cognee/cognee/shared/utils.py

"""This module contains utility functions for the cognee."""

import os
from typing import BinaryIO, Union

import requests
import hashlib
from datetime import datetime, timezone
import graphistry
import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
import tiktoken
import nltk
import base64
import time

import logging
import sys

from cognee.base_config import get_base_config
from cognee.infrastructure.databases.graph import get_graph_engine

from uuid import uuid4
import pathlib
import nltk
from cognee.shared.exceptions import IngestionError

# Analytics Proxy Url, currently hosted by Vercel
proxy_url = "https://test.prometh.ai"



def get_entities(tagged_tokens):
    nltk.download("maxent_ne_chunker", quiet=True)
    from nltk.chunk import ne_chunk

    return ne_chunk(tagged_tokens)


def extract_pos_tags(sentence):
    """Extract Part-of-Speech (POS) tags for words in a sentence."""

    # Ensure that the necessary NLTK resources are downloaded
    nltk.download("words", quiet=True)
    nltk.download("punkt", quiet=True)
    nltk.download("averaged_perceptron_tagger", quiet=True)

    from nltk.tag import pos_tag
    from nltk.tokenize import word_tokenize

    # Tokenize the sentence into words
    tokens = word_tokenize(sentence)

    # Tag each word with its corresponding POS tag
    pos_tags = pos_tag(tokens)

    return pos_tags


def get_anonymous_id():
    """Creates or reads a anonymous user id"""
    home_dir = str(pathlib.Path(pathlib.Path(__file__).parent.parent.parent.resolve()))

    if not os.path.isdir(home_dir):
        os.makedirs(home_dir, exist_ok=True)
    anonymous_id_file = os.path.join(home_dir, ".anon_id")
    if not os.path.isfile(anonymous_id_file):
        anonymous_id = str(uuid4())
        with open(anonymous_id_file, "w", encoding="utf-8") as f:
            f.write(anonymous_id)
    else:
        with open(anonymous_id_file, "r", encoding="utf-8") as f:
            anonymous_id = f.read()
    return anonymous_id


def send_telemetry(event_name: str, user_id, additional_properties: dict = {}):
    if os.getenv("TELEMETRY_DISABLED"):
        return

    env = os.getenv("ENV")
    if env in ["test", "dev"]:
        return

    current_time = datetime.now(timezone.utc)
    payload = {
        "anonymous_id": str(get_anonymous_id()),
        "event_name": event_name,
        "user_properties": {
            "user_id": str(user_id),
        },
        "properties": {
            "time": current_time.strftime("%m/%d/%Y"),
            "user_id": str(user_id),
            **additional_properties,
        },
    }

    response = requests.post(proxy_url, json=payload)

    if response.status_code != 200:
        print(f"Error sending telemetry through proxy: {response.status_code}")


def num_tokens_from_string(string: str, encoding_name: str) -> int:
    """Returns the number of tokens in a text string."""

    # tiktoken.get_encoding("cl100k_base")
    encoding = tiktoken.encoding_for_model(encoding_name)
    num_tokens = len(encoding.encode(string))
    return num_tokens


def get_file_content_hash(file_obj: Union[str, BinaryIO]) -> str:
    h = hashlib.md5()

    try:
        if isinstance(file_obj, str):
            with open(file_obj, "rb") as file:
                while True:
                    # Reading is buffered, so we can read smaller chunks.
                    chunk = file.read(h.block_size)
                    if not chunk:
                        break
                    h.update(chunk)
        else:
            while True:
                # Reading is buffered, so we can read smaller chunks.
                chunk = file_obj.read(h.block_size)
                if not chunk:
                    break
                h.update(chunk)

        return h.hexdigest()
    except IOError as e:
        raise IngestionError(message=f"Failed to load data from {file}: {e}")


def trim_text_to_max_tokens(text: str, max_tokens: int, encoding_name: str) -> str:
    """
    Trims the text so that the number of tokens does not exceed max_tokens.

    Args:
    text (str): Original text string to be trimmed.
    max_tokens (int): Maximum number of tokens allowed.
    encoding_name (str): The name of the token encoding to use.

