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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "06c94f96",
+   "metadata": {},
+   "source": [
+    "# LangSmith Prompts in Code\n",
+    "\n",
+    "## What are Prompts in LangSmith?\n",
+    "\n",
+    "**Prompts** are the instructions you give to Large Language Models (LLMs) to guide their behavior. LangSmith provides a centralized way to:\n",
+    "- **Store** prompts as versioned templates\n",
+    "- **Manage** different versions with commit history\n",
+    "- **Collaborate** with your team on prompt improvements\n",
+    "- **Reuse** prompts across different applications\n",
+    "\n",
+    "## Why Use LangSmith for Prompt Management?\n",
+    "\n",
+    "1. **Version Control**: Track changes to your prompts over time\n",
+    "2. **Collaboration**: Multiple team members can iterate on prompts\n",
+    "3. **Centralized Storage**: One source of truth for all your prompts\n",
+    "4. **Environment Management**: Use tags like `prod`, `dev`, `staging`\n",
+    "5. **Dynamic Templates**: Create reusable prompts with variables\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "Before starting, make sure you have:\n",
+    "- A LangSmith account (sign up at https://smith.langchain.com)\n",
+    "- Your LangSmith API key set as an environment variable\n",
+    "- Required Python packages installed\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ecec640",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "from langsmith import Client\n",
+    "from langchain_core.prompts import ChatPromptTemplate, PromptTemplate\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "# Load environment variables from .env file\n",
+    "load_dotenv()\n",
+    "\n",
+    "# Verify that required environment variables are set\n",
+    "if not os.getenv(\"LANGSMITH_API_KEY\"):\n",
+    "    print(\"⚠️  Warning: LANGSMITH_API_KEY not found!\")\n",
+    "    print(\"   Make sure you have a .env file with LANGSMITH_API_KEY set\")\n",
+    "    print(\"   Or set it manually: os.environ['LANGSMITH_API_KEY'] = 'your-key'\")\n",
+    "\n",
+    "if not os.getenv(\"OPENAI_API_KEY\"):\n",
+    "    print(\"⚠️  Warning: OPENAI_API_KEY not found!\")\n",
+    "    print(\"   Make sure you have a .env file with OPENAI_API_KEY set\")\n",
+    "\n",
+    "# Initialize LangSmith client\n",
+    "client = Client()\n",
+    "\n",
+    "print(\"✅ Setup complete! LangSmith client initialized.\")\n",
+    "print(f\"📍 Project: {os.getenv('LANGSMITH_PROJECT', 'default')}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff25a001",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## 1. Creating Your First Prompt\n",
+    "\n",
+    "Let's start by creating a simple prompt template with variables. We'll use `ChatPromptTemplate` which is the recommended format for chat-based models.\n",
+    "\n",
+    "### Key Concepts:\n",
+    "- **System Message**: Sets the behavior/persona of the AI\n",
+    "- **User Message**: Contains the user's input, often with variables like `{question}`\n",
+    "- **Variables**: Placeholders that get filled in at runtime (e.g., `{topic}`, `{question}`)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97cea521",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example 1: Simple chatbot prompt\n",
+    "simple_prompt = ChatPromptTemplate([\n",
+    "    (\"system\", \"You are a helpful and friendly AI assistant.\"),\n",
+    "    (\"user\", \"{question}\"),\n",
+    "])\n",
+    "\n",
+    "# Push this prompt to LangSmith\n",
+    "client.push_prompt(\"my-first-prompt\", object=simple_prompt)\n",
+    "\n",
+    "print(\"✅ Prompt 'my-first-prompt' created!\")\n",
+    "print(\"📝 You can view it in LangSmith UI under Prompts section\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e099951f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example 2: More complex prompt with multiple variables\n",
+    "expert_prompt = ChatPromptTemplate([\n",
+    "    (\"system\", \"You are an expert in {domain}. Provide detailed, accurate answers.\"),\n",
+    "    (\"user\", \"Question: {question}\\n\\nContext: {context}\"),\n",
+    "])\n",
+    "\n",
+    "# Push to LangSmith\n",
+    "client.push_prompt(\"expert-assistant\", object=expert_prompt)\n",
+    "\n",
+    "print(\"✅ Prompt 'expert-assistant' created!\")\n",
+    "print(\"📝 This prompt has 3 variables: domain, question, and context\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcc7237a",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## 2. Pulling and Using Prompts\n",
+    "\n",
+    "Once you've created a prompt in LangSmith, you can pull it from anywhere in your code. This is useful for:\n",
+    "- **Reusing prompts** across different applications\n",
+    "- **Separating prompt logic** from your code\n",
+    "- **Allowing non-developers** to update prompts without changing code\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "767367da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Pull a prompt from LangSmith\n",
