{ "cells": [ { "cell_type": "markdown", "id": "9f97dd1e", "metadata": {}, "source": [ "# Libraries" ] }, { "cell_type": "code", "execution_count": 145, "id": "9e974df6", "metadata": {}, "outputs": [], "source": [ "import os\n", "import re\n", "from typing import TypedDict, List, Optional, Annotated\n", "from IPython.display import Image, display\n", "\n", "from langchain_core.documents import Document\n", "from langchain_core.messages import BaseMessage, SystemMessage\n", "from langchain_core.tools import tool\n", "from langgraph.checkpoint.memory import InMemorySaver\n", "from langgraph.graph.message import add_messages\n", "from langchain_ollama import ChatOllama, OllamaEmbeddings\n", "from langchain_elasticsearch import ElasticsearchStore\n", "from langgraph.graph import StateGraph, END\n", "from langgraph.prebuilt import ToolNode\n", "from langfuse import get_client, Langfuse\n", "from langfuse.langchain import CallbackHandler\n", "\n", "from typing import TypedDict, List, Optional, Annotated, Literal\n", "from pydantic import BaseModel, Field\n", "from langchain_core.messages import BaseMessage, SystemMessage, AIMessage" ] }, { "cell_type": "code", "execution_count": 146, "id": "30edcecc", "metadata": {}, "outputs": [], "source": [ "ES_URL = os.getenv(\"ELASTICSEARCH_LOCAL_URL\")\n", "INDEX_NAME = os.getenv(\"ELASTICSEARCH_INDEX\")\n", "BASE_URL = os.getenv(\"OLLAMA_LOCAL_URL\")\n", "MODEL_NAME = os.getenv(\"OLLAMA_MODEL_NAME\")\n", "EMB_MODEL_NAME = os.getenv(\"OLLAMA_EMB_MODEL_NAME\")\n", "LANGFUSE_PUBLIC_KEY = os.getenv(\"LANGFUSE_PUBLIC_KEY\")\n", "LANGFUSE_SECRET_KEY = os.getenv(\"LANGFUSE_SECRET_KEY\")\n", "LANGFUSE_HOST = os.getenv(\"LANGFUSE_HOST\")\n", "\n", "\n", "embeddings = OllamaEmbeddings(base_url=BASE_URL, model=EMB_MODEL_NAME)\n", "llm = ChatOllama(base_url=BASE_URL, model=MODEL_NAME, temperature=0)\n", "\n", "vector_store = ElasticsearchStore(\n", " es_url=ES_URL,\n", " index_name=INDEX_NAME,\n", " embedding=embeddings,\n", " query_field=\"text\",\n", " vector_query_field=\"vector\",\n", ")" ] }, { "cell_type": "markdown", "id": "873ea2f6", "metadata": {}, "source": [ "### State" ] }, { "cell_type": "code", "execution_count": 147, "id": "5f8c88cf", "metadata": {}, "outputs": [], "source": [ "class AgentState(TypedDict):\n", " messages: Annotated[list, add_messages]" ] }, { "cell_type": "code", "execution_count": 148, "id": "30473bce", "metadata": {}, "outputs": [], "source": [ "class AgentResponse(BaseModel):\n", " \"\"\"\n", " Structured output contract for final assistant responses.\n", " \"\"\"\n", " language: Literal[\"en\"] = Field(\n", " description=\"ISO code. Must always be 'en'.\"\n", " )\n", " content: str = Field(\n", " description=\"Final answer in English.\"\n", " )" ] }, { "cell_type": "markdown", "id": "1d60c120", "metadata": {}, "source": [ "### Tools" ] }, { "cell_type": "code", "execution_count": 149, "id": "f0a21230", "metadata": {}, "outputs": [], "source": [ "retrieve_kwargs = {\"k\": 5}" ] }, { "cell_type": "code", "execution_count": 150, "id": "f9359747", "metadata": {}, "outputs": [], "source": [ "def format_context(docs: List[Document]) -> str:\n", " chunks: List[str] = []\n", " for i, doc in enumerate(docs, 1):\n", " source = (doc.metadata or {}).get(\"source\", \"Untitled\")\n", " source_id = (doc.metadata or {}).get(\"id\", f\"chunk-{i}\")\n", " text = doc.page_content or \"\"\n", " chunks.append(f\"[{i}] id={source_id} source={source}\\n{text}\")\n", " return \"\\n\\n\".join(chunks)\n", "\n", "\n", "@tool\n", "def retrieve(query: str) -> str:\n", " \"\"\"This tool retrieves relevant documents from the vector store based on the input query and formats them for the agent's response.\n", " Args:\n", " query (str): The input query for which to retrieve relevant documents.