docs(gfql): Comprehensive Let bindings and Call operations documentation

LET_SEQUENCING_DOCS_TASKS.md

@@ -0,0 +1,149 @@
+# Let Sequencing Documentation Tasks
+
+## Raw Task List
+
+1. **10-minute GFQL Guide**
+   - Add sequencing GFQL programs section with `let`
+   - Include PageRank example: compute PageRank, then explore 2 hops from high-scoring nodes
+   - Location: Find appropriate place for ninja edit
+
+2. **Overview -> Quick Examples**
+   - Add similar `let` sequencing example
+   - Show the power of composing operations
+
+3. **Remote Mode Documentation**
+   - Add `let` examples (more important here due to no Python escape hatch)
+   - Show how to sequence operations in pure GFQL
+
+4. **Fix chain_let References**
+   - Find all "chain_let" references in docs
+   - Update to ".let()" syntax
+   - Verify correctness across all docs
+
+5. **Update .chain() Syntax**
+   - Find all .chain(...) references
+   - Update to .gfql(Chain(...))
+   - OR: If implicit coercion .gfql([...]) => .gfql(Chain([...])) is supported:
+     - Do code scan to verify
+     - Add pytest if needed
+     - Document the coercion clearly
+
+6. **GFQL Quick Reference**
+   - Move .let() earlier in the reference
+   - Place before predicates section
+   - Show as fundamental composition tool
+
+7. **GFQL Language Spec**
+   - Fix top-level to show both chain and let as allowed
+   - Add chain+let section before Operations section
+   - Ensure proper hierarchy
+
+8. **Wire Protocol Documentation**
+   - Add chain+let section before operations
+   - Show protocol representation
+
+9. **hop_and_chain_graph_pattern_mining Notebook**
+   - Add `let` example
+   - Validate with docs build that runs notebooks
+
+10. **call_operations Documentation**
+    - Update title to include "and Let bindings"
+    - Show how Call and Let work together
+
+11. **gfql_remote Notebook**
+    - Add let+call example
+    - Show remote execution patterns
+
+12. **Multi-hop Section Updates**
+    - Add forward reference to "Complex Pattern Reuse"
+    - Improve flow between sections
+
+13. **"Between" Section**
+    - Has "Complex Pattern Reuse" 
+    - Fix chain_let references
+
+14. **Combining GFQL Section**
+    - Start early with Python vs pure GFQL equivalents
+    - Show the relationship clearly
+
+## Reordered by PR
+
+All tasks should be on **PR #708** (top of docs stack): "docs(gfql): Comprehensive Let bindings and Call operations documentation"
+
+## Granular Implementation Steps
+
+### Phase 1: Preparation
+1. Switch to PR #708 branch
+2. Pull latest changes
+3. Create working list of all files to modify
+
+### Phase 2: Find and Fix chain_let References
+4. Search for all "chain_let" occurrences in docs
+5. List each file and line number
+6. Update each to ".let()" syntax
+7. Verify context makes sense
+
+### Phase 3: Update .chain() Syntax
+8. Search for all .chain() calls in docs
+9. Check if implicit coercion is supported in code
+10. If yes: Document coercion behavior
+11. If no: Update all to .gfql(Chain(...))
