Agents now work in chainlit

app.py

@@ -1,8 +1,11 @@
+import requests
 import asyncio
 import json
 import os
+import getpass
 from dataclasses import dataclass
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Tuple
+import re
 
 import chainlit as cl
 from dotenv import load_dotenv
@@ -14,20 +17,59 @@ from langchain_openai.embeddings import OpenAIEmbeddings
 from langchain_qdrant import QdrantVectorStore
 from qdrant_client import QdrantClient
 
+from langchain_core.messages import HumanMessage, BaseMessage
+import langgraph.graph
+
+from pstuts_rag.agents import PsTutsTeamState, create_team_supervisor
+from pstuts_rag.agent_tavily import create_tavily_node
+
 import pstuts_rag.datastore
 import pstuts_rag.rag
+
+from pstuts_rag.agent_rag import create_rag_node
+
 from pstuts_rag.loader import load_json_files
+from pstuts_rag.prompt_templates import SUPERVISOR_SYSTEM
+
+import nest_asyncio
+from uuid import uuid4
+
+# Apply nested asyncio to enable nested event loops
+nest_asyncio.apply()
+
+# Generate a unique ID for this application instance
+unique_id = uuid4().hex[0:8]
+
+VIDEOARCHIVE = "VideoArchiveSearch"
+ADOBEHELP = "AdobeHelp"
 
 
 @dataclass
 class ApplicationParameters:
+    """
+    Configuration parameters for the application.
+
+    Attributes:
+        filename: List of JSON file paths to load data from
+        embedding_model: Name of the OpenAI embedding model to use
+        n_context_docs: Number of context documents to retrieve
+        tool_calling_model: Name of the OpenAI model to use for tool calling
+    """
+
     filename = [f"data/{f}.json" for f in ["dev"]]
     embedding_model = "text-embedding-3-small"
     n_context_docs = 2
-    llm_model = "gpt-4.1-mini"
+    tool_calling_model = "gpt-4.1-mini"
 
 
 def set_api_key_if_not_present(key_name, prompt_message=""):
+    """
+    Sets an API key in the environment if it's not already present.
+
+    Args:
+        key_name: Name of the environment variable to set
+        prompt_message: Custom prompt message for getpass (defaults to key_name)
+    """
     if len(prompt_message) == 0:
         prompt_message = key_name
     if key_name not in os.environ or not os.environ[key_name]:
@@ -35,6 +77,25 @@ def set_api_key_if_not_present(key_name, prompt_message=""):
 
 
 class ApplicationState:
+    """
+    Maintains the state of the application and its components.
+
+    Attributes:
+        embeddings: OpenAI embeddings model for vector operations
+        docs: List of loaded documents
+        qdrant_client: Client for Qdrant vector database
+        vector_store: Vector store for document retrieval
+        datastore_manager: Manager for data storage and retrieval
+        rag_factory: Factory for creating RAG chains
+        llm: Language model instance
+        rag_chain: Retrieval-augmented generation chain
+        ai_graph: Compiled AI agent graph
+        ai_graph_sketch: State graph for AI agent orchestration
+        tasks: List of asyncio tasks
+        hasLoaded: Event to track when loading is complete
+        pointsLoaded: Number of data points loaded into the database
+    """
+
     embeddings: OpenAIEmbeddings = None
     docs: List[Document] = []
     qdrant_client: QdrantClient = None
@@ -44,82 +105,284 @@ class ApplicationState:
     llm: BaseChatModel
     rag_chain: Runnable
 
+    ai_graph: Runnable
+    ai_graph_sketch: langgraph.graph.StateGraph
+
+    tasks: List[asyncio.Task] = []
+
     hasLoaded: asyncio.Event = asyncio.Event()
     pointsLoaded: int = 0
 
     def __init__(self) -> None:
+        """
+        Initialize the application state and set up environment variables.
+        """
         load_dotenv()
         set_api_key_if_not_present("OPENAI_API_KEY")
+        set_api_key_if_not_present("TAVILY_API_KEY")
+        os.environ["LANGCHAIN_TRACING_V2"] = "true"
+        os.environ["LANGCHAIN_PROJECT"] = (
+            f"AIE - MBUDISIC - HF - CERT - {unique_id}"
+        )
+        set_api_key_if_not_present("LANGCHAIN_API_KEY")
 