    Returns:
    str: Trimmed version of text or original text if under the limit.
    """
    # First check the number of tokens
    num_tokens = num_tokens_from_string(text, encoding_name)

    # If the number of tokens is within the limit, return the text as is
    if num_tokens <= max_tokens:
        return text

    # If the number exceeds the limit, trim the text
    # This is a simple trim, it may cut words in half; consider using word boundaries for a cleaner cut
    encoded_text = tiktoken.get_encoding(encoding_name).encode(text)
    trimmed_encoded_text = encoded_text[:max_tokens]
    # Decoding the trimmed text
    trimmed_text = tiktoken.get_encoding(encoding_name).decode(trimmed_encoded_text)
    return trimmed_text


def generate_color_palette(unique_layers):
    colormap = plt.cm.get_cmap("viridis", len(unique_layers))
    colors = [colormap(i) for i in range(len(unique_layers))]
    hex_colors = [
        "#%02x%02x%02x" % (int(rgb[0] * 255), int(rgb[1] * 255), int(rgb[2] * 255))
        for rgb in colors
    ]

    return dict(zip(unique_layers, hex_colors))


async def register_graphistry():
    config = get_base_config()
    graphistry.register(
        api=3, username=config.graphistry_username, password=config.graphistry_password
    )


def prepare_edges(graph, source, target, edge_key):
    edge_list = [
        {
            source: str(edge[0]),
            target: str(edge[1]),
            edge_key: str(edge[2]),
        }
        for edge in graph.edges(keys=True, data=True)
    ]

    return pd.DataFrame(edge_list)


def prepare_nodes(graph, include_size=False):
    nodes_data = []
    for node in graph.nodes:
        node_info = graph.nodes[node]

        if not node_info:
            continue

        node_data = {
            "id": str(node),
            "name": node_info["name"] if "name" in node_info else str(node),
        }

        if include_size:
            default_size = 10  # Default node size
            larger_size = 20  # Size for nodes with specific keywords in their ID
            keywords = ["DOCUMENT", "User"]
            node_size = (
                larger_size if any(keyword in str(node) for keyword in keywords) else default_size
            )
            node_data["size"] = node_size

        nodes_data.append(node_data)

    return pd.DataFrame(nodes_data)


async def render_graph(
    graph, include_nodes=False, include_color=False, include_size=False, include_labels=False
):
    await register_graphistry()

    if not isinstance(graph, nx.MultiDiGraph):
        graph_engine = await get_graph_engine()
        networkx_graph = nx.MultiDiGraph()

        (nodes, edges) = await graph_engine.get_graph_data()

        networkx_graph.add_nodes_from(nodes)
        networkx_graph.add_edges_from(edges)

        graph = networkx_graph

    edges = prepare_edges(graph, "source_node", "target_node", "relationship_name")
    plotter = graphistry.edges(edges, "source_node", "target_node")
    plotter = plotter.bind(edge_label="relationship_name")

    if include_nodes:
        nodes = prepare_nodes(graph, include_size=include_size)
        plotter = plotter.nodes(nodes, "id")

        if include_size:
            plotter = plotter.bind(point_size="size")

        if include_color:
            pass
            # unique_layers = nodes["layer_description"].unique()
            # color_palette = generate_color_palette(unique_layers)
            # plotter = plotter.encode_point_color("layer_description", categorical_mapping=color_palette,
            #                                      default_mapping="silver")

        if include_labels:
            plotter = plotter.bind(point_label="name")

    # Visualization
    url = plotter.plot(render=False, as_files=True, memoize=False)
    print(f"Graph is visualized at: {url}")
    return url


# def sanitize_df(df):
#     """Replace NaNs and infinities in a DataFrame with None, making it JSON compliant."""
#     return df.replace([np.inf, -np.inf, np.nan], None)







logging.basicConfig(level=logging.INFO)


async def convert_to_serializable_graph(G):
    """
    Convert a graph into a serializable format with stringified node and edge attributes.
    """

    (nodes, edges) = G

    networkx_graph = nx.MultiDiGraph()
    networkx_graph.add_nodes_from(nodes)
    networkx_graph.add_edges_from(edges)

