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Inspecting the context data used to generate the response
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<div class="md-content" data-md-component="content">
<article class="md-content__inner md-typeset">
<h1>Local Search</h1>
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#26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{--jp-shadow-base-lightness: 0;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-400);--jp-border-color1: var(--md-grey-400);--jp-border-color2: var(--md-grey-300);--jp-border-color3: var(--md-grey-200);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe 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rgba(255, 255, 255, 1);--jp-content-font-color2: rgba(255, 255, 255, .7);--jp-content-font-color3: rgba(255, 255, 255, .5);--jp-content-link-color: var(--md-blue-300);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: #111111;--jp-layout-color1: var(--md-grey-900);--jp-layout-color2: var(--md-grey-800);--jp-layout-color3: var(--md-grey-700);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: white;--jp-inverse-layout-color1: white;--jp-inverse-layout-color2: var(--md-grey-200);--jp-inverse-layout-color3: var(--md-grey-400);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-700);--jp-brand-color1: var(--md-blue-500);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-700);--jp-accent-color1: var(--md-green-500);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-700);--jp-warn-color1: var(--md-orange-500);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-700);--jp-error-color1: var(--md-red-500);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-700);--jp-success-color1: var(--md-green-500);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-700);--jp-info-color1: var(--md-cyan-500);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--jp-layout-color1);--jp-cell-editor-border-color: var(--md-grey-700);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: 1;--jp-cell-prompt-not-active-font-color: var(--md-grey-300);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: rgba(33, 150, 243, .24);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: rgba(244, 67, 54, .28);--jp-rendermime-table-row-background: var(--md-grey-900);--jp-rendermime-table-row-hover-background: rgba(3, 169, 244, .2);--jp-dialog-background: rgba(0, 0, 0, .6);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color2);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .8);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--jp-layout-color0);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color0);--jp-input-hover-background: var(--jp-layout-color2);--jp-input-background: var(--md-grey-800);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: var(--jp-layout-color2);--jp-editor-selected-focused-background: rgba(33, 150, 243, .24);--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: var(--md-green-500);--jp-mirror-editor-atom-color: var(--md-blue-300);--jp-mirror-editor-number-color: var(--md-green-400);--jp-mirror-editor-def-color: var(--md-blue-600);--jp-mirror-editor-variable-color: var(--md-grey-300);--jp-mirror-editor-variable-2-color: var(--md-blue-400);--jp-mirror-editor-variable-3-color: var(--md-green-600);--jp-mirror-editor-punctuation-color: var(--md-blue-400);--jp-mirror-editor-property-color: var(--md-blue-400);--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ff7070;--jp-mirror-editor-string-2-color: var(--md-purple-300);--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: 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<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Copyright (c) 2024 Microsoft Corporation.</span>
<span class="c1"># Licensed under the MIT License.</span>
</pre></div>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">tiktoken</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">graphrag.query.context_builder.entity_extraction</span><span class="w"> </span><span class="kn">import</span> <span class="n">EntityVectorStoreKey</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">graphrag.query.indexer_adapters</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
<span class="n">read_indexer_covariates</span><span class="p">,</span>
<span class="n">read_indexer_entities</span><span class="p">,</span>
<span class="n">read_indexer_relationships</span><span class="p">,</span>
<span class="n">read_indexer_reports</span><span class="p">,</span>
<span class="n">read_indexer_text_units</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">graphrag.query.question_gen.local_gen</span><span class="w"> </span><span class="kn">import</span> <span class="n">LocalQuestionGen</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">graphrag.query.structured_search.local_search.mixed_context</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
<span class="n">LocalSearchMixedContext</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">graphrag.query.structured_search.local_search.search</span><span class="w"> </span><span class="kn">import</span> <span class="n">LocalSearch</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">graphrag.vector_stores.lancedb</span><span class="w"> </span><span class="kn">import</span> <span class="n">LanceDBVectorStore</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-2">import os
import pandas as pd
import tiktoken
from graphrag.query.context_builder.entity_extraction import EntityVectorStoreKey
from graphrag.query.indexer_adapters import (
read_indexer_covariates,
read_indexer_entities,
read_indexer_relationships,
read_indexer_reports,
read_indexer_text_units,
)
from graphrag.query.question_gen.local_gen import LocalQuestionGen
from graphrag.query.structured_search.local_search.mixed_context import (
LocalSearchMixedContext,
)
from graphrag.query.structured_search.local_search.search import LocalSearch
from graphrag.vector_stores.lancedb import LanceDBVectorStore</div>
</div>
</div>
</div>
</div>
</div>
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</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
<h2 id="local-search-example">Local Search Example<a class="anchor-link" href="#local-search-example"></a></h2><p>Local search method generates answers by combining relevant data from the AI-extracted knowledge-graph with text chunks of the raw documents. This method is suitable for questions that require an understanding of specific entities mentioned in the documents (e.g. What are the healing properties of chamomile?).</p>
</div>
</div>
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<h3 id="load-text-units-and-graph-data-tables-as-context-for-local-search">Load text units and graph data tables as context for local search<a class="anchor-link" href="#load-text-units-and-graph-data-tables-as-context-for-local-search"></a></h3><ul>
<li>In this test we first load indexing outputs from parquet files to dataframes, then convert these dataframes into collections of data objects aligning with the knowledge model.</li>
</ul>
</div>
</div>
</div>
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<h3 id="load-tables-to-dataframes">Load tables to dataframes<a class="anchor-link" href="#load-tables-to-dataframes"></a></h3>
</div>
</div>
</div>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">INPUT_DIR</span> <span class="o">=</span> <span class="s2">"./inputs/operation dulce"</span>
<span class="n">LANCEDB_URI</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">INPUT_DIR</span><span class="si">}</span><span class="s2">/lancedb"</span>
<span class="n">COMMUNITY_REPORT_TABLE</span> <span class="o">=</span> <span class="s2">"community_reports"</span>
<span class="n">ENTITY_TABLE</span> <span class="o">=</span> <span class="s2">"entities"</span>
<span class="n">COMMUNITY_TABLE</span> <span class="o">=</span> <span class="s2">"communities"</span>
<span class="n">RELATIONSHIP_TABLE</span> <span class="o">=</span> <span class="s2">"relationships"</span>
<span class="n">COVARIATE_TABLE</span> <span class="o">=</span> <span class="s2">"covariates"</span>
<span class="n">TEXT_UNIT_TABLE</span> <span class="o">=</span> <span class="s2">"text_units"</span>
<span class="n">COMMUNITY_LEVEL</span> <span class="o">=</span> <span class="mi">2</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-3">INPUT_DIR = "./inputs/operation dulce"
LANCEDB_URI = f"{INPUT_DIR}/lancedb"
COMMUNITY_REPORT_TABLE = "community_reports"
ENTITY_TABLE = "entities"
COMMUNITY_TABLE = "communities"
RELATIONSHIP_TABLE = "relationships"
COVARIATE_TABLE = "covariates"
TEXT_UNIT_TABLE = "text_units"
COMMUNITY_LEVEL = 2</div>
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<h4 id="read-entities">Read entities<a class="anchor-link" href="#read-entities"></a></h4>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># read nodes table to get community and degree data</span>
<span class="n">entity_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">INPUT_DIR</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">ENTITY_TABLE</span><span class="si">}</span><span class="s2">.parquet"</span><span class="p">)</span>
<span class="n">community_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">INPUT_DIR</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">COMMUNITY_TABLE</span><span class="si">}</span><span class="s2">.parquet"</span><span class="p">)</span>
<span class="n">entities</span> <span class="o">=</span> <span class="n">read_indexer_entities</span><span class="p">(</span><span class="n">entity_df</span><span class="p">,</span> <span class="n">community_df</span><span class="p">,</span> <span class="n">COMMUNITY_LEVEL</span><span class="p">)</span>
<span class="c1"># load description embeddings to an in-memory lancedb vectorstore</span>
<span class="c1"># to connect to a remote db, specify url and port values.</span>
<span class="n">description_embedding_store</span> <span class="o">=</span> <span class="n">LanceDBVectorStore</span><span class="p">(</span>
<span class="n">collection_name</span><span class="o">=</span><span class="s2">"default-entity-description"</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">description_embedding_store</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="n">db_uri</span><span class="o">=</span><span class="n">LANCEDB_URI</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Entity count: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">entity_df</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">entity_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-4"># read nodes table to get community and degree data
entity_df = pd.read_parquet(f"{INPUT_DIR}/{ENTITY_TABLE}.parquet")
community_df = pd.read_parquet(f"{INPUT_DIR}/{COMMUNITY_TABLE}.parquet")
entities = read_indexer_entities(entity_df, community_df, COMMUNITY_LEVEL)