 
-state = ApplicationState()
+# Initialize global application state
+app_state = ApplicationState()
 params = ApplicationParameters()
+ai_state = PsTutsTeamState(
+    messages=[],
+    team_members=[VIDEOARCHIVE, ADOBEHELP],
+    next="START",
+)
+
 
+async def fill_the_db(
+    state: ApplicationState,
+):
+    """
+    Populates the vector database with document data if it's empty.
 
-async def fill_the_db():
+    Args:
+        state: Application state containing the datastore manager
+
+    Returns:
+        0 if database already has documents, otherwise None
+    """
     if state.datastore_manager.count_docs() == 0:
         data: List[Dict[str, Any]] = await load_json_files(params.filename)
         state.pointsLoaded = await state.datastore_manager.populate_database(
             raw_docs=data
         )
         await cl.Message(
-            content=f"✅ The database has been loaded with {state.pointsLoaded} elements!"
+            content=f"✅ The database has been loaded with {app_state.pointsLoaded} elements!"
         ).send()
+    else:
+        return 0
 
 
 async def build_the_chain():
-    state.rag_factory = pstuts_rag.rag.RAGChainFactory(
-        retriever=state.datastore_manager.get_retriever()
+    """
+    Builds the RAG chain using the application state components.
+
+    Sets up the retrieval-augmented generation factory, initializes the language model,
+    and creates the RAG chain.
+    """
+    app_state.rag_factory = pstuts_rag.rag.RAGChainFactory(
+        retriever=app_state.datastore_manager.get_retriever()
+    )
+    app_state.llm = ChatOpenAI(model=params.tool_calling_model, temperature=0)
+    app_state.rag_chain = app_state.rag_factory.get_rag_chain(app_state.llm)
+
+
+async def build_the_graph(current_state: ApplicationState):
+    """
+    Builds the agent graph for routing user queries.
+
+    Creates the necessary nodes (Adobe help, RAG search, supervisor), defines their
+    connections, and compiles the graph into a runnable chain.
+
+    Args:
+        current_state: Current application state with required components
+    """
+    adobe_help_node, _, _ = create_tavily_node(
+        llm=app_state.llm, name=ADOBEHELP
+    )
+
+    rag_node, _ = create_rag_node(
+        retriever=app_state.datastore_manager.get_retriever(),
+        llm=app_state.llm,
+        name=VIDEOARCHIVE,
+    )
+
+    supervisor_agent = create_team_supervisor(
+        app_state.llm,
+        SUPERVISOR_SYSTEM,
+        [VIDEOARCHIVE, ADOBEHELP],
+    )
+
+    ai_graph = langgraph.graph.StateGraph(PsTutsTeamState)
+
+    ai_graph.add_node(VIDEOARCHIVE, rag_node)
+    ai_graph.add_node(ADOBEHELP, adobe_help_node)
+    ai_graph.add_node("supervisor", supervisor_agent)
+
+    edges = [
+        [VIDEOARCHIVE, "supervisor"],
+        [ADOBEHELP, "supervisor"],
+    ]
+
+    [ai_graph.add_edge(*p) for p in edges]
+
+    ai_graph.add_conditional_edges(
+        "supervisor",
+        lambda x: x["next"],
+        {
+            VIDEOARCHIVE: VIDEOARCHIVE,
+            ADOBEHELP: ADOBEHELP,
+            "FINISH": langgraph.graph.END,
+        },
     )
-    state.llm = ChatOpenAI(model=params.llm_model, temperature=0)
-    state.rag_chain = state.rag_factory.get_rag_chain(state.llm)
-    pass
+
+    ai_graph.set_entry_point("supervisor")
+    app_state.ai_graph_sketch = ai_graph
+    app_state.ai_graph = enter_chain | ai_graph.compile()
+
+
+def enter_chain(message: str):
+    """
+    Entry point for the agent graph chain.
+
+    Transforms a user message into the state format expected by the agent graph.
+
+    Args:
+        message: User's input message
+
+    Returns:
+        Dictionary with the message and team members information
+    """
+    results = {
+        "messages": [HumanMessage(content=message)],
+        "team_members": [VIDEOARCHIVE, ADOBEHELP],
+    }
+    return results
 
