meta-llama · connortreacy · Jan 16, 2025 · Jan 16, 2025 · Jan 16, 2025 · Jan 16, 2025
diff --git a/3p-integrations/aws/getting_started_llama_3_on_amazon_bedrock.ipynb b/3p-integrations/aws/getting_started_llama_3_on_amazon_bedrock.ipynb
@@ -8,8 +8,7 @@
    "source": [
     "# Using Amazon Bedrock with Llama\n",
     "\n",
-    "Open this notebook in <a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/llama_api_providers/examples_with_aws/getting_started_llama2_on_amazon_bedrock.ipynb\"><img data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\" src=\"https://camo.githubusercontent.com/f5e0d0538a9c2972b5d413e0ace04cecd8efd828d133133933dfffec282a4e1b/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\"></a>\n",
-    "\n",
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/aws/getting_started_llama_3_on_amazon_bedrock.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "Use this notebook to quickly get started with Llama on Bedrock. You can access the Amazon Bedrock API using the AWS Python SDK.\n",
     "\n",

diff --git a/3p-integrations/aws/prompt_engineering_with_llama_2_on_amazon_bedrock.ipynb b/3p-integrations/aws/prompt_engineering_with_llama_2_on_amazon_bedrock.ipynb
@@ -7,8 +7,7 @@
    "source": [
     "# Prompt Engineering with Llama 2 - Using Amazon Bedrock + LangChain\n",
     "\n",
-    "Open this notebook in <a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/Prompt_Engineering_with_Llama_2.ipynb\"><img data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\" src=\"https://camo.githubusercontent.com/f5e0d0538a9c2972b5d413e0ace04cecd8efd828d133133933dfffec282a4e1b/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\"></a>\n",
-    "\n",
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/aws/prompt_engineering_with_llama_2_on_amazon_bedrock.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "Prompt engineering is using natural language to produce a desired response from a large language model (LLM).\n",
     "\n",

diff --git a/3p-integrations/aws/react_llama_3_bedrock_wk.ipynb b/3p-integrations/aws/react_llama_3_bedrock_wk.ipynb
@@ -7,7 +7,7 @@
     "# Advanced Techniques\n",
     "## 1. ReAct\n",
     "\n",
-    "Open this notebook in <a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/llama_api_providers/examples_with_aws/ReAct_Llama_2_Bedrock-WK.ipynb\"><img data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\" src=\"https://camo.githubusercontent.com/f5e0d0538a9c2972b5d413e0ace04cecd8efd828d133133933dfffec282a4e1b/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\"></a>\n",
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/aws/react_llama_3_bedrock_wk.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "LLMs abilities for reasoning (e.g. chain-of-thought CoT prompting) and acting have primarily been studied as separate topics. **ReAct** [Shunyu Yao et al. ICLR 2023](https://arxiv.org/pdf/2210.03629.pdf) (Reason and Act) is a method to generate both reasoning traces and task-specific actions in an interleaved manner.\n",
     "\n",

diff --git a/3p-integrations/e2b-ai-analyst/README.md b/3p-integrations/e2b-ai-analyst/README.md
@@ -1,8 +1,6 @@
 # AI Analyst with Llama and E2B
 This is an AI-powered code and data analysis tool powered by Meta Llama and the [E2B SDK](https://e2b.dev/docs).
 
-![Preview](https://private-user-images.githubusercontent.com/33395784/382129362-3bc8b017-4a09-416c-b55c-ce53da7e5560.gif?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.Kaab1leGbgBiQhfh6bV1VHTc8QfinP7ufqwmn7Ra57c)
-
 → Try on [ai-analyst.e2b.dev](https://ai-analyst.e2b.dev/)
 
 ## Features
@@ -54,4 +52,4 @@ In `.env.local`, add an API key for at least one LLM provider:
 
 For Ollama, provide the base URL instead of the API key:
 
-- Ollama: `OLLAMA_BASE_URL`
+- Ollama: `OLLAMA_BASE_URL`
diff --git a/3p-integrations/groq/llama3_cookbook_groq.ipynb b/3p-integrations/groq/llama3_cookbook_groq.ipynb
@@ -7,7 +7,8 @@
    "source": [
     "# Llama 3 Cookbook with LlamaIndex and Groq\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/llama_api_providers/llama3_cookbook_groq.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/groq/llama3_cookbook_groq.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "\n",
     "\n",
     "Meta developed and released the Meta [Llama 3](https://ai.meta.com/blog/meta-llama-3/) family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks.\n",
     "\n",

diff --git a/3p-integrations/langchain/langgraph_rag_agent.ipynb b/3p-integrations/langchain/langgraph_rag_agent.ipynb
@@ -5,7 +5,7 @@
    "id": "6912ab05-f66a-40a9-a4a5-4deb80d2e0d9",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/langchain/langgraph_rag_agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/langchain/langgraph_rag_agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
   },
   {

diff --git a/3p-integrations/langchain/langgraph_rag_agent_local.ipynb b/3p-integrations/langchain/langgraph_rag_agent_local.ipynb
@@ -5,7 +5,7 @@
    "id": "1f53f753-12c6-4fac-b910-6e96677d8a49",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/langchain/langgraph_rag_agent_local.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/langchain/langgraph_rag_agent_local.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
   },
   {