    # Create a new graph to store the serializable version
    new_G = nx.MultiDiGraph()

    # Serialize nodes
    for node, data in networkx_graph.nodes(data=True):
        serializable_data = {k: str(v) for k, v in data.items()}
        new_G.add_node(str(node), **serializable_data)

    # Serialize edges
    for u, v, data in networkx_graph.edges(data=True):
        serializable_data = {k: str(v) for k, v in data.items()}
        new_G.add_edge(str(u), str(v), **serializable_data)

    return new_G


def generate_layout_positions(G, layout_func, layout_scale):
    """
    Generate layout positions for the graph using the specified layout function.
    """
    positions = layout_func(G)
    return {str(node): (x * layout_scale, y * layout_scale) for node, (x, y) in positions.items()}


def assign_node_colors(G, node_attribute, palette):
    """
    Assign colors to nodes based on a specified attribute and a given palette.
    """
    unique_attrs = set(G.nodes[node].get(node_attribute, "Unknown") for node in G.nodes)
    color_map = {attr: palette[i % len(palette)] for i, attr in enumerate(unique_attrs)}
    return [color_map[G.nodes[node].get(node_attribute, "Unknown")] for node in G.nodes], color_map


def embed_logo(p, layout_scale, logo_alpha, position):
    """
    Embed a logo into the graph visualization as a watermark.
    """

    # svg_logo = """<svg width="1294" height="324" viewBox="0 0 1294 324" fill="none" xmlns="http://www.w3.org/2000/svg">
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    # Convert the SVG to a ReportLab Drawing

    logo_url = 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"

    # Finally, add the PNG image to your Bokeh plot as an image_url
    p.image_url(
        url=[logo_url],
        x=-layout_scale * 0.5,
        y=layout_scale * 0.5,
        w=layout_scale,
        h=layout_scale,
        anchor=position,
        global_alpha=logo_alpha,
    )


def style_and_render_graph(p, G, layout_positions, node_attribute, node_colors, centrality):
    """
    Apply styling and render the graph into the plot.
    """
    from bokeh.plotting import figure, from_networkx
    from bokeh.models import Circle, MultiLine, HoverTool, ColumnDataSource, Range1d
    from bokeh.plotting import output_file, show

    from bokeh.embed import file_html
    from bokeh.resources import CDN

    graph_renderer = from_networkx(G, layout_positions)
    node_radii = [0.02 + 0.1 * centrality[node] for node in G.nodes()]
    graph_renderer.node_renderer.data_source.data["radius"] = node_radii
    graph_renderer.node_renderer.data_source.data["fill_color"] = node_colors
    graph_renderer.node_renderer.glyph = Circle(
        radius="radius",
        fill_color="fill_color",
        fill_alpha=0.9,
        line_color="#000000",
        line_width=1.5,
    )
    graph_renderer.edge_renderer.glyph = MultiLine(
        line_color="#000000",
        line_alpha=0.3,
        line_width=1.5,
    )
    p.renderers.append(graph_renderer)
    return graph_renderer


async def create_cognee_style_network_with_logo(
    G,
    output_filename="cognee_network_with_logo.html",
    title="Cognee-Style Network",
    label="group",
    layout_func=nx.spring_layout,
    layout_scale=3.0,
    logo_alpha=0.1,
    bokeh_object=False,
):
    """
    Create a Cognee-inspired network visualization with an embedded logo.
    """
    from bokeh.plotting import figure, from_networkx
    from bokeh.models import Circle, MultiLine, HoverTool, ColumnDataSource, Range1d
    from bokeh.plotting import output_file, show

    from bokeh.embed import file_html
    from bokeh.resources import CDN
    from bokeh.io import export_png

    logging.info("Converting graph to serializable format...")
    G = await convert_to_serializable_graph(G)

    logging.info("Generating layout positions...")
    layout_positions = generate_layout_positions(G, layout_func, layout_scale)

    logging.info("Assigning node colors...")
    palette = ["#6510F4", "#0DFF00", "#FFFFFF"]
    node_colors, color_map = assign_node_colors(G, label, palette)

    logging.info("Calculating centrality...")
    centrality = nx.degree_centrality(G)

