t81_559_class_06_5_embed_db.ipynb

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    "<a href=\"https://colab.research.google.com/github/jeffheaton/app_generative_ai/blob/main/t81_559_class_06_5_embed_db.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "euOZxlIMhstX"
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   "source": [
    "# T81-559: Applications of Generative Artificial Intelligence\n",
    "**Module 6: Retrieval-Augmented Generation (RAG)**\n",
    "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n",
    "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d4Yov72PhstY"
   },
   "source": [
    "# Module 6 Material\n",
    "\n",
    "* Part 6.1: Introduction to Retrieval-Augmented Generation (RAG) [[Video]](https://www.youtube.com/watch?v=qA52K0K181Q) [[Notebook]](t81_559_class_06_1_rag.ipydb)\n",
    "* Part 6.2: Introduction to ChromaDB [[Video]](https://www.youtube.com/watch?v=R53lo4sevLQ) [[Notebook]](t81_559_class_06_2_chromadb.ipynb)\n",
    "* Part 6.3: Understanding Embeddings [[Video]](https://www.youtube.com/watch?v=Tq82Gl2ZZNM) [[Notebook]](t81_559_class_06_3_embeddings.ipynb)\n",
    "* Part 6.4: Question Answering Over Documents [[Video]](https://www.youtube.com/watch?v=hCwL_lW-gP0) [[Notebook]](t81_559_class_06_4_qa.ipynb)\n",
    "* **Part 6.5: Embedding Databases** [[Video]](https://www.youtube.com/watch?v=BG2gT4uYxhM) [[Notebook]](t81_559_class_06_5_embed_db.ipynb)\n"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "# Google CoLab Instructions\n",
    "\n",
    "The following code ensures that Google CoLab is running and maps Google Drive if needed."
   ]
  },
  {
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    "id": "xsI496h5hstZ",
    "outputId": "be64947d-e729-49f6-b18a-2e6e243f80e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Building 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=df9ab1eac2c2fa93cf526eadbf6a930d3a30227d2d2e3265dd7fcd5b7e292be5\n",
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      "Successfully built pypika\n",
      "Installing collected packages: pypika, monotonic, mmh3, websockets, uvloop, ujson, types-requests, python-multipart, python-dotenv, overrides, orjson, opentelemetry-util-http, opentelemetry-proto, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, mypy-extensions, marshmallow, jsonpointer, importlib-metadata, humanfriendly, httptools, h11, dnspython, deprecated, chroma-hnswlib, bcrypt, backoff, asgiref, watchfiles, uvicorn, typing-inspect, tiktoken, starlette, posthog, opentelemetry-exporter-otlp-proto-common, opentelemetry-api, nvidia-cusparse-cu12, nvidia-cudnn-cu12, langchainhub, jsonpatch, httpcore, email_validator, coloredlogs, opentelemetry-semantic-conventions, opentelemetry-instrumentation, onnxruntime, nvidia-cusolver-cu12, langsmith, kubernetes, httpx, dataclasses-json, opentelemetry-sdk, opentelemetry-instrumentation-asgi, openai, langchain-core, fastapi-cli, sentence-transformers, opentelemetry-instrumentation-fastapi, opentelemetry-exporter-otlp-proto-grpc, langchain-text-splitters, langchain_openai, fastapi, langchain, chromadb, langchain_community\n",
      "  Attempting uninstall: importlib-metadata\n",
      "    Found existing installation: importlib_metadata 7.2.0\n",
      "    Uninstalling importlib_metadata-7.2.0:\n",
      "      Successfully uninstalled importlib_metadata-7.2.0\n",
      "Successfully installed asgiref-3.8.1 backoff-2.2.1 bcrypt-4.1.3 chroma-hnswlib-0.7.3 chromadb-0.5.3 coloredlogs-15.0.1 dataclasses-json-0.6.7 deprecated-1.2.14 dnspython-2.6.1 email_validator-2.2.0 fastapi-0.111.0 fastapi-cli-0.0.4 h11-0.14.0 httpcore-1.0.5 httptools-0.6.1 httpx-0.27.0 humanfriendly-10.0 importlib-metadata-7.1.0 jsonpatch-1.33 jsonpointer-3.0.0 kubernetes-30.1.0 langchain-0.2.6 langchain-core-0.2.10 langchain-text-splitters-0.2.2 langchain_community-0.2.6 langchain_openai-0.1.10 langchainhub-0.1.20 langsmith-0.1.82 marshmallow-3.21.3 mmh3-4.1.0 monotonic-1.6 mypy-extensions-1.0.