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jack ning
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modules/python/app/redisVector.py
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194
modules/python/app/redisVector.py
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'''
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Author: jackning 270580156@qq.com
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Date: 2024-08-29 14:49:54
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LastEditors: jackning 270580156@qq.com
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LastEditTime: 2024-09-03 09:14:22
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Description: bytedesk.com https://github.com/Bytedesk/bytedesk
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Please be aware of the BSL license restrictions before installing Bytedesk IM –
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selling, reselling, or hosting Bytedesk IM as a service is a breach of the terms and automatically terminates your rights under the license.
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仅支持企业内部员工自用，严禁私自用于销售、二次销售或者部署SaaS方式销售
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Business Source License 1.1: https://github.com/Bytedesk/bytedesk/blob/main/LICENSE
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contact: 270580156@qq.com
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技术/商务联系：270580156@qq.com
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Copyright (c) 2024 by bytedesk.com, All Rights Reserved.
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'''
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#
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import logging
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from typing import List
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from langchain_redis import RedisConfig, RedisVectorStore
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from langchain_huggingface import HuggingFaceEmbeddings
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from langchain_community.vectorstores.redis.filters import RedisFilter
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from langchain.docstore.document import Document
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from redisvl.query.filter import Tag
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from zhipuai import ZhipuAI
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from app.config import get_settings
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from app.redisPubsub import publishAnswerMessage, publishAnswerFinished
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zhipuAi = ZhipuAI(api_key=get_settings().ZHIPU_API_KEY) # 填写您自己的APIKey
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redis_index_schema = {
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"tag": [{"name": "kb_uid"}, {"name": "file_uid"}],
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}
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# https://python.langchain.com/v0.2/docs/integrations/vectorstores/redis/
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class MyRedisVector:
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#
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embeddings: HuggingFaceEmbeddings
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vector_store: RedisVectorStore
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#
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def __init__(self):
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self.embeddings = HuggingFaceEmbeddings(
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model_name=get_settings().EMBEDDINGS_PATH)
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#
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config = RedisConfig(
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index_name=get_settings().REDIS_INDEX_NAME,
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key_prefix=get_settings().REDIS_KEY_PREFIX,
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redis_url=get_settings().REDIS_URL,
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redis_index_schema=redis_index_schema,
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metadata_schema=[
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{
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"name": "kb_uid",
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"type": "tag",
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},
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{
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"name": "file_uid",
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"type": "tag",
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}
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]
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)
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self.vector_store = RedisVectorStore(self.embeddings, config=config)
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return
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def add_docs(self, docs: List[Document]) -> List[str]:
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# 将docs中的文档存储到vector_store中
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results = self.vector_store.add_documents(docs)
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logging.info(f"add_texts result: {results}")
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return results
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def delete_docs(self, docIds: List[str]) -> int:
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# 将docs从vector_store中删除
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# 判断docIds是否为空，空则返回0
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if not docIds:
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return 0
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# 返回被删除的文档数量
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return self.vector_store.index.drop_keys(docIds)
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# FIXME: RuntimeError: Index has not been created. Must be created before calling search
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def search_docs(self, kbUid: str, query: str) -> List[Document]:
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kb_filter = Tag("kb_uid") == kbUid
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results = self.vector_store.similarity_search(
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query, k=3, filter=kb_filter)
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# logging.info(f'search_store results {results}, {len(results)}')
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return results
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# 4.2 使用Retriever在向量库中搜索问题
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# https://python.langchain.com/docs/integrations/vectorstores/redis#redis-as-retriever
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def search_as_retriever(self, kbUid: str, query: str) -> List[Document]:
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# query向量化？langchain文档demo中没有向量化
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# kb_filter = RedisFilter.tag("kb_uid") == kbUid
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kb_filter = Tag("kb_uid") == kbUid
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# 有三种搜索算法
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# 1 默认算法
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retriever = self.vector_store.as_retriever(
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search_type="similarity",
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search_kwargs={
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"k": 3,
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"filter": kb_filter
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}
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)
