Add LangChain recipes (#485)

recipes/use_cases/agents/langchain/README.md

@@ -0,0 +1,61 @@
+# LangChain <> Llama3 Cookbooks
+
+LLM agents use [planning, memory, and tools](https://lilianweng.github.io/posts/2023-06-23-agent/) to accomplish tasks.
+
+LangChain offers several different ways to implement agents.
+
+(1) Use [AgentExecutor](https://python.langchain.com/docs/modules/agents/quick_start/) with [tool-calling](https://python.langchain.com/docs/integrations/chat/) versions of Llama 3.
+
+(2) Use [LangGraph](https://python.langchain.com/docs/langgraph), a library from LangChain that can be used to build reliable agents with Llama 3.
+
+---
+
+### AgentExecutor Agent
+
+AgentExecutor is the runtime for an agent. AgentExecutor calls the agent, executes the actions it chooses, passes the action outputs back to the agent, and repeats.
+
+Our first notebook, `tool-calling-agent`, shows how to build a [tool calling agent](https://python.langchain.com/docs/modules/agents/agent_types/tool_calling/) with AgentExecutor and Llama 3.
+
+This shows how to build an agent that uses web search and retrieval tools.
+
+--- 
+
+### LangGraph Agent
+
+[LangGraph](https://python.langchain.com/docs/langgraph) is a library from LangChain that can be used to build reliable agents.
+
+LangGraph can be used to build agents with a few pieces:
+- **Planning:** Define a control flow of steps that you want the agent to take (a graph)
+- **Memory:** Persist information (graph state) across these steps
+- **Tool use:** Modify state at any step
+
+Our second notebook, `langgraph-agent`, shows how to build a Llama 3 powered agent that uses web search and retrieval tool in LangGraph.
+
+It discusses some of the trade-offs between AgentExecutor and LangGraph.
+
+--- 
+
+### LangGraph RAG Agent
+
+Our third notebook, `langgraph-rag-agent`, shows how to apply LangGraph to build advanced Llama 3 powered RAG agents that use ideas from 3 papers:
+
+* Corrective-RAG (CRAG) [paper](https://arxiv.org/pdf/2401.15884.pdf) uses self-grading on retrieved documents and web-search fallback if documents are not relevant.
+* Self-RAG [paper](https://arxiv.org/abs/2310.11511) adds self-grading on generations for hallucinations and for ability to answer the question.
+* Adaptive RAG [paper](https://arxiv.org/abs/2403.14403) routes queries between different RAG approaches based on their complexity.
+
+We implement each approach as a control flow in LangGraph:
+- **Planning:** The sequence of RAG steps (e.g., retrieval, grading, and generation) that we want the agent to take
+- **Memory:** All the RAG-related information (input question, retrieved documents, etc) that we want to pass between steps
+- **Tool use:** All the tools needed for RAG (e.g., decide web search or vectorstore retrieval based on the question)
+
+We will build from CRAG (blue, below) to Self-RAG (green) and finally to Adaptive RAG (red):
+
+![Screenshot 2024-05-03 at 10 50 02 AM](https://github.com/rlancemartin/llama-recipes/assets/122662504/ec4aa1cd-3c7e-4cd1-a1e7-7deddc4033a8)
+
+--- 
+
+### Local LangGraph RAG Agent
+
+Our fourth notebook, `langgraph-rag-agent-local`, shows how to apply LangGraph to build advanced RAG agents using Llama 3 that run locally and reliably.
+
+See this [video overview](https://www.youtube.com/watch?v=sgnrL7yo1TE) for more detail.