+    "pulled_prompt = client.pull_prompt(\"my-first-prompt\")\n",
+    "\n",
+    "print(\"✅ Prompt pulled successfully!\")\n",
+    "print(f\"\\nPrompt structure:\")\n",
+    "print(pulled_prompt)\n",
+    "\n",
+    "# You can also inspect the messages\n",
+    "print(f\"\\nPrompt messages:\")\n",
+    "for message in pulled_prompt.messages:\n",
+    "    print(f\"  - {message}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e064e19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Format the prompt with actual values\n",
+    "formatted_messages = pulled_prompt.format_messages(question=\"What is the capital of France?\")\n",
+    "\n",
+    "print(\"📨 Formatted messages:\")\n",
+    "for msg in formatted_messages:\n",
+    "    print(f\"\\n{msg.type.upper()}:\")\n",
+    "    print(f\"  {msg.content}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c50e6033",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "complex_pulled_prompt = client.pull_prompt(\"expert-assistant\")\n",
+    "\n",
+    "complex_formatted_messages = complex_pulled_prompt.format_messages(domain=\"AI\", question=\"Why would you use LangGraph?\", context=\"LangGraph is a framework for building production-grade LLM applications.\")\n",
+    "\n",
+    "print(\" Formatted messages:\")\n",
+    "for msg in complex_formatted_messages:\n",
+    "    print(f\"\\n{msg.type.upper()}:\")\n",
+    "    print(f\"  {msg.content}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d800143e",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## 3. Using Prompts with LLMs\n",
+    "\n",
+    "Now let's combine our prompts with actual language models. LangSmith supports storing prompts **with** model configurations.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb5c08a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a chain: prompt + model\n",
+    "model = ChatOpenAI(model=\"gpt-4o\", temperature=0.7)\n",
+    "\n",
+    "# Option 1: Create chain manually\n",
+    "prompt_from_ls = client.pull_prompt(\"my-first-prompt\")\n",
+    "chain = prompt_from_ls | model\n",
+    "\n",
+    "# Use the chain\n",
+    "response = chain.invoke({\"question\": \"What are the benefits of using LangSmith for prompt management?\"})\n",
+    "print(\"🤖 AI Response:\")\n",
+    "print(response.content)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee9c8154",
+   "metadata": {},
+   "source": [
+    "### Storing Prompts with Model Configurations\n",
+    "\n",
+    "You can store the prompt **and** model configuration together. This is useful when you want to version both the prompt and the specific model settings.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "476742e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a joke generator prompt\n",
+    "joke_prompt = ChatPromptTemplate([\n",
+    "    (\"system\", \"You are a witty comedian who tells funny, clean jokes.\"),\n",
+    "    (\"user\", \"Tell me a joke about {topic}\"),\n",
+    "])\n",
+    "\n",
+    "# Create a chain with specific model configuration\n",
+    "joke_model = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0.9)  # Higher temperature for creativity\n",
+    "joke_chain = joke_prompt | joke_model\n",
+    "\n",
+    "# Push the ENTIRE chain (prompt + model) to LangSmith\n",
+    "client.push_prompt(\"joke-generator-with-model\", object=joke_chain)\n",
+    "\n",
+    "print(\"✅ Prompt with model configuration saved!\")\n",
+    "print(\"📝 This includes both the prompt template AND the model settings\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6fa6cf67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Pull the chain (prompt + model) from LangSmith\n",
+    "# Use include_model=True to get both prompt and model\n",
+    "joke_chain_from_ls = client.pull_prompt(\"joke-generator-with-model\", include_model=True)\n",
+    "\n",
+    "# Use it directly - no need to attach a model!\n",
+    "response = joke_chain_from_ls.invoke({\"topic\": \"programming\"})\n",
+    "print(\"😄 Joke:\")\n",
+    "print(response.content)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efeab0f2",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## 4. Prompt Versioning and Commit Tags\n",
+    "\n",
+    "Every time you push a prompt to LangSmith, it creates a new **commit** (version). This is similar to Git commits!\n",
+    "\n",
+    "### Why Use Commit Tags?\n",
+    "\n",
+    "Commit tags let you:\n",
+    "- Mark specific versions as `production`, `staging`, or `dev`\n",
+    "- Reference stable versions without hardcoding commit IDs\n",
+    "- Easily roll back to previous versions\n",
+    "- Switch between versions without changing code\n",
+    "\n",
+    "### Best Practices:\n",
+    "\n",
+    "1. **Use tags for environments**: Tag prompts with `prod`, `dev`, `staging`\n",
+    "2. **Version your releases**: Use tags like `v1.0`, `v1.1`, `v2.0`\n",
+    "3. **Test before promoting**: Test in `dev` before tagging as `prod`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61f80af8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Update an existing prompt\n",
