Agents.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# LLMs"
      ],
      "metadata": {
        "id": "JFT2z_xJSqan"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hugging Face"
      ],
      "metadata": {
        "id": "c3HssISYStt3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install --upgrade transformers langchain-huggingface"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AUagYHe1N9-n",
        "outputId": "e73ee621-40d8-4c25-f897-bc35e262a4b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting langchain-core<0.3,>=0.1.52 (from langchain-huggingface)\n",
            "  Downloading langchain_core-0.2.38-py3-none-any.whl.metadata (6.2 kB)\n",
            "Collecting sentence-transformers>=2.6.0 (from langchain-huggingface)\n",
            "  Downloading sentence_transformers-3.0.1-py3-none-any.whl.metadata (10 kB)\n",
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            "Collecting langsmith<0.2.0,>=0.1.75 (from langchain-core<0.3,>=0.1.52->langchain-huggingface)\n",
            "  Downloading langsmith-0.1.117-py3-none-any.whl.metadata (13 kB)\n",
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            "Collecting tenacity!=8.4.0,<9.0.0,>=8.1.0 (from langchain-core<0.3,>=0.1.52->langchain-huggingface)\n",
            "  Downloading tenacity-8.5.0-py3-none-any.whl.metadata (1.2 kB)\n",
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            "  Downloading jsonpointer-3.0.0-py2.py3-none-any.whl.metadata (2.3 kB)\n",
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            "  Downloading httpx-0.27.2-py3-none-any.whl.metadata (7.1 kB)\n",
            "Collecting orjson<4.0.0,>=3.9.14 (from langsmith<0.2.0,>=0.1.75->langchain-core<0.3,>=0.1.52->langchain-huggingface)\n",
            "  Downloading orjson-3.10.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (50 kB)\n",
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            "Collecting httpcore==1.* (from httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.75->langchain-core<0.3,>=0.1.52->langchain-huggingface)\n",
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            "Collecting h11<0.15,>=0.13 (from httpcore==1.*->httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.75->langchain-core<0.3,>=0.1.52->langchain-huggingface)\n",
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            "\u001b[?25hInstalling collected packages: tenacity, orjson, jsonpointer, h11, jsonpatch, httpcore, httpx, langsmith, sentence-transformers, langchain-core, langchain-huggingface\n",
            "  Attempting uninstall: tenacity\n",
            "    Found existing installation: tenacity 9.0.0\n",
            "    Uninstalling tenacity-9.0.0:\n",
            "      Successfully uninstalled tenacity-9.0.0\n",
            "Successfully installed h11-0.14.0 httpcore-1.0.5 httpx-0.27.2 jsonpatch-1.33 jsonpointer-3.0.0 langchain-core-0.2.38 langchain-huggingface-0.0.3 langsmith-0.1.117 orjson-3.10.7 sentence-transformers-3.0.1 tenacity-8.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_huggingface.llms import HuggingFacePipeline\n",
        "\n",
        "hf = HuggingFacePipeline.from_model_id(\n",
        "    model_id=\"gpt2\",\n",
        "    task=\"text-generation\",\n",
        "    pipeline_kwargs={\"max_new_tokens\": 10},\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400,
          "referenced_widgets": [
            "d680d44e06fb4357b8d4a9cd6e07aff6",
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            "65b5bcbe0bf046709b47e3d0da55cccd",
            "31c30b5f14394803859d215e0daa4ad2",
            "d6bc0a6a5b144d5cbbfc895c55c79c3c",
            "51e1a421c3804921a1a4b50780319325",
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            "3c614a65b40a49dc89e1c80d19aa7010",
            "c81232c8dd7044fc9b9b07eb060b4893",
            "18005100b0d24a7fa0405f8395b75b42",
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            "2e359a3f181f406d907044ec9b3e02e9",
            "c5e64d0ee75b47fa8dd6f7fc3ebc1c1d",
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            "cda5eb0e3f074acab08454db2d6b48db",
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            "42e1aec3ff7f45008f2d689382fb720e",
            "d16375394b484b299f7dae39d0ee0d0d",
            "57d4e49d1550471e80382350b78e2f4d",
            "f37abf2970b14bb0bc926937861d8cd6",
            "ad13b8cc872f4e6bb844f794c65992ae",
            "a4d3b32914274d7b99ed5a730b274430",
            "0874b56085664168bb968832ac0e85a9",
            "7c220d2816b04a70be9aaeaa126eb346",
            "b445948fcd1a432b8768ded07c1577db",
            "b87a81d72f224f99b5f08baff910a537",
            "5b87bc58ec39437f908fd8713cd441b5",
            "4df311f6b0eb4faaa78bfb7d1248df86",
            "c25fadee5f69478b832744369da1cbfe"
          ]
        },
        "id": "VvL1wWcMOiWo",
        "outputId": "b16037c3-facb-4383-eae9-949f07e59a97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d680d44e06fb4357b8d4a9cd6e07aff6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d9bc64e56614e2cab0e1f368471cb06"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b9ebb7cea8e14ff2b65c523eabbdbb2c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7391690d6d0c4d398af470cb4c08cc6d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "682b728804244db294a21240d6f67ce7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3c614a65b40a49dc89e1c80d19aa7010"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "57d4e49d1550471e80382350b78e2f4d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "question = \"What is electroencephalography?\"\n",
        "hf.invoke(question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "BOHTVKv5OtkM",
        "outputId": "42396b94-18c0-4d99-f5f9-9b1f14bd36c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'What is electroencephalography?\\n\\nIt is a powerful and efficient way to'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Instalación"
      ],
      "metadata": {
        "id": "sEhTKaHUUsgK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install langchain-google-genai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Unb0qYv-UuN-",
        "outputId": "bcbd39d9-1f64-4db3-bfa9-2e7a8ea98435"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain-google-genai\n",
            "  Downloading langchain_google_genai-1.0.10-py3-none-any.whl.metadata (3.8 kB)\n",
            "Requirement already satisfied: google-generativeai<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from langchain-google-genai) (0.7.2)\n",
            "Collecting langchain-core<0.3,>=0.2.33 (from langchain-google-genai)\n",
            "  Downloading langchain_core-0.2.38-py3-none-any.whl.metadata (6.2 kB)\n",
            "Requirement already satisfied: google-ai-generativelanguage==0.6.6 in /usr/local/lib/python3.10/dist-packages (from google-generativeai<0.8.0,>=0.7.0->langchain-google-genai) (0.6.6)\n",
            "Requirement already satisfied: google-api-core in /usr/local/lib/python3.10/dist-packages (from google-generativeai<0.8.0,>=0.7.0->langchain-google-genai) (2.19.2)\n",
            "Requirement already satisfied: google-api-python-client in /usr/local/lib/python3.10/dist-packages (from google-generativeai<0.8.0,>=0.7.0->langchain-google-genai) (2.137.0)\n",
            "Requirement already satisfied: google-auth>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from google-generativeai<0.8.0,>=0.7.0->langchain-google-genai) (2.27.0)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from google-generativeai<0.8.0,>=0.7.0->langchain-google-genai) (3.20.3)\n",
            "Requirement already satisfied: pydantic in /usr/local/lib/python3.10/dist-packages (from google-generativeai<0.8.0,>=0.7.0->langchain-google-genai) (2.8.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from google-generativeai<0.8.0,>=0.7.0->langchain-google-genai) (4.66.5)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from google-generativeai<0.8.0,>=0.7.0->langchain-google-genai) (4.12.2)\n",
            "Requirement already satisfied: proto-plus<2.0.0dev,>=1.22.3 in /usr/local/lib/python3.10/dist-packages (from google-ai-generativelanguage==0.6.6->google-generativeai<0.8.0,>=0.7.0->langchain-google-genai) (1.24.0)\n",
            "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.3,>=0.2.33->langchain-google-genai) (6.0.2)\n",
            "Collecting jsonpatch<2.0,>=1.33 (from langchain-core<0.3,>=0.2.33->langchain-google-genai)\n",
            "  Downloading jsonpatch-1.33-py2.py3-none-any.whl.metadata (3.0 kB)\n",
            "Collecting langsmith<0.2.0,>=0.1.75 (from langchain-core<0.3,>=0.2.33->langchain-google-genai)\n",
            "  Downloading langsmith-0.1.117-py3-none-any.whl.metadata (13 kB)\n",
            "Requirement already satisfied: packaging<25,>=23.2 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.3,>=0.2.33->langchain-google-genai) (24.1)\n",
            "Collecting tenacity!=8.4.0,<9.0.0,>=8.1.0 (from langchain-core<0.3,>=0.2.33->langchain-google-genai)\n",
            "  Downloading tenacity-8.5.0-py3-none-any.whl.metadata (1.2 kB)\n",
            "Requirement already satisfied: googleapis-common-protos<2.0.dev0,>=1.56.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core->google-generativeai<0.8.0,>=0.7.0->langchain-google-genai) (1.65.0)\n",
            "Requirement already satisfied: requests<3.0.0.dev0,>=2.18.0 in /usr/local/lib/python3.10/dist-packages (from google-api-core->google-generativeai<0.8.0,>=0.7.0->langchain-google-genai) (2.32.3)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth>=2.15.0->google-generativeai<0.8.0,>=0.7.0->langchain-google-genai) (5.5.0)\n",
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            "\u001b[?25hInstalling collected packages: tenacity, orjson, jsonpointer, h11, jsonpatch, httpcore, httpx, langsmith, langchain-core, langchain-google-genai\n",
            "  Attempting uninstall: tenacity\n",
            "    Found existing installation: tenacity 9.0.0\n",
            "    Uninstalling tenacity-9.0.0:\n",
            "      Successfully uninstalled tenacity-9.0.0\n",
            "Successfully installed h11-0.14.0 httpcore-1.0.5 httpx-0.27.2 jsonpatch-1.33 jsonpointer-3.0.0 langchain-core-0.2.38 langchain-google-genai-1.0.10 langsmith-0.1.117 orjson-3.10.7 tenacity-8.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from getpass import getpass\n",
        "\n",
        "os.environ['GOOGLE_API_KEY'] = getpass('API_KEY: ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82X3dxvdXBX0",
        "outputId": "4820017f-e8e1-49e1-de9f-3f90b53980d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "API_KEY: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_google_genai import GoogleGenerativeAI"
      ],
      "metadata": {
        "id": "eo9Gmi5eauuo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "OpenAI"
      ],
      "metadata": {
        "id": "iXsa3vgIbUMt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "import os\n",
        "\n",
        "if \"OPENAI_API_KEY\" not in os.environ:\n",
        "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdfGABrSbV_6",
        "outputId": "1f441108-7061-4c7a-81b1-fb0f884fb591"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your OpenAI API key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DB"
      ],
      "metadata": {
        "id": "Z9WUXMYLcKfB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install chromadb pinecone"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvJ6f6vpbV8C",
        "outputId": "e5c328bc-eb75-47a4-b759-9bf5b3873836"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Downloading chromadb-0.5.5-py3-none-any.whl.metadata (6.8 kB)\n",
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            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25hDownloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)\n",
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            "\u001b[?25hDownloading asgiref-3.8.1-py3-none-any.whl (23 kB)\n",
            "Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n",
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            "\u001b[?25hBuilding wheels for collected packages: pypika\n",
            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pypika: filename=PyPika-0.48.9-py2.py3-none-any.whl size=53726 sha256=364680df60909dfc08d253c99693a452ef8169ee37b32cc2eaba94e8e8731a0b\n",
            "  Stored in directory: /root/.cache/pip/wheels/e1/26/51/d0bffb3d2fd82256676d7ad3003faea3bd6dddc9577af665f4\n",
            "Successfully built pypika\n",
            "Installing collected packages: pypika, monotonic, mmh3, websockets, uvloop, uvicorn, python-dotenv, pinecone-plugin-interface, overrides, opentelemetry-util-http, opentelemetry-proto, humanfriendly, httptools, deprecated, chroma-hnswlib, bcrypt, backoff, asgiref, watchfiles, starlette, posthog, pinecone-plugin-inference, opentelemetry-exporter-otlp-proto-common, opentelemetry-api, coloredlogs, pinecone, opentelemetry-semantic-conventions, opentelemetry-instrumentation, onnxruntime, kubernetes, fastapi, opentelemetry-sdk, opentelemetry-instrumentation-asgi, opentelemetry-instrumentation-fastapi, opentelemetry-exporter-otlp-proto-grpc, chromadb\n",
            "Successfully installed asgiref-3.8.1 backoff-2.2.1 bcrypt-4.2.0 chroma-hnswlib-0.7.6 chromadb-0.5.5 coloredlogs-15.0.1 deprecated-1.2.14 fastapi-0.114.0 httptools-0.6.1 humanfriendly-10.0 kubernetes-30.1.0 mmh3-4.1.0 monotonic-1.6 onnxruntime-1.19.2 opentelemetry-api-1.27.0 opentelemetry-exporter-otlp-proto-common-1.27.0 opentelemetry-exporter-otlp-proto-grpc-1.27.0 opentelemetry-instrumentation-0.48b0 opentelemetry-instrumentation-asgi-0.48b0 opentelemetry-instrumentation-fastapi-0.48b0 opentelemetry-proto-1.27.0 opentelemetry-sdk-1.27.0 opentelemetry-semantic-conventions-0.48b0 opentelemetry-util-http-0.48b0 overrides-7.7.0 pinecone-5.1.0 pinecone-plugin-inference-1.0.3 pinecone-plugin-interface-0.0.7 posthog-3.6.4 pypika-0.48.9 python-dotenv-1.0.1 starlette-0.38.5 uvicorn-0.30.6 uvloop-0.20.0 watchfiles-0.24.0 websockets-13.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ChatModels"
      ],
      "metadata": {
        "id": "CdROrqt8ijcM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## OpenAI"
      ],
      "metadata": {
        "id": "UQbbCTVOi-wf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "import os\n",
        "\n",
        "if \"OPENAI_API_KEY\" not in os.environ:\n",
        "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
      ],
      "metadata": {
        "id": "pmM2HWaabksx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qU langchain-openai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SkZl4a0eilSS",
        "outputId": "0966d22f-0d9d-4793-e91d-78a7bdc91261"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/52.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.0/52.0 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m373.5/373.5 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m29.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m318.9/318.9 kB\u001b[0m \u001b[31m18.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_openai import ChatOpenAI\n",
        "\n",
        "llm = ChatOpenAI(model='gpt-3.5-turbo',\n",
        "                 temperature=0,\n",
        "                 max_tokens = 10)"
      ],
      "metadata": {
        "id": "7jAEGba6jr9b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "messages = [(\n",
        "    'system',\n",
        "    'Eres un profesor de programacion en python. Enseña a programar'\n",
        "    ),\n",
        "    (\n",
        "    'human', 'Quiero instalar python en windows'\n",
        "    )\n",
        "           ]"
      ],
      "metadata": {
        "id": "94UirQEmkf0S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ai_msg = llm.invoke(messages)"
      ],
      "metadata": {
        "id": "hTkaOZPGlCZr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ai_msg.content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "wruWuPqKlPtq",
        "outputId": "796d198f-4fce-45bf-bc9a-ad79ae6d1951"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'¡Claro! Aquí te dejo los'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "\n",
        "# Crear una plantilla de prompt para traducciones\n",
        "prompt = ChatPromptTemplate.from_messages(\n",
        "    [\n",
        "        (\n",
        "            \"system\",\n",
        "            \"Eres un asistente útil que traduce {input_language} a {output_language}.\",\n",
        "        ),\n",
        "        (\"human\", \"{input}\"),\n",
        "    ]\n",
        ")\n",
        "\n",
        "# Encadenar la plantilla con el modelo de Google AI\n",
        "chain = prompt | llm\n",
        "\n",
        "# Invocar la cadena con parámetros específicos\n",
        "chain.invoke(\n",
        "    {\n",
        "        \"input_language\": \"English\",\n",
        "        \"output_language\": \"German\",\n",
        "        \"input\": \"I love programming.\",\n",
        "    }\n",
        ")"
      ],
      "metadata": {
        "id": "R_c3AahzMjsi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Chat Models"
      ],
      "metadata": {
        "id": "mBA_vUmqLuY8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qU langchain-openai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hJp4bYEeNZLi",
        "outputId": "ce041d1c-3a31-496c-df86-57de60447394"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m141.9/141.9 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "import os\n",
        "\n",
        "if \"OPENAI_API_KEY\" not in os.environ:\n",
        "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
      ],
      "metadata": {
        "id": "KheCAxfsLw7y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_openai import ChatOpenAI\n",
        "\n",
