From 2daa2da68e455507a42e6aaa6ad3613e4c3aaa9e Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Thu, 11 Sep 2025 08:23:44 -0400 Subject: [PATCH 1/5] Add viz dependency group --- libs/langchain-mongodb/pyproject.toml | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/libs/langchain-mongodb/pyproject.toml b/libs/langchain-mongodb/pyproject.toml index a537a8b0..f76dab9a 100644 --- a/libs/langchain-mongodb/pyproject.toml +++ b/libs/langchain-mongodb/pyproject.toml @@ -44,6 +44,15 @@ dev = [ "typing-extensions>=4.12.2", ] +viz = [ + "networkx", + "pandas", + "numpy", + "holoviews", + "jupyter", + "scikit-image", +] + [tool.pytest.ini_options] addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" markers = [ From 0d8bdcf8d0821405348967c9cfc414044cda4f2b Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Thu, 11 Sep 2025 08:24:56 -0400 Subject: [PATCH 2/5] Add start of Network_Graph notebook --- .../examples/Network_Graphs.ipynb | 2040 +++++++++++++++++ 1 file changed, 2040 insertions(+) create mode 100644 libs/langchain-mongodb/examples/Network_Graphs.ipynb diff --git a/libs/langchain-mongodb/examples/Network_Graphs.ipynb b/libs/langchain-mongodb/examples/Network_Graphs.ipynb new file mode 100644 index 00000000..ce8926c8 --- /dev/null +++ b/libs/langchain-mongodb/examples/Network_Graphs.ipynb @@ -0,0 +1,2040 @@ +{ + "cells": [ + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-03T18:47:38.936293Z", + "start_time": "2025-09-03T18:47:31.008723Z" + } + }, + "source": [ + "import networkx as nx\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import holoviews as hv\n", + "from holoviews import opts\n", + "\n", + "hv.extension('bokeh')\n", + "\n", + "defaults = dict(width=400, height=400)\n", + "hv.opts.defaults(\n", + " opts.EdgePaths(**defaults), opts.Graph(**defaults), opts.Nodes(**defaults))" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "%opts magic unavailable (pyparsing cannot be imported)\n", + "%compositor magic unavailable (pyparsing cannot be imported)\n" + ] + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have 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\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualizing and working with network graphs is a common problem in many different disciplines. HoloViews provides the ability to represent and visualize graphs very simply and easily with facilities for interactively exploring the nodes and edges of the graph, especially using the bokeh plotting interface.\n", + "\n", + "The ``Graph`` ``Element`` differs from other elements in HoloViews in that it consists of multiple sub-elements. The data of the ``Graph`` element itself are the abstract edges between the nodes. By default the element will automatically compute concrete ``x`` and ``y`` positions for the nodes and represent them using a ``Nodes`` element, which is stored on the Graph. The abstract edges and concrete node positions are sufficient to render the ``Graph`` by drawing straight-line edges between the nodes. In order to supply explicit edge paths we can also declare ``EdgePaths``, providing explicit coordinates for each edge to follow.\n", + "\n", + "To summarize a ``Graph`` consists of three different components:\n", + "\n", + "* The ``Graph`` itself holds the abstract edges stored as a table of node indices.\n", + "* The ``Nodes`` hold the concrete ``x`` and ``y`` positions of each node along with a node ``index``. The ``Nodes`` may also define any number of value dimensions, which can be revealed when hovering over the nodes or to color the nodes by.\n", + "* The ``EdgePaths`` can optionally be supplied to declare explicit node paths.\n", + "\n", + "#### A simple Graph\n", + "\n", + "Let's start by declaring a very simple graph connecting one node to all others. If we simply supply the abstract connectivity of the ``Graph``, it will automatically compute a layout for the nodes using the ``layout_nodes`` operation, which defaults to a circular layout:" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-03T18:48:24.994443Z", + "start_time": "2025-09-03T18:48:24.830375Z" + } + }, + "source": [ + "# Declare abstract edges\n", + "N = 8\n", + "node_indices = np.arange(N, dtype=np.int32)\n", + "source = np.zeros(N, dtype=np.int32)\n", + "target = node_indices\n", + "\n", + "\n", + "simple_graph = hv.Graph(((source, target),))\n", + "simple_graph" + ], + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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\n", + "" + ], + "application/vnd.holoviews_exec.v0+json": "", + "text/plain": [ + ":Layout\n", + " .Nodes.I :Nodes [x,y,index]\n", + " .EdgePaths.I :EdgePaths [x,y]" + ] + }, + "execution_count": 3, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "566494cb-8fc6-4332-9fbd-c820b61653d1" + } + }, + "output_type": "execute_result" + } + ], + "execution_count": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Displaying directed graphs\n", + "\n", + "When specifying the graph edges the source and target node are listed in order, if the graph is actually a directed graph this may used to indicate the directionality of the graph. By setting ``directed=True`` as a plot option it is possible to indicate the directionality of each edge using an arrow:" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-03T18:48:49.440050Z", + "start_time": "2025-09-03T18:48:49.363030Z" + } + }, + "source": [ + "simple_graph.relabel('Directed Graph').opts(directed=True, node_size=5, arrowhead_length=0.05)" + ], + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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\n", + "" + ], + "application/vnd.holoviews_exec.v0+json": "", + "text/plain": [ + ":Graph [start,end]" + ] + }, + "execution_count": 4, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "169dead4-2143-4adf-a8c5-6e845eeea631" + } + }, + "output_type": "execute_result" + } + ], + "execution_count": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The length of the arrows can be set as an fraction of the overall graph extent using the ``arrowhead_length`` option." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Supplying explicit paths\n", + "\n", + "Next we will extend this example by supplying explicit edges:" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-03T18:49:22.311062Z", + "start_time": "2025-09-03T18:49:22.235562Z" + } + }, + "source": [ + "def bezier(start, end, control, steps=None):\n", + " if steps is None:\n", + " steps = np.linspace(0, 1, 100)\n", + " return (1-steps)**2*start + 2*(1-steps)*steps*control+steps**2*end\n", + "\n", + "x, y = simple_graph.nodes.array([0, 1]).T\n", + "\n", + "paths = []\n", + "for node_index in node_indices:\n", + " ex, ey = x[node_index], y[node_index]\n", + " paths.append(np.column_stack([bezier(x[0], ex, 0), bezier(y[0], ey, 0)]))\n", + "\n", + "bezier_graph = hv.Graph(((source, target), (x, y, node_indices), paths))\n", + "bezier_graph" + ], + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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We will load the edge and node data using pandas and then color each node by their friendship group using many of the things we learned above." + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-03T18:50:58.798922Z", + "start_time": "2025-09-03T18:50:58.506894Z" + } + }, + "source": [ + "kwargs = dict(width=800, height=800, xaxis=None, yaxis=None)\n", + "opts.defaults(opts.Nodes(**kwargs), opts.Graph(**kwargs))\n", + "\n", + "colors = ['#000000', *hv.Cycle('Category20').values]\n", + "edges_df = pd.read_csv('../assets/fb_edges.csv')\n", + "fb_nodes = hv.Nodes(pd.read_csv('../assets/fb_nodes.csv')).sort()\n", + "fb_graph = hv.Graph((edges_df, fb_nodes), label='Facebook Circles')\n", + "\n", + "fb_graph.opts(cmap=colors, node_size=10, edge_line_width=1,\n", + " node_line_color='gray', node_color='circle')" + ], + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '../assets/fb_edges.csv'", + "output_type": "error", + "traceback": [ + "\u001B[31m---------------------------------------------------------------------------\u001B[39m", + "\u001B[31mFileNotFoundError\u001B[39m Traceback (most recent call last)", + "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[16]\u001B[39m\u001B[32m, line 5\u001B[39m\n\u001B[32m 2\u001B[39m opts.defaults(opts.Nodes(**kwargs), opts.Graph(**kwargs))\n\u001B[32m 4\u001B[39m colors = [\u001B[33m'\u001B[39m\u001B[33m#000000\u001B[39m\u001B[33m'\u001B[39m, *hv.Cycle(\u001B[33m'\u001B[39m\u001B[33mCategory20\u001B[39m\u001B[33m'\u001B[39m).values]\n\u001B[32m----> \u001B[39m\u001B[32m5\u001B[39m edges_df = \u001B[43mpd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mread_csv\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m../assets/fb_edges.csv\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m 6\u001B[39m fb_nodes = hv.Nodes(pd.read_csv(\u001B[33m'\u001B[39m\u001B[33m../assets/fb_nodes.csv\u001B[39m\u001B[33m'\u001B[39m)).sort()\n\u001B[32m 7\u001B[39m fb_graph = hv.Graph((edges_df, fb_nodes), label=\u001B[33m'\u001B[39m\u001B[33mFacebook Circles\u001B[39m\u001B[33m'\u001B[39m)\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/src/langchain-mongodb/libs/langchain-mongodb/.venv/lib/python3.13/site-packages/pandas/io/parsers/readers.py:1026\u001B[39m, in \u001B[36mread_csv\u001B[39m\u001B[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001B[39m\n\u001B[32m 1013\u001B[39m kwds_defaults = _refine_defaults_read(\n\u001B[32m 1014\u001B[39m dialect,\n\u001B[32m 1015\u001B[39m delimiter,\n\u001B[32m (...)\u001B[39m\u001B[32m 1022\u001B[39m dtype_backend=dtype_backend,\n\u001B[32m 1023\u001B[39m )\n\u001B[32m 1024\u001B[39m kwds.update(kwds_defaults)\n\u001B[32m-> \u001B[39m\u001B[32m1026\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_read\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfilepath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/src/langchain-mongodb/libs/langchain-mongodb/.venv/lib/python3.13/site-packages/pandas/io/parsers/readers.py:620\u001B[39m, in \u001B[36m_read\u001B[39m\u001B[34m(filepath_or_buffer, kwds)\u001B[39m\n\u001B[32m 617\u001B[39m _validate_names(kwds.get(\u001B[33m\"\u001B[39m\u001B[33mnames\u001B[39m\u001B[33m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m))\n\u001B[32m 619\u001B[39m \u001B[38;5;66;03m# Create the parser.\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m620\u001B[39m parser = \u001B[43mTextFileReader\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfilepath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 622\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m chunksize \u001B[38;5;129;01mor\u001B[39;00m iterator:\n\u001B[32m 623\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m parser\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/src/langchain-mongodb/libs/langchain-mongodb/.venv/lib/python3.13/site-packages/pandas/io/parsers/readers.py:1620\u001B[39m, in \u001B[36mTextFileReader.