+
+### Phase 4: Core Documentation Updates
+12. Find 10-minute GFQL guide file
+13. Add "Sequencing Programs with Let" section
+14. Write PageRank + 2-hop example
+15. Find Overview -> Quick Examples
+16. Add let sequencing example there
+17. Find Remote Mode docs
+18. Add comprehensive let examples for remote
+
+### Phase 5: Reference Documentation
+19. Find GFQL Quick Reference
+20. Restructure to introduce .let() before predicates
+21. Find GFQL Language Spec
+22. Update top-level grammar to include let
+23. Add chain+let section before Operations
+24. Find Wire Protocol docs
+25. Add chain+let protocol section before ops
+
+### Phase 6: Notebook Updates
+26. Find hop_and_chain_graph_pattern_mining.ipynb
+27. Add let example with pattern reuse
+28. Find gfql_remote.ipynb
+29. Add let+call remote example
+30. Update notebook outputs
+
+### Phase 7: Cross-references and Titles
+31. Find Multi-hop section
+32. Add forward reference to Complex Pattern Reuse
+33. Find call_operations docs
+34. Update title to include "and Let Bindings"
+35. Find Combining GFQL section
+36. Add early Python vs GFQL comparison
+
+### Phase 8: Validation
+37. Run flake8/ruff linting
+38. Run mypy type checking
+39. Run docs build locally
+40. Run notebook tests
+41. Review all changes
+
+### Phase 9: Commit and Push
+42. Stage all changes
+43. Create detailed commit message
+44. Push to PR #708
+
+## Validation Checklist
+- [ ] All chain_let replaced with .let()
+- [ ] All .chain() updated appropriately
+- [ ] PageRank example works in 10-min guide
+- [ ] Remote examples are comprehensive
+- [ ] Quick Ref has proper ordering
+- [ ] Language Spec has let at top level
+- [ ] Notebooks execute without errors
+- [ ] Cross-references are correct
+- [ ] No lint errors
+- [ ] No type errors
+- [ ] Docs build succeeds
+- [ ] Notebook tests pass

demos/gfql/gfql_remote.ipynb

@@ -1401,6 +1401,82 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fs1pabrqfaj",
+   "source": "## Combining Let Bindings with Call Operations\n\nLet bindings in GFQL allow you to create named intermediate results and compose complex operations. When combined with call operations in remote mode, you can orchestrate sophisticated graph analyses entirely on the server, minimizing data transfer and leveraging server-side GPU acceleration.",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bs7rghntlp",
+   "source": "### Example 1: PageRank Analysis with Filtering\n\nThis example demonstrates using let bindings to:\n1. Compute PageRank scores\n2. Filter high-value nodes\n3. Extract subgraphs around important nodes\n4. Return results for visualization",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "id": "wurwk0xplp",
+   "source": "# Create a more complex graph for demonstration\ncomplex_edges = pd.DataFrame({\n    's': ['a', 'b', 'c', 'd', 'e', 'f', 'a', 'b', 'c', 'd'],\n    'd': ['b', 'c', 'd', 'e', 'f', 'a', 'c', 'd', 'e', 'f'],\n    'weight': [1, 2, 1, 3, 1, 2, 1, 2, 1, 1],\n    'type': ['follow', 'mention', 'follow', 'follow', 'mention', 'follow', 'mention', 'follow', 'follow', 'mention']\n})\n\ng_complex = graphistry.edges(complex_edges, 's', 'd').upload()\nprint(f\"Uploaded graph with {len(complex_edges)} edges\")",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "id": "mwr3948llv",
+   "source": "%%time\n\n# Define a complex query using let bindings with call operations\npagerank_analysis_query = {\n    'let': {\n        # Step 1: Compute PageRank scores\n        'with_pagerank': {\n            'call': {\n                'method': 'compute_pagerank',\n                'args': [],\n                'kwargs': {}\n            }\n        },\n        \n        # Step 2: Filter nodes with high PageRank scores\n        'important_nodes': {\n            'pipe': [\n                {'var': 'with_pagerank'},\n                {\n                    'n': {\n                        'filter': {\n                            'gte': [\n                                {'col': 'pagerank'},\n                                0.15  # Threshold for \"important\" nodes\n                            ]\n                        }\n                    }\n                }\n            ]\n        },\n        \n        # Step 3: Get 1-hop neighborhoods of important nodes\n        'important_neighborhoods': {\n            'pipe': [\n                {'var': 'important_nodes'},\n                {'e_undirected': {'hops': 1}},\n                {'n': {}}\n            ]\n        }\n    },\n    \n    # Return the final result\n    'in': {'var': 'important_neighborhoods'}\n}\n\n# Execute the query remotely\nresult = g_complex.chain_remote([pagerank_analysis_query])\n\nprint(f\"Result has {len(result._nodes)} nodes and {len(result._edges)} edges\")\nprint(\"\\nNodes with PageRank scores:\")\nprint(result._nodes)",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "jdzoowghv2q",
+   "source": "### Example 2: Multi-Stage Analysis with Different Edge Types\n\nThis example shows how to use let bindings to analyze different edge types separately and combine the results:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "id": "40m2wl0yn3m",
