diff --git a/documentation/v0.2.0/SUMMARY.md b/documentation/v0.2.0/SUMMARY.md index b275b2a1..ce351f74 100644 --- a/documentation/v0.2.0/SUMMARY.md +++ b/documentation/v0.2.0/SUMMARY.md @@ -49,8 +49,10 @@ - [💬 Chat on PDF](example-use-cases/chat-on-pdf.md) - [💬 Chat on CSV](example-use-cases/chat-on-csv.md) -- [📖 Document Search](example-use-cases/document-search.md) -- [🚀 Notebooks](example-use-cases/notebooks.md) +- [💬 Similarity Search on JSON](example-use-cases/json_similary_search.md) +- [💬 Document Search](example-use-cases/document-search.md) +- [💬 RAG pipeline](example-use-cases/rag_pipelines.md) +- [💬 Information Retrieval Pipeline]() --- diff --git a/documentation/v0.2.0/example-use-cases/rag_pipeline.jpeg b/documentation/v0.2.0/example-use-cases/rag_pipeline.jpeg new file mode 100644 index 00000000..7d0d8928 Binary files /dev/null and b/documentation/v0.2.0/example-use-cases/rag_pipeline.jpeg differ diff --git a/documentation/v0.2.0/example-use-cases/rag_pipelines.md b/documentation/v0.2.0/example-use-cases/rag_pipelines.md new file mode 100644 index 00000000..8b3ca187 --- /dev/null +++ b/documentation/v0.2.0/example-use-cases/rag_pipelines.md @@ -0,0 +1,130 @@ +# Knowledge Base Q&A: + +Provide direct, dynamic answers from databases, making data access swift and user-friendly. + +## GenAI Stack Workflow for Knowledge Base Question & Answer + +![rag_genai_stack](rag_pipeline.jpeg) + +## Installing GenAI Stack + +``` +!pip install genai_stack +``` + +Componets used for implementation +- gpt-3.5-turbo as LLM +- Chromadb as Vectorstore +- sentence-transformers/all-mpnet-base-v2 sentence transformer for text embeddings +- Langchain Framework + +## Import Required GenAI Stack Components + +```py +from genai_stack.stack.stack import Stack +from genai_stack.etl.langchain import LangchainETL +from genai_stack.embedding.langchain import LangchainEmbedding +from genai_stack.vectordb.chromadb import ChromaDB +from genai_stack.prompt_engine.engine import PromptEngine +from genai_stack.model.gpt3_5 import OpenAIGpt35Model +from genai_stack.retriever import LangChainRetriever +from genai_stack.memory.langchain import ConversationBufferMemory +``` + +## Instantiate ETL component by providing configuration according to source data type. + +- Here the input source is .pdf file + +```py +etl = LangchainETL.from_kwargs(name="PyPDFLoader", fields={"file_path": "YOUR PDF DOCUMENT PATH"}) +``` + +## Instantiate the LLM + +```py +llm = OpenAIGpt35Model.from_kwargs(parameters={"openai_api_key": "YOUR OPENAI API KEY"}) + +## Instantiate Embedding component using open source Huggingface Model + +```py +config = { + "model_name": "sentence-transformers/all-mpnet-base-v2", + "model_kwargs": {"device": "cpu"}, + "encode_kwargs": {"normalize_embeddings": False}, +} +embedding = LangchainEmbedding.from_kwargs(name="HuggingFaceEmbeddings", fields=config) +``` + +## Instantiate the Vectorstore + +```py +chromadb = ChromaDB.from_kwargs() +chromadb +``` + +## Instantiate the Retriver + +```py +retriever = LangChainRetriever.from_kwargs() +``` + +## Instantiate the Prompt engine + +Prompt engine constructs the prompt template for instructing the LLM + +```py +promptengine = PromptEngine.from_kwargs(should_validate=False) +``` + +## Instantiate Memory + +```py +memory = ConversationBufferMemory.from_kwargs() +``` + +## Setup the GenAI Stack + +```py +stack = Stack( + etl=etl, + embedding=embedding, + vectordb=chromadb, + model=llm, + prompt_engine=promptengine, + retriever=retriever, + memory=memory +) +``` + +## Performing the ETL operations + +1. extracting the content +2. transforrmation(creating embeddings), +3. load(storing the knowledge base in the vectordb) + +```py +etl.run() +``` + +## Ask