402 lines
30 KiB
Plaintext
402 lines
30 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "9f97dd1e",
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"metadata": {},
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"source": [
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"# Libraries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "9e974df6",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from typing import TypedDict, List, Optional, Annotated\n",
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"from IPython.display import Image, display\n",
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"\n",
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"from langchain_core.documents import Document\n",
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"from langchain_core.messages import BaseMessage, SystemMessage\n",
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"from langchain_core.tools import tool\n",
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"from langgraph.checkpoint.memory import InMemorySaver\n",
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"from langgraph.graph.message import add_messages\n",
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"from langchain_ollama import ChatOllama, OllamaEmbeddings\n",
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"from langchain_elasticsearch import ElasticsearchStore\n",
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"from langgraph.graph import StateGraph, END\n",
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"from langgraph.prebuilt import ToolNode\n",
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"from langfuse import observe, get_client"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "30edcecc",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"DEBUG: LANGFUSE_HOST from env = http://45.77.119.180\n",
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"DEBUG: Langfuse client base_url = NOT SET\n"
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]
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}
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],
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"source": [
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"ES_URL = os.getenv(\"ELASTICSEARCH_LOCAL_URL\")\n",
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"INDEX_NAME = os.getenv(\"ELASTICSEARCH_INDEX\")\n",
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"BASE_URL = os.getenv(\"LLM_BASE_LOCAL_URL\")\n",
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"MODEL_NAME = os.getenv(\"OLLAMA_MODEL_NAME\")\n",
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"LANGFUSE_PUBLIC_KEY = os.getenv(\"LANGFUSE_PUBLIC_KEY\")\n",
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"LANGFUSE_SECRET_KEY = os.getenv(\"LANGFUSE_SECRET_KEY\")\n",
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"LANGFUSE_HOST = os.getenv(\"LANGFUSE_HOST\")\n",
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"\n",
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"print(f\"DEBUG: LANGFUSE_HOST from env = {LANGFUSE_HOST}\")\n",
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"\n",
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"# Initialize Langfuse client\n",
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"langfuse = get_client()\n",
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"\n",
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"# Print actual client configuration\n",
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"print(f\"DEBUG: Langfuse client base_url = {langfuse.base_url if hasattr(langfuse, 'base_url') else 'NOT SET'}\")\n",
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"\n",
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"embeddings = OllamaEmbeddings(base_url=BASE_URL, model=MODEL_NAME)\n",
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"llm = ChatOllama(base_url=BASE_URL, model=MODEL_NAME)\n",
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"\n",
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"vector_store = ElasticsearchStore(\n",
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" es_url=ES_URL,\n",
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" index_name=INDEX_NAME,\n",
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" embedding=embeddings,\n",
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" query_field=\"text\",\n",
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" vector_query_field=\"vector\",\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "ad98841b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Langfuse Configuration:\n",
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" Host: http://45.77.119.180\n",
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" Public Key: **********\n",
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" Secret Key: **********\n",
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"\n",
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"✓ All Langfuse environment variables are set\n",
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" Tracing will be sent to Langfuse when you run the agent\n"
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]
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}
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],
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"source": [
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"print(f\"Langfuse Configuration:\")\n",
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"print(f\" Host: {LANGFUSE_HOST}\")\n",
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"print(f\" Public Key: {'*' * 10 if LANGFUSE_PUBLIC_KEY else 'NOT SET'}\")\n",