    logging.info("Preparing Bokeh output...")
    output_file(output_filename)
    p = figure(
        title=title,
        tools="pan,wheel_zoom,save,reset,hover",
        active_scroll="wheel_zoom",
        width=1200,
        height=900,
        background_fill_color="#F4F4F4",
        x_range=Range1d(-layout_scale, layout_scale),
        y_range=Range1d(-layout_scale, layout_scale),
    )
    p.toolbar.logo = None
    p.axis.visible = False
    p.grid.visible = False

    logging.info("Embedding logo into visualization...")
    embed_logo(p, layout_scale, logo_alpha, "bottom_right")
    embed_logo(p, layout_scale, logo_alpha, "top_left")

    logging.info("Styling and rendering graph...")
    style_and_render_graph(p, G, layout_positions, label, node_colors, centrality)

    logging.info("Adding hover tool...")
    hover_tool = HoverTool(
        tooltips=[
            ("Node", "@index"),
            (label.capitalize(), f"@{label}"),
            ("Centrality", "@radius{0.00}"),
        ],
    )
    p.add_tools(hover_tool)

    # Get the latest Unix timestamp as an integer
    timestamp = int(time.time())

    # Construct your filename
    filename = f"{timestamp}.png"



    logging.info(f"Saving visualization to {output_filename}...")
    html_content = file_html(p, CDN, title)
    with open(output_filename, "w") as f:
        f.write(html_content)

    return html_content


def graph_to_tuple(graph):
    """
    Converts a networkx graph to a tuple of (nodes, edges).

    :param graph: A networkx graph.
    :return: A tuple (nodes, edges).
    """
    nodes = list(graph.nodes(data=True))  # Get nodes with attributes
    edges = list(graph.edges(data=True))  # Get edges with attributes
    return (nodes, edges)


def setup_logging(log_level=logging.INFO):
    """Sets up the logging configuration."""
    formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s\n")

    stream_handler = logging.StreamHandler(sys.stdout)
    stream_handler.setFormatter(formatter)
    stream_handler.setLevel(log_level)

    root_logger = logging.getLogger()

    if root_logger.hasHandlers():
        root_logger.handlers.clear()

    root_logger.addHandler(stream_handler)
    root_logger.setLevel(log_level)


# ---------------- Example Usage ----------------
if __name__ == "__main__":
    import networkx as nx

    # Create a sample graph
    nodes = [
        (1, {"group": "A"}),
        (2, {"group": "A"}),
        (3, {"group": "B"}),
        (4, {"group": "B"}),
        (5, {"group": "C"}),
    ]
    edges = [(1, 2), (2, 3), (3, 4), (4, 5), (5, 1)]

    # Create a NetworkX graph
    G = nx.Graph()
    G.add_nodes_from(nodes)
    G.add_edges_from(edges)

    G = graph_to_tuple(G)

    import asyncio

    output_html = asyncio.run(
        create_cognee_style_network_with_logo(
            G,
            output_filename="example_network.html",
            title="Example Cognee Network",
            label="group",  # Attribute to use for coloring nodes
            layout_func=nx.spring_layout,  # Layout function
            layout_scale=3.0,  # Scale for the layout
            logo_alpha=0.2,
        )
    )

    # Call the function
    # output_html = await create_cognee_style_network_with_logo(
    #     G=G,
    #     output_filename="example_network.html",
    #     title="Example Cognee Network",
    #     node_attribute="group",  # Attribute to use for coloring nodes
    #     layout_func=nx.spring_layout,  # Layout function
    #     layout_scale=3.0,  # Scale for the layout
    #     logo_alpha=0.2,  # Transparency of the logo
    # )

    # Print the output filename
    print("Network visualization saved as example_network.html")

#     # Create a random geometric graph
#     G = nx.random_geometric_graph(50, 0.3)
#     # Assign random group attributes for coloring
#     for i, node in enumerate(G.nodes()):
#         G.nodes[node]["group"] = f"Group {i % 3 + 1}"
#
#     create_cognee_graph(
#         G,
#         output_filename="cognee_style_network_with_logo.html",
#         title="Cognee-Graph Network",
#         node_attribute="group",
#         layout_func=nx.spring_layout,
#         layout_scale=3.0,  # Replace with your logo file path
#     )