RAG开源项目Qanything源码阅读3-在线推理

原文：前沿重器[47] | RAG开源项目Qanything源码阅读3-在线推理
项目：https://github.com/netease-youdao/QAnything
第一篇：RAG开源项目Qanything源码阅读1-概述+服务
第二篇：RAG开源项目Qanything源码阅读2-离线文件处理

0，推理大概流程

检索&粗排
精排
检索文档后处理
prompt和请求大模型

1，外部服务

回顾一下在“前沿重器[45] RAG开源项目Qanything源码阅读1-概述+服务”中提到的服务核心文件，所有的接口都是在qanything_kernel\qanything_server\sanic_api.py里面启动的：

app.add_route(document, "/api/docs", methods=['GET'])
app.add_route(new_knowledge_base, "/api/local_doc_qa/new_knowledge_base", methods=['POST'])  # tags=["新建知识库"]
app.add_route(upload_weblink, "/api/local_doc_qa/upload_weblink", methods=['POST'])  # tags=["上传网页链接"]
app.add_route(upload_files, "/api/local_doc_qa/upload_files", methods=['POST'])  # tags=["上传文件"] 
app.add_route(local_doc_chat, "/api/local_doc_qa/local_doc_chat", methods=['POST'])  # tags=["问答接口"] 
app.add_route(list_kbs, "/api/local_doc_qa/list_knowledge_base", methods=['POST'])  # tags=["知识库列表"] 
app.add_route(list_docs, "/api/local_doc_qa/list_files", methods=['POST'])  # tags=["文件列表"]
app.add_route(get_total_status, "/api/local_doc_qa/get_total_status", methods=['POST'])  # tags=["获取所有知识库状态"]
app.add_route(clean_files_by_status, "/api/local_doc_qa/clean_files_by_status", methods=['POST'])  # tags=["清理数据库"]
app.add_route(delete_docs, "/api/local_doc_qa/delete_files", methods=['POST'])  # tags=["删除文件"] 
app.add_route(delete_knowledge_base, "/api/local_doc_qa/delete_knowledge_base", methods=['POST'])  # tags=["删除知识库"] 
app.add_route(rename_knowledge_base, "/api/local_doc_qa/rename_knowledge_base", methods=['POST'])  # tags=["重命名知识库"]

而推理，就是这里的local_doc_chat，直接看这个函数，就在qanything_kernel\qanything_server\handler.py里面。