 
 @cl.on_chat_start
 async def on_chat_start():
-    state.qdrant_client = QdrantClient(":memory:")
+    """
+    Initializes the application when a new chat session starts.
+
+    Sets up the language model, vector database components, and spawns tasks
+    for database population and graph building.
+    """
+    app_state.llm = ChatOpenAI(model=params.tool_calling_model, temperature=0)
+    app_state.qdrant_client = QdrantClient(":memory:")
 
-    state.datastore_manager = pstuts_rag.datastore.DatastoreManager(
-        qdrant_client=state.qdrant_client, name="local_test"
+    app_state.embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+
+    app_state.datastore_manager = pstuts_rag.datastore.DatastoreManager(
+        qdrant_client=app_state.qdrant_client,
+        name="local_test",
+        embeddings=app_state.embeddings,
     )
-    asyncio.run(main=fill_the_db())
-    asyncio.run(main=build_the_chain())
+
+    app_state.tasks.append(asyncio.create_task(fill_the_db(app_state)))
+
+    app_state.tasks.append(asyncio.create_task(build_the_graph(app_state)))
+
+
+def process_response(
+    response_message: BaseMessage,
+) -> Tuple[str, List[cl.Message]]:
+    """
+    Processes a response from the AI agents.
+
+    Extracts the main text and video references from the response,
+    and creates message elements for displaying video content.
+
+    Args:
+        response: Response object from the AI agent
+
+    Returns:
+        Tuple containing the text response and a list of message elements with video references
+    """
+    streamed_text = f"[_from: {response_message.name}_]\n"
+    msg_references = []
+
+    if response_message.name == VIDEOARCHIVE:
+        text, references = pstuts_rag.rag.RAGChainFactory.unpack_references(
+            str(response_message.content)
+        )
+        streamed_text += text
+
+        if len(references) > 0:
+            references = json.loads(references)
+            print(references)
+
+            for ref in references:
+                msg_references.append(
+                    cl.Message(
+                        content=(
+                            f"Watch {ref["title"]} from timestamp "
+                            f"{round(ref["start"] // 60)}m:{round(ref["start"] % 60)}s"
+                        ),
+                        elements=[
+                            cl.Video(
+                                name=ref["title"],
+                                url=f"{ref["source"]}#t={ref["start"]}",
+                                display="side",
+                            )
+                        ],
+                    )
+                )
+    else:
+        streamed_text += str(response_message.content)
+
+        # Find all URLs in the content
+        urls = re.findall(
+            r"https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+[/\w\.-]*(?:\?[/\w\.-=&%]*)?",
+            str(response_message.content),
+        )
+        print(urls)
+        links = []
+        # Create a list of unique URLs
+        for idx, u in enumerate(list(set(urls))):
+
+            url = "https://api.microlink.io"
+            params = {
+                "url": u,
+                "screenshot": True,
+            }
+
+            payload = requests.get(url, params)
+
+            if payload:
+                print(f"Successful screenshot\n{payload.json()}")
+                links.append(
+                    cl.Image(
+                        name=f"Website {idx} Preview: {u}",
+                        display="side",  # Show in the sidebar
+                        url=payload.json()["data"]["screenshot"]["url"],
+                    )
+                )
+
+        print(links)
+        msg_references.append(
+            cl.Message(
+                content="\n".join([l.url for l in links]), elements=links
+            )
+        )
+
+    return streamed_text, msg_references
 
 
 @cl.on_message
 async def main(message: cl.Message):
-    # Send a response back to the user
-    msg = cl.Message(content="")
-    response = await state.rag_chain.ainvoke({"question": message.content})
+    """
+    Processes incoming user messages and sends responses.
 
