commit f6f46972c370914c802ee1023f7a312388f6aa46 Author: Javier Infante Date: Sun May 24 10:37:53 2026 +0200 initial commit: slides ready + upgraded diff --git a/slides/.gitignore b/slides/.gitignore new file mode 100644 index 0000000..4dbc289 --- /dev/null +++ b/slides/.gitignore @@ -0,0 +1,8 @@ +node_modules/ +dist/ +.env +.env.local +npm-debug.log* +yarn-debug.log* +yarn-error.log* +.DS_Store diff --git a/slides/README.md b/slides/README.md new file mode 100644 index 0000000..227d92c --- /dev/null +++ b/slides/README.md @@ -0,0 +1,26 @@ +# Slides LangChain.js + +Presentación sobre LangChain.js - Desarrollo de aplicaciones con LLMs. + +## Ver online + +https://jabiinfante.github.io/slides-langchain.js/ + +## Desarrollo local + +```bash +npm install +npm run dev +``` + +## Build + +```bash +npm run build +``` + +## Deploy + +```bash +npm run deploy +``` diff --git a/slides/changes.md b/slides/changes.md new file mode 100644 index 0000000..1d47186 --- /dev/null +++ b/slides/changes.md @@ -0,0 +1,371 @@ +# Cambios necesarios para actualizar el curso LangChain.js + +> Fecha de revision: 2026-05-22 +> Las slides fueron preparadas para Enero 2026 (v0.3.x). Desde entonces se ha lanzado **LangChain v1** y **LangGraph v1**, que traen cambios significativos. +> **Todos los cambios críticos e importantes han sido aplicados.** El documento se conserva como referencia histórica. + +## Versiones actuales en npm (mayo 2026) + +| Paquete | Slides | Actual | Cambio | +|---------|--------|--------|--------| +| `langchain` | v0.3.x | **v1.2.35** | MAJOR | +| `@langchain/core` | v0.3.x | **v1.1.34** | MAJOR | +| `@langchain/openai` | - | **v1.3.0** | MAJOR | +| `@langchain/anthropic` | - | **v1.3.25** | MAJOR | +| `@langchain/google-genai` | en uso | **v2.1.26** (DEPRECATED) | Ver punto 3 | +| `@langchain/google` | no mencionado | **v0.1.7** (NUEVO, reemplaza google-genai) | NUEVO | +| `@langchain/community` | en uso | **v1.1.24** | MAJOR | +| `@langchain/langgraph` | en uso | **v1.2.3** | MAJOR | +| `@langchain/classic` | no existe en slides | **v1.0.24** (NUEVO) | NUEVO | +| `@langchain/qdrant` | en uso | **v1.0.1** | MAJOR | +| `@langchain/pinecone` | - | **v1.0.1** | MAJOR | + +--- + +## 1. CRITICO: Versiones en slides/setup/03.md + +**Fichero**: `pages/setup/03.md:52` +**Problema**: Dice `Versiones actuales (Enero 2026): langchain v0.3.x · @langchain/core v0.3.x` +**Correccion**: Cambiar a `Versiones actuales (Marzo 2026): langchain v1.x · @langchain/core v1.x` + +--- + +## 2. CRITICO: Nuevo paquete @langchain/classic + +**Ficheros afectados**: `pages/setup/02.md` +**Problema**: La arquitectura de paquetes no menciona `@langchain/classic`, que es donde se ha movido la funcionalidad legacy en v1. +**Que se movio a @langchain/classic**: +- `MemoryVectorStore` (antes en `langchain/vectorstores/memory`) +- `DirectoryLoader` (antes en `langchain/document_loaders/fs/directory`) +- `TextLoader` (antes en `langchain/document_loaders/fs/text`) +- Legacy chains (`LLMChain`, `ConversationChain`, etc.) +- Retrievers legacy +- Hub module +- `ChatPromptTemplate` ahora se importa desde `@langchain/classic/prompts` en los ejemplos de proveedores (aunque sigue disponible en `@langchain/core/prompts`) + +**Accion**: Anadir `@langchain/classic` al diagrama de arquitectura de paquetes en `setup/02.md` + +--- + +## 3. CRITICO: @langchain/google-genai DEPRECATED + +**Ficheros afectados**: +- `pages/setup/02.md:54` - lista de proveedores +- `pages/setup/03.md:31` - instalacion +- `pages/setup/13.md` - ejemplo completo con Gemini +- `pages/models/08b.md` - ChatGoogleGenerativeAI +- `pages/models/10.md:38` - providers disponibles +- `pages/rag/06.md:22` - GoogleGenerativeAIEmbeddings +- `pages/rag/07.md` - no afectado directamente + +**Problema**: La documentacion oficial dice textualmente: +> "This library is based on a deprecated library from Google and will be replaced by the ChatGoogle library. New implementations should use the ChatGoogle library instead." + +**Correccion**: +```typescript +// ANTES (deprecated) +import { ChatGoogleGenerativeAI } from "@langchain/google-genai"; +const model = new ChatGoogleGenerativeAI({ model: "gemini-2.0-flash-exp" }); + +// AHORA (recomendado) +import { ChatGoogle } from "@langchain/google"; +const model = new ChatGoogle({ model: "gemini-2.5-flash" }); +``` + +**Instalacion**: `npm install @langchain/google` en lugar de `npm install @langchain/google-genai` + +--- + +## 4. CRITICO: createReactAgent -> createAgent + +**Ficheros afectados**: +- `pages/models/15.md` - ejemplo con Tavily usa `createReactAgent` de `@langchain/langgraph/prebuilt` +- `pages/models/16.md` - comparacion bindTools vs createReactAgent + +**Problema**: `createReactAgent` de `@langchain/langgraph/prebuilt` ha sido reemplazado por `createAgent` de `langchain` en v1. + +**Correccion para models/15.md**: +```typescript +// ANTES +import { ChatOpenAI } from "@langchain/openai"; +import { TavilySearchResults } from "@langchain/community/tools/tavily_search"; +import { createReactAgent } from "@langchain/langgraph/prebuilt"; + +const model = new ChatOpenAI({ model: "gpt-4o-mini" }); +const agent = createReactAgent({ llm: model, tools: [searchTool] }); + +// AHORA +import { createAgent } from "langchain"; +import { TavilySearchResults } from "@langchain/community/tools/tavily_search"; + +const searchTool = new TavilySearchResults({ apiKey: process.env.TAVILY_API_KEY, maxResults: 5 }); +const agent = createAgent({ + model: "openai:gpt-4.1-mini", + tools: [searchTool], +}); +``` + +**Correccion para models/16.md**: Cambiar el titulo y contenido del panel derecho de `createReactAgent()` a `createAgent()`. La comparacion sigue siendo valida conceptualmente (bindTools para control manual vs createAgent para loop automatico). + +> NOTA: La seccion de agentes (pages/agents/) ya usa `createAgent` correctamente. Solo models/15.md y models/16.md estan desactualizados. + +--- + +## 5. IMPORTANTE: Nombres de modelos desactualizados + +### OpenAI +**Ficheros**: Multiples (models/, messages/, rag/, setup/, agents/) +- `gpt-4o-mini` -> `gpt-4.1-mini` (segun docs actuales de LangChain) +- `gpt-4o` -> `gpt-4.1` (segun docs actuales de LangChain) +- `gpt-4-turbo` en `models/10.md:18` -> `gpt-4.1` o posterior + +**Nota**: Los modelos `gpt-4o` y `gpt-4o-mini` siguen funcionando pero ya no son los mas recientes. La documentacion oficial de LangChain ahora muestra `gpt-4.1-mini` y `gpt-5.2` como ejemplos. + +### Anthropic +- `claude-sonnet-4-20250514` en `models/08.md:41` -> `claude-sonnet-4-6` (ultimo segun docs) +- `claude-sonnet-4-5-20250929` en `agents/05.md:17` -> mantener o actualizar a `claude-sonnet-4-6` + +### Google Gemini +- `gemini-2.0-flash-exp` en `models/08b.md:19` -> `gemini-2.5-flash` (segun docs actuales) +- `gemini-pro` en `models/10.md:38` -> `gemini-2.5-flash-lite` (modelo recomendado actual) +- `gemini-2.5-flash` en `setup/13.md:16` -> OK, este esta correcto + +### Embeddings +- `embedding-001` en `rag/06.md:25` -> verificar nombre actual del modelo de embeddings de Google +- `llama2` en `rag/07.md:32` (OllamaEmbeddings) -> `llama3.2` o modelo mas reciente + +--- + +## 6. IMPORTANTE: MemoryVectorStore - import path cambiado + +**Fichero**: `pages/rag/11.md:12` +**Problema**: +```typescript +// ANTES (v0.3.x) +import { MemoryVectorStore } from "langchain/vectorstores/memory"; + +// AHORA (v1.x) +import { MemoryVectorStore } from "@langchain/classic/vectorstores/memory"; +``` + +**Nota**: En v1, `MemoryVectorStore` se ha movido a `@langchain/classic`. Requiere instalar: `npm install @langchain/classic` + +--- + +## 7. IMPORTANTE: initChatModel - import path cambiado + +**Fichero**: `pages/models/09.md:22` +**Problema**: +```typescript +// ANTES (v0.3.x) +import { initChatModel } from "@langchain/core/chat_models/universal"; + +// AHORA (v1.x) +import { initChatModel } from "langchain"; +``` + +--- + +## 8. IMPORTANTE: Document Loaders - import paths cambiados + +**Fichero**: `pages/pending/06c.md` +**Problema**: Los loaders que estaban en `langchain/` se han movido a `@langchain/classic/`: +```typescript +// ANTES +import { DirectoryLoader } from "langchain/document_loaders/fs/directory"; +import { TextLoader } from "langchain/document_loaders/fs/text"; + +// AHORA +import { DirectoryLoader } from "@langchain/classic/document_loaders/fs/directory"; +import { TextLoader } from "@langchain/classic/document_loaders/fs/text"; +``` +Los loaders de community (`PDFLoader`, `CSVLoader`) mantienen su path en `@langchain/community/...`. + +--- + +## 9. IMPORTANTE: OllamaEmbeddings - import path + +**Fichero**: `pages/rag/07.md:29` +**Problema**: +```typescript +// ANTES +import { OllamaEmbeddings } from "@langchain/community/embeddings/ollama"; + +// AHORA (paquete dedicado) +import { OllamaEmbeddings } from "@langchain/ollama"; +``` +Requiere: `npm install @langchain/ollama` + +--- + +## 10. MENOR: favicon en slides.md + +**Fichero**: `slides.md:10` +**Problema**: `favicon: https://js.langchain.com/v0.2/img/favicon.ico` - URL con referencia a v0.2 +**Correccion**: Actualizar a la URL actual de la documentacion: `https://docs.langchain.com/...` o usar favicon local + +--- + +## 11. NUEVO CONCEPTO: Standard Content Blocks (v1) + +**No existe en las slides actualmente.** +**Relevancia**: Es una de las 3 mejoras principales de LangChain v1. + +`contentBlocks` es una nueva propiedad en los mensajes que proporciona acceso unificado a features modernos de LLMs (reasoning tokens, imagenes, etc.) de forma estandarizada entre proveedores: + +```typescript +import { AIMessage } from "@langchain/core/messages"; + +// Los mensajes ahora tienen .contentBlocks para acceso tipado +const reasoning = message.contentBlocks + .filter((b) => b.type === "reasoning") + .map((b) => b.reasoning); +``` + +**Sugerencia**: Considerar anadir una slide en la seccion de mensajes (messages/) o en la seccion de agentes explicando este nuevo concepto. + +--- + +## 12. NUEVO CONCEPTO: @langchain/classic + +**No explicado en las slides.** +En v1, la funcionalidad legacy se ha movido a `@langchain/classic`: +- Legacy chains (LLMChain, ConversationChain) +- Retrievers legacy +- Hub module +- MemoryVectorStore +- Algunos document loaders + +**Sugerencia**: Mencionar en la seccion de estructura/setup que en v1 existe este paquete para compatibilidad hacia atras. + +--- + +## 13. MENOR: Modelos disponibles en providers (models/10.md) + +**Fichero**: `pages/models/10.md` +Los comentarios de modelos disponibles estan desactualizados: +- OpenAI: `gpt-4o, gpt-4o-mini, gpt-4-turbo` -> `gpt-4.1, gpt-4.1-mini, gpt-5.2` +- Google: `gemini-2.0-flash-exp, gemini-pro` -> `gemini-2.5-flash, gemini-2.5-flash-lite` +- Anthropic: `claude-sonnet-4, claude-opus-4` -> `claude-sonnet-4-6, claude-opus-4-6` +- Groq: `llama-3.1-70b, mixtral-8x7b` -> verificar disponibilidad actual +- Ollama: `llama3.1:7b` -> `llama3.2:7b` o posterior + +--- + +## 14. MENOR: Structured Output con createAgent (v1 mejora) + +**Ficheros**: Los slides de structured output ya cubren `withStructuredOutput()` correctamente. +**Novedad v1**: En `createAgent`, structured output ahora se genera en el loop principal (sin llamada LLM adicional) via `responseFormat`: + +```typescript +import { createAgent } from "langchain"; +import * as z from "zod"; + +const agent = createAgent({ + model: "gpt-4.1-mini", + tools: [getWeather], + responseFormat: z.object({ + temperature: z.number(), + condition: z.string(), + }), +}); +const result = await agent.invoke({ messages: [...] }); +console.log(result.structuredResponse); +``` + +**Sugerencia**: Verificar si esto ya esta cubierto en las slides de agents/structured output. + +--- + +## 15. MENOR: URLs de documentacion desactualizadas + +**Ficheros afectados**: +- `pages/models/11.md` - enlace `js.langchain.com/docs/integrations/chat` +- `pages/models/13.md` - enlace `js.langchain.com/docs/integrations/tools` +- `pages/rag/29.md` - enlace `js.langchain.com/docs/tutorials/rag` +- `pages/pending/09.md` - enlace `js.langchain.com/docs` + +**Problema**: Las URLs antiguas `js.langchain.com/docs/...` han cambiado a `docs.langchain.com/oss/javascript/...` +**Correccion**: +- `js.langchain.com/docs/integrations/chat` -> `docs.langchain.com/oss/javascript/integrations/chat` +- `js.langchain.com/docs/integrations/tools` -> `docs.langchain.com/oss/javascript/integrations/tools` +- `js.langchain.com/docs/tutorials/rag` -> `docs.langchain.com/oss/javascript/langchain/rag` +- `js.langchain.com/docs` -> `docs.langchain.com/oss/javascript` + +--- + +## 16. MENOR: Precios y modelos en setup/04.md + +**Fichero**: `pages/setup/04.md` +**Problema**: La tabla de comparativa dice "Enero 2025" y lista modelos desactualizados: +- "GPT-4o" -> "GPT-4.1" (o posterior) +- "Gemini 2.0 Flash" -> "Gemini 2.5 Flash" +- "Claude 4 Sonnet" -> modelo actual +- Precios probablemente han cambiado + +**Accion**: Actualizar la tabla con modelos y precios actuales (marzo 2026) + +--- + +## 17. MENOR: setup/07.md modelos recomendados para produccion + +**Fichero**: `pages/setup/07.md` +**Problema**: Recomienda "Claude 4 Sonnet o GPT-4o" para produccion. Los nombres de modelo deberian reflejar los ultimos disponibles. + +--- + +## 18. MENOR: Modelos Ollama en setup/09.md + +**Fichero**: `pages/setup/09.md` +**Problema**: Referencia a "Llama 3.1 7B/13B/70B" en la tabla de hardware. Llama 3.2 ya esta disponible y setup/14.md ya lo usa correctamente. + +--- + +## 19. MENOR: Modelos en agents/ y alternatives/ desactualizados + +**Ficheros afectados**: +- `pages/agents/05.md:17` y `pages/agents/06.md:31` - usan `claude-sonnet-4-5-20250929` +- `pages/alternatives/06.md:40` - usa `claude-sonnet-4-20250514` en ejemplo de Anthropic SDK nativo +- `pages/alternatives/06.md:22` - usa `gpt-4o` en ejemplo de OpenAI SDK nativo +- `pages/alternatives/07.md:15` - usa `gpt-4o` en Assistants API +- `pages/alternatives/07.md:32` - usa `gemini-2.0-flash` en Google GenAI SDK + +**Correccion**: Actualizar nombres de modelos a los ultimos disponibles (ver punto 5) + +--- + +## NOTA POSITIVA: Secciones ya actualizadas a v1 + +Las siguientes secciones **ya estan correctamente actualizadas** para LangChain v1: +- **agents/** (01-23): Usa `createAgent`, middleware, `humanInTheLoopMiddleware`, etc. +- **messages/** (01-11): `ChatPromptTemplate`, `MessagesPlaceholder`, `SystemMessage` siguen siendo validos +- **introduction/06.md**: Ya menciona "LangChain (v1)" +- **langsmith/05.md**: Ya usa `createAgent` de `"langchain"` + +--- + +## Resumen de prioridades + +### Criticos (el curso no funciona correctamente sin estos cambios) +1. Actualizar version numbers en setup/03.md (v0.3.x -> v1.x) +2. Actualizar `@langchain/google-genai` a `@langchain/google` en multiples ficheros +3. Reemplazar `createReactAgent` por `createAgent` en models/15.md y models/16.md +4. Actualizar import de `MemoryVectorStore` en rag/11.md +5. Actualizar import de `initChatModel` en models/09.md +6. Anadir `@langchain/classic` a la arquitectura de paquetes + +### Importantes (el codigo funciona pero con warnings/deprecaciones) +7. Actualizar nombres de modelos (gpt-4o-mini -> gpt-4.1-mini, etc.) +8. Actualizar imports de document loaders en pending/06c.md +9. Actualizar OllamaEmbeddings import path en rag/07.md + +### Menores / Mejoras +10. Actualizar favicon URL en slides.md +11. Considerar anadir slide sobre Standard Content Blocks +12. Actualizar lista de modelos en models/10.md +13. Mencionar @langchain/classic en el modulo de estructura +14. Documentar responseFormat en createAgent +15. Actualizar URLs de documentacion (js.langchain.com -> docs.langchain.com) +16. Actualizar tabla de precios/modelos en setup/04.md +17. Actualizar modelos recomendados en setup/07.mdhttps://docs.langchain.com/oss/javascript/langchain/messages +18. 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"devDependencies": { + "playwright-chromium": "^1.58.2" + } +} diff --git a/slides/pages/agents/01.md b/slides/pages/agents/01.md new file mode 100644 index 0000000..bdeefb8 --- /dev/null +++ b/slides/pages/agents/01.md @@ -0,0 +1,14 @@ +--- +layout: cover +class: text-center +--- + +# Módulo 5: Agentes + +Tools, Structured Output, Runtime y Human-in-the-Loop + +
+ + LLMs que pueden actuar en el mundo real + +
diff --git a/slides/pages/agents/02.md b/slides/pages/agents/02.md new file mode 100644 index 0000000..2f2fd7c --- /dev/null +++ b/slides/pages/agents/02.md @@ -0,0 +1,41 @@ +--- +layout: default +--- + +# ¿Qué es un Agente? + +
+ + + +
+ +Un **agente** es un sistema que usa un LLM para decidir qué acciones tomar + +
+ + + +
+ + +
+

❌ Sin Agentes

+

Flujo fijo y predecible

+

Usuario → LLM → Respuesta

+

El LLM solo genera texto

+
+ + + +
+

✅ Con Agentes

+

Flujo dinámico y adaptativo

+

Usuario → LLM → Tool → LLM → Respuesta

+

El LLM decide qué herramientas usar

+
+ + +
+ +
diff --git a/slides/pages/agents/03.md b/slides/pages/agents/03.md new file mode 100644 index 0000000..adbe232 --- /dev/null +++ b/slides/pages/agents/03.md @@ -0,0 +1,37 @@ +--- +layout: default +--- + +# Casos de Uso de Agentes + +
+ + +
+