+    "updated_prompt = ChatPromptTemplate([\n",
+    "    (\"system\", \"You are a helpful and friendly AI assistant. Always be concise and clear.\"),\n",
+    "    (\"user\", \"{question}\"),\n",
+    "])\n",
+    "\n",
+    "# Push the updated version\n",
+    "client.push_prompt(\"my-first-prompt\", object=updated_prompt)\n",
+    "\n",
+    "print(\"✅ Prompt updated!\")\n",
+    "print(\"📝 A new commit has been created in LangSmith\")\n",
+    "print(\"💡 You can tag this commit in the UI as 'v1.1' or 'production'\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21417cb5",
+   "metadata": {},
+   "source": [
+    "### Pulling Specific Versions\n",
+    "\n",
+    "You can pull prompts by:\n",
+    "- **Latest version** (default): `client.pull_prompt(\"prompt-name\")`\n",
+    "- **Specific tag**: `client.pull_prompt(\"prompt-name:tag-name\")`  \n",
+    "- **Specific commit**: `client.pull_prompt(\"prompt-name\", version=\"commit-hash\")`\n",
+    "\n",
+    "> **Note**: Tags must be created in the LangSmith UI. Navigate to your prompt → Commits tab → Add Commit Tag\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd2b23bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example: Pull the latest version\n",
+    "latest_prompt = client.pull_prompt(\"my-first-prompt\")\n",
+    "print(\"✅ Pulled latest version of prompt\")\n",
+    "\n",
+    "# Example: Pull a specific tagged version (after creating tag in UI)\n",
+    "production_prompt = client.pull_prompt(\"my-first-prompt:production\")\n",
+    "dev_prompt = client.pull_prompt(\"my-first-prompt:dev\")\n",
+    "\n",
+    "# Display prompts in a readable format\n",
+    "print(\"\\n📝 Production Prompt:\")\n",
+    "for msg in production_prompt.messages:\n",
+    "    if hasattr(msg, 'prompt'):\n",
+    "        print(f\"  {msg.__class__.__name__.replace('MessagePromptTemplate', '')}: {msg.prompt.template}\")\n",
+    "\n",
+    "print(\"\\n📝 Dev Prompt:\")\n",
+    "for msg in dev_prompt.messages:\n",
+    "    if hasattr(msg, 'prompt'):\n",
+    "        print(f\"  {msg.__class__.__name__.replace('MessagePromptTemplate', '')}: {msg.prompt.template}\")\n",
+    "\n",
+    "# This allows you to switch between environments easily!\n",
+    "print(\"\\n💡 Tip: Use tags to reference different versions in different environments:\")\n",
+    "print(\"   - Development: pull_prompt('my-prompt:dev')\")\n",
+    "print(\"   - Staging: pull_prompt('my-prompt:staging')\")\n",
+    "print(\"   - Production: pull_prompt('my-prompt:production')\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2a3715f",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## 5. Using Public Prompts from LangChain Hub\n",
+    "\n",
+    "LangSmith has a **public prompt hub** where the community shares useful prompts. You can browse, fork, and use these prompts!\n",
+    "\n",
+    "### How to Use Public Prompts:\n",
+    "\n",
+    "1. Browse prompts at: **LangSmith UI → Prompts → Browse Public Prompts**\n",
+    "2. Pull public prompts using: `{author-handle}/{prompt-name}`\n",
+    "\n",
+    "> **Note**: Public prompts are user-generated and unverified. Review them before use!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7d5ddf1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example: Pull a public prompt from LangChain Hub\n",
+    "# The langchain-ai organization has many useful prompts\n",
+    "\n",
+    "# Note: This will only work if you have access to the hub\n",
+    "# You can also use the legacy langchain.hub (from langchain import hub)\n",
+    "try:\n",
+    "    # Pull a public evaluation prompt as an example\n",
+    "    public_prompt = client.pull_prompt(\"langchain-ai/retrieval-qa-chat\")\n",
+    "    print(\"✅ Successfully pulled public prompt from LangChain Hub\")\n",
+    "    print(f\"\\nPrompt structure:\")\n",
+    "    print(public_prompt)\n",
+    "except Exception as e:\n",
+    "    print(f\"⚠️ Could not pull public prompt: {e}\")\n",
+    "    print(\"💡 You can browse public prompts in the LangSmith UI\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "854f1bc5",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## 6. Advanced Patterns and Best Practices\n",
+    "\n",
+    "### Pattern 1: Multi-Turn Conversations\n",
+    "\n",
+    "Create prompts that support chat history for conversational applications.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3387365",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import HumanMessage, AIMessage\n",
+    "\n",
+    "# Create a conversational prompt with history\n",
+    "conversational_prompt = ChatPromptTemplate([\n",
+    "    (\"system\", \"You are a helpful assistant. Use the conversation history to provide context-aware responses.\"),\n",
+    "    (\"placeholder\", \"{chat_history}\"),  # This will be filled with past messages\n",
+    "    (\"user\", \"{question}\"),\n",
+    "])\n",
+    "\n",
+    "# Push to LangSmith\n",
+    "client.push_prompt(\"conversational-assistant\", object=conversational_prompt)\n",