-    text, references = pstuts_rag.rag.RAGChainFactory.unpack_references(
-        response.content
-    )
-    if isinstance(text, str):
-        for token in [char for char in text]:
-            await msg.stream_token(token)
-
-    await msg.send()
-
-    references = json.loads(references)
-    print(references)
-
-    msg_references = [
-        (
-            f"Watch {ref["title"]} from timestamp "
-            f"{round(ref["start"] // 60)}m:{round(ref["start"] % 60)}s",
-            cl.Video(
-                name=ref["title"],
-                url=f"{ref["source"]}#t={ref["start"]}",
-                display="side",
-            ),
-        )
-        for ref in references
-    ]
-    await cl.Message(content="Related videos").send()
-    for e in msg_references:
-        await cl.Message(content=e[0], elements=[e[1]]).send()
+    Streams the AI agent's response, processes it to extract text and video references,
+    and sends the content back to the user's chat interface.
+
+    Args:
+        message: User's input message
+    """
+    for s in app_state.ai_graph.stream(
+        message.content, {"recursion_limit": 20}
+    ):
+        if "__end__" not in s and "supervisor" not in s.keys():
+            for [node_type, node_response] in s.items():
+                print(f"Processing {node_type} messages")
+                for message in node_response["messages"]:
+                    print(f"Message {message}")
+                    msg = cl.Message(content="")
+                    text, references = process_response(message)
+                    for token in [char for char in text]:
+                        await msg.stream_token(token)
+                    await msg.send()
+                    for m in references:
+                        await m.send()
 
 
 if __name__ == "__main__":