__init__\u001B[39m\u001B[34m(self, f, engine, **kwds)\u001B[39m\n\u001B[32m 1617\u001B[39m \u001B[38;5;28mself\u001B[39m.options[\u001B[33m\"\u001B[39m\u001B[33mhas_index_names\u001B[39m\u001B[33m\"\u001B[39m] = kwds[\u001B[33m\"\u001B[39m\u001B[33mhas_index_names\u001B[39m\u001B[33m\"\u001B[39m]\n\u001B[32m 1619\u001B[39m \u001B[38;5;28mself\u001B[39m.handles: IOHandles | \u001B[38;5;28;01mNone\u001B[39;00m = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1620\u001B[39m \u001B[38;5;28mself\u001B[39m._engine = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_make_engine\u001B[49m\u001B[43m(\u001B[49m\u001B[43mf\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mengine\u001B[49m\u001B[43m)\u001B[49m\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/src/langchain-mongodb/libs/langchain-mongodb/.venv/lib/python3.13/site-packages/pandas/io/parsers/readers.py:1880\u001B[39m, in \u001B[36mTextFileReader._make_engine\u001B[39m\u001B[34m(self, f, engine)\u001B[39m\n\u001B[32m 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"\u001B[31mFileNotFoundError\u001B[39m: [Errno 2] No such file or directory: '../assets/fb_edges.csv'" + ] + } + ], + "execution_count": 16 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bundling graphs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The datashader library provides algorithms for bundling the edges of a graph and HoloViews provides convenient wrappers around the libraries. Note that these operations need ``scikit-image`` which you can install using:\n", + "\n", + "```\n", + "conda install scikit-image\n", + "```\n", + "\n", + "or\n", + "\n", + "```\n", + "pip install scikit-image\n", + "```" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-03T18:53:09.019406Z", + "start_time": "2025-09-03T18:53:08.663808Z" + } + }, + "source": [ + "from holoviews.operation.datashader import bundle_graph, datashade\n", + "\n", + "bundled = bundle_graph(fb_graph)\n", + "bundled" + ], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'datashader'", + "output_type": "error", + "traceback": [ + "\u001B[31m---------------------------------------------------------------------------\u001B[39m", + "\u001B[31mModuleNotFoundError\u001B[39m Traceback (most recent call last)", + "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[17]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mholoviews\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01moperation\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mdatashader\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m bundle_graph, datashade\n\u001B[32m 3\u001B[39m bundled = bundle_graph(fb_graph)\n\u001B[32m 4\u001B[39m bundled\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/src/langchain-mongodb/libs/langchain-mongodb/.venv/lib/python3.13/site-packages/holoviews/operation/datashader.py:7\u001B[39m\n\u001B[32m 4\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mfunctools\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m partial\n\u001B[32m 5\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mtyping\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m TYPE_CHECKING\n\u001B[32m----> \u001B[39m\u001B[32m7\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mdatashader\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mas\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mds\u001B[39;00m\n\u001B[32m 8\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mdatashader\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mreductions\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mas\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mrd\u001B[39;00m\n\u001B[32m 9\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mdatashader\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mtransfer_functions\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mas\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mtf\u001B[39;00m\n", + "\u001B[31mModuleNotFoundError\u001B[39m: No module named 'datashader'" + ] + } + ], + "execution_count": 17 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Datashading graphs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For graphs with a large number of edges we can datashade the paths and display the nodes separately. This loses some of the interactive features but will let you visualize quite large graphs:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(datashade(bundled, normalization='linear', width=800, height=800) * bundled.nodes).opts(\n", + " opts.Nodes(color='circle', size=10, width=1000, cmap=colors, legend_position='right'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Applying selections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively we can select the nodes and edges by an attribute that resides on either. In this case we will select the nodes and edges for a particular circle and then overlay just the selected part of the graph on the datashaded plot. Note that selections on the ``Graph`` itself will select all nodes that connect to one of the selected nodes. In this way a smaller subgraph can be highlighted and the larger graph can be datashaded." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "datashade(bundle_graph(fb_graph), normalization='linear', width=800, height=800) *\\\n", + "bundled.select(circle='circle15').opts(node_fill_color='white')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To select just nodes that are in 'circle15' set the ``selection_mode='nodes'`` overriding the default of 'edges':" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bundled.select(circle='circle15', selection_mode='nodes')" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From bb4a01f20d11bb45af8258ad18eac8bba0e4697e Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Thu, 25 Sep 2025 10:36:17 -0400 Subject: [PATCH 3/5] Added viz to project.optional-dependencies --- libs/langchain-mongodb/pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/libs/langchain-mongodb/pyproject.toml b/libs/langchain-mongodb/pyproject.toml index f76dab9a..9c404140 100644 --- a/libs/langchain-mongodb/pyproject.toml +++ b/libs/langchain-mongodb/pyproject.toml @@ -44,6 +44,7 @@ dev = [ "typing-extensions>=4.12.2", ] +[project.optional-dependencies] viz = [ "networkx", "pandas", @@ -51,6 +52,7 @@ viz = [ "holoviews", "jupyter", "scikit-image", + "pyparsing", ] [tool.pytest.ini_options] From 2b7573725e6528b28b0c217a8deddb6f8ff97ef0 Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Mon, 29 Sep 2025 12:59:40 -0400 Subject: [PATCH 4/5] Split out networkx. Cleaned up API. Testing via notebook not yet checked in. --- .../langchain_mongodb/graphrag/__init__.py | 3 + .../langchain_mongodb/graphrag/graph.py | 142 +- libs/langchain-mongodb/pyproject.toml | 14 +- libs/langchain-mongodb/uv.lock | 2344 ++++++++++++++++- uv.lock | 6 +- 5 files changed, 2435 insertions(+), 74 deletions(-) diff --git a/libs/langchain-mongodb/langchain_mongodb/graphrag/__init__.py b/libs/langchain-mongodb/langchain_mongodb/graphrag/__init__.py index e69de29b..f392f064 100644 --- a/libs/langchain-mongodb/langchain_mongodb/graphrag/__init__.py +++ b/libs/langchain-mongodb/langchain_mongodb/graphrag/__init__.py @@ -0,0 +1,3 @@ +from langchain_mongodb.graphrag.graph import MongoDBGraphStore + +__all__ = ["MongoDBGraphStore"] diff --git a/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py b/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py index 5f0dfd38..0c10770a 100644 --- a/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py +++ b/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py @@ -4,7 +4,7 @@ import logging from copy import deepcopy from importlib.metadata import version -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union +from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union from langchain_core.documents import Document from langchain_core.language_models.chat_models import BaseChatModel @@ -30,6 +30,9 @@ Entity: TypeAlias = Dict[str, Any] """Represents an Entity in the knowledge graph with specific schema. See .schema""" + import holoviews # type: ignore[import-untyped] + import networkx + logger = logging.getLogger(__name__) @@ -544,3 +547,140 @@ def chat_response( entity_schema=entity_schema, ) ) + + def to_networkx(self, **kwargs: Any) -> networkx.DiGraph: + """Utility converts Entity Collection to `NetworkX DiGraph `_ + + NOTE: Requires optional-dependency "viz", i.e. `uv sync --extra viz` + + Args: + **kwargs: Keywork arguments passed to json formatting, and constructors of graph, nodes, and edges. + + Returns: networkx.DiGraph + """ + + try: + import json + + import networkx as nx + except ImportError as e: + raise ImportError( + "Install optional-dependency `viz` for networkx or to view in Holoviews" + ) from e + + def _safe_get(lst: list, i: int, default: Any = "") -> Any: + return lst[i] if i < len(lst) else default + + # First pass: Add all nodes with their attributes + nx_graph = nx.DiGraph(**kwargs) + for doc in self.collection.find({}): + # Add node with all attributes + node_id = doc["_id"] + node_attrs = {} + node_attrs["type"] = json.dumps(doc.get("type", ""), **kwargs) + node_attrs["attributes"] = json.dumps(doc.get("attributes", {}), **kwargs) + nx_graph.add_node(node_id, **node_attrs, **kwargs) + + # Second pass: Add edges based on relationships + for doc in self.collection.find({}): + source_id = doc["_id"] + relationships = doc.get("relationships", {}) + # relationships can contain numerous target_ids, each with type and attributes + target_ids = relationships.get("target_ids", []) + n_targets = len(target_ids) + types = relationships.get("types", []) + attrs = relationships.get("attributes", []) + + for t in range(n_targets): + # Add edge and attributes + edge_attrs = {} + edge_attrs["type"] = json.dumps(_safe_get(types, t), **kwargs) + edge_attrs["attributes"] = json.dumps(_safe_get(attrs, t), **kwargs) + if nx_graph.has_node(target_ids[t]): + nx_graph.add_edge(source_id, target_ids[t], **edge_attrs, **kwargs) + else: + logger.warning( + f"{source_id=} references {target_ids[t]=} not found in collection" + ) + + return nx_graph + + def view( + self, + layout: Optional[Callable] = None, + nx_opts: Optional[dict] = None, + edge_opts: Optional[dict] = None, + node_opts: Optional[dict] = None, + ) -> holoviews.Graph: + """Draws a Knowledge Graph as Holoviews/Bokeh interactive plot. + + We first convert the entity collection to a NetworkX Graph, + and then convert it to a Holoviews Graph via their API. + + Both of these libraries are incredibly feature rich. + We encourage those interested in visualization and/or graph analysis + to dig deeper into their documentation. + The customization options are truly stunning. + + The default layout chosen is the spring_layout. + This maximizes the distance between nodes. As our entities have a type field, + however, another good layout choice is: + `layout=nx.multipartite_layout, nx_opts["subset_key"]= "type"` + + NOTE: Requires optional-dependency "viz", i.e. `uv sync --extra viz` + + Args: + layout: `networkx layout. `_ + Defaults to networkx.spring_layout. + nx_opts: Keyword arguments for layout function. + edge_opts: Keyword arguments to draw edges. + node_opts: Keyword arguments to draw nodes. + + Returns: `holoviews.Graph `_ + + """ + try: + import holoviews as hv + import networkx as nx + + hv.extension("bokeh") + except ImportError as e: + raise ImportError("To view graph, install optional-dependency `viz`") from e + + hv.opts.defaults( + hv.opts.Graph(xaxis=None, yaxis=None), hv.opts.Nodes(xaxis=None, yaxis=None) + ) + + if layout is None: + layout = nx.spring_layout + # Convert entity collection to NetworkX graph. + nx_opts = {} if nx_opts is None else nx_opts + nx_graph = self.to_networkx(**nx_opts) + # Convert to HoloViews Graph + hv_graph = hv.Graph.from_networkx(nx_graph, layout, **nx_opts) + # Display with hover tools over edges and nodes + edge_opts = {} if edge_opts is None else edge_opts + node_opts = {} if node_opts is None else node_opts + return hv_graph.opts( + inspection_policy="edges", **edge_opts + ) * hv_graph.nodes.opts(**node_opts) + + +# TODO +# Discuss API +# Document +# Choose reasonable defaults +# - Currently I have sidestepped them leaving standard defaults. +# - dpi, width, height +# - where do we specify these? hv.opts? +# - Documents layouts. => +# - spring_layout, (100-1000 nodes) +# - nx.kamada_kawai_layout, (< 100 nodes) +# - # These others require: brew install graphviz. pip install pydot graphviz +# layout=nx.nx_pydot.graphviz_layout, nx_opts={"prog":"sfdp"}) (1000+ nodes) , +# others: prog="dot" for Hierarchical Knowledge Graphs, prog=". +# ** layout=nx.multipartite_layout, subset_key = "type", # If you have different node types (e.g., Person, Organization, Location) +# lambda x: nx.nx_pydot.graphviz_layout(x, prog="twopi") +# Export collection to play with in notebook ==> view_graphstore.ipynb +# Create a much larger graph to test defaults. => Use Claude Code to build tests. +# Test save. 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!== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n 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const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n 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root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));", - "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if 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const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) 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&& Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n 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\n", - "\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Visualizing and working with network graphs is a common problem in many different disciplines. HoloViews provides the ability to represent and visualize graphs very simply and easily with facilities for interactively exploring the nodes and edges of the graph, especially using the bokeh plotting interface.\n", - "\n", - "The ``Graph`` ``Element`` differs from other elements in HoloViews in that it consists of multiple sub-elements. The data of the ``Graph`` element itself are the abstract edges between the nodes. By default the element will automatically compute concrete ``x`` and ``y`` positions for the nodes and represent them using a ``Nodes`` element, which is stored on the Graph. The abstract edges and concrete node positions are sufficient to render the ``Graph`` by drawing straight-line edges between the nodes. In order to supply explicit edge paths we can also declare ``EdgePaths``, providing explicit coordinates for each edge to follow.\n", - "\n", - "To summarize a ``Graph`` consists of three different components:\n", - "\n", - "* The ``Graph`` itself holds the abstract edges stored as a table of node indices.\n", - "* The ``Nodes`` hold the concrete ``x`` and ``y`` positions of each node along with a node ``index``. The ``Nodes`` may also define any number of value dimensions, which can be revealed when hovering over the nodes or to color the nodes by.\n", - "* The ``EdgePaths`` can optionally be supplied to declare explicit node paths.\n", - "\n", - "#### A simple Graph\n", - "\n", - "Let's start by declaring a very simple graph connecting one node to all others. If we simply supply the abstract connectivity of the ``Graph``, it will automatically compute a layout for the nodes using the ``layout_nodes`` operation, which defaults to a circular layout:" - ] - }, - { - "cell_type": "code", - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-03T18:48:24.994443Z", - "start_time": "2025-09-03T18:48:24.830375Z" - } - }, - "source": [ - "# Declare abstract edges\n", - "N = 8\n", - "node_indices = np.arange(N, dtype=np.int32)\n", - "source = np.zeros(N, dtype=np.int32)\n", - "target = node_indices\n", - "\n", - "\n", - "simple_graph = hv.Graph(((source, target),))\n", - "simple_graph" - ], - "outputs": [ - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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\n", - "" - ], - "application/vnd.holoviews_exec.v0+json": "", - "text/plain": [ - ":Layout\n", - " .Nodes.I :Nodes [x,y,index]\n", - " .EdgePaths.I :EdgePaths [x,y]" - ] - }, - "execution_count": 3, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "566494cb-8fc6-4332-9fbd-c820b61653d1" - } - }, - "output_type": "execute_result" - } - ], - "execution_count": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Displaying directed graphs\n", - "\n", - "When specifying the graph edges the source and target node