🔍 Búsqueda y análisis

+

Buscar información en APIs, bases de datos o web

+
+ + + +
+

🛠️ Automatización

+

Enviar emails, crear tickets, actualizar sistemas

+
+ + + +
+

💼 Asistentes empresariales

+

Consultar CRM, generar reportes, analizar datos

+
+ + + +
+

🤖 Agentes autónomos

+

Tomar decisiones complejas con múltiples pasos

+
+ + +
diff --git a/slides/pages/agents/04.md b/slides/pages/agents/04.md new file mode 100644 index 0000000..ae78c5e --- /dev/null +++ b/slides/pages/agents/04.md @@ -0,0 +1,45 @@ +--- +layout: default +--- + +# createAgent: Creando el Agente + +
+ + + +
+ +**createAgent** es la forma estándar de crear agentes en LangChain v1 + +
+ + + + + +```typescript +import { createAgent } from "langchain"; + +const agent = createAgent({ + model: "mistral:mistral-large-latest", // Modelo como string + tools: [weatherTool], // Array de tools + systemPrompt: "Eres un asistente útil.", +}); + +const result = await agent.invoke({ + messages: [{ role: "user", content: "¿Qué tiempo hace en Madrid?" }] +}); +``` + + + + + +
+El agente automatiza el ciclo: llamar al LLM -> ejecutar tools -> procesar resultados -> repetir +
+ + + +
diff --git a/slides/pages/agents/05.md b/slides/pages/agents/05.md new file mode 100644 index 0000000..95572f9 --- /dev/null +++ b/slides/pages/agents/05.md @@ -0,0 +1,42 @@ +--- +layout: default +--- + +# Configuración del Modelo + +
+ + + +**Opción 1: String con formato `provider:model`** + +```typescript +const agent = createAgent({ + model: "mistral:mistral-large-latest", // Mistral + // model: "openai:gpt-4.1", // OpenAI + // model: "anthropic:claude-sonnet-4-6", // Anthropic + tools: [], +}); +``` + + + + + +**Opción 2: Instancia del modelo con configuración avanzada** + +```typescript +import { ChatMistralAI } from "@langchain/mistralai"; + +const model = new ChatMistralAI({ + model: "mistral-large-latest", + temperature: 0.1, + maxTokens: 1000, +}); + +const agent = createAgent({ model, tools: [] }); +``` + + + +
diff --git a/slides/pages/agents/06.md b/slides/pages/agents/06.md new file mode 100644 index 0000000..ea887b2 --- /dev/null +++ b/slides/pages/agents/06.md @@ -0,0 +1,43 @@ +--- +layout: default +--- + +# systemPrompt: Configurando el Comportamiento + +
+ + + +**String simple:** + +```typescript +const agent = createAgent({ + model: "mistral:mistral-large-latest", + tools: [weatherTool], + systemPrompt: "Eres un asistente meteorológico. Sé conciso y preciso.", +}); +``` + + + + + +**SystemMessage (para features avanzados como cache de Anthropic):** + +```typescript +import { SystemMessage } from "langchain"; + +const agent = createAgent({ + model: "anthropic:claude-sonnet-4-6", + tools: [weatherTool], + systemPrompt: new SystemMessage({ + content: [ + { type: "text", text: "Eres un experto en clima...", cache_control: { type: "ephemeral" } } + ], + }), +}); +``` + + + +
diff --git a/slides/pages/agents/07.md b/slides/pages/agents/07.md new file mode 100644 index 0000000..27cb17a --- /dev/null +++ b/slides/pages/agents/07.md @@ -0,0 +1,44 @@ +--- +layout: default +--- + +# Tools: Definiendo Herramientas + +
+ + + +```typescript +import { tool } from "@langchain/core/tools"; +import { z } from "zod"; + +const weatherTool = tool( + async ({ location }) => { + // Llamar a API del tiempo + return `El tiempo en ${location} es soleado, 22°C`; + }, + { + name: "get_weather", + description: "Obtiene el tiempo actual de una ubicación", + schema: z.object({ + location: z.string().describe("Ciudad o ubicación"), + }), + } +); +``` + + + + + +```typescript +// El agente decide automáticamente cuándo usar cada tool +const agent = createAgent({ + model: "mistral:mistral-large-latest", + tools: [weatherTool, calculatorTool, searchTool], +}); +``` + + + +
diff --git a/slides/pages/agents/08.md b/slides/pages/agents/08.md new file mode 100644 index 0000000..fe98f0f --- /dev/null +++ b/slides/pages/agents/08.md @@ -0,0 +1,42 @@ +--- +layout: default +--- + +# Streaming: Modos de Transmisión + +
+ + + +**`updates`**: Emite evento después de cada paso del agente + +```typescript +for await (const chunk of await agent.stream( + { messages: [{ role: "user", content: "¿Tiempo en Madrid?" }] }, + { streamMode: "updates" } +)) { + console.log(chunk); + // { model: { messages: [AIMessage con tool_call] } } + // { tools: { messages: [ToolMessage con resultado] } } + // { model: { messages: [AIMessage respuesta final] } } +} +``` + + + + + +**`values`**: Emite el estado completo después de cada paso + +```typescript +for await (const chunk of await agent.stream( + { messages: [{ role: "user", content: "¿Tiempo en Madrid?" }] }, + { streamMode: "values" } +)) { + console.log(chunk.messages); // Array completo de mensajes hasta ese punto +} +``` + + + +
diff --git a/slides/pages/agents/09.md b/slides/pages/agents/09.md new file mode 100644 index 0000000..c05fa8a --- /dev/null +++ b/slides/pages/agents/09.md @@ -0,0 +1,40 @@ +--- +layout: default +--- + +# Streaming: Tokens y Mensajes + +
+ + + +**`messages`**: Stream de tokens del LLM + metadata + +```typescript +for await (const [mode, chunk] of await agent.stream( + { messages: [{ role: "user", content: "Explica qué es TypeScript" }] }, + { streamMode: ["updates", "messages"] } +)) { + if (mode === "messages") { + const [token, metadata] = chunk; + if (token.content) { + process.stdout.write(token.content); // Token a token + } + } else if (mode === "updates") { + // Paso completo del agente + console.log("\n[Paso completado]", Object.keys(chunk)); + } +} +``` + + + + + +
+Combina múltiples modos: `streamMode: ["updates", "messages"]` +
+ + + +
diff --git a/slides/pages/agents/10.md b/slides/pages/agents/10.md new file mode 100644 index 0000000..3018b39 --- /dev/null +++ b/slides/pages/agents/10.md @@ -0,0 +1,39 @@ +--- +layout: default +--- + +# Checkpointer: MemorySaver + +
+ + + +**MemorySaver**: Persistencia en memoria (desarrollo/testing) + +```typescript +import { createAgent } from "langchain"; +import { MemorySaver } from "@langchain/langgraph"; + +const agent = createAgent({ + model: "mistral:mistral-large-latest", + tools: [weatherTool], + checkpointer: new MemorySaver(), // En memoria RAM +}); + +const config = { configurable: { thread_id: "conv-1" } }; + +// Primera conversación +await agent.invoke( + { messages: [{ role: "user", content: "Hola, soy María" }] }, config +); + +// Segunda conversación - recuerda el contexto +await agent.invoke( + { messages: [{ role: "user", content: "¿Cómo me llamo?" }] }, config +); +// "Tu nombre es María" +``` + + + +
diff --git a/slides/pages/agents/11.md b/slides/pages/agents/11.md new file mode 100644 index 0000000..95916fb --- /dev/null +++ b/slides/pages/agents/11.md @@ -0,0 +1,47 @@ +--- +layout: default +--- + +# Checkpointer: SqliteSaver + +
+ + + +**SqliteSaver**: Persistencia en SQLite (local) + +```bash +npm install @langchain/langgraph-checkpoint-sqlite +``` + + + + + +```typescript +import { createAgent } from "langchain"; +import { SqliteSaver } from "@langchain/langgraph-checkpoint-sqlite"; + +const checkpointer = SqliteSaver.fromConnString("./agent_memory.db"); +await checkpointer.setup(); // Crear tablas + +const agent = createAgent({ + model: "mistral:mistral-large-latest", + tools: [weatherTool], + checkpointer, +}); + +// El estado persiste aunque reinicies la aplicación +``` + + + + + +
+Ideal para desarrollo local y aplicaciones de usuario único +
+ + + +
diff --git a/slides/pages/agents/12.md b/slides/pages/agents/12.md new file mode 100644 index 0000000..74986c9 --- /dev/null +++ b/slides/pages/agents/12.md @@ -0,0 +1,47 @@ +--- +layout: default +--- + +# Checkpointer: PostgresSaver + +
+ + + +**PostgresSaver**: Persistencia en PostgreSQL (producción) + +```bash +npm install @langchain/langgraph-checkpoint-postgres +``` + + + + + +```typescript +import { createAgent } from "langchain"; +import { PostgresSaver } from "@langchain/langgraph-checkpoint-postgres"; + +const DB_URI = "postgresql://user:pass@localhost:5432/mydb"; +const checkpointer = PostgresSaver.fromConnString(DB_URI); +await checkpointer.setup(); + +const agent = createAgent({ + model: "mistral:mistral-large-latest", + tools: [weatherTool], + checkpointer, +}); +``` + + + + + +
+También disponibles: **MongoDB** (`@langchain/langgraph-checkpoint-mongodb`), +**Redis** (`@langchain/langgraph-checkpoint-redis`), y **Custom** (implementar `BaseCheckpointSaver`) +
+ + + +
diff --git a/slides/pages/agents/12b.md b/slides/pages/agents/12b.md new file mode 100644 index 0000000..7beca14 --- /dev/null +++ b/slides/pages/agents/12b.md @@ -0,0 +1,51 @@ +--- +layout: default +--- + +# Middleware: Arquitectura + +
+ + + +
+ +**Middleware**: Hooks que interceptan el flujo del agente en puntos clave + +
+ + + + + +
+ +
+ beforeAgent - Antes de iniciar el agente +
+ +
+ beforeModel - Antes de cada llamada al LLM +
+ +
+ wrapModelCall - Envuelve la llamada al LLM (interceptar/modificar) +
+ +
+ wrapToolCall - Envuelve la ejecución de tools +
+ +
+ afterModel - Después de cada respuesta del LLM +
+ +
+ afterAgent - Después de completar el agente +
+ +
+ + + +
diff --git a/slides/pages/agents/13.md b/slides/pages/agents/13.md new file mode 100644 index 0000000..939abac --- /dev/null +++ b/slides/pages/agents/13.md @@ -0,0 +1,36 @@ +--- +layout: default +--- + +# Middleware Custom + +
+ + + +```typescript +import { createAgent, createMiddleware } from "langchain"; + +const loggingMiddleware = createMiddleware({ + name: "LoggingMiddleware", + beforeModel: (state) => { + console.log(`Llamando modelo con ${state.messages.length} mensajes`); + return; // No modificar estado + }, + afterModel: (state) => { + const lastMsg = state.messages[state.messages.length - 1]; + console.log(`Modelo respondió: ${lastMsg.content?.slice(0, 50)}...`); + return; + }, +}); + +const agent = createAgent({ + model: "mistral:mistral-large-latest", + tools: [weatherTool], + middleware: [loggingMiddleware], +}); +``` + + + +
diff --git a/slides/pages/agents/14.md b/slides/pages/agents/14.md new file mode 100644 index 0000000..2500de0 --- /dev/null +++ b/slides/pages/agents/14.md @@ -0,0 +1,46 @@ +--- +layout: default +--- + +# dynamicSystemPromptMiddleware + +
+ + + +```typescript +import * as z from "zod"; +import { createAgent, dynamicSystemPromptMiddleware } from "langchain"; + +const contextSchema = z.object({ + userRole: z.enum(["expert", "beginner"]), +}); + +const agent = createAgent({ + model: "mistral:mistral-large-latest", + tools: [searchTool], + contextSchema, + middleware: [ + dynamicSystemPromptMiddleware>((state, runtime) => { + const userRole = runtime.context.userRole; + let prompt = "Eres un asistente útil."; + + if (userRole === "expert") { + prompt += " Proporciona respuestas técnicas detalladas."; + } else { + prompt += " Explica conceptos de forma simple, sin jerga técnica."; + } + return prompt; + }), + ], +}); + +await agent.invoke( + { messages: [{ role: "user", content: "Explica machine learning" }] }, + { context: { userRole: "beginner" } } +); +``` + + + +
diff --git a/slides/pages/agents/15.md b/slides/pages/agents/15.md new file mode 100644 index 0000000..a617ae0 --- /dev/null +++ b/slides/pages/agents/15.md @@ -0,0 +1,48 @@ +--- +layout: default +--- + +# toolCallLimitMiddleware: Limitar Tools + +
+ + + +```typescript +import { createAgent, toolCallLimitMiddleware } from "langchain"; + +const agent = createAgent({ + model: "mistral:mistral-large-latest", + tools: [searchTool, databaseTool, scraperTool], + middleware: [ + // Límite global: máx 20 por thread, 10 por run + toolCallLimitMiddleware({ threadLimit: 20, runLimit: 10 }), + + // Límite específico para búsquedas + toolCallLimitMiddleware({ + toolName: "search", + threadLimit: 5, + runLimit: 3, + }), + + // Límite estricto para scraping (lanza error si excede) + toolCallLimitMiddleware({ + toolName: "scrape_webpage", + runLimit: 2, + exitBehavior: "error", // "continue" | "error" | "end" + }), + ], +}); +``` + + + + + +
+Requiere checkpointer para `threadLimit`. `runLimit` se resetea en cada invocación. +
+ + + +
diff --git a/slides/pages/agents/16.md b/slides/pages/agents/16.md new file mode 100644 index 0000000..7e73e52 --- /dev/null +++ b/slides/pages/agents/16.md @@ -0,0 +1,44 @@ +--- +layout: default +--- + +# summarizationMiddleware: Resumir Historial + +
+ + + +```typescript +import { createAgent, summarizationMiddleware } from "langchain"; + +const agent = createAgent({ + model: "mistral:mistral-large-latest", + tools: [searchTool, calculatorTool], + middleware: [ + summarizationMiddleware({ + model: "mistral:mistral-small-latest", // Modelo para resumir (más barato) + trigger: { tokens: 4000 }, // Resumir cuando supere 4000 tokens + keep: { messages: 20 }, // Mantener los últimos 20 mensajes + }), + ], +}); +``` + + + + + +
+ +**Funcionamiento automático:** + +1. Detecta cuando el historial supera el límite de tokens +2. Resume los mensajes antiguos usando el modelo especificado +3. Reemplaza permanentemente los mensajes antiguos por el resumen +4. Mantiene los mensajes recientes intactos para contexto + +
+ + + +
diff --git a/slides/pages/agents/17.md b/slides/pages/agents/17.md new file mode 100644 index 0000000..4559e63 --- /dev/null +++ b/slides/pages/agents/17.md @@ -0,0 +1,46 @@ +--- +layout: default +--- + +# Middleware: Limitar Historial (Trim) + +
+ + + +```typescript +import { RemoveMessage } from "@langchain/core/messages"; +import { createAgent, createMiddleware } from "langchain"; +import { MemorySaver, REMOVE_ALL_MESSAGES } from "@langchain/langgraph"; + +const trimMessages = createMiddleware({ + name: "TrimMessages", + beforeModel: (state) => { + const messages = state.messages; + if (messages.length <= 5) return; // No recortar si hay pocos + + // Mantener primer mensaje + últimos 4 + const firstMsg = messages[0]; + const recentMsgs = messages.slice(-4); + const newMessages = [firstMsg, ...recentMsgs]; + + return { + messages: [ + new RemoveMessage({ id: REMOVE_ALL_MESSAGES }), + ...newMessages, + ], + }; + }, +}); + +const agent = createAgent({ + model: "mistral:mistral-large-latest", + tools: [], + middleware: [trimMessages], + checkpointer: new MemorySaver(), +}); +``` + + + +
diff --git a/slides/pages/agents/18.md b/slides/pages/agents/18.md new file mode 100644 index 0000000..cf5f095 --- /dev/null +++ b/slides/pages/agents/18.md @@ -0,0 +1,40 @@ +--- +layout: default +--- + +# Middleware: wrapModelCall + +
+ + + +Permite interceptar y modificar la llamada al modelo: + +```typescript +import { ChatMistralAI } from "@langchain/mistralai"; +import { createAgent, createMiddleware } from "langchain"; + +const basicModel = new ChatMistralAI({ model: "mistral-small-latest" }); +const advancedModel = new ChatMistralAI({ model: "mistral-large-latest" }); + +const dynamicModelSelection = createMiddleware({ + name: "DynamicModelSelection", + wrapModelCall: (request, handler) => { + // Elegir modelo según complejidad de la conversación + const messageCount = request.messages.length; + const model = messageCount > 10 ? advancedModel : basicModel; + + return handler({ ...request, model }); + }, +}); + +const agent = createAgent({ + model: basicModel, + tools: [searchTool], + middleware: [dynamicModelSelection], +}); +``` + + + +
diff --git a/slides/pages/agents/19.md b/slides/pages/agents/19.md new file mode 100644 index 0000000..d30e552 --- /dev/null +++ b/slides/pages/agents/19.md @@ -0,0 +1,40 @@ +--- +layout: default +--- + +# Middleware: wrapToolCall + +
+ + + +Intercepta la ejecución de tools para manejo de errores: + +```typescript +import { createAgent, createMiddleware, ToolMessage } from "langchain"; + +const handleToolErrors = createMiddleware({ + name: "HandleToolErrors", + wrapToolCall: async (request, handler) => { + try { + return await handler(request); + } catch (error) { + // Devolver mensaje de error al modelo para que reintente + return new ToolMessage({ + content: `Error en tool: ${error}. Por favor revisa los parámetros.`, + tool_call_id: request.toolCall.id!, + }); + } + }, +}); + +const agent = createAgent({ + model: "mistral:mistral-large-latest", + tools: [databaseTool, apiTool], + middleware: [handleToolErrors], +}); +``` + + + +
diff --git a/slides/pages/agents/20.md b/slides/pages/agents/20.md new file mode 100644 index 0000000..89881d9 --- /dev/null +++ b/slides/pages/agents/20.md @@ -0,0 +1,51 @@ +--- +layout: default +--- + +# Middleware Built-in: Resumen + +
+ + +
+ summarizationMiddleware - Resumir historial cuando supera tokens +
+ + + +
+ humanInTheLoopMiddleware - Pausar para aprobación humana +
+ + + +
+ toolCallLimitMiddleware - Limitar llamadas a tools +
+ + + +
+ modelCallLimitMiddleware - Limitar llamadas al modelo +
+ + + +
+ modelFallbackMiddleware - Fallback a modelos alternativos +
+ + + +
+ modelRetryMiddleware - Reintentar con backoff exponencial +
+ + + +
+ piiRedactionMiddleware - Redactar información personal +
+ + +
diff --git a/slides/pages/agents/21.md b/slides/pages/agents/21.md new file mode 100644 index 0000000..0ade2ad --- /dev/null +++ b/slides/pages/agents/21.md @@ -0,0 +1,46 @@ +--- +layout: default +--- + +# Combinando Middleware + +
+ + + +```typescript +import { + createAgent, + summarizationMiddleware, + humanInTheLoopMiddleware, + toolCallLimitMiddleware, + modelRetryMiddleware, +} from "langchain"; +import { MemorySaver } from "@langchain/langgraph"; + +const agent = createAgent({ + model: "mistral:mistral-large-latest", + tools: [searchTool, sendEmailTool, databaseTool], + checkpointer: new MemorySaver(), + middleware: [ + modelRetryMiddleware({ maxRetries: 3, backoffFactor: 2 }), + summarizationMiddleware({ model: "mistral:mistral-small-latest", trigger: { tokens: 4000 } }), + toolCallLimitMiddleware({ toolName: "search", runLimit: 5 }), + humanInTheLoopMiddleware({ + interruptOn: { send_email: { allowedDecisions: ["approve", "reject"] } }, + }), + ], +}); +``` + + + + + +
+El orden de los middleware importa: se ejecutan en secuencia +
+ + + +
diff --git a/slides/pages/agents/22.md b/slides/pages/agents/22.md new file mode 100644 index 0000000..22eb19e --- /dev/null +++ b/slides/pages/agents/22.md @@ -0,0 +1,49 @@ +--- +layout: default +--- + +

