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tenacity, orjson, jsonpointer, geomet, jsonpatch, cassandra-driver, langsmith, cassio, langchain-core, langgraph-checkpoint, langchain-text-splitters, langgraph, langchain\n", " Attempting uninstall: tenacity\n", " Found existing installation: tenacity 9.0.0\n", " Uninstalling tenacity-9.0.0:\n", " Successfully uninstalled tenacity-9.0.0\n", "Successfully installed cassandra-driver-3.29.1 cassio-0.1.8 geomet-0.2.1.post1 jsonpatch-1.33 jsonpointer-3.0.0 langchain-0.2.14 langchain-core-0.2.32 langchain-text-splitters-0.2.2 langgraph-0.2.4 langgraph-checkpoint-1.0.3 langsmith-0.1.99 orjson-3.10.7 tenacity-8.5.0\n" ] } ], "source": [ "!pip install langchain langgraph cassio" ] }, { "cell_type": "code", "source": [ "import cassio\n", "## connection of the ASTRA DB\n", "ASTRA_DB_APPLICATION_TOKEN=\"AstraCS:ZipfJaTtlEwiJoZekjQZYHbu:7c9851effffb5e953885379e0dbd62c799ad62f251e27bc834867a925691a4f8\" # enter the \"AstraCS:...\" string found in in your Token JSON file\"\n", "ASTRA_DB_ID=\"fca479ba-bf54-46c1-90ab-b14b2c764181\"\n", "cassio.init(token=ASTRA_DB_APPLICATION_TOKEN,database_id=ASTRA_DB_ID)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "U_4-ZQeLRE28", "outputId": "5d2bc62f-1a4c-497d-9e7d-3bf04ff519fb" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:cassandra.cluster:Downgrading core protocol version from 66 to 65 for fca479ba-bf54-46c1-90ab-b14b2c764181-us-east-2.db.astra.datastax.com:29042:0750e59f-441b-37bb-b0a7-e097c5d725f7. To avoid this, it is best practice to explicitly set Cluster(protocol_version) to the version supported by your cluster. http://datastax.github.io/python-driver/api/cassandra/cluster.html#cassandra.cluster.Cluster.protocol_version\n", "WARNING:cassandra.cluster:Downgrading core protocol version from 65 to 5 for fca479ba-bf54-46c1-90ab-b14b2c764181-us-east-2.db.astra.datastax.com:29042:0750e59f-441b-37bb-b0a7-e097c5d725f7. To avoid this, it is best practice to explicitly set Cluster(protocol_version) to the version supported by your cluster. http://datastax.github.io/python-driver/api/cassandra/cluster.html#cassandra.cluster.Cluster.protocol_version\n", "WARNING:cassandra.cluster:Downgrading core protocol version from 5 to 4 for fca479ba-bf54-46c1-90ab-b14b2c764181-us-east-2.db.astra.datastax.com:29042:0750e59f-441b-37bb-b0a7-e097c5d725f7. To avoid this, it is best practice to explicitly set Cluster(protocol_version) to the version supported by your cluster. http://datastax.github.io/python-driver/api/cassandra/cluster.html#cassandra.cluster.Cluster.protocol_version\n" ] } ] }, { "cell_type": "code", "source": [ "!pip install langchain_community" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "47F0DTbjSuOt", "outputId": "82e4ef13-e5b4-486a-fc35-81be07d7eb90" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting langchain_community\n", " Downloading langchain_community-0.2.12-py3-none-any.whl.metadata (2.7 kB)\n", "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (6.0.2)\n", "Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (2.0.32)\n", "Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (3.10.2)\n", "Collecting dataclasses-json<0.7,>=0.5.7 (from langchain_community)\n", " Downloading dataclasses_json-0.6.7-py3-none-any.whl.metadata (25 kB)\n", "Requirement already satisfied: langchain<0.3.0,>=0.2.13 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (0.2.14)\n", "Requirement already satisfied: langchain-core<0.3.0,>=0.2.30 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (0.2.32)\n", "Requirement already satisfied: langsmith<0.2.0,>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (0.1.99)\n", "Requirement already satisfied: numpy<2,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (1.26.4)\n", "Requirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (2.32.3)\n", "Requirement already satisfied: tenacity!