async def local_doc_chat(req: request):local_doc_qa: LocalDocQA = req.app.ctx.local_doc_qauser_id = safe_get(req, 'user_id')if user_id is None:return sanic_json({"code": 2002, "msg": f'输入非法！request.json：{req.json}，请检查！'})is_valid = validate_user_id(user_id)if not is_valid:return sanic_json({"code": 2005, "msg": get_invalid_user_id_msg(user_id=user_id)})debug_logger.info('local_doc_chat %s', user_id)kb_ids = safe_get(req, 'kb_ids')question = safe_get(req, 'question')rerank = safe_get(req, 'rerank', default=True)debug_logger.info('rerank %s', rerank)streaming = safe_get(req, 'streaming', False)history = safe_get(req, 'history', [])debug_logger.info("history: %s ", history)debug_logger.info("question: %s", question)debug_logger.info("kb_ids: %s", kb_ids)debug_logger.info("user_id: %s", user_id)not_exist_kb_ids = local_doc_qa.milvus_summary.check_kb_exist(user_id, kb_ids)if not_exist_kb_ids:return sanic_json({"code": 2003, "msg": "fail, knowledge Base {} not found".format(not_exist_kb_ids)})file_infos = []milvus_kb = local_doc_qa.match_milvus_kb(user_id, kb_ids)for kb_id in kb_ids:file_infos.extend(local_doc_qa.milvus_summary.get_files(user_id, kb_id))valid_files = [fi for fi in file_infos if fi[2] == 'green']if len(valid_files) == 0:return sanic_json({"code": 200, "msg": "当前知识库为空，请上传文件或等待文件解析完毕", "question": question,"response": "All knowledge bases {} are empty or haven't green file, please upload files".format(kb_ids), "history": history, "source_documents": [{}]})else:debug_logger.info("streaming: %s", streaming)if streaming:debug_logger.info("start generate answer")async def generate_answer(response):debug_logger.info("start generate...")for resp, next_history in local_doc_qa.get_knowledge_based_answer(query=question, milvus_kb=milvus_kb, chat_history=history, streaming=True, rerank=rerank):chunk_data = resp["result"]if not chunk_data:continuechunk_str = chunk_data[6:]if chunk_str.startswith("[DONE]"):source_documents = []for inum, doc in enumerate(resp["source_documents"]):source_info = {'file_id': doc.metadata['file_id'],'file_name': doc.metadata['file_name'],'content': doc.page_content,'retrieval_query': doc.metadata['retrieval_query'],'score': str(doc.metadata['score'])}source_documents.append(source_info)retrieval_documents = format_source_documents(resp["retrieval_documents"])source_documents = format_source_documents(resp["source_documents"])chat_data = {'user_info': user_id, 'kb_ids': kb_ids, 'query': question, 'history': history,'prompt': resp['prompt'], 'result': next_history[-1][1],'retrieval_documents': retrieval_documents, 'source_documents': source_documents}qa_logger.info("chat_data: %s", chat_data)debug_logger.info("response: %s", chat_data['result'])stream_res = {"code": 200,"msg": "success","question": question,# "response":next_history[-1][1],"response": "","history": next_history,"source_documents": source_documents,}else:chunk_js = json.loads(chunk_str)delta_answer = chunk_js["answer"]stream_res = {"code": 200,"msg": "success","question": "","response": delta_answer,"history": [],"source_documents": [],}await response.write(f"data: {json.dumps(stream_res, ensure_ascii=False)}\n\n")if chunk_str.startswith("[DONE]"):await response.eof()await asyncio.sleep(0.001)response_stream = ResponseStream(generate_answer, content_type='text/event-stream')return response_streamelse:for resp, history in local_doc_qa.get_knowledge_based_answer(query=question, milvus_kb=milvus_kb, chat_history=history, streaming=False, rerank=rerank):passretrieval_documents = format_source_documents(resp["retrieval_documents"])source_documents = format_source_documents(resp["source_documents"])chat_data = {'user_id': user_id, 'kb_ids': kb_ids, 'query': question, 'history': history,'retrieval_documents': retrieval_documents, 'prompt': resp['prompt'], 'result': resp['result'],'`': source_documents}qa_logger.info("chat_data: %s", chat_data)debug_logger.info("response: %s", chat_data['result'])return sanic_json({"code": 200, "msg": "success chat", "question": question, "response": resp["result"],"history": history, "source_documents": source_documents})

上面代码的重点内容：

首先因为是正式项目，在鉴权、数据库检测上都做了很多健壮性的处理，例如，对user_id的判别、对数据库及其对应用户的权限判别check_kb_exist，再者还有知识库的判空等。
此处有区分是否使用了流式streaming。
最终结果的输出有进行结构化，结构化这事的业务代码专门弄了个函数format_source_documents。
这里区分了retrieval_documents和source_documents，两者有所区别，在后面展开聊关键算法流程的时候会展开讲。
get_knowledge_based_answer是内部获取知识点并进行生成的关键函数，就是上一条所说的关键算法流程。

