LCEL(Langchain_Expression_Language).ipynb

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        "<a href=\"https://colab.research.google.com/github/sunnysavita10/Generative-AI-Indepth-Basic-to-Advance/blob/main/LCEL(Langchain_Expression_Language).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "## Transitioning from Old class to New Pipe Base Operator\n",
        "\n",
        "## 1. Understanding `Runnables`\n",
        "- `Runnables` are self-contained units of work.\n",
        "- Can be executed in isolation or combined for complex operations.\n",
        "- Provides flexibility in execution (sync, async, parallel).\n",
        "\n",
        "## 2. `RunnableParallel`\n",
        "- Executes tasks concurrently.\n",
        "- Useful for performance enhancement in scenarios where tasks can run independently.\n",
        "- Syntax example:\n",
        "    ```python\n",
        "    from some_module import RunnableParallel\n",
        "    ```\n",
        "\n",
        "## 3. `RunnablePassthrough`\n",
        "- A simple `Runnable` that passes inputs directly to outputs without modification.\n",
        "- Helpful for debugging or chaining in pipelines.\n",
        "- Example use case:\n",
        "    ```python\n",
        "    from some_module import RunnablePassthrough\n",
        "    passthrough = RunnablePassthrough()\n",
        "    result = passthrough.run(input_data)\n",
        "    ```\n",
        "\n",
        "## 4. `RunnableLambda`\n",
        "- Allows quick, inline definitions of small, custom functions.\n",
        "- Example:\n",
        "    ```python\n",
        "    from some_module import RunnableLambda\n",
        "    lambda_op = RunnableLambda(lambda x: x * 2)\n",
        "    result = lambda_op.run(5)  # Output: 10\n",
        "    ```\n",
        "\n",
        "## 5. Assign Functions\n",
        "- Used to assign values or parameters during execution.\n",
        "- Useful in data pipelines to update intermediate values.\n",
        "\n",
        "## 6. Performance Improvement (Inference Speed)\n",
        "- Focus on optimizing the inference speed by leveraging parallel execution.\n",
        "- Use `RunnableParallel` or batching techniques.\n",
        "- Consider optimizing data pipelines by removing unnecessary steps.\n",
        "\n",
        "## 7. Async Invoke\n",
        "- Executes operations asynchronously, improving the overall throughput of the system.\n",
        "- Syntax example:\n",
        "    ```python\n",
        "    async def async_operation():\n",
        "        result = await some_async_function()\n",
        "    ```\n",
        "\n",
        "## 8. Batch Support\n",
        "- Handles multiple inputs at once to improve performance.\n",
        "- Can be combined with `RunnableParallel` for parallel batch execution.\n",
        "\n",
        "## 9. Async Batch Execution\n",
        "- Combines asynchronous execution with batch processing for high-performance tasks.\n",
        "- Reduces overall execution time for larger datasets.\n",
        "\n",
        "## 10. Using `Itemgetter` with `LCEL`\n",
        "- `Itemgetter` is used to extract specific items from collections.\n",
        "- When combined with `LCEL` (LangChain Execution Layer), it can streamline complex operations.\n",
        "\n",
        "## 11. Bind Tools\n",
        "- `Bind` tools help to connect different steps in the pipeline.\n",
        "- Ensures smooth data flow between various `Runnable` components.\n",
        "\n",
        "## 12. Stream Support\n",
        "- Keep your pipelines more responsive by incorporating stream support for data.\n",
        "- This allows continuous data processing and near real-time outputs.\n",
        "  \n"
      ],
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          "text": [
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            "  Downloading jsonpatch-1.33-py2.py3-none-any.whl.metadata (3.0 kB)\n",
            "Collecting langsmith<0.2.0,>=0.1.117 (from langchain-core<0.4,>=0.3.0->langchain_google_genai)\n",
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            "Collecting langchain_huggingface\n",
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            "Collecting sentence-transformers>=2.6.0 (from langchain_huggingface)\n",
            "  Downloading sentence_transformers-3.1.0-py3-none-any.whl.metadata (23 kB)\n",
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            "Downloading langchain_huggingface-0.1.0-py3-none-any.whl (20 kB)\n",
            "Downloading sentence_transformers-3.1.0-py3-none-any.whl (249 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m249.1/249.1 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentence-transformers, langchain_huggingface\n",
            "Successfully installed langchain_huggingface-0.1.0 sentence-transformers-3.1.0\n",
            "Collecting langchain_groq\n",