# load description embeddings to an in-memory lancedb vectorstore
# to connect to a remote db, specify url and port values.
description_embedding_store = LanceDBVectorStore(
collection_name="default-entity-description",
)
description_embedding_store.connect(db_uri=LANCEDB_URI)
print(f"Entity count: {len(entity_df)}")
entity_df.head()</div>
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<pre>Entity count: 18
</pre>
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<style scoped="">
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}
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text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>id</th>
<th>human_readable_id</th>
<th>title</th>
<th>type</th>
<th>description</th>
<th>text_unit_ids</th>
<th>frequency</th>
<th>degree</th>
<th>x</th>
<th>y</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>425a7862-0aef-4f69-a4c8-8bd42151c9d4</td>
<td>0</td>
<td>ALEX MERCER</td>
<td>PERSON</td>
<td>Agent Alex Mercer is a determined individual w...</td>
<td>[8e938693af886bfd081acbbe8384c3671446bff84a134...</td>
<td>4</td>
<td>9</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<th>1</th>
<td>bcdbf1fc-0dc1-460f-bc71-2781729c96ba</td>
<td>1</td>
<td>TAYLOR CRUZ</td>
<td>PERSON</td>
<td>Agent Taylor Cruz is a commanding and authorit...</td>
<td>[8e938693af886bfd081acbbe8384c3671446bff84a134...</td>
<td>4</td>
<td>8</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<th>2</th>
<td>ef02ef24-5762-46ce-93ce-7dea6fc86595</td>
<td>2</td>
<td>JORDAN HAYES</td>
<td>PERSON</td>
<td>Dr. Jordan Hayes is a scientist and a member o...</td>
<td>[8e938693af886bfd081acbbe8384c3671446bff84a134...</td>
<td>4</td>
<td>9</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<th>3</th>
<td>8b163d27-e43a-4a2c-a26f-866778d8720e</td>
<td>3</td>
<td>SAM RIVERA</td>
<td>PERSON</td>
<td>Sam Rivera is a cybersecurity expert and a tal...</td>
<td>[8e938693af886bfd081acbbe8384c3671446bff84a134...</td>
<td>4</td>
<td>8</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<th>4</th>
<td>542aa5bd-ba2d-400a-8488-c52d50bc300d</td>
<td>4</td>
<td>PARANORMAL MILITARY SQUAD</td>
<td>ORGANIZATION</td>
<td>The PARANORMAL MILITARY SQUAD is an elite grou...</td>
<td>[8e938693af886bfd081acbbe8384c3671446bff84a134...</td>
<td>2</td>
<td>6</td>
<td>0</td>
<td>0</td>
</tr>
</tbody>
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<h4 id="read-relationships">Read relationships<a class="anchor-link" href="#read-relationships"></a></h4>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">relationship_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">INPUT_DIR</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">RELATIONSHIP_TABLE</span><span class="si">}</span><span class="s2">.parquet"</span><span class="p">)</span>
<span class="n">relationships</span> <span class="o">=</span> <span class="n">read_indexer_relationships</span><span class="p">(</span><span class="n">relationship_df</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Relationship count: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">relationship_df</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">relationship_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-5">relationship_df = pd.read_parquet(f"{INPUT_DIR}/{RELATIONSHIP_TABLE}.parquet")
relationships = read_indexer_relationships(relationship_df)
print(f"Relationship count: {len(relationship_df)}")
relationship_df.head()</div>
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<pre>Relationship count: 54
</pre>
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}
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vertical-align: top;
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text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>id</th>
<th>human_readable_id</th>
<th>source</th>
<th>target</th>
<th>description</th>
<th>weight</th>
<th>combined_degree</th>
<th>text_unit_ids</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>2bfad9f4-5abd-48d0-8db3-a9cad9120413</td>
<td>0</td>
<td>ALEX MERCER</td>
<td>TAYLOR CRUZ</td>
<td>Alex Mercer and Taylor Cruz are both agents wo...</td>
<td>37.0</td>
<td>17</td>
<td>[8e938693af886bfd081acbbe8384c3671446bff84a134...</td>
</tr>
<tr>
<th>1</th>
<td>6cbb838f-9e83-4086-a684-15c8ed709e52</td>
<td>1</td>
<td>ALEX MERCER</td>
<td>JORDAN HAYES</td>
<td>Alex Mercer and Jordan Hayes are both agents w...</td>
<td>42.0</td>
<td>18</td>
<td>[8e938693af886bfd081acbbe8384c3671446bff84a134...</td>
</tr>
<tr>
<th>2</th>
<td>bfdc25f1-80ca-477b-a304-94465b69e680</td>
<td>2</td>
<td>ALEX MERCER</td>
<td>SAM RIVERA</td>
<td>Alex Mercer and Sam Rivera are both agents and...</td>
<td>26.0</td>
<td>17</td>
<td>[8e938693af886bfd081acbbe8384c3671446bff84a134...</td>
</tr>
<tr>
<th>3</th>
<td>7a7e943d-a4f5-487b-9625-5d0907c4c26d</td>
<td>3</td>
<td>ALEX MERCER</td>
<td>PARANORMAL MILITARY SQUAD</td>
<td>Alex Mercer is a member of the Paranormal Mili...</td>
<td>17.0</td>
<td>15</td>
<td>[8e938693af886bfd081acbbe8384c3671446bff84a134...</td>
</tr>
<tr>
<th>4</th>
<td>5e00bcb9-a17e-4c27-8241-6ebb286a7fc6</td>
<td>4</td>
<td>ALEX MERCER</td>
<td>DULCE</td>
<td>Alex Mercer is preparing to lead the team into...</td>
<td>15.0</td>
<td>14</td>
<td>[8e938693af886bfd081acbbe8384c3671446bff84a134...</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># NOTE: covariates are turned off by default, because they generally need prompt tuning to be valuable</span>
<span class="c1"># Please see the GRAPHRAG_CLAIM_* settings</span>
<span class="n">covariate_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">INPUT_DIR</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">COVARIATE_TABLE</span><span class="si">}</span><span class="s2">.parquet"</span><span class="p">)</span>
<span class="n">claims</span> <span class="o">=</span> <span class="n">read_indexer_covariates</span><span class="p">(</span><span class="n">covariate_df</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Claim records: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">claims</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">covariates</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"claims"</span><span class="p">:</span> <span class="n">claims</span><span class="p">}</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-6"># NOTE: covariates are turned off by default, because they generally need prompt tuning to be valuable
# Please see the GRAPHRAG_CLAIM_* settings
covariate_df = pd.read_parquet(f"{INPUT_DIR}/{COVARIATE_TABLE}.parquet")
claims = read_indexer_covariates(covariate_df)
print(f"Claim records: {len(claims)}")
covariates = {"claims": claims}</div>
</div>
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<pre>Claim records: 17
</pre>
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<h4 id="read-community-reports">Read community reports<a class="anchor-link" href="#read-community-reports"></a></h4>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">report_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">INPUT_DIR</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">COMMUNITY_REPORT_TABLE</span><span class="si">}</span><span class="s2">.parquet"</span><span class="p">)</span>
<span class="n">reports</span> <span class="o">=</span> <span class="n">read_indexer_reports</span><span class="p">(</span><span class="n">report_df</span><span class="p">,</span> <span class="n">community_df</span><span class="p">,</span> <span class="n">COMMUNITY_LEVEL</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Report records: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">report_df</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">report_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-7">report_df = pd.read_parquet(f"{INPUT_DIR}/{COMMUNITY_REPORT_TABLE}.parquet")
reports = read_indexer_reports(report_df, community_df, COMMUNITY_LEVEL)
print(f"Report records: {len(report_df)}")
report_df.head()</div>