# qanything_kernel\utils\general_utils.py def format_source_documents(ori_source_documents):source_documents = []for inum, doc in enumerate(ori_source_documents):# for inum, doc in enumerate(answer_source_documents):# doc_source = doc.metadata['source']file_id = doc.metadata['file_id']file_name = doc.metadata['file_name']# source_str = doc_source if isURL(doc_source) else os.path.split(doc_source)[-1]source_info = {'file_id': doc.metadata['file_id'],'file_name': doc.metadata['file_name'],'content': doc.page_content,'retrieval_query': doc.metadata['retrieval_query'],'kernel': doc.metadata['kernel'],'score': str(doc.metadata['score']),'embed_version': doc.metadata['embed_version']}source_documents.append(source_info)return source_documents

2，RAG推理流程

get_knowledge_based_answer的函数很简单，不过单独拿出来，对可读性是有挺大帮助的。
RAG说白了就是先搜后交给大模型生成，终于讲到这段代码了，流程在这里qanything_kernel\core\local_doc_qa.py。

   
# qanything_kernel\core\local_doc_qa.py
@get_timedef get_knowledge_based_answer(self, query, milvus_kb, chat_history=None, streaming: bool = STREAMING,rerank: bool = False):if chat_history is None:chat_history = []retrieval_queries = [query]source_documents = self.get_source_documents(retrieval_queries, milvus_kb)deduplicated_docs = self.deduplicate_documents(source_documents)retrieval_documents = sorted(deduplicated_docs, key=lambda x: x.metadata['score'], reverse=True)if rerank and len(retrieval_documents) > 1:debug_logger.info(f"use rerank, rerank docs num: {len(retrieval_documents)}")retrieval_documents = self.rerank_documents(query, retrieval_documents)source_documents = self.reprocess_source_documents(query=query,source_docs=retrieval_documents,history=chat_history,prompt_template=PROMPT_TEMPLATE)prompt = self.generate_prompt(query=query,source_docs=source_documents,prompt_template=PROMPT_TEMPLATE)t1 = time.time()for answer_result in self.llm.generatorAnswer(prompt=prompt,history=chat_history,streaming=streaming):resp = answer_result.llm_output["answer"]prompt = answer_result.prompthistory = answer_result.history# logging.info(f"[debug] get_knowledge_based_answer history = {history}")history[-1][0] = queryresponse = {"query": query,"prompt": prompt,"result": resp,"retrieval_documents": retrieval_documents,"source_documents": source_documents}yield response, historyt2 = time.time()debug_logger.info(f"LLM time: {t2 - t1}")

首先注意到这里有个装饰器@get_time。可以用来记录执行的时间。

def get_time(func):def inner(*arg, **kwargs):s_time = time.time()res = func(*arg, **kwargs)e_time = time.time()print('函数 {} 执行耗时: {} 秒'.format(func.__name__, e_time - s_time))return resreturn inner

2.1 检索&粗排

get_source_documents是检索的过程，即给定了retrieval_queries和milvus_kb，即query和所需要查的数据库，开始进行查询。这个的返回结果，会放在retrieval_documents里面，即**“检索到的文档”**，下面是源码。

def get_source_documents(self, queries, milvus_kb, cosine_thresh=None, top_k=None):milvus_kb: MilvusClientif not top_k:top_k = self.top_ksource_documents = []embs = self.embeddings._get_len_safe_embeddings(queries)t1 = time.time()batch_result = milvus_kb.search_emb_async(embs=embs, top_k=top_k, queries=queries)t2 = time.time()debug_logger.info(f"milvus search time: {t2 - t1}")for query, query_docs in zip(queries, batch_result):for doc in query_docs:doc.metadata['retrieval_query'] = query  # 添加查询到文档的元数据中doc.metadata['embed_version'] = self.embeddings.embed_versionsource_documents.append(doc)if cosine_thresh:source_documents = [item for item in source_documents if float(item.metadata['score']) > cosine_thresh]return source_documents