            "  Downloading langchain_groq-0.2.0-py3-none-any.whl.metadata (2.9 kB)\n",
            "Collecting groq<1,>=0.4.1 (from langchain_groq)\n",
            "  Downloading groq-0.11.0-py3-none-any.whl.metadata (13 kB)\n",
            "Requirement already satisfied: langchain-core<0.4,>=0.3 in /usr/local/lib/python3.10/dist-packages (from langchain_groq) (0.3.0)\n",
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            "Downloading langchain_groq-0.2.0-py3-none-any.whl (14 kB)\n",
            "Downloading groq-0.11.0-py3-none-any.whl (106 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.5/106.5 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: groq, langchain_groq\n",
            "Successfully installed groq-0.11.0 langchain_groq-0.2.0\n"
          ]
        }
      ],
      "source": [
        "!pip install langchain_google_genai\n",
        "!pip install langchain_community\n",
        "!pip install langchain\n",
        "!pip install langchain_huggingface\n",
        "!pip install langchain_groq"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "GROQ_API_KEY=userdata.get('GROQ_API_KEY')\n",
        "import os\n",
        "os.environ[\"GROQ_API_KEY\"]=GROQ_API_KEY"
      ],
      "metadata": {
        "id": "rxKWe5nxCpRj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "GOOGLE_API_KEY=userdata.get('GOOGLE_API_KEY')\n",
        "import os\n",
        "os.environ[\"GOOGLE_API_KEY\"]=GOOGLE_API_KEY"
      ],
      "metadata": {
        "id": "gigsz8A_Cswd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_google_genai import GoogleGenerativeAIEmbeddings\n",
        "embeddings = GoogleGenerativeAIEmbeddings(model=\"models/embedding-001\")\n",
        "from langchain_google_genai import ChatGoogleGenerativeAI\n",
        "llm = ChatGoogleGenerativeAI(model=\"gemini-1.0-pro\")"
      ],
      "metadata": {
        "id": "-T6Y59BhC_05"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''from langchain_huggingface import HuggingFaceEmbeddings\n",
        "embeddings=HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
        "from langchain_groq import ChatGroq\n",
        "import os\n",
        "llm=ChatGroq(model_name=\"Gemma2-9b-It\")'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424,
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        "id": "PP7O_-5CDLs5",
        "outputId": "621a1489-08f7-46bd-b1e0-93901ae9b04c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
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        },
        {
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          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
            ],
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d12085da77d84d0ab0884a36c3b63a8c"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d1ae802dba1149ca9e4def59a72ec3ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0201bd357a964ef28e97bf63947147a0"
            }
          },
          "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": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e8e8700a0b45490992fae82f6e04ccf0"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# this is my simple chain (old chaining concept)"
      ],
      "metadata": {
        "id": "imELHoVZMR53"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "template= 'Hi! I am learning {skill}. Can you suggest me top 5 things to learn?\\n'"
      ],
      "metadata": {
        "id": "pZOC6k1_DSpM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain import PromptTemplate"
      ],
      "metadata": {
        "id": "l6nUqQ7NDf0h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = PromptTemplate(template=template,input_variables=[\"skill\"])"
      ],
      "metadata": {
        "id": "cqgbDPABDhkL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iVMhFTXfDpmz",
        "outputId": "9d269bdd-2fce-4ef4-aea7-83444fd39597"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_variables=['skill'] input_types={} partial_variables={} template='Hi! I am learning {skill}. Can you suggest me top 5 things to learn?\\n'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain import LLMChain"
      ],
      "metadata": {
        "id": "2n2FbzaUDsHx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm_chain = LLMChain(prompt=prompt,llm=llm)"
      ],
      "metadata": {
        "id": "bgv6v3kRD1wh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(llm_chain.run('Data Science'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y0vv9CH-EAuR",
        "outputId": "e77a91e6-e5aa-4388-b500-754f41c4134d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**Top 5 Essential Skills for Data Science Learners:**\n",
            "\n",
            "1. **Programming Languages:** Python and R are the most popular programming languages for data science. Focus on mastering data manipulation, analysis, and visualization capabilities.\n",
            "\n",
