“llamaindex”的搜索结果

帮助你使用FAISS实现大规模向量相似性搜索，涵盖索引选择、保存与加载及GPU使用，并调整搜索速度、准确率和内存占用。

• 实现大规模向量相似性搜索
• 按速度、准确率和内存选择索引
• 使用GPU批量搜索

Build LlamaIndex applications that retrieve relevant material before generating answers, a method called RAG. Connect documents, websites, and databases to support document questions, conversational search, structured summaries, and checks of answer relevance and source consistency.

中文简介正在准备中。当前显示英语简介。

• Building question answering over internal documents
• Creating a chatbot that remembers the conversation
• Searching information across files, websites, and databases

Guides building AI applications that search documents before answering questions, an approach called RAG. Covers importing files and web data, creating searchable indexes, adding conversational interfaces, and evaluating whether answers match the retrieved material.

中文简介正在准备中。当前显示英语简介。

• Build a question-answering system for internal documents
• Create a chatbot that remembers conversation context
• Search information across files, web pages, and databases

LlamaIndex 官方用户文档离线知识库，用于检索并回答 LlamaIndex Python 框架的安装、RAG、数据加载、索引、检索与查询、Agent、Workflow、模型、Embedding、向量库、评估、可观测性、部署、LlamaCloud 和 LlamaParse 等问题，也可生成有文档依据的示例代码与排障建议。当用户提到 LlamaIndex、llama-index、LlamaParse、VectorStoreIndex、QueryEngine、Retriever、AgentWorkflow 或相关集成用法时使用；纯源码贡献、内部实现审查或与 LlamaIndex 无关的通用 AI 问题不使用。

中文简介正在准备中。当前显示原文说明。

Build search and retrieval systems with Pinecone, a managed database for numerical representations of content. Covers data uploads, filtered similarity searches, combined semantic and keyword search, and connections to LangChain and LlamaIndex.

中文简介正在准备中。当前显示英语简介。

• Building retrieval for AI answers grounded in documents
• Combining semantic and keyword search
• Filtering results by category or price

使用Chroma构建按语义相似度检索文档的系统，保存和更新正文及附加信息，按条件筛选结果，并用于开发结合检索资料的AI应用原型。

• 按语义相似度搜索文档
• 试作基于检索资料的AI回答
• 将文档数据保存在本地

Operating-system distillation of LlamaIndex — the leading RAG / document-agent framework. Activate when the calling agent must build, debug, harden, or evaluate a Retrieval-Augmented Generation pipeline over unstructured/private data, decide between RAG primitives (Index types, retrievers, query engines, routers, agents), or pick LlamaIndex vs LangChain / Haystack / raw vector store for a coding task. Encodes the 5-layer mental model (Documents → Nodes → Indices → Retrievers → Query Engines / Response Synthesizers), the canonical RAG bootstrap SOP from baseline `VectorStoreIndex` through hybrid + reranker + eval-loop hardening, the official 13-failure-mode checklist, and 5 dilemma cases distilled from docs, GitHub issues, and 2025 production post-mortems.

中文简介正在准备中。当前显示原文说明。

Build search and recommendation features with Pinecone, a managed database for numerical representations of content. Covers data loading, combined meaning and keyword search, filtering by attributes, and separating data by user.

中文简介正在准备中。当前显示英语简介。

• Build document retrieval for AI answers
• Combine meaning and keyword search
• Separate search data by user

Build document search and AI retrieval workflows with Chroma, a database for numerical representations of text. Cover document storage, meaning-based search, metadata filters, persistence, and connections to LangChain and LlamaIndex.

中文简介正在准备中。当前显示英语简介。

• Building document search based on meaning
• Adding document retrieval to AI answers
• Filtering search results by document attributes

Implement, update, test, or document TypeScript provider packages, the adapters connecting supported services and frameworks. This skill focuses on work under ts/packages/providers and directs the agent to read the provider workflow before making changes.

中文简介正在准备中。当前显示英语简介。

• Implement a TypeScript provider
• Modify a service or framework adapter
• Test provider-specific changes

Build search and recommendation features with Pinecone, a managed database for numerical representations of content. Covers data loading, combined meaning and keyword search, filtering by attributes, and separating data by user.