=8.4.0,<9.0.0,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (8.5.0)\n", "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (2.3.5)\n", "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (1.3.1)\n", "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (24.2.0)\n", "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (1.4.1)\n", "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (6.0.5)\n", "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (1.9.4)\n", "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (4.0.3)\n", "Collecting marshmallow<4.0.0,>=3.18.0 (from dataclasses-json<0.7,>=0.5.7->langchain_community)\n", " Downloading marshmallow-3.21.3-py3-none-any.whl.metadata (7.1 kB)\n", "Collecting typing-inspect<1,>=0.4.0 (from dataclasses-json<0.7,>=0.5.7->langchain_community)\n", " Downloading typing_inspect-0.9.0-py3-none-any.whl.metadata (1.5 kB)\n", "Requirement already satisfied: langchain-text-splitters<0.3.0,>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from langchain<0.3.0,>=0.2.13->langchain_community) (0.2.2)\n", "Requirement already satisfied: pydantic<3,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain<0.3.0,>=0.2.13->langchain_community) (2.8.2)\n", "Requirement already satisfied: jsonpatch<2.0,>=1.33 in /usr/local/lib/python3.10/dist-packages (from 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mypy_extensions-1.0.0-py3-none-any.whl.metadata (1.1 kB)\n", "Downloading langchain_community-0.2.12-py3-none-any.whl (2.3 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m44.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hDownloading dataclasses_json-0.6.7-py3-none-any.whl (28 kB)\n", "Downloading marshmallow-3.21.3-py3-none-any.whl (49 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.2/49.2 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hDownloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)\n", "Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n", "Installing collected packages: mypy-extensions, marshmallow, typing-inspect, dataclasses-json, langchain_community\n", "Successfully installed dataclasses-json-0.6.7 langchain_community-0.2.12 marshmallow-3.21.3 mypy-extensions-1.0.0 typing-inspect-0.9.0\n" ] } ] }, { "cell_type": "code", "source": [ "!pip install -U langchain_community tiktoken langchain-groq langchainhub chromadb langchain langgraph langchain_huggingface" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "g4takLNeS4TC", "outputId": "cd0eae5c-ceb6-4ad7-e33f-6b6607f3aa63" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: langchain_community in /usr/local/lib/python3.10/dist-packages (0.2.12)\n", "Collecting tiktoken\n", " Downloading tiktoken-0.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.6 kB)\n", "Collecting langchain-groq\n", " Downloading langchain_groq-0.1.9-py3-none-any.whl.metadata (2.9 kB)\n", "Collecting langchainhub\n", " Downloading langchainhub-0.1.21-py3-none-any.whl.metadata (659 bytes)\n", "Collecting chromadb\n", " Downloading chromadb-0.5.5-py3-none-any.whl.metadata (6.8 kB)\n", "Requirement already satisfied: langchain in 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filename=PyPika-0.48.9-py2.py3-none-any.whl size=53726 sha256=7081b6b6483d9bc4db361a4bb8dee172d15f2086d86183dd25325a4635ff76fe\n", " Stored in directory: /root/.cache/pip/wheels/e1/26/51/d0bffb3d2fd82256676d7ad3003faea3bd6dddc9577af665f4\n", "Successfully built pypika\n", "Installing collected packages: pypika, monotonic, mmh3, websockets, uvloop, types-requests, python-dotenv, overrides, 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, importlib-metadata, humanfriendly, httptools, h11, deprecated, chroma-hnswlib, bcrypt, backoff, asgiref, watchfiles, uvicorn, tiktoken, starlette, posthog, opentelemetry-exporter-otlp-proto-common, opentelemetry-api, nvidia-cusparse-cu12, nvidia-cudnn-cu12, langchainhub, httpcore, coloredlogs, opentelemetry-semantic-conventions, opentelemetry-instrumentation, onnxruntime, nvidia-cusolver-cu12, kubernetes, httpx, fastapi, opentelemetry-sdk, opentelemetry-instrumentation-asgi, groq, sentence-transformers, opentelemetry-instrumentation-fastapi, opentelemetry-exporter-otlp-proto-grpc, langchain-groq, langchain_huggingface, chromadb\n", " Attempting