            "2. **Statistics and Probability:** Understand fundamental statistical concepts such as probability distributions, hypothesis testing, and regression analysis. These skills are crucial for data analysis and modeling.\n",
            "\n",
            "3. **Machine Learning Algorithms:** Explore different machine learning algorithms, including supervised (e.g., linear regression, decision trees) and unsupervised (e.g., clustering, dimensionality reduction) techniques.\n",
            "\n",
            "4. **Data Manipulation and Visualization:** Learn to efficiently import, clean, manipulate, and visualize data using libraries like pandas, NumPy, and Matplotlib. Effective data visualization is key for communicating insights.\n",
            "\n",
            "5. **Cloud Computing:** Familiarize yourself with cloud platforms like AWS, Azure, or Google Cloud. Cloud computing offers scalable and cost-effective solutions for handling large datasets and computational tasks.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(llm_chain.run({'skill':'Data Science'}))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSlUMR_BELrZ",
        "outputId": "a5e201d1-f434-4193-a7ee-db7caed30369"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**Top 5 Things to Learn for Data Science:**\n",
            "\n",
            "1. **Programming Language:** Proficiency in a programming language is essential for data manipulation, analysis, and visualization. Python and R are the most popular choices for data science.\n",
            "\n",
            "2. **Data Manipulation and Analysis:** Understand how to clean, transform, and explore data using tools like Pandas (Python) or dplyr (R). Learn statistical concepts like descriptive statistics, hypothesis testing, and regression analysis.\n",
            "\n",
            "3. **Machine Learning:** Study supervised and unsupervised learning algorithms, such as linear regression, logistic regression, decision trees, and clustering. Understand the principles of model selection, evaluation, and deployment.\n",
            "\n",
            "4. **Data Visualization:** Learn to create informative and visually appealing visualizations using libraries like Matplotlib (Python) or ggplot2 (R). This enables effective communication of insights and facilitates data exploration.\n",
            "\n",
            "5. **Cloud Computing:** Familiarize yourself with cloud platforms like AWS or Azure. Cloud computing provides scalable and cost-effective resources for data storage, processing, and analysis. It also facilitates collaboration and remote work.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# this is a implementation  using LCEL"
      ],
      "metadata": {
        "id": "Sw-ow6nHNIZQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "llm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BkHP_36eEXNp",
        "outputId": "989af42e-3465-4ea3-bf53-3a792bdd8557"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ChatGoogleGenerativeAI(model='models/gemini-1.0-pro', google_api_key=SecretStr('**********'), client=<google.ai.generativelanguage_v1beta.services.generative_service.client.GenerativeServiceClient object at 0x7b10ed2405e0>, async_client=<google.ai.generativelanguage_v1beta.services.generative_service.async_client.GenerativeServiceAsyncClient object at 0x7b10ed2418d0>, default_metadata=())"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J-SyysJqMd5T",
        "outputId": "a7c01f22-7c96-491d-ee4f-43cbfb7c312f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PromptTemplate(input_variables=['skill'], input_types={}, partial_variables={}, template='Hi! I am learning {skill}. Can you suggest me top 5 things to learn?\\n')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chain = prompt | llm"
      ],
      "metadata": {
        "id": "0x3jF1zHMgrT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(chain.invoke({'skill':'Big Data'}))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HlWhjIxJMlMz",
        "outputId": "718b5767-42b4-45a1-d663-de5d1ec77080"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "content='**Top 5 Essential Concepts for Big Data:**\\n\\n1. **Big Data Frameworks:** Understand the fundamentals of Hadoop, Spark, and other popular frameworks used for processing and managing large datasets.\\n2. **Data Ingestion and Storage:** Learn the techniques for importing and storing data from various sources, including structured, semi-structured, and unstructured data.\\n3. **Data Analysis and Visualization:** Explore methods for analyzing big data, including statistical techniques, machine learning algorithms, and data visualization tools.\\n4. **Data Management and Security:** Understand the principles of data management, data governance, and data security in a big data environment.\\n5. **Cloud Computing for Big Data:** Familiarize yourself with the role of cloud platforms, such as AWS, Azure, and GCP, in supporting big data storage, processing, and analysis.' additional_kwargs={} response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'safety_ratings': [{'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability': 'NEGLIGIBLE', 'blocked': False}]} id='run-6e7a956f-de9a-410c-8015-789952d62d96-0' usage_metadata={'input_tokens': 21, 'output_tokens': 168, 'total_tokens': 189}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.output_parsers import StrOutputParser"