+   "source": "%%time\n\n# Analyze different edge types and combine results\nedge_type_analysis = {\n    'let': {\n        # Analyze follow edges\n        'follow_network': {\n            'pipe': [\n                {\n                    'e_undirected': {\n                        'filter': {\n                            'eq': [\n                                {'col': 'type'},\n                                'follow'\n                            ]\n                        }\n                    }\n                },\n                {'n': {}}\n            ]\n        },\n        \n        # Compute centrality on follow network\n        'follow_centrality': {\n            'pipe': [\n                {'var': 'follow_network'},\n                {\n                    'call': {\n                        'method': 'compute_degree_centrality',\n                        'args': [],\n                        'kwargs': {}\n                    }\n                }\n            ]\n        },\n        \n        # Find mention patterns\n        'mention_edges': {\n            'e_undirected': {\n                'filter': {\n                    'eq': [\n                        {'col': 'type'},\n                        'mention'\n                    ]\n                }\n            }\n        },\n        \n        # Get nodes that are both highly connected and frequently mentioned\n        'influential_nodes': {\n            'pipe': [\n                {'var': 'follow_centrality'},\n                {\n                    'n': {\n                        'filter': {\n                            'gte': [\n                                {'col': 'degree_centrality'},\n                                0.5\n                            ]\n                        }\n                    }\n                },\n                {'var': 'mention_edges'},\n                {'n': {}}\n            ]\n        }\n    },\n    \n    'in': {'var': 'influential_nodes'}\n}\n\ninfluential_result = g_complex.chain_remote([edge_type_analysis])\n\nprint(f\"Found {len(influential_result._nodes)} influential nodes\")\nprint(f\"Connected by {len(influential_result._edges)} edges\")\nprint(\"\\nInfluential nodes with centrality scores:\")\nprint(influential_result._nodes)",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5y02h8p7mkk",
+   "source": "### Example 3: Conditional Analysis with Let Bindings\n\nThis example demonstrates using let bindings to perform conditional analysis based on graph properties:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "id": "gmy30drvbts",
+   "source": "%%time\n\n# Complex analysis with multiple algorithms\ncomprehensive_analysis = {\n    'let': {\n        # Base graph with all computations\n        'enriched_graph': {\n            'pipe': [\n                {'call': {'method': 'compute_pagerank', 'args': [], 'kwargs': {}}},\n                {'call': {'method': 'compute_degree_centrality', 'args': [], 'kwargs': {}}},\n                # Add edge weight normalization\n                {\n                    'call': {\n                        'method': 'edges',\n                        'args': [],\n                        'kwargs': {\n                            'normalize': {\n                                'weight': {\n                                    'min': 0,\n                                    'max': 1\n                                }\n                            }\n                        }\n                    }\n                }\n            ]\n        },\n        \n        # Find bridge nodes (high betweenness)\n        'bridge_nodes': {\n            'pipe': [\n                {'var': 'enriched_graph'},\n                {\n                    'n': {\n                        'filter': {\n                            'and': [\n                                {'gte': [{'col': 'pagerank'}, 0.1]},\n                                {'lte': [{'col': 'degree_centrality'}, 0.7]}\n                            ]\n                        }\n                    }\n                }\n            ]\n        },\n        \n        # Find hub nodes (high degree centrality)\n        'hub_nodes': {\n            'pipe': [\n                {'var': 'enriched_graph'},\n                {\n                    'n': {\n                        'filter': {\n                            'gte': [{'col': 'degree_centrality'}, 0.7]\n                        }\n                    }\n                }\n            ]\n        },\n        \n        # Get connections between bridges and hubs\n        'critical_paths': {\n            'pipe': [\n                {'var': 'bridge_nodes'},\n                {'e_forward': {'to_nodes': {'var': 'hub_nodes'}}},\n                {'n': {}}\n            ]\n        }\n    },\n    \n    'in': {'var': 'critical_paths'}\n}\n\n# Execute remotely with GPU acceleration\ncritical_paths_result = g_complex.chain_remote([comprehensive_analysis], engine='cudf')\n\nprint(f\"Critical paths network: {len(critical_paths_result._nodes)} nodes, {len(critical_paths_result._edges)} edges\")\n\n# Check if we got results\nif len(critical_paths_result._nodes) > 0:\n    print(\"\\nCritical path nodes:\")\n    print(critical_paths_result._nodes)\nelse:\n    print(\"\\nNo critical paths found with current thresholds\")",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "wbt02937wz",