+    "\n",
+    "# Simulate a conversation\n",
+    "chat_history = [\n",
+    "    HumanMessage(content=\"My name is Alice\"),\n",
+    "    AIMessage(content=\"Nice to meet you, Alice! How can I help you today?\"),\n",
+    "]\n",
+    "\n",
+    "# Create chain and invoke with history\n",
+    "conv_chain = conversational_prompt | model\n",
+    "response = conv_chain.invoke({\n",
+    "    \"chat_history\": chat_history,\n",
+    "    \"question\": \"What's my name?\"\n",
+    "})\n",
+    "\n",
+    "print(\"🤖 AI Response (with memory):\")\n",
+    "print(response.content)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd6cc4c6",
+   "metadata": {},
+   "source": [
+    "### Pattern 2: Few-Shot Prompting\n",
+    "\n",
+    "Few-shot prompting provides examples to guide the model's behavior.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "829bedcc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a few-shot classification prompt\n",
+    "few_shot_prompt = ChatPromptTemplate([\n",
+    "    (\"system\", \"\"\"You are a sentiment classifier. Classify the sentiment as positive, negative, or neutral.\n",
+    "\n",
+    "Examples:\n",
+    "Input: \"I love this product!\"\n",
+    "Output: positive\n",
+    "\n",
+    "Input: \"This is terrible.\"\n",
+    "Output: negative\n",
+    "\n",
+    "Input: \"It's okay, nothing special.\"\n",
+    "Output: neutral\"\"\"),\n",
+    "    (\"user\", \"Input: {text}\\nOutput:\"),\n",
+    "])\n",
+    "\n",
+    "# Push to LangSmith\n",
+    "client.push_prompt(\"sentiment-classifier\", object=few_shot_prompt)\n",
+    "\n",
+    "# Test it\n",
+    "sentiment_chain = few_shot_prompt | model\n",
+    "result = sentiment_chain.invoke({\"text\": \"This is absolutely amazing!\"})\n",
+    "\n",
+    "print(\"🎯 Classification Result:\")\n",
+    "print(result.content)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1cd0514",
+   "metadata": {},
+   "source": [
+    "### Pattern 3: RAG (Retrieval-Augmented Generation) Prompts\n",
+    "\n",
+    "RAG prompts incorporate retrieved context to answer questions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2706f74a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a RAG prompt\n",
+    "rag_prompt = ChatPromptTemplate([\n",
+    "    (\"system\", \"\"\"You are a helpful assistant that answers questions based on the provided context.\n",
+    "    \n",
+    "Instructions:\n",
+    "- Use ONLY the information from the context to answer\n",
+    "- If the answer is not in the context, say \"I don't have enough information to answer that.\"\n",
+    "- Be concise and accurate\"\"\"),\n",
+    "    (\"user\", \"\"\"Context: {context}\n",
+    "\n",
+    "Question: {question}\n",
+    "\n",
+    "Answer:\"\"\"),\n",
+    "])\n",
+    "\n",
+    "# Push to LangSmith\n",
+    "client.push_prompt(\"rag-qa-assistant\", object=rag_prompt)\n",
+    "\n",
+    "# Test it with sample context\n",
+    "context = \"\"\"\n",
+    "LangSmith is a platform for building production-grade LLM applications.\n",
+    "It provides tools for debugging, testing, evaluating, and monitoring.\n",
+    "LangSmith supports prompt versioning and team collaboration.\n",
+    "\"\"\"\n",
+    "\n",
+    "rag_chain = rag_prompt | model\n",
+    "result = rag_chain.invoke({\n",
+    "    \"context\": context,\n",
+    "    \"question\": \"What is LangSmith?\"\n",
+    "})\n",
+    "\n",
+    "print(\"📚 RAG Response:\")\n",
+    "print(result.content)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb0a146f",
+   "metadata": {},
+   "source": [
+    "### Pattern 4: Template Formats - F-String vs Mustache\n",
+    "\n",
+    "LangSmith supports two template formats for variables:\n",
+    "- **F-string** (default): Uses `{variable}` - Simple and familiar to Python developers\n",
+    "- **Mustache**: Uses `{{variable}}` - More powerful with conditionals, loops, and nested keys\n",
+    "\n",
+    "> 💡 **When to use Mustache**: Use mustache when you need conditional logic, loops, or complex variable handling\n",
+    "\n",
+    "Reference: [LangSmith Prompt Engineering Concepts](https://docs.langchain.com/langsmith/prompt-engineering-concepts)\n",
+    "\n",
+    "Mustache Reference: [Mustache Manual](https://mustache.github.io/mustache.5.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20ddaa49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example 1: F-string format (default)\n",
+    "fstring_prompt = ChatPromptTemplate([\n",
+    "    (\"system\", \"You are a helpful assistant.\"),\n",
+    "    (\"user\", \"Hello, {name}! How can I help you today?\"),\n",
+    "])\n",
+    "\n",
+    "client.push_prompt(\"fstring-example\", object=fstring_prompt)\n",
+    "\n",
+    "# Test it\n",
+    "result = fstring_prompt.format_messages(name=\"Alice\")\n",
+    "print(\"📝 F-string format output:\")\n",
+    "for msg in result:\n",
+    "    print(f\"{msg.type}: {msg.content}\")\n",
+    "\n",
+    "print(\"\\n✅ F-string is simple and works great for basic variable substitution!\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2909c6ce",