notebooks/transcript_agents.ipynb

@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 282,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 283,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,18 +31,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 284,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n"
@@ -50,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 285,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 286,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 287,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 288,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 289,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 290,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -143,9 +134,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 291,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_7954/3445616894.py:1: LangChainDeprecationWarning: The method `BaseTool.__call__` was deprecated in langchain-core 0.1.47 and will be removed in 1.0. Use :meth:`~invoke` instead.\n",
+      "  adobe_search(\"What is crop?\")\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -154,7 +153,7 @@
        "  'content': 'Cropping is the easiest way to remove unwanted objects or people at the edges of a photograph. Anything outside the crop boundary will disappear from your image',\n",
        "  'score': 0.585789},\n",
        " {'title': 'Crop, resize, and resample images in Photoshop Elements',\n",
-       "  'url': 'https://helpx.adobe.com/au/photoshop-elements/kb/crop-resize-resample-photoshop-elements.html',\n",
+       "  'url': 'https://helpx.adobe.com/photoshop-elements/kb/crop-resize-resample-photoshop-elements.html',\n",
        "  'content': 'When you crop an image, you trim away material from the edges to show a smaller area, often for artistic reasons.',\n",
        "  'score': 0.585789},\n",
        " {'title': 'How to crop and straighten photos in Photoshop',\n",
@@ -167,11 +166,11 @@
        "  'score': 0.5367749},\n",
        " {'title': 'Crop a photo using the Crop tool',\n",
        "  'url': 'https://helpx.adobe.com/photoshop/using/tool-techniques/crop-tool.html',\n",
-       "  'content': 'The Crop tool allows you to select an area of a photo and remove or crop everything outside the selected area. Photoshop Crop Tool.',\n",
-       "  'score': 0.49567938}]"
+       "  'content': 'The Crop tool allows you to select an area of a photo and remove or crop everything outside the selected area.',\n",
+       "  'score': 0.49465415}]"
       ]
      },
-     "execution_count": 291,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -182,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 292,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -191,35 +190,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 293,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "('To create and use clipping masks in Adobe Photoshop, follow these steps:\\n'\n",
+      "('To create a new layer in Adobe Photoshop, you can do any of the following:\\n'\n",
       " '\\n'\n",
-      " '1. Arrange your layers in the Layers panel so that the layer you want to use '\n",
-      " 'as a mask is directly below the layer you want to mask.\\n'\n",
+      " '1. Click the \"Create a New Layer\" button at the bottom of the Layers panel. '\n",
+      " 'This creates a new transparent layer above the currently selected layer.\\n'\n",
       " '\\n'\n",
-      " '2. Select the top layer (the one you want to be clipped) in the Layers '\n",
-      " 'panel.\\n'\n",
+      " '2. Choose Layer > New > Layer from the top menu. This opens a dialog box '\n",
+      " 'where you can name the layer and set options before creating it.\\n'\n",
       " '\\n'\n",
-      " '3. Choose Layer > Create Clipping Mask from the menu. Alternatively, you can '\n",
-      " 'right-click the top layer and select \"Create Clipping Mask.\"\\n'\n",
+      " '3. Alt-click (Windows) or Option-click (Mac) the \"Create a New Layer\" button '\n",
+      " 'to open the New Layer dialog box and set layer options.\\n'\n",
       " '\\n'\n",
-      " '4. The top layer will now be clipped to the shape and transparency of the '\n",
-      " 'layer below it, meaning it will only show through where the bottom layer has '\n",
-      " 'pixels.\\n'\n",
+      " '4. Ctrl-click (Windows) or Command-click (Mac) the \"Create a New Layer\" '\n",
+      " 'button to add a new layer below the currently selected layer.\\n'\n",
       " '\\n'\n",
-      " 'You can use two or more layers as a clipping mask, and you can release the '\n",
-      " 'clipping mask by selecting the clipped layer and choosing Layer > Release '\n",
-      " 'Clipping Mask.\\n'\n",
+      " 'Additionally, you can create a new layer from a selection by making a '\n",
+      " 'selection and then choosing:\\n'\n",
+      " '- Layer > New > Layer Via Copy (copies the selection to a new layer)\\n'\n",
+      " '- Layer > New > Layer Via Cut (cuts the selection and pastes it into a new '\n",
+      " 'layer)\\n'\n",
       " '\\n'\n",
-      " \"For more detailed instructions, you can visit Adobe's official help page on \"\n",
-      " 'clipping masks:\\n'\n",
-      " 'https://helpx.adobe.com/photoshop/using/revealing-layers-clipping-masks.html')\n"
+      " 'A new layer appears above the selected layer or within the selected group in '\n",
+      " 'the Layers panel.\\n'\n",
+      " '\\n'\n",
+      " \"For more details, you can visit Adobe's official help page on creating and \"\n",
+      " 'managing layers:\\n'\n",
+      " 'https://helpx.adobe.com/photoshop/using/create-layers-groups.html\\n'\n",
+      " '\\n'\n",
+      " '**URL**\\n'\n",
+      " 'https://helpx.adobe.com/photoshop/using/create-layers-groups.html')\n"
      ]
     }
    ],
@@ -230,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 294,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -239,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 295,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -249,19 +255,22 @@