a question + +```py +question = input("Ask a question: ") + +response = retriever.retrieve(question) +print(response['output']) +``` + +## Helper Function to generate response + +```py +process = "y" +while process == 'y': + question = 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+ ], + "metadata": { + "id": "CqWw1dqBO7WA" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Installing GenAI Stack version 2" + ], + "metadata": { + "id": "c1hycpJVHos6" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TmF3lrdfLjZq", + "outputId": "11236069-00da-4e83-d8eb-c22c0ea5bbf1" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting git+https://github.com/aiplanethub/genai-stack.git@0.2.0\n", + " Cloning https://github.com/aiplanethub/genai-stack.git (to revision 0.2.0) to /tmp/pip-req-build-hsmd3mo9\n", + " Running command git clone --filter=blob:none --quiet https://github.com/aiplanethub/genai-stack.git /tmp/pip-req-build-hsmd3mo9\n", + " Running command git checkout -b 0.2.0 --track origin/0.2.0\n", + " Switched to a new branch '0.2.0'\n", + " Branch '0.2.0' set up to track remote branch '0.2.0' from 'origin'.\n", + " Resolved https://github.com/aiplanethub/genai-stack.git to commit 4960a901d9326df683cad87a7c41843273e396c9\n", + " 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: Click>=7.0 in /usr/local/lib/python3.10/dist-packages (from genai-stack==0.1.0) (8.1.7)\n", + "Requirement already satisfied: requests>=2.28 in /usr/local/lib/python3.10/dist-packages (from genai-stack==0.1.0) (2.31.0)\n", + "Requirement already satisfied: langchain>=0.0.232 in /usr/local/lib/python3.10/dist-packages (from genai-stack==0.1.0) (0.0.285)\n", + "Requirement already satisfied: llama-hub in /usr/local/lib/python3.10/dist-packages (from genai-stack==0.1.0) (0.0.29)\n", + "Requirement already satisfied: llama-index in /usr/local/lib/python3.10/dist-packages (from genai-stack==0.1.0) (0.8.23.post1)\n", + "Requirement already 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genai-stack==0.1.0) (2.0.1+cu118)\n", + "Requirement already satisfied: chromadb==0.4.5 in /usr/local/lib/python3.10/dist-packages (from genai-stack==0.1.0) (0.4.5)\n", + "Requirement already satisfied: pypdf==3.14.0 in /usr/local/lib/python3.10/dist-packages (from genai-stack==0.1.0) (3.14.0)\n", + "Requirement already satisfied: sentence-transformers==2.2.2 in /usr/local/lib/python3.10/dist-packages (from genai-stack==0.1.0) (2.2.2)\n", + "Requirement already satisfied: pydantic<2.0,>=1.9 in /usr/local/lib/python3.10/dist-packages (from chromadb==0.4.5->genai-stack==0.1.0) (1.10.12)\n", + "Requirement already satisfied: chroma-hnswlib==0.7.2 in /usr/local/lib/python3.10/dist-packages (from chromadb==0.4.5->genai-stack==0.1.0) (0.7.2)\n", + "Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from chromadb==0.4.5->genai-stack==0.1.0) (1.23.5)\n", + "Requirement already satisfied: posthog>=2.4.0 in /usr/local/lib/python3.10/dist-packages (from chromadb==0.4.5->genai-stack==0.1.0) (3.0.2)\n", + "Requirement already satisfied: typing-extensions>=4.5.0 in /usr/local/lib/python3.10/dist-packages (from chromadb==0.4.5->genai-stack==0.1.0) (4.5.0)\n", + "Requirement already satisfied: pulsar-client>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from chromadb==0.4.5->genai-stack==0.1.0) (3.3.0)\n", + "Requirement already satisfied: onnxruntime>=1.14.1 in /usr/local/lib/python3.10/dist-packages (from chromadb==0.4.5->genai-stack==0.1.0) (1.15.1)\n", + "Requirement already satisfied: tokenizers>=0.13.2 in /usr/local/lib/python3.10/dist-packages (from chromadb==0.4.5->genai-stack==0.1.0) (0.13.3)\n", + "Requirement already satisfied: pypika>=0.48.9 in /usr/local/lib/python3.10/dist-packages (from