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"print(f\" Secret Key: {'*' * 10 if LANGFUSE_SECRET_KEY else 'NOT SET'}\")\n",
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"\n",
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"if all([LANGFUSE_HOST, LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY]):\n",
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" print(\"\\n✓ All Langfuse environment variables are set\")\n",
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" print(\" Tracing will be sent to Langfuse when you run the agent\")\n",
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"else:\n",
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" print(\"\\n⚠ Some Langfuse variables are missing - tracing may not work\")\n",
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" print(f\" Set LANGFUSE_HOST, LANGFUSE_PUBLIC_KEY, and LANGFUSE_SECRET_KEY\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "873ea2f6",
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"metadata": {},
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"source": [
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"### State"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "5f8c88cf",
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"metadata": {},
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"outputs": [],
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"source": [
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"class AgentState(TypedDict):\n",
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" messages: Annotated[list, add_messages]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1d60c120",
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"metadata": {},
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"source": [
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"### Tools"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"id": "f9359747",
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"metadata": {},
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"outputs": [],
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"source": [
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"def format_context(docs: List[Document]) -> str:\n",
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" chunks: List[str] = []\n",
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" for i, doc in enumerate(docs, 1):\n",
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" title = (doc.metadata or {}).get(\"title\", \"Untitled\")\n",
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" source_id = (doc.metadata or {}).get(\"id\", f\"chunk-{i}\")\n",
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" text = doc.page_content or \"\"\n",
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" chunks.append(f\"[{i}] id={source_id} title={title}\\n{text}\")\n",
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" return \"\\n\\n\".join(chunks)\n",
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"\n",
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"\n",
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"@tool(\"retrieve\", return_direct=False)\n",
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"def retrieve(query: str, k: int = 4, title_filter: Optional[str] = None) -> str:\n",
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" \"\"\"Retrieve relevant context from Elasticsearch for a given query.\"\"\"\n",
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" search_kwargs = {\"k\": k}\n",
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" if title_filter:\n",
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" search_kwargs[\"filter\"] = {\"term\": {\"metadata.title.keyword\": title_filter}}\n",
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"\n",
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" retriever = vector_store.as_retriever(\n",
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" search_type=\"similarity\",\n",
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" search_kwargs=search_kwargs,\n",
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" )\n",
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" docs = retriever.invoke(query)\n",
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" return format_context(docs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"id": "e5247ab9",
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"metadata": {},
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"outputs": [],
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"source": [
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"def should_continue(state: AgentState) -> str:\n",
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" last = state[\"messages\"][-1]\n",
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" # If the model requested tool calls, go execute them\n",
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" if getattr(last, \"tool_calls\", None):\n",
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" return \"tools\"\n",
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" return \"end\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"id": "a644f6fa",
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"metadata": {},
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"outputs": [],
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"source": [
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"tools = [retrieve]\n",
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"tool_node = ToolNode(tools)\n",
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"memory = InMemorySaver()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "395966e2",
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"metadata": {},
