"text": "", "generation_info": null, "message": { "content": "", "additional_kwargs": { "function_call": { "name": "Calculator", "arguments": "{\n \"__arg1\": \"2^33\"\n}" } }, "example": false } } ] ], "llm_output": { "token_usage": { "prompt_tokens": 1...
message =convert_dict_to_message(res["message"]) generation_info =dict(finish_reason=res.get("finish_reason"))if"index"inres: generation_info["index"] = res["index"] gen =ChatGeneration( message=message, generation_info=generation_info, ) generations.append(gen) token_usage = response.get...
generations=[[Generation(text='\n\nQ: Why did the scarecrow win an award?\n\nA: Because he was outstanding in his field!', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you...
• 最下层深色部分：LangChain的Python和JavaScript库。包含无数组件的接口和集成，以及将这些组件组合到...
generation_info["usage"] = usage default_chunk_class = chunk.__class__ chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info) yield chunk if run_manager: 31 changes: 31 additions & 0 deletions 31 langchain_searxng/components/llm/custom/zhipuai/zhipuai_info.py Original...
LLMResult(generations=[[Generation(text='\n\nQ: What did the fish say when it hit the wall?\nA: Dam!', generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {}, 'model_name': 'text-davinci-003'}, run=[RunInfo(run_id=UUID('c47c9b6f...
分块(Chunking)是构建检索增强型生成(RAG)(https://zilliz.com.cn/use-cases/llm-retrieval-augmented-generation)应用程序中最具挑战性的问题。分块是指切分文本的过程,虽然听起来非常简单,但要处理的细节问题不少。根据文本内容的类型,需要采用不同的分块策略。
RAG 是retrieval-augmented-generation的缩写,翻译为中文的意思就检索增强,以基于最新,最准确的数据建立LLM 的语料知识库。 LLM 现存的痛点 我们知道 LLM 的知识库是通过现有的网络公开的数据作为数据源来训练的,现在公开的很多模型他们基于的训练数据会比我们现在网络上公开的数据早很多,那自然就会产生一种问题,网络上...
import UnstructuredMarkdownLoader, UnstructuredURLLoader from langchain.chains import LLMChain, SimpleSequentialChain, RetrievalQA, ConversationalRetrievalChain from transformers import BitsAndBytesConfig, AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline import warnings warnings.filterwarnings('ignore...
{ "generations": [ [ { "text": "Hi there! How can I assist you today?", "generationInfo": { "prompt": 0, "completion": 0, "finish_reason": "stop" }, "message": { "lc": 1, "type": "constructor", "id": [ "langchain_core", "messages", "AIMessageChunk" ], "kwargs"...