+   "metadata": {},
+   "source": [
+    "#### Mustache Template Syntax Explained\n",
+    "\n",
+    "##### The Three Key Symbols\n",
+    "\n",
+    "**1. `#` (Hash) - \"If True\" / \"Loop\"**\n",
+    "```mustache\n",
+    "{{#variable}}\n",
+    "  This content shows when variable is truthy (true, non-empty, exists)\n",
+    "{{/variable}}\n",
+    "```\n",
+    "\n",
+    "**2. `/` (Slash) - \"Closing Tag\"**\n",
+    "```mustache\n",
+    "{{#variable}}\n",
+    "  Content here\n",
+    "{{/variable}}  ← This closes the block started with #\n",
+    "```\n",
+    "\n",
+    "**3. `^` (Caret) - \"If False\" / \"Negation\"**\n",
+    "```mustache\n",
+    "{{^variable}}\n",
+    "  This content shows when variable is falsy (false, empty, null)\n",
+    "{{/variable}}\n",
+    "```\n",
+    "\n",
+    "##### Key Rules\n",
+    "\n",
+    "1. **Every `#` needs a matching `/`** - They work like opening and closing tags\n",
+    "2. **`#variable`** = \"Show this if variable is truthy OR loop if it's an array\"\n",
+    "3. **`^variable`** = \"Show this if variable is falsy\" (opposite of `#`)\n",
+    "4. **Always close with `/variable`** - Whether you used `#` or `^` to open\n",
+    "\n",
+    "---\n",
+    "\n",
+    "##### Visual Pattern\n",
+    "\n",
+    "Think of it like HTML tags:\n",
+    "\n",
+    "```\n",
+    "#  = Opening tag  (like <if>)\n",
+    "/  = Closing tag  (like </if>)\n",
+    "^  = Negation tag (like <if-not>)\n",
+    "```\n",
+    "\n",
+    "```mustache\n",
+    "{{#condition}}        ← If TRUE\n",
+    "  Content A\n",
+    "{{/condition}}        ← Close\n",
+    "\n",
+    "{{^condition}}        ← If FALSE  \n",
+    "  Content B\n",
+    "{{/condition}}        ← Close\n",
+    "```\n",
+    "\n",
+    "---\n",
+    "\n",
+    "##### Quick Cheat Sheet\n",
+    "\n",
+    "| Syntax | Meaning | Use Case |\n",
+    "|--------|---------|----------|\n",
+    "| `{{variable}}` | Insert value | Simple substitution |\n",
+    "| `{{#variable}}...{{/variable}}` | If true / Loop | Show content if truthy, or iterate array |\n",
+    "| `{{^variable}}...{{/variable}}` | If false | Show content if falsy |\n",
+    "| `{{/variable}}` | Close block | Required to close `#` or `^` |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e408a8f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example 2: Mustache format with conditionals\n",
+    "# Note: To use mustache in the LangSmith Playground, switch the template format in settings\n",
+    "\n",
+    "from langchain_core.prompts import PromptTemplate\n",
+    "\n",
+    "# Mustache prompt with conditional logic\n",
+    "mustache_prompt_text = \"\"\"{{#is_logged_in}}\n",
+    "Welcome back, {{name}}! Here are your recent activities.\n",
+    "{{/is_logged_in}}{{^is_logged_in}}\n",
+    "Please log in to continue, {{name}}.\n",
+    "{{/is_logged_in}}\"\"\"\n",
+    "\n",
+    "# Create a PromptTemplate with mustache-style variables\n",
+    "mustache_prompt = PromptTemplate.from_template(\n",
+    "    mustache_prompt_text,\n",
+    "    template_format=\"mustache\"\n",
+    ")\n",
+    "\n",
+    "print(\"📝 Mustache format examples:\")\n",
+    "print(\"\\n1. When user IS logged in:\")\n",
+    "result1 = mustache_prompt.format(is_logged_in=True, name=\"Alice\")\n",
+    "print(result1)\n",
+    "\n",
+    "print(\"\\n2. When user is NOT logged in:\")\n",
+    "result2 = mustache_prompt.format(is_logged_in=False, name=\"Bob\")\n",
+    "print(result2)\n",
+    "\n",
+    "print(\"\\n✅ Mustache supports conditionals with {{#variable}} and {{^variable}} (negation)!\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89827e84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example 3: Real-world mustache example - Personalized customer support\n",
+    "customer_support_mustache = \"\"\"You are a customer support agent.\n",
+    "\n",
+    "Customer Information:\n",
+    "- Name: {{customer_name}}\n",
+    "- Account Type: {{account_type}}\n",
+    "{{#is_premium}}\n",
+    "- Premium Features: Enabled\n",
+    "- Priority Support: Yes\n",
+    "{{/is_premium}}\n",
+    "{{^is_premium}}\n",
+    "- Premium Features: Not available (upgrade to access)\n",
+    "- Support: Standard queue\n",
+    "{{/is_premium}}\n",
+    "\n",
+    "{{#has_open_ticket}}\n",
+    "IMPORTANT: This customer has an open ticket #{{ticket_id}}.\n",
+    "Reference this ticket in your response.\n",
+    "{{/has_open_ticket}}\n",
+    "\n",
+    "Respond professionally to: {{question}}\"\"\"\n",
+    "\n",
+    "mustache_support = PromptTemplate.from_template(\n",
+    "    customer_support_mustache,\n",
+    "    template_format=\"mustache\"\n",
+    ")\n",
+    "\n",
+    "# Push to LangSmith\n",
+    "# Note: When viewing in the UI, switch to mustache format in prompt settings\n",
+    "client.push_prompt(\"mustache-customer-support\", object=mustache_support)\n",
+    "\n",
+    "# Test with different scenarios\n",