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"source": [
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"### Agent"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"id": "36d0f54e",
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"metadata": {},
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"outputs": [],
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"source": [
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"def agent(state: AgentState) -> AgentState:\n",
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" messages: List[BaseMessage] = state[\"messages\"]\n",
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"\n",
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" system = SystemMessage(\n",
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" content=(\n",
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" \"You are a helpful assistant. You must use the tools provided to respond.\\n\"\n",
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" \"If you don't have enough info, ask a precise follow-up question.\"\n",
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" )\n",
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" )\n",
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"\n",
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" # IMPORTANT: bind tools so the model can emit tool calls\n",
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" # Also bind the langfuse handler for tracing\n",
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" model = llm.bind_tools(tools)\n",
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"\n",
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" resp = model.invoke([system, *messages], config={\"callbacks\": [langfuse_handler]})\n",
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" return {\"messages\": [*messages, resp]}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ef55bca3",
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"metadata": {},
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"source": [
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"### Graph"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"id": "fae46a58",
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"metadata": {},
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"outputs": [],
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"source": [
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"graph = StateGraph(AgentState)\n",
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"graph.add_node(\"agent\", agent)\n",
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"graph.add_node(\"tools\", tool_node)\n",
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"\n",
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"graph.set_entry_point(\"agent\")\n",
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"graph.add_conditional_edges(\"agent\", should_continue, {\"tools\": \"tools\", \"end\": END})\n",
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"graph.add_edge(\"tools\", \"agent\")\n",
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"\n",
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"agent_graph = graph.compile(checkpointer=memory)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"id": "2fec3fdb",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"try:\n",
|
|
" display(Image(agent_graph.get_graph().draw_mermaid_png()))\n",
|
|
"except Exception:\n",
|
|
" pass"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "1e9aff05",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Test"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"id": "a7f4fbf6",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"user_input = \"What does vertical farming proposes?\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"id": "8569cf39",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"config = {\"configurable\": {\"thread_id\": \"1\"}, \n",
|
|
" \"callbacks\": [langfuse_handler],\n",
|
|
" \"run_name\": \"rag-local-test\"}\n",
|
|
"\n",
|
|
"\n",
|
|
"@observe(name=\"agent-graph-stream\")\n",
|
|
"def stream_graph_updates(user_input: str):\n",
|
|
" for event in agent_graph.stream(\n",
|
|
" {\"messages\": [{\"role\": \"user\", \"content\": user_input}]},\n",
|
|
" config=config,\n",
|
|
" stream_mode=\"values\",\n",
|
|
" ):\n",
|
|
" event[\"messages\"][-1].pretty_print()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "53b89690",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Starting agent...\n",
|
|
"================================\u001b[1m Human Message \u001b[0m=================================\n",
|
|
"\n",
|
|
"What does vertical farming proposes?\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Failed to export span batch code: 404, reason: <!DOCTYPE html><html lang=\"en\"><head><meta charSet=\"utf-8\"/><meta name=\"viewport\" content=\"width=device-width\"/><meta name=\"next-head-count\" content=\"2\"/><link rel=\"preload\" href=\"/_next/static/css/1d1bf8ebf039f876.css\" as=\"style\"/><link rel=\"stylesheet\" href=\"/_next/static/css/1d1bf8ebf039f876.css\" data-n-g=\"\"/><noscript data-n-css=\"\"></noscript><script defer=\"\" nomodule=\"\" src=\"/_next/static/chunks/polyfills-42372ed130431b0a.js\"></script><script src=\"/_next/static/chunks/webpack-fafe7122277f5772.js\" defer=\"\"></script><script src=\"/_next/static/chunks/framework-d2dcb18632b956b2.js\" defer=\"\"></script><script src=\"/_next/static/chunks/main-19694d19b6f6c228.js\" defer=\"\"></script><script src=\"/_next/static/chunks/pages/_app-9e6c56ce79ebaf0a.js\" defer=\"\"></script><script src=\"/_next/static/chunks/pages/_error-881d11b9e6da73d1.js\" defer=\"\"></script><script src=\"/_next/static/Eu5QEyLpa3Avvo-CHkMDG/_buildManifest.js\" defer=\"\"></script><script src=\"/_next/static/Eu5QEyLpa3Avvo-CHkMDG/_ssgManifest.js\" defer=\"\"></script></head><body><div id=\"__next\"><script>!function(){try{var d=document.documentElement,c=d.classList;c.remove('light','dark');var e=localStorage.getItem('theme');if('system'===e||(!e&&true)){var t='(prefers-color-scheme: dark)',m=window.matchMedia(t);if(m.media!