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 onnxruntime-1.18.0 openai-1.35.4 opentelemetry-api-1.25.0 opentelemetry-exporter-otlp-proto-common-1.25.0 opentelemetry-exporter-otlp-proto-grpc-1.25.0 opentelemetry-instrumentation-0.46b0 opentelemetry-instrumentation-asgi-0.46b0 opentelemetry-instrumentation-fastapi-0.46b0 opentelemetry-proto-1.25.0 opentelemetry-sdk-1.25.0 opentelemetry-semantic-conventions-0.46b0 opentelemetry-util-http-0.46b0 orjson-3.10.5 overrides-7.7.0 posthog-3.5.0 pypika-0.48.9 python-dotenv-1.0.1 python-multipart-0.0.9 sentence-transformers-3.0.1 starlette-0.37.2 tiktoken-0.7.0 types-requests-2.32.0.20240622 typing-inspect-0.9.0 ujson-5.10.0 uvicorn-0.30.1 uvloop-0.19.0 watchfiles-0.22.0 websockets-12.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "try:\n",
    "    from google.colab import drive, userdata\n",
    "    COLAB = True\n",
    "    print(\"Note: using Google CoLab\")\n",
    "except:\n",
    "    print(\"Note: not using Google CoLab\")\n",
    "    COLAB = False\n",
    "\n",
    "# OpenAI Secrets\n",
    "if COLAB:\n",
    "    os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY')\n",
    "\n",
    "# Install needed libraries in CoLab\n",
    "if COLAB:\n",
    "    !pip install langchain langchain_openai chromadb langchain_community sentence-transformers langchainhub"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pC9A-LaYhsta"
   },
   "source": [
    "# 6.5: Embedding Databases\n",
    "\n",
    "\n",
    "In this section, we will explore the process of running ChromaDB as a server to facilitate access to RAG LLm. Initially, we'll begin by loading a locally saved embedding database, setting the foundation for our server's data handling capabilities. Once the database is successfully loaded, we will proceed to demonstrate how it can be reloaded, ensuring that our system can maintain up-to-date information and recover seamlessly from any disruptions. Finally, we will shift our focus to accessing the database through an HTTP client, which will enable users to interact with the server remotely, retrieving and utilizing embeddings as needed for efficient and scalable language model operations. Throughout this part, we aim to provide a comprehensive guide to setting up and managing ChromaDB in a way that supports robust RAG LLm access.\n",
    "\n",
    "To illustrate the capabilities of the server, we will utilize a sample dataset I created, containing synthetic data of employee biographies from five fictional companies. This dataset is crafted to demonstrate how RAG can effectively retrieve and utilize specific information that a foundation model would not inherently possess. By doing so, it provides a clear example of how RAG can be applied in a real-world corporate setting.\n",
    "\n",
    "Here is an example of such a generated biography:\n",
    "\n",
    "> Elena Martinez is a seasoned Robotics Engineer at FutureTech, a leading innovator in artificial intelligence and robotics based in Silicon Valley. With a Master's degree in Mechanical Engineering from MIT and over a decade of experience, Elena has been pivotal in the development of autonomous robotic systems designed to enhance urban mobility and accessibility. Her groundbreaking work includes the creation of the first AI-powered robotic assistant that can seamlessly interact with urban environments to aid the elderly and disabled. A passionate advocate for women in STEM, Elena also leads FutureTech's outreach program, aiming to inspire the next generation of female engineers through workshops and mentorships. Her contributions have not only propelled FutureTech to new heights but have also set new standards in robotics applications for social good.\n",
    "\n",
    "\n",
    "This biography showcases the type of detailed, company-specific information that RAG can retrieve and incorporate into its responses, enhancing the relevance and accuracy of the generated content. Through the integration of such tailored data, RAG not only enriches the output of LLMs but also ensures that the responses are aligned with the unique context and needs of the organization.\n",
    "\n",