       "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "Name: AdobeHelp\n",
       "\n",
-      "Layers in Adobe Photoshop are fundamental elements that allow you to work on different parts of an image independently without affecting other parts. Think of layers as stacked, transparent sheets of glass, where each layer can contain images, text, effects, or objects. You can edit, move, and apply changes to one layer without altering the content on other layers.\n",
+      "Layers in Adobe Photoshop are fundamental elements used for editing and composing images. They are like stacked, transparent sheets of glass, each containing images, text, effects, or objects. Layers allow you to work on different parts of an image independently without affecting other layers. This makes editing nondestructive and flexible.\n",
+      "\n",
+      "You can think of layers as separate pieces of content stacked on top of each other. The Layers panel in Photoshop shows these layers arranged in a stack, where you can manage their order, visibility, and properties such as opacity and blending modes. Layers enable you to composite multiple images, add text, apply filters, and make adjustments selectively.\n",
       "\n",
-      "Layers help you make nondestructive edits by stacking and managing images, text, and graphics separately. They are arranged in a stack in the Layers panel, usually located in the bottom right of the workspace. You can add multiple layers to composite images, add text, apply filters, and create complex designs.\n",
+      "The bottommost layer is usually the Background layer, which is locked by default but can be converted into a regular layer for more editing options.\n",
       "\n",
-      "The bottommost layer is often the Background layer, which is locked by default but can be converted to a regular layer for more flexibility.\n",
+      "In summary, layers help you:\n",
+      "- Edit parts of an image independently\n",
+      "- Stack multiple images or elements\n",
+      "- Apply effects and adjustments nondestructively\n",
+      "- Manage complex compositions easily\n",
       "\n",
-      "In summary, layers let you:\n",
-      "- Work on different elements independently\n",
-      "- Apply effects and adjustments to specific parts\n",
-      "- Rearrange content by changing the stacking order\n",
-      "- Control opacity and blending modes for creative effects\n",
+      "For more detailed information, you can visit Adobe's official help page on layers:\n",
+      "https://helpx.adobe.com/photoshop/web/edit-images/manage-layers/about-layers.html\n",
       "\n",
-      "For more details, you can visit Adobe's official help page about layers:  \n",
+      "**URL**\n",
       "https://helpx.adobe.com/photoshop/web/edit-images/manage-layers/about-layers.html\n"
      ]
     }
@@ -282,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 296,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -304,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 297,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -327,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 298,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -350,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 299,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -360,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 300,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -378,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 301,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -388,7 +397,7 @@
       "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "Name: VideoArchiveSearch\n",
       "\n",
-      "Layers are the building blocks of any image in Photoshop CC. You can think of layers like separate flat panes of glass stacked on top of each other, with each layer containing separate pieces of content. Some parts of a layer can be transparent, allowing you to see through to the layers below. This setup lets you edit parts of an image independently without affecting the rest of the image. You work with layers in the Layers panel, where you can toggle their visibility on and off using the Eye icon. (See explanation around 0:28–2:00 and 1:25–2:32 in the video) 🎨🖼️\n",
+      "Layers are the building blocks of any image in Photoshop CC. You can think of layers like separate flat panes of glass stacked on top of each other, with each layer containing separate pieces of content. Some parts of a layer can be transparent, allowing you to see through to the layers below. This setup lets you edit parts of an image independently without affecting the rest of the image. You manage and work with layers in the Layers panel, where you can toggle their visibility on and off using the Eye icon. (See explanation around 0:28 to 1:00 and 1:25 to 2:32) 🎨🖼️\n",
       "**REFERENCES**\n",
       "[\n",
       "  {\n",
@@ -421,9 +430,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 302,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mbudisic/Documents/PsTuts-RAG/pstuts_rag/pstuts_rag/agents.py:79: LangChainDeprecationWarning: The method `BaseChatOpenAI.bind_functions` was deprecated in langchain-openai 0.2.1 and will be removed in 1.0.0. Use :meth:`~langchain_openai.chat_models.base.ChatOpenAI.bind_tools` instead.\n",
+      "  | llm.bind_functions(functions=[function_def], function_call=\"route\")\n"
+     ]
+    }
+   ],
    "source": [
     "from pstuts_rag.agents import create_team_supervisor\n",
     "from pstuts_rag.prompt_templates import SUPERVISOR_SYSTEM\n",
@@ -437,16 +455,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 303,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<langgraph.graph.state.StateGraph at 0x71685a01e120>"
+       "<langgraph.graph.state.StateGraph at 0x794d6582ecf0>"
       ]
      },
-     "execution_count": 303,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -478,7 +496,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 304,