        "model = ChatOpenAI(model=\"gpt-4o\")"
      ],
      "metadata": {
        "id": "rcSt0J-8L-sa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.messages import AIMessage, HumanMessage, SystemMessage\n",
        "\n",
        "messages = [ SystemMessage(content='Eres un asistente útil.'),\n",
        "            HumanMessage(content='Me ayudas a organizar las tareas del dia?'),\n",
        "             AIMessage(content='Claro! Que tareas necesitas completar hoy?'),\n",
        "             HumanMessage(content='Tengo que enviar un correo importante, hacer ejercicio y estudiar para un examen'),\n",
        "             AIMessage(content='Aquí tienes tu lista de tareas: 1. Enviar correo. 2. Hacer ejercicio. 3. Estudiar para el examen')\n",
        "]\n"
      ],
      "metadata": {
        "id": "I8O4supXNjzc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response = model.invoke(messages)\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9NhYhh13QVKR",
        "outputId": "a3c08b21-50f0-4d13-fa51-1e6713e25281"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "content='¡Perfecto! Vamos a organizarlas de manera eficiente:\\n\\n1. **Enviar correo importante**\\n   - **Prioridad:** Alta\\n   - **Tiempo estimado:** 30 minutos\\n   - **Consejo:** Hazlo a primera hora del día para que no te distraiga el resto de tus tareas.\\n\\n2. **Hacer ejercicio**\\n   - **Prioridad:** Media\\n   - **Tiempo estimado:** 1 hora\\n   - **Consejo:** Programarlo a media mañana o después del correo para recargar energías y despejar la mente.\\n\\n3. **Estudiar para el examen**\\n   - **Prioridad:** Alta\\n   - **Tiempo estimado:** 2-3 horas\\n   - **Consejo:** Divide el estudio en bloques de 1 hora con pequeños descansos entre ellos para maximizar la concentración.\\n\\n**Propuesta de Horario:**\\n\\n- **08:00 - 08:30:** Enviar correo importante\\n- **08:30 - 09:00:** Desayuno y pequeño descanso\\n- **09:00 - 10:00:** Hacer ejercicio\\n- **10:00 - 10:15:** Descanso y refrigerio\\n- **10:15 - 12:15:** Primer bloque de estudio para el examen\\n- **12:15 - 12:30:** Descanso corto\\n- **12:30 - 13:30:** Segundo bloque de estudio para el examen\\n- **13:30 en adelante:** Almuerzo y tiempo libre o ajuste según necesidades\\n\\n¿Te parece bien esta organización o hay algo que quisieras ajustar?' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 332, 'prompt_tokens': 91, 'total_tokens': 423}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_25624ae3a5', 'finish_reason': 'stop', 'logprobs': None} id='run-1579954c-ce29-4efa-a567-9c63a701b9fd-0' usage_metadata={'input_tokens': 91, 'output_tokens': 332, 'total_tokens': 423}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(response.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GVveArYGQgJ0",
        "outputId": "a78f29c5-ed57-4f31-bfe2-244fbbab39b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "¡Perfecto! Vamos a organizarlas de manera eficiente:\n",
            "\n",
            "1. **Enviar correo importante**\n",
            "   - **Prioridad:** Alta\n",
            "   - **Tiempo estimado:** 30 minutos\n",
            "   - **Consejo:** Hazlo a primera hora del día para que no te distraiga el resto de tus tareas.\n",
            "\n",
            "2. **Hacer ejercicio**\n",
            "   - **Prioridad:** Media\n",
            "   - **Tiempo estimado:** 1 hora\n",
            "   - **Consejo:** Programarlo a media mañana o después del correo para recargar energías y despejar la mente.\n",
            "\n",
            "3. **Estudiar para el examen**\n",
            "   - **Prioridad:** Alta\n",
            "   - **Tiempo estimado:** 2-3 horas\n",
            "   - **Consejo:** Divide el estudio en bloques de 1 hora con pequeños descansos entre ellos para maximizar la concentración.\n",
            "\n",
            "**Propuesta de Horario:**\n",
            "\n",
            "- **08:00 - 08:30:** Enviar correo importante\n",
            "- **08:30 - 09:00:** Desayuno y pequeño descanso\n",
            "- **09:00 - 10:00:** Hacer ejercicio\n",
            "- **10:00 - 10:15:** Descanso y refrigerio\n",
            "- **10:15 - 12:15:** Primer bloque de estudio para el examen\n",
            "- **12:15 - 12:30:** Descanso corto\n",
            "- **12:30 - 13:30:** Segundo bloque de estudio para el examen\n",
            "- **13:30 en adelante:** Almuerzo y tiempo libre o ajuste según necesidades\n",
            "\n",
            "¿Te parece bien esta organización o hay algo que quisieras ajustar?\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.messages import trim_messages\n",
        "trim_messages(\n",
        "    messages,\n",
        "    max_tokens=100,\n",
        "    strategy='last',\n",
        "    token_counter=ChatOpenAI(model='gpt-4o'),\n",
        "    include_system = True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MvYvJRrZRGYc",
        "outputId": "ea3de8a0-dbde-4489-9c1c-470c5a1b631c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[SystemMessage(content='Eres un asistente útil.'),\n",
              " HumanMessage(content='Me ayudas a organizar las tareas del dia?'),\n",
              " AIMessage(content='Claro! Que tareas necesitas completar hoy?'),\n",
              " HumanMessage(content='Tengo que enviar un correo importante, hacer ejercicio y estudiar para un examen'),\n",
              " AIMessage(content='Aquí tienes tu lista de tareas: 1. Enviar correo. 2. Hacer ejercicio. 3. Estudiar para el examen')]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prompt Templates"
      ],
      "metadata": {
        "id": "IbDUIZ76eWQC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "import os\n",
        "\n",
        "if \"OPENAI_API_KEY\" not in os.environ:\n",
        "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
      ],
      "metadata": {
        "id": "I8nCuFbQeYfT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qU langchain-openai langchain"
      ],
      "metadata": {
        "id": "slkgMkJhebRi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_openai import ChatOpenAI\n",
        "\n",
        "model = ChatOpenAI(model=\"gpt-4o\")"
      ],
      "metadata": {
        "id": "crGjWomieaie"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.prompts import PromptTemplate\n",
        "\n",
        "prompt_template = PromptTemplate.from_template('Dime un chiste {topic}')\n",
        "\n",
        "print(prompt_template.invoke({'topic':'gatos'}))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SSrpN8AQeaTI",
        "outputId": "ea610283-aa23-443c-90ec-58fb76cfb378"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "text='Dime un chiste gatos'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "\n",
        "prompt_template = ChatPromptTemplate.from_messages([\n",
        "    ('system', 'Eres un asistente útil'),\n",
        "    ('user', 'Dime un chiste {topic}')\n",
        "])\n",
        "print(prompt_template.invoke({'topic':'gatos'}))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jj_ji0DUi4rw",
        "outputId": "af60cc67-a5b8-4220-f432-684d1b7e82ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "messages=[SystemMessage(content='Eres un asistente útil'), HumanMessage(content='Dime un chiste gatos')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
        "from langchain_core.messages import HumanMessage\n",
        "\n",
        "prompt_template = ChatPromptTemplate.from_messages([\n",
        "    ('system', 'Eres un asistente útil'),\n",
        "    MessagesPlaceholder('msgs')\n",
        "\n",
        "])\n",
        "\n",
        "print(prompt_template.invoke({'msgs': [HumanMessage(content='hola'),HumanMessage(content='adios')]}))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X9oi-Xfokzgs",
        "outputId": "b4874c30-c2c8-45a7-c3eb-1838b0e21e72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "messages=[SystemMessage(content='Eres un asistente útil'), HumanMessage(content='hola'), HumanMessage(content='adios')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Few Shot prompting"
      ],
      "metadata": {
        "id": "sXrUvWBHosG3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = ChatOpenAI(model=\"gpt-4o\")"
      ],
      "metadata": {
        "id": "oMnO0gYKqC18"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.invoke('Cuanto es 2 🦜 9').content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "1ZflB7K2qDpi",
        "outputId": "a668b219-e79c-460a-a2f8-d4b429ec7103"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Parece que usaste un emoji de loro (🦜) en tu pregunta. Si por \"2 🦜 9\" te refieres a una operación matemática estándar como \"2 + 9\", la respuesta sería 11.\\n\\nSin embargo, si \"🦜\" está destinado a representar algo más específico o simbólico, por favor proporciona más contexto para poder ayudarte mejor.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate\n",
        "\n",
        "examples = [{'input': '2 🦜 2', 'output': '4'},\n",
        "            {'input': '2 🦜 3', 'output': '5'}]"
      ],
      "metadata": {
        "id": "GvspCwn7qDnH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "example_prompt = ChatPromptTemplate(\n",
        "    [('human', '{input}'),\n",
        "     ('ai', '{output}')]\n",
        ")"
      ],
      "metadata": {
        "id": "KBr1Xqo8qDko"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "few_shot_prompt = FewShotChatMessagePromptTemplate(\n",
        "    example_prompt=example_prompt,\n",
        "    examples=examples\n",
        ")"
      ],
      "metadata": {
        "id": "PTde6WI5qEe8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(few_shot_prompt.invoke({}).to_messages)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OAaEBrWlsIYY",
        "outputId": "9307e384-772e-47ce-ec1f-71eef3a9f6ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<bound method ChatPromptValue.to_messages of ChatPromptValue(messages=[HumanMessage(content='2 🦜 2'), AIMessage(content='4'), HumanMessage(content='2 🦜 3'), AIMessage(content='5')])>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "main_prompt = ChatPromptTemplate.from_messages(\n",
        "    [('system', 'Eres un mago de las matematicas.'),\n",
        "     few_shot_prompt,\n",
        "     ('human', '{input}')\n",
        "     ]\n",
        ")"
      ],
      "metadata": {
        "id": "OfQNQ-71tqoj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain = main_prompt | model"
      ],
      "metadata": {
        "id": "9s3Ll3e4uodU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain.invoke({'input': 'Cuanto es 2 🦜 9'}).content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "zx3bacV3u0I6",
        "outputId": "1c851579-6a07-49f4-bae5-5aa24a7183aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2 🦜 9 es igual a 11.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LCEL"
      ],
      "metadata": {
        "id": "mYPTIAWM3v-N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "\n",
        "prompt_template = ChatPromptTemplate.from_messages([\n",
        "    ('system', \"Traduce lo siguiente al {language}: \"),\n",
        "    ('human', '{text}')\n",
        "])"
      ],
      "metadata": {
        "id": "DqgxJhlA3s_N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.output_parsers import StrOutputParser\n",
        "\n",
        "parser = StrOutputParser()\n",
        "\n",
        "chain = prompt_template | model | parser"
      ],
      "metadata": {
        "id": "J3DhCZhd5zsh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response = chain.invoke({'language': 'Italian', 'text': 'hello'})"
      ],
      "metadata": {
        "id": "FYqV7RVi6DMa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(response)"
      ],
      "metadata": {
        "id": "n0zSGxCF6ew0",
        "outputId": "62024d52-dccc-4b3b-a938-b16e85f3c898",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ciao\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ejercicio: Chat Memory"
      ],
      "metadata": {
        "id": "CIqT1kWLHnDu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_openai import ChatOpenAI\n",
        "\n",
        "model = ChatOpenAI(model=\"gpt-4o\")"
      ],
      "metadata": {
        "id": "a-m63CIgHqPR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.schema import AIMessage, HumanMessage, SystemMessage\n",
        "\n",
        "chat_history = []\n",
        "if not chat_history:\n",
        "  system_message = SystemMessage(content='Eres un asistente útil')\n",
        "  chat_history.append(system_message)"
      ],
      "metadata": {
        "id": "hnhIsdOVHvxY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = input('Haz una pregunta: ')\n",
        "chat_history.append(HumanMessage(content=query))\n",
        "\n",
        "response = model.invoke(chat_history).content\n",
        "chat_history.append(AIMessage(content=response))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVZA3b_VHvuy",
        "outputId": "46e038cd-369b-466b-c740-da296d973903"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Haz una pregunta: adios\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for message in chat_history:\n",
        "  print(message)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "weSMzq4IHvma",
        "outputId": "1bbf7fb3-29e8-4c86-b5d2-3f919919635d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "content='Eres un asistente útil'\n",
            "content='hola'\n",
            "content='¡Hola! ¿En qué puedo ayudarte hoy?'\n",
            "content='cual es tu nombre'\n",
            "content='Soy un asistente virtual desarrollado por OpenAI, y no tengo un nombre propio. Pero estoy aquí para ayudarte con lo que necesites. ¿En qué puedo asistirte hoy?'\n",
            "content='adios'\n",
            "content='¡Adiós! Si necesitas ayuda en el futuro, no dudes en volver. ¡Que tengas un buen día!'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chat_history"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XyM6kNZyHwoV",
        "outputId": "fca984ce-bbc8-4ff7-c8d5-bb582971fa6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[SystemMessage(content='Eres un asistente útil'),\n",
              " HumanMessage(content='hola'),\n",
              " AIMessage(content='¡Hola! ¿En qué puedo ayudarte hoy?'),\n",
              " HumanMessage(content='cual es tu nombre'),\n",
              " AIMessage(content='Soy un asistente virtual desarrollado por OpenAI, y no tengo un nombre propio. Pero estoy aquí para ayudarte con lo que necesites. ¿En qué puedo asistirte hoy?'),\n",
              " HumanMessage(content='adios'),\n",
              " AIMessage(content='¡Adiós! Si necesitas ayuda en el futuro, no dudes en volver. ¡Que tengas un buen día!')]"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loaders PDF y CSV"
      ],
      "metadata": {
        "id": "dOH_Y-rZWGxt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install --upgrade --quiet pypdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_b-MuKuWvA2",
        "outputId": "98a2228e-4027-475c-8c2a-5f0605c59175"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/295.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.8/295.8 kB\u001b[0m \u001b[31m14.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain_community"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZVXZIDQhbhSV",
        "outputId": "5bdde08e-0434-4f1a-968b-52d42e39dbd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain_community\n",
            "  Downloading langchain_community-0.2.16-py3-none-any.whl.metadata (2.7 kB)\n",
            "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (6.0.2)\n",
            "Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (2.0.32)\n",
            "Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (3.10.5)\n",
            "Collecting dataclasses-json<0.7,>=0.5.7 (from langchain_community)\n",
            "  Downloading dataclasses_json-0.6.7-py3-none-any.whl.metadata (25 kB)\n",
            "Collecting langchain<0.3.0,>=0.2.16 (from langchain_community)\n",
            "  Downloading langchain-0.2.16-py3-none-any.whl.metadata (7.1 kB)\n",
            "Collecting langchain-core<0.3.0,>=0.2.38 (from langchain_community)\n",
            "  Downloading langchain_core-0.2.39-py3-none-any.whl.metadata (6.2 kB)\n",
            "Collecting langsmith<0.2.0,>=0.1.0 (from langchain_community)\n",
            "  Downloading langsmith-0.1.117-py3-none-any.whl.metadata (13 kB)\n",
            "Requirement already satisfied: numpy<2,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (1.26.4)\n",
            "Requirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (2.32.3)\n",
            "Collecting tenacity!=8.4.0,<9.0.0,>=8.1.0 (from langchain_community)\n",
            "  Downloading tenacity-8.5.0-py3-none-any.whl.metadata (1.2 kB)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (2.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (24.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (4.0.3)\n",
            "Collecting marshmallow<4.0.0,>=3.18.0 (from dataclasses-json<0.7,>=0.5.7->langchain_community)\n",
            "  Downloading marshmallow-3.22.0-py3-none-any.whl.metadata (7.2 kB)\n",
            "Collecting typing-inspect<1,>=0.4.0 (from dataclasses-json<0.7,>=0.5.7->langchain_community)\n",
            "  Downloading typing_inspect-0.9.0-py3-none-any.whl.metadata (1.5 kB)\n",
            "Collecting langchain-text-splitters<0.3.0,>=0.2.0 (from langchain<0.3.0,>=0.2.16->langchain_community)\n",
            "  Downloading langchain_text_splitters-0.2.4-py3-none-any.whl.metadata (2.3 kB)\n",
            "Requirement already satisfied: pydantic<3,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain<0.3.0,>=0.2.16->langchain_community) (2.8.2)\n",
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            "Requirement already satisfied: packaging<25,>=23.2 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.3.0,>=0.2.38->langchain_community) (24.1)\n",