Human-in-the-Loop

+ +
+ + + +````md magic-move +```typescript +import { createAgent, humanInTheLoopMiddleware } from "langchain"; +import { Command, MemorySaver } from "@langchain/langgraph"; + +const agent = createAgent({ + model: "gpt-4.1", + tools: [sendEmailTool, deleteRecordTool], + checkpointer: new MemorySaver(), + middleware: [ + humanInTheLoopMiddleware({ + interruptOn: { + send_email: { allowedDecisions: ["approve", "edit", "reject"] }, + delete_record: { allowedDecisions: ["approve", "reject"] }, + }, + }), + ], +}); +``` +```typescript +const config = { configurable: { thread_id: "thread-1" } }; + +// Se pausa antes de ejecutar tools sensibles +let result = await agent.invoke({ messages: [...] }, config); + +// Aprobar y continuar +result = await agent.invoke( + new Command({ resume: { decisions: [{ type: "approve" }] } }), + config +); +``` +```` + + + +
+ + +--- diff --git a/slides/pages/agents/23.md b/slides/pages/agents/23.md new file mode 100644 index 0000000..6bcbbed --- /dev/null +++ b/slides/pages/agents/23.md @@ -0,0 +1,51 @@ +--- +layout: default +--- + +# Resumen: Agentes LangChain v1 + +
+ + +
+ createAgent - Punto de entrada principal +
+ + + +
+ model - String ("mistral:mistral-large-latest") o instancia ChatModel +
+ + + +
+ systemPrompt - String o SystemMessage +
+ + + +
+ tools - Array de tools definidas con Zod +
+ + + +
+ checkpointer - MemorySaver | SqliteSaver | PostgresSaver +
+ + + +
+ middleware - Array para customización (prompts, límites, HITL) +
+ + + +
+ stream() - updates | values | messages +
+ + +
diff --git a/slides/pages/alternatives/01.md b/slides/pages/alternatives/01.md new file mode 100644 index 0000000..6fa34a7 --- /dev/null +++ b/slides/pages/alternatives/01.md @@ -0,0 +1,20 @@ +--- +layout: cover +class: text-center +--- + +# Alternativas en el Ecosistema JS/TS + +Frameworks y SDKs para aplicaciones LLM en TypeScript + +
+ + Pinceladas rápidas del mercado actual (Mayo 2026) + +
+ +
+ Nota: Nos centramos exclusivamente en opciones JavaScript/TypeScript +
+ + diff --git a/slides/pages/alternatives/02.md b/slides/pages/alternatives/02.md new file mode 100644 index 0000000..baa886a --- /dev/null +++ b/slides/pages/alternatives/02.md @@ -0,0 +1,31 @@ +--- +layout: default +--- + +# El Panorama JS/TS para LLMs + + + +### Tres niveles de abstracción + +``` +┌─────────────────────────────────────────────────────┐ +│ Frameworks (LangChain, LlamaIndex) │ ← Mayor abstracción +├─────────────────────────────────────────────────────┤ +│ SDKs unificados (Vercel AI SDK) │ ← Abstracción media +├─────────────────────────────────────────────────────┤ +│ APIs nativas (OpenAI SDK, Anthropic SDK, etc.) │ ← Control total +└─────────────────────────────────────────────────────┘ +``` + +### La pregunta no es "cuál es mejor" + +Es: **¿qué nivel de abstracción necesito?** + +- **Prototipo rápido** → API nativa o Vercel AI +- **RAG complejo** → LlamaIndex o LangChain +- **Agentes sofisticados** → LangGraph / DeepAgents o Vercel AI SDK 6 +- **UI streaming en React** → Vercel AI SDK + + + diff --git a/slides/pages/alternatives/03.md b/slides/pages/alternatives/03.md new file mode 100644 index 0000000..52aa23c --- /dev/null +++ b/slides/pages/alternatives/03.md @@ -0,0 +1,50 @@ +--- +layout: default +--- + +# LlamaIndex.TS + +El "hermano pequeño" de LlamaIndex (Python) + + + +### Realidad del ecosistema TS + +
+
+ +**Lo bueno** +- Especializado en RAG +- API similar a la versión Python +- Conectores de datos incluidos +- Activamente desarrollado + +
+
+ +**La realidad** +- Mejorando rápido (agentes, LiteParse) +- Documentación aún por detrás de Python +- Comunidad más pequeña +- Features llegan después + +
+
+ +### Ejemplo básico + +```typescript +import { Document, VectorStoreIndex } from "llamaindex"; + +const doc = new Document({ text: "Contenido..." }); +const index = await VectorStoreIndex.fromDocuments([doc]); +const response = await index.asQueryEngine().query({ query: "Mi pregunta" }); +``` + +
+⚠️ Ha mejorado mucho, pero si tu proyecto permite Python, LlamaIndex Python sigue siendo más completo. +
+ + + + diff --git a/slides/pages/alternatives/04.md b/slides/pages/alternatives/04.md new file mode 100644 index 0000000..06943b3 --- /dev/null +++ b/slides/pages/alternatives/04.md @@ -0,0 +1,36 @@ +--- +layout: default +--- + +# Vercel AI SDK + +El SDK moderno para aplicaciones AI en JS/TS + + + +### Por qué destaca + +- **Provider-agnostic**: OpenAI, Anthropic, Google, Mistral, Groq... +- **Streaming nativo**: Pensado para UIs reactivas +- **React-first**: Hooks listos para usar (`useChat`, `useCompletion`) +- **Edge-ready**: Funciona en Vercel, Cloudflare Workers, etc. +- **Ligero**: Pocas abstracciones, código directo + +### Ya en v6 — El SDK que más ha crecido + +```typescript +import { openai } from '@ai-sdk/openai'; +import { generateText } from 'ai'; + +const { text } = await generateText({ + model: openai('gpt-4.1'), + prompt: 'Explica qué es RAG', +}); +``` + +
+✅ Excelente para: Apps Next.js/React, APIs serverless, prototipos rápidos +
+ + + diff --git a/slides/pages/alternatives/05.md b/slides/pages/alternatives/05.md new file mode 100644 index 0000000..ceb4f28 --- /dev/null +++ b/slides/pages/alternatives/05.md @@ -0,0 +1,40 @@ +--- +layout: default +--- + +# Vercel AI SDK 6 - Agentes + + + +### Agent class (nuevo en v6) + +```typescript +import { openai } from '@ai-sdk/openai'; +import { Agent, tool } from 'ai'; +import { z } from 'zod'; + +const agent = new Agent({ + model: openai('gpt-4.1'), + tools: { + weather: tool({ + description: 'Obtiene el clima', + parameters: z.object({ city: z.string() }), + execute: async ({ city }) => ({ temp: 22, condition: 'Soleado' }), + }), + }, + maxSteps: 5, +}); +``` + +### ¿Agentes completos? + +| Tiene ✅ | No tiene ❌ | +|----------|-------------| +| Agent class reutilizable | Persistencia de estado (threads) | +| Human-in-the-loop (`needsApproval`) | Tools built-in (code interpreter) | +| Tools custom + Streaming | Orquestación multi-agente nativa | + + + + + diff --git a/slides/pages/alternatives/06.md b/slides/pages/alternatives/06.md new file mode 100644 index 0000000..63cd776 --- /dev/null +++ b/slides/pages/alternatives/06.md @@ -0,0 +1,52 @@ +--- +layout: default +--- + +# APIs Nativas de los Providers + +Cada provider tiene su SDK oficial para Node.js/TS + + + +
+ +
+ +### OpenAI SDK +```typescript +import OpenAI from "openai"; +const openai = new OpenAI(); + +// Function calling nativo +const response = await openai.chat.completions.create({ + model: "gpt-4.1", + messages: [...], + tools: [{ type: "function", function: {...} }], +}); +``` +- Assistants API (agentes con estado) +- File search, code interpreter built-in + +
+ +
+ +### Anthropic SDK + Agent SDK +```typescript +import Anthropic from "@anthropic-ai/sdk"; +const anthropic = new Anthropic(); + +const message = await anthropic.messages.create({ + model: "claude-sonnet-4-6", + messages: [...], + tools: [{ name: "...", input_schema: {...} }], +}); +``` +- Tool use muy potente +- **Claude Agent SDK** (`@anthropic-ai/claude-agent-sdk`) + +
+ +
+ + diff --git a/slides/pages/alternatives/07.md b/slides/pages/alternatives/07.md new file mode 100644 index 0000000..c7df5b9 --- /dev/null +++ b/slides/pages/alternatives/07.md @@ -0,0 +1,42 @@ +--- +layout: default +--- + +# ¿Agentes con APIs Nativas? + +Cada provider tiene su propio enfoque + + + +### OpenAI: Assistants API + +```typescript +const assistant = await openai.beta.assistants.create({ + name: "Analyst", tools: [{ type: "code_interpreter" }], model: "gpt-4.1", +}); +const thread = await openai.beta.threads.create(); +const run = await openai.beta.threads.runs.create(thread.id, { assistant_id: assistant.id }); +``` + +- Agentes con estado, tools built-in (code interpreter, file search) + +### Anthropic: Claude Agent SDK +- Agent SDK con tools built-in (Bash, Read, Edit, Web...) +- O tool use manual si prefieres control total + +### Google (Gemini): @google/genai SDK + +```typescript +import { GoogleGenAI } from "@google/genai"; +const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY }); +const response = await ai.models.generateContent({ + model: "gemini-2.5-flash", contents: "Explica RAG", +}); +``` + +- Function calling similar a OpenAI +- Vertex AI Agents Builder para low-code enterprise + + + + diff --git a/slides/pages/alternatives/08.md b/slides/pages/alternatives/08.md new file mode 100644 index 0000000..8a67de9 --- /dev/null +++ b/slides/pages/alternatives/08.md @@ -0,0 +1,16 @@ +--- +layout: default +--- + +# Comparativa Rápida: Ecosistema JS/TS + +| | LangChain.js | LlamaIndex.TS | Vercel AI | APIs Nativas | +|---|:---:|:---:|:---:|:---:| +| **Madurez TS** | ⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | +| **RAG** | ✅ Completo | ✅ Especializado | ⚠️ Básico | 🔧 Manual | +| **Agentes** | ✅ LangGraph | ⚠️ Básico | ✅ Agent class | 🔧 Varía | +| **Streaming UI** | ✅ | ⚠️ | ✅✅ | ✅ | +| **Curva aprendizaje** | Media-Alta | Media | Baja | Baja | +| **Vendor lock-in** | Bajo | Bajo | Bajo | Alto | + + diff --git a/slides/pages/alternatives/09.md b/slides/pages/alternatives/09.md new file mode 100644 index 0000000..3da6281 --- /dev/null +++ b/slides/pages/alternatives/09.md @@ -0,0 +1,25 @@ +--- +layout: default +--- + +# Recomendación Pragmática + + + +| Caso de uso | Recomendación | +|-------------|---------------| +| **Prototipo/MVP** | Vercel AI SDK o API nativa | +| **RAG** | LangChain.js (o Python si puedes) | +| **Agentes complejos** | LangGraph / DeepAgents | +| **App React con chat** | Vercel AI SDK | +| **Control total** | APIs nativas del provider | + +
+ +1. **Empieza simple** → API nativa o Vercel AI +2. **Añade complejidad cuando la necesites** → LangChain/LangGraph +3. **No sobre-ingenierices** → La herramienta mínima que resuelva el problema + +
+ + diff --git a/slides/pages/alternatives/10.md b/slides/pages/alternatives/10.md new file mode 100644 index 0000000..6c80e08 --- /dev/null +++ b/slides/pages/alternatives/10.md @@ -0,0 +1,31 @@ +--- +layout: default +--- + +# Reflexión Final + + + +### El ecosistema JS/TS para LLMs está madurando + +- **2023**: LangChain dominaba, pocas alternativas +- **2024**: Explosión de opciones, Vercel AI despega +- **2025**: Vercel AI SDK 5/6, Claude Agent SDK, DeepAgents +- **2026**: Ecosistema TS ya compite seriamente con Python + +### Pero seamos honestos... + +
+ +**Python sigue adelante** en el ecosistema LLM: +- Más ejemplos, tutoriales, comunidad +- Features nuevas llegan antes +- Mejor soporte de ML/embeddings locales + +Si tu stack lo permite, Python puede ser más productivo para AI. + +
+ + + + diff --git a/slides/pages/alternatives/11.md b/slides/pages/alternatives/11.md new file mode 100644 index 0000000..87acf91 --- /dev/null +++ b/slides/pages/alternatives/11.md @@ -0,0 +1,38 @@ +--- +layout: default +--- + +# ¿Cuándo JS/TS es viable para AI? + + + +### Casos donde tiene sentido + +- Tu equipo es **JS/TS puro** y no quieres añadir Python +- Necesitas **edge computing** (Cloudflare Workers, Vercel Edge) +- Tu aplicación es **web-first** con mucho frontend +- Quieres un **stack unificado** (mismo lenguaje front y back) + +### El ecosistema está mejorando + +
+
+ +**Vercel AI SDK** está muy maduro y bien mantenido + +
+
+ +**LangChain.js** sigue de cerca a Python + +
+
+ +
+ +**Conclusión**: JS/TS ya es una opción seria para aplicaciones LLM en producción, especialmente si tu contexto lo favorece. + +
+ + + diff --git a/slides/pages/ending.md b/slides/pages/ending.md new file mode 100644 index 0000000..7a7ff65 --- /dev/null +++ b/slides/pages/ending.md @@ -0,0 +1,30 @@ +--- +layout: center +class: text-center +--- + +# Eskerrik asko! 🎉 + +
+ +Gracias por vuestra atención + +
+ +
+ +¿Dudas o consultas? + +
+ +
+ +📧 **jabi@nykk.io** + +
+ +
+ +LangChain.js: De cero a producción + +
diff --git a/slides/pages/introduction/01.md b/slides/pages/introduction/01.md new file mode 100644 index 0000000..db891b4 --- /dev/null +++ b/slides/pages/introduction/01.md @@ -0,0 +1,15 @@ +--- +layout: cover +class: text-center +--- + +# ¿Qué es LangChain? + +Framework para desarrollar aplicaciones con Modelos de Lenguaje (LLMs) + +
+ + Conectando LLMs con el mundo real + +
+ diff --git a/slides/pages/introduction/02.md b/slides/pages/introduction/02.md new file mode 100644 index 0000000..b89558f --- /dev/null +++ b/slides/pages/introduction/02.md @@ -0,0 +1,33 @@ +--- +layout: default +--- + +# Historia de LangChain + +
+ + + +
+

Octubre 2022: Nace LangChain en Python 🐍

+

Creado por Harrison Chase • Enfoque inicial: encadenar prompts y LLMs

+
+ +
+

Diciembre 2022: Lanzamiento de LangChain.js ⚡

+

Apenas 2 meses después • Port oficial • Para desarrolladores web

+
+ +
+

2023: Explosión de adopción en ambos ecosistemas

+

Múltiples proveedores de LLMs • 110K+ stars en GitHub

+
+ +
+

2024-2025: Maduración hacia producción

+

LangGraph: Control de bajo nivel • LangSmith: Observabilidad y testing

+
+ + + +
diff --git a/slides/pages/introduction/03.md b/slides/pages/introduction/03.md new file mode 100644 index 0000000..65aaa2b --- /dev/null +++ b/slides/pages/introduction/03.md @@ -0,0 +1,34 @@ +--- +layout: center +class: text-center +--- + +# Filosofía de LangChain + +
+ +
+
🔌
+
Flexibilidad
+
Sin vendor lock-in
+
+ +
+
🔗
+
Integración
+
LLMs + Datos externos
+
+ +
+
🤖
+
Agentes
+
Decisiones autónomas
+
+ +
+
🏗️
+
Producción
+
Del prototipo a la realidad
+
+ +
diff --git a/slides/pages/introduction/04.md b/slides/pages/introduction/04.md new file mode 100644 index 0000000..cb394db --- /dev/null +++ b/slides/pages/introduction/04.md @@ -0,0 +1,33 @@ +--- +layout: default +--- + +# Principios Fundamentales + +
+ +
+

1. Flexibilidad de Modelos

+

+ Estandarización de inputs/outputs entre proveedores de LLMs. + Cambia fácilmente entre OpenAI, Anthropic, Google, etc. +

+
+ +
+

2. Orquestación Compleja

+

+ Los modelos coordinan flujos de trabajo con datos externos y herramientas. + Más allá de la simple generación de texto. +

+
+ +
+

3. Fiabilidad en Producción

+

+ Observabilidad, testing y debugging para aplicaciones reales. + El prototipo es fácil, producción es el reto. +

+
+ +
diff --git a/slides/pages/introduction/05.md b/slides/pages/introduction/05.md new file mode 100644 index 0000000..945ab0b --- /dev/null +++ b/slides/pages/introduction/05.md @@ -0,0 +1,38 @@ +--- +layout: default +--- + +# Creencias Fundamentales + +
+ + + +
+

🚀 Los LLMs son tecnología poderosa

+

Una innovación que vale la pena construir sobre ella

+
+ +
+

🔗 Los LLMs son mejores con datos externos

+

Combinar LLMs con fuentes de datos externas potencia sus capacidades

+
+ +
+

🤖 El futuro es agéntico

+

Las aplicaciones exhibirán capacidades autónomas y de toma de decisiones

+
+ +
+

🌱 Estamos en una etapa temprana

+

El campo está en constante evolución con espacio sustancial para crecer

+
+ +
+

💪 Producción es difícil

+

Prototipar es fácil, lograr fiabilidad en producción es el verdadero desafío

+
+ + + +
diff --git a/slides/pages/introduction/06.md b/slides/pages/introduction/06.md new file mode 100644 index 0000000..df2fbbe --- /dev/null +++ b/slides/pages/introduction/06.md @@ -0,0 +1,31 @@ +--- +layout: center +class: text-center +--- + +# Ecosistema LangChain + +
+ +
+
⛓️
+
LangChain (v1)
+
Interfaz productiva para agentes
+
createAgent + Middleware
+
+ +
+
🕸️
+
LangGraph
+
Motor de runtime confiable
+
Persistencia, streaming, control granular
+
+ +
+
🔍
+
LangSmith
+
Observabilidad + Evaluación
+
Tracing automático, debugging visual
+
+ +
diff --git a/slides/pages/introduction/07.md b/slides/pages/introduction/07.md new file mode 100644 index 0000000..01db7e4 --- /dev/null +++ b/slides/pages/introduction/07.md @@ -0,0 +1,42 @@ +--- +layout: default +--- + +# ¿Quién usa LangChain? + +
+ +
+ +## 🏢 Empresas en Producción + +- **LinkedIn** - Agentes con LangGraph +- **Uber** - Sistemas agénticos +- **Replit** - Asistentes de código +- **Klarna** - Chatbots de atención +- **Snowflake** - Integración de datos +- **J.P. Morgan** - Aplicaciones financieras +- **BCG** - Consultoría con IA + +
+ +
+ +## 📊 Números de Adopción + +- **1,306** empresas verificadas +- **132K+** aplicaciones LLM construidas +- **130M+** descargas totales +- **51%** usando agentes en producción +- **78%** planean usar agentes pronto + +### 💼 Casos de Uso Comunes + +- Chat sobre documentos +- Atención al cliente +- Asistentes personalizados +- Análisis de datos + +
+ +
diff --git a/slides/pages/introduction/08.md b/slides/pages/introduction/08.md new file mode 100644 index 0000000..a33c715 --- /dev/null +++ b/slides/pages/introduction/08.md @@ -0,0 +1,52 @@ +--- +layout: default +--- + +# Modelo de Negocio de LangChain + +
+ +
+ +## 🏢 La Empresa + + +- **Fundadores**: Harrison Chase y Ankush Gola +- **Fundación**: Octubre 2022 +- **Sede**: San Francisco, CA +- **CEO**: Harrison Chase +- **Equipo**: ~136 empleados (2025) + +### 💰 Financiación + +- **$260M** levantados en total +- **$1.25B** de valoración (Oct 2025) +- **Inversores**: Sequoia, Benchmark, IVP, CapitalG + + +
+ +
+ +## 💼 Modelo de Negocio + + +**Freemium B2B** +- Framework open-source (gratis) +- Monetización vía LangSmith + +**Revenue 2025**: $16M ARR +- Crecimiento: $8.5M → $16M en 1 año +- **1,000+** clientes empresariales + + +### 💎 LangSmith Pricing + +- **Developer**: Gratis (5k traces/mes) +- **Plus**: $39/usuario/mes (10k traces) +- **Enterprise**: Personalizado + self-hosted + + +
+ +
diff --git a/slides/pages/introduction/09.md b/slides/pages/introduction/09.md new file mode 100644 index 0000000..03b7c75 --- /dev/null +++ b/slides/pages/introduction/09.md @@ -0,0 +1,27 @@ +--- +layout: two-cols +--- + +# LangChain.js + +La implementación JavaScript/TypeScript del framework LangChain + +::right:: + + + +## ¿Por qué LangChain.js? + +- **Desarrollo web nativo** + - Frontend y backend con la misma tecnología + +- **Ecosistema JavaScript** + - NPM, TypeScript, Node.js + +- **Paridad con Python** + - Mismos conceptos y APIs + +- **Comunidad activa** + - Mantenido oficialmente + + diff --git a/slides/pages/introduction/10.md b/slides/pages/introduction/10.md new file mode 100644 index 0000000..f61ae04 --- /dev/null +++ b/slides/pages/introduction/10.md @@ -0,0 +1,54 @@ +--- +layout: default +--- + +# LangChain.js: Entornos de Ejecución + +
+ +
+ +## 🖥️ Runtimes Soportados + + + +- **Node.js** (20.x, 22.x, 24.x) + - ESM y CommonJS + +- **Deno** - Runtime seguro + +- **Bun** - Runtime ultra-rápido + +- **Navegadores** - Client-side + + + +
+ +
+ +## ☁️ Edge & Serverless + + + +- **Cloudflare Workers** + +- **Vercel Edge Functions** + +- **Next.js** (Server, Edge, Browser) + +- **Supabase Edge Functions** + + + + + +### 💪 Ventaja Clave + +**Una sola codebase** para frontend, backend, edge y serverless + + + +
+ +
diff --git a/slides/pages/introduction/11.md b/slides/pages/introduction/11.md new file mode 100644 index 0000000..ed6e2ef --- /dev/null +++ b/slides/pages/introduction/11.md @@ -0,0 +1,59 @@ +--- +layout: default +--- + +# Arquitectura de Componentes + +

Los componentes trabajan juntos para crear aplicaciones AI sofisticadas

+ +
+ +## 🧩 Seis Categorías Principales + +
+ + +
+

📝 Models

+

Chat, LLMs, embeddings

+
+ + + +
+

🔧 Tools

+

APIs, búsquedas, DBs

+
+ + + +
+

🤖 Agents

+

Orquestación y razonamiento

+
+ + + +
+

💾 Memory

+

Historia y estado

+
+ + + +
+

🔍 Retrievers

+

Búsqueda vectorial y web

+
+ + + +
+

📄 Documents

+

Loaders, splitters

+
+ + +
+ +
diff --git a/slides/pages/langsmith/01.md b/slides/pages/langsmith/01.md new file mode 100644 index 0000000..071e504 --- /dev/null +++ b/slides/pages/langsmith/01.md @@ -0,0 +1,14 @@ +--- +layout: cover +class: text-center +--- + +# Módulo 6: LangSmith + +Observabilidad, debugging y evaluación + +
+ + Llevando tu aplicación a producción + +
diff --git a/slides/pages/langsmith/02.md b/slides/pages/langsmith/02.md new file mode 100644 index 0000000..64f5581 --- /dev/null +++ b/slides/pages/langsmith/02.md @@ -0,0 +1,44 @@ +--- +layout: default +--- + +# ¿Qué es LangSmith? + +
+ + + +
+ +**LangSmith**: Plataforma de observabilidad y testing para aplicaciones LLM + +
+ + + +
+ + +
+