chromadb==0.4.5->genai-stack==0.1.0) (0.48.9)\n", + "Requirement already satisfied: tqdm>=4.65.0 in /usr/local/lib/python3.10/dist-packages (from chromadb==0.4.5->genai-stack==0.1.0) (4.66.1)\n", + "Requirement already satisfied: overrides>=7.3.1 in /usr/local/lib/python3.10/dist-packages (from chromadb==0.4.5->genai-stack==0.1.0) (7.4.0)\n", + "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.10/dist-packages (from chromadb==0.4.5->genai-stack==0.1.0) (6.0.1)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2==3.1.2->genai-stack==0.1.0) (2.1.3)\n", + "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema==4.18.6->genai-stack==0.1.0) (23.1.0)\n", + "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema==4.18.6->genai-stack==0.1.0) (2023.7.1)\n", + "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema==4.18.6->genai-stack==0.1.0) (0.30.2)\n", + "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from 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"uFkGYlgRRXBd" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Import Required GenAI Stack Components" + ], + "metadata": { + "id": "DdnrxAO4HScY" + } + }, + { + "cell_type": "code", + "source": [ + "from genai_stack.stack.stack import Stack\n", + "from genai_stack.etl.langchain import LangchainETL\n", + "from genai_stack.embedding.langchain import LangchainEmbedding\n", + "from genai_stack.vectordb.chromadb import ChromaDB\n", + "from genai_stack.prompt_engine.engine import PromptEngine\n", + "from genai_stack.model.gpt3_5 import OpenAIGpt35Model\n", + "from genai_stack.retriever import LangChainRetriever\n", + "from genai_stack.memory.langchain import ConversationBufferMemory" + ], + "metadata": { + "id": "DAEFKXHoF5wh" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Instantiate ETL component by providing configuration according to source data type.\n", + "\n", + "* Here the input source is .pdf file\n", + "\n", + "\n" + ], + "metadata": { + "id": "fSTVjT6Zpo8D" + } + }, + { + "cell_type": "code", + "source": [ + "etl = LangchainETL.from_kwargs(name=\"PyPDFLoader\", fields={\"file_path\": \"YOUR PDF DOCUMENT PATH\"})" + ], + "metadata": { + "id": "XWE464Nep9Ar" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Instantiate the LLM" + ], + "metadata": { + "id": "IDS9qukTpW6G" + } + }, + { + "cell_type": "code", + "source": [ + "llm = OpenAIGpt35Model.from_kwargs(parameters={\"openai_api_key\": \"YOUR OPENAI API KEY\"})" + ], + "metadata": { + "id": "5znp5Ci-pZDZ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Instantiate Embedding component using open source Huggingface Model" + ], + "metadata": { + "id": "dW_X-xFeqFIX" + } + }, + { + "cell_type": "code", + "source": [ + "config = {\n", + " \"model_name\": \"sentence-transformers/all-mpnet-base-v2\",\n", + " \"model_kwargs\": {\"device\": \"cpu\"},\n", + " \"encode_kwargs\": {\"normalize_embeddings\": False},\n", + "}\n", + "embedding = LangchainEmbedding.from_kwargs(name=\"HuggingFaceEmbeddings\", fields=config)" + ], + "metadata": { + "id": "WS1GRYXoqLb4" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Instantiate the Vectorstore" + ], + "metadata": { + "id": "hQSmg4w4qXGU" + } + }, + { + "cell_type": "code", + "source": [ + "chromadb = ChromaDB.from_kwargs()\n", + "chromadb\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bE4sPVOoLplm", + "outputId": "35a41626-4af6-48dc-a14a-7d37a8927b1b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 17 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Instantiate the Retriver" + ], + "metadata": { + "id": "ckyZd6cHqlJL" + } + }, + { + "cell_type": "code", + "source": [ + "retriever = LangChainRetriever.from_kwargs()" + ], + "metadata": { + "id": "Z_QfrkJlO_W7" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Instantiate