      ],
      "metadata": {
        "id": "D1g1TnedM08c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parser = StrOutputParser()"
      ],
      "metadata": {
        "id": "7hLevRtnM4v1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain = prompt | llm | parser"
      ],
      "metadata": {
        "id": "Y8m8mq1wM7wj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(chain.invoke({'skill':'Machine Learning'}))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rQ4mSKFgM_rM",
        "outputId": "d1e81fcf-175e-4efc-d103-718a5a74401c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**Top 5 Essential Concepts to Learn in Machine Learning:**\n",
            "\n",
            "1. **Supervised Learning:** Understanding how algorithms learn from labeled data and predict outcomes. This includes concepts like linear regression, logistic regression, and decision trees.\n",
            "\n",
            "2. **Unsupervised Learning:** Exploring algorithms that learn from unlabeled data to find patterns and structures. This encompasses methods like clustering, dimensionality reduction, and anomaly detection.\n",
            "\n",
            "3. **Model Evaluation:** Mastering techniques to assess the performance of machine learning models, such as accuracy, precision, recall, and confusion matrices.\n",
            "\n",
            "4. **Feature Engineering:** Learning how to transform and select the most informative features from raw data to improve model performance. This involves data cleaning, normalization, and feature selection techniques.\n",
            "\n",
            "5. **Model Tuning and Optimization:** Understanding how to adjust model parameters and hyperparameters to enhance its predictive capabilities. This includes methods like cross-validation, regularization, and gradient descent.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# lets discuss about the runnables"
      ],
      "metadata": {
        "id": "99PwfyAjNNpv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.runnables import RunnableParallel, RunnablePassthrough , RunnableLambda"
      ],
      "metadata": {
        "id": "6o2eHdFXNDhT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain = RunnablePassthrough()"
      ],
      "metadata": {
        "id": "thIShr5mNhm_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain.invoke('Welcome to this youtube channel')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "AQRkFFeMNmLb",
        "outputId": "8999112b-e24b-4d5a-b053-4229ec0f5ee0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Welcome to this youtube channel'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chain = RunnablePassthrough() | RunnablePassthrough() | RunnablePassthrough()"
      ],
      "metadata": {
        "id": "uZ_-Vw1RNzls"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain.invoke('Welcome to my sunny\"s youtube channel')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "G1TYVRLYN83A",
        "outputId": "9784600c-fa77-4ff5-9745-3b762d6178ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Welcome to my sunny\"s youtube channel'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def string_upper(input):\n",
        "  return input.upper()"
      ],
      "metadata": {
        "id": "fPpAEYVJORUf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain = RunnablePassthrough() | RunnableLambda(string_upper)"
      ],
      "metadata": {
        "id": "RkrgncYDN-ej"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain.invoke('Welcome to my sunny\"s youtube channel')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "TfkJXhuKOYGj",
        "outputId": "59974653-d974-4513-b418-837231482548"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'WELCOME TO MY SUNNY\"S YOUTUBE CHANNEL'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "string_upper.invoke('Welcome to my sunny\"s youtube channel')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "hbipHFRNOm3A",
        "outputId": "f4655d86-4682-416e-cf2f-5e078e298f62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'function' object has no attribute 'invoke'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-44-9ecdd511418b>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstring_upper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minvoke\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Welcome to my sunny\"s youtube channel'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'function' object has no attribute 'invoke'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chain = RunnableLambda(string_upper)"
