langchain_simple_introductory_pipeline.ipynb

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        "print('I just followed a sample sequential way of langchain chatbot building')\n",
        "print('i did not run on any data or sources , even i did not run the code, Just arranged all together' )\n",
        "print('as if i can work on sequentially later and do powerfull stuffs on each point')"
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            "I just followed a sample sequential way of langchain chatbot building\n",
            "i did not run on any data or sources , even i did not run the code, Just arranged all together\n",
            "as if i can work on sequentially later and do powerfull stuffs on each point\n"
          ]
        }
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      "source": [
        "## sample pipeline for langchain\n",
        "llm > promt > external sources > retriever > vector_stores > tools (retrieving, web_searching) > agent(tools) > fast_api > prompts_settings for agents_ to run"
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        "!pip install langchain langchain-openai chromadb tavily\n",
        "\n",
        "from typing import List\n",
        "\n",
        "# for deployment possibly\n",
        "from fastapi import FastAPI\n",
        "# ____________________________________________________________________ #\n",
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "from langchain_openai import ChatOpenAI\n",
        "from langchain_community.document_loaders import WebBaseLoader\n",
        "from langchain_openai import OpenAIEmbeddings\n",
        "from langchain_community.vectorstores import FAISS\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "# tool\n",
        "from langchain.tools.retriever import create_retriever_tool\n",
        "from langchain_community.tools.tavily_search import TavilySearchResults\n",
        "# _____________________________________________________________________ #\n",
        "from langchain_openai import ChatOpenAI\n",
        "from langchain import hub\n",
        "# agent\n",
        "from langchain.agents import create_openai_functions_agent\n",
        "from langchain.agents import AgentExecutor\n",
        "# _____________________________________________________________________ #\n",
        "from langchain.pydantic_v1 import BaseModel, Field\n",
        "from langchain_core.messages import BaseMessage\n",
        "from langserve import add_routes\n",
        "\n",
        "# 1. Load Retriever\n",
        "loader = WebBaseLoader(\"https://docs.smith.langchain.com/overview\")\n",
        "docs = loader.load()\n",
        "text_splitter = RecursiveCharacterTextSplitter()\n",
        "documents = text_splitter.split_documents(docs)\n",
        "embeddings = OpenAIEmbeddings()\n",
        "vector = FAISS.from_documents(documents, embeddings)\n",
        "retriever = vector.as_retriever()\n",
        "\n",
        "# 2. Create Tools\n",
        "retriever_tool = create_retriever_tool(\n",
        "    retriever,\n",
        "    \"langsmith_search\",\n",
        "    \"Search for information about LangSmith. For any questions about LangSmith, you must use this tool!\",\n",
        ")\n",
        "search = TavilySearchResults()\n",
        "tools = [retriever_tool, search]\n",
        "\n",
        "\n",
        "# 3. Create Agent\n",
        "prompt = hub.pull(\"hwchase17/openai-functions-agent\")\n",
        "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
        "agent = create_openai_functions_agent(llm, tools, prompt)\n",
        "agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)\n",
        "\n",
        "\n",
        "# 4. App definition\n",
        "app = FastAPI(\n",
        "  title=\"LangChain Server\",\n",
        "  version=\"1.0\",\n",
        "  description=\"A simple API server using LangChain's Runnable interfaces\",\n",
        ")\n"
      ]
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        "\n",
        "\n",
        "```\n",
        "# Deal with prompt templates for retrieving datas from\n",
        "external sources like documents or webpage.\n",
        "```\n",
        "\n"
      ],
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        "# 5. AgentExecuter(some input output prompts)\n",
        "\n",
        "class Input(BaseModel):\n",
        "    input: str\n",
        "    chat_history: List[BaseMessage] = Field(\n",
        "        ...,\n",
        "        extra={\"widget\": {\"type\": \"chat\", \"input\": \"location\"}},\n",
        "    )\n",
        "\n",
        "\n",
        "class Output(BaseModel):\n",
        "    output: str\n",
        "\n",
        "add_routes(\n",
        "    app,\n",
        "    agent_executor.with_types(input_type=Input, output_type=Output),\n",
        "    path=\"/agent\",\n",
        ")\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    import uvicorn\n",
        "\n",
        "    uvicorn.run(app, host=\"localhost\", port=8000)"
      ],
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