🔍 Observabilidad

+

Ver cada paso del agente: prompts, respuestas, tools ejecutadas

+
+ + + +
+

🐛 Debugging

+

Identificar errores, latencias y costos en producción

+
+ + + +
+

✅ Evaluación

+

Datasets de prueba y métricas de calidad

+
+ + +
+ +
diff --git a/slides/pages/langsmith/03.md b/slides/pages/langsmith/03.md new file mode 100644 index 0000000..20bd243 --- /dev/null +++ b/slides/pages/langsmith/03.md @@ -0,0 +1,29 @@ +--- +layout: default +--- + +# ¿Por qué LangSmith? + +
+ + +
+

❌ Sin LangSmith

+

• No sabes qué prompts se enviaron exactamente

+

• Difícil reproducir errores

+

• Imposible saber el costo real por usuario

+

• Testing manual y tedioso

+
+ + + +
+

✅ Con LangSmith

+

• Trazas completas de cada ejecución

+

• Replay de conversaciones problemáticas

+

• Dashboard de costos y latencias

+

• Datasets y evaluaciones automáticas

+
+ + +
diff --git a/slides/pages/langsmith/04.md b/slides/pages/langsmith/04.md new file mode 100644 index 0000000..f6f3755 --- /dev/null +++ b/slides/pages/langsmith/04.md @@ -0,0 +1,42 @@ +--- +layout: default +--- + +# Configuración de LangSmith + +
+ + + +**Paso 1:** Crear cuenta gratuita en [smith.langchain.com](https://smith.langchain.com) + + + + + +**Paso 2:** Obtener API Key desde Settings → API Keys + + + + + +**Paso 3:** Configurar variables de entorno + +```bash +# .env +LANGCHAIN_TRACING_V2=true +LANGCHAIN_API_KEY=lsv2_pt_... +LANGCHAIN_PROJECT=mi-proyecto +``` + + + + + +
+✨ Automático: LangChain detecta las variables y envía trazas a LangSmith +
+ + + +
diff --git a/slides/pages/langsmith/05.md b/slides/pages/langsmith/05.md new file mode 100644 index 0000000..8a2f004 --- /dev/null +++ b/slides/pages/langsmith/05.md @@ -0,0 +1,33 @@ +--- +layout: default +--- + +# Ejemplo con LangSmith + +
+ +```typescript +import { createAgent } from "langchain"; + +// Con las variables de entorno configuradas, +// las trazas se envían automáticamente a LangSmith + +const agent = createAgent({ + model: "openai:gpt-4.1-mini", + tools: [weatherTool], +}); + +const result = await agent.invoke({ + messages: [{ role: "user", content: "¿Qué tiempo hace en Madrid?" }] +}); + +// ✅ Ve la traza completa en https://smith.langchain.com +// - Input del usuario +// - Decisión del LLM de usar la tool +// - Ejecución de la tool +// - Respuesta final +// - Tokens consumidos +// - Latencia de cada paso +``` + +
diff --git a/slides/pages/langsmith/06.md b/slides/pages/langsmith/06.md new file mode 100644 index 0000000..9593597 --- /dev/null +++ b/slides/pages/langsmith/06.md @@ -0,0 +1,37 @@ +--- +layout: default +--- + +# Plan Gratuito + +
+ + + +
+

🎁 Developer (Gratis)

+

• 5,000 traces/mes

+

• Retención de 14 días

+

• 1 usuario

+

• Perfecto para desarrollo y testing

+
+ + + + + +
+📚 Planes de pago desde $39/mes (Plus) para equipos y producción +
+ + + + + +
+[smith.langchain.com/pricing](https://smith.langchain.com/pricing) +
+ + + +
diff --git a/slides/pages/langsmith/07.md b/slides/pages/langsmith/07.md new file mode 100644 index 0000000..7676ab6 --- /dev/null +++ b/slides/pages/langsmith/07.md @@ -0,0 +1,30 @@ +--- +layout: default +--- + +# Próximos Pasos + +
+ + +
+

🚀 En producción

+

Activa LangSmith para monitorear tu aplicación en real-time

+
+ + + +
+

📚 Explora datasets

+

Crea casos de prueba y evalúa mejoras en tus prompts

+
+ + + +
+

📖 Documentación

+

[docs.smith.langchain.com](https://docs.smith.langchain.com)

+
+ + +
diff --git a/slides/pages/messages/01.md b/slides/pages/messages/01.md new file mode 100644 index 0000000..2a75961 --- /dev/null +++ b/slides/pages/messages/01.md @@ -0,0 +1,14 @@ +--- +layout: cover +class: text-center +--- + +# Módulo 4: Mensajes y Prompts + +Sistema de mensajes, templates y gestión de contexto + +
+ + Comunicándote efectivamente con los LLMs + +
diff --git a/slides/pages/messages/02.md b/slides/pages/messages/02.md new file mode 100644 index 0000000..8125128 --- /dev/null +++ b/slides/pages/messages/02.md @@ -0,0 +1,29 @@ +--- +layout: default +--- + +# Tipos de Mensajes + +
+ +Los **Chat Models** trabajan con mensajes tipados, no con texto plano. Cada tipo tiene una **carga semántica** que el modelo interpreta de forma diferente + +
+ + +
+ 💼 SystemMessage (role: "system") +

Instrucciones iniciales que condicionan todo el comportamiento del modelo. Define persona, reglas, restricciones y formato de respuesta. Tiene la mayor prioridad en la atención del modelo.

+
+ + + +
+ 👤 HumanMessage (role: "user") +

Input del usuario. Es lo que el modelo debe responder directamente. Puede contener texto, imágenes, audio y archivos (multimodal).

+
+ + +
+ +
diff --git a/slides/pages/messages/02b.md b/slides/pages/messages/02b.md new file mode 100644 index 0000000..e9e6ffd --- /dev/null +++ b/slides/pages/messages/02b.md @@ -0,0 +1,33 @@ +--- +layout: default +--- + +# Tipos de Mensajes (cont.) + +
+ +
+ + +
+ 🤖 AIMessage (role: "assistant") +

Respuesta previa del modelo. Se usa para historial de conversación y para few-shot learning. Puede incluir tool_calls, usage_metadata y response_metadata.

+
+ + + +
+ 🔧 ToolMessage (role: "tool") +

Resultado de una herramienta ejecutada. Requiere tool_call_id que enlaza con el AIMessage que solicitó la herramienta. Cierra el ciclo de tool-calling.

+
+ + + +
+💡 También puedes usar la notación abreviada con tuplas: ["system", "..."], ["human", "..."], ["ai", "..."] dentro de ChatPromptTemplate +
+ + +
+ +
diff --git a/slides/pages/messages/02c.md b/slides/pages/messages/02c.md new file mode 100644 index 0000000..3d5171f --- /dev/null +++ b/slides/pages/messages/02c.md @@ -0,0 +1,39 @@ +--- +layout: default +--- + +# Implicaciones: System vs Human + +
+ +El tipo de mensaje **no es solo una etiqueta**, cambia cómo el modelo procesa la información + +
+ + +
+

💼 System vs Human

+

Poner instrucciones en SystemMessage vs en HumanMessage no es equivalente:

+
    +
  • System tiene mayor prioridad — el modelo lo trata como "ley"
    • +
  • Human se trata como "petición" — puede ser ignorado si contradice al system
    • +
  • En Anthropic, system va en un parámetro separado de la API
    • +
+
+ + + +
+

🤖 AIMessage manual

+

Puedes inyectar AIMessages falsos en el historial:

+
    +
  • El modelo "cree" que él mismo dijo eso
    • +
  • Útil para few-shot: enseñar el patrón de respuesta deseado
    • +
  • Útil para prefilling: forzar el inicio de la respuesta
    • +
+
+ + +
+ +
diff --git a/slides/pages/messages/02d.md b/slides/pages/messages/02d.md new file mode 100644 index 0000000..66ae35c --- /dev/null +++ b/slides/pages/messages/02d.md @@ -0,0 +1,39 @@ +--- +layout: default +--- + +# Implicaciones: Tool Calling y Orden + +
+ +
+ + +
+

🔧 Ciclo de Tool Calling

+

El flujo completo requiere 3 mensajes:

+
    +
  1. AIMessage con tool_calls → modelo pide ejecutar
    2. +
  2. ToolMessage con resultado → tu app ejecuta y devuelve
    3. +
  3. AIMessage final → modelo interpreta el resultado
    4. +
+

Lo veremos en detalle en el módulo de Agentes

+
+ + + +
+

📋 El orden importa

+

La secuencia de mensajes define el contexto:

+
    +
  • Los mensajes recientes tienen más peso que los antiguos
    • +
  • El modelo espera alternancia Human → AI → Human
    • +
  • Romper la alternancia puede causar comportamiento inesperado
    • +
+

Por eso la gestión del historial es clave

+
+ + +
+ +
diff --git a/slides/pages/messages/03.md b/slides/pages/messages/03.md new file mode 100644 index 0000000..310a590 --- /dev/null +++ b/slides/pages/messages/03.md @@ -0,0 +1,29 @@ +--- +layout: default +--- + +# Ejemplo: Usando Mensajes + +
+ +```typescript +import { ChatOpenAI } from "@langchain/openai"; +import { + SystemMessage, + HumanMessage +} from "@langchain/core/messages"; + +const model = new ChatOpenAI({ model: "gpt-4.1-mini" }); + +const messages = [ + new SystemMessage("Eres un experto en JavaScript y TypeScript"), + new HumanMessage("¿Qué es async/await?") +]; + +const response = await model.invoke(messages); + +console.log(response.content); +// Respuesta contextualizada como experto en JS/TS +``` + +
diff --git a/slides/pages/messages/04.md b/slides/pages/messages/04.md new file mode 100644 index 0000000..72b3748 --- /dev/null +++ b/slides/pages/messages/04.md @@ -0,0 +1,47 @@ +--- +layout: default +--- + +# Prompt Templates + +
+ + + +
+ +**ChatPromptTemplate** permite reutilizar prompts con variables dinámicas y la notación abreviada de roles + +
+ + + + + +```typescript +import { ChatPromptTemplate } from "@langchain/core/prompts"; + +const template = ChatPromptTemplate.fromMessages([ + ["system", "Eres un experto en {topic}"], // ← shorthand de SystemMessage + ["human", "{question}"] // ← shorthand de HumanMessage +]); + +const prompt = await template.invoke({ + topic: "JavaScript", + question: "¿Qué es async/await?" +}); + +const response = await model.invoke(prompt); +``` + + + + + +
+💡 Los templates son útiles para reutilización, testing y separar prompts de la lógica. Pero para casos simples, puedes usar mensajes directamente. +
+ + + +
diff --git a/slides/pages/messages/05.md b/slides/pages/messages/05.md new file mode 100644 index 0000000..757ee20 --- /dev/null +++ b/slides/pages/messages/05.md @@ -0,0 +1,37 @@ +--- +layout: default +--- + +# Ventajas de Templates + +
+ + +
+

✅ Reutilización

+

El mismo template con diferentes variables

+
+ + + +
+

✅ Mantenibilidad

+

Cambiar el prompt en un solo lugar

+
+ + + +
+

✅ Testing

+

Probar con múltiples combinaciones de variables

+
+ + + +
+

✅ Separación de Responsabilidades

+

Prompts independientes de la lógica de negocio

+
+ + +
diff --git a/slides/pages/messages/06.md b/slides/pages/messages/06.md new file mode 100644 index 0000000..5614871 --- /dev/null +++ b/slides/pages/messages/06.md @@ -0,0 +1,41 @@ +--- +layout: default +--- + +# Few-Shot Prompting + +
+ + + +
+ +**Few-shot**: Dar ejemplos al LLM para que aprenda el patrón deseado + +
+ + + + + +```typescript +import { ChatPromptTemplate } from "@langchain/core/prompts"; + +const template = ChatPromptTemplate.fromMessages([ + ["system", "Clasifica el sentimiento de tweets"], + ["human", "Me encanta este producto!"], + ["ai", "positivo"], + ["human", "No funciona bien :("], + ["ai", "negativo"], + ["human", "{tweet}"] // El tweet a clasificar +]); + +const response = await model.invoke( + await template.invoke({ tweet: "Buena experiencia" }) +); +// → "positivo" +``` + + + +
diff --git a/slides/pages/messages/07.md b/slides/pages/messages/07.md new file mode 100644 index 0000000..acc938c --- /dev/null +++ b/slides/pages/messages/07.md @@ -0,0 +1,40 @@ +--- +layout: default +--- + +# Historial de Conversación + +
+ + + +
+ +Para mantener **contexto** entre mensajes, acumula el historial + +
+ + + + + +```typescript +import { ChatOpenAI } from "@langchain/openai"; +import { HumanMessage, AIMessage } from "@langchain/core/messages"; + +const model = new ChatOpenAI({ model: "gpt-4.1-mini" }); + +const history = [ + new HumanMessage("Hola, me llamo Juan"), + new AIMessage("Hola Juan, ¿en qué puedo ayudarte?"), + new HumanMessage("¿Cuál es mi nombre?") +]; + +const response = await model.invoke(history); +console.log(response.content); +// → "Tu nombre es Juan" +``` + + + +
diff --git a/slides/pages/messages/08.md b/slides/pages/messages/08.md new file mode 100644 index 0000000..e834a95 --- /dev/null +++ b/slides/pages/messages/08.md @@ -0,0 +1,44 @@ +--- +layout: default +--- + +# Gestión de Historial + +
+ + + +
+ +**Problema**: El historial crece indefinidamente → Tokens infinitos → Costos altos + +
+ + + +
+ + +
+

📏 Limitar por cantidad

+

Mantener solo los últimos N mensajes

+
+ + + +
+

📊 Limitar por tokens

+

Mantener solo los últimos N tokens

+
+ + + +
+

📝 Resumir

+

Comprimir conversaciones antiguas en un resumen

+
+ + +
+ +
diff --git a/slides/pages/messages/09.md b/slides/pages/messages/09.md new file mode 100644 index 0000000..2c8a226 --- /dev/null +++ b/slides/pages/messages/09.md @@ -0,0 +1,34 @@ +--- +layout: default +--- + +# Ejemplo: Limitando Historial + +
+ +```typescript +import { ChatOpenAI } from "@langchain/openai"; +import { HumanMessage, AIMessage } from "@langchain/core/messages"; + +const model = new ChatOpenAI({ model: "gpt-4.1-mini" }); + +// Simular un historial largo +const fullHistory = [ + new HumanMessage("Mensaje 1"), + new AIMessage("Respuesta 1"), + new HumanMessage("Mensaje 2"), + new AIMessage("Respuesta 2"), + new HumanMessage("Mensaje 3"), + new AIMessage("Respuesta 3"), +]; + +// Mantener solo los últimos 4 mensajes (2 turnos) +const recentHistory = fullHistory.slice(-4); + +const response = await model.invoke([ + ...recentHistory, + new HumanMessage("Nueva pregunta") +]); +``` + +
diff --git a/slides/pages/messages/10.md b/slides/pages/messages/10.md new file mode 100644 index 0000000..ccc775c --- /dev/null +++ b/slides/pages/messages/10.md @@ -0,0 +1,51 @@ +--- +layout: default +--- + +# Placeholder Variables + +
+ + + +
+ +**MessagesPlaceholder**: Insertar arrays de mensajes dinámicamente en templates + +
+ + + + + +```typescript +import { ChatPromptTemplate, MessagesPlaceholder } from "@langchain/core/prompts"; + +const template = ChatPromptTemplate.fromMessages([ + ["system", "Eres un asistente útil"], + new MessagesPlaceholder("history"), // Array de mensajes + ["human", "{input}"] +]); + +const response = await model.invoke( + await template.invoke({ + history: [ + new HumanMessage("Hola"), + new AIMessage("¡Hola! ¿En qué puedo ayudarte?") + ], + input: "¿Qué tiempo hace?" + }) +); +``` + + + + + +
+💡 ["system", "..."] y ["human", "..."] son la forma abreviada de SystemMessage y HumanMessage en ChatPromptTemplate +
+ + + +
diff --git a/slides/pages/messages/11.md b/slides/pages/messages/11.md new file mode 100644 index 0000000..cc82285 --- /dev/null +++ b/slides/pages/messages/11.md @@ -0,0 +1,43 @@ +--- +layout: default +--- + +# Partial Templates + +
+ + + +
+ +**Partial**: Pre-rellenar algunas variables, completar otras después + +
+ + + + + +```typescript +import { ChatPromptTemplate } from "@langchain/core/prompts"; + +const template = ChatPromptTemplate.fromMessages([ + ["system", "Eres un experto en {topic} que habla {language}"], + ["human", "{question}"] +]); + +// Pre-configurar topic y language +const spanishJSTemplate = await template.partial({ + topic: "JavaScript", + language: "español" +}); + +// Ahora solo necesitas la pregunta +const response = await model.invoke( + await spanishJSTemplate.invoke({ question: "¿Qué es async/await?" }) +); +``` + + + +
diff --git a/slides/pages/models/01.md b/slides/pages/models/01.md new file mode 100644 index 0000000..030fd51 --- /dev/null +++ b/slides/pages/models/01.md @@ -0,0 +1,14 @@ +--- +layout: cover +class: text-center +--- + +# Módulo 3: Modelos + +Trabajando con LLMs y Chat Models en LangChain.js + +
+ + La base de cualquier aplicación con IA + +
diff --git a/slides/pages/models/02.md b/slides/pages/models/02.md new file mode 100644 index 0000000..1fc6f47 --- /dev/null +++ b/slides/pages/models/02.md @@ -0,0 +1,43 @@ +--- +layout: default +--- + +# Chat Models + +
+ +
+

💬 La interfaz estándar en LangChain

+

Todos los modelos en LangChain se usan como Chat Models

+

📥 Entrada: Lista de mensajes (System, Human, AI)

+

📤 Salida: Mensaje estructurado (AIMessage)

+
+ +
+ +
+

Mistral

+

Mistral Large, Small

+
+ +
+

Anthropic

+

Claude Sonnet, Haiku

+
+ +
+

Google

+

Gemini Pro, Flash

+
+ +
+ + + +
+ ✅ Misma interfaz para todos → fácil cambiar de proveedor +
+ + + +
diff --git a/slides/pages/models/02b.md b/slides/pages/models/02b.md new file mode 100644 index 0000000..7efb488 --- /dev/null +++ b/slides/pages/models/02b.md @@ -0,0 +1,44 @@ +--- +layout: default +--- + +# ¿Qué es un Token? + +
+ + + +
+ +**Token** = Unidad básica de texto que procesa un LLM + +No es exactamente una palabra, ni un carácter + +
+ + + + + +
+ +
+

Ejemplos de Tokenización

+

"Hola mundo" → 2 tokens

+

"LangChain" → 2 tokens (Lang + Chain)

+

"desarrollador" → 3 tokens

+

"🚀" → 1-2 tokens

+
+ +
+

Regla General

+

1 token ≈ 4 caracteres en inglés

+

1 token ≈ ¾ de palabra en inglés

+

100 tokens ≈ 75 palabras

+
+ +
+ + + +
diff --git a/slides/pages/models/02c.md b/slides/pages/models/02c.md new file mode 100644 index 0000000..b5d8e7d --- /dev/null +++ b/slides/pages/models/02c.md @@ -0,0 +1,41 @@ +--- +layout: default +--- + +# ¿Cómo se Cuentan los Tokens? + +
+ + + +
+ +Los tokens se cuentan en **entrada (input)** y **salida (output)** por separado + +
+ + + + + +
+

💰 Costos

+

Los LLMs cobran por tokens procesados

+

Output suele ser más caro que input (3-5x)

+

Ejemplo: GPT-4o → $2.50/1M tokens input, $10/1M tokens output

+

📊 Precios actualizados por modelo (JSON)

+
+ + + + + +
+

🔧 Herramienta para Contar Tokens

+

OpenAI Tokenizer (funciona para la mayoría de modelos):

+

platform.openai.com/tokenizer

+
+ + + +
diff --git a/slides/pages/models/03.md b/slides/pages/models/03.md new file mode 100644 index 0000000..3a01e00 --- /dev/null +++ b/slides/pages/models/03.md @@ -0,0 +1,25 @@ +--- +layout: default +--- + +# Parámetros Principales + +
+ + +
+

🌡️ temperature

+

Controla la aleatoriedad de las respuestas

+

0.0 = Determinista | 1.0 = Creativo | 2.0 = Muy aleatorio

+
+ + + +
+

📏 maxTokens

+

Límite máximo de tokens en la respuesta

+

Ejemplo: 500 (aprox. 375 palabras)

+
+ + +
diff --git a/slides/pages/models/03b.md b/slides/pages/models/03b.md new file mode 100644 index 0000000..a0a7d4e --- /dev/null +++ b/slides/pages/models/03b.md @@ -0,0 +1,31 @@ +--- +layout: default +--- + +# Parámetros Principales + +
+ + +
+

🎯 topP

+

Nucleus sampling (alternativa a temperature)

