feat: es_client

This commit is contained in:
李如威 2025-12-18 16:28:12 +08:00
parent 78965ded3e
commit 63480e312e
3 changed files with 86 additions and 82 deletions

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@ -1,45 +1,102 @@
import httpx from datetime import datetime, timezone
from pipeline.config import config from pipeline.config import config
from elasticsearch import AsyncElasticsearch
class AsyncES: class AsyncES:
def __init__(self): def __init__(self):
self.base = f"{config['es_host']}:{config['es_port']}" self.base = f"{config['es_host']}:{config['es_port']}"
self.auth = (config["es_user"], config['es_password']) self.auth = (config["es_user"], config["es_password"])
self.client = AsyncElasticsearch(self.base, basic_auth=self.auth)
self.mapping = {
"settings": {
"analysis": {
"analyzer": {
"ik_smart": {
"tokenizer": "ik_smart",
},
}
}
},
"mappings": {
"properties": {
"type": {"type": "keyword"}, # 分类/类型
"doc_id": {"type": "keyword"}, # 业务文档 ID便于聚合
"title": {"type": "text", "analyzer": "ik_smart"}, # 文档标题
"created_at": {"type": "date"}, # 创建时间
"content": {"type": "text", "analyzer": "ik_smart"}, # 切片内容
"embedding": { # 向量字段
"type": "dense_vector",
"dims": 768,
"index": True,
"similarity": "cosine",
},
},
},
}
async def create_index(self, index: str): async def create_index(self, index: str):
""" """
创建 index知识库 创建 index知识库
""" """
async with httpx.AsyncClient() as client: is_exists = await self.client.indices.exists(index)
resp = await client.put( if is_exists:
f"{self.base}/{index}", return True
auth=self.auth, else:
json={ await self.client.indices.create(index=index, body=self.mapping)
"settings": {"analysis": {"analyzer": {"default": {"type": "standard"}}}}, return True
"mappings": {
"properties": {
"text": {"type": "text"},
"kb": {"type": "keyword"},
}
},
},
)
return resp.json()
async def add_doc(self, index: str, text: str, kb: str): async def add_doc(self, index: str, es_id: str, doc_id: str, content: str, embedding: list, title: str = "", doc_type: str = "", created_at: datetime = None):
""" """
写入文档普通文本 写入文档普通文本
""" """
async with httpx.AsyncClient() as client: if create_at is None:
resp = await client.post(f"{self.base}/{index}/_doc", auth=self.auth, json={"text": text, "kb": kb}) create_at = datetime.now(timezone.utc)
return resp.json() doc = {"doc_id": doc_id, "title": title, "type": doc_type, "content": content, "embedding": embedding, "created_at": created_at.isoformat()}
await self.client.index(index=index, id=es_id, document=doc)
async def bm25_search(self, index: str, query: str, top_k: int = 10): async def hybrid_search_es(self, index: str, query_text: str, query_vector: list, top_k: int = 5, doc_type: str = None):
""" """
BM25 Elasticsearch 内执行 BM25 + 向量混合检
""" """
async with httpx.AsyncClient() as client: # 基础查询:中文 BM25 匹配 content 或 title
resp = await client.post(f"{self.base}/{index}/_search", auth=self.auth, json={"size": top_k, "query": {"match": {"text": query}}}) must_clauses = [{"multi_match": {"query": query_text, "fields": ["content", "title"]}}]
return resp.json() # 可选类型过滤
if doc_type:
must_clauses.append({"term": {"type": doc_type}})
body = {
"size": top_k,
"query": {
"function_score": {
"query": {"bool": {"must": must_clauses}},
"functions": [
{
"script_score": {
"script": {
# cosineSimilarity 返回 [-1,1],加 1 保证非负
"source": "cosineSimilarity(params.query_vector, 'embedding') + 1.0",
"params": {"query_vector": query_vector},
}
}
}
],
"boost_mode": "sum", # BM25 得分 + 向量相似度相加
}
},
}
es_client = AsyncES res = await self.client.search(index=index, body=body)
return [
{
"es_id": hit["_id"],
"doc_id": hit["_source"].get("doc_id"),
"title": hit["_source"].get("title"),
"type": hit["_source"].get("type"),
"created_at": hit["_source"].get("created_at"),
"score": hit["_score"],
"content": hit["_source"].get("content"),
}
for hit in res["hits"]["hits"]
]
es_client = AsyncES()

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@ -1,53 +0,0 @@
from qdrant_client import AsyncQdrantClient
from qdrant_client.models import (
VectorParams,
Distance,
PointStruct,
)
from pipeline.config import config
class AsyncQD:
def __init__(self):
self.client = AsyncQdrantClient(
host=config["qdrant_host"],
port=config["qdrant_port"],
api_key=config["qdrant_api_key"],
)
async def ensure_collection(self, name: str, dim: int):
"""
如果 collection 不存在则创建
适用于多知识库每个知识库一个 collection
"""
try:
await self.client.get_collection(name)
except Exception:
await self.client.recreate_collection(
collection_name=name,
vectors_config=VectorParams(size=dim, distance=Distance.COSINE),
)
async def upsert_vectors(self, collection: str, vectors: list, payloads: list):
"""
写入向量id 自增或自行维护
- vectors: [[float...], ...]
- payloads: [{"text": "...", "kb": "..."}]
"""
points = [PointStruct(id=i, vector=vectors[i], payload=payloads[i]) for i in range(len(vectors))]
await self.client.upsert(collection_name=collection, points=points)
async def vector_search(self, collection: str, query_vector: list, top_k: int = 5):
"""
查询向量相似度
"""
result = await self.client.search(
collection_name=collection,
query_vector=query_vector,
limit=top_k,
)
return result
qd_client = AsyncQD()

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@ -12,9 +12,9 @@ async def test_embedding():
shared = { shared = {
"files": [ "files": [
"./files/带图片的.pdf", # "./files/带图片的.pdf",
# "./files/大白智问-API接入文档-V1.2.2.pdf", # "./files/大白智问-API接入文档-V1.2.2.pdf",
# "./files/我来帮您创建一个美观简洁的微信小程序订单详情页面。首先让我了解一下当前的项目结构.md", "./files/我来帮您创建一个美观简洁的微信小程序订单详情页面。首先让我了解一下当前的项目结构.md",
# "./files/deepsearch状态.txt", # "./files/deepsearch状态.txt",
] ]
} }