+    "print(\"🎯 Scenario 1: Premium customer with open ticket\")\n",
+    "scenario1 = mustache_support.format(\n",
+    "    customer_name=\"Alice Johnson\",\n",
+    "    account_type=\"Enterprise\",\n",
+    "    is_premium=True,\n",
+    "    has_open_ticket=True,\n",
+    "    ticket_id=\"12345\",\n",
+    "    question=\"I need help with the integration\"\n",
+    ")\n",
+    "print(scenario1)\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*60)\n",
+    "print(\"🎯 Scenario 2: Standard customer, no ticket\")\n",
+    "scenario2 = mustache_support.format(\n",
+    "    customer_name=\"Bob Smith\",\n",
+    "    account_type=\"Basic\",\n",
+    "    is_premium=False,\n",
+    "    has_open_ticket=False,\n",
+    "    question=\"How do I upgrade my account?\"\n",
+    ")\n",
+    "print(scenario2)\n",
+    "\n",
+    "print(\"\\n✅ Mustache conditionals enable dynamic, context-aware prompts!\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9df00f5",
+   "metadata": {},
+   "source": [
+    "#### Mustache Template Features\n",
+    "\n",
+    "Mustache provides several powerful features beyond simple variable substitution:\n",
+    "\n",
+    "**1. Conditionals:**\n",
+    "- `{{#variable}}...{{/variable}}` - Shows content if variable is truthy\n",
+    "- `{{^variable}}...{{/variable}}` - Shows content if variable is falsy (negation)\n",
+    "- `{{#variable}}...{{else}}...{{/variable}}` - If-else logic\n",
+    "\n",
+    "**2. Loops:**\n",
+    "```\n",
+    "{{#items}}\n",
+    "  - {{name}}: {{price}}\n",
+    "{{/items}}\n",
+    "```\n",
+    "\n",
+    "**3. Nested Keys:**\n",
+    "```\n",
+    "{{user.profile.name}}\n",
+    "{{user.settings.theme}}\n",
+    "```\n",
+    "\n",
+    "**Using Mustache in LangSmith Playground:**\n",
+    "1. Open your prompt in the Playground\n",
+    "2. Click the gear icon (⚙️) next to the model name\n",
+    "3. Go to \"Template Format\" section\n",
+    "4. Select \"mustache\" instead of \"f-string\"\n",
+    "5. For conditional variables, manually add JSON in the 'inputs' section\n",
+    "\n",
+    "**Example JSON for conditionals:**\n",
+    "```json\n",
+    "{\n",
+    "  \"name\": \"Alice\",\n",
+    "  \"is_logged_in\": true,\n",
+    "  \"has_open_ticket\": false\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "> 📚 Learn more: [Mustache Documentation](https://mustache.github.io/mustache.5.html)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9fb7677a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example 4: Mustache with loops - Product recommendation\n",
+    "product_recommendation = \"\"\"You are a helpful shopping assistant.\n",
+    "\n",
+    "Customer: {{customer_name}}\n",
+    "\n",
+    "Available Products:\n",
+    "{{#products}}\n",
+    "- {{name}}: ${{price}}\n",
+    "  {{#on_sale}}⭐ ON SALE! {{discount}}% off{{/on_sale}}\n",
+    "{{/products}}\n",
+    "\n",
+    "Based on these products and the customer's interest in {{interest}}, provide a personalized recommendation.\"\"\"\n",
+    "\n",
+    "mustache_shopping = PromptTemplate.from_template(\n",
+    "    product_recommendation,\n",
+    "    template_format=\"mustache\"\n",
+    ")\n",
+    "\n",
+    "# Test with product data\n",
+    "products_data = [\n",
+    "    {\"name\": \"Laptop Pro\", \"price\": \"1299\", \"on_sale\": True, \"discount\": 15},\n",
+    "    {\"name\": \"Wireless Mouse\", \"price\": \"49\", \"on_sale\": False},\n",
+    "    {\"name\": \"USB-C Hub\", \"price\": \"79\", \"on_sale\": True, \"discount\": 20},\n",
+    "]\n",
+    "\n",
+    "result = mustache_shopping.format(\n",
+    "    customer_name=\"Sarah\",\n",
+    "    products=products_data,\n",
+    "    interest=\"productivity tools\"\n",
+    ")\n",
+    "\n",
+    "print(\"🛍️ Product Recommendation Prompt:\")\n",
+    "print(result)\n",
+    "print(\"\\n✅ Mustache loops make it easy to handle dynamic lists of data!\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee2f36e5",
+   "metadata": {},
+   "source": [
+    "#### When to Use F-String vs Mustache\n",
+    "\n",
+    "| Feature | F-String `{var}` | Mustache `{{var}}` |\n",
+    "|---------|------------------|-------------------|\n",
+    "| **Simple variables** | ✅ Perfect | ✅ Works well |\n",
+    "| **Conditionals** | ❌ Not supported | ✅ Full support |\n",
+    "| **Loops** | ❌ Not supported | ✅ Full support |\n",
+    "| **Nested keys** | ⚠️ Limited | ✅ Full support |\n",
+    "| **Ease of use** | ⭐⭐⭐⭐⭐ Very simple | ⭐⭐⭐ More complex |\n",
+    "| **Python familiarity** | ✅ Native Python | ⚠️ New syntax |\n",
+    "\n",
+    "**Choose F-String when:**\n",
+    "- You only need simple variable substitution\n",
+    "- Your team is familiar with Python\n",
+    "- You don't need conditional logic\n",
+    "\n",
+    "**Choose Mustache when:**\n",
+    "- You need conditional content based on variables\n",
+    "- You're working with dynamic lists/arrays\n",
+    "- You need complex nested data structures\n",