+

0.1 = Conservador | 0.9 = Diverso

+
+ + + +
+

🔁 n

+

Número de respuestas a generar

+

Útil para comparar variaciones

+
+ + + +
+ ⚠️ Estos parámetros dependen de cada modelo y pueden no estar disponibles en todos los proveedores. +
+ + +
diff --git a/slides/pages/models/04.md b/slides/pages/models/04.md new file mode 100644 index 0000000..23531e4 --- /dev/null +++ b/slides/pages/models/04.md @@ -0,0 +1,34 @@ +--- +layout: default +--- + +# Ejemplo: Configurando Parámetros + +
+ +```typescript +import { ChatOpenAI } from "@langchain/openai"; + +const model = new ChatOpenAI({ + model: "gpt-4.1-mini", + temperature: 0.7, // Balance entre creatividad y coherencia + maxTokens: 500, // Limitar longitud de respuesta + topP: 0.9, // Nucleus sampling +}); + +const response = await model.invoke( + "Explica qué es un agente en LangChain" +); + +console.log(response.content); +``` + + + +
+💡 Tip: Usa temperature: 0 para respuestas deterministas (útil en tests) +
+ + + +
diff --git a/slides/pages/models/05.md b/slides/pages/models/05.md new file mode 100644 index 0000000..3a56eb6 --- /dev/null +++ b/slides/pages/models/05.md @@ -0,0 +1,42 @@ +--- +layout: default +--- + +# Streaming: Respuestas en Tiempo Real + +
+ + + +
+ +**¿Por qué streaming?** + +- ✅ Experiencia de usuario mejorada (como ChatGPT) +- ✅ Feedback inmediato (no esperar 10-20 segundos) +- ✅ Procesar mientras se genera + +
+ + + + + +```typescript +import { ChatOpenAI } from "@langchain/openai"; + +const model = new ChatOpenAI({ + model: "gpt-4.1-mini", + streaming: true, +}); + +const stream = await model.stream("Explica qué es LangChain"); + +for await (const chunk of stream) { + console.log(chunk.content); // Imprime palabra por palabra +} +``` + + + +
diff --git a/slides/pages/models/06.md b/slides/pages/models/06.md new file mode 100644 index 0000000..298011e --- /dev/null +++ b/slides/pages/models/06.md @@ -0,0 +1,45 @@ +--- +layout: default +--- + +# Batch: Procesamiento en Lote + +
+ + + +
+ +**¿Cuándo usar batch?** + +- ✅ Procesar múltiples inputs a la vez +- ✅ Optimización de costos (algunos proveedores ofrecen descuentos) +- ✅ Mejor throughput que llamadas individuales + +
+ + + + + +```typescript +import { ChatOpenAI } from "@langchain/openai"; + +const model = new ChatOpenAI({ model: "gpt-4.1-mini" }); + +const inputs = [ + "¿Qué es un agente?", + "¿Qué es RAG?", + "¿Qué es LangSmith?" +]; + +const responses = await model.batch(inputs); + +responses.forEach((response, i) => { + console.log(`Pregunta ${i + 1}:`, response.content); +}); +``` + + + +
diff --git a/slides/pages/models/07.md b/slides/pages/models/07.md new file mode 100644 index 0000000..ad46621 --- /dev/null +++ b/slides/pages/models/07.md @@ -0,0 +1,43 @@ +--- +layout: default +--- + +# Structured Output + +
+ + + +
+ +**Problema:** Las respuestas de LLMs son texto plano, difíciles de procesar + +**Solución:** Forzar al modelo a responder con JSON estructurado + +
+ + + + + +```typescript +import { ChatOpenAI } from "@langchain/openai"; +import { z } from "zod"; + +// Definir el esquema con Zod +const schema = z.object({ + sentiment: z.enum(["positive", "negative", "neutral"]), + score: z.number().min(0).max(1), + keywords: z.array(z.string()), +}); + +const model = new ChatOpenAI({ model: "gpt-4.1-mini" }) + .withStructuredOutput(schema); + +const result = await model.invoke("Me encanta LangChain!"); +// { sentiment: "positive", score: 0.95, keywords: ["encanta", "langchain"] } +``` + + + +
diff --git a/slides/pages/models/08.md b/slides/pages/models/08.md new file mode 100644 index 0000000..94252ea --- /dev/null +++ b/slides/pages/models/08.md @@ -0,0 +1,49 @@ +--- +layout: default +--- + +# Intercambiabilidad de Modelos + +
+ + + +
+ +**Ventaja clave de LangChain:** Cambiar de proveedor sin reescribir código + +
+ + + +
+ + + +```typescript +// OpenAI +import { ChatOpenAI } from "@langchain/openai"; + +const model = new ChatOpenAI({ + model: "gpt-4.1-mini" +}); +``` + + + + + +```typescript +// Anthropic Claude +import { ChatAnthropic } from "@langchain/anthropic"; + +const model = new ChatAnthropic({ + model: "claude-sonnet-4-6" +}); +``` + + + +
+ +
diff --git a/slides/pages/models/08b.md b/slides/pages/models/08b.md new file mode 100644 index 0000000..d7232a1 --- /dev/null +++ b/slides/pages/models/08b.md @@ -0,0 +1,47 @@ +--- +layout: default +--- + +# Intercambiabilidad de Modelos + +
+ +
+ + + +```typescript +// Google Gemini +import { ChatGoogle } from "@langchain/google"; + +const model = new ChatGoogle({ + model: "gemini-2.5-flash" +}); +``` + + + + + +```typescript +// Ollama (Local) +import { ChatOllama } from "@langchain/ollama"; + +const model = new ChatOllama({ + model: "llama3.2" +}); +``` + + + +
+ + + +
+⚡ Todos usan la misma interfaz: .invoke(), .stream(), .batch() +
+ + + +
diff --git a/slides/pages/models/09.md b/slides/pages/models/09.md new file mode 100644 index 0000000..23c781e --- /dev/null +++ b/slides/pages/models/09.md @@ -0,0 +1,51 @@ +--- +layout: default +--- + +# initChatModel: Configuración Universal + +
+ + + +
+ +**Forma simplificada** de inicializar modelos sin imports específicos + +
+ + + + + +```typescript +import { initChatModel } from "langchain"; + +// En lugar de importar ChatOpenAI, ChatAnthropic, etc. +const model = await initChatModel("gpt-4.1-mini", { + modelProvider: "openai", + temperature: 0.7, +}); + +const response = await model.invoke("Hola!"); +``` + + + + + +
+✅ Ventaja: Cambiar de proveedor modificando solo el string +
+ + + + + +
+⚠️ No es magia: aunque no veas el import, el paquete del proveedor (@langchain/openai, @langchain/mistralai, etc.) debe estar instalado en tu proyecto. initChatModel lo resuelve internamente (under the hood). +
+ + + +
diff --git a/slides/pages/models/10.md b/slides/pages/models/10.md new file mode 100644 index 0000000..a2bb540 --- /dev/null +++ b/slides/pages/models/10.md @@ -0,0 +1,100 @@ +--- +layout: default +--- + +# Providers Disponibles + +
+ +
+ +
+ + + +**OpenAI** +```typescript +modelProvider: "openai" +// gpt-4.1, gpt-4.1-mini, gpt-5.2 +``` + + + + + +**Anthropic** +```typescript +modelProvider: "anthropic" +// claude-sonnet-4-6, claude-opus-4-6 +``` + + + + + +**Google** +```typescript +modelProvider: "google" +// gemini-2.5-flash, gemini-2.5-flash-lite +``` + + + + + +**Azure OpenAI** +```typescript +modelProvider: "azure_openai" +``` + + + +
+ +
+ + + +**Mistral** +```typescript +modelProvider: "mistral" +// mistral-large, mistral-small +``` + + + + + +**Groq** +```typescript +modelProvider: "groq" +// llama-3.3-70b, mixtral-8x7b +``` + + + + + +**Ollama** +```typescript +modelProvider: "ollama" +// llama3.2:7b, mistral:7b +``` + + + + + +**Cohere** +```typescript +modelProvider: "cohere" +// command-r, command-r-plus +``` + + + +
+ +
+ +
diff --git a/slides/pages/models/11.md b/slides/pages/models/11.md new file mode 100644 index 0000000..a196d90 --- /dev/null +++ b/slides/pages/models/11.md @@ -0,0 +1,78 @@ +--- +layout: default +--- + +# Más Providers + +
+ +
+ +
+ + + +**Fireworks** +```typescript +modelProvider: "fireworks" +``` + + + + + +**Together AI** +```typescript +modelProvider: "together_ai" +``` + + + + + +**Bedrock (AWS)** +```typescript +modelProvider: "bedrock" +``` + + + +
+ +
+ + + +**Vertex AI (Google Cloud)** +```typescript +modelProvider: "google-vertexai" +``` + + + + + +**Cloudflare** +```typescript +modelProvider: "cloudflare" +``` + + + +
+ +
+ + + +
+ +📚 Lista completa de providers + +[docs.langchain.com/integrations/chat](https://docs.langchain.com/oss/javascript/integrations/chat) + +
+ + + +
diff --git a/slides/pages/models/12.md b/slides/pages/models/12.md new file mode 100644 index 0000000..0498412 --- /dev/null +++ b/slides/pages/models/12.md @@ -0,0 +1,44 @@ +--- +layout: default +--- + +# Binding Tools al Modelo + +
+ + + +
+ +Puedes vincular tools directamente al modelo con `.bindTools()` + +
+ + + + + +```typescript +import { ChatOpenAI } from "@langchain/openai"; +import { tool } from "@langchain/core/tools"; +import { z } from "zod"; + +const weatherTool = tool( + async ({ location }) => `El tiempo en ${location} es soleado`, + { + name: "get_weather", + description: "Obtiene el tiempo de una ubicación", + schema: z.object({ location: z.string() }), + } +); + +const model = new ChatOpenAI({ model: "gpt-4.1-mini" }); +const modelWithTools = model.bindTools([weatherTool]); + +const response = await modelWithTools.invoke("¿Qué tiempo hace en Madrid?"); +console.log(response.tool_calls); // Tool que el LLM decidió usar +``` + + + +
diff --git a/slides/pages/models/13.md b/slides/pages/models/13.md new file mode 100644 index 0000000..1c36e0f --- /dev/null +++ b/slides/pages/models/13.md @@ -0,0 +1,75 @@ +--- +layout: default +--- + +# Tools Integradas en LangChain.js + +
+ + + +LangChain.js incluye numerosas **herramientas pre-construidas** listas para usar con agentes + + + +
+ +
+ + + +**Busqueda Web** +- `TavilySearchResults` - Busqueda optimizada para LLMs +- `DuckDuckGoSearch` - Busqueda gratuita +- `SerpAPI` / `SearchApi` - Google Search +- `ExaSearchResults` - Busqueda semantica + + + + + +**Conocimiento** +- `WikipediaQueryRun` - Consultas a Wikipedia +- `WolframAlphaTool` - Calculos y datos +- `GoogleScholar` - Articulos academicos + + + +
+ +
+ + + +**Productividad** +- `GmailTools` - Leer/enviar emails +- `GoogleCalendarTools` - Gestionar eventos +- `DiscordTool` - Integracion Discord + + + + + +**Desarrollo** +- `StackExchangeTool` - Q&A tecnico +- `PythonInterpreter` - Ejecutar codigo +- `WebBrowserTool` - Navegar paginas + + + +
+ +
+ + + +
+ +Ver listado completo: [docs.langchain.com/integrations/tools](https://docs.langchain.com/oss/javascript/integrations/tools) + +
+ + + +
+ diff --git a/slides/pages/models/14.md b/slides/pages/models/14.md new file mode 100644 index 0000000..a8ddddc --- /dev/null +++ b/slides/pages/models/14.md @@ -0,0 +1,50 @@ +--- +layout: default +--- + +# Usando Tools Integradas + +
+ + + +```typescript +import { TavilySearchResults } from "@langchain/community/tools/tavily_search"; +import { WikipediaQueryRun } from "@langchain/community/tools/wikipedia_query_run"; +import { Calculator } from "@langchain/community/tools/calculator"; + +// Crear instancias de las tools +const tavilySearch = new TavilySearchResults({ + apiKey: process.env.TAVILY_API_KEY, + maxResults: 3, +}); + +const wikipedia = new WikipediaQueryRun({ + topKResults: 2, + maxDocContentLength: 4000, +}); + +const calculator = new Calculator(); + +// Usar con un agente +const tools = [tavilySearch, wikipedia, calculator]; +``` + + + + + +
+ +**Paquete:** La mayoria de tools integradas estan en `@langchain/community` + +```bash +npm install @langchain/community +``` + +
+ + + +
+ diff --git a/slides/pages/models/15.md b/slides/pages/models/15.md new file mode 100644 index 0000000..7ea63f7 --- /dev/null +++ b/slides/pages/models/15.md @@ -0,0 +1,41 @@ +--- +layout: default +--- + +# Ejemplo: Agente con Tavily Search + +
+ +```typescript +import { createAgent } from "langchain"; +import { TavilySearchResults } from "@langchain/community/tools/tavily_search"; + +const searchTool = new TavilySearchResults({ apiKey: process.env.TAVILY_API_KEY, maxResults: 5 }); +const agent = createAgent({ + model: "openai:gpt-4.1-mini", + tools: [searchTool], +}); + +const result = await agent.invoke({ + messages: [{ role: "user", content: "¿Cuales son las ultimas noticias sobre IA?" }], +}); +``` + +
+ +
+ +**Tavily** - Optimizado para LLMs: extrae contenido relevante, filtra anuncios/ruido, API key gratuita limitada + +
+ +
+ +**Docs**: [langchain.com/integrations/tools](https://docs.langchain.com/oss/javascript/integrations/tools) + +
+ +
+ +
+ diff --git a/slides/pages/models/16.md b/slides/pages/models/16.md new file mode 100644 index 0000000..ca9c4ef --- /dev/null +++ b/slides/pages/models/16.md @@ -0,0 +1,43 @@ +--- +layout: default +--- + +# Diferencia: bindTools vs createAgent + +
+ +
+ + +
+

🔧 .bindTools()

+

• El LLM decide si usar la tool

+

• Devuelve tool_calls

+

• Tú ejecutas la tool manualmente

+

• Control total del flujo

+

Ideal para flujos personalizados

+
+ + + +
+

🤖 createAgent() (LangChain v1)

