LLMPredictor:用于使用大型语言模型(LLM)生成预测。ServiceContext:提供协调各种服务所需的上下文数据。KnowledgeGraphIndex:用于构建和操作知识图谱。SimpleGraphStore:用作存储图数据的简单仓库。HuggingFaceInferenceAPI:用于利用开源LLM的模块。 4、引入LLM HF_TOKEN = "Your Huggaingface api key " llm = HuggingFace...
自定义 LLMs 默认情况,llamaIndex 使用text-davinci-003,也可以用别的构建 Index from llama_index import LLMPredictor, GPTSimpleVectorIndex, PromptHelper, ServiceContext from langchain import OpenAI ... # define LLM llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="text-...
# define an LLMPredictor set number of output tokens llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0, max_tokens=512, model_name='gpt-3.5-turbo')) service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor) storage_context = StorageContext.from_defaults()\ ## define ...
from llama_indeximport(SimpleDirectoryReader,LLMPredictor,ServiceContext,ResponseSynthesizer)from llama_index.indices.document_summaryimportGPTDocumentSummaryIndex from langchain.chat_modelsimportChatOpenAI # load docs,define service context...# build the index response_synthesizer=ResponseSynthesizer.from_args(r...
现在LLM可以部署能够与用户进行交互式对话的模型。在本例中,选择 Llama 2-chat 模型之一,该模型通过以下方式识别 代码语言:javascript 复制 my_model=JumpStartModel(model_id="meta-textgeneration-llama-2-70b-f") 该模型需要使用 部署到实时端点predictor = my_model.deploy()。 SageMaker 将返回模型的端点名称,...
llm_predictor=llm_predictor , node_parser=node_parser, chunk_size=1024) set_global_service_context(service_context) db = chromadb. PersistentClient(path="./chroma_db") chroma_collection = db.get_or_create_collection("datartchromaDB") vector_store = ChromaVectorStore(chroma_collection=chroma_...
fromllama_indeximportLLMPredictor, VectorStoreIndex fromlangchainimportOpenAI os.environ["OPENAI_API_KEY"]="api-key" index=VectorStoreIndex(nodes) 建立檢索器 我們將使用 VectorIndexRetriever,它會根據相似度檢索出前 k 個匹配的文件。在這個例子中,我們將 k 設為 2。
llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="text-davinci-003", max_tokens =128)) num_output =256max_chunk_overlap =20max_input_size =4096prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap) service_context = ServiceContext.from_defaults(llm_...
我在Python 代码中遇到了 Llama.index 的一些导入问题。我正在尝试使用多个模块,包括 LLMPredictor、 PromptHelper、 SimpleDirectoryReader 等,但我收到了专门针对 GPTVectorStoreIndex 的错误: File "d:\l\zira_0.0.2\main.py", line 5, in <module> from llama_index import ( ImportError: cannot import...
Regarding the HuggingFace API token, based on a similar issue in the repository, you might need to load the LLM from HuggingFace and then pass the llm into the llm_predictor. Here is the code: from llama_index import LLMPredictor, ServiceContext llm_predictor = LLMPredictor(llm=llm) service...