      ],
      "metadata": {
        "id": "dytKiiXVOm5O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain.invoke('Welcome to my sunny\"s youtube channel')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "d9LJwI12PAi1",
        "outputId": "263e35f7-4ab8-489f-8dfe-8183cdea32f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'WELCOME TO MY SUNNY\"S YOUTUBE CHANNEL'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chain = RunnableParallel({'x':RunnablePassthrough(),'y':RunnablePassthrough()})"
      ],
      "metadata": {
        "id": "3ZGkNnXjPEWU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain.invoke(\"Sunny\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9aBTG_OsPPA8",
        "outputId": "b1ea167d-e1b9-4c25-fe31-0858854cf4bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'x': 'Sunny', 'y': 'Sunny'}"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chain.invoke({'Youtube': '@sunnysavita10','Blog': \"Sunny's blog\"})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSP5r-qxPSjU",
        "outputId": "3f3431bb-1255-42e0-fb48-64c0079666eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'x': {'Youtube': '@sunnysavita10', 'Blog': \"Sunny's blog\"},\n",
              " 'y': {'Youtube': '@sunnysavita10', 'Blog': \"Sunny's blog\"}}"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lambda x: x['Blog']"
      ],
      "metadata": {
        "id": "wmX6hCbPTJrI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain = RunnableParallel({'x':RunnablePassthrough(),'Blog':lambda x: x['Blog']})"
      ],
      "metadata": {
        "id": "xD3lHrcIQazJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain.invoke({'Youtube': '@sunnysavita10','Blog': \"Sunny's blog\"})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3hM3gBqtQcCa",
        "outputId": "665da4fd-07b5-4d97-d70d-1d630b4df5f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'x': {'Youtube': '@sunnysavita10', 'Blog': \"Sunny's blog\"},\n",
              " 'Blog': \"Sunny's blog\"}"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def fetch_website(input: dict):\n",
        "    output = input.get('Website','Not found')\n",
        "    return output"
      ],
      "metadata": {
        "id": "U4SUTxQ0QdUW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mydict={'Youtube': '@sunnysavita10','Blog': \"Sunny's blog\"}"
      ],
      "metadata": {
        "id": "15vR9qJzTs5M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mydict.get(\"website\",\"Not found\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "DChnkIoFTwpn",
        "outputId": "554609b8-8c43-4dcd-b11d-4508af64d145"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Not found'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chain = RunnableParallel({'Website':RunnablePassthrough() | RunnableLambda(fetch_website),\n",
        "                          'Blog':lambda z: z['Blog']})"
      ],
      "metadata": {
        "id": "r4ApiixHQe0n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain.invoke({'Youtube': '@sunnysavita10','Blog': \"Sunny's blog\"})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7tSnqZ9-QgCI",
        "outputId": "172f2641-4016-4191-bcc2-a0ee696700fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Website': 'Not found', 'Blog': \"Sunny's blog\"}"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chain.invoke({'Youtube': '@sunnysavita10','Blog': \"Sunny's blog\" , 'Website' : 'sunnysavita.com'})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P9UWY3o2QhcI",
        "outputId": "6536bcbc-28c3-4802-a672-264f50b3e13f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Website': 'sunnysavita.com', 'Blog': \"Sunny's blog\"}"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def extra_func(input):\n",
        "    return 'Happy Learning'"
      ],
      "metadata": {
        "id": "4xKCFIioQi8Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain = RunnableParallel({'x' : RunnablePassthrough()}).assign(extra=RunnableLambda(extra_func))"
      ],
      "metadata": {
        "id": "ymBvP7r9QkTv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain = RunnableParallel({'x' : RunnablePassthrough()}).assign(y=RunnableLambda(extra_func))"
      ],
      "metadata": {
        "id": "W6ZoETBaUf4f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain.invoke('Hello')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cIYfRydBQliP",
        "outputId": "1fa3bb6c-c429-4a48-e353-9674a2ab7c9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'x': 'Hello', 'y': 'Happy Learning'}"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install chromadb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NfjIKZsvUZR2",
        "outputId": "61a639fc-65c7-4d83-bba8-5795271e99c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting chromadb\n",