+

• El agente decide y ejecuta

+

• Loop automático

+

• Ejecuta tools automáticamente

+

• Estado persistente incluido

+

Ideal para agentes autónomos

+
+ + +
+ + + +
+💡 Usa bindTools() para control, createAgent() (LangChain v1) para simplicidad +
+ + + +
diff --git a/slides/pages/pending/01.md b/slides/pages/pending/01.md new file mode 100644 index 0000000..1c7b5a0 --- /dev/null +++ b/slides/pages/pending/01.md @@ -0,0 +1,14 @@ +--- +layout: cover +class: text-center +--- + +# Temas Pendientes + +Lo que nos dejamos en el tintero + +
+ + Hay mucho más por explorar en el ecosistema LangChain... + +
diff --git a/slides/pages/pending/02.md b/slides/pages/pending/02.md new file mode 100644 index 0000000..3c0ec5b --- /dev/null +++ b/slides/pages/pending/02.md @@ -0,0 +1,22 @@ +--- +layout: default +--- + +# Sistemas Multi-Agente (Subagentes) + + + +### Patrones principales en LangChain/LangGraph + +| Patrón | Descripción | +|--------|-------------| +| **Subagents** | Un agente supervisor coordina subagentes como tools | +| **Handoffs** | El comportamiento cambia dinámicamente según el estado | +| **Skills** | Prompts y conocimiento especializado cargado bajo demanda | +| **Router** | Clasifica input y lo dirige a agentes especializados | +| **Custom Workflow** | Flujos con LangGraph mezclando lógica determinista y agéntica | + +[Docs: Multi-agent](https://docs.langchain.com/oss/javascript/langchain/multi-agent/index) + + + diff --git a/slides/pages/pending/02b.md b/slides/pages/pending/02b.md new file mode 100644 index 0000000..c4cbc5d --- /dev/null +++ b/slides/pages/pending/02b.md @@ -0,0 +1,37 @@ +--- +layout: default +--- + +# Deep Agents + + + +### Librería standalone para agentes complejos + +Inspirada en Claude Code, Deep Research y Manus. + +### Características principales + +- **Planificación**: Capacidad de planificar tareas multi-paso +- **File systems**: Sistema de archivos virtual para gestión de contexto +- **Subagentes**: Spawn de agentes especializados para subtareas +- **Long-term memory**: Integración con almacenamiento persistente + +### Ejemplo básico + +```typescript +import { createDeepAgent } from "@langchain/deepagents"; + +const agent = createDeepAgent({ + model: "gpt-4.1", + filesystem: true, + planning: true, +}); + +await agent.invoke("Research and write a report about AI trends"); +``` + +[Docs: Deep Agents](https://docs.langchain.com/oss/javascript/deepagents/overview) + + + diff --git a/slides/pages/pending/03.md b/slides/pages/pending/03.md new file mode 100644 index 0000000..cfc77ce --- /dev/null +++ b/slides/pages/pending/03.md @@ -0,0 +1,32 @@ +--- +layout: default +--- + +# Long-term Memory con Semantic Search + + + +### InMemoryStore con embeddings + +```typescript +import { InMemoryStore } from "@langchain/langgraph"; +import { OpenAIEmbeddings } from "@langchain/openai"; + +const embeddings = new OpenAIEmbeddings({ model: "text-embedding-3-small" }); + +const store = new InMemoryStore({ + index: { embeddings, dims: 1536 } +}); + +// Guardar memorias +await store.put(["user_123", "memories"], "1", { text: "I love pizza" }); + +// Buscar por similitud semántica +const items = await store.search( + ["user_123", "memories"], + { query: "I'm hungry", limit: 2 } +); +``` + + + diff --git a/slides/pages/pending/04.md b/slides/pages/pending/04.md new file mode 100644 index 0000000..d3f0733 --- /dev/null +++ b/slides/pages/pending/04.md @@ -0,0 +1,30 @@ +--- +layout: default +--- + +# Integración MCP (Model Context Protocol) + + + +### MCP en LangChain.js + +Protocolo abierto que estandariza cómo las aplicaciones proporcionan tools y contexto a LLMs. + +```typescript +import { ChatOpenAI, tools } from "@langchain/openai"; + +const model = new ChatOpenAI({ model: "gpt-4.1" }); + +const response = await model.invoke("Roll 2d4+1", { + tools: [ + tools.mcp({ + serverLabel: "dmcp", + serverDescription: "A D&D MCP server for dice rolling", + serverUrl: "https://dmcp-server.deno.dev/sse", + requireApproval: "never", + }), + ], +}); +``` + + diff --git a/slides/pages/pending/05.md b/slides/pages/pending/05.md new file mode 100644 index 0000000..75611ff --- /dev/null +++ b/slides/pages/pending/05.md @@ -0,0 +1,38 @@ +--- +layout: default +--- + +# Context Engineering + + + +### Inyección de dependencias en runtime + +```typescript +const contextSchema = z.object({ + userId: z.string(), + apiKey: z.string(), +}); + +const fetchUserData = tool( + async ({ query }, runtime: ToolRuntime) => { + const { userId, apiKey } = runtime.context; + return await performQuery(query, apiKey, userId); + }, + { name: "fetch_user_data", schema: z.object({ query: z.string() }) } +); + +const agent = createAgent({ + model: "gpt-4.1", + tools: [fetchUserData], + contextSchema +}); + +// Cada usuario tiene su propio contexto +await agent.invoke(messages, { + context: { userId: "user_123", apiKey: "sk-..." } +}); +``` + + + diff --git a/slides/pages/pending/05b.md b/slides/pages/pending/05b.md new file mode 100644 index 0000000..1e1bb73 --- /dev/null +++ b/slides/pages/pending/05b.md @@ -0,0 +1,26 @@ +--- +layout: default +--- + +# Tipos de Contexto en LangGraph + + + +### Tres dimensiones de contexto + +| Tipo | Mutabilidad | Alcance | Uso | +|------|-------------|---------|-----| +| **Static (config)** | Inmutable | Single run | user ID, API keys, DB connections | +| **Dynamic (state)** | Mutable | Single run | historial, resultados intermedios | +| **Store** | Mutable | Cross-conversation | memorias persistentes, preferencias | + +### Casos de uso + +- **Autenticación por usuario**: Inyectar API keys específicas por tenant +- **Permisos dinámicos**: Ajustar herramientas disponibles según rol +- **Personalización**: Cargar preferencias del usuario desde el store +- **Multi-tenancy**: Aislar datos entre organizaciones + +[Docs: Context Engineering](https://docs.langchain.com/oss/javascript/langchain/context-engineering) + + diff --git a/slides/pages/pending/08.md b/slides/pages/pending/08.md new file mode 100644 index 0000000..584bebf --- /dev/null +++ b/slides/pages/pending/08.md @@ -0,0 +1,32 @@ +--- +layout: default +--- + +# Testing de Aplicaciones LLM + + + +### Dos enfoques complementarios + +**Unit tests**: Componentes aislados con mocks, validaciones rápidas y predecibles. + +**Integration tests**: Llamadas reales para confirmar que todo funciona junto. + +### AgentEvals + +```typescript +import { createTrajectoryMatchEvaluator } from "agentevals"; + +// Trajectory Match: compara contra trayectoria de referencia +const evaluator = createTrajectoryMatchEvaluator({ matchMode: "subset" }); + +// LLM-as-Judge: validación cualitativa con un modelo +const llmEvaluator = createLLMJudgeEvaluator({ model: "gpt-4.1" }); +``` + +### Integración con LangSmith +Configura `LANGSMITH_API_KEY` y `LANGSMITH_TRACING` para registrar experimentos automáticamente con Vitest/Jest. + +[Docs: Testing](https://docs.langchain.com/oss/javascript/langchain/test) + + diff --git a/slides/pages/pending/09.md b/slides/pages/pending/09.md new file mode 100644 index 0000000..8bdbb9b --- /dev/null +++ b/slides/pages/pending/09.md @@ -0,0 +1,27 @@ +--- +layout: default +--- + +# Recursos para continuar aprendiendo + + + +### Documentación oficial +- **LangChain.js Docs**: [docs.langchain.com](https://docs.langchain.com/oss/javascript) +- **LangGraph.js**: [docs.langchain.com/langgraph](https://docs.langchain.com/langgraph) +- **LangSmith**: [docs.smith.langchain.com](https://docs.smith.langchain.com) + +### Cursos y tutoriales +- **LangChain Academy**: Cursos oficiales gratuitos +- **DeepLearning.AI**: Cursos sobre LLMs y LangChain +- **YouTube**: Canal oficial de LangChain + +### Comunidad +- **Discord**: Comunidad activa de LangChain +- **GitHub Discussions**: Preguntas y respuestas +- **Twitter/X**: @LangChainAI para novedades + +### Chat de LangChain +- **Chat**: [chat.langchain.com](https://chat.langchain.com) - Asistente IA entrenado con la documentación oficial + + diff --git a/slides/pages/rag/01.md b/slides/pages/rag/01.md new file mode 100644 index 0000000..7e2aff5 --- /dev/null +++ b/slides/pages/rag/01.md @@ -0,0 +1,14 @@ +--- +layout: cover +class: text-center +--- + +# Módulo 7: RAG + +Retrieval Augmented Generation + +
+ + Extendiendo el conocimiento de los LLMs con tus datos + +
diff --git a/slides/pages/rag/02.md b/slides/pages/rag/02.md new file mode 100644 index 0000000..dd2ba44 --- /dev/null +++ b/slides/pages/rag/02.md @@ -0,0 +1,25 @@ +--- +layout: default +--- + +# ¿Qué es RAG? + + + +**Retrieval Augmented Generation** combina búsqueda de información con generación de texto + +- **Problema**: Los LLMs tienen conocimiento limitado + - Datos de entrenamiento hasta cierta fecha + - No conocen información específica de tu empresa + - No pueden acceder a bases de datos en tiempo real + +- **Solución RAG**: + - Buscar información relevante en tus documentos + - Proporcionar ese contexto al LLM + - Generar respuestas basadas en tus datos + +
+💡 En resumen: Dale al LLM la información que necesita para responder correctamente +
+ + diff --git a/slides/pages/rag/03.md b/slides/pages/rag/03.md new file mode 100644 index 0000000..b6a4f7e --- /dev/null +++ b/slides/pages/rag/03.md @@ -0,0 +1,28 @@ +--- +layout: default +--- + +# Flujo típico de RAG + +
+ +```mermaid +graph LR + A[Pregunta del usuario] --> B[Convertir a embedding] + B --> C[Buscar en BD vectorial] + C --> D[Recuperar documentos relevantes] + D --> E[Construir prompt con contexto] + E --> F[LLM genera respuesta] + F --> G[Respuesta al usuario] +``` + +
+ + + +### Dos fases principales + +1. **Indexación** (una vez): Convertir documentos a embeddings y almacenarlos +2. **Recuperación** (cada consulta): Buscar documentos relevantes y generar respuesta + + diff --git a/slides/pages/rag/03b.md b/slides/pages/rag/03b.md new file mode 100644 index 0000000..0a363f2 --- /dev/null +++ b/slides/pages/rag/03b.md @@ -0,0 +1,22 @@ +--- +layout: default +--- + +# Arquitecturas RAG + + + +### 1. 2-Step RAG (Simple) +- Recuperación **siempre antes** de la generación · Flujo lineal y predecible +- Ideal para: FAQs, chatbots de documentación · ⚡ Alto control, baja flexibilidad + +### 2. Agentic RAG +- Un **agente decide cuándo y cómo** recuperar información · Razona y usa herramientas +- Ideal para: Asistentes de investigación · ⚡ Bajo control, alta flexibilidad + +### 3. Hybrid RAG +- Combina ambos con **validación intermedia** · Query + retrieval + answer validation +- Ideal para: Q&A de dominio específico · ⚡ Control y flexibilidad medios + + + diff --git a/slides/pages/rag/04.md b/slides/pages/rag/04.md new file mode 100644 index 0000000..68692e4 --- /dev/null +++ b/slides/pages/rag/04.md @@ -0,0 +1,14 @@ +--- +layout: cover +class: text-center +--- + +# Embeddings + +La base de la búsqueda semántica + +
+ + Convirtiendo texto en vectores numéricos + +
diff --git a/slides/pages/rag/05.md b/slides/pages/rag/05.md new file mode 100644 index 0000000..a424a5d --- /dev/null +++ b/slides/pages/rag/05.md @@ -0,0 +1,27 @@ +--- +layout: default +--- + +# ¿Qué son los Embeddings? + + + +**Embeddings** son representaciones numéricas de texto que capturan su significado semántico + +- Vector de números (típicamente 768, 1536, o 3072 dimensiones) +- Textos con significado similar tienen embeddings cercanos +- Permite búsqueda por similitud semántica + +### Ejemplo conceptual + +``` +"perro" → [0.2, 0.8, 0.1, ...] +"gato" → [0.3, 0.7, 0.2, ...] ← Similar a perro +"coche" → [0.9, 0.1, 0.8, ...] ← Muy diferente +``` + +
+✨ La "magia" está en que textos con significados parecidos tienen vectores parecidos +
+ + diff --git a/slides/pages/rag/06.md b/slides/pages/rag/06.md new file mode 100644 index 0000000..ea5f102 --- /dev/null +++ b/slides/pages/rag/06.md @@ -0,0 +1,29 @@ +--- +layout: default +--- + +# Proveedores de Embeddings + +LangChain soporta múltiples proveedores de embeddings: + + + +### OpenAI +```typescript +import { OpenAIEmbeddings } from "@langchain/openai"; + +const embeddings = new OpenAIEmbeddings({ + model: "text-embedding-3-small", // o text-embedding-3-large +}); +``` + +### Google (Vertex AI / Gemini) +```typescript +import { GoogleEmbeddings } from "@langchain/google"; + +const embeddings = new GoogleEmbeddings({ + model: "text-embedding-004", +}); +``` + + diff --git a/slides/pages/rag/07.md b/slides/pages/rag/07.md new file mode 100644 index 0000000..67c3c60 --- /dev/null +++ b/slides/pages/rag/07.md @@ -0,0 +1,36 @@ +--- +layout: default +--- + +# Más proveedores de Embeddings + + + +### Mistral AI +```typescript +import { MistralAIEmbeddings } from "@langchain/mistralai"; + +const embeddings = new MistralAIEmbeddings({ + model: "mistral-embed", +}); +``` + +### Cohere +```typescript +import { CohereEmbeddings } from "@langchain/cohere"; + +const embeddings = new CohereEmbeddings({ + model: "embed-multilingual-v3.0", +}); +``` + +### Ollama (local) +```typescript +import { OllamaEmbeddings } from "@langchain/ollama"; + +const embeddings = new OllamaEmbeddings({ + model: "llama3.2", // o cualquier modelo compatible +}); +``` + + diff --git a/slides/pages/rag/08.md b/slides/pages/rag/08.md new file mode 100644 index 0000000..b6ae286 --- /dev/null +++ b/slides/pages/rag/08.md @@ -0,0 +1,27 @@ +--- +layout: default +--- + +# ¿Cómo elegir un proveedor? + + + +### Criterios a considerar + +- **Calidad**: OpenAI y Cohere son los más precisos +- **Coste**: Varía mucho entre proveedores +- **Latencia**: Los modelos locales (Ollama) son más rápidos +- **Privacidad**: Ollama permite procesamiento offline +- **Dimensiones**: Más dimensiones = más precisión pero más coste de almacenamiento +- **Idioma**: Algunos modelos funcionan mejor con español (Cohere multilingual) + +### Recomendaciones + +| Caso de uso | Proveedor recomendado | +|-------------|----------------------| +| Máxima calidad | OpenAI `text-embedding-3-large` | +| Mejor relación calidad/precio | OpenAI `text-embedding-3-small` | +| Privacidad/Local | Ollama | +| Multiidioma | Cohere `embed-multilingual-v3.0` | + + diff --git a/slides/pages/rag/09.md b/slides/pages/rag/09.md new file mode 100644 index 0000000..669276d --- /dev/null +++ b/slides/pages/rag/09.md @@ -0,0 +1,14 @@ +--- +layout: cover +class: text-center +--- + +# Bases de Datos Vectoriales + +Almacenando y buscando embeddings + +
+ + Búsqueda eficiente por similitud + +
diff --git a/slides/pages/rag/10.md b/slides/pages/rag/10.md new file mode 100644 index 0000000..467054b --- /dev/null +++ b/slides/pages/rag/10.md @@ -0,0 +1,27 @@ +--- +layout: default +--- + +# ¿Qué es una Base de Datos Vectorial? + + + +**Vector Store** o **Base de Datos Vectorial** almacena embeddings y permite búsquedas por similitud + +- Optimizadas para búsqueda por similitud coseno/euclidiana +- Indexación eficiente de vectores de alta dimensión +- Retornan los N documentos más similares a una consulta + +### Tipos + +1. **In-Memory**: Ideal para desarrollo y prototipos + - `MemoryVectorStore` + +2. **Persistentes**: Para producción + - Qdrant, Pinecone, Weaviate, Chroma, etc. + +
+⚠️ En producción, siempre usa una BD vectorial persistente +
+ + diff --git a/slides/pages/rag/11.md b/slides/pages/rag/11.md new file mode 100644 index 0000000..6c3fdf3 --- /dev/null +++ b/slides/pages/rag/11.md @@ -0,0 +1,34 @@ +--- +layout: default +--- + +# MemoryVectorStore - Para desarrollo + + + +**MemoryVectorStore** almacena embeddings en memoria (se pierden al cerrar) + +```typescript +import { MemoryVectorStore } from "@langchain/classic/vectorstores/memory"; +import { OpenAIEmbeddings } from "@langchain/openai"; + +const vectorStore = await MemoryVectorStore.fromTexts( + [ + "LangChain es un framework para aplicaciones con LLMs", + "Los embeddings capturan el significado del texto", + "RAG combina búsqueda con generación de texto", + ], + [{ id: 1 }, { id: 2 }, { id: 3 }], // metadata + new OpenAIEmbeddings() +); + +// Buscar documentos similares +const results = await vectorStore.similaritySearch( + "¿Qué es RAG?", + 2 // número de resultados +); + +console.log(results); +``` + + diff --git a/slides/pages/rag/12.md b/slides/pages/rag/12.md new file mode 100644 index 0000000..98c806c --- /dev/null +++ b/slides/pages/rag/12.md @@ -0,0 +1,28 @@ +--- +layout: default +--- + +# Bases de Datos Vectoriales en Producción + + + +### Opciones populares + +| BD Vectorial | Características | Mejor para | +|--------------|----------------|------------| +| **Qdrant** | Open source, Rust, muy rápido | Autohosting, alta performance | +| **Pinecone** | Managed, fácil de usar | Startups, no querer gestionar infra | +| **Weaviate** | Open source, GraphQL | Búsquedas complejas | +| **Chroma** | Open source, Python-first | Prototipado rápido | +| **Milvus** | Open source, escalable | Grandes volúmenes de datos | +| **pgvector** | Extensión PostgreSQL | Ya usas PostgreSQL | + + + + + +
+💡 Recomendación: Qdrant ofrece el mejor balance entre rendimiento, facilidad de uso y coste +
+ + diff --git a/slides/pages/rag/13.md b/slides/pages/rag/13.md new file mode 100644 index 0000000..cdb0040 --- /dev/null +++ b/slides/pages/rag/13.md @@ -0,0 +1,29 @@ +--- +layout: default +--- + +# Qdrant - Configuración + + + +### Instalación + +```bash +npm install @langchain/qdrant @qdrant/js-client-rest +``` + +### Iniciar Qdrant con Docker + +```bash +docker run -p 6333:6333 qdrant/qdrant +``` + +### O usar Qdrant Cloud + +Crear una cuenta gratuita en [qdrant.tech](https://qdrant.tech) + +- 1GB de almacenamiento gratis +- Clusters gestionados +- Sin necesidad de infraestructura + + diff --git a/slides/pages/rag/14.md b/slides/pages/rag/14.md new file mode 100644 index 0000000..1c66fe7 --- /dev/null +++ b/slides/pages/rag/14.md @@ -0,0 +1,26 @@ +--- +layout: default +--- + +# Qdrant - Crear e insertar + +```typescript +import { QdrantVectorStore } from "@langchain/qdrant"; +import { OpenAIEmbeddings } from "@langchain/openai"; + +const vectorStore = await QdrantVectorStore.fromTexts( + [ + "LangChain es un framework para aplicaciones con LLMs", + "Los embeddings capturan el significado del texto", + "RAG combina búsqueda con generación de texto", + ], + [{ source: "doc1" }, { source: "doc2" }, { source: "doc3" }], + new OpenAIEmbeddings(), + { + url: "http://localhost:6333", + collectionName: "langchain_docs", + } +); + +console.log("Documentos indexados correctamente"); +``` diff --git a/slides/pages/rag/15.md b/slides/pages/rag/15.md new file mode 100644 index 0000000..70a8ed6 --- /dev/null +++ b/slides/pages/rag/15.md @@ -0,0 +1,30 @@ +--- +layout: default +--- + +# Qdrant - Búsqueda + +```typescript +import { QdrantVectorStore } from "@langchain/qdrant"; +import { OpenAIEmbeddings } from "@langchain/openai"; + +// Conectar a colección existente +const vectorStore = await QdrantVectorStore.fromExistingCollection( + new OpenAIEmbeddings(), + { + url: "http://localhost:6333", + collectionName: "langchain_docs", + } +); + +// Búsqueda por similitud +const results = await vectorStore.similaritySearch( + "¿Qué es RAG?", + 3 // top 3 resultados +); + +results.forEach((doc, i) => { + console.log(`${i + 1}. ${doc.pageContent}`); + console.log(` Metadata:`, doc.metadata); +}); +``` diff --git a/slides/pages/rag/16.md b/slides/pages/rag/16.md new file mode 100644 index 0000000..e345585 --- /dev/null +++ b/slides/pages/rag/16.md @@ -0,0 +1,33 @@ +--- +layout: default +--- + +# Qdrant - Búsqueda con score + +```typescript +// Búsqueda con puntuación de similitud +const resultsWithScore = await vectorStore.similaritySearchWithScore( + "embeddings y vectores", + 3 +); + +resultsWithScore.forEach(([doc, score], i) => { + console.log(`${i + 1}. Score: ${score.toFixed(3)}`); + console.log(` ${doc.pageContent}`); +}); +``` + + + +### Salida ejemplo + +``` +1. Score: 0.892 + Los embeddings capturan el significado del texto +2. Score: 0.745 + RAG combina búsqueda con generación de texto +3. Score: 0.621 + LangChain es un framework para aplicaciones con LLMs +``` + + diff --git a/slides/pages/rag/16b.md b/slides/pages/rag/16b.md new file mode 100644 index 0000000..a113236 --- /dev/null +++ b/slides/pages/rag/16b.md @@ -0,0 +1,14 @@ +--- +layout: cover +class: text-center +--- + +# Document Loaders y Splitters + +Cargando y procesando documentos para RAG + +
+ + Del documento fuente al vector store + +