+   "source": "### Example 4: Visualization-Ready Analysis\n\nThis example shows how to prepare data for visualization by enriching it with multiple metrics and creating a focused subgraph:",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "id": "4glzmgi1u3s",
+   "source": "%%time\n\n# Prepare visualization-ready data with all enrichments\nviz_prep_query = {\n    'let': {\n        # Compute all metrics\n        'with_metrics': {\n            'pipe': [\n                {'call': {'method': 'compute_pagerank', 'args': [], 'kwargs': {}}},\n                {'call': {'method': 'compute_degree_centrality', 'args': [], 'kwargs': {}}},\n                # Add node colors based on PageRank\n                {\n                    'call': {\n                        'method': 'nodes',\n                        'args': [],\n                        'kwargs': {\n                            'assign': {\n                                'node_color': {\n                                    'case': [\n                                        {\n                                            'when': {'gte': [{'col': 'pagerank'}, 0.2]},\n                                            'then': 65280  # Green for high PageRank\n                                        },\n                                        {\n                                            'when': {'gte': [{'col': 'pagerank'}, 0.15]},\n                                            'then': 16776960  # Yellow for medium\n                                        }\n                                    ],\n                                    'else': 16711680  # Red for low\n                                },\n                                'node_size': {\n                                    'mul': [\n                                        {'col': 'degree_centrality'},\n                                        50  # Scale factor\n                                    ]\n                                }\n                            }\n                        }\n                    }\n                }\n            ]\n        },\n        \n        # Add edge styling based on type and weight\n        'styled_graph': {\n            'pipe': [\n                {'var': 'with_metrics'},\n                {\n                    'call': {\n                        'method': 'edges',\n                        'args': [],\n                        'kwargs': {\n                            'assign': {\n                                'edge_color': {\n                                    'case': [\n                                        {\n                                            'when': {'eq': [{'col': 'type'}, 'follow']},\n                                            'then': 255  # Blue for follows\n                                        }\n                                    ],\n                                    'else': 16711935  # Magenta for mentions\n                                },\n                                'edge_weight': {\n                                    'col': 'weight'\n                                }\n                            }\n                        }\n                    }\n                }\n            ]\n        },\n        \n        # Focus on top nodes and their connections\n        'viz_subgraph': {\n            'pipe': [\n                {'var': 'styled_graph'},\n                {\n                    'n': {\n                        'filter': {\n                            'or': [\n                                {'gte': [{'col': 'pagerank'}, 0.15]},\n                                {'gte': [{'col': 'degree_centrality'}, 0.6]}\n                            ]\n                        }\n                    }\n                },\n                {'e_undirected': {'hops': 1}},\n                {'n': {}}\n            ]\n        }\n    },\n    \n    'in': {'var': 'viz_subgraph'}\n}\n\n# Get visualization-ready data\nviz_result = g_complex.chain_remote([viz_prep_query])\n\nprint(f\"Visualization subgraph: {len(viz_result._nodes)} nodes, {len(viz_result._edges)} edges\")\nprint(\"\\nNodes with visualization attributes:\")\nprint(viz_result._nodes)\nprint(\"\\nEdges with styling:\")\nprint(viz_result._edges)\n\n# Ready to visualize\n# viz_result.plot()  # Uncomment to create visualization",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "p1o68dnaemk",
+   "source": "### Key Benefits of Let Bindings with Remote Calls\n\n1. **Server-Side Orchestration**: All operations happen on the server, minimizing data transfer\n2. **Named Intermediate Results**: Create readable, reusable steps in complex analyses\n3. **GPU Acceleration**: Leverage server GPU for compute-intensive operations like PageRank\n4. **Composability**: Build complex workflows from simple building blocks\n5. **Efficiency**: Avoid redundant computations by reusing named results\n\nWhen working with large graphs, this approach is particularly powerful as it allows you to:\n- Perform multiple analyses without downloading intermediate results\n- Chain together different algorithms and filters\n- Prepare visualization-ready data entirely on the server\n- Return only the final, filtered results you need",
+   "metadata": {}
   }
  ],
  "metadata": {
@@ -1424,4 +1500,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}