_get_len_safe_embeddings给定query获取向量。在上一期RAG开源项目Qanything源码阅读2-离线文件处理有讲过，这个内部是请求一个向量模型的服务，背后的模型是需要和离线文件处理那个模型一致，所以部署同一个就会比较稳当，当然的，接口也是triton，一个grpc接口，有关GRPC，上次忘了放链接，这次放这里心法利器[6] | python grpc实践，非常建议大家详细了解并且学会。
search_emb_async是用于做向量检索的。这个就是pymilvus的核心功能了。
此处，查询出来还要过一个阈值卡控，对相似度达不到阈值的文档，需要过滤，阈值设置在cosine_thresh。

_get_len_safe_embeddings 使用的embedding 代码（可跳过，继续回到 `get_knowledge_based_answer`）

# qanything_kernel\connector\embedding\embedding_for_local.py
"""Wrapper around YouDao embedding models."""
from typing import Listfrom qanything_kernel.connector.embedding.embedding_client import EmbeddingClient
from qanything_kernel.configs.model_config import LOCAL_EMBED_SERVICE_URL, LOCAL_EMBED_MODEL_NAME, LOCAL_EMBED_MAX_LENGTH, LOCAL_EMBED_BATCH
from qanything_kernel.utils.custom_log import debug_logger
import concurrent.futures
from tqdm import tqdm embedding_client = EmbeddingClient(server_url=LOCAL_EMBED_SERVICE_URL,model_name=LOCAL_EMBED_MODEL_NAME,model_version='1',resp_wait_s=120,tokenizer_path='qanything_kernel/connector/embedding/embedding_model_0630')class YouDaoLocalEmbeddings:def __init__(self):passdef _get_embedding(self, queries):embeddings = embedding_client.get_embedding(queries, max_length=LOCAL_EMBED_MAX_LENGTH)return embeddingsdef _get_len_safe_embeddings(self, texts: List[str]) -> List[List[float]]:all_embeddings = []batch_size = LOCAL_EMBED_BATCHwith concurrent.futures.ThreadPoolExecutor() as executor:futures = []for i in range(0, len(texts), batch_size):batch = texts[i:i + batch_size]future = executor.submit(self._get_embedding, batch)futures.append(future)debug_logger.info(f'embedding number: {len(futures)}')for future in tqdm(futures):embeddings = future.result()all_embeddings += embeddingsreturn all_embeddings@propertydef embed_version(self):return embedding_client.getModelVersion()

回到 get_knowledge_based_answer
留意到 qanything_kernel\core\local_doc_qa.py 文件里的 get_knowledge_based_answer 里这一串代码：

retrieval_documents = sorted(deduplicated_docs, key=lambda x: x.metadata['score'], reverse=True)
if rerank and len(retrieval_documents) > 1:debug_logger.info(f"use rerank, rerank docs num: {len(retrieval_documents)}")retrieval_documents = self.rerank_documents(query, retrieval_documents)

此处注意，这里的检索还涉及一个过程“粗排”（上面第一行代码），这个粗排是指查询数据库的时候，需要根据相似度进行排序，只取TOPN，毕竟如果不进行这个TOP的卡控，那数据库里所有的数据都会被查出来，这没什么意义了。这里之所以叫粗排，是因为这种相似度的对比是比较粗略的，只能过滤掉“肯定不是”的那些无关结果。具体“哪个好”，用额外的、更精准的模型来做会更好，达到“优中取优”的目的。

2.2 检索&粗排

继续关注这里的 qanything_kernel\core\local_doc_qa.py 的 get_knowledge_based_answer里调用的 rerank_documents，这个就是精排，或者像这里说的重排。

def rerank_documents(self, query, source_documents):return self.rerank_documents_for_local(query, source_documents)def rerank_documents_for_local(self, query, source_documents):if len(query) > 300:  # tokens数量超过300时不使用local rerankreturn source_documentssource_documents_reranked = []try:response = requests.post(f"{self.local_rerank_service_url}/rerank",json={"passages": [doc.page_content for doc in source_documents], "query": query})scores = response.json()for idx, score in enumerate(scores):source_documents[idx].metadata['score'] = scoreif score < 0.35 and len(source_documents_reranked) > 0:continuesource_documents_reranked.append(source_documents[idx])source_documents_reranked = sorted(source_documents_reranked, key=lambda x: x.metadata['score'], reverse=True)except Exception as e:debug_logger.error("rerank error: %s", traceback.format_exc())debug_logger.warning("rerank error, use origin retrieval docs")source_documents_reranked = sorted(source_documents, key=lambda x: x.metadata['score'], reverse=True)return source_documents_reranked