diff --git a/slides/pages/rag/16c.md b/slides/pages/rag/16c.md new file mode 100644 index 0000000..314b073 --- /dev/null +++ b/slides/pages/rag/16c.md @@ -0,0 +1,28 @@ +--- +layout: default +--- + +# Document Loaders + + + +**Document Loaders** transforman datos de distintas fuentes en objetos `Document` de LangChain + +```typescript +// Estructura de un Document +interface Document { + pageContent: string; // contenido del texto + metadata: Record; // información sobre la fuente +} +``` + +### Categorías principales + +| Tipo | Ejemplos | Paquete | +|------|----------|---------| +| **Archivos locales** | PDF, DOCX, CSV, JSON, EPUB, PPTX | `@langchain/community` | +| **Web** | Cheerio, Playwright, Puppeteer | `@langchain/community` | +| **Servicios** | GitHub, Notion, Confluence, Jira | `@langchain/community` | +| **Audio** | OpenAI Whisper, AssemblyAI | `@langchain/community` | + + diff --git a/slides/pages/rag/16d.md b/slides/pages/rag/16d.md new file mode 100644 index 0000000..d64b1b7 --- /dev/null +++ b/slides/pages/rag/16d.md @@ -0,0 +1,37 @@ +--- +layout: default +--- + +# PDFLoader + + + +### Instalación + +```bash +npm install @langchain/community pdf-parse +``` + +### Uso + +```typescript +import { PDFLoader } from "@langchain/community/document_loaders/fs/pdf"; + +// Carga cada página como un Document independiente +const loader = new PDFLoader("./documentos/manual.pdf"); +const docs = await loader.load(); + +console.log(`Cargadas ${docs.length} páginas`); +console.log(docs[0].pageContent.substring(0, 200)); +console.log(docs[0].metadata); +// { source: './documentos/manual.pdf', pdf: {...}, loc: { pageNumber: 1 } } +``` + +### Opciones útiles + +```typescript +// Cargar todo el PDF como un solo documento +const loader = new PDFLoader("./manual.pdf", { splitPages: false }); +``` + + diff --git a/slides/pages/rag/16e.md b/slides/pages/rag/16e.md new file mode 100644 index 0000000..f207277 --- /dev/null +++ b/slides/pages/rag/16e.md @@ -0,0 +1,39 @@ +--- +layout: default +--- + +# CheerioWebBaseLoader + + + +Carga páginas web como documentos (sin ejecutar JavaScript) + +### Instalación + +```bash +npm install @langchain/community cheerio +``` + +### Uso + +```typescript +import "cheerio"; +import { CheerioWebBaseLoader } from + "@langchain/community/document_loaders/web/cheerio"; + +const loader = new CheerioWebBaseLoader( + "https://blog.langchain.dev/what-is-an-agent/", + { selector: "article" } // selector CSS para filtrar contenido +); +const docs = await loader.load(); + +console.log(docs[0].pageContent.substring(0, 300)); +console.log(docs[0].metadata); +// { source: 'https://blog.langchain.dev/...', title: '...' } +``` + +
+⚠️ Para SPAs o páginas con JS dinámico, usa Playwright o Puppeteer +
+ + diff --git a/slides/pages/rag/16f.md b/slides/pages/rag/16f.md new file mode 100644 index 0000000..bf3d68a --- /dev/null +++ b/slides/pages/rag/16f.md @@ -0,0 +1,33 @@ +--- +layout: default +--- + +# Otros Document Loaders útiles + + + +### CSV +```typescript +import { CSVLoader } from "@langchain/community/document_loaders/fs/csv"; +const loader = new CSVLoader("./datos/productos.csv"); +``` + +### DOCX (Microsoft Word) +```typescript +import { DocxLoader } from "@langchain/community/document_loaders/fs/docx"; +const loader = new DocxLoader("./docs/informe.docx"); +``` + +### Texto plano y Markdown +```typescript +import { TextLoader } from "@langchain/community/document_loaders/fs/text"; +const loader = new TextLoader("./docs/README.md"); +``` + +### JSON +```typescript +import { JSONLoader } from "@langchain/community/document_loaders/fs/json"; +const loader = new JSONLoader("./datos/respuestas.json"); +``` + + diff --git a/slides/pages/rag/16f2.md b/slides/pages/rag/16f2.md new file mode 100644 index 0000000..427d32e --- /dev/null +++ b/slides/pages/rag/16f2.md @@ -0,0 +1,36 @@ +--- +layout: default +--- + +# DirectoryLoader + + + +Carga todos los archivos de un directorio, mapeando extensiones a loaders + +```typescript +import { DirectoryLoader } from + "@langchain/community/document_loaders/fs/directory"; +import { PDFLoader } from + "@langchain/community/document_loaders/fs/pdf"; +import { TextLoader } from + "@langchain/community/document_loaders/fs/text"; +import { CSVLoader } from + "@langchain/community/document_loaders/fs/csv"; + +const loader = new DirectoryLoader("./documentos/", { + ".pdf": (path) => new PDFLoader(path), + ".txt": (path) => new TextLoader(path), + ".md": (path) => new TextLoader(path), + ".csv": (path) => new CSVLoader(path), +}); + +const docs = await loader.load(); +console.log(`Cargados ${docs.length} documentos`); +``` + +
+💡 Muy útil para indexar carpetas enteras de documentación de una sola vez +
+ + diff --git a/slides/pages/rag/16g.md b/slides/pages/rag/16g.md new file mode 100644 index 0000000..5e1e704 --- /dev/null +++ b/slides/pages/rag/16g.md @@ -0,0 +1,31 @@ +--- +layout: default +--- + +# Text Splitters + + + +Los documentos largos deben dividirse en **chunks** antes de crear embeddings + +### RecursiveCharacterTextSplitter (recomendado) + +```typescript +import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters"; + +const splitter = new RecursiveCharacterTextSplitter({ + chunkSize: 1000, // máximo de caracteres por chunk + chunkOverlap: 200, // solapamiento entre chunks +}); +const chunks = await splitter.splitDocuments(docs); +``` + +Intenta mantener unidades de texto lógicas (párrafos → frases → palabras) + +### ¿Por qué dividir? + +- Los embeddings funcionan mejor con textos cortos y cohesivos +- Enviamos solo contexto relevante al LLM (ahorramos tokens) +- Mejora la precisión de la búsqueda por similitud + + diff --git a/slides/pages/rag/16h.md b/slides/pages/rag/16h.md new file mode 100644 index 0000000..e633b6c --- /dev/null +++ b/slides/pages/rag/16h.md @@ -0,0 +1,21 @@ +--- +layout: default +--- + +# Estrategias de Chunking + + + +### ¿Cómo elegir tamaño de chunk? + +| Estrategia | chunkSize | chunkOverlap | Mejor para | +|------------|-----------|--------------|------------| +| **Fino** | 200-500 | 50 | FAQ, preguntas cortas | +| **Medio** | 500-1000 | 100-200 | Documentación técnica | +| **Grueso** | 1000-2000 | 200-400 | Artículos largos, informes | + +
+💡 Experimenta con distintos tamaños — el óptimo depende del tipo de documento y las preguntas que esperas recibir +
+ + diff --git a/slides/pages/rag/16h2.md b/slides/pages/rag/16h2.md new file mode 100644 index 0000000..466c73c --- /dev/null +++ b/slides/pages/rag/16h2.md @@ -0,0 +1,36 @@ +--- +layout: default +--- + +# Splitters por tipo de contenido + + + +`RecursiveCharacterTextSplitter.fromLanguage()` usa separadores adaptados a cada formato + +### Markdown — respeta encabezados y listas +```typescript +import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters"; + +const mdSplitter = RecursiveCharacterTextSplitter.fromLanguage("markdown", { + chunkSize: 1000, chunkOverlap: 200, +}); +``` + +### HTML — respeta estructura de etiquetas +```typescript +const htmlSplitter = RecursiveCharacterTextSplitter.fromLanguage("html", { + chunkSize: 1000, chunkOverlap: 200, +}); +``` + +### Código JS/TS — respeta funciones y clases +```typescript +const codeSplitter = RecursiveCharacterTextSplitter.fromLanguage("js", { + chunkSize: 1500, chunkOverlap: 200, +}); +``` + +Otros lenguajes soportados: `python`, `java`, `go`, `rust`, `ruby`, `php`... + + diff --git a/slides/pages/rag/16i.md b/slides/pages/rag/16i.md new file mode 100644 index 0000000..a8e4e2c --- /dev/null +++ b/slides/pages/rag/16i.md @@ -0,0 +1,31 @@ +--- +layout: default +--- + +# Extractores y parsers avanzados + + + +### Unstructured.io +Servicio de extracción que soporta **+30 tipos de archivo** (PDF, DOCX, PPTX, imágenes, HTML...) + +```typescript +import { UnstructuredLoader } from + "@langchain/community/document_loaders/fs/unstructured"; + +const loader = new UnstructuredLoader("./docs/informe.pdf", { + apiKey: process.env.UNSTRUCTURED_API_KEY, + apiUrl: "https://api.unstructuredapp.io/general/v0/general", +}); +const docs = await loader.load(); +``` + +- Extrae texto, tablas, imágenes con OCR +- API cloud o self-hosted +- Ideal para documentos complejos con layouts mixtos + +
+💡 Unstructured es especialmente útil para PDFs escaneados o con tablas complejas donde pdf-parse se queda corto +
+ + diff --git a/slides/pages/rag/16j.md b/slides/pages/rag/16j.md new file mode 100644 index 0000000..e8b2ee6 --- /dev/null +++ b/slides/pages/rag/16j.md @@ -0,0 +1,25 @@ +--- +layout: default +--- + +# Otros extractores a conocer + + + +Cuando `pdf-parse` no es suficiente (PDFs escaneados, tablas complejas, layouts mixtos): + +| Extractor | Descripción | JS/TS | Tablas | OCR | +|-----------|-------------|-------|--------|-----| +| **Docling** (IBM) | Comprensión avanzada de layout | ❌ Solo Python | ✅ | ✅ | +| **Azure Doc Intelligence** | Servicio cloud de Microsoft | ✅ | ✅ | ✅ | +| **Amazon Textract** | Servicio cloud de AWS | ❌ Solo Python | ✅ | ✅ | +| **LlamaParse** | Parser de LlamaIndex, API cloud | ✅ via API | ✅ | ✅ | + +### Docling — mención especial + +- Proyecto open source de IBM con excelente calidad de extracción +- Integración oficial solo con LangChain Python (`langchain-docling`) +- Desde Node.js: consumir su API REST o usar como preproceso a markdown. + + + diff --git a/slides/pages/rag/17.md b/slides/pages/rag/17.md new file mode 100644 index 0000000..473f14b --- /dev/null +++ b/slides/pages/rag/17.md @@ -0,0 +1,14 @@ +--- +layout: cover +class: text-center +--- + +# Ejemplo Práctico: RAG con PDFs + +Búsqueda semántica sobre documentos + +
+ + Del PDF a respuestas inteligentes + +
diff --git a/slides/pages/rag/18.md b/slides/pages/rag/18.md new file mode 100644 index 0000000..2ebc5c6 --- /dev/null +++ b/slides/pages/rag/18.md @@ -0,0 +1,34 @@ +--- +layout: default +--- + +# Paso 1: Cargar el PDF y dividir en chunks + +```typescript +import { PDFLoader } from "@langchain/community/document_loaders/fs/pdf"; +import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters"; + +// Cargar PDF (cada página → un Document) +const loader = new PDFLoader("./docs/manual-usuario.pdf"); +const docs = await loader.load(); +console.log(`📥 ${docs.length} páginas cargadas`); + +// Dividir en chunks pequeños para mejor búsqueda +const splitter = new RecursiveCharacterTextSplitter({ + chunkSize: 1000, + chunkOverlap: 200, +}); +const chunks = await splitter.splitDocuments(docs); +console.log(`📄 Dividido en ${chunks.length} chunks`); +``` + + + +
+ +- **chunkSize**: tamaño máximo de cada trozo en caracteres +- **chunkOverlap**: solapamiento entre chunks para no perder contexto + +
+ + diff --git a/slides/pages/rag/19.md b/slides/pages/rag/19.md new file mode 100644 index 0000000..52b080d --- /dev/null +++ b/slides/pages/rag/19.md @@ -0,0 +1,34 @@ +--- +layout: default +--- + +# Paso 2: Indexar en Qdrant + +```typescript +import { QdrantVectorStore } from "@langchain/qdrant"; +import { OpenAIEmbeddings } from "@langchain/openai"; + +const embeddings = new OpenAIEmbeddings(); + +// Crear colección e insertar chunks con sus embeddings +const vectorStore = await QdrantVectorStore.fromDocuments( + chunks, + embeddings, + { + url: "http://localhost:6333", + collectionName: "manuales", + } +); + +console.log(`✅ ${chunks.length} chunks indexados en Qdrant`); +``` + + + +
+ +`fromDocuments` hace todo: genera embeddings + crea la colección + inserta los vectores + +
+ + diff --git a/slides/pages/rag/20.md b/slides/pages/rag/20.md new file mode 100644 index 0000000..c54ce47 --- /dev/null +++ b/slides/pages/rag/20.md @@ -0,0 +1,33 @@ +--- +layout: default +--- + +# Paso 3: Buscar documentos relevantes + +```typescript +// Conectar a colección existente +const vectorStore = await QdrantVectorStore.fromExistingCollection( + new OpenAIEmbeddings(), + { url: "http://localhost:6333", collectionName: "manuales" } +); + +// Búsqueda semántica +const pregunta = "¿Cómo resetear la contraseña?"; +const resultados = await vectorStore.similaritySearch(pregunta, 3); + +resultados.forEach((doc, i) => { + console.log(`\n--- Resultado ${i + 1} ---`); + console.log(doc.pageContent.substring(0, 200)); + console.log("Fuente:", doc.metadata.source); +}); +``` + + + +
+ +`similaritySearch(query, k)` devuelve los **k** chunks más similares semánticamente a la query + +
+ + diff --git a/slides/pages/rag/21.md b/slides/pages/rag/21.md new file mode 100644 index 0000000..21a3bdd --- /dev/null +++ b/slides/pages/rag/21.md @@ -0,0 +1,37 @@ +--- +layout: default +--- + +# Paso 4: RAG - Búsqueda + Generación + +```typescript +import { ChatOpenAI } from "@langchain/openai"; +import { HumanMessage, SystemMessage } from "@langchain/core/messages"; + +const llm = new ChatOpenAI({ model: "gpt-4.1-mini" }); + +// Buscar contexto relevante +const pregunta = "¿Cómo resetear la contraseña?"; +const docs = await vectorStore.similaritySearch(pregunta, 3); +const contexto = docs.map(d => d.pageContent).join("\n\n"); + +// Generar respuesta con contexto +const respuesta = await llm.invoke([ + new SystemMessage( + `Responde basándote SOLO en este contexto:\n\n${contexto}` + ), + new HumanMessage(pregunta), +]); + +console.log(respuesta.content); +``` + + + +
+ +Esto es RAG: **recuperar** contexto relevante y pasárselo al LLM para que **genere** la respuesta + +
+ + diff --git a/slides/pages/rag/22.md b/slides/pages/rag/22.md new file mode 100644 index 0000000..559e920 --- /dev/null +++ b/slides/pages/rag/22.md @@ -0,0 +1,27 @@ +--- +layout: default +--- + +# Búsquedas simples + +```typescript +import { QdrantVectorStore } from "@langchain/qdrant"; +import { OpenAIEmbeddings } from "@langchain/openai"; + +// Conectar a la colección +const vectorStore = await QdrantVectorStore.fromExistingCollection( + new OpenAIEmbeddings(), + { url: "http://localhost:6333", collectionName: "manuales" } +); + +// Buscar +const pregunta = "¿Cómo resetear la contraseña?"; +const resultados = await vectorStore.similaritySearch(pregunta, 3); + +resultados.forEach((doc, i) => { + console.log(`\n--- Resultado ${i + 1} ---`); + console.log(doc.pageContent); + console.log("Fuente:", doc.metadata.source); + console.log("Página:", doc.metadata.loc?.pageNumber); +}); +``` diff --git a/slides/pages/rag/23.md b/slides/pages/rag/23.md new file mode 100644 index 0000000..5e813c0 --- /dev/null +++ b/slides/pages/rag/23.md @@ -0,0 +1,33 @@ +--- +layout: default +--- + +# RAG completo: Búsqueda + Generación + +```typescript +import { ChatOpenAI } from "@langchain/openai"; +import { PromptTemplate } from "@langchain/core/prompts"; + +const llm = new ChatOpenAI({ model: "gpt-4.1-mini" }); + +// Buscar contexto relevante +const pregunta = "¿Cómo resetear la contraseña?"; +const docs = await vectorStore.similaritySearch(pregunta, 3); +const contexto = docs.map(d => d.pageContent).join("\n\n"); + +// Crear prompt con contexto +const prompt = PromptTemplate.fromTemplate(` +Responde la pregunta basándote SOLO en el siguiente contexto: + +Contexto: +{contexto} + +Pregunta: {pregunta} + +Respuesta:`); + +const chain = prompt.pipe(llm); +const respuesta = await chain.invoke({ contexto, pregunta }); + +console.log(respuesta.content); +``` diff --git a/slides/pages/rag/24.md b/slides/pages/rag/24.md new file mode 100644 index 0000000..57879de --- /dev/null +++ b/slides/pages/rag/24.md @@ -0,0 +1,27 @@ +--- +layout: default +--- + +# Retrievers: Abstracción de búsqueda + + + +**Retriever** es una interfaz estándar para recuperar documentos + +```typescript +// Convertir vector store en retriever +const retriever = vectorStore.asRetriever({ + k: 3, // número de documentos a recuperar +}); + +// Usar el retriever +const docs = await retriever.invoke("¿Cómo resetear la contraseña?"); +``` + +### Ventajas + +- Interfaz unificada para cualquier fuente de datos +- Fácil de intercambiar (Qdrant → Pinecone) +- Compatible con chains predefinidas de LangChain + + diff --git a/slides/pages/rag/24b.md b/slides/pages/rag/24b.md new file mode 100644 index 0000000..572e715 --- /dev/null +++ b/slides/pages/rag/24b.md @@ -0,0 +1,32 @@ +--- +layout: default +--- + +# Reranking: mejorando la relevancia + + + +La búsqueda vectorial recupera documentos **similares**, pero no siempre los más **relevantes**. Un **reranker** reordena los resultados usando un modelo especializado. + +```typescript +import { CohereRerank } from "@langchain/cohere"; + +const reranker = new CohereRerank({ + apiKey: process.env.COHERE_API_KEY, + model: "rerank-v4.0-fast", // multilingüe por defecto + topN: 3, +}); + +// .compressDocuments() → devuelve los topN Document[] listos para usar en chains +const rerankedDocs = await reranker.compressDocuments(docs, query); + +// .rerank() → devuelve TODOS los índices + scores (más control manual) +const results = await reranker.rerank(docs, query); +// [{ index: 2, relevanceScore: 0.95 }, { index: 0, relevanceScore: 0.72 }, ...] +``` + +
+✨ Patrón común: recuperar ~10 docs con vector search → reranking → top 3 al LLM +
+ + diff --git a/slides/pages/rag/24c.md b/slides/pages/rag/24c.md new file mode 100644 index 0000000..f86b900 --- /dev/null +++ b/slides/pages/rag/24c.md @@ -0,0 +1,20 @@ +--- +layout: default +--- + +# Proveedores de Reranking + + + +| Proveedor | Paquete | Free tier | Self-hosted | Multiidioma | +|-----------|---------|-----------|-------------|-------------| +| **Cohere** | `@langchain/cohere` | Trial API key | ❌ | ✅ `rerank-v4.0-pro/fast` | +| **MixedBread AI** | `@langchain/community` | 100 queries/mes | BYOC (enterprise) | ✅ | +| **IBM watsonx** | `@langchain/community` | Lite plan | ❌ | ✅ | + + +
+💡 El reranker también ayuda a filtrar duplicados: los chunks redundantes quedan con scores muy similares y se pueden descartar +
+ + diff --git a/slides/pages/rag/24d.md b/slides/pages/rag/24d.md new file mode 100644 index 0000000..1d6fee2 --- /dev/null +++ b/slides/pages/rag/24d.md @@ -0,0 +1,24 @@ +--- +layout: default +--- + +# Reranking self-hosted + + + +### BAAI/bge-reranker-v2-m3 + +- Modelo open source (Apache 2.0), 0.6B params, multilingüe +- Funciona en **CPU** — no necesitas GPU +- Primera descarga ~600MB, luego se cachea + +### Runtimes para servir el modelo + +| Runtime | Descripción | +|---------|-------------| +| **Infinity** | Runtime ligero y sencillo de configurar. Soporta embeddings y reranking. Ideal para empezar | +| **TEI** (Text Embeddings Inference) | Runtime de HuggingFace, más optimizado. Tiene imágenes Docker separadas para CPU y GPU | + +Ambos se despliegan con Docker y exponen una API REST compatible + + diff --git a/slides/pages/rag/24e.md b/slides/pages/rag/24e.md new file mode 100644 index 0000000..89ac772 --- /dev/null +++ b/slides/pages/rag/24e.md @@ -0,0 +1,36 @@ +--- +layout: default +--- + +# Consumir reranker self-hosted desde Node.js + + + +No hay integración oficial con LangChain.js, pero la API REST es muy sencilla: + +```typescript +async function rerank(query: string, docs: string[], topN = 3) { + const res = await fetch("http://localhost:7997/rerank", { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ query, documents: docs }), + }); + const results = await res.json(); + // [{ index: 2, relevance_score: 0.95 }, { index: 0, relevance_score: 0.72 }, ...] + + return results + .sort((a, b) => b.relevance_score - a.relevance_score) + .slice(0, topN); +} + +// Uso con documentos de LangChain +const searchResults = await vectorStore.similaritySearch(query, 10); +const ranked = await rerank(query, searchResults.map(d => d.pageContent)); +const topDocs = ranked.map(r => searchResults[r.index]); +``` + +
+💡 Misma API para Infinity y TEI — cambiar de runtime no requiere cambiar código +
+ + diff --git a/slides/pages/rag/26.md b/slides/pages/rag/26.md new file mode 100644 index 0000000..7b47b9b --- /dev/null +++ b/slides/pages/rag/26.md @@ -0,0 +1,38 @@ +--- +layout: two-cols-header +--- + +# Mejores prácticas RAG + +::left:: + + + +### Chunking +- Tamaño típico: 500-1500 caracteres +- Overlap: 10-20% del tamaño del chunk +- Experimenta con diferentes estrategias + +### Embeddings +- Usa el mismo modelo para indexar y buscar +- Considera modelos multiidioma para español +- `text-embedding-3-small` buen balance + + + +::right:: + + + +### Búsqueda +- Recupera 3-5 docs (más no siempre es mejor) +- Usa metadata para filtrar resultados +- Considera hybrid search (keyword + semántica) + +### Prompts +- Instruye al LLM a basarse SOLO en el contexto +- Pide citas/referencias cuando sea posible +- Maneja casos sin información relevante + + + diff --git a/slides/pages/rag/27.md b/slides/pages/rag/27.md new file mode 100644 index 0000000..f154e41 --- /dev/null +++ b/slides/pages/rag/27.md @@ -0,0 +1,38 @@ +--- +layout: two-cols-header +--- + +# Técnicas avanzadas de RAG + +::left:: + + + +### Parent Document Retriever +- Busca con chunks pequeños +- Retorna documentos padres completos +- Mejor contexto sin perder precisión + +### Multi-Query Retriever +- Genera múltiples variaciones de la pregunta +- Busca con todas las variaciones +- Combina resultados + + + +::right:: + + + +### Contextual Compression +- Filtra partes irrelevantes de docs +- Reduce tokens enviados al LLM +- Mejora relevancia + +### Self-Query Retriever +- Extrae filtros de metadata de la pregunta +- "Docs de 2023 sobre Python" → `{year: 2023, topic: "Python"}` + + + + diff --git a/slides/pages/rag/29.md b/slides/pages/rag/29.md new file mode 100644 index 0000000..b4bfcdf --- /dev/null +++ b/slides/pages/rag/29.md @@ -0,0 +1,24 @@ +--- +layout: default +--- + +# Recursos adicionales + + + +### Documentación oficial +- [LangChain RAG Tutorial](https://docs.langchain.com/oss/javascript/tutorials/rag) +- [Qdrant LangChain Integration](https://qdrant.tech/documentation/frameworks/langchain/) +- [OpenAI Embeddings Guide](https://platform.openai.com/docs/guides/embeddings) + +### Herramientas útiles +- [Qdrant Cloud](https://qdrant.tech) - BD vectorial gestionada +- [LangSmith](https://smith.langchain.com) - Debugging y evaluación +- [Embedding comparisons](https://huggingface.co/spaces/mteb/leaderboard) - MTEB Leaderboard + +### Alternativas a explorar +- **LlamaIndex**: Framework especializado en RAG +- **Haystack**: Pipeline flexible para búsqueda +- **txtai**: Búsqueda semántica ligera + + diff --git a/slides/pages/setup/01.md b/slides/pages/setup/01.md new file mode 100644 index 0000000..95cbe13 --- /dev/null +++ b/slides/pages/setup/01.md @@ -0,0 +1,14 @@ +--- +layout: cover +class: text-center +--- + +# Instalación y Setup + +Módulo 2: Primeros Pasos con LangChain.js + +
+ + Configurando tu entorno de desarrollo + +
diff --git a/slides/pages/setup/02.md b/slides/pages/setup/02.md new file mode 100644 index 0000000..80ddeca --- /dev/null +++ b/slides/pages/setup/02.md @@ -0,0 +1,94 @@ +--- +layout: default +--- + +