+    "- Non-developers will be editing prompts in the UI\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b396350",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## 7. Best Practices for LangSmith Prompts\n",
+    "\n",
+    "### 🎯 Naming Conventions\n",
+    "\n",
+    "- Use descriptive, kebab-case names: `customer-support-chatbot`\n",
+    "- Include purpose in name: `sentiment-classifier`, `code-reviewer`\n",
+    "- Avoid generic names like `prompt1`, `test-prompt`\n",
+    "\n",
+    "### 📋 Prompt Design Tips\n",
+    "\n",
+    "1. **Be Specific**: Clearly define the task and expected output format\n",
+    "2. **Use System Messages**: Set the AI's role and behavior\n",
+    "3. **Include Examples**: Few-shot examples improve consistency\n",
+    "4. **Handle Edge Cases**: Tell the model what to do when uncertain\n",
+    "5. **Test Thoroughly**: Use the Playground to test with various inputs\n",
+    "\n",
+    "### 🔄 Version Management\n",
+    "\n",
+    "- **Tag Important Versions**: Mark stable versions with tags\n",
+    "- **Use Environment Tags**: Separate `dev`, `staging`, `prod`\n",
+    "- **Document Changes**: Add clear commit messages in the UI\n",
+    "- **Test Before Promoting**: Always test in lower environments first\n",
+    "\n",
+    "### 🤝 Team Collaboration\n",
+    "\n",
+    "- **Share Prompts**: Use LangSmith as the single source of truth\n",
+    "- **Iterate in Playground**: Test changes before pushing\n",
+    "- **Review Together**: Use the UI to compare versions\n",
+    "- **Set Permissions**: Control who can edit production prompts\n",
+    "\n",
+    "### ⚡ Performance Tips\n",
+    "\n",
+    "- **Be Concise**: Shorter prompts are faster and cheaper\n",
+    "- **Optimize Variables**: Only include necessary context\n",
+    "- **Cache When Possible**: Reuse prompts across requests\n",
+    "- **Monitor Usage**: Track prompt performance in LangSmith\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e71608d",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## 8. Complete Example: Building a Production-Ready Prompt\n",
+    "\n",
+    "Let's put it all together with a real-world example: a customer support chatbot.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c336ff6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 1: Create a production-ready prompt\n",
+    "customer_support_prompt = ChatPromptTemplate([\n",
+    "    (\"system\", \"\"\"You are a professional customer support agent for TechCorp, a software company.\n",
+    "\n",
+    "Your responsibilities:\n",
+    "- Be friendly, empathetic, and professional\n",
+    "- Provide accurate information from the knowledge base\n",
+    "- If you don't know something, offer to escalate to a human agent\n",
+    "- Keep responses concise (under 150 words)\n",
+    "\n",
+    "Company policies:\n",
+    "- Refunds available within 30 days\n",
+    "- 24/7 technical support available\n",
+    "- Enterprise customers get priority support\"\"\"),\n",
+    "    (\"placeholder\", \"{chat_history}\"),\n",
+    "    (\"user\", \"\"\"Customer: {customer_name}\n",
+    "Customer Type: {customer_type}\n",
+    "Question: {question}\"\"\"),\n",
+    "])\n",
+    "\n",
+    "# Step 2: Combine with model configuration\n",
+    "support_model = ChatOpenAI(\n",
+    "    model=\"gpt-4o-mini\",\n",
+    "    temperature=0.7,  # Balanced creativity\n",
+    "    max_tokens=200    # Keep responses concise\n",
+    ")\n",
+    "\n",
+    "support_chain = customer_support_prompt | support_model\n",
+    "\n",
+    "# Step 3: Push to LangSmith with descriptive name\n",
+    "client.push_prompt(\"customer-support-agent-v1\", object=support_chain)\n",
+    "\n",
+    "print(\"✅ Production-ready customer support prompt created!\")\n",
+    "print(\"📝 Next steps:\")\n",
+    "print(\"   1. Test in LangSmith Playground\")\n",
+    "print(\"   2. Run evaluations on test dataset\")\n",
+    "print(\"   3. Tag as 'staging' for testing\")\n",
+    "print(\"   4. After validation, tag as 'production'\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "944ae21a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 4: Use the prompt in your application\n",
+    "def handle_customer_query(customer_name, customer_type, question, history=None):\n",
+    "    \"\"\"\n",
+    "    Production function that uses the LangSmith prompt\n",
+    "    \"\"\"\n",
+    "    # Pull the production version\n",
+    "    support_chain = client.pull_prompt(\"customer-support-agent-v1:production\", include_model=True)\n",
+    "    \n",
+    "    # Invoke with customer data\n",
+    "    response = support_chain.invoke({\n",
+    "        \"customer_name\": customer_name,\n",
+    "        \"customer_type\": customer_type,\n",
+    "        \"question\": question,\n",
+    "        \"chat_history\": history or []\n",
+    "    })\n",
+    "    \n",
+    "    return response.content\n",
+    "\n",
+    "# Test the function\n",
+    "# Note: This will fail if 'production' tag doesn't exist yet\n",
+    "# You can remove ':production' to use the latest version\n",
+    "try:\n",