uninstall: importlib-metadata\n", " Found existing installation: importlib_metadata 8.2.0\n", " Uninstalling importlib_metadata-8.2.0:\n", " Successfully uninstalled importlib_metadata-8.2.0\n", "Successfully installed asgiref-3.8.1 backoff-2.2.1 bcrypt-4.2.0 chroma-hnswlib-0.7.6 chromadb-0.5.5 coloredlogs-15.0.1 deprecated-1.2.14 fastapi-0.112.1 groq-0.9.0 h11-0.14.0 httpcore-1.0.5 httptools-0.6.1 httpx-0.27.0 humanfriendly-10.0 importlib-metadata-8.0.0 kubernetes-30.1.0 langchain-groq-0.1.9 langchain_huggingface-0.0.3 langchainhub-0.1.21 mmh3-4.1.0 monotonic-1.6 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-cu12-12.6.20 nvidia-nvtx-cu12-12.1.105 onnxruntime-1.19.0 opentelemetry-api-1.26.0 opentelemetry-exporter-otlp-proto-common-1.26.0 opentelemetry-exporter-otlp-proto-grpc-1.26.0 opentelemetry-instrumentation-0.47b0 opentelemetry-instrumentation-asgi-0.47b0 opentelemetry-instrumentation-fastapi-0.47b0 opentelemetry-proto-1.26.0 opentelemetry-sdk-1.26.0 opentelemetry-semantic-conventions-0.47b0 opentelemetry-util-http-0.47b0 overrides-7.7.0 posthog-3.5.0 pypika-0.48.9 python-dotenv-1.0.1 sentence-transformers-3.0.1 starlette-0.38.2 tiktoken-0.7.0 types-requests-2.32.0.20240712 uvicorn-0.30.6 uvloop-0.20.0 watchfiles-0.23.0 websockets-12.0\n" ] } ] }, { "cell_type": "code", "source": [ "### Build Index\n", "\n", "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import WebBaseLoader\n", "from langchain_community.vectorstores import Chroma\n", "\n", "\n", "### from langchain_cohere import CohereEmbeddings\n", "\n", "\n", "\n", "# Docs to index\n", "urls = [\n", " \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n", " \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n", " \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n", "]\n", "\n", "# Load\n", "docs = [WebBaseLoader(url).load() for url in urls]\n", "docs_list = [item for sublist in docs for item in sublist]\n", "\n", "# Split\n", "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n", " chunk_size=500, chunk_overlap=0\n", ")\n", "doc_splits = text_splitter.split_documents(docs_list)\n", "\n" ], "metadata": { "id": "Cg8aOn7sSDao" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from langchain_huggingface import HuggingFaceEmbeddings\n", "embeddings=HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424, "referenced_widgets": [ "29bbe29ae8d14b15bf902254e6bbb297", "4621a5ea9dab45e3937d11538024ff79", "da9a7c10f62f4dfa8db62cbfa8b4d855", "689a1504546a4d69900a5b902d47e3fa", "913fc392e6fa4c3d91b86224db1d1293", "49c353ef209b4b8abbfa134490a4a2ae", "0d5d7dccb4c64ea9ab091bdd5426fa1e", "99a5377c3fc249a7933ea9a2b822af22", "d7a2b1e95c5540b490331cb7ac5209e7", "a12edcd2abf34ddd8a82eb290cd412c6", "8f0122713dc545a7ac2901b93c166778", "9f29dc6484ea46aca138e40c8313f427", "4c598536de8248d097dde009d78c3c5a", "02c5b566d8a243e1babacd2ba5175e52", "4258f67757484d21af7528797a651326", "65116c2e349f42cf8dea447cd5b74fd6", "395138178f384f0f943a90732539e4db", "26db5f7359b84635a4b56e88768832b4", "7b287eabcb254977b12dd111a9266b0c", "0f5d7066b6494da6b7c63d92c728eebb", "815365e3958f483188183fcd6c9f153a", "9eab8d0c0955446fad9f69beeb974724", 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/root/.cache/pip/wheels/f0/69/93/a47e9d621be168e9e33c7ce60524393c0b92ae83cf6c6e89c5\n", "Successfully built wikipedia sgmllib3k\n", "Installing collected packages: sgmllib3k, feedparser, wikipedia, arxiv\n", "Successfully installed arxiv-2.1.3 feedparser-6.0.11 sgmllib3k-1.0.0 wikipedia-1.4.0\n" ] } ] }, { "cell_type": "code", "source": [ "### Working With Tools\n", "from langchain_community.utilities import