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<pre>Report records: 2
</pre>
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<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>id</th>
<th>human_readable_id</th>
<th>community</th>
<th>level</th>
<th>parent</th>
<th>children</th>
<th>title</th>
<th>summary</th>
<th>full_content</th>
<th>rank</th>
<th>rating_explanation</th>
<th>findings</th>
<th>full_content_json</th>
<th>period</th>
<th>size</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>6c3a555680d647ac8be866a129c7b0ea</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>-1</td>
<td>[]</td>
<td>Operation: Dulce and Dulce Base Exploration</td>
<td>The community revolves around 'Operation: Dulc...</td>
<td># Operation: Dulce and Dulce Base Exploration\...</td>
<td>8.5</td>
<td>The impact severity rating is high due to the ...</td>
<td>[{'explanation': 'Operation: Dulce is a signif...</td>
<td>{\n "title": "Operation: Dulce and Dulce Ba...</td>
<td>2025-03-04</td>
<td>7</td>
</tr>
<tr>
<th>1</th>
<td>0127331a1ea34b8ba19de2c2a4cb3bc9</td>
<td>1</td>
<td>1</td>
<td>0</td>
<td>-1</td>
<td>[]</td>
<td>Paranormal Military Squad and Operation: Dulce</td>
<td>The community centers around the Paranormal Mi...</td>
<td># Paranormal Military Squad and Operation: Dul...</td>
<td>8.5</td>
<td>The impact severity rating is high due to the ...</td>
<td>[{'explanation': 'Agent Alex Mercer is a key f...</td>
<td>{\n "title": "Paranormal Military Squad and...</td>
<td>2025-03-04</td>
<td>9</td>
</tr>
</tbody>
</table>
</div>
</div>
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<h4 id="read-text-units">Read text units<a class="anchor-link" href="#read-text-units"></a></h4>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">text_unit_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">INPUT_DIR</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">TEXT_UNIT_TABLE</span><span class="si">}</span><span class="s2">.parquet"</span><span class="p">)</span>
<span class="n">text_units</span> <span class="o">=</span> <span class="n">read_indexer_text_units</span><span class="p">(</span><span class="n">text_unit_df</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Text unit records: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">text_unit_df</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">text_unit_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-8">text_unit_df = pd.read_parquet(f"{INPUT_DIR}/{TEXT_UNIT_TABLE}.parquet")
text_units = read_indexer_text_units(text_unit_df)
print(f"Text unit records: {len(text_unit_df)}")
text_unit_df.head()</div>
</div>
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<pre>Text unit records: 5
</pre>
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<style scoped="">
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vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>id</th>
<th>human_readable_id</th>
<th>text</th>
<th>n_tokens</th>
<th>document_ids</th>
<th>entity_ids</th>
<th>relationship_ids</th>
<th>covariate_ids</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>8e938693af886bfd081acbbe8384c3671446bff84a134a...</td>
<td>1</td>
<td># Operation: Dulce\n\n## Chapter 1\n\nThe thru...</td>
<td>1200</td>
<td>[6e81f882f89dd5596e1925dd3ae8a4f0a0edcb55b35a8...</td>
<td>[425a7862-0aef-4f69-a4c8-8bd42151c9d4, bcdbf1f...</td>
<td>[2bfad9f4-5abd-48d0-8db3-a9cad9120413, 6cbb838...</td>
<td>[745d28dd-be20-411b-85ff-1c69ca70e7b3, 9cba185...</td>
</tr>
<tr>
<th>1</th>
<td>fd1f46d32e1df6cd429542aeda3d64ddf3745ccb80f443...</td>
<td>2</td>
<td>, the hollow echo of the bay a stark reminder ...</td>
<td>1200</td>
<td>[6e81f882f89dd5596e1925dd3ae8a4f0a0edcb55b35a8...</td>
<td>[425a7862-0aef-4f69-a4c8-8bd42151c9d4, bcdbf1f...</td>
<td>[2bfad9f4-5abd-48d0-8db3-a9cad9120413, 6cbb838...</td>
<td>[4f9b461f-5e8f-465d-9586-e2fc81787062, 0f74618...</td>
</tr>
<tr>
<th>2</th>
<td>7296d9a1f046854d59079dc183de8a054c27c4843d2979...</td>
<td>3</td>
<td>differently than praise from others. This was...</td>
<td>1200</td>
<td>[6e81f882f89dd5596e1925dd3ae8a4f0a0edcb55b35a8...</td>
<td>[425a7862-0aef-4f69-a4c8-8bd42151c9d4, bcdbf1f...</td>
<td>[2bfad9f4-5abd-48d0-8db3-a9cad9120413, 6cbb838...</td>
<td>[3ef1be9c-4080-4fac-99bd-c4a636248904, 8730b20...</td>
</tr>
<tr>
<th>3</th>
<td>ac72722a02ac71242a2a91fca323198d04197daf60515d...</td>
<td>4</td>
<td>contrast to the rigid silence enveloping the ...</td>
<td>1200</td>
<td>[6e81f882f89dd5596e1925dd3ae8a4f0a0edcb55b35a8...</td>
<td>[425a7862-0aef-4f69-a4c8-8bd42151c9d4, bcdbf1f...</td>
<td>[2bfad9f4-5abd-48d0-8db3-a9cad9120413, 6cbb838...</td>
<td>[2c292047-b79a-4958-ab57-7bf7d7a22c92, 3cbd18a...</td>
</tr>
<tr>
<th>4</th>
<td>4c277337d461a16aaf8f9760ddb8b44ef220e948a2341d...</td>
<td>5</td>
<td>a mask of duty.\n\nIn the midst of the descen...</td>
<td>35</td>
<td>[6e81f882f89dd5596e1925dd3ae8a4f0a0edcb55b35a8...</td>
<td>[d084d615-3584-4ec8-9931-90aa6075c764, 4b84859...</td>
<td>[6efdc42e-69a2-47c0-97ec-4b296cd16d5e]</td>
<td>[db8da02f-f889-4bb5-8e81-ab2a72e380bb]</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">graphrag.config.enums</span><span class="w"> </span><span class="kn">import</span> <span class="n">ModelType</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">graphrag.config.models.language_model_config</span><span class="w"> </span><span class="kn">import</span> <span class="n">LanguageModelConfig</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">graphrag.language_model.manager</span><span class="w"> </span><span class="kn">import</span> <span class="n">ModelManager</span>
<span class="n">api_key</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"GRAPHRAG_API_KEY"</span><span class="p">]</span>
<span class="n">llm_model</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"GRAPHRAG_LLM_MODEL"</span><span class="p">]</span>
<span class="n">embedding_model</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"GRAPHRAG_EMBEDDING_MODEL"</span><span class="p">]</span>
<span class="n">chat_config</span> <span class="o">=</span> <span class="n">LanguageModelConfig</span><span class="p">(</span>
<span class="n">api_key</span><span class="o">=</span><span class="n">api_key</span><span class="p">,</span>
<span class="nb">type</span><span class="o">=</span><span class="n">ModelType</span><span class="o">.</span><span class="n">OpenAIChat</span><span class="p">,</span>
<span class="n">model</span><span class="o">=</span><span class="n">llm_model</span><span class="p">,</span>
<span class="n">max_retries</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">chat_model</span> <span class="o">=</span> <span class="n">ModelManager</span><span class="p">()</span><span class="o">.</span><span class="n">get_or_create_chat_model</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s2">"local_search"</span><span class="p">,</span>
<span class="n">model_type</span><span class="o">=</span><span class="n">ModelType</span><span class="o">.</span><span class="n">OpenAIChat</span><span class="p">,</span>
<span class="n">config</span><span class="o">=</span><span class="n">chat_config</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">token_encoder</span> <span class="o">=</span> <span class="n">tiktoken</span><span class="o">.</span><span class="n">encoding_for_model</span><span class="p">(</span><span class="n">llm_model</span><span class="p">)</span>
<span class="n">embedding_config</span> <span class="o">=</span> <span class="n">LanguageModelConfig</span><span class="p">(</span>
<span class="n">api_key</span><span class="o">=</span><span class="n">api_key</span><span class="p">,</span>
<span class="nb">type</span><span class="o">=</span><span class="n">ModelType</span><span class="o">.</span><span class="n">OpenAIEmbedding</span><span class="p">,</span>
<span class="n">model</span><span class="o">=</span><span class="n">embedding_model</span><span class="p">,</span>
