""
script_query = {
"script_score": {
"query": {"match_all": {}},
"script": {
"source": "cosineSimilarity(params.query_vector, 'cont_vector') + 1.0",
"params": {"query_vector": query_vector}
}
}
}
print(f"request body ------------ : {script_query}")
response = es.search(
index=index_name,
body={
"size": top_k,
"query": script_query,
"_source": ["cont"] # 只返回内容字段
}
)
similar_docs = []
for hit in response['hits']['hits']:
doc_info = {
"id": hit['_id'],
"score": hit['_score'],
"cont": hit['_source']['cont']
}
similar_docs.append(doc_info)
return similar_docs
def getSimilarDocumentById(doc_id: str) -> Dict:
"""
根据文档ID获取文档内容。
:param doc_id: 文档的ID。
:return: 包含文档信息的字典。
"""
response = es.search(index=index_name, body={"query": {"term": {"_id": doc_id}}})
# response = es.get(index=index_name, id=doc_id)
print(f"Search response: {response}")
doc = response['hits']['hits'][0]['_source']
print(f"Document _source: {doc}")
print(f"doc vector: {doc['cont_vector']}")
similarDocs = getSimilarDocuments(doc['cont_vector'], top_k=5)
for d in similarDocs:
print(f"ID: {d['id']}, Score: {d['score']}, Content: {d['cont'][:50]}...")
if __name__ == "__main__":
test_doc_id = "01146c1cac8a28009068c089d4d83f537f" # 替换为你想测试的文档ID
getSimilarDocumentById(test_doc_id)