feat: es_client
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@ -1,45 +1,102 @@
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import httpx
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from datetime import datetime, timezone
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from pipeline.config import config
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from elasticsearch import AsyncElasticsearch
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class AsyncES:
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def __init__(self):
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self.base = f"{config['es_host']}:{config['es_port']}"
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self.auth = (config["es_user"], config['es_password'])
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self.auth = (config["es_user"], config["es_password"])
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self.client = AsyncElasticsearch(self.base, basic_auth=self.auth)
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self.mapping = {
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"settings": {
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"analysis": {
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"analyzer": {
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"ik_smart": {
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"tokenizer": "ik_smart",
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},
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}
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}
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},
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"mappings": {
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"properties": {
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"type": {"type": "keyword"}, # 分类/类型
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"doc_id": {"type": "keyword"}, # 业务文档 ID,便于聚合
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"title": {"type": "text", "analyzer": "ik_smart"}, # 文档标题
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"created_at": {"type": "date"}, # 创建时间
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"content": {"type": "text", "analyzer": "ik_smart"}, # 切片内容
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"embedding": { # 向量字段
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"type": "dense_vector",
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"dims": 768,
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"index": True,
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"similarity": "cosine",
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},
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},
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},
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}
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async def create_index(self, index: str):
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"""
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创建 index(知识库)
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"""
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async with httpx.AsyncClient() as client:
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resp = await client.put(
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f"{self.base}/{index}",
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auth=self.auth,
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json={
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"settings": {"analysis": {"analyzer": {"default": {"type": "standard"}}}},
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"mappings": {
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"properties": {
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"text": {"type": "text"},
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"kb": {"type": "keyword"},
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}
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},
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},
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)
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return resp.json()
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is_exists = await self.client.indices.exists(index)
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if is_exists:
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return True
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else:
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await self.client.indices.create(index=index, body=self.mapping)
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return True
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async def add_doc(self, index: str, text: str, kb: str):
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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):
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"""
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写入文档(普通文本)
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"""
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async with httpx.AsyncClient() as client:
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resp = await client.post(f"{self.base}/{index}/_doc", auth=self.auth, json={"text": text, "kb": kb})
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return resp.json()
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if create_at is None:
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create_at = datetime.now(timezone.utc)
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doc = {"doc_id": doc_id, "title": title, "type": doc_type, "content": content, "embedding": embedding, "created_at": created_at.isoformat()}
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await self.client.index(index=index, id=es_id, document=doc)
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async def bm25_search(self, index: str, query: str, top_k: int = 10):
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async def hybrid_search_es(self, index: str, query_text: str, query_vector: list, top_k: int = 5, doc_type: str = None):
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"""
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BM25 搜索
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在 Elasticsearch 内执行 BM25 + 向量混合检索
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"""
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async with httpx.AsyncClient() as client:
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resp = await client.post(f"{self.base}/{index}/_search", auth=self.auth, json={"size": top_k, "query": {"match": {"text": query}}})
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return resp.json()
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# 基础查询:中文 BM25 匹配 content 或 title
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must_clauses = [{"multi_match": {"query": query_text, "fields": ["content", "title"]}}]
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# 可选类型过滤
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if doc_type:
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must_clauses.append({"term": {"type": doc_type}})
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body = {
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"size": top_k,
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"query": {
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"function_score": {
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"query": {"bool": {"must": must_clauses}},
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"functions": [
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{
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"script_score": {
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"script": {
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# cosineSimilarity 返回 [-1,1],加 1 保证非负
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"source": "cosineSimilarity(params.query_vector, 'embedding') + 1.0",
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"params": {"query_vector": query_vector},
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}
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}
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}
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],
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"boost_mode": "sum", # BM25 得分 + 向量相似度相加
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}
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},
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}
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es_client = AsyncES
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res = await self.client.search(index=index, body=body)
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return [
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{
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"es_id": hit["_id"],
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"doc_id": hit["_source"].get("doc_id"),
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"title": hit["_source"].get("title"),
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"type": hit["_source"].get("type"),
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"created_at": hit["_source"].get("created_at"),
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"score": hit["_score"],
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"content": hit["_source"].get("content"),
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}
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for hit in res["hits"]["hits"]
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]
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es_client = AsyncES()
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@ -1,53 +0,0 @@
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from qdrant_client import AsyncQdrantClient
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from qdrant_client.models import (
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VectorParams,
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Distance,
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PointStruct,
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)
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from pipeline.config import config
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class AsyncQD:
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def __init__(self):
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self.client = AsyncQdrantClient(
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host=config["qdrant_host"],
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port=config["qdrant_port"],
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api_key=config["qdrant_api_key"],
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)
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async def ensure_collection(self, name: str, dim: int):
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"""
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如果 collection 不存在则创建。
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适用于多知识库:每个知识库一个 collection。
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"""
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try:
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await self.client.get_collection(name)
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except Exception:
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await self.client.recreate_collection(
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collection_name=name,
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vectors_config=VectorParams(size=dim, distance=Distance.COSINE),
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)
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async def upsert_vectors(self, collection: str, vectors: list, payloads: list):
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"""
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写入向量(id 自增或自行维护)
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- vectors: [[float...], ...]
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- payloads: [{"text": "...", "kb": "..."}]
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"""
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points = [PointStruct(id=i, vector=vectors[i], payload=payloads[i]) for i in range(len(vectors))]
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await self.client.upsert(collection_name=collection, points=points)
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async def vector_search(self, collection: str, query_vector: list, top_k: int = 5):
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"""
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查询向量相似度
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"""
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result = await self.client.search(
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collection_name=collection,
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query_vector=query_vector,
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limit=top_k,
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)
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return result
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qd_client = AsyncQD()
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@ -12,9 +12,9 @@ async def test_embedding():
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shared = {
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"files": [
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"./files/带图片的.pdf",
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# "./files/带图片的.pdf",
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# "./files/大白智问-API接入文档-V1.2.2.pdf",
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# "./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",
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"./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",
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# "./files/deepsearch状态.txt",
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]
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}
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