<span class="n">max_retries</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">text_embedder</span> <span class="o">=</span> <span class="n">ModelManager</span><span class="p">()</span><span class="o">.</span><span class="n">get_or_create_embedding_model</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s2">"local_search_embedding"</span><span class="p">,</span>
<span class="n">model_type</span><span class="o">=</span><span class="n">ModelType</span><span class="o">.</span><span class="n">OpenAIEmbedding</span><span class="p">,</span>
<span class="n">config</span><span class="o">=</span><span class="n">embedding_config</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-9">from graphrag.config.enums import ModelType
from graphrag.config.models.language_model_config import LanguageModelConfig
from graphrag.language_model.manager import ModelManager
api_key = os.environ["GRAPHRAG_API_KEY"]
llm_model = os.environ["GRAPHRAG_LLM_MODEL"]
embedding_model = os.environ["GRAPHRAG_EMBEDDING_MODEL"]
chat_config = LanguageModelConfig(
api_key=api_key,
type=ModelType.OpenAIChat,
model=llm_model,
max_retries=20,
)
chat_model = ModelManager().get_or_create_chat_model(
name="local_search",
model_type=ModelType.OpenAIChat,
config=chat_config,
)
token_encoder = tiktoken.encoding_for_model(llm_model)
embedding_config = LanguageModelConfig(
api_key=api_key,
type=ModelType.OpenAIEmbedding,
model=embedding_model,
max_retries=20,
)
text_embedder = ModelManager().get_or_create_embedding_model(
name="local_search_embedding",
model_type=ModelType.OpenAIEmbedding,
config=embedding_config,
)</div>
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<h3 id="create-local-search-context-builder">Create local search context builder<a class="anchor-link" href="#create-local-search-context-builder"></a></h3>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">context_builder</span> <span class="o">=</span> <span class="n">LocalSearchMixedContext</span><span class="p">(</span>
<span class="n">community_reports</span><span class="o">=</span><span class="n">reports</span><span class="p">,</span>
<span class="n">text_units</span><span class="o">=</span><span class="n">text_units</span><span class="p">,</span>
<span class="n">entities</span><span class="o">=</span><span class="n">entities</span><span class="p">,</span>
<span class="n">relationships</span><span class="o">=</span><span class="n">relationships</span><span class="p">,</span>
<span class="c1"># if you did not run covariates during indexing, set this to None</span>
<span class="n">covariates</span><span class="o">=</span><span class="n">covariates</span><span class="p">,</span>
<span class="n">entity_text_embeddings</span><span class="o">=</span><span class="n">description_embedding_store</span><span class="p">,</span>
<span class="n">embedding_vectorstore_key</span><span class="o">=</span><span class="n">EntityVectorStoreKey</span><span class="o">.</span><span class="n">ID</span><span class="p">,</span> <span class="c1"># if the vectorstore uses entity title as ids, set this to EntityVectorStoreKey.TITLE</span>
<span class="n">text_embedder</span><span class="o">=</span><span class="n">text_embedder</span><span class="p">,</span>
<span class="n">token_encoder</span><span class="o">=</span><span class="n">token_encoder</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-10">context_builder = LocalSearchMixedContext(
community_reports=reports,
text_units=text_units,
entities=entities,
relationships=relationships,
# if you did not run covariates during indexing, set this to None
covariates=covariates,
entity_text_embeddings=description_embedding_store,
embedding_vectorstore_key=EntityVectorStoreKey.ID, # if the vectorstore uses entity title as ids, set this to EntityVectorStoreKey.TITLE
text_embedder=text_embedder,
token_encoder=token_encoder,
)</div>
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<h3 id="create-local-search-engine">Create local search engine<a class="anchor-link" href="#create-local-search-engine"></a></h3>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># text_unit_prop: proportion of context window dedicated to related text units</span>
<span class="c1"># community_prop: proportion of context window dedicated to community reports.</span>
<span class="c1"># The remaining proportion is dedicated to entities and relationships. Sum of text_unit_prop and community_prop should be &lt;= 1</span>
<span class="c1"># conversation_history_max_turns: maximum number of turns to include in the conversation history.</span>
<span class="c1"># conversation_history_user_turns_only: if True, only include user queries in the conversation history.</span>
<span class="c1"># top_k_mapped_entities: number of related entities to retrieve from the entity description embedding store.</span>
<span class="c1"># top_k_relationships: control the number of out-of-network relationships to pull into the context window.</span>
<span class="c1"># include_entity_rank: if True, include the entity rank in the entity table in the context window. Default entity rank = node degree.</span>
<span class="c1"># include_relationship_weight: if True, include the relationship weight in the context window.</span>
<span class="c1"># include_community_rank: if True, include the community rank in the context window.</span>
<span class="c1"># return_candidate_context: if True, return a set of dataframes containing all candidate entity/relationship/covariate records that</span>
<span class="c1"># could be relevant. Note that not all of these records will be included in the context window. The "in_context" column in these</span>
<span class="c1"># dataframes indicates whether the record is included in the context window.</span>
<span class="c1"># max_tokens: maximum number of tokens to use for the context window.</span>
<span class="n">local_context_params</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"text_unit_prop"</span><span class="p">:</span> <span class="mf">0.5</span><span class="p">,</span>
<span class="s2">"community_prop"</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">,</span>
<span class="s2">"conversation_history_max_turns"</span><span class="p">:</span> <span class="mi">5</span><span class="p">,</span>
<span class="s2">"conversation_history_user_turns_only"</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
<span class="s2">"top_k_mapped_entities"</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span>
<span class="s2">"top_k_relationships"</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span>
<span class="s2">"include_entity_rank"</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
<span class="s2">"include_relationship_weight"</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
<span class="s2">"include_community_rank"</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s2">"return_candidate_context"</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s2">"embedding_vectorstore_key"</span><span class="p">:</span> <span class="n">EntityVectorStoreKey</span><span class="o">.</span><span class="n">ID</span><span class="p">,</span> <span class="c1"># set this to EntityVectorStoreKey.TITLE if the vectorstore uses entity title as ids</span>
<span class="s2">"max_tokens"</span><span class="p">:</span> <span class="mi">12_000</span><span class="p">,</span> <span class="c1"># change this based on the token limit you have on your model (if you are using a model with 8k limit, a good setting could be 5000)</span>
<span class="p">}</span>
<span class="n">model_params</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"max_tokens"</span><span class="p">:</span> <span class="mi">2_000</span><span class="p">,</span> <span class="c1"># change this based on the token limit you have on your model (if you are using a model with 8k limit, a good setting could be 1000=1500)</span>
<span class="s2">"temperature"</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span>
<span class="p">}</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-11"># text_unit_prop: proportion of context window dedicated to related text units