            "  Downloading chromadb-0.5.5-py3-none-any.whl.metadata (6.8 kB)\n",
            "Requirement already satisfied: build>=1.0.3 in /usr/local/lib/python3.10/dist-packages (from chromadb) (1.2.2)\n",
            "Requirement already satisfied: pydantic>=1.9 in /usr/local/lib/python3.10/dist-packages (from chromadb) (2.9.1)\n",
            "Collecting chroma-hnswlib==0.7.6 (from chromadb)\n",
            "  Downloading chroma_hnswlib-0.7.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (252 bytes)\n",
            "Collecting fastapi>=0.95.2 (from chromadb)\n",
            "  Downloading fastapi-0.114.2-py3-none-any.whl.metadata (27 kB)\n",
            "Collecting uvicorn>=0.18.3 (from uvicorn[standard]>=0.18.3->chromadb)\n",
            "  Downloading uvicorn-0.30.6-py3-none-any.whl.metadata (6.6 kB)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.22.5 in /usr/local/lib/python3.10/dist-packages (from chromadb) (1.26.4)\n",
            "Collecting posthog>=2.4.0 (from chromadb)\n",
            "  Downloading posthog-3.6.6-py2.py3-none-any.whl.metadata (2.0 kB)\n",
            "Requirement already satisfied: typing-extensions>=4.5.0 in /usr/local/lib/python3.10/dist-packages (from chromadb) (4.12.2)\n",
            "Collecting onnxruntime>=1.14.1 (from chromadb)\n",
            "  Downloading onnxruntime-1.19.2-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (4.5 kB)\n",
            "Collecting opentelemetry-api>=1.2.0 (from chromadb)\n",
            "  Downloading opentelemetry_api-1.27.0-py3-none-any.whl.metadata (1.4 kB)\n",
            "Collecting opentelemetry-exporter-otlp-proto-grpc>=1.2.0 (from chromadb)\n",
            "  Downloading opentelemetry_exporter_otlp_proto_grpc-1.27.0-py3-none-any.whl.metadata (2.3 kB)\n",
            "Collecting opentelemetry-instrumentation-fastapi>=0.41b0 (from chromadb)\n",
            "  Downloading opentelemetry_instrumentation_fastapi-0.48b0-py3-none-any.whl.metadata (2.1 kB)\n",
            "Collecting opentelemetry-sdk>=1.2.0 (from chromadb)\n",
            "  Downloading opentelemetry_sdk-1.27.0-py3-none-any.whl.metadata (1.5 kB)\n",
            "Requirement already satisfied: tokenizers>=0.13.2 in /usr/local/lib/python3.10/dist-packages (from chromadb) (0.19.1)\n",
            "Collecting pypika>=0.48.9 (from chromadb)\n",
            "  Downloading PyPika-0.48.9.tar.gz (67 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.3/67.3 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: tqdm>=4.65.0 in /usr/local/lib/python3.10/dist-packages (from chromadb) (4.66.5)\n",
            "Collecting overrides>=7.3.1 (from chromadb)\n",
            "  Downloading overrides-7.7.0-py3-none-any.whl.metadata (5.8 kB)\n",
            "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.10/dist-packages (from chromadb) (6.4.5)\n",
            "Requirement already satisfied: grpcio>=1.58.0 in /usr/local/lib/python3.10/dist-packages (from chromadb) (1.64.1)\n",
            "Collecting bcrypt>=4.0.1 (from chromadb)\n",
            "  Downloading bcrypt-4.2.0-cp39-abi3-manylinux_2_28_x86_64.whl.metadata (9.6 kB)\n",
            "Requirement already satisfied: typer>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from chromadb) (0.12.5)\n",
            "Collecting kubernetes>=28.1.0 (from chromadb)\n",
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            "\u001b[?25hBuilding wheels for collected packages: pypika\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.document_loaders import TextLoader, DirectoryLoader\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain_community.vectorstores import Chroma\n",
        "\n",
        "### Reading the txt files from source directory\n",
        "\n",
        "loader = DirectoryLoader('./source', glob=\"./*.txt\", loader_cls=TextLoader)\n",
        "docs = loader.load()\n",
        "\n",
        "### Creating Chunks using RecursiveCharacterTextSplitter\n",
        "\n",
        "text_splitter = RecursiveCharacterTextSplitter(\n",
        "    chunk_size=50,\n",
        "    chunk_overlap=10,\n",
        "    length_function=len\n",
        ")\n",
        "new_docs = text_splitter.split_documents(documents=docs)\n",
        "doc_strings = [doc.page_content for doc in new_docs]\n",
        "\n",
        "###  BGE Embddings\n",
        "\n",
        "'''from langchain.embeddings import HuggingFaceBgeEmbeddings\n",
        "\n",
        "model_name = \"BAAI/bge-base-en-v1.5\"\n",
        "model_kwargs = {'device': 'cpu'}\n",
        "encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity\n",
        "embeddings = HuggingFaceBgeEmbeddings(\n",
        "    model_name=model_name,\n",
        "    model_kwargs=model_kwargs,\n",
        "    encode_kwargs=encode_kwargs,\n",
        ")\n",
        "'''\n",
        "\n",
        "### Creating Retriever using Vector DB\n",
        "\n",