are listed in order, if the graph is actually a directed graph this may used to indicate the directionality of the graph. By setting ``directed=True`` as a plot option it is possible to indicate the directionality of each edge using an arrow:" - ] - }, - { - "cell_type": "code", - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-03T18:48:49.440050Z", - "start_time": "2025-09-03T18:48:49.363030Z" - } - }, - "source": [ - "simple_graph.relabel('Directed Graph').opts(directed=True, node_size=5, arrowhead_length=0.05)" - ], - "outputs": [ - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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\n", - "" - ], - "application/vnd.holoviews_exec.v0+json": "", - "text/plain": [ - ":Graph [start,end]" - ] - }, - "execution_count": 4, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "169dead4-2143-4adf-a8c5-6e845eeea631" - } - }, - "output_type": "execute_result" - } - ], - "execution_count": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The length of the arrows can be set as an fraction of the overall graph extent using the ``arrowhead_length`` option." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Supplying explicit paths\n", - "\n", - "Next we will extend this example by supplying explicit edges:" - ] - }, - { - "cell_type": "code", - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-03T18:49:22.311062Z", - "start_time": "2025-09-03T18:49:22.235562Z" - } - }, - "source": [ - "def bezier(start, end, control, steps=None):\n", - " if steps is None:\n", - " steps = np.linspace(0, 1, 100)\n", - " return (1-steps)**2*start + 2*(1-steps)*steps*control+steps**2*end\n", - "\n", - "x, y = simple_graph.nodes.array([0, 1]).T\n", - "\n", - "paths = []\n", - "for node_index in node_indices:\n", - " ex, ey = x[node_index], y[node_index]\n", - " paths.append(np.column_stack([bezier(x[0], ex, 0), bezier(y[0], ey, 0)]))\n", - "\n", - "bezier_graph = hv.Graph(((source, target), (x, y, node_indices), paths))\n", - "bezier_graph" - ], - "outputs": [ - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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We will load the edge and node data using pandas and then color each node by their friendship group using many of the things we learned above." - ] - }, - { - "cell_type": "code", - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-03T18:50:58.798922Z", - "start_time": "2025-09-03T18:50:58.506894Z" - } - }, - "source": [ - "kwargs = dict(width=800, height=800, xaxis=None, yaxis=None)\n", - "opts.defaults(opts.Nodes(**kwargs), opts.Graph(**kwargs))\n", - "\n", - "colors = ['#000000', *hv.Cycle('Category20').values]\n", - "edges_df = pd.read_csv('../assets/fb_edges.csv')\n", - "fb_nodes = hv.Nodes(pd.read_csv('../assets/fb_nodes.csv')).sort()\n", - "fb_graph = hv.Graph((edges_df, fb_nodes), label='Facebook Circles')\n", - "\n", - "fb_graph.opts(cmap=colors, node_size=10, edge_line_width=1,\n", - " node_line_color='gray', node_color='circle')" - ], - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: '../assets/fb_edges.csv'", - "output_type": "error", - "traceback": [ - "\u001B[31m---------------------------------------------------------------------------\u001B[39m", - "\u001B[31mFileNotFoundError\u001B[39m Traceback (most recent call last)", - "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[16]\u001B[39m\u001B[32m, line 5\u001B[39m\n\u001B[32m 2\u001B[39m opts.defaults(opts.Nodes(**kwargs), opts.Graph(**kwargs))\n\u001B[32m 4\u001B[39m colors = [\u001B[33m'\u001B[39m\u001B[33m#000000\u001B[39m\u001B[33m'\u001B[39m, *hv.Cycle(\u001B[33m'\u001B[39m\u001B[33mCategory20\u001B[39m\u001B[33m'\u001B[39m).values]\n\u001B[32m----> \u001B[39m\u001B[32m5\u001B[39m edges_df = \u001B[43mpd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mread_csv\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m../assets/fb_edges.csv\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m 6\u001B[39m fb_nodes = hv.Nodes(pd.read_csv(\u001B[33m'\u001B[39m\u001B[33m../assets/fb_nodes.csv\u001B[39m\u001B[33m'\u001B[39m)).sort()\n\u001B[32m 7\u001B[39m fb_graph = hv.Graph((edges_df, fb_nodes), label=\u001B[33m'\u001B[39m\u001B[33mFacebook Circles\u001B[39m\u001B[33m'\u001B[39m)\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/src/langchain-mongodb/libs/langchain-mongodb/.venv/lib/python3.13/site-packages/pandas/io/parsers/readers.py:1026\u001B[39m, in \u001B[36mread_csv\u001B[39m\u001B[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001B[39m\n\u001B[32m 1013\u001B[39m kwds_defaults = _refine_defaults_read(\n\u001B[32m 1014\u001B[39m dialect,\n\u001B[32m 1015\u001B[39m delimiter,\n\u001B[32m (...)\u001B[39m\u001B[32m 1022\u001B[39m dtype_backend=dtype_backend,\n\u001B[32m 1023\u001B[39m )\n\u001B[32m 1024\u001B[39m kwds.update(kwds_defaults)\n\u001B[32m-> \u001B[39m\u001B[32m1026\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_read\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfilepath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/src/langchain-mongodb/libs/langchain-mongodb/.venv/lib/python3.13/site-packages/pandas/io/parsers/readers.py:620\u001B[39m, in \u001B[36m_read\u001B[39m\u001B[34m(filepath_or_buffer, kwds)\u001B[39m\n\u001B[32m 617\u001B[39m _validate_names(kwds.get(\u001B[33m\"\u001B[39m\u001B[33mnames\u001B[39m\u001B[33m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m))\n\u001B[32m 619\u001B[39m \u001B[38;5;66;03m# Create the parser.