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            "Collecting httpcore==1.* (from httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.0->langchain_community)\n",
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            "Collecting h11<0.15,>=0.13 (from httpcore==1.*->httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.0->langchain_community)\n",
            "  Downloading h11-0.14.0-py3-none-any.whl.metadata (8.2 kB)\n",
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            "  Downloading jsonpointer-3.0.0-py2.py3-none-any.whl.metadata (2.3 kB)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain<0.3.0,>=0.2.16->langchain_community) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.20.1 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain<0.3.0,>=0.2.16->langchain_community) (2.20.1)\n",
            "Collecting mypy-extensions>=0.3.0 (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain_community)\n",
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            "Downloading langchain-0.2.16-py3-none-any.whl (1.0 MB)\n",
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            "\u001b[?25hDownloading tenacity-8.5.0-py3-none-any.whl (28 kB)\n",
            "Downloading httpx-0.27.2-py3-none-any.whl (76 kB)\n",
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            "\u001b[?25hDownloading jsonpatch-1.33-py2.py3-none-any.whl (12 kB)\n",
            "Downloading langchain_text_splitters-0.2.4-py3-none-any.whl (25 kB)\n",
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            "\u001b[?25hDownloading orjson-3.10.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (141 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m141.9/141.9 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)\n",
            "Downloading jsonpointer-3.0.0-py2.py3-none-any.whl (7.6 kB)\n",
            "Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
            "Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: tenacity, orjson, mypy-extensions, marshmallow, jsonpointer, h11, typing-inspect, jsonpatch, httpcore, httpx, dataclasses-json, langsmith, langchain-core, langchain-text-splitters, langchain, langchain_community\n",
            "  Attempting uninstall: tenacity\n",
            "    Found existing installation: tenacity 9.0.0\n",
            "    Uninstalling tenacity-9.0.0:\n",
            "      Successfully uninstalled tenacity-9.0.0\n",
            "Successfully installed dataclasses-json-0.6.7 h11-0.14.0 httpcore-1.0.5 httpx-0.27.2 jsonpatch-1.33 jsonpointer-3.0.0 langchain-0.2.16 langchain-core-0.2.39 langchain-text-splitters-0.2.4 langchain_community-0.2.16 langsmith-0.1.117 marshmallow-3.22.0 mypy-extensions-1.0.0 orjson-3.10.7 tenacity-8.5.0 typing-inspect-0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.document_loaders import PyPDFLoader\n",
        "\n",
        "file_path = (\n",
        "    \"/content/lorem-ipsum.pdf\"\n",
        ")\n",
        "loader = PyPDFLoader(file_path)\n",
        "pages = loader.load_and_split()\n",
        "\n",
        "pages[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nh35EHBVWvcV",
        "outputId": "7a116fb8-c66f-4e70-d7f6-b00112b93277"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qU langchain-openai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pSBththWZk6R",
        "outputId": "f690530a-0538-49a9-d9b9-4424476ff8e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/52.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.0/52.0 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/373.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m373.5/373.5 kB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.1 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m49.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/318.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m318.9/318.9 kB\u001b[0m \u001b[31m22.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "import os\n",
        "\n",
        "if \"OPENAI_API_KEY\" not in os.environ:\n",
        "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NZdCr4saZlkt",
        "outputId": "74158684-8f6d-4a90-bef3-cea20cb703bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your OpenAI API key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install faiss-gpu"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KEoyx195dAOw",
        "outputId": "4ec2c309-1509-4b03-d786-4c287de57cdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting faiss-gpu\n",
            "  Downloading faiss_gpu-1.7.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.4 kB)\n",
            "Downloading faiss_gpu-1.7.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (85.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.5/85.5 MB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: faiss-gpu\n",
            "Successfully installed faiss-gpu-1.7.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.vectorstores import FAISS\n",
        "from langchain_openai import OpenAIEmbeddings\n",
        "\n",
        "faiss_index = FAISS.from_documents(pages, OpenAIEmbeddings())\n",
        "docs = faiss_index.similarity_search(\"Lorem ipsum\", k=2)\n",
        "for doc in docs:\n",
        "    print(str(doc.metadata[\"page\"]) + \":\", doc.page_content[:300])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dld5J1opeJwM",
        "outputId": "c23fe514-6a2f-416d-fee7-87899a0bb35d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0: Test document  PDF \n",
            " Lorem ipsum dolor sit amet, consectetur  adipiscing  elit. Nulla est purus, ultrices in porttitor \n",
            "in, accumsan  non quam. Nam consectetur  porttitor rhoncus. Curabitur  eu est et leo feugiat \n",
            "auctor vel quis lorem. Ut et ligula dolor, sit amet consequat  lorem. Aliquam porta er\n",
            "0: quam, a volutpat purus. Etiam sollicitudin  arcu vel elit bibendum  et imperdiet  risus tincidunt.  \n",
            "Etiam elit velit, posuere ut pulvinar ac, condimentum  eget justo. Fusce a erat velit. Vivamus \n",
            "imperdiet  ultrices orci in hendrerit.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.document_loaders.csv_loader import CSVLoader\n",
        "\n",
        "file_path = (\n",
        "    \"/content/Online_Retail.csv\"\n",
        ")\n",
        "\n",
        "loader = CSVLoader(file_path=file_path)\n",
        "data = loader.load()\n",
        "\n",
        "for record in data[:2]:\n",
        "    print(record)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5nJmGZTechr",
        "outputId": "5dbe5455-9a70-42c7-d06e-ce362a23478e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "page_content='InvoiceNo: 536365\n",
            "StockCode: 85123A\n",
            "Description: WHITE HANGING HEART T-LIGHT HOLDER\n",
            "Quantity: 6\n",
            "InvoiceDate: 12/1/10 8:26\n",
            "UnitPrice: 2.55\n",
            "CustomerID: 17850\n",
            "Country: United Kingdom' metadata={'source': '/content/Online_Retail.csv', 'row': 0}\n",
            "page_content='InvoiceNo: 536365\n",
            "StockCode: 71053\n",
            "Description: WHITE METAL LANTERN\n",
            "Quantity: 6\n",
            "InvoiceDate: 12/1/10 8:26\n",
            "UnitPrice: 3.39\n",
            "CustomerID: 17850\n",
            "Country: United Kingdom' metadata={'source': '/content/Online_Retail.csv', 'row': 1}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loader HTML y Directorio"
      ],
      "metadata": {
        "id": "RieRWtFDmB0z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install bs4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E3SGpcgoo8QU",
        "outputId": "26e40818-95ab-4908-c75f-532743b9b000"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: bs4 in /usr/local/lib/python3.10/dist-packages (0.0.2)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from bs4) (4.12.3)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->bs4) (2.6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.document_loaders import BSHTMLLoader\n",
        "\n",
        "file_path = '/content/example.html'\n",
        "\n",
        "loader = BSHTMLLoader(file_path)\n",
        "data = loader.load()\n",
        "\n",
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uaGSgRd4mNX5",
        "outputId": "142b3e6c-85af-4376-af58-289bffeddd21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Document(metadata={'source': '/content/example.html', 'title': 'Inteligencia Artificial: Un Enfoque Moderno'}, page_content='\\n\\n\\n\\nInteligencia Artificial: Un Enfoque Moderno\\n\\n\\n\\nInteligencia Artificial: Un Enfoque Moderno\\nPor Stuart Russell y Peter Norvig\\n\\n\\n\\nCapítulo 1: Introducción\\nCapítulo 2: Resolución de Problemas\\nCapítulo 3: Representación del Conocimiento\\n\\n\\n\\nCapítulo 1: Introducción\\nLa inteligencia artificial (IA) es el campo de estudio que trata de diseñar agentes que actúan inteligentemente. Un agente es algo que percibe su entorno a través de sensores y actúa sobre ese entorno a través de actuadores.\\n\\n\\nCapítulo 2: Resolución de Problemas\\nLa resolución de problemas es una parte fundamental de la inteligencia artificial. Los agentes inteligentes deben ser capaces de formular metas y desarrollar estrategias para alcanzarlas.\\n\\n\\nCapítulo 3: Representación del Conocimiento\\nLa representación del conocimiento es crucial para la inteligencia artificial, ya que permite que los agentes razonen acerca del mundo que los rodea. Esto incluye el uso de lógica, redes semánticas y otros mecanismos.\\n\\n\\nEste es un ejemplo basado en el libro \"Inteligencia Artificial: Un Enfoque Moderno\".\\n\\n\\n\\n')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Splitters"
      ],
      "metadata": {
        "id": "neNpNpHAt3kE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install -qU langchain-text-splitters"
      ],
      "metadata": {
        "id": "gPmQqLGPwaG-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_text_splitters import RecursiveCharacterTextSplitter"
      ],
      "metadata": {
        "id": "QiIqnFm7zrhL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('example.txt') as f:\n",
        "  state_of_the_union = f.read()"
      ],
      "metadata": {
        "id": "VAwHL9O90Llp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_splitter = RecursiveCharacterTextSplitter(\n",
        "    chunk_size=10,\n",
        "    chunk_overlap=2,\n",
        "    length_function=len,\n",
        "    is_separator_regex=False,\n",
        ")"
      ],
      "metadata": {
        "id": "QS9AqcG90Lii"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = text_splitter.create_documents([state_of_the_union])"
      ],
      "metadata": {
        "id": "bbC2V_Ol0Le7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sIB-__vg0LUv",
        "outputId": "785147d9-dc3a-4f51-d063-aa33e53a3ae5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(page_content='Lorem'),\n",
              " Document(page_content='ipsum'),\n",
              " Document(page_content='dolor sit'),\n",
              " Document(page_content='amet,'),\n",
              " Document(page_content='consectet'),\n",
              " Document(page_content='etur'),\n",
              " Document(page_content='adipiscin'),\n",
              " Document(page_content='ing'),\n",
              " Document(page_content='elit. Sed'),\n",
              " Document(page_content='do'),\n",
              " Document(page_content='eiusmod'),\n",
              " Document(page_content='tempor'),\n",
              " Document(page_content='incididun'),\n",
              " Document(page_content='unt'),\n",
              " Document(page_content='ut labore'),\n",
              " Document(page_content='et dolore'),\n",
              " Document(page_content='magna'),\n",
              " Document(page_content='aliqua.'),\n",
              " Document(page_content='Ut enim'),\n",
              " Document(page_content='ad minim'),\n",
              " Document(page_content='veniam,'),\n",
              " Document(page_content='quis'),\n",
              " Document(page_content='nostrud'),\n",
              " Document(page_content='exercitat'),\n",
              " Document(page_content='ation'),\n",
              " Document(page_content='ullamco'),\n",
              " Document(page_content='laboris'),\n",
              " Document(page_content='nisi ut'),\n",
              " Document(page_content='aliquip'),\n",
              " Document(page_content='ex ea'),\n",
              " Document(page_content='commodo'),\n",
              " Document(page_content='consequat'),\n",
              " Document(page_content='at.'),\n",
              " Document(page_content='Duis aute'),\n",
              " Document(page_content='irure'),\n",
              " Document(page_content='dolor in'),\n",
              " Document(page_content='reprehend'),\n",
              " Document(page_content='nderit'),\n",
              " Document(page_content='in'),\n",
              " Document(page_content='voluptate'),\n",
              " Document(page_content='velit'),\n",
              " Document(page_content='esse'),\n",
              " Document(page_content='cillum'),\n",
              " Document(page_content='dolore eu'),\n",
              " Document(page_content='fugiat'),\n",
              " Document(page_content='nulla'),\n",
              " Document(page_content='pariatur.'),\n",
              " Document(page_content='Excepteur'),\n",
              " Document(page_content='sint'),\n",
              " Document(page_content='occaecat'),\n",
              " Document(page_content='cupidatat'),\n",
              " Document(page_content='non'),\n",
              " Document(page_content='proident,'),\n",
              " Document(page_content='sunt in'),\n",
              " Document(page_content='culpa qui'),\n",
              " Document(page_content='officia'),\n",
              " Document(page_content='deserunt'),\n",
              " Document(page_content='mollit'),\n",
              " Document(page_content='anim id'),\n",
              " Document(page_content='est'),\n",
              " Document(page_content='laborum.')]"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SVE75KVO2ND4",
        "outputId": "ebd50a06-2e36-43bc-c49c-8a651d4aa3ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Document(page_content='Lorem')"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yS8928fq2Wru",
        "outputId": "ff7ec8f0-d44f-4bec-dd05-bd98e7f2e08b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Document(page_content='ipsum')"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Embeddings"
      ],
      "metadata": {
        "id": "WubAru0NL9_A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain-openai"
      ],
      "metadata": {
        "id": "6P9nuAChL_ch"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "import os\n",
        "\n",
        "if \"OPENAI_API_KEY\" not in os.environ:\n",
        "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
      ],
      "metadata": {
        "id": "Wu74uWXpL_1L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_openai import OpenAIEmbeddings\n",
        "\n",
        "embeddings_model = OpenAIEmbeddings()\n",
        "embeddings = embeddings_model.embed_documents(\n",
        "    [\n",
        "        'Hola!',\n",
        "        'Holas, cómo estás?',\n",
        "        'Cual es tu nombre?',\n",
        "        'Me llamo Daniel',\n",
        "        'Hola Daniel'\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "id": "d2zLtdWkMAgw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dGVTk3zMNQiq",
        "outputId": "7831b629-4408-4ee9-fc0e-41c35bcb7fee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[-0.02080022543668747,\n",
              "  -0.005842472892254591,\n",
              "  -0.00405265623703599,\n",
              "  -0.019035978242754936,\n",
              "  -0.032165560871362686,\n",
              "  0.02198917604982853,\n",
              "  -0.014152335934340954,\n",
              "  -0.004004714544862509,\n",
              "  0.003477358026430011,\n",
              "  -0.015443560667335987,\n",
              "  0.03449232131242752,\n",
              "  -0.005717824678868055,\n",
              "  -0.009307046420872211,\n",
              "  -0.011365335434675217,\n",
              "  -0.003352710045874119,\n",
              "  -0.011659376323223114,\n",
              "  0.018000440672039986,\n",
              "  -0.01581430807709694,\n",
              "  0.023868482559919357,\n",
              "  -0.010700546205043793,\n",
              "  -0.025811713188886642,\n",
              "  1.9913208234356716e-05,\n",
              "  0.015302931889891624,\n",
              "  -0.020928069949150085,\n",
              "  -0.010317014530301094,\n",
              "  -0.0019656021613627672,\n",
              "  0.007076167967170477,\n",
              "  -0.020109867677092552,\n",
              "  0.025517670437693596,\n",
              "  -0.044106196612119675,\n",
              "  0.02214258909225464,\n",
              "  0.010847566649317741,\n",
              "  -0.014305748045444489,\n",
              "  -0.006750165484845638,\n",
              "  -0.004458561073988676,\n",
              "  -0.025965124368667603,\n",
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              "  ...]]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(embeddings[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v2bD8WR8NQfi",
        "outputId": "dcb3331f-4a15-4fce-8887-17644be1cb85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1536"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embedded_query = embeddings_model.embed_query('Cual es el nombre mencionado en la conversación?')"
