Continuous Learning(持续学习):互动式 RAG 系统会从每次互动中学习,不断完善其知识库,从而确保后续回答更加准确,更符合实际情况。 第三项技术展示了先进的 RAG 方法如何对未来的人工智能应用至关重要,它提供了一种动态、自适应和以用户为中心的信息检索和处理方法。 技术4:Contextual compression in Advanced RAG(先进...
顺序保持RAG与普通RAG。如图4所示,当检索到的块数量较少时(例如,8),所提出的顺序保持RAG与普通RAG的优势并不显著。相反,当检索到的块数量较大时,我们的顺序保持RAG显著优于普通RAG。具体来说,在EN.QA数据集上,当检索到的块数量为128时,普通RAG仅实现了38.40 F1分数,而我们的顺序保持RAG实现了44.43 F1分数。在...
Step 4: Build a Graph RAG Chatbot in LangChain Create a Neo4j Vector Chain Create a Neo4j Cypher Chain Create Wait Time Functions Create the Chatbot Agent Step 5: Deploy the LangChain Agent Serve the Agent With FastAPI Create a Chat UI With Streamlit Orchestrate the Project With Docker Compos...
RAG is an information retrieval process whereby the outputs produced by an LLM are optimized. LLMs rely on the knowledge gained from the data they have been generated upon to generate responses. Meanwhile, RAG points to an external knowledge base. By combining both solutions, RAG can be used ...
task, using a RAG pipeline along with the continuous self-instruct fine-tuning framework. We build this as a compound AI system and use DSPy to drive the RAG inference, prompt optimization, LLM fine-tuning, and performance evaluation. The overall workflow...
In this diagram, various factors like complexity, cost, and quality are represented along a single dimension. The takeaway? RAG is simpler and less expensive, but its quality might not match up. My advice usually was: start with RAG, gauge its performance, and if found lacking, shift to fi...
Aggregating data with RAG This diagram outlines a workflow involving data processing, embedding, querying, and inference within OCI services. This workflow highlights the integration of various components to process data, create embeddings, store, and query vector data, and perform inference using advanc...
In this diagram, various factors like complexity, cost, and quality are represented along a single dimension. The takeaway? RAG is simpler and less expensive, but its quality might not match up. My advice usually was: start with RAG, gauge its performance, and if found lacking, shift ...
🎨 Additional RAG Settings: Customize your RAG process with the ability to edit the TOP K value. Navigate to Documents > Settings > General to make changes. 🖥️ UI Improvements: Tooltips now available in the input area and sidebar handle. More tooltips will be added across other parts ...
Llamaindex - High-level concepts: Main concepts to know when building RAG pipelines. Pinecone - Retrieval Augmentation: Overview of the retrieval augmentation process. LangChain - Q&A with RAG: Step-by-step tutorial to build a typical RAG pipeline. LangChain - Memory types: List of different ty...