the Promptengine\n", + "\n", + "* prompt engine constructs the prompt template for instructing the LLM" + ], + "metadata": { + "id": "6eGU8It-rgCW" + } + }, + { + "cell_type": "code", + "source": [ + "promptengine = PromptEngine.from_kwargs(should_validate=False)" + ], + "metadata": { + "id": "O7P_qsmFry_H" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Instantiate Memory" + ], + "metadata": { + "id": "O1ZbaAeNs1_S" + } + }, + { + "cell_type": "code", + "source": [ + "memory = ConversationBufferMemory.from_kwargs()" + ], + "metadata": { + "id": "rBQjWE3ws4oD" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Setup the GenAI Stack" + ], + "metadata": { + "id": "i1LXqz6RtBf3" + } + }, + { + "cell_type": "code", + "source": [ + "stack = Stack(\n", + " etl=etl,\n", + " embedding=embedding,\n", + " vectordb=chromadb,\n", + " model=llm,\n", + " prompt_engine=promptengine,\n", + " retriever=retriever,\n", + " memory=memory\n", + ")" + ], + "metadata": { + "id": "H0UpnsUNPbG7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 465, + "referenced_widgets": [ + "41eb29b0e8154decafdbebeed8e3d35c", + "a22be9851c03486f93057267f5319d3a", + "7d63cdc5f7a341e4882f40bc5845e607", + "1a487fa84e3e4fa19ce9a5cc573f1b8f", + "1903ea316b21478e9889f0ecb17656ec", + "4d206cd66a09438c8cd18bbd09cb0d7b", + "613315d06afd46e1b0b1d8ff082951af", + "a6ba43a1962443809c218d3b3beae4be", + "f98acf32072847409b0596e232981a0a", + "300168b994cc4296a555c37507370ba8", + "f6d117b73d864b14848522fd13a15e5c", + "a5702c011bd4411fa65034ad8bfa31cd", + "89a7a40823a149549b8454d0084942a0", + "895223499ad84ae3ad2eb79611f523aa", + "05e1441ce1a74d48b228bb49aa2e6385", + 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transformers3-0.0.0a1 typing-inspect-0.9.0 urllib3-1.26.17 uvicorn-0.23.0 uvloop-0.18.0 validators-0.22.0 watchfiles-0.21.0 weaviate-client-3.24.2 websockets-11.0.3\n" + ] + } + ], + "source": [ + "!pip install genai_stack" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QeIrtUI1_2s0" + }, + "source": [ + "## Setup your API Key" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cs6UPxk4sIGg" + }, + "outputs": [], + "source": [ + "import os\n", + "from getpass import getpass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "S2vfFbmssZfW", + "outputId": "afd599e4-fa4d-4381-94b1-1385fa18a26c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter OpenAI API Key:··········\n" + ] + } + ], + "source": [ + "api_key = getpass(\"Enter OpenAI API Key:\")\n", + "os.environ['OPENAI_API_KEY'] = api_key" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9ahC5bx7Dfl_" + }, + "source": [ + "## Import required modules" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vEfjWMuVPpCY" + }, + "outputs": [], + "source": [ + "from genai_stack.stack.stack import Stack\n", + "from genai_stack.etl.langchain import LangchainETL\n", + "from genai_stack.embedding.langchain import LangchainEmbedding\n", + "from genai_stack.vectordb.chromadb import ChromaDB\n", + "from genai_stack.prompt_engine.engine import PromptEngine\n", + "from genai_stack.model.gpt3_5 import OpenAIGpt35Model\n", + "from genai_stack.retriever.langchain import LangChainRetriever\n", + "from genai_stack.memory.langchain import ConversationBufferMemory" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "p7TVObv1DiLg" + }, + "source": [ + "## ETL - \"Extract, Transform, and Load.