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['VideoArchiveSearch', 'AdobeHelp', 'supervisor'])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adobe_help_graph.nodes.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -488,7 +533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 305,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -498,7 +543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 306,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -513,10 +558,10 @@
       "                                  *                                   \n",
       "                            +------------+                            \n",
       "                            | supervisor |                            \n",
-      "                       .....+------------+.....                       \n",
-      "                   ....            .           ....                   \n",
-      "              .....                .               .....              \n",
-      "           ...                     .                    ...           \n",
+      "                       *****+------------+.....                       \n",
+      "                   ****            .           ....                   \n",
+      "              *****                .               .....              \n",
+      "           ***                     .                    ...           \n",
       "+-----------+           +--------------------+           +---------+  \n",
       "| AdobeHelp |           | VideoArchiveSearch |           | __end__ |  \n",
       "+-----------+           +--------------------+           +---------+  \n"
@@ -532,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 307,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -556,27 +601,24 @@
     "                for response in s.values():\n",
     "                    for msg in response['messages']:\n",
     "                        msg.pretty_print()\n",
-    "            else:\n",
-    "                print(s)\n",
     "            print(\"---\")\n",
     "            \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 308,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'supervisor': {'next': 'VideoArchiveSearch'}}\n",
       "---\n",
       "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "Name: VideoArchiveSearch\n",
       "\n",
-      "Layers are the building blocks of any image in Photoshop CC. You can think of layers like separate flat panes of glass stacked on top of each other, where each layer contains separate pieces of content. Some parts of a layer can be transparent, allowing you to see through to the layers below. This setup lets you edit parts of an image independently without affecting the rest of the image. You work with layers in the Layers panel, where you can toggle their visibility on and off to see what each layer contains (explained around 0:28 to 1:03 and 1:25 to 2:33 in the video).\n",
+      "Layers are the building blocks of any image in Photoshop CC. You can think of layers like separate flat panes of glass stacked on top of each other, where each layer contains separate pieces of content. Some parts of a layer can be transparent, allowing you to see through to the layers below. This setup lets you edit parts of an image independently without affecting the rest of the image. You manage and work with layers in the Layers panel, where you can toggle their visibility on and off using the Eye icon. (See 0:28–1:03 and 1:25–2:32) 🎨🖼️\n",
       "**REFERENCES**\n",
       "[\n",
       "  {\n",
@@ -593,7 +635,6 @@
       "  }\n",
       "]\n",
       "---\n",
-      "{'supervisor': {'next': 'FINISH'}}\n",
       "---\n"
      ]
     }
@@ -604,40 +645,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 309,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'supervisor': {'next': 'VideoArchiveSearch'}}\n",
       "---\n",
       "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "Name: VideoArchiveSearch\n",
       "\n",
       "I don't know. This isn’t covered in the training videos.\n",
       "---\n",
-      "{'supervisor': {'next': 'AdobeHelp'}}\n",
       "---\n",
       "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "Name: AdobeHelp\n",
       "\n",
-      "To crop a layer in Adobe Photoshop, you can use the Crop tool in a way that targets the active layer or a selection within that layer. Here's how to do it:\n",
+      "To crop a layer in Adobe Photoshop, you can use the Crop tool in a way that targets the active layer or a selection within that layer. Here's how you can do it:\n",
       "\n",
       "1. Select the layer you want to crop by clicking its thumbnail in the Layers panel.\n",
       "2. Choose the Crop tool from the toolbar.\n",
-      "3. A bounding box will appear around the active layer or your selection.\n",
+      "3. A bounding box will appear around the active layer or the selection.\n",
       "4. Drag the handles of the bounding box to set the crop size.\n",
       "5. You can also rotate the layer by moving the rotation dial below the bounding box if needed.\n",
-      "6. Once you have the desired crop area, apply the crop.\n",
+      "6. Apply touch gestures or mouse actions to pan, zoom, or reset the crop area.\n",
+      "7. When satisfied, confirm the crop to apply it to the layer.\n",
       "\n",
       "This method allows you to crop and rotate an active layer or the contents of a selection non-destructively.\n",
       "\n",
-      "For more details, you can visit Adobe's official help page on cropping and rotating layers:\n",
+      "For more detailed instructions, you can visit Adobe's official help page on cropping and rotating layers:\n",
+      "https://helpx.adobe.com/photoshop/using/crop-move-rotate-photos.html\n",
+      "\n",
+      "**URL**\n",
       "https://helpx.adobe.com/photoshop/using/crop-move-rotate-photos.html\n",
       "---\n",
-      "{'supervisor': {'next': 'FINISH'}}\n",
       "---\n"
      ]
     }