        "db = Chroma.from_documents(new_docs, embeddings)\n",
        "retriever = db.as_retriever(search_kwargs={\"k\": 4})"
      ],
      "metadata": {
        "id": "oxWNmfPJUrZs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "template = \"\"\"Answer the question based only on the following context:\n",
        "{context}\n",
        "\n",
        "Question: {question}\n",
        "\"\"\"\n",
        "prompt = PromptTemplate.from_template(template)\n"
      ],
      "metadata": {
        "id": "mRV0rT9bVRqo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "retrieval_chain = (\n",
        "    RunnableParallel({\"context\": retriever, \"question\": RunnablePassthrough()})\n",
        "    | prompt\n",
        "    | llm\n",
        "    | StrOutputParser()\n",
        "    )"
      ],
      "metadata": {
        "id": "gRn_Zn3fUusE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "question =\"what is llama3? can you highlight 3 important points?\""
      ],
      "metadata": {
        "id": "xH0TxuFLU06x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "retrieval_chain.invoke(question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "lQbsuB43Vc-L",
        "outputId": "0a35d312-2949-4368-856c-d89d4e729da3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'- Llama 3 is a large language model developed by Meta AI.\\n- It was released in April 2024.\\n- It is used in various applications, including language translation, question answering, and text generation.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "start_time = time.time()\n",
        "\n",
        "result = retrieval_chain.invoke(question)\n",
        "\n",
        "print('Time taken:',time.time() - start_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kHqEw55uVgVd",
        "outputId": "37a7e6e7-2c1d-40f0-a352-59b14c01095b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time taken: 1.6216003894805908\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <coroutine object RunnableSequence.ainvoke at 0x7b0fd7ee3c30>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/google/colab/_variable_inspector.py\", line 27, in run\n",
            "KeyError: '__builtins__'\n",
            "Exception ignored in: <coroutine object RunnableSequence.ainvoke at 0x7b0fd7ee3c30>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/google/colab/_variable_inspector.py\", line 27, in run\n",
            "KeyError: '__builtins__'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start_time = time.time()\n",
        "\n",
        "result = retrieval_chain.ainvoke(question)\n",
        "\n",
        "print('Time taken:',time.time() - start_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tk71kT4ZVnrT",
        "outputId": "6d2de3fd-57b3-4a66-9057-5be9fbabc25d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time taken: 0.00014090538024902344\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start_time = time.time()\n",
        "\n",
        "batch_output = retrieval_chain.batch([\n",
        "                        \"what is llama3?\",\n",
        "                        \"can you highlight 3 main properties?\"\n",
        "                       ])\n",
        "\n",
        "print('Time taken:',time.time() - start_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2bjWjZHKV4WS",
        "outputId": "b92165ae-7cd2-4b15-eb39-8fc2d325a30f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time taken: 0.9684896469116211\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YlEAL3OPV55X",
        "outputId": "5144a8bc-0a40-484d-d17b-a301c3d1bc0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Llama 3 is a family of large language models developed by Meta AI.',\n",
              " 'The provided context does not mention anything about the main properties, so I cannot extract the requested data from the provided context.']"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start_time = time.time()\n",
        "\n",
        "batch_output = await retrieval_chain.abatch([\n",
        "                        \"what is llama3?\",\n",
        "                        \"can you highlight 3 main properties?\"\n",
        "                       ])\n",
        "\n",
        "print('Time taken:',time.time() - start_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A0Odh6PiV7i4",
        "outputId": "0d03839c-6f1c-4938-9936-5b0c99ebed2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time taken: 0.9242796897888184\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_output"
      ],
      "metadata": {
        "id": "uye5uA4SV83i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_dict = {'Youtube': '@sunnysavita10','Blog': \"sunny's blog\" , 'Website' : 'sunnysavita.com'}\n",
        "my_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "im5Guk7eWDwl",
        "outputId": "86fb4bcc-e6cd-493f-dbdf-a0bcc10a449c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Youtube': '@sunnysavita10',\n",