\"\n", + "\n", + "- Add your data here. Check documentation for the required loaders" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FxQ9zTuasoLK" + }, + "outputs": [], + "source": [ + "etl = LangchainETL.from_kwargs(name=\"WebBaseLoader\",\n", + " fields={\"web_path\": [\n", + " \"https://aiplanet.com\",\n", + " \"https://aimarketplace.co\"\n", + " ]\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FS87zjIwDvne" + }, + "source": [ + "## Create Embeddings to store in VectorDB" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-gZdeZpDs8-6" + }, + "outputs": [], + "source": [ + "config = {\n", + " \"model_name\": \"sentence-transformers/all-mpnet-base-v2\",\n", + " \"model_kwargs\": {\"device\": \"cpu\"},\n", + " \"encode_kwargs\": {\"normalize_embeddings\": False},\n", + "}\n", + "embedding = LangchainEmbedding.from_kwargs(name=\"HuggingFaceEmbeddings\", fields=config)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Mh1appk9D09D" + }, + "source": [ + "## Define the VectorDB" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "e1qTWu6Vs9ua" + }, + "outputs": [], + "source": [ + "chromadb = ChromaDB.from_kwargs()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YEccaQgvD3ln" + }, + "source": [ + "## Define your LLM - Large Language Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dwLeBUTQtBFL" + }, + "outputs": [], + "source": [ + "llm = OpenAIGpt35Model.from_kwargs(parameters={\"openai_api_key\": api_key})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P6duBcfjD7pF" + }, + "source": [ + "## Add Retrieval and Stack all the components" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 483, + "referenced_widgets": [ + "f96546c4a7884e9db702dfadab247519", + 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"application/vnd.jupyter.widget-view+json": { + "model_id": "f96546c4a7884e9db702dfadab247519", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading (…)a8e1d/.gitattributes: 0%| | 0.00/1.18k [00:00" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prompt_engine = PromptEngine.from_kwargs(should_validate=False)\n", + "retriever = LangChainRetriever.from_kwargs()\n", + "memory = ConversationBufferMemory.from_kwargs()\n", + "Stack(\n", + " etl=etl,\n", + " embedding=embedding,\n", + " vectordb=chromadb,\n", + " model=llm,\n", + " prompt_engine=prompt_engine,\n", + " retriever=retriever,\n", + " memory=memory\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dEHtQQPkD_ng" + }, + "source": [ + "## Run your ETL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "R9AU8SSltKaX" + }, + "outputs": [], + "source": [ + "etl.run()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QrUamZzWtPa_" + }, + "outputs": [], + "source": [ + "prompt1 = \"Why choose models from AI Marketplace?\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MdLkaSeDtUbg" + }, + "outputs": [], + "source": [ + "response = retriever.retrieve(prompt1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Chde1cIDtXVm", + "outputId": "63343353-983b-455f-b3ee-1dab2e816686" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Models from AI Marketplace are a good choice because they are ready-to-use and can accelerate innovation. They simplify integration across diverse applications and bring ideas to life. Additionally, AI Marketplace offers a large collection of models for different industries and use cases, allowing users to explore diverse AI models. The marketplace also provides cost-saving solutions with pre-built models, eliminating the need for a data science team, and offering flexible scaling and pay-as-you-go pricing. Finally, AI Marketplace offers business-focused models that can optimize operations and drive growth.\n" + ] + } + ], + "source": [ + "print(response['output'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3DLv-rWOtfm3" + }, + "outputs": [], + "source": [ + "prompt2 = \"What is the total community members count at AI Planet?