    "This sample data is stored at the following URL's, each file holds people from one of the companies.\n",
    "\n",
    "* https://data.heatonresearch.com/data/t81-559/bios/DD.txt\n",
    "* https://data.heatonresearch.com/data/t81-559/bios/FT.txt\n",
    "* https://data.heatonresearch.com/data/t81-559/bios/GS.txt\n",
    "* https://data.heatonresearch.com/data/t81-559/bios/NGS.txt\n",
    "* https://data.heatonresearch.com/data/t81-559/bios/TI.txt\n",
    "\n",
    "The following code loads these documents and saves them to a ChromaDB database. We have seen this code previously.\n",
    "\n",
    "## Load the ChromaDB Database\n",
    "\n"
   ]
  },
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    },
    "id": "HYtVPMTHExaT",
    "outputId": "6f6bcfe0-bd6c-43dd-8369-7ebe8d461ac8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading: https://data.heatonresearch.com/data/t81-559/bios/DD.txt\n",
      "Reading: https://data.heatonresearch.com/data/t81-559/bios/FT.txt\n",
      "Reading: https://data.heatonresearch.com/data/t81-559/bios/GS.txt\n",
      "Reading: https://data.heatonresearch.com/data/t81-559/bios/NGS.txt\n",
      "Reading: https://data.heatonresearch.com/data/t81-559/bios/TI.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:139: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 0.3.0. An updated version of the class exists in the langchain-huggingface package and should be used instead. To use it run `pip install -U langchain-huggingface` and import as `from langchain_huggingface import HuggingFaceEmbeddings`.\n",
      "  warn_deprecated(\n",
      "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:11: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  from tqdm.autonotebook import tqdm, trange\n",
      "/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"
     ]
    },
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       "model_id": "34765850b1b9457484057087b6dc881b",
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      "text/plain": [
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      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
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      ]
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   ],
   "source": [
    "from langchain.chains.summarize import load_summarize_chain\n",
    "from langchain.document_loaders import TextLoader\n",
    "from langchain import OpenAI, PromptTemplate\n",
    "from langchain_openai import ChatOpenAI\n",
    "from IPython.display import display_markdown\n",
    "from langchain.indexes import VectorstoreIndexCreator\n",
    "from langchain_openai import OpenAIEmbeddings\n",
    "from langchain_community.vectorstores.inmemory import InMemoryVectorStore\n",
    "from langchain.schema import Document\n",
    "import requests\n",
    "\n",
    "urls = [\n",
    "    \"https://data.heatonresearch.com/data/t81-559/bios/DD.txt\",\n",
    "    \"https://data.heatonresearch.com/data/t81-559/bios/FT.txt\",\n",
    "    \"https://data.heatonresearch.com/data/t81-559/bios/GS.txt\",\n",
    "    \"https://data.heatonresearch.com/data/t81-559/bios/NGS.txt\",\n",
    "    \"https://data.heatonresearch.com/data/t81-559/bios/TI.txt\"\n",
    "]\n",
    "\n",
    "def chunk_text(text, chunk_size, overlap):\n",
    "    chunks = []\n",
    "    for i in range(0, len(text), chunk_size - overlap):\n",
    "        chunks.append(text[i:i + chunk_size])\n",
    "    return chunks\n",
    "\n",
    "chunk_size = 900\n",
    "overlap = 300\n",
    "\n",
    "documents = []\n",
    "\n",
    "for url in urls:\n",
    "    print(f\"Reading: {url}\")\n",
    "    response = requests.get(url)\n",
    "    response.raise_for_status()  # Ensure we notice bad responses\n",
    "    content = response.text\n",
    "    chunks = chunk_text(content, chunk_size, overlap)\n",
    "    for chunk in chunks:\n",
    "        document = Document(page_content=chunk)\n",
    "        documents.append(document)\n",
    "\n",
    "from langchain_text_splitters import CharacterTextSplitter\n",
    "from langchain_community.embeddings.sentence_transformer import (\n",
