改用 httpx 实现所有 client 交互
This commit is contained in:
parent
4345dfb9cb
commit
fcaac9c0f1
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@ -8,5 +8,4 @@ scikit-learn
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aiofiles
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pillow
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loguru
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aiohttp
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elasticsearch>=8.0.0,<9.0.0
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httpx
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@ -0,0 +1,191 @@
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import httpx
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from datetime import datetime, timezone
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from src.pipeline.config import config
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from src.pipeline.core.utils import logger
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class AsyncES:
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def __init__(
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self,
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timeout: float = 30.0,
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max_connections: int = 50,
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max_keepalive: int = 10,
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):
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self.base = f"{config['es_host']}:{config['es_port']}".rstrip("/")
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self.auth = (config["es_user"], config["es_password"])
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self.client = httpx.AsyncClient(
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http2=False,
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trust_env=False,
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timeout=httpx.Timeout(timeout),
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limits=httpx.Limits(
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max_connections=max_connections,
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max_keepalive_connections=max_keepalive,
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),
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auth=self.auth,
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headers={
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"Content-Type": "application/json",
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"Accept": "application/json",
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},
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)
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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"},
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"doc_id": {"type": "keyword"},
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"title": {"type": "text", "analyzer": "ik_smart"},
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"created_at": {"type": "date"},
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"content": {"type": "text", "analyzer": "ik_smart"},
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"embedding": {
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"type": "dense_vector",
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"dims": config['embedding_dims'],
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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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# -------------------------
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# Index
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# -------------------------
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async def create_index(self, index: str) -> bool:
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url = f"{self.base}/{index}"
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try:
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resp = await self.client.put(url, json=self.mapping)
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if resp.status_code in (200, 201):
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return True
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if resp.status_code == 400 and "resource_already_exists" in resp.text:
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return True
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logger.error(f"Create index failed: {resp.text}")
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return False
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except Exception:
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logger.exception("Create index error")
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return False
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# -------------------------
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# Index document
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# -------------------------
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async def add_doc(
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self,
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index: str,
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es_id: str,
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doc_id: str,
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content: str,
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embedding: list,
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title: str = "",
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doc_type: str = "",
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created_at: datetime | None = None,
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):
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if created_at is None:
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created_at = datetime.now(timezone.utc)
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doc = {
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"doc_id": doc_id,
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"title": title,
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"type": doc_type,
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"content": content,
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"embedding": embedding,
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"created_at": created_at.isoformat(),
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}
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url = f"{self.base}/{index}/_doc/{es_id}"
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try:
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resp = await self.client.put(url, json=doc)
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resp.raise_for_status()
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except Exception:
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logger.exception("Index document failed")
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# -------------------------
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# Hybrid search
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# -------------------------
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async def hybrid_search_es(
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self,
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index: str,
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query_text: str,
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query_vector: list,
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top_k: int = 5,
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doc_type: str | None = None,
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):
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must_clauses = [
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{
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"multi_match": {
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"query": query_text,
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"fields": ["content", "title"],
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}
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}
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]
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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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"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",
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}
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},
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}
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url = f"{self.base}/{index}/_search"
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resp = await self.client.post(url, json=body)
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resp.raise_for_status()
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hits = resp.json()["hits"]["hits"]
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return [
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{
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"es_id": h["_id"],
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"doc_id": h["_source"].get("doc_id"),
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"title": h["_source"].get("title"),
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"type": h["_source"].get("type"),
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"created_at": h["_source"].get("created_at"),
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"score": h["_score"],
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"content": h["_source"].get("content"),
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}
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for h in hits
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]
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async def close(self):
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await self.client.aclose()
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client: AsyncES | None = None
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async def init_client():
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global client
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client = AsyncES()
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async def close_client():
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await client.close()
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@ -1,125 +0,0 @@
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from codecs import ignore_errors
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from datetime import datetime, timezone
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from venv import logger
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from src.pipeline.config import config
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from elasticsearch import AsyncElasticsearch, ApiError
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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.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": 1024, # 必须和 embedding 模型的纬度一样
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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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try:
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await self.client.indices.create(
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index=index,
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body=self.mapping,
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)
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return True
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except ApiError as e:
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logger.error(e.message)
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return False
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async def add_doc(
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self,
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index: str,
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es_id: str,
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doc_id: str,