# community_prop: proportion of context window dedicated to community reports.
# The remaining proportion is dedicated to entities and relationships. Sum of text_unit_prop and community_prop should be &lt;= 1
# conversation_history_max_turns: maximum number of turns to include in the conversation history.
# conversation_history_user_turns_only: if True, only include user queries in the conversation history.
# top_k_mapped_entities: number of related entities to retrieve from the entity description embedding store.
# top_k_relationships: control the number of out-of-network relationships to pull into the context window.
# include_entity_rank: if True, include the entity rank in the entity table in the context window. Default entity rank = node degree.
# include_relationship_weight: if True, include the relationship weight in the context window.
# include_community_rank: if True, include the community rank in the context window.
# return_candidate_context: if True, return a set of dataframes containing all candidate entity/relationship/covariate records that
# could be relevant. Note that not all of these records will be included in the context window. The "in_context" column in these
# dataframes indicates whether the record is included in the context window.
# max_tokens: maximum number of tokens to use for the context window.
local_context_params = {
"text_unit_prop": 0.5,
"community_prop": 0.1,
"conversation_history_max_turns": 5,
"conversation_history_user_turns_only": True,
"top_k_mapped_entities": 10,
"top_k_relationships": 10,
"include_entity_rank": True,
"include_relationship_weight": True,
"include_community_rank": False,
"return_candidate_context": False,
"embedding_vectorstore_key": EntityVectorStoreKey.ID, # set this to EntityVectorStoreKey.TITLE if the vectorstore uses entity title as ids
"max_tokens": 12_000, # change this based on the token limit you have on your model (if you are using a model with 8k limit, a good setting could be 5000)
}
model_params = {
"max_tokens": 2_000, # change this based on the token limit you have on your model (if you are using a model with 8k limit, a good setting could be 1000=1500)
"temperature": 0.0,
}</div>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">search_engine</span> <span class="o">=</span> <span class="n">LocalSearch</span><span class="p">(</span>
<span class="n">model</span><span class="o">=</span><span class="n">chat_model</span><span class="p">,</span>
<span class="n">context_builder</span><span class="o">=</span><span class="n">context_builder</span><span class="p">,</span>
<span class="n">token_encoder</span><span class="o">=</span><span class="n">token_encoder</span><span class="p">,</span>
<span class="n">model_params</span><span class="o">=</span><span class="n">model_params</span><span class="p">,</span>
<span class="n">context_builder_params</span><span class="o">=</span><span class="n">local_context_params</span><span class="p">,</span>
<span class="n">response_type</span><span class="o">=</span><span class="s2">"multiple paragraphs"</span><span class="p">,</span> <span class="c1"># free form text describing the response type and format, can be anything, e.g. prioritized list, single paragraph, multiple paragraphs, multiple-page report</span>
<span class="p">)</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-12">search_engine = LocalSearch(
model=chat_model,
context_builder=context_builder,
token_encoder=token_encoder,
model_params=model_params,
context_builder_params=local_context_params,
response_type="multiple paragraphs", # free form text describing the response type and format, can be anything, e.g. prioritized list, single paragraph, multiple paragraphs, multiple-page report
)</div>
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<h3 id="run-local-search-on-sample-queries">Run local search on sample queries<a class="anchor-link" href="#run-local-search-on-sample-queries"></a></h3>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="k">await</span> <span class="n">search_engine</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="s2">"Tell me about Agent Mercer"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">response</span><span class="p">)</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-13">result = await search_engine.search("Tell me about Agent Mercer")
print(result.response)</div>
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<pre>
<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">AuthenticationError</span> Traceback (most recent call last)
Cell <span class="ansi-green-fg">In[13], line 1</span>
<span class="ansi-green-fg">----&gt; 1</span> result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> search_engine<span style="color: rgb(98,98,98)">.</span>search(<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">Tell me about Agent Mercer</span><span style="color: rgb(175,0,0)">"</span>)
<span class="ansi-green-intense-fg ansi-bold"> 2</span> <span style="color: rgb(0,135,0)">print</span>(result<span style="color: rgb(98,98,98)">.</span>response)
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/query/structured_search/local_search/search.py:63</span>, in <span class="ansi-cyan-fg">LocalSearch.search</span><span class="ansi-blue-fg">(self, query, conversation_history, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 61</span> search_prompt <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">"</span>
<span class="ansi-green-intense-fg ansi-bold"> 62</span> llm_calls, prompt_tokens, output_tokens <span style="color: rgb(98,98,98)">=</span> {}, {}, {}
<span class="ansi-green-fg">---&gt; 63</span> context_result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">context_builder</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">build_context</span><span class="ansi-yellow-bg">(</span>
<span class="ansi-green-intense-fg ansi-bold"> 64</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">query</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">query</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 65</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">conversation_history</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">conversation_history</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 66</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 67</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">context_builder_params</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 68</span> <span class="ansi-yellow-bg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 69</span> llm_calls[<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">build_context</span><span style="color: rgb(175,0,0)">"</span>] <span style="color: rgb(98,98,98)">=</span> context_result<span style="color: rgb(98,98,98)">.</span>llm_calls
<span class="ansi-green-intense-fg ansi-bold"> 70</span> prompt_tokens[<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">build_context</span><span style="color: rgb(175,0,0)">"</span>] <span style="color: rgb(98,98,98)">=</span> context_result<span style="color: rgb(98,98,98)">.</span>prompt_tokens
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/query/structured_search/local_search/mixed_context.py:139</span>, in <span class="ansi-cyan-fg">LocalSearchMixedContext.build_context</span><span class="ansi-blue-fg">(self, query, conversation_history, include_entity_names, exclude_entity_names, conversation_history_max_turns, conversation_history_user_turns_only, max_context_tokens, text_unit_prop, community_prop, top_k_mapped_entities, top_k_relationships, include_community_rank, include_entity_rank, rank_description, include_relationship_weight, relationship_ranking_attribute, return_candidate_context, use_community_summary, min_community_rank, community_context_name, column_delimiter, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 134</span> pre_user_questions <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(175,0,0)">"</span><span class="ansi-bold" style="color: rgb(175,95,0)">\n</span><span style="color: rgb(175,0,0)">"</span><span style="color: rgb(98,98,98)">.</span>join(