pstuts_rag/pstuts_rag/agents.py

@@ -19,13 +19,18 @@ class PsTutsTeamState(TypedDict):
     next: str
 
 
-def agent_node(state, agent, name, outputfield: str = "output"):
+def agent_node(
+    state: PsTutsTeamState,
+    agent: Runnable,
+    name: str,
+    output_field: str = "output",
+):
     """agent_node calls the invoke function of the agent Runnable"""
     # Initialize team_members if it's not already in the state
     if "team_members" not in state:
         state["team_members"] = []
     result = agent.invoke(state)
-    return {"messages": [HumanMessage(content=result[outputfield], name=name)]}
+    return {"messages": [AIMessage(content=result[output_field], name=name)]}
 
 
 def create_agent(
@@ -47,7 +52,7 @@ def create_agent(
     return executor
 
 
-def create_team_supervisor(llm: ChatOpenAI, system_prompt, members):
+def create_team_supervisor(llm: BaseChatModel, system_prompt, members):
     """An LLM-based router."""
     options = ["FINISH"] + members
     function_def = {

pstuts_rag/pstuts_rag/prompt_templates.py

@@ -67,9 +67,6 @@ for Adobe Photoshop help topics using the tavily search engine.
 Users may provide you with partial questions - try your best to determine their intent.
 
 If Tavily provides no references, respond with "I don't know".
-
-IMPORTANT: Include ALL urls from all references Tavily provides. 
-Separate them from the rest of the text using a line containing "**URL**"
 """
 
 SUPERVISOR_SYSTEM = """You are the Supervisor for an agentic RAG system. Your job is to 

pstuts_rag/pstuts_rag/rag.py

@@ -119,9 +119,7 @@ class RAGChainFactory:
             return text, references
 
         else:
-            raise ValueError(
-                f"No '**References:**' section found in input:\n{content}"
-            )
+            return content, ""
 
     def __init__(
         self,

pyproject.toml

@@ -92,3 +92,8 @@ extension-pkg-allow-list = "numpy, torch"  # compiled deps that astroid cannot p
 
 [tool.pylint.TYPECHECK]
 ignored-modules   = "pkg_resources"        # suppress noisy vendored imports
+
+[dependency-groups]
+dev = [
+    "ipdb>=0.13.13",
+]

test_screenshot.py

@@ -0,0 +1,11 @@
+import requests
+
+url = "https://api.microlink.io"
+params = {
+    "url": "https://www.framatome.com",
+    "screenshot": True,
+}
+
+response = requests.get(url, params)
+
+print(response.json())

uv.lock

@@ -940,6 +940,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/2c/e1/e6716421ea10d38022b952c159d5161ca1193197fb744506875fbb87ea7b/iniconfig-2.1.0-py3-none-any.whl", hash = "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760", size = 6050 },
 ]
 
+[[package]]
+name = "ipdb"
+version = "0.13.13"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "decorator" },
+    { name = "ipython" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/3d/1b/7e07e7b752017f7693a0f4d41c13e5ca29ce8cbcfdcc1fd6c4ad8c0a27a0/ipdb-0.13.13.tar.gz", hash = "sha256:e3ac6018ef05126d442af680aad863006ec19d02290561ac88b8b1c0b0cfc726", size = 17042 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0c/4c/b075da0092003d9a55cf2ecc1cae9384a1ca4f650d51b00fc59875fe76f6/ipdb-0.13.13-py3-none-any.whl", hash = "sha256:45529994741c4ab6d2388bfa5d7b725c2cf7fe9deffabdb8a6113aa5ed449ed4", size = 12130 },
+]
+
 [[package]]
 name = "ipykernel"
 version = "6.29.5"
@@ -2370,6 +2383,11 @@ dev = [
     { name = "pytest" },
 ]
 
+[package.dev-dependencies]
+dev = [
+    { name = "ipdb" },
+]
+
 [package.metadata]
 requires-dist = [
     { name = "aiohttp", specifier = ">=3.8.0" },
@@ -2412,6 +2430,9 @@ requires-dist = [
 ]
 provides-extras = ["dev"]
 
+[package.metadata.requires-dev]
+dev = [{ name = "ipdb", specifier = ">=0.13.13" }]
+
 [[package]]
 name = "psutil"
 version = "7.0.0"