    "    SentenceTransformerEmbeddings,\n",
    ")\n",
    "from langchain.vectorstores import Chroma\n",
    "\n",
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "docs = text_splitter.split_documents(documents)\n",
    "\n",
    "embedding_function = SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
    "\n",
    "#db = Chroma.from_documents(docs, embedding_function)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ELhuBPP-agc_"
   },
   "source": [
    "Finally, we will load a ChromaDB database from these documents; however, we will now specify a directory to store this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "gYehOnJFJPI1"
   },
   "outputs": [],
   "source": [
    "db = Chroma.from_documents(docs, embedding_function, persist_directory=\"/content/chroma_db\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E2h0nvlva1ah"
   },
   "source": [
    "We can see that this is a regular Chroma object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 274
    },
    "id": "F_1FgCiUYzTH",
    "outputId": "222cdfdc-b546-414c-a291-29e0051257a4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
       "      pre.function-repr-contents {\n",
       "        overflow-x: auto;\n",
       "        padding: 8px 12px;\n",
       "        max-height: 500px;\n",
       "      }\n",
       "\n",
       "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
       "        cursor: pointer;\n",
       "        max-height: 100px;\n",
       "      }\n",
       "    </style>\n",
       "    <pre style=\"white-space: initial; background:\n",
       "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
       "         border-bottom: 1px solid var(--colab-border-color);\"><b>langchain_community.vectorstores.chroma.Chroma</b><br/>def __init__(collection_name: str=_LANGCHAIN_DEFAULT_COLLECTION_NAME, embedding_function: Optional[Embeddings]=None, persist_directory: Optional[str]=None, client_settings: Optional[chromadb.config.Settings]=None, collection_metadata: Optional[Dict]=None, client: Optional[chromadb.Client]=None, relevance_score_fn: Optional[Callable[[float], float]]=None) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/langchain_community/vectorstores/chroma.py</a>`ChromaDB` vector store.\n",
       "\n",
       "To use, you should have the ``chromadb`` python package installed.\n",
       "\n",
       "Example:\n",
       "    .. code-block:: python\n",
       "\n",
       "            from langchain_community.vectorstores import Chroma\n",
       "            from langchain_community.embeddings.openai import OpenAIEmbeddings\n",
       "\n",
       "            embeddings = OpenAIEmbeddings()\n",
       "            vectorstore = Chroma(&quot;langchain_store&quot;, embeddings)</pre>\n",
       "      <script>\n",
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       "        };\n",
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       "      </script>\n",
       "      </div>"
      ],
      "text/plain": [
       "langchain_community.vectorstores.chroma.Chroma"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9CM8vZuJbCrn"
   },
   "source": [
    "The following code shows the structure of the embedding database, as stored by ChromaDB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EhILCyOZMpmk",
    "outputId": "d5c34abd-b9a1-46c0-bddb-5c582535b462"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 5904\n",
      "drwxr-xr-x 3 root root    4096 Jun 26 16:31 .\n",
      "drwxr-xr-x 1 root root    4096 Jun 26 16:30 ..\n",
      "-rw-r--r-- 1 root root 6029312 Jun 26 16:31 chroma.sqlite3\n",
      "drwxr-xr-x 2 root root    4096 Jun 26 16:31 e7f2b890-35c6-4a12-b5ff-a12a08fe90c8\n"
     ]
    }
   ],
   "source": [
    "!ls -al /content/chroma_db"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z_6oBXkOJqq1"
   },
   "source": [
    "## Reload Database from Disk\n",
    "\n",
    "We can now reload this data into a new memory-based ChromaDB. This technique is much more efficient than reloading the data using the from_documents method previously employed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "x-2isPrvJw5W"