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# 2 similarity_distance_threshold retriever which allows the user to specify the vector distance
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# retriever = self.vector_store.as_retriever(
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# search_type="similarity_distance_threshold",
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# search_kwargs={"k": 3, "distance_threshold": 0.1,
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# "filter": kb_filter},
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# )
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# 3 the similarity_score_threshold allows the user to define the minimum score for similar documents
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# retriever = self.vector_store.as_retriever(
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# search_type="similarity_score_threshold",
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# search_kwargs={"score_threshold": 0.9,
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# "k": 3, "filter": kb_filter},
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# )
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#
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results = retriever.get_relevant_documents(query)
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print(f'search_as_retriever results {results}, {len(results)}')
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return results
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# 5.Generation 将问题和搜索结果传给大模型，返回答案
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def query_llm(self, messageUid: str, threadTopic: str, kbUid: str, question: str, search_results: List[Document]):
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# logging.info(f'query_llm search_results {search_results}, {len(search_results)}')
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# 拼接
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# context = "\n".join(search_results)
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context = "\n".join([doc.page_content for doc in search_results])
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# logging.info(f'query_llm context {context}')
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# 基于本地知识问答的提示词模
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# TODO: write an english version
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# <问题 > {query} < /问题 >
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prompt_template = f'''
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### Human: <指令>根据已知信息，简洁和专业的来回答问题。如果无法从中得到答案，请说 “根据已知信息无法回答该问题”，不允许在答案中添加编造成分，答案请使用跟问题中同样的语言。 </指令>
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<已知信息>{ context }</已知信息>###
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Assistant:'''
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print(f'prompt_template: { { prompt_template } }')
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# https://open.bigmodel.cn/dev/api#sdk_example
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response = zhipuAi.chat.completions.create(
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model="glm-4-flash", # 免费版
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# prompt=[{"role": "user", "content": prompt_template}],
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messages=[
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{"role": "system", "content": prompt_template},
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{"role": "user", "content": question},
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],
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top_p=0.7,
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temperature=0.3,
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stream=True
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)
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#
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counter = 0
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for chunk in response:
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# ChatCompletionChunk(id='202408291708554ae5d8eac6e94630', choices=[Choice(delta=ChoiceDelta(content='凡', role='assistant', tool_calls=None), finish_reason=None, index=0)], created=1724922535, model='glm-4-air', usage=None, extra_json=None)
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# ChatCompletionChunk(id='202408291708554ae5d8eac6e94630', choices=[Choice(delta=ChoiceDelta(content='', role='assistant', tool_calls=None), finish_reason='stop', index=0)], created=1724922535, model='glm-4-air', usage=CompletionUsage(prompt_tokens=453, completion_tokens=34, total_tokens=487), extra_json=None)
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# print(f'chunk: { chunk }')
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# INFO: query_llm chunk: , stop, glm-4-air, CompletionUsage(prompt_tokens=453, completion_tokens=34, total_tokens=487)
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#
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counter += 1
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id_to_publish = counter
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#
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answer = chunk.choices[0].delta.content
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model = chunk.model
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created = chunk.created
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finish_reason = chunk.choices[0].finish_reason
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logging.info(
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f'query_llm: {counter} {chunk.choices[0].delta.content}, {chunk.choices[0].finish_reason}, {chunk.model}, {chunk.created}, {chunk.usage}')
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if finish_reason == 'stop':
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promptTokens = chunk.usage.prompt_tokens
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completionTokens = chunk.usage.completion_tokens
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totalTokens = chunk.usage.total_tokens
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publishAnswerFinished(
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id=id_to_publish,
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uid=messageUid,
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threadTopic=threadTopic,
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kbUid=kbUid,
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question=question,
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answer=answer,
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model=model,
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created=created,
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promptTokens=promptTokens,
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completionTokens=completionTokens,
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totalTokens=totalTokens
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)
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else:
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publishAnswerMessage(
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id=id_to_publish,
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uid=messageUid,
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threadTopic=threadTopic,
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kbUid=kbUid,
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question=question,
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answer=answer,
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model=model,
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created=created
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)
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myredisVector = MyRedisVector()
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#
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