\n", " \"\"\"\n", " retriever = vector_store.as_retriever(\n", " search_type=\"similarity\",\n", " search_kwargs=retrieve_kwargs,\n", " )\n", " docs = retriever.invoke(query)\n", " return format_context(docs)" ] }, { "cell_type": "code", "execution_count": 151, "id": "e5247ab9", "metadata": {}, "outputs": [], "source": [ "def should_continue(state: AgentState) -> str:\n", " last = state[\"messages\"][-1]\n", " \n", " if getattr(last, \"tool_calls\", None):\n", " return \"tools\"\n", " return \"end\"" ] }, { "cell_type": "code", "execution_count": 152, "id": "a644f6fa", "metadata": {}, "outputs": [], "source": [ "tools = [retrieve]\n", "tool_node = ToolNode(tools)\n", "memory = InMemorySaver()" ] }, { "cell_type": "markdown", "id": "395966e2", "metadata": {}, "source": [ "### Agent" ] }, { "cell_type": "code", "execution_count": 153, "id": "36d0f54e", "metadata": {}, "outputs": [], "source": [ "def _message_text(message: BaseMessage) -> str:\n", " content = getattr(message, \"content\", \"\")\n", " if isinstance(content, str):\n", " return content\n", " if isinstance(content, list):\n", " return \" \".join(str(item) for item in content)\n", " return str(content)\n", "\n", "\n", "def _last_user_query(messages: List[BaseMessage]) -> str:\n", " for message in reversed(messages):\n", " message_type = getattr(message, \"type\", \"\")\n", " if message_type == \"human\":\n", " return _message_text(message)\n", " return _message_text(messages[-1]) if messages else \"\"\n", "\n", "\n", "def _last_assistant_text(messages: List[BaseMessage]) -> str:\n", " for message in reversed(messages):\n", " message_type = getattr(message, \"type\", \"\")\n", " if message_type == \"ai\":\n", " return _message_text(message)\n", " return \"\"\n", "\n", "\n", "MAX_LANGUAGE_RETRIES = 5\n", "\n", "\n", "def agent(state: AgentState) -> AgentState:\n", " messages: List[BaseMessage] = state[\"messages\"]\n", " user_query = _last_user_query(messages)\n", "\n", " retrieved_context = retrieve.invoke({\"query\": user_query})\n", "\n", " system = SystemMessage(\n", " content=(\n", " \"\"\"\n", " You are an AVAP language assistant focused on accurate and grounded answers.\n", "\n", " Rules:\n", " 1. ALWAYS use the provided retrieval context from Elasticsearch FIRST.\n", " 2. Use ONLY the retrieved context to produce the final answer. Do NOT invent AVAP commands.\n", " 3. ALWAYS reply in English only. Never respond in any other language.\n", " 4. If asked for code/snippet, include exactly ONE fenced AVAP block (```avap ... ```).\n", " 5. Never output internal reasoning or unrelated text.\n", "\n", " Retrieved context:\n", " \"\"\"\n", " + retrieved_context\n", " )\n", " )\n", "\n", "\n", "\n", " structured_model = llm.with_structured_output(AgentResponse)\n", " structured_response: AgentResponse = structured_model.invoke(\n", " [system, *messages]\n", " )\n", "\n", " # Keep LangGraph message flow unchanged\n", " final_message = AIMessage(content=structured_response.content)\n", " return {\"messages\": [*messages, final_message]}\n", "\n", " # model = llm.bind_tools(tools)\n", " # response = model.invoke([system, *messages])\n", "\n", " # return {\"messages\": [*messages, response]}" ] }, { "cell_type": "markdown", "id": "ef55bca3", "metadata": {}, "source": [ "### Graph" ] }, { "cell_type": "code", "execution_count": 154, "id": "fae46a58", "metadata": {}, "outputs": [], "source": [ "graph = StateGraph(AgentState)\n", "graph.add_node(\"agent\", agent)\n", "\n", "\n", "graph.set_entry_point(\"agent\")\n", "graph.add_edge(\"agent\", END)\n", "\n", "# Alternative mode (single pass) - kept commented for quick rollback.\n", "# graph.set_entry_point(\"agent\")\n", "# graph.add_edge(\"agent\", END)\n", "\n", "agent_graph = graph.compile()" ] }, { "cell_type": "code", "execution_count": 155, "id": "2fec3fdb", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "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": 156, "id": "8569cf39", "metadata": {}, "outputs": [], "source": [ "config = {\"configurable\": {\"thread_id\": \"5\"}, \n", " #\"callbacks\": [langfuse_handler],\n", " #\"run_name\": \"rag-local-test\"differences between getDatetime() and getTimeStamp() functions in AVAP\n", " }\n", "\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": 162, "id": "a1a1f3cf", "metadata": {}, "outputs": [], "source": [ "user_input = (\n", " \"Generate two variables, asigning them one number to each, do it in AVAP language.\"\n", ")" ] }, { "cell_type": "code", "execution_count": 163, "id": "53b89690", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================\u001b[1m Human Message \u001b[0m=================================\n", "\n", "Generate two variables, asigning them one number to each, do it in AVAP language.\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", "user: generate two variables, assigning them one number to each, you assign themize in, avap language.\n" ] } ], "source": [ "a = stream_graph_updates(user_input)" ] }, { "cell_type": "code", "execution_count": null, "id": "136f420c", "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 }