   },
   "outputs": [],
   "source": [
    "db2 = Chroma(persist_directory=\"/content/chroma_db\", embedding_function=embedding_function)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X9Mjb9KnbW4T"
   },
   "source": [
    "The following code we previously discussed demonstrates that the reloaded database is operational."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "XdHWSI1NJ_WH"
   },
   "outputs": [],
   "source": [
    "from langchain import hub\n",
    "from langchain_core.runnables import RunnablePassthrough\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "\n",
    "MODEL = 'gpt-4o-mini'\n",
    "\n",
    "llm = ChatOpenAI(\n",
    "        model=MODEL,\n",
    "        temperature=0.2,\n",
    "        n=1\n",
    "    )\n",
    "\n",
    "rag_prompt = hub.pull(\"rlm/rag-prompt\")\n",
    "\n",
    "def format_documents(documents):\n",
    "    return \"\\n\\n\".join(doc.page_content for doc in documents)\n",
    "\n",
    "retriever = db2.as_retriever()\n",
    "\n",
    "qa_chain = (\n",
    "    {\"context\": retriever | format_documents, \"question\": RunnablePassthrough()}\n",
    "    | rag_prompt\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L0_poIKdbpzo"
   },
   "source": [
    "We perform a query that accesses the RAG enabled data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "odbi4SeVKRJ0",
    "outputId": "705fcbb5-6756-413e-f560-000e2852bb6b"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'Elena Martinez works for Digital Dynamics.'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qa_chain.invoke(\"What company does Elena Martinez work for?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c-TI6Ft_Jt57"
   },
   "source": [
    "## Run Database as Server\n",
    "\n",
    "You can use the following command to start a ChromaDB server that uses the previously created directory.\n",
    "\n",
    "```\n",
    "chroma run --host 127.0.0.1 --path /content/chroma_db &\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-ySeKBhjcNu0"
   },
   "source": [
    "We can now construct a client to communicate with this database server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "SPxDQnHTWFxv"
   },
   "outputs": [],
   "source": [
    "import chromadb\n",
    "import langchain_community\n",
    "\n",
    "client = chromadb.HttpClient(host='127.0.0.1', port=8000)\n",
    "db3 = langchain_community.vectorstores.chroma.Chroma(\n",
    "    client=client,embedding_function=embedding_function)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3jGOl1PGbh_k"
   },
   "source": [
    "The following code we previously discussed demonstrates that the client HTTP database is operational."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "RgiFmGyrWQKw"
   },
   "outputs": [],
   "source": [
    "from langchain import hub\n",
    "from langchain_core.runnables import RunnablePassthrough\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "\n",
    "MODEL = 'gpt-4o-mini'\n",
    "\n",
    "llm = ChatOpenAI(\n",
    "        model=MODEL,\n",
    "        temperature=0.2,\n",
    "        n=1\n",
    "    )\n",
    "\n",
    "rag_prompt = hub.pull(\"rlm/rag-prompt\")\n",
    "\n",
    "def format_documents(documents):\n",
    "    return \"\\n\\n\".join(doc.page_content for doc in documents)\n",
    "\n",
    "retriever = db3.as_retriever()\n",
    "\n",
    "qa_chain = (\n",
    "    {\"context\": retriever | format_documents, \"question\": RunnablePassthrough()}\n",
    "    | rag_prompt\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V7Nd5Tuxbldr"
   },
   "source": [
    "We perform a query that accesses the RAG enabled data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "wvKB2dBRXFtx",
    "outputId": "e68fb841-f0cf-4861-aa50-e326c0a1f738"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'Samantha Clarke works for Digital Dynamics.'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qa_chain.invoke(\"What company does Elena Martinez work for?\")"
   ]
  }
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