recipes/use_cases/agents/langchain/tool-calling-agent.ipynb

@@ -0,0 +1,297 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9856250d-51bb-49d3-962d-483178cfafab",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/use_cases/agents/langchain/tool-calling-agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "158a3f92-139a-4e92-9273-e018b027fc54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install -U langchain_groq langchain langchain_community sentence-transformers tavily-python tiktoken langchainhub chromadb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "745f7d9f-15c4-41c8-94f8-09e1426581cc",
+   "metadata": {},
+   "source": [
+    "# Tool calling agent with Llama 3\n",
+    "\n",
+    "[Tool calling](https://python.langchain.com/docs/modules/agents/agent_types/tool_calling/) allows an LLM to detect when one or more tools should be called.\n",
+    "\n",
+    "It will then respond with the inputs that should be passed to those tools. \n",
+    "\n",
+    "LangChain has a general agent that works with tool-calling LLMs.\n",
+    "\n",
+    "### Tools \n",
+    "\n",
+    "Let's define a few tools.\n",
+    "\n",
+    "`Retriever`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16569e69-890c-4e0c-947b-35ed2e0cf0c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ['TAVILY_API_KEY'] = 'YOUR_TAVILY_API_KEY'\n",
+    "os.environ['GROQ_API_KEY'] = 'YOUR_GROQ_API_KEY'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6debf81d-84d1-4645-aa25-07d24cdcbc2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_community.embeddings import HuggingFaceEmbeddings\n",
+    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
+    "\n",
+    "loader = WebBaseLoader(\"https://docs.smith.langchain.com/overview\")\n",
+    "docs = loader.load()\n",
+    "documents = RecursiveCharacterTextSplitter(\n",
+    "    chunk_size=1000, chunk_overlap=200\n",
+    ").split_documents(docs)\n",
+    "vector = Chroma.from_documents(documents, HuggingFaceEmbeddings())\n",
+    "retriever = vector.as_retriever()\n",
+    "\n",
+    "from langchain.tools.retriever import create_retriever_tool\n",
+    "retriever_tool = create_retriever_tool(\n",
+    "    retriever,\n",
+    "    \"langsmith_search\",\n",
+    "    \"Search for information about LangSmith. For any questions about LangSmith, you must use this tool!\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8454286f-f6d3-4ac0-a583-16315189d151",
+   "metadata": {},
+   "source": [
+    "`Web search`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a4d9feb-80b7-4355-9cba-34e816400aa5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "search = TavilySearchResults()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42215e7b-3170-4311-8438-5a7b385ebb64",
+   "metadata": {},
+   "source": [
+    "`Custom`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c130481d-dc6f-48e0-b795-7e3a4438fb6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import tool\n",
+    "\n",
+    "@tool\n",
+    "def magic_function(input: int) -> int:\n",
+    "    \"\"\"Applies a magic function to an input.\"\"\"\n",
+    "    return input + 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bfc0cfbe-d5ce-4c26-859f-5d29be121bef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = [magic_function, search, retriever_tool] "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e88c2e1d-1503-4659-be4d-98900a69253f",
+   "metadata": {},
+   "source": [
+    "### LLM\n",
+    "\n",
+    "Here, we need a Llama 3 model that supports tool use.\n",
+    "\n",
+    "This can be accomplished via prompt engineering (e.g., see [here](https://replicate.com/hamelsmu/llama-3-70b-instruct-awq-with-tools)) or fine-tuning (e.g., see [here](https://huggingface.co/mzbac/llama-3-8B-Instruct-function-calling) and [here](https://huggingface.co/mzbac/llama-3-8B-Instruct-function-calling)).\n",
+    "\n",
+    "We can review LLMs that support tool calling [here](https://python.langchain.com/docs/integrations/chat/) and Groq is included.\n",
+    "\n",
+    "[Here](https://github.com/groq/groq-api-cookbook/blob/main/llama3-stock-market-function-calling/llama3-stock-market-function-calling.ipynb) is a notebook by Groq on function calling with Llama 3 and LangChain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99c919d2-198d-4c3b-85ba-0772bf7db383",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_groq import ChatGroq\n",
+    "llm = ChatGroq(temperature=0, model=\"llama3-70b-8192\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "695ffb74-c278-4420-b10b-b18210d824eb",
+   "metadata": {},
+   "source": [
+    "### Agent\n",
+    "\n",
+    "We use LangChain [tool calling agent](https://python.langchain.com/docs/modules/agents/agent_types/tool_calling/). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fae083a8-864c-4394-93e5-36d22aaa5fe3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prompt \n",
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", \"You are a helpful assistant\"),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "        MessagesPlaceholder(\"agent_scratchpad\"),\n",
+    "    ]\n",
+    ")\n",
+    "prompt.pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "421f9565-bc1a-4141-aae7-c6bcae2c63fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Run\n",
+    "from langchain.agents import AgentExecutor, create_tool_calling_agent, tool\n",
+    "agent = create_tool_calling_agent(llm, tools, prompt)\n",
+    "agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "229372f4-abb3-4418-9444-cadc548a8155",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor.invoke({\"input\": \"what is the value of magic_function(3)?\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "016b447f-d374-4fc7-a1fe-0ce56856a763",
+   "metadata": {},
+   "source": [
+    "Trace: \n",
+    "\n",
+    "https://smith.langchain.com/public/adf06494-94d6-4e93-98f3-60e65d2f2c19/r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6914b16c-be7a-4838-b080-b6af6b6e1417",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor.invoke({\"input\": \"whats the weather in sf?\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e363535-29b8-45d8-85b6-10d2e21f93bc",
+   "metadata": {},
+   "source": [
+    "Trace: https://smith.langchain.com/public/64a62781-7e3c-4acf-ae72-ce49ccb82960/r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ce1b38c-a22a-4035-a9a1-2ea0da419ade",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor.invoke({\"input\": \"how can langsmith help with testing?\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e28cc79b-f6de-45fb-b7e5-84c119ba57da",
+   "metadata": {},
+   "source": [
+    "This last question failed to run. \n",
+    "\n",
+    "Trace: https://smith.langchain.com/public/960a40e9-24f1-42a0-859d-2e0a30018d1c/r\n",
+    "\n",
+    "We can see that the agent correctly decides to query the vectorstore for a question about LangSmith. But it then inexplicably attempts web search. And it appears to get stuck in a loop of calling various tools before crashing.\n",
+    "\n",
+    "Of course, this is using a non-fine-tuned (only prompting) version of llama3 for tool-use. But, it illustates the reliability challenge with using Agent Executor. It is sensitive to the LLM's capacity for tool-use! \n",
+    "\n",
+    "In the next notebook, we will show an alternative way to implement this agent using LangGraph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55e7518c-e7d8-4ce7-9a4a-7909fb3a8b88",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

scripts/spellcheck_conf/wordlist.txt

@@ -1310,3 +1310,7 @@ leaderboards
 txn
 ollama
 tavily
+AgentExecutor
+LangGraph
+langgraph
+vectorstore