\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m620\u001B[39m parser = \u001B[43mTextFileReader\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfilepath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 622\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m chunksize \u001B[38;5;129;01mor\u001B[39;00m iterator:\n\u001B[32m 623\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m parser\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/src/langchain-mongodb/libs/langchain-mongodb/.venv/lib/python3.13/site-packages/pandas/io/parsers/readers.py:1620\u001B[39m, in \u001B[36mTextFileReader.__init__\u001B[39m\u001B[34m(self, f, engine, **kwds)\u001B[39m\n\u001B[32m 1617\u001B[39m \u001B[38;5;28mself\u001B[39m.options[\u001B[33m\"\u001B[39m\u001B[33mhas_index_names\u001B[39m\u001B[33m\"\u001B[39m] = kwds[\u001B[33m\"\u001B[39m\u001B[33mhas_index_names\u001B[39m\u001B[33m\"\u001B[39m]\n\u001B[32m 1619\u001B[39m \u001B[38;5;28mself\u001B[39m.handles: IOHandles | \u001B[38;5;28;01mNone\u001B[39;00m = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1620\u001B[39m \u001B[38;5;28mself\u001B[39m._engine = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_make_engine\u001B[49m\u001B[43m(\u001B[49m\u001B[43mf\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mengine\u001B[49m\u001B[43m)\u001B[49m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/src/langchain-mongodb/libs/langchain-mongodb/.venv/lib/python3.13/site-packages/pandas/io/parsers/readers.py:1880\u001B[39m, in \u001B[36mTextFileReader._make_engine\u001B[39m\u001B[34m(self, f, engine)\u001B[39m\n\u001B[32m 1878\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[33m\"\u001B[39m\u001B[33mb\u001B[39m\u001B[33m\"\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m mode:\n\u001B[32m 1879\u001B[39m mode += \u001B[33m\"\u001B[39m\u001B[33mb\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m-> \u001B[39m\u001B[32m1880\u001B[39m \u001B[38;5;28mself\u001B[39m.handles = \u001B[43mget_handle\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1881\u001B[39m \u001B[43m \u001B[49m\u001B[43mf\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1882\u001B[39m \u001B[43m \u001B[49m\u001B[43mmode\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1883\u001B[39m \u001B[43m \u001B[49m\u001B[43mencoding\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43moptions\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mencoding\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1884\u001B[39m \u001B[43m \u001B[49m\u001B[43mcompression\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43moptions\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mcompression\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1885\u001B[39m \u001B[43m \u001B[49m\u001B[43mmemory_map\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43moptions\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mmemory_map\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1886\u001B[39m \u001B[43m \u001B[49m\u001B[43mis_text\u001B[49m\u001B[43m=\u001B[49m\u001B[43mis_text\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1887\u001B[39m \u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43moptions\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mencoding_errors\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mstrict\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1888\u001B[39m \u001B[43m \u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43moptions\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mstorage_options\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1889\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1890\u001B[39m \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28mself\u001B[39m.handles \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m 1891\u001B[39m f = \u001B[38;5;28mself\u001B[39m.handles.handle\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/src/langchain-mongodb/libs/langchain-mongodb/.venv/lib/python3.13/site-packages/pandas/io/common.py:873\u001B[39m, in \u001B[36mget_handle\u001B[39m\u001B[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001B[39m\n\u001B[32m 868\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(handle, \u001B[38;5;28mstr\u001B[39m):\n\u001B[32m 869\u001B[39m \u001B[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001B[39;00m\n\u001B[32m 870\u001B[39m \u001B[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001B[39;00m\n\u001B[32m 871\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m ioargs.encoding \u001B[38;5;129;01mand\u001B[39;00m \u001B[33m\"\u001B[39m\u001B[33mb\u001B[39m\u001B[33m\"\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m ioargs.mode:\n\u001B[32m 872\u001B[39m \u001B[38;5;66;03m# Encoding\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m873\u001B[39m handle = \u001B[38;5;28;43mopen\u001B[39;49m\u001B[43m(\u001B[49m\n\u001B[32m 874\u001B[39m \u001B[43m \u001B[49m\u001B[43mhandle\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 875\u001B[39m \u001B[43m \u001B[49m\u001B[43mioargs\u001B[49m\u001B[43m.\u001B[49m\u001B[43mmode\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 876\u001B[39m \u001B[43m \u001B[49m\u001B[43mencoding\u001B[49m\u001B[43m=\u001B[49m\u001B[43mioargs\u001B[49m\u001B[43m.