ArxivAPIWrapper,WikipediaAPIWrapper\n", "from langchain_community.tools import ArxivQueryRun,WikipediaQueryRun\n", "\n", "## Arxiv and wikipedia Tools\n", "arxiv_wrapper=ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=200)\n", "arxiv=ArxivQueryRun(api_wrapper=arxiv_wrapper)\n", "\n", "api_wrapper=WikipediaAPIWrapper(top_k_results=1,doc_content_chars_max=200)\n", "wiki=WikipediaQueryRun(api_wrapper=api_wrapper)" ], "metadata": { "id": "guhXPR0MeOO5" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "## Graph\n", "\n", "from typing import List\n", "\n", "from typing_extensions import TypedDict\n", "\n", "\n", "class GraphState(TypedDict):\n", " \"\"\"\n", " Represents the state of our graph.\n", "\n", " Attributes:\n", " question: question\n", " generation: LLM generation\n", " documents: list of documents\n", " \"\"\"\n", "\n", " question: str\n", " generation: str\n", " documents: List[str]" ], "metadata": { "id": "Axw3sFySeeTo" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from langchain.schema import Document\n", "\n", "\n", "def retrieve(state):\n", " \"\"\"\n", " Retrieve documents\n", "\n", " Args:\n", " state (dict): The current graph state\n", "\n", " Returns:\n", " state (dict): New key added to state, documents, that contains retrieved documents\n", " \"\"\"\n", " print(\"---RETRIEVE---\")\n", " question = state[\"question\"]\n", "\n", " # Retrieval\n", " documents = retriever.invoke(question)\n", " return {\"documents\": documents, \"question\": question}" ], "metadata": { "id": "kEHsSowYeVXr" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def wiki_search(state):\n", " \"\"\"\n", " wiki search based on the re-phrased question.\n", "\n", " Args:\n", " state (dict): The current graph state\n", "\n", " Returns:\n", " state (dict): Updates documents key with appended web results\n", " \"\"\"\n", "\n", " print(\"---wikipedia---\")\n", " print(\"---HELLO--\")\n", " question = state[\"question\"]\n", " print(question)\n", "\n", " # Wiki search\n", " docs = wiki.invoke({\"query\": question})\n", " #print(docs[\"summary\"])\n", " wiki_results = docs\n", " wiki_results = Document(page_content=wiki_results)\n", "\n", " return {\"documents\": wiki_results, \"question\": question}" ], "metadata": { "id": "lhcrLf2KebJ4" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "### Edges ###\n", "\n", "\n", "def route_question(state):\n", " \"\"\"\n", " Route question to wiki search or RAG.\n", "\n", " Args:\n", " state (dict): The current graph state\n", "\n", " Returns:\n", " str: Next node to call\n", " \"\"\"\n", "\n", " print(\"---ROUTE QUESTION---\")\n", " question = state[\"question\"]\n", " source = question_router.invoke({\"question\": question})\n", " if source.datasource == \"wiki_search\":\n", " print(\"---ROUTE QUESTION TO Wiki SEARCH---\")\n", " return \"wiki_search\"\n", " elif source.datasource == \"vectorstore\":\n", " print(\"---ROUTE QUESTION TO RAG---\")\n", " return \"vectorstore\"" ], "metadata": { "id": "H5XFlwvvenKE" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "### Edges ###\n", "\n", "\n", "def route_question(state):\n", " \"\"\"\n", " Route question to wiki search or RAG.\n", "\n", " Args:\n", " state (dict): The current graph state\n", "\n", " Returns:\n", " str: Next node to call\n", " \"\"\"\n", "\n", " print(\"---ROUTE QUESTION---\")\n", " question = state[\"question\"]\n", " source = question_router.invoke({\"question\": question})\n", " if source.datasource == \"wiki_search\":\n", " print(\"---ROUTE QUESTION TO Wiki SEARCH---\")\n", " return \"wiki_search\"\n", " elif source.datasource == \"vectorstore\":\n", " print(\"---ROUTE QUESTION TO RAG---\")\n", " return \"vectorstore\"" ], "metadata": { "id": "EDwy1gZwioPn" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from langgraph.graph import END, StateGraph, START\n", "\n", "workflow = StateGraph(GraphState)\n", "# Define the