封装openai 的 Embeddings 模型接口 defget_embeddings(texts, model="text-embedding-ada-002", dimensions=None): '''封装 OpenAI 的 Embedding 模型接口''' ifmodel =="text-embedding-ada-002": dimensions = None ifdimensions: data = client.embeddings.create( input=texts, model=model, dimensions=dimen...
query_engine = summary_index.as_query_engine( service_context=ServiceContext.from_defaults( llm=OpenAI(model="gpt-3.5-turbo") ) ) summary_txt = str(query_engine.query(query_str)) new_doc = Document(text=summary_txt, metadata=new_metadata) return new_doc async def aprocess...
This query will run on a database whose schema is represented in this string: ...省略 ### Response: Based on your instructions, here is the SQL query I have generated to answer the question `{question}`: ```sql """.format(question=question) response_model = smr_client.invoke_endpoint...
一个方法就是保存到数据库里面,这个是需要引入数据库相关的以来的,另外一个方法就是保存到文件里面去,但是这个涉及到我们的消息和文本的转换,消息就是我们和大模型对话的,你想要把他进行持久化的存储,这个时候需要转换成为我们的文本文件类型的语言,例如这个txt之类的,保存的时候需要转换,从文本文件读取成为消息的时候...
gitclonehttps://github.com/open-kf/rag-gpt.git &&cdrag-gpt 2.配置环境变量 在启动RAG-GPT服务之前,需要修改相关配置,以便程序正确初始化。 如果使用智谱AI作为LLM底座 cp env_of_zhipuai.env .env 文件中的变量 LLM_NAME="ZhipuAI"ZHIPUAI_API_KEY="xxxx"GLM_MODEL_NAME="glm-3-turbo"MIN_RELEVANCE...
print("Creat embedding model...") embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-zh-v1.5",trust_remote_code=True) with open('knowledge.txt', 'r') as file: line = file.readline() while line: # Generate embeddings using encoder from HuggingFace. ...
) embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-zh-v1.5",trust_remote_code=True) # load documents MilvusReader = download_loader("MilvusReader") reader = MilvusReader( host="localhost", port=19530, user="<user>", password="<password>", use_secure=False ) # Example query...
This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access a GPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval. ...
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-zh-v1.5",trust_remote_code=True)# load documentsMilvusReader = download_loader("MilvusReader") reader = MilvusReader(host="localhost",port=19530,user="<user>",password="<password>",use_secure=False)# Example query vector:documents...
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