中文简介正在准备中。当前显示英语简介。

• Build document retrieval for AI answers
• Combine meaning and keyword search
• Separate search data by user

Build document search and AI retrieval workflows with Chroma, a database for numerical representations of text. Cover document storage, meaning-based search, metadata filters, persistence, and connections to LangChain and LlamaIndex.

中文简介正在准备中。当前显示英语简介。

• Building document search based on meaning
• Adding document retrieval to AI answers
• Filtering search results by document attributes

LlamaIndex agent and query engine setup for RAG-powered agents

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Neutral, framework-agnostic decision tree for project kickoff: "which agent / RAG / LLM framework should I reach for?" Synthesizes the ecosystem sections of 7 landmark-project SOPs (LangGraph, LlamaIndex, DSPy, CrewAI, vLLM, Aider, Dify) into one layered rubric. Core stance: frameworks are LAYERS, not competitors — a real project usually combines DSPy (compile) + LlamaIndex (retrieve) + LangGraph (orchestrate) + vLLM (serve), and you choose ONE per layer, not one to rule all. Use when starting any LLM/agent/RAG project, or whenever the "which framework?" question is asked. Deliberately neutral — unlike vendor docs and the LangChain-biased `framework-selection` on skill.sh, this skill has no horse in the race.

中文简介正在准备中。当前显示原文说明。

Re-ingest-correctness SOP for production RAG. Activate when a calling agent builds, reviews, or debugs an ingestion pipeline that runs more than once over a changing corpus — scheduled re-index, incremental updates, CI re-ingest, or a "retrieval has duplicates / shows deleted docs" bug. Encodes the rule — **ingestion must be idempotent: a document's content hash decides insert/update/skip, so re-running over unchanged docs is a no-op** — plus the docstore + doc-hash upsert machinery (LlamaIndex `IngestionPipeline` + `DocstoreStrategy`), the delete-propagation problem, and cross-framework equivalents (LangChain `index()` + `RecordManager`, manual hash table). ENHANCE overlay over [[llamaindex]]: the IngestionPipeline exists in the base skill but the re-ingest-correctness contract is not surfaced.

中文简介正在准备中。当前显示原文说明。

LlamaIndex.TS data framework for RAG, indexing, retrieval, query engines, chat engines, and agentic workflows in TypeScript

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Framework de dados para construir aplicações LLM com RAG. Especializado em ingestão de documentos (300+ conectores), indexação e consultas. Inclui índices vetoriais, mecanismos de consulta, agentes e suporte multimodal. Use para perguntas sobre documentos, chatbots, recuperação de conhecimento ou construção de pipelines RAG. Ideal para aplicações LLM centradas em dados.

中文简介正在准备中。当前显示原文说明。

Expert guidance for LlamaIndex development including RAG applications, vector stores, document processing, query engines, and building production AI applications.

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Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.

中文简介正在准备中。当前显示原文说明。

使用Qdrant将文本等内容表示为向量，实现相似内容搜索。支持按类别或日期筛选、为AI回答检索资料、实时推荐，以及内存和分布式部署的配置调整。

• 为AI回答检索参考资料
• 按类别和日期筛选相似内容
• 实现实时推荐

Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.

中文简介正在准备中。当前显示英语简介。

• Generate document embeddings for RAG
• Find sentences with similar meanings
• Group documents by topic

Build similarity search with FAISS to find nearby matches in large collections of numeric vectors. The skill guides index selection, memory reduction, GPU use, and saving indexes while balancing search speed and accuracy.

中文简介正在准备中。当前显示英语简介。

• Find similar items in large vector datasets
• Choose an index for search speed and accuracy
• Reduce memory use for vector search

Set up Langfuse to record LLM application activity, costs, and response times. Manage prompt versions, organize evaluation datasets, and compare results to investigate failures and guide improvements using recorded evidence.

中文简介正在准备中。当前显示英语简介。

• Investigating failures in LLM applications
• Comparing prompt versions and evaluation scores
• Monitoring LLM costs and response times

Add DeepEval tracing to Python AI applications so execution steps appear in Confident AI. Choose a supported integration or manual @observe instrumentation, then capture inputs, outputs, step types, tags, and metadata.

中文简介正在准备中。当前显示英语简介。

• Trace execution steps in a Python AI application
• Select tracing integrations for an existing framework
• Add manual tracing with @observe