简单地，这里就是把所有召回回来的文章请求到重排服务来算分，根据算分来进行过滤和排序，筛选出最优的文章。和向量模型类似，一样是用triton部署的，看模型名像是QAEnsemble_embed_rerank。

2.3 检索文档后处理

更进一步，需要对文档进行后处理，即reprocess_source_documents函数。qanything_kernel\core\local_doc_qa.py

#source_documents = self.reprocess_source_documents(query=query,
#                                                           source_docs=retrieval_documents,
#                                                           history=chat_history,
#                                                           prompt_template=PROMPT_TEMPLATE)def reprocess_source_documents(self, query: str,source_docs: List[Document],history: List[str],prompt_template: str) -> List[Document]:# 组装prompt,根据max_tokenquery_token_num = self.llm.num_tokens_from_messages([query])history_token_num = self.llm.num_tokens_from_messages([x for sublist in history for x in sublist])template_token_num = self.llm.num_tokens_from_messages([prompt_template])# logging.info(f"<self.llm.token_window, self.llm.max_token, self.llm.offcut_token, query_token_num, history_token_num, template_token_num>, types = {type(self.llm.token_window), type(self.llm.max_token), type(self.llm.offcut_token), type(query_token_num), type(history_token_num), type(template_token_num)}, values = {query_token_num, history_token_num, template_token_num}")limited_token_nums = self.llm.token_window - self.llm.max_token - self.llm.offcut_token - query_token_num - history_token_num - template_token_numnew_source_docs = []total_token_num = 0for doc in source_docs:doc_token_num = self.llm.num_tokens_from_docs([doc])if total_token_num + doc_token_num <= limited_token_nums:new_source_docs.append(doc)total_token_num += doc_token_numelse:remaining_token_num = limited_token_nums - total_token_numdoc_content = doc.page_contentdoc_content_token_num = self.llm.num_tokens_from_messages([doc_content])while doc_content_token_num > remaining_token_num:# Truncate the doc content to fit the remaining tokensif len(doc_content) > 2 * self.llm.truncate_len:doc_content = doc_content[self.llm.truncate_len: -self.llm.truncate_len]else:  # 如果最后不够truncate_len长度的2倍，说明不够切了，直接赋值为空doc_content = ""breakdoc_content_token_num = self.llm.num_tokens_from_messages([doc_content])doc.page_content = doc_contentnew_source_docs.append(doc)breakdebug_logger.info(f"limited token nums: {limited_token_nums}")debug_logger.info(f"template token nums: {template_token_num}")debug_logger.info(f"query token nums: {query_token_num}")debug_logger.info(f"history token nums: {history_token_num}")debug_logger.info(f"new_source_docs token nums: {self.llm.num_tokens_from_docs(new_source_docs)}")return new_source_docs

这里的llm，是一个自己封装好的大模型工具，具体是在qanything_kernel\connector\llm\llm_for_fastchat.py这个位置。里面支持计算token、请求大模型等通用功能。这个工具可以结合自己场景的需求搬过去直接使用。
计算limited_token_nums主要是方便组装prompt，避免某些文字被吃掉。
这里是需要对文档进行新的拼接和调整，如果查询的文档太多太长，则需要截断，且截断的时候需要注意，要保证截断的位置必须是完整地句子，如果不够长直接不切了。