<span class="ansi-green-intense-fg ansi-bold"> 135</span> conversation_history<span style="color: rgb(98,98,98)">.</span>get_user_turns(conversation_history_max_turns)
<span class="ansi-green-intense-fg ansi-bold"> 136</span> )
<span class="ansi-green-intense-fg ansi-bold"> 137</span> query <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(175,0,0)">f</span><span style="color: rgb(175,0,0)">"</span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>query<span class="ansi-bold" style="color: rgb(175,95,135)">}</span><span class="ansi-bold" style="color: rgb(175,95,0)">\n</span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>pre_user_questions<span class="ansi-bold" style="color: rgb(175,95,135)">}</span><span style="color: rgb(175,0,0)">"</span>
<span class="ansi-green-fg">--&gt; 139</span> selected_entities <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">map_query_to_entities</span><span class="ansi-yellow-bg">(</span>
<span class="ansi-green-intense-fg ansi-bold"> 140</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">query</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">query</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 141</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">text_embedding_vectorstore</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">entity_text_embeddings</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 142</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 143</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">all_entities_dict</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">entities</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 144</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">embedding_vectorstore_key</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">embedding_vectorstore_key</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 145</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">include_entity_names</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">include_entity_names</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 146</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">exclude_entity_names</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">exclude_entity_names</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 147</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">k</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">top_k_mapped_entities</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 148</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">oversample_scaler</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">2</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 149</span> <span class="ansi-yellow-bg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 151</span> <span style="color: rgb(95,135,135)"># build context</span>
<span class="ansi-green-intense-fg ansi-bold"> 152</span> final_context <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">list</span>[<span style="color: rgb(0,135,0)">str</span>]()
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/query/context_builder/entity_extraction.py:58</span>, in <span class="ansi-cyan-fg">map_query_to_entities</span><span class="ansi-blue-fg">(query, text_embedding_vectorstore, text_embedder, all_entities_dict, embedding_vectorstore_key, include_entity_names, exclude_entity_names, k, oversample_scaler)</span>
<span class="ansi-green-intense-fg ansi-bold"> 54</span> matched_entities <span style="color: rgb(98,98,98)">=</span> []
<span class="ansi-green-intense-fg ansi-bold"> 55</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> query <span style="color: rgb(98,98,98)">!=</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">"</span>:
<span class="ansi-green-intense-fg ansi-bold"> 56</span> <span style="color: rgb(95,135,135)"># get entities with highest semantic similarity to query</span>
<span class="ansi-green-intense-fg ansi-bold"> 57</span> <span style="color: rgb(95,135,135)"># oversample to account for excluded entities</span>
<span class="ansi-green-fg">---&gt; 58</span> search_results <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">text_embedding_vectorstore</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">similarity_search_by_text</span><span class="ansi-yellow-bg">(</span>
<span class="ansi-green-intense-fg ansi-bold"> 59</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">text</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">query</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 60</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">lambda</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">:</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">embed</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 61</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">k</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">k</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">oversample_scaler</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 62</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 63</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> result <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> search_results:
<span class="ansi-green-intense-fg ansi-bold"> 64</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> embedding_vectorstore_key <span style="color: rgb(98,98,98)">==</span> EntityVectorStoreKey<span style="color: rgb(98,98,98)">.</span>ID <span class="ansi-bold" style="color: rgb(175,0,255)">and</span> <span style="color: rgb(0,135,0)">isinstance</span>(
<span class="ansi-green-intense-fg ansi-bold"> 65</span> result<span style="color: rgb(98,98,98)">.</span>document<span style="color: rgb(98,98,98)">.</span>id, <span style="color: rgb(0,135,0)">str</span>
<span class="ansi-green-intense-fg ansi-bold"> 66</span> ):
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/vector_stores/lancedb.py:134</span>, in <span class="ansi-cyan-fg">LanceDBVectorStore.similarity_search_by_text</span><span class="ansi-blue-fg">(self, text, text_embedder, k, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 130</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span><span style="color: rgb(188,188,188)"> </span><span style="color: rgb(0,0,255)">similarity_search_by_text</span>(
<span class="ansi-green-intense-fg ansi-bold"> 131</span> <span style="color: rgb(0,135,0)">self</span>, text: <span style="color: rgb(0,135,0)">str</span>, text_embedder: TextEmbedder, k: <span style="color: rgb(0,135,0)">int</span> <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(98,98,98)">10</span>, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs: Any
<span class="ansi-green-intense-fg ansi-bold"> 132</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> <span style="color: rgb(0,135,0)">list</span>[VectorStoreSearchResult]:
<span class="ansi-green-intense-fg ansi-bold"> 133</span> <span style="color: rgb(188,188,188)"> </span><span style="color: rgb(175,0,0)">"""Perform a similarity search using a given input text."""</span>
<span class="ansi-green-fg">--&gt; 134</span> query_embedding <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">text</span><span class="ansi-yellow-bg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 135</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> query_embedding:
<span class="ansi-green-intense-fg ansi-bold"> 136</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>similarity_search_by_vector(query_embedding, k)
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/query/context_builder/entity_extraction.py:60</span>, in <span class="ansi-cyan-fg">map_query_to_entities.&lt;locals&gt;.&lt;lambda&gt;</span><span class="ansi-blue-fg">(t)</span>