      ],
      "metadata": {
        "id": "sZnMQx6JP7Ia"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embedded_query"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tBCHmyqYQGJE",
        "outputId": "d6f5b2da-bd14-4721-e88d-6440019fdea5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 0.027061602100729942,\n",
              " -0.0421842597424984,\n",
              " -0.003430841024965048,\n",
              " 0.019962424412369728,\n",
              " 0.002304344205185771,\n",
              " 0.007696124259382486,\n",
              " 0.03129800036549568,\n",
              " 0.22635206580162048,\n",
              " 0.008793736808001995,\n",
              " -0.01871717907488346,\n",
              " 0.039231620728969574,\n",
              " -0.0011770448181778193,\n",
              " 0.03278715908527374,\n",
              " 0.019217845052480698,\n",
              " 0.019808372482657433,\n",
              " 0.005468805320560932,\n",
              " 0.01725369691848755,\n",
              " -0.017446260899305344,\n",
              " -0.018152326345443726,\n",
              " -0.004053463228046894,\n",
              " 0.013723364099860191,\n",
              " 0.022106299176812172,\n",
              " -0.03905189409852028,\n",
              " -0.03630465269088745,\n",
              " -0.030039917677640915,\n",
              " 0.006543952040374279,\n",
              " -0.039616744965314865,\n",
              " 0.010488295927643776,\n",
              " 0.008922112174332142,\n",
              " 0.010199450887739658,\n",
              " -0.022632639855146408,\n",
              " 0.024635300040245056,\n",
              " 0.029269663617014885,\n",
              " 0.01202880498021841,\n",
              " 0.009140350855886936,\n",
              " 0.009255888871848583,\n",
              " -0.005430292803794146,\n",
              " -0.013299724087119102,\n",
              " 0.006338551174849272,\n",
              " 0.0017090017208829522,\n",
              " 0.0062326411716639996,\n",
              " -0.014352405443787575,\n",
              " -0.006662699859589338,\n",
              " 0.009563990868628025,\n",
              " -0.0007610272732563317,\n",
              " 0.008768061175942421,\n",
              " 0.0025947941467165947,\n",
              " -0.004220351576805115,\n",
              " -0.018935417756438255,\n",
              " -0.022683989256620407,\n",
              " -0.015777375549077988,\n",
              " 0.002222504699602723,\n",
              " 0.01549494918435812,\n",
              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Chroma"
      ],
      "metadata": {
        "id": "mx_qiONaU5hd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "import os\n",
        "\n",
        "if \"OPENAI_API_KEY\" not in os.environ:\n",
        "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R5Nkb7BwWC0s",
        "outputId": "323a4260-67e3-4412-895a-0120654f75cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your OpenAI API key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qU \"langchain-chroma>=0.1.2\" langchain_openai"
      ],
      "metadata": {
        "id": "qFKtQ-FcVTUj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_openai import OpenAIEmbeddings\n",
        "\n",
        "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-large\")"
      ],
      "metadata": {
        "id": "3n2e80SJYR6b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_chroma import Chroma\n",
        "\n",
        "vector_store = Chroma(\n",
        "    collection_name=\"example_collection\",\n",
        "    embedding_function=embeddings,\n",
        "    persist_directory=\"./chroma_langchain_db\",\n",
        ")"
      ],
      "metadata": {
        "id": "Y83DvySgZDot"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from uuid import uuid4\n",
        "\n",
        "from langchain_core.documents import Document\n",
        "\n",
        "document_1 = Document(\n",
        "    page_content=\"I had chocalate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
        "    metadata={\"source\": \"tweet\"},\n",
        "    id=1,\n",
        ")\n",
        "\n",
        "document_2 = Document(\n",
        "    page_content=\"The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.\",\n",
        "    metadata={\"source\": \"news\"},\n",
        "    id=2,\n",
        ")\n",
        "\n",
        "document_3 = Document(\n",
        "    page_content=\"Building an exciting new project with LangChain - come check it out!\",\n",
        "    metadata={\"source\": \"tweet\"},\n",
        "    id=3,\n",
        ")\n",
        "\n",
        "document_4 = Document(\n",
        "    page_content=\"Robbers broke into the city bank and stole $1 million in cash.\",\n",
        "    metadata={\"source\": \"news\"},\n",
        "    id=4,\n",
        ")\n",
        "\n",
        "document_5 = Document(\n",
        "    page_content=\"Wow! That was an amazing movie. I can't wait to see it again.\",\n",
        "    metadata={\"source\": \"tweet\"},\n",
        "    id=5,\n",
        ")\n",
        "\n",
        "document_6 = Document(\n",
        "    page_content=\"Is the new iPhone worth the price? Read this review to find out.\",\n",
        "    metadata={\"source\": \"website\"},\n",
        "    id=6,\n",
        ")\n",
        "\n",
        "document_7 = Document(\n",
        "    page_content=\"The top 10 soccer players in the world right now.\",\n",
        "    metadata={\"source\": \"website\"},\n",
        "    id=7,\n",
        ")\n",
        "\n",
        "document_8 = Document(\n",
        "    page_content=\"LangGraph is the best framework for building stateful, agentic applications!\",\n",
        "    metadata={\"source\": \"tweet\"},\n",
        "    id=8,\n",
        ")\n",
        "\n",
        "document_9 = Document(\n",
        "    page_content=\"The stock market is down 500 points today due to fears of a recession.\",\n",
        "    metadata={\"source\": \"news\"},\n",
        "    id=9,\n",
        ")\n",
        "\n",
        "document_10 = Document(\n",
        "    page_content=\"I have a bad feeling I am going to get deleted :(\",\n",
        "    metadata={\"source\": \"tweet\"},\n",
        "    id=10,\n",
        ")\n",
        "\n",
        "documents = [\n",
        "    document_1,\n",
        "    document_2,\n",
        "    document_3,\n",
        "    document_4,\n",
        "    document_5,\n",
        "    document_6,\n",
        "    document_7,\n",
        "    document_8,\n",
        "    document_9,\n",
        "    document_10,\n",
        "]\n",
        "uuids = [str(uuid4()) for _ in range(len(documents))]\n",
        "\n",
        "vector_store.add_documents(documents=documents, ids=uuids)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i5qEUVBLbkgw",
        "outputId": "f826d3cd-d133-401f-86ea-d1e2f4111e53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ce5a96e2-2013-4bcb-9019-c0bbeb36ed7d',\n",
              " '1c5e5064-05ec-477f-b010-47c7fe935fe8',\n",
              " '958512f6-a7cc-4b42-b825-82adfc8ed3a3',\n",
              " '8993d1df-da78-43b2-ba3c-1bd699afd9ac',\n",
              " 'b35e5890-221b-4c9e-8878-9d8039e21854',\n",
              " 'a901a30a-c4e0-4628-80a4-e01ad087b66f',\n",
              " 'edd6e85b-9b3b-4cb9-a714-fa1800c22ee8',\n",
              " '8a8e6061-57bd-48e1-8703-29423781dadc',\n",
              " 'c413395b-92bf-4735-9ccb-e1706e46c1b6',\n",
              " 'f336ea87-43ef-4e73-9fe8-9c9eb5975aeb']"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results = vector_store.similarity_search(\n",
        "    \"LangChain provides abstractions to make working with LLMs easy\",\n",
        "    k=2,\n",
        "    filter={\"source\": \"tweet\"},\n",
        ")\n",
        "for res in results:\n",
        "    print(f\"* {res.page_content} [{res.metadata}]\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NR9s5FRwdLZd",
        "outputId": "a40ff171-ff18-47d7-c355-4869bb0c1501"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]\n",
            "* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Integración de Cadenas en Proyectos : Runnable, OutputParser y Streaming"
      ],
      "metadata": {
        "id": "UGDEyW9DfI4K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ykiO3iSwffpi",
        "outputId": "eabc0d27-7e80-4ea2-fd74-57592762cabd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain\n",
            "  Downloading langchain-0.2.16-py3-none-any.whl.metadata (7.1 kB)\n",
            "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (6.0.2)\n",
            "Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.0.34)\n",
            "Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (3.10.5)\n",
            "Requirement already satisfied: async-timeout<5.0.0,>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (4.0.3)\n",
            "Collecting langchain-core<0.3.0,>=0.2.38 (from langchain)\n",
            "  Downloading langchain_core-0.2.39-py3-none-any.whl.metadata (6.2 kB)\n",
            "Collecting langchain-text-splitters<0.3.0,>=0.2.0 (from langchain)\n",
            "  Downloading langchain_text_splitters-0.2.4-py3-none-any.whl.metadata (2.3 kB)\n",
            "Collecting langsmith<0.2.0,>=0.1.17 (from langchain)\n",
            "  Downloading langsmith-0.1.118-py3-none-any.whl.metadata (13 kB)\n",
            "Requirement already satisfied: numpy<2,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain) (1.26.4)\n",
            "Requirement already satisfied: pydantic<3,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.9.1)\n",
            "Requirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.32.3)\n",
            "Collecting tenacity!=8.4.0,<9.0.0,>=8.1.0 (from langchain)\n",
            "  Downloading tenacity-8.5.0-py3-none-any.whl.metadata (1.2 kB)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (2.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (24.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.11.1)\n",
            "Collecting jsonpatch<2.0,>=1.33 (from langchain-core<0.3.0,>=0.2.38->langchain)\n",
            "  Downloading jsonpatch-1.33-py2.py3-none-any.whl.metadata (3.0 kB)\n",
            "Requirement already satisfied: packaging<25,>=23.2 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.3.0,>=0.2.38->langchain) (24.1)\n",
            "Requirement already satisfied: typing-extensions>=4.7 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.3.0,>=0.2.38->langchain) (4.12.2)\n",
            "Collecting httpx<1,>=0.23.0 (from langsmith<0.2.0,>=0.1.17->langchain)\n",
            "  Downloading httpx-0.27.2-py3-none-any.whl.metadata (7.1 kB)\n",
            "Collecting orjson<4.0.0,>=3.9.14 (from langsmith<0.2.0,>=0.1.17->langchain)\n",
            "  Downloading orjson-3.10.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (50 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.4/50.4 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.23.3 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain) (2.23.3)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain) (2024.8.30)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from SQLAlchemy<3,>=1.4->langchain) (3.0.3)\n",
            "Requirement already satisfied: anyio in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.17->langchain) (3.7.1)\n",
            "Collecting httpcore==1.* (from httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.17->langchain)\n",
            "  Downloading httpcore-1.0.5-py3-none-any.whl.metadata (20 kB)\n",
            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.17->langchain) (1.3.1)\n",
            "Collecting h11<0.15,>=0.13 (from httpcore==1.*->httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.17->langchain)\n",
            "  Downloading h11-0.14.0-py3-none-any.whl.metadata (8.2 kB)\n",
            "Collecting jsonpointer>=1.9 (from jsonpatch<2.0,>=1.33->langchain-core<0.3.0,>=0.2.38->langchain)\n",
            "  Downloading jsonpointer-3.0.0-py2.py3-none-any.whl.metadata (2.3 kB)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.17->langchain) (1.2.2)\n",
            "Downloading langchain-0.2.16-py3-none-any.whl (1.0 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m20.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading langchain_core-0.2.39-py3-none-any.whl (396 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m396.6/396.6 kB\u001b[0m \u001b[31m22.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading langchain_text_splitters-0.2.4-py3-none-any.whl (25 kB)\n",
            "Downloading langsmith-0.1.118-py3-none-any.whl (289 kB)\n",
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            "\u001b[?25hDownloading tenacity-8.5.0-py3-none-any.whl (28 kB)\n",
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            "\u001b[?25hDownloading jsonpatch-1.33-py2.py3-none-any.whl (12 kB)\n",
            "Downloading orjson-3.10.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (141 kB)\n",
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            "\u001b[?25hDownloading jsonpointer-3.0.0-py2.py3-none-any.whl (7.6 kB)\n",
            "Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
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            "\u001b[?25hInstalling collected packages: tenacity, orjson, jsonpointer, h11, jsonpatch, httpcore, httpx, langsmith, langchain-core, langchain-text-splitters, langchain\n",
            "  Attempting uninstall: tenacity\n",
            "    Found existing installation: tenacity 9.0.0\n",
            "    Uninstalling tenacity-9.0.0:\n",
            "      Successfully uninstalled tenacity-9.0.0\n",
            "Successfully installed h11-0.14.0 httpcore-1.0.5 httpx-0.27.2 jsonpatch-1.33 jsonpointer-3.0.0 langchain-0.2.16 langchain-core-0.2.39 langchain-text-splitters-0.2.4 langsmith-0.1.118 orjson-3.10.7 tenacity-8.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.runnables import RunnableLambda\n",
        "\n",
        "sequence = RunnableLambda(lambda x: x +1 ) | RunnableLambda(lambda x: x * 2 )"
      ],
      "metadata": {
        "id": "ljKUjozlf9E3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sequence.invoke(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rpJOo1bof9AP",
        "outputId": "4eaec62f-858f-4ed1-de5d-f80b48af7733"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sequence = RunnableLambda(lambda x: x +1 ) | {\n",
        "    'index_1' : RunnableLambda(lambda x: x * 2 ),\n",
        "    'index_2' : RunnableLambda(lambda x: x * 5 )\n",
        "}"
      ],
      "metadata": {
        "id": "BYUV4NB2in3A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sequence.invoke(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_lLVdhYk0qa",
        "outputId": "401dc7bb-5f70-4f3f-80fa-b06d8e8da666"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'index_1': 22, 'index_2': 55}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qU langchain langchain_openai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ePSGhiPVk0fC",
        "outputId": "9c116afa-b143-4464-b243-a3b7fad11513"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/52.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.0/52.0 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/373.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m373.5/373.5 kB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.1 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m42.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "import os\n",
        "\n",
        "if \"OPENAI_API_KEY\" not in os.environ:\n",
        "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87Gt40B-l2zy",
        "outputId": "cf401ba2-4600-4700-c2db-d57f011f9e5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your OpenAI API key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.output_parsers import JsonOutputParser\n",
        "from langchain_core.prompts import PromptTemplate\n",
        "from langchain_openai import ChatOpenAI"
      ],
      "metadata": {
        "id": "FQTrZy5pm1eU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = ChatOpenAI()"
      ],
      "metadata": {
        "id": "sqXkKSionFFd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "joke_query = 'Tell me a joke'"
      ],
      "metadata": {
        "id": "-lFc_4h0nFDI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parser = JsonOutputParser()"
      ],
      "metadata": {
        "id": "Zec99U3bnFAj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = PromptTemplate(\n",
        "    template = 'Answer the user query. \\n{format_instructions}\\n{query}',\n",
        "    input_variables = ['query'],\n",
        "    partial_variables = { 'format_instructions': parser.get_format_instructions()}\n",
        ")"
      ],
      "metadata": {
        "id": "suwNTTr-nrpt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain = prompt | model | parser"
      ],
      "metadata": {
        "id": "FG2Owo36o28s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response = chain.invoke({\"query\": joke_query})"
      ],
      "metadata": {
        "id": "M33O9i94qyrK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_f_D4toq7bK",
        "outputId": "5b5768c9-95a8-480c-df36-d19cd79ab991"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'joke': \"Why don't scientists trust atoms? Because they make up everything!\"}"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "for s in chain.stream({\"query\": joke_query}):\n",
        "  print(s)\n",
        "  time.sleep(0.3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_hyqlb2sb0l",
        "outputId": "5e3a333a-a807-43d2-a7a4-d1b019f18c21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{}\n",
            "{'joke': ''}\n",
            "{'joke': 'Why'}\n",
            "{'joke': 'Why couldn'}\n",
            "{'joke': \"Why couldn't\"}\n",
            "{'joke': \"Why couldn't the\"}\n",
            "{'joke': \"Why couldn't the bicycle\"}\n",
            "{'joke': \"Why couldn't the bicycle stand\"}\n",
            "{'joke': \"Why couldn't the bicycle stand up\"}\n",
