Feature/academy module 4

deeplearning-ai/README.md

@@ -0,0 +1 @@
+# DeepLearning AI

module-0/basics.ipynb → langchain-academy/module-0/basics.ipynb

@@ -53,18 +53,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "c2a15227",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os, getpass\n",
+    "# import os, getpass\n",
+    "\n",
+    "# def _set_env(var: str):\n",
+    "#     if not os.environ.get(var):\n",
+    "#         os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "# _set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0ffb6a23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load environment variables\n",
+    "from dotenv import load_dotenv\n",
+    "import os\n",
     "\n",
-    "def _set_env(var: str):\n",
-    "    if not os.environ.get(var):\n",
-    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "load_dotenv(dotenv_path='C:\\MIConsulting\\LangChain\\langchain-academy\\.env')\n",
     "\n",
-    "_set_env(\"OPENAI_API_KEY\")"
+    "# OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")\n",
+    "GOOGLEAI_API_KEY = os.getenv(\"GOOGLEAI_API_KEY\")\n",
+    "TAVILY_API_KEY = os.getenv(\"TAVILY_API_KEY\")"
    ]
   },
   {
@@ -84,14 +102,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "id": "e19a54d3",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_openai import ChatOpenAI\n",
-    "gpt4o_chat = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
-    "gpt35_chat = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+    "# from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "# gpt4o_chat = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
+    "# gpt35_chat = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "16e79f2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "\n",
+    "gemini15_chat = ChatGoogleGenerativeAI(\n",
+    "    model=\"gemini-1.5-pro\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=None,\n",
+    "    timeout=None,\n",
+    "    max_retries=2,\n",
+    "    api_key=GOOGLEAI_API_KEY,\n",
+    ")"
    ]
   },
   {
@@ -109,17 +147,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 19,
    "id": "b1280e1b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Hello! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 11, 'total_tokens': 20}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5', 'finish_reason': 'stop', 'logprobs': None}, id='run-d3c4bc85-ef14-49f6-ba7e-91bf455cffee-0', usage_metadata={'input_tokens': 11, 'output_tokens': 9, 'total_tokens': 20})"
+       "AIMessage(content='Hello world back to you!\\n', additional_kwargs={}, response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'safety_ratings': []}, id='run-07967d2a-4feb-48f8-9f50-4c7aee2d331f-0', usage_metadata={'input_tokens': 3, 'output_tokens': 7, 'total_tokens': 10, 'input_token_details': {'cache_read': 0}})"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -134,7 +172,9 @@
     "messages = [msg]\n",
     "\n",
     "# Invoke the model with a list of messages \n",
-    "gpt4o_chat.invoke(messages)"
+    "# gpt4o_chat.invoke(messages)\n",
+    "# gpt35_chat.invoke(messages)\n",
+    "gemini15_chat.invoke(messages)"
    ]
   },
   {
@@ -147,44 +187,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 20,
    "id": "f27c6c9a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Hello! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 9, 'total_tokens': 18}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5', 'finish_reason': 'stop', 'logprobs': None}, id='run-d6f6b682-e29a-44de-b45e-79fad1e405e5-0', usage_metadata={'input_tokens': 9, 'output_tokens': 9, 'total_tokens': 18})"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gpt4o_chat.invoke(\"hello world\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "fdc2f0ca",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Hello! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 9, 'total_tokens': 18}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-c75d3f0f-2d71-47be-b14c-42b8dd2b4b08-0', usage_metadata={'input_tokens': 9, 'output_tokens': 9, 'total_tokens': 18})"
+       "AIMessage(content='Hello world!\\n', additional_kwargs={}, response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'safety_ratings': []}, id='run-add51968-5aad-45f1-b010-25e112d3b76f-0', usage_metadata={'input_tokens': 3, 'output_tokens': 4, 'total_tokens': 7, 'input_token_details': {'cache_read': 0}})"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "gpt35_chat.invoke(\"hello world\")"
+    "# gpt4o_chat.invoke(\"hello world\")\n",
+    "gemini15_chat.invoke(\"hello world\")"
    ]
   },
   {
@@ -214,39 +234,40 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "_set_env(\"TAVILY_API_KEY\")"
+    "# _set_env(\"TAVILY_API_KEY\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 23,
    "id": "52d69da9",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "\n",
     "tavily_search = TavilySearchResults(max_results=3)\n",
     "search_docs = tavily_search.invoke(\"What is LangGraph?\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 24,
    "id": "d06f87e6",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[{'url': 'https://www.datacamp.com/tutorial/langgraph-tutorial',\n",
+       "[{'url': 'https://langchain-ai.github.io/langgraph/',\n",
+       "  'content': 'Overview¶. LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions.'},\n",
+       " {'url': 'https://www.datacamp.com/tutorial/langgraph-tutorial',\n",
        "  'content': 'LangGraph is a library within the LangChain ecosystem designed to tackle these challenges head-on. LangGraph provides a framework for defining, coordinating, and executing multiple LLM agents (or chains) in a structured manner.'},\n",
-       " {'url': 'https://langchain-ai.github.io/langgraph/',\n",
-       "  'content': 'Overview LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a ...'},\n",
-       " {'url': 'https://www.youtube.com/watch?v=nmDFSVRnr4Q',\n",
-       "  'content': 'LangGraph is an extension of LangChain enabling Multi-Agent conversation and cyclic chains. This video explains the basics of LangGraph and codesLangChain in...'}]"
+       " {'url': 'https://medium.com/@cplog/introduction-to-langgraph-a-beginners-guide-14f9be027141',\n",
+       "  'content': 'LangGraph is a versatile tool for building complex, stateful applications with LLMs. By understanding its core concepts and working through simple examples, beginners can start to leverage its'}]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -266,7 +287,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "lc-academy",
    "language": "python",
    "name": "python3"
   },
@@ -280,7 +301,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.1"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,