\u001B[49m\u001B[43mencoding\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 877\u001B[39m \u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[43m=\u001B[49m\u001B[43merrors\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 878\u001B[39m \u001B[43m \u001B[49m\u001B[43mnewline\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m 879\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 880\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 881\u001B[39m \u001B[38;5;66;03m# Binary mode\u001B[39;00m\n\u001B[32m 882\u001B[39m handle = \u001B[38;5;28mopen\u001B[39m(handle, ioargs.mode)\n", - "\u001B[31mFileNotFoundError\u001B[39m: [Errno 2] No such file or directory: '../assets/fb_edges.csv'" - ] - } - ], - "execution_count": 16 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bundling graphs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The datashader library provides algorithms for bundling the edges of a graph and HoloViews provides convenient wrappers around the libraries. Note that these operations need ``scikit-image`` which you can install using:\n", - "\n", - "```\n", - "conda install scikit-image\n", - "```\n", - "\n", - "or\n", - "\n", - "```\n", - "pip install scikit-image\n", - "```" - ] - }, - { - "cell_type": "code", - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-03T18:53:09.019406Z", - "start_time": "2025-09-03T18:53:08.663808Z" - } - }, - "source": [ - "from holoviews.operation.datashader import bundle_graph, datashade\n", - "\n", - "bundled = bundle_graph(fb_graph)\n", - "bundled" - ], - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'datashader'", - "output_type": "error", - "traceback": [ - "\u001B[31m---------------------------------------------------------------------------\u001B[39m", - "\u001B[31mModuleNotFoundError\u001B[39m Traceback (most recent call last)", - "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[17]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mholoviews\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01moperation\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mdatashader\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m bundle_graph, datashade\n\u001B[32m 3\u001B[39m bundled = bundle_graph(fb_graph)\n\u001B[32m 4\u001B[39m bundled\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/src/langchain-mongodb/libs/langchain-mongodb/.venv/lib/python3.13/site-packages/holoviews/operation/datashader.py:7\u001B[39m\n\u001B[32m 4\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mfunctools\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m partial\n\u001B[32m 5\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mtyping\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m TYPE_CHECKING\n\u001B[32m----> \u001B[39m\u001B[32m7\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mdatashader\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mas\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mds\u001B[39;00m\n\u001B[32m 8\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mdatashader\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mreductions\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mas\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mrd\u001B[39;00m\n\u001B[32m 9\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mdatashader\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mtransfer_functions\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mas\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mtf\u001B[39;00m\n", - "\u001B[31mModuleNotFoundError\u001B[39m: No module named 'datashader'" - ] - } - ], - "execution_count": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Datashading graphs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For graphs with a large number of edges we can datashade the paths and display the nodes separately. This loses some of the interactive features but will let you visualize quite large graphs:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(datashade(bundled, normalization='linear', width=800, height=800) * bundled.nodes).opts(\n", - " opts.Nodes(color='circle', size=10, width=1000, cmap=colors, legend_position='right'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Applying selections" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively we can select the nodes and edges by an attribute that resides on either. In this case we will select the nodes and edges for a particular circle and then overlay just the selected part of the graph on the datashaded plot. Note that selections on the ``Graph`` itself will select all nodes that connect to one of the selected nodes. In this way a smaller subgraph can be highlighted and the larger graph can be datashaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datashade(bundle_graph(fb_graph), normalization='linear', width=800, height=800) *\\\n", - "bundled.select(circle='circle15').opts(node_fill_color='white')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To select just nodes that are in 'circle15' set the ``selection_mode='nodes'`` overriding the default of 'edges':" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bundled.select(circle='circle15', selection_mode='nodes')" - ] - } - ], - "metadata": { - "language_info": { - "name": "python", - "pygments_lexer": "ipython3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py b/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py index 0c10770a..58013b82 100644 --- a/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py +++ b/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py @@ -667,7 +667,7 @@ def view( # TODO -# Discuss API +# Discuss API => split out json_opts and nx_opts # Document # Choose reasonable defaults # - Currently I have sidestepped them leaving standard defaults.