\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SalOePZetsXQ" + }, + "outputs": [], + "source": [ + "response = retriever.retrieve(prompt2)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "widgets": 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Run etl - - ```bash - genai-stack etl --config_file etl.json - ``` - -2. Run model - ```bash - genai-stack start --config_file model.json - ``` diff --git a/examples/chat_on_pdf/etl.json b/examples/chat_on_pdf/etl.json deleted file mode 100644 index 9e8198e1..00000000 --- a/examples/chat_on_pdf/etl.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "etl": "langchain", - "source": { - "name": "PyPDFLoader", - "fields": { - "file_path": "/home/sln/dphi_projects/llaim/TrainTicket-31-Mar-2023.pdf" - } - }, - "vectordb": { - "name": "chromadb", - "class_name": "genai_stack" - } -} diff --git a/examples/chat_on_pdf/model.json b/examples/chat_on_pdf/model.json deleted file mode 100644 index ce13f159..00000000 --- a/examples/chat_on_pdf/model.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "model": { - "name": "gpt4all" - }, - "retriever": { - "name": "langchain" - }, - "vectordb": { - "name": "chromadb", - "class_name": "genai_stack" - } -} diff --git a/examples/chat_on_webpage/Readme.md b/examples/chat_on_webpage/Readme.md deleted file mode 100644 index be165a1b..00000000 --- a/examples/chat_on_webpage/Readme.md +++ /dev/null @@ -1,14 +0,0 @@ -## Chat with a Webpage - -**Note:** Edit the valid_web_url path in the etl.json file. - -1. Run etl - - ```bash - genai-stack etl --config_file etl.json - ``` - -2. Run model - ```bash - genai-stack start --config_file model.json - ``` diff --git a/examples/chat_on_webpage/etl.json b/examples/chat_on_webpage/etl.json deleted file mode 100644 index 7e3cca77..00000000 --- a/examples/chat_on_webpage/etl.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "etl": "langchain", - "source": { - "name": "WebBaseLoader", - "fields": { - "web_path": "valid_web_url" - } - }, - "vectordb": { - "name": "chromadb", - "class_name": "genai_stack" - } -} \ No newline at end of file diff --git a/examples/chat_on_webpage/model.json b/examples/chat_on_webpage/model.json deleted file mode 100644 index 042de947..00000000 --- a/examples/chat_on_webpage/model.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "model": { - "name": "gpt4all" - }, - "retriever": { - "name": "langchain" - }, - "vectordb": { - "name": "chromadb", - "class_name": "genai_stack" - } -} \ No newline at end of file diff --git a/genai_stack/model/hf_hub.py b/genai_stack/model/hf_hub.py new file mode 100644 index 00000000..9fb25722 --- /dev/null +++ b/genai_stack/model/hf_hub.py @@ -0,0 +1,42 @@ +from typing import Optional, Dict +from langchain.llms import HuggingFaceHub +from genai_stack.model.base import BaseModel, BaseModelConfig, BaseModelConfigModel + + +class HuggingFaceHubParameters(BaseModelConfigModel): + """ + Data Model for the configs + """ + repo_id: Optional[str] = "HuggingFaceH4/zephyr-7b-beta" + """Model name to use.""" + model_kwargs: Optional[Dict] = None + """Key word arguments passed to the model.""" + task: str = "text-generation" + """Valid tasks: 'text2text-generation', 'text-generation', 'summarization'""" + huggingface_api_token: str + "HuggingFace Access Token" + +class HuggingFaceHubConfigModel(BaseModelConfigModel): + """ + Parameters for the configs + """ + parameters: HuggingFaceHubParameters + +class HuggingFaceHubConfig(BaseModelConfig): + data_model = HuggingFaceHubConfigModel + +class HuggingFaceHubModel(BaseModel): + config_class = HuggingFaceHubConfig + + def _post_init(self, *args, **kwargs): + self.model = self.load() + + def load(self): + model = HuggingFaceHub.from_model_id( + **self.config.parameters.dict() + ) + return model + + def predict(self, prompt: str): + response = self.model.predict(prompt) + return {"output": response}