<span class="ansi-green-intense-fg ansi-bold"> 54</span> matched_entities <span style="color: rgb(98,98,98)">=</span> []
<span class="ansi-green-intense-fg ansi-bold"> 55</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> query <span style="color: rgb(98,98,98)">!=</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">"</span>:
<span class="ansi-green-intense-fg ansi-bold"> 56</span> <span style="color: rgb(95,135,135)"># get entities with highest semantic similarity to query</span>
<span class="ansi-green-intense-fg ansi-bold"> 57</span> <span style="color: rgb(95,135,135)"># oversample to account for excluded entities</span>
<span class="ansi-green-intense-fg ansi-bold"> 58</span> search_results <span style="color: rgb(98,98,98)">=</span> text_embedding_vectorstore<span style="color: rgb(98,98,98)">.</span>similarity_search_by_text(
<span class="ansi-green-intense-fg ansi-bold"> 59</span> text<span style="color: rgb(98,98,98)">=</span>query,
<span class="ansi-green-fg">---&gt; 60</span> text_embedder<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">lambda</span> t: <span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">embed</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">)</span>,
<span class="ansi-green-intense-fg ansi-bold"> 61</span> k<span style="color: rgb(98,98,98)">=</span>k <span style="color: rgb(98,98,98)">*</span> oversample_scaler,
<span class="ansi-green-intense-fg ansi-bold"> 62</span> )
<span class="ansi-green-intense-fg ansi-bold"> 63</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> result <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> search_results:
<span class="ansi-green-intense-fg ansi-bold"> 64</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> embedding_vectorstore_key <span style="color: rgb(98,98,98)">==</span> EntityVectorStoreKey<span style="color: rgb(98,98,98)">.</span>ID <span class="ansi-bold" style="color: rgb(175,0,255)">and</span> <span style="color: rgb(0,135,0)">isinstance</span>(
<span class="ansi-green-intense-fg ansi-bold"> 65</span> result<span style="color: rgb(98,98,98)">.</span>document<span style="color: rgb(98,98,98)">.</span>id, <span style="color: rgb(0,135,0)">str</span>
<span class="ansi-green-intense-fg ansi-bold"> 66</span> ):
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/language_model/providers/fnllm/models.py:237</span>, in <span class="ansi-cyan-fg">OpenAIEmbeddingFNLLM.embed</span><span class="ansi-blue-fg">(self, text, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 225</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span><span style="color: rgb(188,188,188)"> </span><span style="color: rgb(0,0,255)">embed</span>(<span style="color: rgb(0,135,0)">self</span>, text: <span style="color: rgb(0,135,0)">str</span>, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> <span style="color: rgb(0,135,0)">list</span>[<span style="color: rgb(0,135,0)">float</span>]:
<span class="ansi-green-intense-fg ansi-bold"> 226</span> <span style="color: rgb(188,188,188)"> </span><span style="color: rgb(175,0,0)">"""</span>
<span class="ansi-green-intense-fg ansi-bold"> 227</span> <span style="color: rgb(175,0,0)"> Embed the given text using the Model.</span>
<span class="ansi-green-intense-fg ansi-bold"> 228</span>
<span class="ansi-green-fg"> (...)</span>
<span class="ansi-green-intense-fg ansi-bold"> 235</span> <span style="color: rgb(175,0,0)"> The embeddings of the text.</span>
<span class="ansi-green-intense-fg ansi-bold"> 236</span> <span style="color: rgb(175,0,0)"> """</span>
<span class="ansi-green-fg">--&gt; 237</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">run_coroutine_sync</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">aembed</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">text</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg">)</span>
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/language_model/providers/fnllm/utils.py:127</span>, in <span class="ansi-cyan-fg">run_coroutine_sync</span><span class="ansi-blue-fg">(coroutine)</span>
<span class="ansi-green-intense-fg ansi-bold"> 125</span> _thr<span style="color: rgb(98,98,98)">.</span>start()
<span class="ansi-green-intense-fg ansi-bold"> 126</span> future <span style="color: rgb(98,98,98)">=</span> asyncio<span style="color: rgb(98,98,98)">.</span>run_coroutine_threadsafe(coroutine, _loop)
<span class="ansi-green-fg">--&gt; 127</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">future</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">result</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">)</span>
File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.11.12/x64/lib/python3.11/concurrent/futures/_base.py:456</span>, in <span class="ansi-cyan-fg">Future.result</span><span class="ansi-blue-fg">(self, timeout)</span>
<span class="ansi-green-intense-fg ansi-bold"> 454</span> <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> CancelledError()
<span class="ansi-green-intense-fg ansi-bold"> 455</span> <span class="ansi-bold" style="color: rgb(0,135,0)">elif</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_state <span style="color: rgb(98,98,98)">==</span> FINISHED:
<span class="ansi-green-fg">--&gt; 456</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">__get_result</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 457</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
<span class="ansi-green-intense-fg ansi-bold"> 458</span> <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> <span class="ansi-bold" style="color: rgb(215,95,95)">TimeoutError</span>()
File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.11.12/x64/lib/python3.11/concurrent/futures/_base.py:401</span>, in <span class="ansi-cyan-fg">Future.__get_result</span><span class="ansi-blue-fg">(self)</span>
<span class="ansi-green-intense-fg ansi-bold"> 399</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_exception:
<span class="ansi-green-intense-fg ansi-bold"> 400</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
<span class="ansi-green-fg">--&gt; 401</span> <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_exception
<span class="ansi-green-intense-fg ansi-bold"> 402</span> <span class="ansi-bold" style="color: rgb(0,135,0)">finally</span>:
<span class="ansi-green-intense-fg ansi-bold"> 403</span> <span style="color: rgb(95,135,135)"># Break a reference cycle with the exception in self._exception</span>
<span class="ansi-green-intense-fg ansi-bold"> 404</span> <span style="color: rgb(0,135,0)">self</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/language_model/providers/fnllm/models.py:204</span>, in <span class="ansi-cyan-fg">OpenAIEmbeddingFNLLM.aembed</span><span class="ansi-blue-fg">(self, text, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 192</span> <span class="ansi-bold" style="color: rgb(0,135,0)">async</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span><span style="color: rgb(188,188,188)"> </span><span style="color: rgb(0,0,255)">aembed</span>(<span style="color: rgb(0,135,0)">self</span>, text: <span style="color: rgb(0,135,0)">str</span>, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> <span style="color: rgb(0,135,0)">list</span>[<span style="color: rgb(0,135,0)">float</span>]:
<span class="ansi-green-intense-fg ansi-bold"> 193</span> <span style="color: rgb(188,188,188)"> </span><span style="color: rgb(175,0,0)">"""</span>
<span class="ansi-green-intense-fg ansi-bold"> 194</span> <span style="color: rgb(175,0,0)"> Embed the given text using the Model.</span>
<span class="ansi-green-intense-fg ansi-bold"> 195</span>
<span class="ansi-green-fg"> (...)</span>
<span class="ansi-green-intense-fg ansi-bold"> 202</span> <span style="color: rgb(175,0,0)"> The embeddings of the text.</span>
<span class="ansi-green-intense-fg ansi-bold"> 203</span> <span style="color: rgb(175,0,0)"> """</span>
<span class="ansi-green-fg">--&gt; 204</span> response <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>model([text], <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)