Arquitectura de Paquetes

+ +
+ +
+ +
+ + + +

Paquetes base

+ +
+ +
+

@langchain/core

+

Primitivas fundamentales: mensajes, prompts, interfaces base. Es el cimiento que usan todos los demás paquetes.

+
+ +
+

langchain

+

Metapaquete principal. Re-exporta lo útil de core y añade herramientas de alto nivel para construir agentes.

+
+ +
+

@langchain/classic

+

Funcionalidad legacy de v0.x: MemoryVectorStore, document loaders, chains antiguas. Para migración gradual.

+
+ +
+ + + +
+ +
+ + + +

Paquetes por proveedor

+ +
+ +
+

@langchain/openai

+

GPT-4.1, GPT-5, Azure

+
+ +
+

@langchain/anthropic

+

Claude 4, 4.5, 4.6

+
+ +
+

@langchain/google

+

Gemini 2.5

+
+ +
+

@langchain/mistralai

+

Mistral, Mixtral

+
+ +
+

@langchain/ollama

+

Modelos locales

+
+ +
+

@langchain/...

+

+30 proveedores

+
+ +
+ + + +
+ +
+ + + +
+ Instala solo los proveedores que necesites: npm i langchain @langchain/openai +
+ + + +
diff --git a/slides/pages/setup/03.md b/slides/pages/setup/03.md new file mode 100644 index 0000000..87ba5d8 --- /dev/null +++ b/slides/pages/setup/03.md @@ -0,0 +1,57 @@ +--- +layout: default +--- + +# Instalación en Node.js + +
+ + + +**Requisitos**: Node.js v20+ y tu entorno TypeScript habitual (tsx, ts-node, Vite, Next.js...) + + + + + +## 1. Paquetes base + +```bash +npm install langchain @langchain/core +``` + + + + + +## 2. Proveedores (los que vayas a usar) + +```bash +npm install @langchain/mistralai # Mistral +npm install @langchain/google # Gemini +npm install @langchain/openai # GPT-4.1, Azure +npm install @langchain/anthropic # Claude +``` + + + + + +## 3. Entorno TypeScript (si no lo tienes) + +```bash +npm install -D typescript tsx @types/node +npx tsx mi-script.ts # Para ejecutar +``` + + + + + +
+Versiones actuales (Mayo 2026): langchain v1.4.1 · @langchain/core v1.1.44 +
+ + + +
diff --git a/slides/pages/setup/04.md b/slides/pages/setup/04.md new file mode 100644 index 0000000..5f8329a --- /dev/null +++ b/slides/pages/setup/04.md @@ -0,0 +1,23 @@ +--- +layout: default +--- + +# ¿Qué LLM usar? + +Comparativa de proveedores principales + +
+ +| Proveedor | Modelo | Empresa | Free Tier | Precio API* | +|-----------|--------|---------|-----------|-------------| +| **OpenAI** | GPT-4.1 | USA | ❌ No | $2/$8 | +| **Google** | Gemini 2.5 Flash | USA | ✅ Sí | $0.15/$0.60 | +| **Anthropic** | Claude 4 Sonnet | USA | ✅ Límite diario | $3/$15 | +| **Mistral** | Mistral Large | Francia (UE) | ✅ Experiment | ~$2/$6 | +| **DeepSeek** | DeepSeek R1 | China | ✅ Demo gratis | $0.55/$2.19 | + +

* Precio por millón de tokens (input/output) - Mayo 2026

+ +

⚠️ Considera las implicaciones de privacidad según tu jurisdicción y tipo de datos

+ +
diff --git a/slides/pages/setup/05.md b/slides/pages/setup/05.md new file mode 100644 index 0000000..b09c124 --- /dev/null +++ b/slides/pages/setup/05.md @@ -0,0 +1,39 @@ +--- +layout: default +--- + +# URLs para obtener API Keys + +
+ +
+ +
+ +### OpenAI +[platform.openai.com](https://platform.openai.com) + +### Google Gemini +[ai.google.dev/gemini-api](https://ai.google.dev/gemini-api) + +### Anthropic (Claude) +[console.anthropic.com](https://console.anthropic.com) + +
+ +
+ +### Mistral AI +[console.mistral.ai](https://console.mistral.ai) + +### DeepSeek +[platform.deepseek.com](https://platform.deepseek.com) + +### Ollama (Local) +[ollama.com](https://ollama.com) + +
+ +
+ +
diff --git a/slides/pages/setup/06.md b/slides/pages/setup/06.md new file mode 100644 index 0000000..ab09fa7 --- /dev/null +++ b/slides/pages/setup/06.md @@ -0,0 +1,17 @@ +--- +layout: default +--- + +# Recomendaciones: Para Empezar + +
+ +
+

🎓 Para aprender y empezar

+

Google Gemini o Mistral Experiment

+

✅ Gratis

+

✅ Sin tarjeta de crédito

+

✅ Límites generosos para desarrollo

+
+ +
diff --git a/slides/pages/setup/07.md b/slides/pages/setup/07.md new file mode 100644 index 0000000..329c89b --- /dev/null +++ b/slides/pages/setup/07.md @@ -0,0 +1,27 @@ +--- +layout: default +--- + +

Recomendaciones: Producción

+ +
+ +
+

💼 Para producción

+

Claude 4 Sonnet o GPT-4.1

+

✅ Mejor precisión y razonamiento

+

⚠️ Requiere presupuesto ($3-15/M tokens)

+
+ +
+

💰 Alto volumen / Económico

+

DeepSeek R1

+

✅ 90% más barato ($0.55-$2.19/M)

+

✅ Excelente relación calidad/precio

+
+ +
+⚠️ Revisa términos de privacidad y residencia de datos según tus necesidades +
+ +
diff --git a/slides/pages/setup/08.md b/slides/pages/setup/08.md new file mode 100644 index 0000000..b597726 --- /dev/null +++ b/slides/pages/setup/08.md @@ -0,0 +1,30 @@ +--- +layout: default +--- + +

Estrategia Recomendada

+ +
+ +
+

🏠 Desarrollo: Ollama (Local)

+

Para testing, iteración rápida y privacidad

+

✅ Gratis • Sin límites • Privado

+

⚠️ Requiere 16GB RAM mínimo

+
+ +
+

☁️ Producción: APIs Cloud

+

Para usuarios finales

+

✅ Mayor calidad • Escalable • Fiable

+
+ + + +
+ 💡 Mejor de ambos mundos: Desarrolla local, despliega cloud +
+ + + +
diff --git a/slides/pages/setup/09.md b/slides/pages/setup/09.md new file mode 100644 index 0000000..9a93fa9 --- /dev/null +++ b/slides/pages/setup/09.md @@ -0,0 +1,50 @@ +--- +layout: default +--- + +

Ollama: Requisitos de Hardware

+ +
+ +

📊 ¿Qué necesitas según el modelo?

+ +
+ +| Tamaño | RAM | VRAM GPU | Modelos Populares | +|--------|-----|----------|-------------------| +| **3B** | 8GB | 4GB | Llama 3.2 3B, Phi-3 Mini | +| **7B** | 16GB | 8GB | Llama 3.2 7B, Mistral 7B | +| **13B** | 32GB | 16GB | Llama 3.2 13B | +| **70B** | 64GB+ | 24GB+ | Llama 3.3 70B | + +
+ + + +

💡 Recomendaciones

+ +
+ +
+

🎓 Para empezar

+

Llama 3.2 3B

+

8GB VRAM suficiente

+
+ +
+

💻 Desarrollo

+

Llama 3.2 7B

+

16GB VRAM + GPU

+
+ +
+

🏢 Producción

+

Usa APIs cloud

+

Más fiable

+
+ +
+ + + +
diff --git a/slides/pages/setup/10.md b/slides/pages/setup/10.md new file mode 100644 index 0000000..db9853d --- /dev/null +++ b/slides/pages/setup/10.md @@ -0,0 +1,48 @@ +--- +layout: default +--- + +# Configuración del Entorno + +
+ + + +## 🔑 Variables de Entorno (.env) + +```bash +# Crea un archivo .env en la raíz +OPENAI_API_KEY=sk-proj-... +ANTHROPIC_API_KEY=sk-ant-... +``` + + + + + +## 📦 Opción A: dotenv (tradicional) + +```bash +npm install dotenv +``` + +```typescript +import 'dotenv/config'; // Al inicio del archivo +``` + + + + + +## ⚡ Opción B: --env-file (Node.js 20+) + +```bash +# Sin instalar nada! +npx tsx --env-file=.env index.ts +``` + +✅ Recomendado: Sin dependencias adicionales + + + +
diff --git a/slides/pages/setup/11.md b/slides/pages/setup/11.md new file mode 100644 index 0000000..29e5e17 --- /dev/null +++ b/slides/pages/setup/11.md @@ -0,0 +1,52 @@ +--- +layout: default +--- + +# API Key en el Constructor + +
+ + + +## 🔧 Pasar la key directamente + +```typescript +import { ChatOpenAI } from "@langchain/openai"; + +const model = new ChatOpenAI({ + apiKey: "sk-proj-xxxxxxxxxxxxx", // Directamente aquí + model: "gpt-4.1-mini" +}); +``` + + + + + +## ⚠️ Cuándo usarlo + +
+ +
+

✅ Variables de entorno

+

Para producción, CI/CD y equipos

+
+ +
+

⚡ Constructor

+

Solo testing rápido y prototipos

+
+ +
+ + + + + +
+🔒 Recuerda: Añade .env a .gitignore +
+ + + +
diff --git a/slides/pages/setup/12.md b/slides/pages/setup/12.md new file mode 100644 index 0000000..f9aa312 --- /dev/null +++ b/slides/pages/setup/12.md @@ -0,0 +1,69 @@ +--- +layout: two-cols +layoutClass: gap-8 +--- + +# Hello World con LangChain.js + +Tu primera aplicación LLM + +```typescript {all|1-2|4-7|9-11|13-15|all} +// index.ts +import { ChatOpenAI } from "@langchain/openai"; + +// 1. Crear el modelo +const model = new ChatOpenAI({ + model: "gpt-4.1-mini", +}); + +// 2. Invocar el modelo +const response = await model.invoke( + "¿Qué es LangChain?" +); + +// 3. Ver la respuesta +console.log(response.content); +``` + +::right:: + +
+ + + +## 🏃 Ejecutar con tsx + +```bash +# Instalar tsx +npm install -D tsx + +# Ejecutar directamente +npx tsx index.ts +``` + + + + + +## 📤 Salida Esperada + +``` +LangChain es un framework para +desarrollar aplicaciones con LLMs. +Permite conectar modelos de lenguaje +con datos externos y herramientas... +``` + + + + + +## 🎯 Conceptos Clave + +- **Model**: ChatOpenAI +- **invoke()**: Llamada síncrona +- **response.content**: El texto generado + + + +
diff --git a/slides/pages/setup/13.md b/slides/pages/setup/13.md new file mode 100644 index 0000000..0989849 --- /dev/null +++ b/slides/pages/setup/13.md @@ -0,0 +1,65 @@ +--- +layout: two-cols +layoutClass: gap-8 +--- + +# Ejemplo con Google Gemini + +Alternativa gratuita a OpenAI + +```typescript {all|1-2|4-7|9-11|all} +// gemini-example.ts +import { ChatGoogle } from "@langchain/google"; + +// 1. Crear el modelo +const model = new ChatGoogle({ + model: "gemini-2.5-flash", +}); + +// 2. Invocar +const response = await model.invoke( + "¿Qué es LangChain?" +); + +console.log(response.content); +``` + +::right:: + +
+ + + +## 📦 Instalación + +```bash +npm install @langchain/google +``` + + + + + +## 🔑 API Key + +```bash +# .env +GOOGLE_API_KEY=AI... +``` + +Obtener gratis en: +[ai.google.dev/gemini-api](https://ai.google.dev/gemini-api) + + + + + +## ⚡ Ejecutar + +```bash +npx tsx --env-file=.env gemini-example.ts +``` + + + +
diff --git a/slides/pages/setup/13b.md b/slides/pages/setup/13b.md new file mode 100644 index 0000000..93b56a8 --- /dev/null +++ b/slides/pages/setup/13b.md @@ -0,0 +1,65 @@ +--- +layout: two-cols +layoutClass: gap-8 +--- + +# Ejemplo con Mistral AI + +Alternativa europea (Francia) + +```typescript {all|1-2|4-7|9-11|all} +// mistral-example.ts +import { ChatMistralAI } from "@langchain/mistralai"; + +// 1. Crear el modelo +const model = new ChatMistralAI({ + model: "mistral-large-latest", +}); + +// 2. Invocar +const response = await model.invoke( + "¿Qué es LangChain?" +); + +console.log(response.content); +``` + +::right:: + +
+ + + +## 📦 Instalación + +```bash +npm install @langchain/mistralai +``` + + + + + +## 🔑 API Key + +```bash +# .env +MISTRAL_API_KEY=... +``` + +Obtener en: +[console.mistral.ai](https://console.mistral.ai) + + + + + +## ⚡ Ejecutar + +```bash +npx tsx --env-file=.env mistral-example.ts +``` + + + +
diff --git a/slides/pages/setup/14.md b/slides/pages/setup/14.md new file mode 100644 index 0000000..188dffb --- /dev/null +++ b/slides/pages/setup/14.md @@ -0,0 +1,44 @@ +--- +layout: default +--- + +# Usar Ollama con LangChain.js + +
+ + + +## 📦 Instalación + +```bash +# 1. Descargar e instalar Ollama +# https://ollama.com/download + +# 2. Descargar un modelo +ollama pull llama3.2 + +# 3. Instalar paquete LangChain +npm install @langchain/ollama +``` + + + + + +## 💻 Código + +```typescript +import { ChatOllama } from "@langchain/ollama"; + +const model = new ChatOllama({ + model: "llama3.2", + baseUrl: "http://localhost:11434", +}); + +const response = await model.invoke("¿Qué es LangChain?"); +console.log(response.content); +``` + + + +
diff --git a/slides/pages/setup/15.md b/slides/pages/setup/15.md new file mode 100644 index 0000000..63750fe --- /dev/null +++ b/slides/pages/setup/15.md @@ -0,0 +1,42 @@ +--- +layout: default +--- + +# ¿Qué es Zod? + +
+ + + +
+ +**Zod** es una librería de validación de esquemas para TypeScript + +
+ + + + + +
+ +**¿Para qué lo usamos en LangChain?** + +- ✅ Definir la estructura esperada de las respuestas del LLM +- ✅ Validar que el LLM devuelve datos en el formato correcto +- ✅ Obtener autocompletado y type safety en TypeScript +- ✅ Convertir respuestas de texto a objetos JavaScript tipados + +
+ + + + + +```bash +npm install zod +``` + + + +
diff --git a/slides/pages/setup/16.md b/slides/pages/setup/16.md new file mode 100644 index 0000000..32c876e --- /dev/null +++ b/slides/pages/setup/16.md @@ -0,0 +1,34 @@ +--- +layout: default +--- + +# Ejemplo de Zod + +
+ +```typescript +import { z } from "zod"; + +// Definir el esquema de datos +const UserSchema = z.object({ + name: z.string(), + age: z.number().min(0).max(120), + email: z.string().email(), + roles: z.array(z.enum(["admin", "user", "guest"])), +}); + +// Inferir el tipo TypeScript automáticamente +type User = z.infer; + +// Validar datos +const result = UserSchema.parse({ + name: "Juan", + age: 30, + email: "juan@example.com", + roles: ["admin", "user"] +}); +// ✅ Si los datos son válidos, devuelve el objeto tipado +// ❌ Si no, lanza un error con detalles del problema +``` + +
diff --git a/slides/pages/structure/01.md b/slides/pages/structure/01.md new file mode 100644 index 0000000..c0b021c --- /dev/null +++ b/slides/pages/structure/01.md @@ -0,0 +1,14 @@ +--- +layout: cover +class: text-center +--- + +# Estructura del Curso + +LangChain.js: De cero a producción + +
+ + Tu hoja de ruta completa + +
diff --git a/slides/pages/structure/02.md b/slides/pages/structure/02.md new file mode 100644 index 0000000..97ccde8 --- /dev/null +++ b/slides/pages/structure/02.md @@ -0,0 +1,30 @@ +--- +layout: default +--- + +# Módulos del Curso (1/3) + +
+ + +
+

📚 Módulo 1: Introducción

+

Qué es LangChain, filosofía, historia y ecosistema

+
+ + + +
+

⚙️ Módulo 2: Instalación y Setup

+

Configuración, APIs, proveedores de LLMs

+
+ + + +
+

🤖 Módulo 3: Modelos

+

Chat Models, LLMs, parámetros, streaming

+
+ + +
diff --git a/slides/pages/structure/02b.md b/slides/pages/structure/02b.md new file mode 100644 index 0000000..a0bc61c --- /dev/null +++ b/slides/pages/structure/02b.md @@ -0,0 +1,30 @@ +--- +layout: default +--- + +# Módulos del Curso (2/3) + +
+ + +
+

💬 Módulo 4: Mensajes y Prompts

+

Sistema de mensajes, templates, contexto, historial

+
+ + + +
+

🎯 Módulo 5: Agentes

+

Tools, structured output, runtime, human-in-the-loop

+
+ + + +
+

🔍 Módulo 6: LangSmith

+

Observabilidad, debugging, evaluación y producción

+
+ + +
diff --git a/slides/pages/structure/02c.md b/slides/pages/structure/02c.md new file mode 100644 index 0000000..5f6624a --- /dev/null +++ b/slides/pages/structure/02c.md @@ -0,0 +1,30 @@ +--- +layout: default +--- + +# Módulos del Curso (3/3) + +
+ + +
+

📄 Módulo 7: RAG

+

Embeddings, bases de datos vectoriales, retrieval

+
+ + + +
+

📝 Módulo 8: Temas Pendientes

+

Lo que nos dejamos en el tintero

+
+ + + +
+

🔄 Módulo 9: Alternativas

+

Otros frameworks del ecosistema LLM

+
+ + +
diff --git a/slides/pages/structure/03.md b/slides/pages/structure/03.md new file mode 100644 index 0000000..cb00003 --- /dev/null +++ b/slides/pages/structure/03.md @@ -0,0 +1,49 @@ +--- +layout: default +--- + +# Progresión del Aprendizaje + +
+ +
+ + +
+
🌱
+

Fundamentos

+

Módulos 1-2

+

Instalación y conceptos básicos

+
+ + + +
+
🔨
+

Construcción

+

Módulos 3-5

+

Modelos, mensajes y agentes

+
+ + + +
+
🚀
+

Producción

+

Módulos 6-7

+

LangSmith y RAG

+
+ + +
+ + +
+
+ 📝 Módulo 8: Temas pendientes + 🔄 Módulo 9: Alternativas +
+
+ + +
diff --git a/slides/pages/structure/04.md b/slides/pages/structure/04.md new file mode 100644 index 0000000..5cc6d96 --- /dev/null +++ b/slides/pages/structure/04.md @@ -0,0 +1,39 @@ +--- +layout: default +--- + +# Al Final del Curso + +
+ +

Serás capaz de:

+ +
+ + +
Crear aplicaciones LLM completas
+ + + +
Implementar agentes autónomos
+ + + +
Gestionar conversaciones con contexto
+ + + +
Construir sistemas RAG con tus datos
+ + + +
Monitorizar y debuggear con LangSmith
+ + + +
Desplegar en producción con confianza
+ + +
+ +
diff --git a/slides/public/EITB.svg b/slides/public/EITB.svg new file mode 100644 index 0000000..5bb31be --- /dev/null +++ b/slides/public/EITB.svg @@ -0,0 +1,28 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/slides/public/cover.png b/slides/public/cover.png new file mode 100644 index 0000000..9f31826 Binary files /dev/null and b/slides/public/cover.png differ diff --git a/slides/public/nykk.png b/slides/public/nykk.png new file mode 100644 index 0000000..9b5c126 Binary files /dev/null and b/slides/public/nykk.png differ diff --git a/slides/slides.md b/slides/slides.md new file mode 100644 index 0000000..b486ada --- /dev/null +++ b/slides/slides.md @@ -0,0 +1,914 @@ +--- +# try also 'default' to start simple +theme: seriph + +# random image from a curated Unsplash collection by Anthony +# like them? see https://unsplash.com/collections/94734566/slidev +background: /cover.png +# some information about your slides (markdown enabled) +title: LangChain.js - De cero a producción +favicon: https://docs.langchain.com/favicon.ico +info: | + ## Langchain.js: De cero a producción + + Curso completo sobre LangChain.js para desarrolladores + + ### Temario: + - Introducción a LangChain y su ecosistema + - Estructura y arquitectura del framework + - Configuración y setup del entorno + - Modelos de lenguaje (LLMs, Chat Models) + - Gestión de mensajes y prompts + - Agentes y herramientas (Tools) + - LangSmith para debugging y monitorización + - **RAG (Retrieval Augmented Generation)** + - Embeddings y proveedores + - Bases de datos vectoriales (Qdrant, Pinecone, etc.) + - Document Loaders y Splitters (PDF, Web, Markdown) + - Extractores avanzados (Unstructured, Docling, etc.) + - Ejemplo práctico con PDFs + - Temas avanzados y pendientes + - Alternativas al ecosistema LangChain + +# apply UnoCSS classes to the current slide +class: text-center +# https://sli.dev/features/drawing +drawings: + persist: false +# slide transition: https://sli.dev/guide/animations.html#slide-transitions +transition: slide-left +# enable MDC Syntax: https://sli.dev/features/mdc +mdc: true +# duration of the presentation +duration: 45min + + +--- + +## Agentes y RAG para desarrolladores web +### eitb · Mayo/Junio 2026 + +
+ + +
+ + +--- +src: ./pages/introduction/01.md +hide: false +--- + +--- +src: ./pages/introduction/02.md +hide: false +--- + +--- +src: ./pages/introduction/03.md +hide: false +--- + +--- +src: ./pages/introduction/04.md +hide: false +--- + +--- +src: ./pages/introduction/05.md +hide: false +--- + +--- +src: ./pages/introduction/06.md +hide: false +--- + +--- +src: ./pages/introduction/07.md +hide: false +--- + +--- +src: ./pages/introduction/08.md +hide: false +--- + +--- +src: ./pages/introduction/09.md +hide: false +--- + +--- +src: ./pages/introduction/10.md +hide: false +--- + +--- +src: ./pages/introduction/11.md +hide: false +--- + +--- +src: ./pages/structure/01.md +hide: false +--- + +--- +src: ./pages/structure/02.md +hide: false +--- + +--- +src: ./pages/structure/02b.md +hide: false +--- + +--- +src: ./pages/structure/02c.md +hide: false +--- + +--- +src: ./pages/structure/03.md +hide: false +--- + +--- +src: ./pages/structure/04.md +hide: false +--- + + +--- +src: 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