nodes\n", "workflow.add_node(\"wiki_search\", wiki_search) # web search\n", "workflow.add_node(\"retrieve\", retrieve) # retrieve\n", "\n", "# Build graph\n", "workflow.add_conditional_edges(\n", " START,\n", " route_question,\n", " {\n", " \"wiki_search\": \"wiki_search\",\n", " \"vectorstore\": \"retrieve\",\n", " },\n", ")\n", "workflow.add_edge( \"retrieve\", END)\n", "workflow.add_edge( \"wiki_search\", END)\n", "# Compile\n", "app = workflow.compile()" ], "metadata": { "id": "8FNZ-4x7eq4T" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from IPython.display import Image, display\n", "\n", "try:\n", " display(Image(app.get_graph().draw_mermaid_png()))\n", "except Exception:\n", " # This requires some extra dependencies and is optional\n", " pass" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 255 }, "id": "zbSQ3ke4hb0Z", "outputId": "bb21ec7a-496d-4a83-9055-bfb9b7aef9b9" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/jpeg": 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\n", "text/plain": [ "" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "from pprint import pprint\n", "\n", "# Run\n", "inputs = {\n", " \"question\": \"What is agent?\"\n", "}\n", "for output in app.stream(inputs):\n", " for key, value in output.items():\n", " # Node\n", " pprint(f\"Node '{key}':\")\n", " # Optional: print full state at each node\n", " # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n", " pprint(\"\\n---\\n\")\n", "\n", "# Final generation\n", "pprint(value['documents'][0].dict()['metadata']['description'])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lqw7mbB1hcMD", "outputId": "907135c8-50da-4f1d-c28d-cc74feb9c38c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "---ROUTE QUESTION---\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:cassandra.protocol:Server warning: Top-K queries can only be run with consistency level ONE / LOCAL_ONE / NODE_LOCAL. Consistency level LOCAL_QUORUM was requested. Downgrading the consistency level to LOCAL_ONE.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "---ROUTE QUESTION TO RAG---\n", "---RETRIEVE---\n", "\"Node 'retrieve':\"\n", "'\\n---\\n'\n", "('Building agents with LLM (large language model) as its core controller is a '\n", " 'cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer '\n", " 'and BabyAGI, serve as inspiring examples. The potentiality of LLM extends '\n", " 'beyond generating well-written copies, stories, essays and programs; it can '\n", " 'be framed as a powerful general problem solver.\\n'\n", " 'Agent System Overview In a LLM-powered autonomous agent system, LLM '\n", " 'functions as the agent’s brain, complemented by several key components:')\n" ] } ] }, { "cell_type": "code", "source": [ "from pprint import pprint\n", "\n", "# Run\n", "inputs = {\n", " \"question\": \"Avengers\"\n", "}\n", "for output in app.stream(inputs):\n", " for key, value in output.items():\n", " # Node\n", " pprint(f\"Node '{key}':\")\n", " # Optional: print full state at each node\n", " # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n", " pprint(\"\\n---\\n\")\n", "\n", "# Final generation\n", "pprint(value['documents'])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uuIkJ28qh7vL", "outputId": "c4fe5b52-bb9f-467e-edb3-d7067103b208" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "---ROUTE QUESTION---\n", "---ROUTE QUESTION TO Wiki SEARCH---\n", "---wikipedia---\n", "---HELLO--\n", "Avengers\n", "\"Node 'wiki_search':\"\n", "'\\n---\\n'\n", "Document(page_content=\"Page: The Avengers (2012 film)\\nSummary: Marvel's The Avengers (titled Marvel Avengers Assemble in the United Kingdom and Ireland and commonly referred to as simply The Avengers) is a 2012 American sup\")\n" ] } ] }, { "cell_type": "markdown", "source": [], "metadata": { "id": "GH10caS3krBd" } }, { "cell_type": "code", "source": [], "metadata": { "id": "TTjTD0VMkjwn" }, "execution_count": null, "outputs": [] } ] }