+    "    result = handle_customer_query(\n",
+    "        customer_name=\"John Smith\",\n",
+    "        customer_type=\"Enterprise\",\n",
+    "        question=\"How do I reset my password?\"\n",
+    "    )\n",
+    "    print(\"💬 Support Agent Response:\")\n",
+    "    print(result)\n",
+    "except Exception as e:\n",
+    "    print(f\"⚠️ Could not use production tag: {e}\")\n",
+    "    print(\"💡 Tag your prompt as 'production' in the UI, or use the latest version instead\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48390dac",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## 9. Using Prompts Without LangChain (Optional)\n",
+    "\n",
+    "LangSmith is framework-agnostic! You can use prompts with OpenAI or Anthropic SDKs directly.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b7a3e61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openai\n",
+    "\n",
+    "# Pull prompt from LangSmith\n",
+    "prompt = client.pull_prompt(\"my-first-prompt\")\n",
+    "\n",
+    "# Convert to OpenAI format\n",
+    "# LangSmith provides conversion methods for popular APIs\n",
+    "messages = prompt.format_messages(question=\"What is machine learning?\")\n",
+    "\n",
+    "# Convert LangChain messages to OpenAI format\n",
+    "openai_messages = [\n",
+    "    {\"role\": msg.type, \"content\": msg.content} \n",
+    "    for msg in messages\n",
+    "]\n",
+    "\n",
+    "print(\"🔄 Converted to OpenAI format:\")\n",
+    "print(openai_messages)\n",
+    "print(\"\\n💡 You can now use this with the OpenAI SDK directly!\")\n",
+    "\n",
+    "# Example (commented out to avoid API calls):\n",
+    "# openai_client = openai.OpenAI()\n",
+    "# response = openai_client.chat.completions.create(\n",
+    "#     model=\"gpt-4o-mini\",\n",
+    "#     messages=openai_messages\n",
+    "# )\n",
+    "# print(response.choices[0].message.content)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71dfc551",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## 10. Key Takeaways\n",
+    "\n",
+    "### What You Learned 🎓\n",
+    "\n",
+    "1. ✅ **Create prompts** using `client.push_prompt()`\n",
+    "2. ✅ **Pull prompts** using `client.pull_prompt()`\n",
+    "3. ✅ **Use variables** to make prompts dynamic and reusable\n",
+    "4. ✅ **Version prompts** with commits and tags\n",
+    "5. ✅ **Store model configs** alongside prompts\n",
+    "6. ✅ **Use environment tags** for deployment workflows\n",
+    "7. ✅ **Apply patterns** like RAG, few-shot, and conversations\n",
+    "8. ✅ **Follow best practices** for production use\n",
+    "\n",
+    "### Quick Reference 📚\n",
+    "\n",
+    "```python\n",
+    "# Create and push\n",
+    "from langsmith import Client\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "client = Client()\n",
+    "prompt = ChatPromptTemplate([(\"system\", \"...\"), (\"user\", \"{var}\")])\n",
+    "client.push_prompt(\"prompt-name\", object=prompt)\n",
+    "\n",
+    "# Pull and use\n",
+    "prompt = client.pull_prompt(\"prompt-name\")\n",
+    "prompt = client.pull_prompt(\"prompt-name:production\")  # With tag\n",
+    "chain = client.pull_prompt(\"name\", include_model=True)  # With model\n",
+    "\n",
+    "# Use\n",
+    "result = chain.invoke({\"var\": \"value\"})\n",
+    "```\n",
+    "\n",
+    "### Next Steps 🚀\n",
+    "\n",
+    "1. **Explore the LangSmith UI**\n",
+    "   - Navigate to the Prompts section\n",
+    "   - Try the Playground to test prompts interactively\n",
+    "   - Compare different versions side-by-side\n",
+    "\n",
+    "2. **Run Evaluations**\n",
+    "   - Create datasets in LangSmith\n",
+    "   - Use the Playground to run experiments\n",
+    "   - Track performance over time\n",
+    "\n",
+    "3. **Set Up Production Workflow**\n",
+    "   - Use `dev` → `staging` → `production` tags\n",
+    "   - Integrate with your CI/CD pipeline\n",
+    "   - Set up webhooks for prompt updates\n",
+    "\n",
+    "4. **Collaborate with Your Team**\n",
+    "   - Share prompts with team members\n",
+    "   - Review and iterate together\n",
+    "   - Document your prompt strategies\n",
+    "\n",
+    "### Additional Resources 📖\n",
+    "\n",
+    "- **LangSmith Documentation**: https://docs.langchain.com/langsmith\n",
+    "- **LangSmith Playground**: https://smith.langchain.com\n",
+    "- **Public Prompt Hub**: Browse community prompts in the UI\n",
+    "- **LangSmith API Reference**: https://docs.langchain.com/langsmith/smith-python-sdk\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## 🎉 Congratulations!\n",
+    "\n",
+    "You're now ready to use LangSmith for professional prompt management. Start building, iterating, and shipping better prompts faster!\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
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+   "pygments_lexer": "ipython3",
+   "version": "3.13.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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index 0000000..b4358da
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