Step 3: Use Custom LLM & Embedding Model | Use Custom Model Index Quick Install 🐳 AutoRAG Docker Guide Data Creation Parsing Chunking QA Creation RAG Optimization Set YAML File Run AutoRAG Run Dashboard Deploy your optimal RAG pipeline (for testing) ...
Orion-14B is a family of models includes a 14B foundation LLM, and a series of models: a chat model, a long context model, a quantized model, a RAG fine-tuned model, and an Agent fine-tuned model. Orion-14B 系列模型包括一个具有140亿参数的多语言基座大模型
Then, when we make the call (using the same messages as described earlier), we also tell the OpenAI model that it can use that tool: chat_completion = await self.openai_client.chat.completions.create( messages=messages, model=self.chatgpt_model, temperature=0.0, max_tokens=100, n...
在微调 LLM 或使用 RAG 之间进行选择时,一个关键的考虑因素是应用程序是否需要访问外部数据源。如果答案是肯定的,RAG 可能是更好的选择。 RAG 系统从定义上来说旨在通过在生成响应之前从知识源中检索相关信息来增强 LLM 的能力。这使得这种技术非常适用于需要查询数据库、文档或其他结构化/非结构化数据存储库的应用...
self, name: str = "style_metric", model_name: str = settings.OPENAI_MODEL_ID ) -> None: self.name = name self.llm_client = litellm_chat_model.LiteLLMChatModel(model_name=model_name) self.prompt_template = """ You are an impartial expert judge. Evaluate the quality of a given ans...
经历了大型语言模型(LLM)快速发展的 2023 年,业内越来越多地讨论起了检索增强生成(Retrieval-Augmented Generation,简称 RAG)技术,这个技术关键之处在于它结合了两个重要的元素:检索和生成。首先,它会通过搜索外部信息源(比如网页或数据库)来收集与问题相关的信息。然后,它会将这些信息巧妙地融入到它的回答中,生成一...
llm = AzureOpenAI( model="YourAzureOpenAICompletionModelName", deployment_name="YourAzureOpenAICompletionDeploymentName", api_key=aoai_api_key, azure_endpoint=aoai_endpoint, api_version=aoai_api_version, ) # You need to deploy your own embedding model as well as your own chat completion model...
To download the benchmark datasets used in this analysis, visit the BEIR GitHubrepositoryand find the download links under the Available Datasets section. Try our embedding model usingNVIDIA AI FoundationEndpoints and refer todocumentationandcode snippets. ...
7.Cloud Deployment Ready: Enjoy hassle-free deployment with cloud-ready architecture, ensuring scalability and accessibility for users across diverse environments. 8.“Bring Your Own LLM” Model: AnythingLLM’s support for various LLMs ensures flexibility, enabling users to leverage...
#setuptheservicecontext service_context=ServiceContext.from_defaults( chunk_size=256, llm=llm, embed_model=embed_model ) #setupthestoragecontext graph_store=SimpleGraphStore() storage_context=StorageContext.from_defaults(graph_store=graph_store) #ConstructtheKnowlegeGraphUndex index=KnowledgeGraphIndex.fr...