            "{'joke': \"Why couldn't the bicycle stand up by\"}\n",
            "{'joke': \"Why couldn't the bicycle stand up by itself\"}\n",
            "{'joke': \"Why couldn't the bicycle stand up by itself?\"}\n",
            "{'joke': \"Why couldn't the bicycle stand up by itself? Because\"}\n",
            "{'joke': \"Why couldn't the bicycle stand up by itself? Because it\"}\n",
            "{'joke': \"Why couldn't the bicycle stand up by itself? Because it was\"}\n",
            "{'joke': \"Why couldn't the bicycle stand up by itself? Because it was two\"}\n",
            "{'joke': \"Why couldn't the bicycle stand up by itself? Because it was two tired\"}\n",
            "{'joke': \"Why couldn't the bicycle stand up by itself? Because it was two tired!\"}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chunks = []\n",
        "async for chunk in model.astream(joke_query):\n",
        "  chunks.append(chunk)\n",
        "  print(chunk.content, end='', flush=True)\n",
        "  time.sleep(0.3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kegW96_5sgYw",
        "outputId": "b9dfa6fd-164e-4bc6-ed8a-8a7c1845de20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Why couldn't the bicycle stand up by itself?\n",
            "\n",
            "Because it was two-tired!"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Chat Memory"
      ],
      "metadata": {
        "id": "Iwkaaov6t_Oq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "import os\n",
        "\n",
        "if \"OPENAI_API_KEY\" not in os.environ:\n",
        "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YsvPWV3sCN56",
        "outputId": "fca0a248-b2dd-4f82-d5d7-6b6a5a59ecd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your OpenAI API key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain_openai langchain"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O7kfzKF7CRLL",
        "outputId": "e0721972-26f9-4c54-f15c-0cee3877deb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain_openai\n",
            "  Downloading langchain_openai-0.1.23-py3-none-any.whl.metadata (2.6 kB)\n",
            "Collecting langchain\n",
            "  Downloading langchain-0.2.16-py3-none-any.whl.metadata (7.1 kB)\n",
            "Collecting langchain-core<0.3.0,>=0.2.35 (from langchain_openai)\n",
            "  Downloading langchain_core-0.2.39-py3-none-any.whl.metadata (6.2 kB)\n",
            "Collecting openai<2.0.0,>=1.40.0 (from langchain_openai)\n",
            "  Downloading openai-1.44.1-py3-none-any.whl.metadata (22 kB)\n",
            "Collecting tiktoken<1,>=0.7 (from langchain_openai)\n",
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            "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (6.0.2)\n",
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            "Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (3.10.5)\n",
            "Requirement already satisfied: async-timeout<5.0.0,>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (4.0.3)\n",
            "Collecting langchain-text-splitters<0.3.0,>=0.2.0 (from langchain)\n",
            "  Downloading langchain_text_splitters-0.2.4-py3-none-any.whl.metadata (2.3 kB)\n",
            "Collecting langsmith<0.2.0,>=0.1.17 (from langchain)\n",
            "  Downloading langsmith-0.1.118-py3-none-any.whl.metadata (13 kB)\n",
            "Requirement already satisfied: numpy<2,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain) (1.26.4)\n",
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            "Collecting tenacity!=8.4.0,<9.0.0,>=8.1.0 (from langchain)\n",
            "  Downloading tenacity-8.5.0-py3-none-any.whl.metadata (1.2 kB)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (2.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.3.1)\n",
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            "\u001b[?25hDownloading jsonpointer-3.0.0-py2.py3-none-any.whl (7.6 kB)\n",
            "Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
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            "\u001b[?25hInstalling collected packages: tenacity, orjson, jsonpointer, jiter, h11, tiktoken, jsonpatch, httpcore, httpx, openai, langsmith, langchain-core, langchain-text-splitters, langchain_openai, langchain\n",
            "  Attempting uninstall: tenacity\n",
            "    Found existing installation: tenacity 9.0.0\n",
            "    Uninstalling tenacity-9.0.0:\n",
            "      Successfully uninstalled tenacity-9.0.0\n",
            "Successfully installed h11-0.14.0 httpcore-1.0.5 httpx-0.27.2 jiter-0.5.0 jsonpatch-1.33 jsonpointer-3.0.0 langchain-0.2.16 langchain-core-0.2.39 langchain-text-splitters-0.2.4 langchain_openai-0.1.23 langsmith-0.1.118 openai-1.44.1 orjson-3.10.7 tenacity-8.5.0 tiktoken-0.7.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_openai import ChatOpenAI\n",
        "from langchain_core.messages import HumanMessage\n",
        "\n",
        "model = ChatOpenAI(model=\"gpt-3.5-turbo\")"
      ],
      "metadata": {
        "id": "23vvxiJnsgT2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.invoke([HumanMessage(content='Hi! my name is bob')]).content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "jdB7fNSFuA6M",
        "outputId": "c870665d-e266-43a9-cb5c-d8930cb70b2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hello Bob! How can I assist you today?'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.invoke([HumanMessage(content='What is my name?')]).content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "UobvpwGvuA3_",
        "outputId": "1cce9941-8b4f-4f1b-a995-9cca3848c72f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"I'm sorry, I don't know your name. Can you please tell me?\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.messages import AIMessage\n",
        "\n",
        "model.invoke(\n",
        "    [\n",
        "        HumanMessage(content=\"Hi! I'm Bob\"),\n",
        "        AIMessage(content=\"Hello Bob! How can I assist you today?\"),\n",
        "        HumanMessage(content=\"What's my name?\"),\n",
        "    ]\n",
        ").content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "5p0RB84vuA1z",
        "outputId": "f369276d-de23-4d5d-fc9e-b8bda92d896c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Your name is Bob.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.chat_history import (\n",
        "    BaseChatMessageHistory,\n",
        "    InMemoryChatMessageHistory,\n",
        ")\n",
        "from langchain_core.runnables.history import RunnableWithMessageHistory"
      ],
      "metadata": {
        "id": "LUsh4oqxuAzU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "store = {}\n",
        "\n",
        "def get_session_history(session_id: str) -> BaseChatMessageHistory:\n",
        "  if session_id not in store:\n",
        "    store[session_id] = InMemoryChatMessageHistory()\n",
        "  return store[session_id]"
      ],
      "metadata": {
        "id": "ntXTERMC4UVe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with_message_history = RunnableWithMessageHistory(model, get_session_history)"
      ],
      "metadata": {
        "id": "uHGbQF7x4_gl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "config = {'configurable': {'session_id': 'abc2'}}"
      ],
      "metadata": {
        "id": "qEBoGIwI5JlB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response = with_message_history.invoke(\n",
        "    [HumanMessage(content='Hi! I am Bob')],\n",
        "     config=config\n",
        ")"
      ],
      "metadata": {
        "id": "RO6yHJEy5Tdc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response.content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "ghVFQpDd6hN8",
        "outputId": "bd57b9f1-1be4-4030-c9dc-32d16290e92b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hello Bob! How can I assist you today?'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = with_message_history.invoke(\n",
        "    [HumanMessage(content='What is my name?')],\n",
        "     config=config\n",
        ")"
      ],
      "metadata": {
        "id": "AC15U9uU6kS_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response.content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "VYWOlqwV6pMY",
        "outputId": "f18ead4b-9860-4ff5-8f4d-6703d070fc08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Your name is Bob.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "store"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kkqYPy8S6sKq",
        "outputId": "fe88412d-742f-473d-f8dc-ec0801cb5f95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'abc2': InMemoryChatMessageHistory(messages=[HumanMessage(content='Hi! I am Bob'), AIMessage(content='Hello Bob! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 12, 'total_tokens': 22}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-fee65ef9-1aa9-4b24-b261-63de0d0655f7-0', usage_metadata={'input_tokens': 12, 'output_tokens': 10, 'total_tokens': 22}), HumanMessage(content='What is my name?'), AIMessage(content='Your name is Bob.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 35, 'total_tokens': 40}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-cf7590de-c07a-4822-b37e-0a150d1da4f3-0', usage_metadata={'input_tokens': 35, 'output_tokens': 5, 'total_tokens': 40})])}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = with_message_history.invoke(\n",
        "    [HumanMessage(content='How are you?')],\n",
        "     config=config\n",
        ")"
      ],
      "metadata": {
        "id": "Cm0PuuPR6zlU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "store"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bcsvaXm_62zp",
        "outputId": "36905f62-be2b-49b8-e602-be476d2cfed1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'abc2': InMemoryChatMessageHistory(messages=[HumanMessage(content='Hi! I am Bob'), AIMessage(content='Hello Bob! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 12, 'total_tokens': 22}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-fee65ef9-1aa9-4b24-b261-63de0d0655f7-0', usage_metadata={'input_tokens': 12, 'output_tokens': 10, 'total_tokens': 22}), HumanMessage(content='What is my name?'), AIMessage(content='Your name is Bob.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 35, 'total_tokens': 40}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-cf7590de-c07a-4822-b37e-0a150d1da4f3-0', usage_metadata={'input_tokens': 35, 'output_tokens': 5, 'total_tokens': 40}), HumanMessage(content='How are you?'), AIMessage(content=\"I'm just a computer program, so I don't have feelings, but I'm here to help you with anything you need. How can I assist you today?\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 33, 'prompt_tokens': 52, 'total_tokens': 85}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-f6a527ec-d4d6-40ea-81cc-682fb60236d7-0', usage_metadata={'input_tokens': 52, 'output_tokens': 33, 'total_tokens': 85})])}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "config = {'configurable': {'session_id': 'abc3'}}"
      ],
      "metadata": {
        "id": "f206f2XJ7vkw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response = with_message_history.invoke(\n",
        "    [HumanMessage(content='What is my name?')],\n",
        "     config=config\n",
        ")\n",
        "response"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gSRGDFJi7yQq",
        "outputId": "fceb3230-ed81-47c9-f753-2462cced3aea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AIMessage(content=\"I'm sorry, I do not have access to personal information such as your name.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 12, 'total_tokens': 29}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-86ff7ae9-1dee-4029-8939-b0752dab2267-0', usage_metadata={'input_tokens': 12, 'output_tokens': 17, 'total_tokens': 29})"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = with_message_history.invoke(\n",
        "    [HumanMessage(content='Hi! I am Carli')],\n",
        "     config=config\n",
        ")\n",
        "response.content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "5TgTRPIT75-a",
        "outputId": "76708618-8f46-4ab1-9ff6-7c6a38bab823"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hello Carli! How can I assist you today?'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = with_message_history.invoke(\n",
        "    [HumanMessage(content='What is my name?')],\n",
        "     config=config\n",
        ")\n",
        "response"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P6Lys8Gg79QE",
        "outputId": "2f06e157-f3a5-4bf9-d26d-0729222b23c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AIMessage(content='Your name is Carli.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 67, 'total_tokens': 73}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-52489772-e003-4ede-9ab2-bb26d09aa3ad-0', usage_metadata={'input_tokens': 67, 'output_tokens': 6, 'total_tokens': 73})"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "store"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BdbFOyS58B9-",
        "outputId": "69e21de9-0fec-406c-986e-8c646e987cff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'abc2': InMemoryChatMessageHistory(messages=[HumanMessage(content='Hi! I am Bob'), AIMessage(content='Hello Bob! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 12, 'total_tokens': 22}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-fee65ef9-1aa9-4b24-b261-63de0d0655f7-0', usage_metadata={'input_tokens': 12, 'output_tokens': 10, 'total_tokens': 22}), HumanMessage(content='What is my name?'), AIMessage(content='Your name is Bob.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 35, 'total_tokens': 40}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-cf7590de-c07a-4822-b37e-0a150d1da4f3-0', usage_metadata={'input_tokens': 35, 'output_tokens': 5, 'total_tokens': 40}), HumanMessage(content='How are you?'), AIMessage(content=\"I'm just a computer program, so I don't have feelings, but I'm here to help you with anything you need. How can I assist you today?\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 33, 'prompt_tokens': 52, 'total_tokens': 85}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-f6a527ec-d4d6-40ea-81cc-682fb60236d7-0', usage_metadata={'input_tokens': 52, 'output_tokens': 33, 'total_tokens': 85})]),\n",
              " 'abc3': InMemoryChatMessageHistory(messages=[HumanMessage(content='What is my name?'), AIMessage(content=\"I'm sorry, I do not have access to personal information such as your name.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 12, 'total_tokens': 29}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-86ff7ae9-1dee-4029-8939-b0752dab2267-0', usage_metadata={'input_tokens': 12, 'output_tokens': 17, 'total_tokens': 29}), HumanMessage(content='Hi! I am Carli'), AIMessage(content='Hello Carli! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 43, 'total_tokens': 54}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-0923a4e2-580c-4430-944a-8391625228dc-0', usage_metadata={'input_tokens': 43, 'output_tokens': 11, 'total_tokens': 54}), HumanMessage(content='What is my name?'), AIMessage(content='Your name is Carli.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 67, 'total_tokens': 73}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-52489772-e003-4ede-9ab2-bb26d09aa3ad-0', usage_metadata={'input_tokens': 67, 'output_tokens': 6, 'total_tokens': 73})])}"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
        "\n",
        "prompt = ChatPromptTemplate.from_messages(\n",
        "    [\n",
        "        (\n",
        "            'system', 'You are a helpful assistant. Answer all question to the best of your abity.'\n",
        "        ),\n",
        "        MessagesPlaceholder(variable_name='messages')\n",
        "\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "id": "2A38PQChAMxZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain  = prompt | model"
      ],
      "metadata": {
        "id": "43qPdEOMAzaP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response = chain.invoke(\n",
        "    {\n",
        "        'messages': [HumanMessage(content='Hi! I am Bob')]\n",
        "    }\n",
        ")\n",
        "response.content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "oD-dcCZJEQRv",
        "outputId": "ecdf4485-6033-40c3-ec1f-ea1ef357be53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hello Bob! How can I assist you today?'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = chain.invoke(\n",
        "    {\n",
        "        'messages': [HumanMessage(content='What is your name?')]\n",
        "    }\n",
        ")\n",
        "response.content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "vi4E17gCEfaO",
        "outputId": "e45ed5bb-4ab0-4fd7-d369-6bfe12e8c2a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'I am a virtual assistant and do not have a personal name. You can just call me assistant. How can I assist you today?'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with_message_history = RunnableWithMessageHistory(chain,get_session_history)"
      ],
      "metadata": {
        "id": "QH3rW6J0EjMH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "config = { 'configurable': {'session_id': 'abc5'}}"
      ],
      "metadata": {
        "id": "a8Ox5nxnEs3G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response = with_message_history.invoke(\n",
        "    [HumanMessage(content='What is your name?')],\n",
        "    config=config\n",
        ")\n",
        "response.content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "YDbh8AJwE0_s",
        "outputId": "d7341f63-d334-4b89-cb4a-22759a23479f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'I am a helpful assistant here to assist you with any questions or tasks you have. You can call me Assistant. How can I assist you today?'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = with_message_history.invoke(\n",
        "    [HumanMessage(content='Hi! I am Bob')],\n",
        "    config=config\n",