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content: str,
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embedding: list,
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title: str = "",
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doc_type: str = "",
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created_at: datetime = None,
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):
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"""
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写入文档(普通文本)
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"""
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if created_at is None:
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created_at = datetime.now(timezone.utc)
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doc = {
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"doc_id": doc_id,
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"title": title,
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"type": doc_type,
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"content": content,
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"embedding": embedding,
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"created_at": created_at.isoformat(),
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}
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await self.client.index(index=index, id=es_id, document=doc)
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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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在 Elasticsearch 内执行 BM25 + 向量混合检索
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"""
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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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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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@ -1,29 +1,60 @@
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import httpx
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from src.pipeline.config import config
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import aiohttp
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from src.pipeline.core.utils import logger
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_embedding_session: aiohttp.ClientSession | None = None
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async def get_embedding(text: str, timeout: int = 30):
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global _embedding_session
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if _embedding_session is None or _embedding_session.closed:
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_embedding_session = aiohttp.ClientSession(
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timeout=aiohttp.ClientTimeout(total=timeout)
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class AsyncLLm:
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def __init__(
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self,
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timeout: float = 30.0,
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max_connections: int = 100,
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max_keepalive: int = 20,
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):
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self.embedding_api = config["embedding_api_host"].rstrip("/") + "/embeddings"
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self.embedding_model = config["embedding_model"]
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self.api_key = config["embedding_api_key"]
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logger.debug(self.embedding_api)
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self.embedding_client = httpx.AsyncClient(
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http2=False,
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trust_env=False,
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timeout=httpx.Timeout(timeout),
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limits=httpx.Limits(
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max_connections=max_connections,
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max_keepalive_connections=max_keepalive,
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),
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.api_key}",
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},
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)
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async with _embedding_session.post(
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config["embedding_api_host"],
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json={
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"model": config["embedding_model"],
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"input": text,
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},
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {config['embedding_api_key']}",
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},
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) as resp:
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if resp.status >= 400:
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return []
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async def embedding(self, text: str) -> list[float]:
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try:
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resp = await self.embedding_client.post(
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self.embedding_api,
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json={"model": self.embedding_model, "input": text},
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)
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data = await resp.json()
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return data["data"][0]["embedding"]
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resp.raise_for_status()
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data = resp.json()
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return data["data"][0]["embedding"]
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except httpx.HTTPStatusError as e:
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logger.error(e)
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logger.error(f"Embedding HTTP error: {e.response.text}")
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except Exception as e:
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logger.exception("Embedding request failed")
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return []
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async def close(self):
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await self.embedding_client.aclose()
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client: AsyncLLm | None = None
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async def init_client():
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global client
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client = AsyncLLm()
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async def close_client():
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await client.close()
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@ -1,8 +1,8 @@
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import uuid
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from src.pipeline.core.pocket_flow import AsyncBatchNode
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from src.pipeline.core.utils import fixed_size_chunk, load_document, logger
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from src.pipeline.core.llm import get_embedding
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from src.pipeline.core.es_client import es_client
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from src.pipeline.core import llm
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from src.pipeline.core import es
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import re
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@ -49,7 +49,7 @@ class EmbeddingDocumentsNode(AsyncBatchNode):
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:param document: {text, file_name}
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"""
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logger.debug(f"开始 embedding: {document["text"].strip()[:10]}...")
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res = {**document, "embedding": await get_embedding(document["text"])}
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res = {**document, "embedding": await llm.client.embedding(document["text"])}
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logger.debug(f"结束 embedding: {document["text"].strip()[:10]}...")
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return res
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@ -95,7 +95,7 @@ class ReadDocumentNode(AsyncBatchNode):
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class WriteToElasticsearchNode(AsyncBatchNode):
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async def prep_async(self, shared):
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index = shared["index"]
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await es_client.create_index(index)
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await es.client.create_index(index)
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return [
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{
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"index": index,
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@ -110,7 +110,7 @@ class WriteToElasticsearchNode(AsyncBatchNode):
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]
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async def exec_async(self, prep_res):
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await es_client.add_doc(**prep_res)
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await es.client.add_doc(**prep_res)
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return True
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async def post_async(self, shared, prep_res, exec_res):
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@ -3,19 +3,20 @@ import json
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from src.pipeline.core.nodes import ReadDocumentNode, ChunkDocumentsNode, EmbeddingDocumentsNode, WriteToElasticsearchNode
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from src.pipeline.core.pocket_flow import AsyncFlow
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from src.pipeline.core.utils import logger
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from src.pipeline.core import llm, es
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@pytest.mark.asyncio
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async def test_embedding():
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await llm.init_client()
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await es.init_client()
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print("\n\ntest_embedding:\n")
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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/大白智问-API接入文档-V1.2.2.pdf",
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"./files/山海经01.txt",
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# "./files/deepsearch状态.txt",
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],
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"documents": [],
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"index": "test_kb",
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@ -31,3 +32,6 @@ async def test_embedding():
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await flow.run_async(shared)
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logger.debug(json.dumps([{**x, "embedding":x["embedding"][:4]} for x in shared["documents"]], indent=4, ensure_ascii=False))
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await llm.close_client()
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await es.close_client()
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