2.4 prompt和请求大模型

然后就是开始生成prompt，generate_prompt。说白了就是一个简单的拼接。另外，这里的prompt拼接，更多使用replace来完成，之前有看过别的模式，例如用字符串的format应该也可以，不过replace的适用范围会更广一些。

def generate_prompt(self, query, source_docs, prompt_template):context = "\n".join([doc.page_content for doc in source_docs])prompt = prompt_template.replace("{question}", query).replace("{context}", context)return prompt

顺带就看看他们的prompt吧，实际上并不复杂。

PROMPT_TEMPLATE = """参考信息：
{context}
---
我的问题或指令：
{question}
---
请根据上述参考信息回答我的问题或回复我的指令。前面的参考信息可能有用，也可能没用，你需要从我给出的参考信息中选出与我的问题最相关的那些，来为你的回答提供依据。回答一定要忠于原文，简洁但不丢信息，不要胡乱编造。我的问题或指令是什么语种，你就用什么语种回复,
你的回复："""

最后一步就是开始请求大模型了。即generatorAnswer函数。

def generatorAnswer(self, prompt: str,history: List[List[str]] = [],streaming: bool = False) -> AnswerResult:if history is None or len(history) == 0:history = [[]]logging.info(f"history_len: {self.history_len}")logging.info(f"prompt: {prompt}")logging.info(f"prompt tokens: {self.num_tokens_from_messages([{'content': prompt}])}")logging.info(f"streaming: {streaming}")response = self._call(prompt, history[:-1], streaming)complete_answer = ""for response_text in response:if response_text:chunk_str = response_text[6:]if not chunk_str.startswith("[DONE]"):chunk_js = json.loads(chunk_str)complete_answer += chunk_js["answer"]history[-1] = [prompt, complete_answer]answer_result = AnswerResult()answer_result.history = historyanswer_result.llm_output = {"answer": response_text}answer_result.prompt = promptyield answer_result

这里就是请求大模型的基本话术了，相对还是比较简单的，一方面是请求大模型，另一方面是解析大模型内的结果。有留意到，这里有对内容做一些校验：

if response_text:chunk_str = response_text[6:]if not chunk_str.startswith("[DONE]"):chunk_js = json.loads(chunk_str)complete_answer += chunk_js["answer"]

可以看出应该是有一些泛用性处理，能解决更多复杂的问题吧。

小结

本文把QAnything项目内的重要的推理部分穿讲了一遍，可以看出这个项目已经非常完成，基本具备上线所需的关键部分，同时也有很严格的校验逻辑，严格程度很高也比较稳定，经过这个学习，自己对工程代码和具体实施的理解有了很大的提升，希望大家也有收获。当然有空再复习一遍应该有更大收获。