        ")\n",
        "response.content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "WPsQLSuSFDxF",
        "outputId": "1116e9f0-03ad-499d-e863-74b5843003d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hello Bob! How can I assist you today?'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RAG"
      ],
      "metadata": {
        "id": "vAJoeFciJxAU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "import os\n",
        "\n",
        "if \"OPENAI_API_KEY\" not in os.environ:\n",
        "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IVahZzArJzX9",
        "outputId": "1e01e660-f493-4772-a794-e8c2e2e9e364"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your OpenAI API key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qU langchain langchain_community unstructured langchain_openai"
      ],
      "metadata": {
        "id": "JZ92QoSeJzVm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade --quiet pypdf"
      ],
      "metadata": {
        "id": "trPUqYrRN26d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.document_loaders import DirectoryLoader, PyPDFLoader"
      ],
      "metadata": {
        "id": "45nBrjsSJzTZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loader = DirectoryLoader(\n",
        "    '/content/drive/MyDrive/PDFs',\n",
        "    glob='**/*.pdf',\n",
        "    loader_cls=PyPDFLoader\n",
        ")"
      ],
      "metadata": {
        "id": "OmkZ5ggyJzAW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pages = loader.load()"
      ],
      "metadata": {
        "id": "qB0pVGDLQAbw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pages"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c64MDnXHQQfV",
        "outputId": "5ac66894-2a87-426d-8c36-24dc4ee72fb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='Informe de Análisis de Ventas - Tienda ABC\\nEste informe presenta un análisis detallado de las ventas realizadas en la Tienda ABC durante el\\núltimo trimestre. El objetivo de este informe es identificar tendencias, analizar el rendimiento de los\\nproductos, y proponer recomendaciones para mejorar las ventas en el próximo período.\\n1. Rendimiento General de Ventas\\nEn el último trimestre, las ventas totales alcanzaron un valor de $150,000, con un incremento del\\n15% respecto al trimestre anterior. El número total de transacciones fue de 2,500, lo que representa\\nun promedio de 833 transacciones por mes.\\n2. Análisis por Categoría de Producto\\nLas siguientes categorías de productos se analizaron según su rendimiento en ventas:\\n- Ropa: Representa el 40% de las ventas totales, con un valor de $60,000.\\n- Calzado: Contribuye con el 30% de las ventas, alcanzando $45,000.\\n- Accesorios: Generaron $30,000, representando el 20% de las ventas.\\n- Otros: El 10% restante, equivalente a $15,000, proviene de productos diversos.\\n3. Productos más Vendidos\\nLos siguientes productos fueron los más vendidos durante el trimestre:\\n- Camiseta Básica: 500 unidades vendidas, con un total de $10,000 en ingresos.\\n- Zapatos Deportivos: 300 unidades vendidas, generando $18,000 en ingresos.\\n- Reloj de Pulsera: 200 unidades vendidas, con un total de $20,000 en ingresos.\\n4. Recomendaciones\\nPara incrementar las ventas en el próximo trimestre, se sugieren las siguientes acciones:'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 1}, page_content='- Lanzar promociones especiales para productos de la categoría de accesorios.\\n- Optimizar la presencia online de la tienda para captar más clientes a través de redes sociales.\\n- Realizar campañas de fidelización para los clientes recurrentes, ofreciendo descuentos exclusivos.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='Guía de Viaje: Descubre París\\nParís, la ciudad del amor, es uno de los destinos turísticos más icónicos del mundo. Con sus\\nmonumentos históricos, su rica cultura y gastronomía, y su ambiente romántico, la capital de\\nFrancia atrae a millones de visitantes cada año. En esta guía encontrarás recomendaciones sobre\\nlos mejores lugares para visitar, comer, y disfrutar en París.\\n1. Lugares Imprescindibles\\nNo puedes visitar París sin conocer los siguientes lugares:\\n- Torre Eiffel: El símbolo de París y una de las estructuras más reconocidas del mundo. No te\\npierdas la vista panorámica desde la cima.\\n- Museo del Louvre: El museo más grande del mundo, hogar de obras maestras como la Mona Lisa\\ny la Venus de Milo.\\n- Catedral de Notre-Dame: Una catedral gótica que impresiona por su arquitectura y su historia.\\n- Campos Elíseos y Arco del Triunfo: Pasea por esta avenida famosa y sube al Arco para una vista\\nespectacular de la ciudad.\\n2. Gastronomía Local\\nParís es un paraíso para los amantes de la comida. Aquí te recomendamos algunos platos típicos y\\nlugares para probarlos:\\n- Croissants y pain au chocolat: Disfruta de estos deliciosos pasteles en cualquier café parisino.\\n- Ratatouille: Un plato tradicional de verduras cocidas al horno, perfecto para vegetarianos.\\n- Crêpes: Tanto dulces como saladas, las crêpes son una delicia en cualquier momento del día.\\n- Restaurante Le Jules Verne: Ubicado en la Torre Eiffel, este restaurante ofrece una experiencia\\ngastronómica inolvidable.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 1}, page_content=\"3. Opciones de Transporte\\nMoverse por París es fácil gracias a su eficiente sistema de transporte público. Las opciones\\nincluyen:\\n- Metro: El metro de París cuenta con 16 líneas que cubren la mayor parte de la ciudad.\\n- Autobús: Ideal para disfrutar del paisaje mientras te desplazas de un lugar a otro.\\n- Bicicletas: Puedes alquilar una bicicleta en las estaciones de Vélib' y explorar la ciudad sobre dos\\nruedas.\\n- Barcos Batobus: Una manera única de moverse por el río Sena y disfrutar de las vistas.\\n4. Alojamiento Recomendado\\nParís ofrece una amplia gama de alojamientos para todos los presupuestos. Algunas opciones\\nrecomendadas son:\\n- Hotel Le Meurice: Un hotel de lujo con vistas al Jardín de las Tullerías.\\n- Hôtel des Grands Boulevards: Una opción boutique, perfecta para una estancia romántica.\\n- Generator Hostel: Si buscas algo más económico, este moderno hostel es ideal para jóvenes\\nviajeros.\"),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 0}, page_content='Recetario de Cocina Saludable\\nEste recetario está diseñado para quienes desean preparar comidas saludables y deliciosas. Cada\\nreceta incluye ingredientes frescos y opciones para hacer las comidas más nutritivas, manteniendo\\nel sabor y la sencillez en su preparación.\\n1. Desayuno - Avena con Frutas\\nIngredientes:\\n- 1 taza de avena\\n- 1 taza de leche de almendras\\n- 1 cucharada de miel\\n- Frutas frescas (fresas, plátano, arándanos)\\nInstrucciones:\\n1. Cocina la avena con la leche de almendras a fuego lento.\\n2. Una vez cocida, añade la miel y mezcla bien.\\n3. Decora con las frutas frescas antes de servir.\\n2. Almuerzo - Ensalada de Quinoa con Aguacate\\nIngredientes:\\n- 1 taza de quinoa cocida\\n- 1 aguacate\\n- 1 tomate\\n- Espinacas frescas\\n- Jugo de limón\\n- Sal y pimienta al gusto'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 1}, page_content='Instrucciones:\\n1. Mezcla la quinoa con el aguacate y el tomate cortados en cubos.\\n2. Añade las espinacas y aliña con jugo de limón, sal y pimienta al gusto.\\n3. Cena - Salmón al Horno con Vegetales\\nIngredientes:\\n- 2 filetes de salmón\\n- Brócoli y zanahorias\\n- Aceite de oliva\\n- Ajo en polvo\\n- Limón\\n- Sal y pimienta\\nInstrucciones:\\n1. Precalienta el horno a 180°C.\\n2. Coloca los filetes de salmón en una bandeja, rocía con aceite de oliva y condimenta con ajo en\\npolvo, sal y pimienta.\\n3. Añade los vegetales cortados y hornea por 20 minutos.\\n4. Sirve con una rodaja de limón sobre el salmón.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='Investigación Científica: Impacto de la IA en la Medicina\\nEste documento explora el impacto de la inteligencia artificial (IA) en el campo de la medicina. A lo\\nlargo de los últimos años, la IA ha demostrado ser una herramienta poderosa para mejorar el\\ndiagnóstico, optimizar los tratamientos y personalizar la atención médica. Este estudio analiza los\\navances actuales y el potencial futuro de la IA en la medicina.\\n1. Aplicaciones Actuales de la IA en Medicina\\nLa IA está siendo utilizada en varios aspectos del cuidado de la salud, incluyendo:\\n- Diagnóstico por imágenes: Algoritmos de IA analizan imágenes médicas para detectar\\nenfermedades como el cáncer de pulmón o el Alzheimer.\\n- Planificación de tratamientos: Herramientas de IA ayudan a personalizar los tratamientos en\\nfunción del perfil genético de cada paciente.\\n- Robótica en cirugía: Robots guiados por IA realizan procedimientos quirúrgicos con mayor\\nprecisión y menor riesgo.\\n2. Desafíos y Limitaciones\\nA pesar de los avances, la integración de la IA en la medicina enfrenta desafíos importantes:\\n- Privacidad de los datos: El uso de grandes volúmenes de datos médicos plantea preocupaciones\\nsobre la privacidad y la seguridad.\\n- Regulaciones: Es necesario que los gobiernos implementen marcos regulatorios adecuados para\\ngarantizar el uso seguro y ético de la IA.\\n- Confianza en los algoritmos: Muchos profesionales médicos son reacios a depender\\ncompletamente de las decisiones de la IA, prefiriendo un enfoque mixto con el juicio humano.\\n3. El Futuro de la IA en la Medicina'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 1}, page_content='El futuro de la IA en la medicina parece prometedor, con desarrollos que podrían transformar la\\natención médica:\\n- Medicina predictiva: La IA será capaz de prever enfermedades antes de que los síntomas\\naparezcan, basándose en análisis genéticos y ambientales.\\n- Optimización de sistemas de salud: Los hospitales utilizarán IA para gestionar recursos, optimizar\\nel flujo de pacientes y reducir los tiempos de espera.\\n- Telemedicina avanzada: La combinación de IA y telemedicina permitirá ofrecer diagnósticos y\\ntratamientos a distancia con gran precisión.')]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
        "\n",
        "text_splitter = RecursiveCharacterTextSplitter(\n",
        "    chunk_size = 100,\n",
        "    chunk_overlap=20\n",
        ")\n",
        "splits = text_splitter.split_documents(pages)"
      ],
      "metadata": {
        "id": "trj263NhQAYb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "splits"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OlxRtXkwQnBg",
        "outputId": "72ddf9bf-fde7-4202-f46b-d1eab84f9d47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='Informe de Análisis de Ventas - Tienda ABC'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='Este informe presenta un análisis detallado de las ventas realizadas en la Tienda ABC durante el'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='último trimestre. El objetivo de este informe es identificar tendencias, analizar el rendimiento de'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='el rendimiento de los'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='productos, y proponer recomendaciones para mejorar las ventas en el próximo período.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='1. Rendimiento General de Ventas'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='En el último trimestre, las ventas totales alcanzaron un valor de $150,000, con un incremento del'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='15% respecto al trimestre anterior. El número total de transacciones fue de 2,500, lo que'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='de 2,500, lo que representa'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='un promedio de 833 transacciones por mes.\\n2. Análisis por Categoría de Producto'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='Las siguientes categorías de productos se analizaron según su rendimiento en ventas:'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='- Ropa: Representa el 40% de las ventas totales, con un valor de $60,000.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='- Calzado: Contribuye con el 30% de las ventas, alcanzando $45,000.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='- Accesorios: Generaron $30,000, representando el 20% de las ventas.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='- Otros: El 10% restante, equivalente a $15,000, proviene de productos diversos.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='3. Productos más Vendidos\\nLos siguientes productos fueron los más vendidos durante el trimestre:'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='- Camiseta Básica: 500 unidades vendidas, con un total de $10,000 en ingresos.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='- Zapatos Deportivos: 300 unidades vendidas, generando $18,000 en ingresos.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='- Reloj de Pulsera: 200 unidades vendidas, con un total de $20,000 en ingresos.\\n4. Recomendaciones'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='Para incrementar las ventas en el próximo trimestre, se sugieren las siguientes acciones:'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 1}, page_content='- Lanzar promociones especiales para productos de la categoría de accesorios.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 1}, page_content='- Optimizar la presencia online de la tienda para captar más clientes a través de redes sociales.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 1}, page_content='- Realizar campañas de fidelización para los clientes recurrentes, ofreciendo descuentos'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 1}, page_content='descuentos exclusivos.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='Guía de Viaje: Descubre París'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='París, la ciudad del amor, es uno de los destinos turísticos más icónicos del mundo. Con sus'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='monumentos históricos, su rica cultura y gastronomía, y su ambiente romántico, la capital de'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='Francia atrae a millones de visitantes cada año. En esta guía encontrarás recomendaciones sobre'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='los mejores lugares para visitar, comer, y disfrutar en París.\\n1. Lugares Imprescindibles'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='No puedes visitar París sin conocer los siguientes lugares:'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='- Torre Eiffel: El símbolo de París y una de las estructuras más reconocidas del mundo. No te'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='pierdas la vista panorámica desde la cima.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='- Museo del Louvre: El museo más grande del mundo, hogar de obras maestras como la Mona Lisa'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='y la Venus de Milo.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='- Catedral de Notre-Dame: Una catedral gótica que impresiona por su arquitectura y su historia.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='- Campos Elíseos y Arco del Triunfo: Pasea por esta avenida famosa y sube al Arco para una vista'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='espectacular de la ciudad.\\n2. Gastronomía Local'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='París es un paraíso para los amantes de la comida. Aquí te recomendamos algunos platos típicos y'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='lugares para probarlos:'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='- Croissants y pain au chocolat: Disfruta de estos deliciosos pasteles en cualquier café parisino.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='- Ratatouille: Un plato tradicional de verduras cocidas al horno, perfecto para vegetarianos.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='- Crêpes: Tanto dulces como saladas, las crêpes son una delicia en cualquier momento del día.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='- Restaurante Le Jules Verne: Ubicado en la Torre Eiffel, este restaurante ofrece una experiencia'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 0}, page_content='gastronómica inolvidable.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 1}, page_content='3. Opciones de Transporte'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 1}, page_content='Moverse por París es fácil gracias a su eficiente sistema de transporte público. Las opciones'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 1}, page_content='incluyen:\\n- Metro: El metro de París cuenta con 16 líneas que cubren la mayor parte de la ciudad.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 1}, page_content='- Autobús: Ideal para disfrutar del paisaje mientras te desplazas de un lugar a otro.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 1}, page_content=\"- Bicicletas: Puedes alquilar una bicicleta en las estaciones de Vélib' y explorar la ciudad sobre\"),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 1}, page_content='la ciudad sobre dos'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 1}, page_content='ruedas.\\n- Barcos Batobus: Una manera única de moverse por el río Sena y disfrutar de las vistas.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 1}, page_content='4. Alojamiento Recomendado'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 1}, page_content='París ofrece una amplia gama de alojamientos para todos los presupuestos. Algunas opciones'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 1}, page_content='recomendadas son:\\n- Hotel Le Meurice: Un hotel de lujo con vistas al Jardín de las Tullerías.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 1}, page_content='- Hôtel des Grands Boulevards: Una opción boutique, perfecta para una estancia romántica.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 1}, page_content='- Generator Hostel: Si buscas algo más económico, este moderno hostel es ideal para jóvenes'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Guia_Viaje_Descubre_Paris.pdf', 'page': 1}, page_content='viajeros.