==t||m.matches){d.style.colorScheme = 'dark';c.add('dark')}else{d.style.colorScheme = 'light';c.add('light')}}else if(e){c.add(e|| '')}if(e==='light'||e==='dark')d.style.colorScheme=e}catch(e){}}()</script><div class=\"flex min-h-full flex-1 flex-col justify-center py-12 sm:px-6 lg:px-8\" data-sentry-component=\"Spinner\" data-sentry-source-file=\"spinner.tsx\"><div class=\"sm:mx-auto sm:w-full sm:max-w-md\"><img src=\"/icon.svg\" width=\"42\" height=\"42\" alt=\"Langfuse Icon\" class=\"mx-auto motion-safe:animate-spin\" data-sentry-component=\"LangfuseIcon\" data-sentry-source-file=\"LangfuseLogo.tsx\"/><h2 class=\"mt-5 text-center text-2xl font-bold leading-9 tracking-tight text-primary\">Loading<!-- --> ...</h2></div></div></div><script id=\"__NEXT_DATA__\" type=\"application/json\">{\"props\":{\"pageProps\":{\"statusCode\":404}},\"page\":\"/_error\",\"query\":{},\"buildId\":\"Eu5QEyLpa3Avvo-CHkMDG\",\"nextExport\":true,\"isFallback\":false,\"gip\":true,\"locale\":\"en\",\"locales\":[\"en\"],\"defaultLocale\":\"en\",\"scriptLoader\":[]}</script></body></html>\n",
|
|
"Failed to export span batch code: 404, reason: <!DOCTYPE html><html lang=\"en\"><head><meta charSet=\"utf-8\"/><meta name=\"viewport\" content=\"width=device-width\"/><meta name=\"next-head-count\" content=\"2\"/><link rel=\"preload\" href=\"/_next/static/css/1d1bf8ebf039f876.css\" as=\"style\"/><link rel=\"stylesheet\" href=\"/_next/static/css/1d1bf8ebf039f876.css\" data-n-g=\"\"/><noscript data-n-css=\"\"></noscript><script defer=\"\" nomodule=\"\" src=\"/_next/static/chunks/polyfills-42372ed130431b0a.js\"></script><script src=\"/_next/static/chunks/webpack-fafe7122277f5772.js\" defer=\"\"></script><script src=\"/_next/static/chunks/framework-d2dcb18632b956b2.js\" defer=\"\"></script><script src=\"/_next/static/chunks/main-19694d19b6f6c228.js\" defer=\"\"></script><script src=\"/_next/static/chunks/pages/_app-9e6c56ce79ebaf0a.js\" defer=\"\"></script><script src=\"/_next/static/chunks/pages/_error-881d11b9e6da73d1.js\" defer=\"\"></script><script src=\"/_next/static/Eu5QEyLpa3Avvo-CHkMDG/_buildManifest.js\" defer=\"\"></script><script src=\"/_next/static/Eu5QEyLpa3Avvo-CHkMDG/_ssgManifest.js\" defer=\"\"></script></head><body><div id=\"__next\"><script>!function(){try{var d=document.documentElement,c=d.classList;c.remove('light','dark');var e=localStorage.getItem('theme');if('system'===e||(!e&&true)){var t='(prefers-color-scheme: dark)',m=window.matchMedia(t);if(m.media!==t||m.matches){d.style.colorScheme = 'dark';c.add('dark')}else{d.style.colorScheme = 'light';c.add('light')}}else if(e){c.add(e|| '')}if(e==='light'||e==='dark')d.style.colorScheme=e}catch(e){}}()</script><div class=\"flex min-h-full flex-1 flex-col justify-center py-12 sm:px-6 lg:px-8\" data-sentry-component=\"Spinner\" data-sentry-source-file=\"spinner.tsx\"><div class=\"sm:mx-auto sm:w-full sm:max-w-md\"><img src=\"/icon.svg\" width=\"42\" height=\"42\" alt=\"Langfuse Icon\" class=\"mx-auto motion-safe:animate-spin\" data-sentry-component=\"LangfuseIcon\" data-sentry-source-file=\"LangfuseLogo.tsx\"/><h2 class=\"mt-5 text-center text-2xl font-bold leading-9 tracking-tight text-primary\">Loading<!-- --> ...</h2></div></div></div><script id=\"__NEXT_DATA__\" type=\"application/json\">{\"props\":{\"pageProps\":{\"statusCode\":404}},\"page\":\"/_error\",\"query\":{},\"buildId\":\"Eu5QEyLpa3Avvo-CHkMDG\",\"nextExport\":true,\"isFallback\":false,\"gip\":true,\"locale\":\"en\",\"locales\":[\"en\"],\"defaultLocale\":\"en\",\"scriptLoader\":[]}</script></body></html>\n"
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|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
|
|
"\n",
|
|
"<think>\n",
|
|
"Okay, the user is asking what vertical farming proposes. First, I need to recall what vertical farming is. From what I remember, vertical farming is a method of growing crops in stacked layers, often in controlled environments. It's a form of hydroponics or aquaponics. The idea is to maximize space efficiency by using vertical structures. \n",
|
|
"\n",
|
|
"But wait, maybe I should check if there's any specific information I'm missing. The user might be looking for details about the technology involved, like using LED lights, hydroponics, or aquaponics. Also, vertical farms can reduce the need for land, water, and pesticides. \n",
|
|
"\n",
|
|
"I should present this information clearly. Let me make sure to mention the key aspects: stacked layers, controlled environment, space efficiency, and sustainability. That should answer the question comprehensively.\n",
|
|
"</think>\n",
|
|
"\n",
|
|
"Vertical farming is a method of growing crops in stacked layers (like vertical gardens) in controlled environments, often using hydroponics or aquaponics. It maximizes space efficiency, reduces water and land use, and promotes sustainability by minimizing resource consumption.\n",
|
|
"\n",
|
|
"Flushing traces to Langfuse...\n",
|
|
"✓ Traces flushed (check your Langfuse dashboard at http://45.77.119.180)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(\"Starting agent...\")\n",
|
|
"stream_graph_updates(user_input)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "a5bfbf18",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "assistance-engine",
|
|
"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": "3.12.11"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|