QAnything在服务的完整性、健壮性，以及文档处理上都有了很多的更新，但都不要指望用上就能达到很高的水准，需要进一步提升还需要更多内里的修炼：

query理解辅助更好地提升检索的准确性
联合训练提升大模型和检索结果的协同
更深入定制的文档处理提升内容的可读性等

补充

`qanything_kernel\connector\llm\llm_for_fastchat.py`

from abc import ABC
import tiktoken
import os
from dotenv import load_dotenv
from openai import OpenAI
from typing import Optional, List
import sys
import json
import requests
import logging
sys.path.append("../../../")
from qanything_kernel.connector.llm.base import (BaseAnswer, AnswerResult)
from qanything_kernel.configs.model_config import LOCAL_LLM_SERVICE_URL, LOCAL_LLM_MODEL_NAME, LOCAL_LLM_MAX_LENGTHload_dotenv()logging.basicConfig(level=logging.INFO)class OpenAICustomLLM(BaseAnswer, ABC):model: str = LOCAL_LLM_MODEL_NAMEtoken_window: int = LOCAL_LLM_MAX_LENGTHmax_token: int = 512offcut_token: int = 50truncate_len: int = 50temperature: float = 0stop_words: str = Nonehistory: List[List[str]] = []history_len: int = 2def __init__(self):super().__init__()# self.client = OpenAI(base_url="http://localhost:7802/v1", api_key="EMPTY")if LOCAL_LLM_SERVICE_URL.startswith("http://"):base_url = f"{LOCAL_LLM_SERVICE_URL}/v1" else:base_url = f"http://{LOCAL_LLM_SERVICE_URL}/v1" self.client = OpenAI(base_url=base_url, api_key="EMPTY")@propertydef _llm_type(self) -> str:return "CustomLLM using FastChat w/ huggingface transformers or vllm backend"@propertydef _history_len(self) -> int:return self.history_lendef set_history_len(self, history_len: int = 10) -> None:self.history_len = history_lendef token_check(self, query: str) -> int:if LOCAL_LLM_SERVICE_URL.startswith("http://"):base_url = f"{LOCAL_LLM_SERVICE_URL}/api/v1/token_check" else:base_url = f"http://{LOCAL_LLM_SERVICE_URL}/api/v1/token_check" headers = {"Content-Type": "application/json"}response = requests.post(base_url, data=json.dumps({'prompts': [{'model': self.model, 'prompt': query, 'max_tokens': self.max_token}]}),headers=headers, timeout=60)# {'prompts': [{'fits': True, 'tokenCount': 317, 'contextLength': 8192}]}result = response.json()token_num = 0try:token_num = result['prompts'][0]['tokenCount']return token_numexcept Exception as e:logging.error(f"token_check Exception {base_url} w/ {e}")return token_numdef num_tokens_from_messages(self, message_texts):num_tokens = 0for message in message_texts:num_tokens += self.token_check(message)return num_tokensdef num_tokens_from_docs(self, docs):num_tokens = 0for doc in docs:num_tokens += self.token_check(doc.page_content)return num_tokensdef _call(self, prompt: str, history: List[List[str]], streaming: bool=False) -> str:messages = []for pair in history:question, answer = pairmessages.append({"role": "user", "content": question})messages.append({"role": "assistant", "content": answer})messages.append({"role": "user", "content": prompt})logging.info(messages)try:if streaming:response = self.client.chat.completions.create(model=self.model,messages=messages,stream=True,max_tokens=self.max_token,# temperature=self.temperature,stop=[self.stop_words] if self.stop_words is not None else None,)for event in response:if not isinstance(event, dict):event = event.model_dump()if event["choices"] is None:event_text = event["text"] + " error_code:" + str(event["error_code"])else:event_text = event["choices"][0]['delta']['content']if isinstance(event_text, str) and event_text != "":# logging.info(f"[debug] event_text = [{event_text}]")delta = {'answer': event_text}yield "data: " + json.dumps(delta, ensure_ascii=False)else:response = self.client.chat.completions.create(model=self.model,messages=messages,stream=False,max_tokens=self.max_token,# temperature=self.temperature,stop=[self.stop_words] if self.stop_words is not None else None,)# logging.info(f"[debug] response.choices = [{response.choices}]")event_text = response.choices[0].message.content if response.choices else ""delta = {'answer': event_text}yield "data: " + json.dumps(delta, ensure_ascii=False)except Exception as e:logging.info(f"Error calling API: {e}")delta = {'answer': f"{e}"}yield "data: " + json.dumps(delta, ensure_ascii=False)finally:# logging.info("[debug] try-finally")yield f"data: [DONE]\n\n"def generatorAnswer(self, prompt: str,history: List[List[str]] = [],streaming: bool = False) -> AnswerResult:if history is None or len(history) == 0:history = [[]]logging.info(f"history_len: {self.history_len}")logging.info(f"prompt: {prompt}")logging.info(f"prompt tokens: {self.num_tokens_from_messages([prompt])}")logging.info(f"streaming: {streaming}")response = self._call(prompt, history[:-1], streaming)complete_answer = ""for response_text in response:if response_text:chunk_str = response_text[6:]if not chunk_str.startswith("[DONE]"):chunk_js = json.loads(chunk_str)complete_answer += chunk_js["answer"]history[-1] = [prompt, complete_answer]answer_result = AnswerResult()answer_result.history = historyif streaming:answer_result.llm_output = {"answer": response_text}else:answer_result.llm_output = {"answer": complete_answer}answer_result.prompt = promptyield answer_resultif __name__ == "__main__":base_url = f"http://{LOCAL_LLM_SERVICE_URL}/api/v1/token_check" headers = {"Content-Type": "application/json"}query = "hello"response = requests.post(base_url, data=json.dumps({'prompts': [{'model': LOCAL_LLM_MODEL_NAME, 'prompt': query, 'max_tokens': 512}]}),headers=headers, timeout=60)# {'prompts': [{'fits': True, 'tokenCount': 317, 'contextLength': 8192}]}result = response.json()logging.info(f"[debug] result = {result}")llm = OpenAICustomLLM()streaming = Truechat_history = []prompt = "你是谁"prompt = """参考信息：
中央纪委国家监委网站讯 据山西省纪委监委消息：山西转型综合改革示范区党工委副书记、管委会副主任董良涉嫌严重违纪违法，目前正接受山西省纪委监委纪律审查和监察调查。\\u3000\\u3000董良简历\\u3000\\u3000董良，男，汉族，1964年8月生，河南鹿邑人，在职研究生学历，邮箱random@xxx.com，联系电话131xxxxx909，1984年3月加入中国共产党，1984年8月参加工作\\u3000\\u3000历任太原经济技术开发区管委会副主任、太原武宿综合保税区专职副主任，山西转型综合改革示范区党工委委员、管委会副主任。2021年8月，任山西转型综合改革示范区党工委副书记、管委会副主任。(山西省纪委监委)
---
我的问题或指令：
帮我提取上述人物的中文名，英文名，性别，国籍，现任职位，最高学历，毕业院校，邮箱，电话
---
请根据上述参考信息回答我的问题或回复我的指令。前面的参考信息可能有用，也可能没用，你需要从我给出的参考信息中选出与我的问题最相关的那些，来为你的回答提供依据。回答一定要忠于原文，简洁但不丢信息，不要胡乱编造。我的问题或指令是什么语种，你就用什么语种回复,
你的回复："""final_result = ""for answer_result in llm.generatorAnswer(prompt=prompt,history=chat_history,streaming=streaming):resp = answer_result.llm_output["answer"]if "DONE" not in resp:final_result += json.loads(resp[6:])["answer"]# logging.info(resp)logging.info(f"final_result = {final_result}")

RAG开源项目Qanything源码阅读3-在线推理

0，推理大概流程

1，外部服务

2，RAG推理流程

2.1 检索&粗排

_get_len_safe_embeddings 使用的embedding 代码（可跳过，继续回到 `get_knowledge_based_answer`）

2.2 检索&粗排

2.3 检索文档后处理

2.4 prompt和请求大模型

小结

补充

`qanything_kernel\connector\llm\llm_for_fastchat.py`

推荐新闻

热搜词

RAG开源项目Qanything源码阅读3-在线推理

0，推理大概流程

1，外部服务

2，RAG推理流程

2.1 检索&粗排

_get_len_safe_embeddings 使用的embedding 代码（可跳过，继续回到 get_knowledge_based_answer）

2.2 检索&粗排

2.3 检索文档后处理

2.4 prompt和请求大模型

小结

补充

qanything_kernel\connector\llm\llm_for_fastchat.py

推荐新闻

热搜词

_get_len_safe_embeddings 使用的embedding 代码（可跳过，继续回到 `get_knowledge_based_answer`）

`qanything_kernel\connector\llm\llm_for_fastchat.py`