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 0}, page_content='Recetario de Cocina Saludable'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 0}, page_content='Este recetario está diseñado para quienes desean preparar comidas saludables y deliciosas. Cada'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 0}, page_content='receta incluye ingredientes frescos y opciones para hacer las comidas más nutritivas, manteniendo'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 0}, page_content='el sabor y la sencillez en su preparación.\\n1. Desayuno - Avena con Frutas\\nIngredientes:'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 0}, page_content='Ingredientes:\\n- 1 taza de avena\\n- 1 taza de leche de almendras\\n- 1 cucharada de miel'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 0}, page_content='- Frutas frescas (fresas, plátano, arándanos)\\nInstrucciones:'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 0}, page_content='Instrucciones:\\n1. Cocina la avena con la leche de almendras a fuego lento.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 0}, page_content='2. Una vez cocida, añade la miel y mezcla bien.\\n3. Decora con las frutas frescas antes de servir.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 0}, page_content='2. Almuerzo - Ensalada de Quinoa con Aguacate\\nIngredientes:\\n- 1 taza de quinoa cocida\\n- 1 aguacate'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 0}, page_content='- 1 aguacate\\n- 1 tomate\\n- Espinacas frescas\\n- Jugo de limón\\n- Sal y pimienta al gusto'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 1}, page_content='Instrucciones:\\n1. Mezcla la quinoa con el aguacate y el tomate cortados en cubos.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 1}, page_content='2. Añade las espinacas y aliña con jugo de limón, sal y pimienta al gusto.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 1}, page_content='3. Cena - Salmón al Horno con Vegetales\\nIngredientes:\\n- 2 filetes de salmón\\n- Brócoli y zanahorias'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 1}, page_content='- Aceite de oliva\\n- Ajo en polvo\\n- Limón\\n- Sal y pimienta\\nInstrucciones:'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 1}, page_content='Instrucciones:\\n1. Precalienta el horno a 180°C.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 1}, page_content='2. Coloca los filetes de salmón en una bandeja, rocía con aceite de oliva y condimenta con ajo en'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 1}, page_content='polvo, sal y pimienta.\\n3. Añade los vegetales cortados y hornea por 20 minutos.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Recetario_Cocina_Saludable.pdf', 'page': 1}, page_content='4. Sirve con una rodaja de limón sobre el salmón.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='Investigación Científica: Impacto de la IA en la Medicina'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='Este documento explora el impacto de la inteligencia artificial (IA) en el campo de la medicina. A'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='de la medicina. A lo'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='largo de los últimos años, la IA ha demostrado ser una herramienta poderosa para mejorar el'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='diagnóstico, optimizar los tratamientos y personalizar la atención médica. Este estudio analiza los'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='avances actuales y el potencial futuro de la IA en la medicina.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='1. Aplicaciones Actuales de la IA en Medicina'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='La IA está siendo utilizada en varios aspectos del cuidado de la salud, incluyendo:'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='- Diagnóstico por imágenes: Algoritmos de IA analizan imágenes médicas para detectar'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='enfermedades como el cáncer de pulmón o el Alzheimer.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='- Planificación de tratamientos: Herramientas de IA ayudan a personalizar los tratamientos en'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='función del perfil genético de cada paciente.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='- Robótica en cirugía: Robots guiados por IA realizan procedimientos quirúrgicos con mayor'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='precisión y menor riesgo.\\n2. Desafíos y Limitaciones'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='A pesar de los avances, la integración de la IA en la medicina enfrenta desafíos importantes:'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='- Privacidad de los datos: El uso de grandes volúmenes de datos médicos plantea preocupaciones'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='sobre la privacidad y la seguridad.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='- Regulaciones: Es necesario que los gobiernos implementen marcos regulatorios adecuados para'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='garantizar el uso seguro y ético de la IA.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='- Confianza en los algoritmos: Muchos profesionales médicos son reacios a depender'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='completamente de las decisiones de la IA, prefiriendo un enfoque mixto con el juicio humano.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 0}, page_content='3. El Futuro de la IA en la Medicina'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 1}, page_content='El futuro de la IA en la medicina parece prometedor, con desarrollos que podrían transformar la'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 1}, page_content='atención médica:'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 1}, page_content='- Medicina predictiva: La IA será capaz de prever enfermedades antes de que los síntomas'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 1}, page_content='aparezcan, basándose en análisis genéticos y ambientales.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 1}, page_content='- Optimización de sistemas de salud: Los hospitales utilizarán IA para gestionar recursos,'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 1}, page_content='gestionar recursos, optimizar'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 1}, page_content='el flujo de pacientes y reducir los tiempos de espera.'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 1}, page_content='- Telemedicina avanzada: La combinación de IA y telemedicina permitirá ofrecer diagnósticos y'),\n",
              " Document(metadata={'source': '/content/drive/MyDrive/PDFs/Investigacion_IA_Medicina.pdf', 'page': 1}, page_content='tratamientos a distancia con gran precisión.')]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "splits[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kuaKRAmTQtYF",
        "outputId": "069b6a22-82d2-4a20-85d2-b3759785cffc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Document(metadata={'source': '/content/drive/MyDrive/PDFs/Informe_Analisis_Ventas_Tienda_ABC.pdf', 'page': 0}, page_content='Informe de Análisis de Ventas - Tienda ABC')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain_chroma"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y8etW9fgRcGK",
        "outputId": "79da16e0-0bd4-4fc3-8b53-2522cf6b44a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain_chroma\n",
            "  Downloading langchain_chroma-0.1.3-py3-none-any.whl.metadata (1.5 kB)\n",
            "Collecting chromadb!=0.5.4,!=0.5.5,<0.6.0,>=0.4.0 (from langchain_chroma)\n",
            "  Downloading chromadb-0.5.3-py3-none-any.whl.metadata (6.8 kB)\n",
            "Collecting fastapi<1,>=0.95.2 (from langchain_chroma)\n",
            "  Downloading fastapi-0.114.1-py3-none-any.whl.metadata (27 kB)\n",
            "Requirement already satisfied: langchain-core<0.3,>=0.1.40 in /usr/local/lib/python3.10/dist-packages (from langchain_chroma) (0.2.39)\n",
            "Requirement already satisfied: numpy<2,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain_chroma) (1.26.4)\n",
            "Requirement already satisfied: build>=1.0.3 in /usr/local/lib/python3.10/dist-packages (from chromadb!=0.5.4,!=0.5.5,<0.6.0,>=0.4.0->langchain_chroma) (1.2.2)\n",
            "Requirement already satisfied: requests>=2.28 in /usr/local/lib/python3.10/dist-packages (from chromadb!=0.5.4,!=0.5.5,<0.6.0,>=0.4.0->langchain_chroma) (2.32.3)\n",
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            "Collecting chroma-hnswlib==0.7.3 (from chromadb!=0.5.4,!=0.5.5,<0.6.0,>=0.4.0->langchain_chroma)\n",
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            "\u001b[?25hBuilding wheels for collected packages: pypika\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_chroma import Chroma\n",
        "from langchain_openai import OpenAIEmbeddings\n",
        "\n",
        "vectorstore = Chroma.from_documents(\n",
        "    documents = splits,\n",
        "    embedding=OpenAIEmbeddings()\n",
        ")\n",
        "\n",
        "retriever = vectorstore.as_retriever()"
      ],
      "metadata": {
        "id": "YSKZcS6LR2R4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = (\n",
        "    'Eres un asistente que devuelve información de múltiples PDFS, además incluye emojis a cada una de la s respuestas. tienes el siguiente {context}'\n",
        "\n",
        ")"
      ],
      "metadata": {
        "id": "x-HpV_BTS6HA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_openai import ChatOpenAI\n",
        "\n",
        "llm = ChatOpenAI(\n",
        "    model = 'gpt-4o',\n",
        "    temperature=0,\n",
        "    max_tokens=150\n",
        "\n",
        ")"
      ],
      "metadata": {
        "id": "ZXNi9P6DTjCZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
        "\n",
        "from langchain.chains import create_history_aware_retriever, create_retrieval_chain\n"
      ],
      "metadata": {
        "id": "sBHdVsYsf3uX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "contextualize_q_system_prompt = (\n",
        "    \"Responde segun el historial de chat y la última pregunta del usuario \"\n",
        "    \"si no está en el historial de chat o en el contexto. NO respondas la pregunta \"\n",
        "    \"Además responde de manera profesional a la pregunta del ususario\"\n",
        "\n",
        ")"
      ],
      "metadata": {
        "id": "UuoZY8wYiQtr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "contextualize_q_prompt = ChatPromptTemplate.from_messages(\n",
        "    [\n",
        "        (\n",
        "            'system', contextualize_q_system_prompt\n",
        "        ),\n",
        "        MessagesPlaceholder('chat_history'),\n",
        "        ('human', '{input}')\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "id": "osoAinnbifP-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history_aware_retriever = create_history_aware_retriever(\n",
        "    llm, retriever, contextualize_q_prompt\n",
        ")"
      ],
      "metadata": {
        "id": "nOHESy7mjCrW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chains.combine_documents import create_stuff_documents_chain"
      ],
      "metadata": {
        "id": "GW3z5oMbj01o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "qa_prompt = ChatPromptTemplate.from_messages(\n",
        "    [\n",
        "        ('system', system_prompt),\n",
        "        MessagesPlaceholder('chat_history'),\n",
        "        ('human', '{input}')\n",
        "    ]\n",
        "\n",
        ")"
      ],
      "metadata": {
        "id": "L82wyzRrknF0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "question_answer_chain = create_stuff_documents_chain(\n",
        "    llm,qa_prompt\n",
        ")"
      ],
      "metadata": {
        "id": "wfgSRoQJk7eX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rag_chain = create_retrieval_chain(\n",
        "    history_aware_retriever,question_answer_chain\n",
        ")"
      ],
      "metadata": {
        "id": "kldTbUIylCZb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.chat_history import BaseChatMessageHistory\n",
        "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
        "from langchain_community.chat_message_histories import ChatMessageHistory\n",
        "\n",
        "\n",
        "store = {}\n",
        "\n",
        "\n",
        "def get_session_history(session_id: str) -> BaseChatMessageHistory:\n",
        "    if session_id not in store:\n",
        "        store[session_id] = ChatMessageHistory()\n",
        "    return store[session_id]"
      ],
      "metadata": {
        "id": "wdxe6RQnllgs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conversational_rag_chain = RunnableWithMessageHistory(\n",
        "    rag_chain,\n",
        "    get_session_history,\n",
        "    input_messages_key='input',\n",
        "    history_messages_key='chat_history',\n",
        "    output_messages_key='answer'\n",
        ")"
      ],
      "metadata": {
        "id": "_bP5AzEpm6Iz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conversational_rag_chain.invoke(\n",
        "    {\n",
        "        'input': 'Cuales son las ventas del ultimo trimestre?'\n",
        "    },\n",
        "    config={\n",
        "        'configurable': {'session_id':'abc123'}\n",
        "    }\n",
        ")['answer']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "kCMiG-LmnMxl",
        "outputId": "92c2e4ac-902a-42ea-e152-ca5d692df40a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Las ventas totales del último trimestre alcanzaron un valor de $150,000. 📈💰'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(conversational_rag_chain.invoke(\n",
        "    {\n",
        "        'input': 'Cual es la receta de Avena con Frutas?'\n",
        "    },\n",
        "    config={\n",
        "        'configurable': {'session_id':'abc123'}\n",
        "    }\n",
        ")['answer'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j_qf5d3vp5Ao",
        "outputId": "1eaff6b2-4ba2-40f6-fbd8-df3807bd145a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "¡Claro! Aquí tienes la receta de Avena con Frutas:\n",
            "\n",
            "**Ingredientes:**\n",
            "- 1 taza de avena\n",
            "- 1 taza de leche de almendras\n",
            "- 1 cucharada de miel\n",
            "- Frutas frescas (a tu elección)\n",
            "\n",
            "**Instrucciones:**\n",
            "1. Cocina la avena con la leche de almendras a fuego lento. 🍲\n",
            "2. Una vez cocida, añade la miel y mezcla bien. 🍯\n",
            "3. Decora con las frutas frescas antes de servir. 🍓🍌🍇\n",
            "\n",
            "¡Disfruta de tu delicioso y saludable desayuno! 🥣✨\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pinecone"
      ],
      "metadata": {
        "id": "ADJbh6_LhCJC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "import os\n",
        "\n",
        "if \"OPENAI_API_KEY\" not in os.environ:\n",
        "  os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oi5FP2O9hDyx",
        "outputId": "0bdadd1d-4fec-4115-d582-b9186e12dfab"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your OpenAI API key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qU langchain-openai langchain_community"
      ],
      "metadata": {
        "id": "RKiitafdhdUA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain-pinecone"
      ],
      "metadata": {
        "id": "Fw0QHaNehjZF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_openai import ChatOpenAI\n",
        "\n",
        "llm = ChatOpenAI(model='gpt-4o',\n",
        "                 temperature=0,\n",
        "                 max_tokens = 100)"
      ],
      "metadata": {
        "id": "6FkktzbChlks"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm.invoke('Hola').content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "ig6B3p8iqclF",
        "outputId": "0f8cfc2b-ecef-4f9b-9948-779404cd8f9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'¡Hola! ¿En qué puedo ayudarte hoy?'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "import os\n",
        "\n",
        "if \"PINECONE_API_KEY\" not in os.environ:\n",
        "  os.environ[\"PINECONE_API_KEY\"] = getpass.getpass(\"Enter your PINECONE_API_KEY API key: \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RAgXDeeBqvNT",
        "outputId": "de2913e5-3b95-4418-d7c6-23b5cccf9a78"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your PINECONE_API_KEY API key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.document_loaders import TextLoader\n",
        "from langchain_text_splitters import CharacterTextSplitter"
      ],
      "metadata": {
        "id": "NSLSEBKHrNW7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index_name = 'documentation'\n",
        "\n",
        "loader = TextLoader('/content/H.txt')"
      ],
      "metadata": {
        "id": "W1J34Tcgv2Jg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "documents = loader.load()\n",
        "text_splitter =  CharacterTextSplitter(\n",
        "    chunk_size =200,\n",
        "    chunk_overlap = 40\n",
        ")\n",
        "\n",
        "docs = text_splitter.split_documents(documents)"
      ],
      "metadata": {
        "id": "rRktMHXfwAOq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_openai import OpenAIEmbeddings\n",
        "embedding = OpenAIEmbeddings(model='text-embedding-3-large')"
      ],
      "metadata": {
        "id": "DCJA7MRkwxtr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_pinecone import PineconeVectorStore\n",
        "\n",
        "vectorstore_from_docs = PineconeVectorStore.from_documents(\n",
        "    docs,\n",
        "    index_name = index_name,\n",
        "    embedding=embedding\n",
        ")\n"
      ],
      "metadata": {