              " 'Blog': \"sunny's blog\",\n",
              " 'Website': 'sunnysavita.com'}"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from operator import itemgetter\n",
        "\n",
        "website = itemgetter('Website')"
      ],
      "metadata": {
        "id": "tZ4vG0XaWFbB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "website(my_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "zEPPAK3yWGiw",
        "outputId": "a3cabbb1-66d0-4b0e-b919-9a1006a6c434"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'sunnysavita.com'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "template = \"\"\"Answer the question based only on the following context:\n",
        "{context}\n",
        "\n",
        "Question: {question}\n",
        "\n",
        "Answer in the following language: {language}\n",
        "\"\"\"\n",
        "prompt = PromptTemplate.from_template(template)\n"
      ],
      "metadata": {
        "id": "gvKpRIcHYQeh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "retrieval_chain = (\n",
        "    RunnableParallel({\"context\": itemgetter('question') | retriever,\n",
        "                       \"question\": itemgetter('question'),\n",
        "                       \"language\": itemgetter('language')\n",
        "                       })\n",
        "    | prompt\n",
        "    | llm\n",
        "    | StrOutputParser()\n",
        "    )"
      ],
      "metadata": {
        "id": "lhl9vll7WIEY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### itemgetter only works with dictionaries , input has to be a dict\n",
        "\n",
        "response = retrieval_chain.invoke({'question': \"what is llama3?\",\n",
        "                        'language': \"Spnish\"})\n",
        "\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "73Rl5KYZWJCh",
        "outputId": "596b6ccf-bf12-45f4-c6a6-c628c87a5710"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Llama (Large Language Model Meta AI) es una familia\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "template = 'Hi! I am learning {skill}. Can you suggest me top 5 things to learn?\\n'\n",
        "\n",
        "prompt = PromptTemplate.from_template(template=template)\n",
        "\n",
        "chain = prompt | llm"
      ],
      "metadata": {
        "id": "EDevsLI3WL8w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for s in chain.stream({'skill':'Big Data'}):\n",
        "    print(s.content,end='')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pJv0bVtUWNCG",
        "outputId": "c2679e08-6dcc-43af-e29a-898a93c0cd4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**Top 5 Must-Learn Concepts for Big Data:**\n",
            "\n",
            "1. **Data Management and Storage:** Understand various data storage technologies like Hadoop Distributed File System (HDFS), NoSQL databases (e.g., MongoDB, Cassandra), and data warehousing concepts.\n",
            "\n",
            "2. **Data Processing and Analysis:** Learn about frameworks like Apache Spark, MapReduce, and Hadoop for processing and analyzing large datasets. Explore data mining, machine learning, and statistical techniques for extracting insights.\n",
            "\n",
            "3. **Cloud Computing:** Familiarize yourself with cloud platforms like AWS, Azure, and GCP that offer scalable and cost-effective solutions for storing, processing, and analyzing big data.\n",
            "\n",
            "4. **Data Visualization and Communication:** Gain expertise in tools like Tableau, Power BI, and QlikView to effectively communicate data insights to stakeholders. Learn about data visualization best practices and storytelling techniques.\n",
            "\n",
            "5. **Data Governance and Security:** Understand data governance principles, data quality management, and security measures to protect sensitive data in big data environments. Learn about compliance regulations and best practices for data privacy and security."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "from langchain_core.messages import ToolMessage\n",
        "from langchain_core.tools import tool\n",
        "from langchain_core.utils.function_calling import convert_to_openai_tool\n",
        "\n",
        "@tool\n",
        "def multiply(first_number: int, second_number: int):\n",
        "    \"\"\"Multiplies two numbers together.\"\"\"\n",
        "    return first_number * second_number\n",
        "\n",
        "model_with_tools = llm.bind(tools=[convert_to_openai_tool(multiply)])"
      ],
      "metadata": {
        "id": "bpmGR1mbWPdp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response = model_with_tools.invoke('What is 35 * 46?')"
      ],
      "metadata": {
        "id": "mMG5ND6rWRDr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response"
      ],
      "metadata": {
        "id": "PijeN4AUWSFN"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}