diff --git a/3p-integrations/langchain/langgraph_tool_calling_agent.ipynb b/3p-integrations/langchain/langgraph_tool_calling_agent.ipynb
@@ -5,7 +5,7 @@
    "id": "8ac4ba3b-c438-4f2e-8f52-39846beb5642",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/langchain/langgraph_tool_calling_agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/langchain/langgraph_tool_calling_agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
   },
   {

diff --git a/...ns/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L2_Tool_Calling.ipynb b/...ns/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L2_Tool_Calling.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L2_Tool_Calling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L2_Tool_Calling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "This notebook ports the DeepLearning.AI short course [Building Agentic RAG with Llamaindex Lesson 2 Tool Calling](https://learn.deeplearning.ai/courses/building-agentic-rag-with-llamaindex/lesson/3/tool-calling) to using Llama 3. It shows how to use Llama 3 to not only pick a function to execute, but also infer an argument to pass through the function.\n",
     "\n",

diff --git a/...gentic_rag/Building_Agentic_RAG_with_Llamaindex_L3_Building_an_Agent_Reasoning_Loop.ipynb b/...gentic_rag/Building_Agentic_RAG_with_Llamaindex_L3_Building_an_Agent_Reasoning_Loop.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L3_Building_an_Agent_Reasoning_Loop.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L3_Building_an_Agent_Reasoning_Loop.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "This notebook ports the DeepLearning.AI short course [Building Agentic RAG with Llamaindex Lesson 3 Building an Agent Reasoning Loop](https://learn.deeplearning.ai/courses/building-agentic-rag-with-llamaindex/lesson/4/building-an-agent-reasoning-loop) to using Llama 3. It shows how to define a complete agent reasoning loop to reason over tools and multiple steps on a complex question the user asks about a single document while maintaining memory.\n",
     "\n",

diff --git a/...agentic_rag/Building_Agentic_RAG_with_Llamaindex_L4_Building_a_Multi-Document_Agent.ipynb b/...agentic_rag/Building_Agentic_RAG_with_Llamaindex_L4_Building_a_Multi-Document_Agent.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L4_Building_a_Multi-Document_Agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L4_Building_a_Multi-Document_Agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "This notebook ports the DeepLearning.AI short course [Building Agentic RAG with Llamaindex Lesson 4 Building a Multi-Document Agent](https://learn.deeplearning.ai/courses/building-agentic-rag-with-llamaindex/lesson/5/building-a-multi-document-agent) to using Llama 3. It shows how to use an agent to handle multiple documents and increasing degrees of complexity.\n",
     "\n",

diff --git a/3p-integrations/togetherai/knowledge_graphs_with_structured_outputs.ipynb b/3p-integrations/togetherai/knowledge_graphs_with_structured_outputs.ipynb
@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "# Generating Knowledge Graphs with LLMs and Structured Outputs\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/togetherai/knowledge_graphs_with_structured_outputs.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/togetherai/knowledge_graphs_with_structured_outputs.ipynb)"
    ]
   },
   {
@@ -30,60 +30,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "DFAjay1FZVrn",
     "outputId": "d4b17b31-c125-4de5-ad54-6d4d08d81eaa"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: together in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (1.3.3)\n",
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-      "Requirement already satisfied: shellingham>=1.3.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from typer<0.13,>=0.9->together) (1.5.4)\n",
-      "Requirement already satisfied: mdurl~=0.1 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich<14.0.0,>=13.8.1->together) (0.1.2)\n",
-      "Requirement already satisfied: propcache>=0.2.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from yarl<2.0,>=1.12.0->aiohttp<4.0.0,>=3.9.3->together) (0.2.0)\n",
-      "Collecting graphviz\n",
-      "  Using cached graphviz-0.20.3-py3-none-any.whl.metadata (12 kB)\n",
-      "Using cached graphviz-0.20.3-py3-none-any.whl (47 kB)\n",
-      "Installing collected packages: graphviz\n",
-      "Successfully installed graphviz-0.20.3\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install together\n",
     "!pip install graphviz"

diff --git a/3p-integrations/togetherai/pdf_to_podcast_using_llama_on_together.ipynb b/3p-integrations/togetherai/pdf_to_podcast_using_llama_on_together.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/togetherai/pdf_to_podcast_using_llama_on_together.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/togetherai/pdf_to_podcast_using_llama_on_together.ipynb)"
    ]
   },
   {

diff --git a/README.md b/README.md
@@ -17,7 +17,7 @@ This repository covers the most popular community approaches, use-cases and the
 > * [Text to SQL](./end-to-end-use-cases/coding/text2sql/)
 
 
-> Note: We recently did a refactor of the repo, [archive-main](https://github.com/meta-llama/llama-recipes/tree/archive-main) is a snapshot branch from before the refactor
+> Note: We recently did a refactor of the repo, [archive-main](https://github.com/meta-llama/llama-cookbook/tree/archive-main) is a snapshot branch from before the refactor
 
 ## Repository Structure:
 
@@ -44,7 +44,7 @@ A: Checkout the Fine-Tuning FAQ [here](./src/docs/)
 
 - Q: Some links are broken/folders are missing: 
 
-A: We recently did a refactor of the repo, [archive-main](https://github.com/meta-llama/llama-recipes/tree/archive-main) is a snapshot branch from before the refactor
+A: We recently did a refactor of the repo, [archive-main](https://github.com/meta-llama/llama-cookbook/tree/archive-main) is a snapshot branch from before the refactor
 
 - Where can we find details about the latest models?