        "id": "7bKW4PGEw8_y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = 'Donde nacio el hidrogeno'\n",
        "print(vectorstore_from_docs.similarity_search(query))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UdUiZcU4ycAv",
        "outputId": "d88a8bcc-fd4a-41d1-d3e4-fc07fd0bbdbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Document(metadata={'source': '/content/H.txt'}, page_content='Hidrógeno, el primero,\\nnacido en el aliento del cosmos,\\ntan leve que flota entre las manos del universo,\\nportador de la chispa que incendia las estrellas,\\nel origen que nunca se apaga.'), Document(metadata={'source': '/content/H.txt'}, page_content='Hidrógeno, el viajero eterno,\\nla clave en la expansión del todo,\\nel hilo invisible que une\\nlo que fue, lo que es,\\ny lo que será.'), Document(metadata={'source': '/content/H.txt'}, page_content='Invisible, pero esencial,\\nla energía que duerme en cada átomo,\\nen los sueños de un futuro limpio,\\ndonde la chispa de hidrógeno\\nenciende motores sin humo ni sombras.'), Document(metadata={'source': '/content/H.txt'}, page_content='Es el susurro del agua,\\nen la molécula que danza,\\nel alfabeto del viento,\\nen cada brisa que se escapa.')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tools"
      ],
      "metadata": {
        "id": "aN_0ozpHCzXn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qU langchain-openai langchain_community"
      ],
      "metadata": {
        "id": "mSWhK0KbFIXG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import sqlite3\n",
        "\n",
        "import requests\n",
        "from langchain_community.utilities.sql_database import SQLDatabase\n",
        "from sqlalchemy import create_engine\n",
        "from sqlalchemy.pool import StaticPool\n",
        "\n",
        "\n",
        "def get_engine_for_chinook_db():\n",
        "    \"\"\"Pull sql file, populate in-memory database, and create engine.\"\"\"\n",
        "    url = \"https://raw.githubusercontent.com/lerocha/chinook-database/master/ChinookDatabase/DataSources/Chinook_Sqlite.sql\"\n",
        "    response = requests.get(url)\n",
        "    sql_script = response.text\n",
        "\n",
        "    connection = sqlite3.connect(\":memory:\", check_same_thread=False)\n",
        "    connection.executescript(sql_script)\n",
        "    return create_engine(\n",
        "        \"sqlite://\",\n",
        "        creator=lambda: connection,\n",
        "        poolclass=StaticPool,\n",
        "        connect_args={\"check_same_thread\": False},\n",
        "    )\n",
        "\n",
        "\n",
        "engine = get_engine_for_chinook_db()\n",
        "\n",
        "db = SQLDatabase(engine)"
      ],
      "metadata": {
        "id": "Fxkn1xmMDGhA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_openai import ChatOpenAI\n",
        "\n",
        "llm = ChatOpenAI(model='gpt-4o',\n",
        "                 temperature=0,\n",
        "                 max_tokens = 100)"
      ],
      "metadata": {
        "id": "5hd90IcDFWgv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit\n",
        "\n",
        "toolkit = SQLDatabaseToolkit(db=db, llm=llm)"
      ],
      "metadata": {
        "id": "xgat53ixFLDF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Agent"
      ],
      "metadata": {
        "id": "ZxiWDJ7KbCxU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "import os\n",
        "#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\n",
        "os.environ[\"TAVILY_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K6FG_AfCbEEM",
        "outputId": "c26c5e96-da18-481f-f11c-d5b1fafd4393"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your OpenAI API key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "import os\n",
        "#sk-proj-t8XFiRvFA7IKAhuSu8kKT3BlbkFJbCvznM1tliBsa933Bg1v\n",
        "\n",
        "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hPHYIjiib9gj",
        "outputId": "45dc347a-2a19-4adb-f9c2-3d2027e5ab29"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your OpenAI API key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain_community"
      ],
      "metadata": {
        "id": "41G-pQbDbI6W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.tools.tavily_search import TavilySearchResults\n",
        "search = TavilySearchResults(max_results=2)"
      ],
      "metadata": {
        "id": "u40FAadobLsO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "search.invoke(\"que es openai o1-mini\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rMQvXybQbOHa",
        "outputId": "3a1aff43-72e2-4f4b-eaba-19752daf0d88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'url': 'https://openai.com/index/openai-o1-mini-advancing-cost-efficient-reasoning/',\n",
              "  'content': \"OpenAI o1-mini We're releasing OpenAI o1-mini, a cost-efficient reasoning model. We expect o1-mini will be a faster, cost-effective model for applications that require reasoning without broad world knowledge. Today, we are launching o1-mini to tier 5 API users(opens in a new window) at a cost that is 80% cheaper than OpenAI o1-preview. ChatGPT Plus, Team, Enterprise, and Edu users can use o1-mini as an alternative to o1-preview, with higher rate limits and lower latency (see Model Speed). When evaluated on benchmarks requiring intelligence and reasoning, o1-mini performs well compared to o1-preview and o1. Human preference evaluation: We had human raters compare o1-mini to GPT-4o on challenging, open-ended prompts in various domains, using the same methodology as our o1-preview vs GPT-4o comparison. GPT-4o mini\"},\n",
              " {'url': 'https://www.elgrupoinformatico.com/inteligencia-artificial/openai-o1-detalles-capacidad/',\n",
              "  'content': 'Por otro lado, OpenAI ha lanzado una variante más pequeña de este modelo, el OpenAI o1-mini, que es más rápido y económico. OpenAI o1 ya está disponible para desarrolladores y para los usuarios de ChatGPT Plus y Team en su versión estándar y en su versión mini, aunque tendrán límites de mensajes semanales que irán siendo ampliados ...'}]"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip -qU install faiss-gpu"
      ],
      "metadata": {
        "id": "4cVYiCbcbsgP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.document_loaders import WebBaseLoader\n",
        "from langchain_community.vectorstores import FAISS\n",
        "from langchain_openai import OpenAIEmbeddings\n",
        "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
        "\n",
        "loader = WebBaseLoader(\"https://docs.smith.langchain.com/overview\")\n",
        "docs = loader.load()\n",
        "\n",
        "documents = RecursiveCharacterTextSplitter(\n",
        "    chunk_size=1000,\n",
        "    chunk_overlap=200\n",
        ").split_documents(docs)\n",
        "\n",
        "vector = FAISS.from_documents(documents, OpenAIEmbeddings())\n",
        "retriever = vector.as_retriever()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yOX7Q-YGbyMj",
        "outputId": "a2d5008d-8554-445c-8527-ade844c7fa9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:langchain_community.utils.user_agent:USER_AGENT environment variable not set, consider setting it to identify your requests.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "retriever.invoke(\"how to upload a dataset\")[0]"
      ],
      "metadata": {
        "id": "PY02zepgceIx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.tools.retriever import create_retriever_tool\n",
        "\n",
        "txt = '''Busca información sobre LangSmith.\n",
        "¡Para cualquier pregunta sobre LangSmith,\n",
        "debes usar esta herramienta!'''\n",
        "\n",
        "retriever_tool = create_retriever_tool(\n",
        "    retriever,\n",
        "    \"langsmith_search\",\n",
        "    txt,)"
      ],
      "metadata": {
        "id": "bundEF-EdPRW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tools = [search, retriever_tool]"
      ],
      "metadata": {
        "id": "pq5lkIe9dqap"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_with_tools = model.bind_tools(tools)"
      ],
      "metadata": {
        "id": "1I6-hfy8dsVK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.messages import HumanMessage\n",
        "response = model_with_tools.invoke([HumanMessage(content=\"Hi!\")])\n",
        "\n",
        "print(f\"ContentString: {response.content}\")\n",
        "print(f\"ToolCalls: {response.tool_calls}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OZM3xDn5eDsr",
        "outputId": "7d06e30f-dffc-4c22-e50d-ea97473840a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ContentString: Hello! How can I assist you today?\n",
            "ToolCalls: []\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = model_with_tools.invoke([HumanMessage(content=\n",
        "                            \"What's the weather in SF?\")])\n",
        "\n",
        "print(f\"ContentString: {response.content}\")\n",
        "print(f\"ToolCalls: {response.tool_calls}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r2GJXWTMeHh7",
        "outputId": "54bd23f8-7807-43bb-d15b-cc269204e46a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ContentString: \n",
            "ToolCalls: [{'name': 'tavily_search_results_json', 'args': {'query': 'current weather in San Francisco'}, 'id': 'call_7KT49d4Cr5vl5NDLFhrGm2e2', 'type': 'tool_call'}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain import hub\n",
        "\n",
        "# Get the prompt to use - you can modify this!\n",
        "prompt = hub.pull(\"hwchase17/openai-functions-agent\")\n",
        "prompt.messages"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7QlVFCByeJb0",
        "outputId": "17498727-7963-41a6-c4dc-da9fe2e9fb71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langsmith/client.py:322: LangSmithMissingAPIKeyWarning: API key must be provided when using hosted LangSmith API\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/langsmith/client.py:5301: LangChainBetaWarning: The function `loads` is in beta. It is actively being worked on, so the API may change.\n",
            "  prompt = loads(json.dumps(prompt_object.manifest))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=[], template='You are a helpful assistant')),\n",
              " MessagesPlaceholder(variable_name='chat_history', optional=True),\n",
              " HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['input'], template='{input}')),\n",
              " MessagesPlaceholder(variable_name='agent_scratchpad')]"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.agents import create_tool_calling_agent\n",
        "\n",
        "agent = create_tool_calling_agent(model, tools, prompt)"
      ],
      "metadata": {
        "id": "1Kry-lvneSxj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.agents import AgentExecutor\n",
        "\n",
        "agent_executor = AgentExecutor(agent=agent, tools=tools)"
      ],
      "metadata": {
        "id": "IS-I7A5TeTdM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "agent_executor.invoke({\"input\": \"hi!\"})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Y_ELtdMeU4k",
        "outputId": "f50110f6-4b20-446b-e481-eeeae4201ac8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input': 'hi!', 'output': 'Hello! How can I assist you today?'}"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "agent_executor.invoke({\"input\": \"Cómo ayuda langsmith\n",
        "a testing?\"})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t-Mo7uX3eW0N",
        "outputId": "6f6fa59c-58cb-41e5-e9e8-58ce9429820a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input': 'Cómo ayuda langsmith a testing?',\n",
              " 'output': 'LangSmith ayuda en el testing proporcionando herramientas y funcionalidades para evaluar y rastrear el rendimiento de los modelos de lenguaje. Aquí hay un resumen de cómo LangSmith puede ser útil en el proceso de testing:\\n\\n1. **Creación de Conjuntos de Datos de Prueba**: Permite definir conjuntos de datos que actúan como casos de prueba. Estos conjuntos de datos pueden incluir entradas y salidas esperadas para evaluar la precisión del modelo.\\n\\n2. **Evaluación de Exactitud**: LangSmith ofrece evaluadores integrados, como el evaluador de coincidencia exacta, que compara las salidas del modelo con las salidas esperadas para determinar la precisión.\\n\\n3. **Trazabilidad de Ejecuciones**: Proporciona la capacidad de rastrear las ejecuciones del modelo, lo que permite ver cómo el modelo llega a sus respuestas y facilita la identificación de errores o áreas de mejora.\\n\\n4. **Automatización de Evaluaciones**: Permite automatizar el proceso de evaluación, lo que facilita la ejecución de pruebas repetitivas y la obtención de resultados consistentes.\\n\\n5. **Integración con Modelos de Lenguaje**: LangSmith se puede integrar con modelos de lenguaje como GPT-3.5-turbo para realizar evaluaciones y pruebas directamente en el entorno del modelo.\\n\\nEn resumen, LangSmith proporciona un marco estructurado y automatizado para evaluar y mejorar el rendimiento de los modelos de lenguaje, lo que es esencial para garantizar la calidad y precisión en aplicaciones basadas en IA.'}"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(agent_executor.invoke({\"input\": \"Como intalar LangSmith?\"})['output'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9qRMXy4DejCc",
        "outputId": "327e2b80-8380-4c5e-f0ff-10a59aa3b6f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Para instalar LangSmith, sigue estos pasos:\n",
            "\n",
            "1. **Instalar LangSmith**:\n",
            "   - **Python**:\n",
            "     ```bash\n",
            "     pip install -U langsmith\n",
            "     ```\n",
            "   - **TypeScript**:\n",
            "     ```bash\n",
            "     yarn add langsmith\n",
            "     ```\n",
            "\n",
            "2. **Crear una clave API**:\n",
            "   - Dirígete a la página de Configuración en LangSmith.\n",
            "   - Haz clic en \"Crear clave API\".\n",
            "\n",
            "3. **Configurar tu entorno**:\n",
            "   - En tu terminal, exporta las siguientes variables de entorno:\n",
            "     ```bash\n",
            "     export LANGCHAIN_TRACING_V2=true\n",
            "     export LANGCHAIN_API_KEY=<tu-clave-api>\n",
            "     # Si usas la API de OpenAI, también exporta su clave\n",
            "     export OPENAI_API_KEY=<tu-clave-api-openai>\n",
            "     ```\n",
            "\n",
            "4. **Registrar tu primer rastro**:\n",
            "   - Si tu aplicación está construida completamente en LangChain (ya sea en Python o JS), no es necesario usar el SDK de LangSmith directamente.\n",
            "   - Puedes seguir la guía de rastreo específica para usuarios de LangChain proporcionada por LangSmith.\n",
            "\n",
            "Estos pasos te permitirán instalar y configurar LangSmith para empezar a monitorear y evaluar tus aplicaciones de LLM.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Here we pass in an empty list of messages for chat_history because it is the first message in the chat\n",
        "agent_executor.invoke({\"input\": \"Mi nombre es Bob\", \"chat_history\": []})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HfZyphg5elwa",
        "outputId": "20075079-cea8-4d20-cc8e-360e21820ff7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input': 'Mi nombre es Bob',\n",
              " 'chat_history': [],\n",
              " 'output': '¡Hola, Bob! ¿En qué puedo ayudarte hoy?'}"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.messages import AIMessage, HumanMessage\n",
        "agent_executor.invoke(\n",
        "    {\n",
        "        \"chat_history\": [\n",
        "            HumanMessage(content=\"Mi nombre es Bob\"),\n",
        "            AIMessage(content=\"Hola Bob! ¿En qué puedo ayudarte hoy?\"),\n",
        "        ],\n",
        "        \"input\": \"Cuál es mi nombre?\",\n",
        "    }\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6If6ayfYepZu",
        "outputId": "d17fbe07-e069-407e-8592-08e7b44b6dc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'chat_history': [HumanMessage(content='Mi nombre es Bob'),\n",
              "  AIMessage(content='Hola Bob! ¿En qué puedo ayudarte hoy?')],\n",
              " 'input': 'Cuál es mi nombre?',\n",
              " 'output': 'Tu nombre es Bob. ¿Hay algo más en lo que pueda asistirte?'}"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.chat_message_histories import ChatMessageHistory\n",
        "from langchain_core.chat_history import BaseChatMessageHistory\n",
        "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
        "\n",
        "store = {}\n",
        "\n",
        "def get_session_history(session_id: str) -> BaseChatMessageHistory:\n",
        "    if session_id not in store:\n",
        "        store[session_id] = ChatMessageHistory()\n",
        "    return store[session_id]"
      ],
      "metadata": {
        "id": "TaJTCSEJe31v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "agent_with_chat_history = RunnableWithMessageHistory(\n",
        "    agent_executor,\n",
        "    get_session_history,\n",
        "    input_messages_key=\"input\",\n",
        "    history_messages_key=\"chat_history\",\n",
        ")"
      ],
      "metadata": {
        "id": "SaQ64lrle5-4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "agent_with_chat_history.invoke(\n",
        "    {\"input\": \"what's my name?\"},\n",
        "    config={\"configurable\": {\"session_id\": \"<foo>\"}},\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DIdDH3Jce73I",
        "outputId": "46fbe844-aefe-46f5-9165-5fe0f09e641b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input': \"what's my name?\",\n",
              " 'chat_history': [],\n",
              " 'output': \"You haven't shared your name with me yet. How can I assist you today?\"}"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Rk0ooAXIe-ni"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}