<span class="ansi-green-intense-fg ansi-bold"> 205</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> response<span style="color: rgb(98,98,98)">.</span>output<span style="color: rgb(98,98,98)">.</span>embeddings <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>:
<span class="ansi-green-intense-fg ansi-bold"> 206</span> msg <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">No embeddings found in response</span><span style="color: rgb(175,0,0)">"</span>
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/fnllm/base/base_llm.py:144</span>, in <span class="ansi-cyan-fg">BaseLLM.__call__</span><span class="ansi-blue-fg">(self, prompt, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 142</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
<span class="ansi-green-intense-fg ansi-bold"> 143</span> prompt, kwargs <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_rewrite_input(prompt, kwargs)
<span class="ansi-green-fg">--&gt; 144</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_decorated_target(prompt, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)
<span class="ansi-green-intense-fg ansi-bold"> 145</span> <span class="ansi-bold" style="color: rgb(0,135,0)">except</span> <span class="ansi-bold" style="color: rgb(215,95,95)">BaseException</span> <span class="ansi-bold" style="color: rgb(0,135,0)">as</span> e:
<span class="ansi-green-intense-fg ansi-bold"> 146</span> stack_trace <span style="color: rgb(98,98,98)">=</span> traceback<span style="color: rgb(98,98,98)">.</span>format_exc()
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/fnllm/base/services/rate_limiter.py:75</span>, in <span class="ansi-cyan-fg">RateLimiter.decorate.&lt;locals&gt;.invoke</span><span class="ansi-blue-fg">(prompt, **args)</span>
<span class="ansi-green-intense-fg ansi-bold"> 73</span> <span class="ansi-bold" style="color: rgb(0,135,0)">async</span> <span class="ansi-bold" style="color: rgb(0,135,0)">with</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_limiter<span style="color: rgb(98,98,98)">.</span>use(manifest):
<span class="ansi-green-intense-fg ansi-bold"> 74</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_events<span style="color: rgb(98,98,98)">.</span>on_limit_acquired(manifest)
<span class="ansi-green-fg">---&gt; 75</span> result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> delegate(prompt, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>args)
<span class="ansi-green-intense-fg ansi-bold"> 76</span> <span class="ansi-bold" style="color: rgb(0,135,0)">finally</span>:
<span class="ansi-green-intense-fg ansi-bold"> 77</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_events<span style="color: rgb(98,98,98)">.</span>on_limit_released(manifest)
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/fnllm/base/base_llm.py:126</span>, in <span class="ansi-cyan-fg">BaseLLM._decorator_target</span><span class="ansi-blue-fg">(self, prompt, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 121</span> <span style="color: rgb(175,0,0)">"""Target for the decorator chain.</span>
<span class="ansi-green-intense-fg ansi-bold"> 122</span>
<span class="ansi-green-intense-fg ansi-bold"> 123</span> <span style="color: rgb(175,0,0)">Leave signature alone as prompt, kwargs.</span>
<span class="ansi-green-intense-fg ansi-bold"> 124</span> <span style="color: rgb(175,0,0)">"""</span>
<span class="ansi-green-intense-fg ansi-bold"> 125</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_events<span style="color: rgb(98,98,98)">.</span>on_execute_llm()
<span class="ansi-green-fg">--&gt; 126</span> output <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_execute_llm(prompt, kwargs)
<span class="ansi-green-intense-fg ansi-bold"> 127</span> result <span style="color: rgb(98,98,98)">=</span> LLMOutput(output<span style="color: rgb(98,98,98)">=</span>output)
<span class="ansi-green-intense-fg ansi-bold"> 128</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_inject_usage(result)
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/fnllm/openai/llm/openai_embeddings_llm.py:126</span>, in <span class="ansi-cyan-fg">OpenAIEmbeddingsLLMImpl._execute_llm</span><span class="ansi-blue-fg">(self, prompt, kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 121</span> local_model_parameters <span style="color: rgb(98,98,98)">=</span> kwargs<span style="color: rgb(98,98,98)">.</span>get(<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">model_parameters</span><span style="color: rgb(175,0,0)">"</span>)
<span class="ansi-green-intense-fg ansi-bold"> 122</span> embeddings_parameters <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_build_embeddings_parameters(
<span class="ansi-green-intense-fg ansi-bold"> 123</span> local_model_parameters
<span class="ansi-green-intense-fg ansi-bold"> 124</span> )
<span class="ansi-green-fg">--&gt; 126</span> result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_client<span style="color: rgb(98,98,98)">.</span>embeddings<span style="color: rgb(98,98,98)">.</span>create(
<span class="ansi-green-intense-fg ansi-bold"> 127</span> <span style="color: rgb(0,135,0)">input</span><span style="color: rgb(98,98,98)">=</span>prompt,
<span class="ansi-green-intense-fg ansi-bold"> 128</span> <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>embeddings_parameters,
<span class="ansi-green-intense-fg ansi-bold"> 129</span> )
<span class="ansi-green-intense-fg ansi-bold"> 130</span> usage: LLMUsageMetrics <span style="color: rgb(98,98,98)">|</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
<span class="ansi-green-intense-fg ansi-bold"> 131</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> result<span style="color: rgb(98,98,98)">.</span>usage:
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/openai/resources/embeddings.py:243</span>, in <span class="ansi-cyan-fg">AsyncEmbeddings.create</span><span class="ansi-blue-fg">(self, input, model, dimensions, encoding_format, user, extra_headers, extra_query, extra_body, timeout)</span>
<span class="ansi-green-intense-fg ansi-bold"> 237</span> embedding<span style="color: rgb(98,98,98)">.</span>embedding <span style="color: rgb(98,98,98)">=</span> np<span style="color: rgb(98,98,98)">.</span>frombuffer( <span style="color: rgb(95,135,135)"># type: ignore[no-untyped-call]</span>
<span class="ansi-green-intense-fg ansi-bold"> 238</span> base64<span style="color: rgb(98,98,98)">.</span>b64decode(data), dtype<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">float32</span><span style="color: rgb(175,0,0)">"</span>
<span class="ansi-green-intense-fg ansi-bold"> 239</span> )<span style="color: rgb(98,98,98)">.</span>tolist()
<span class="ansi-green-intense-fg ansi-bold"> 241</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> obj
<span class="ansi-green-fg">--&gt; 243</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_post(
<span class="ansi-green-intense-fg ansi-bold"> 244</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">/embeddings</span><span style="color: rgb(175,0,0)">"</span>,
<span class="ansi-green-intense-fg ansi-bold"> 245</span> body<span style="color: rgb(98,98,98)">=</span>maybe_transform(params, embedding_create_params<span style="color: rgb(98,98,98)">.</span>EmbeddingCreateParams),
<span class="ansi-green-intense-fg ansi-bold"> 246</span> options<span style="color: rgb(98,98,98)">=</span>make_request_options(
<span class="ansi-green-intense-fg ansi-bold"> 247</span> extra_headers<span style="color: rgb(98,98,98)">=</span>extra_headers,
<span class="ansi-green-intense-fg ansi-bold"> 248</span> extra_query<span style="color: rgb(98,98,98)">=</span>extra_query,
<span class="ansi-green-intense-fg ansi-bold"> 249</span> extra_body<span style="color: rgb(98,98,98)">=</span>extra_body,
<span class="ansi-green-intense-fg ansi-bold"> 250</span> timeout<span style="color: rgb(98,98,98)">=</span>timeout,
<span class="ansi-green-intense-fg ansi-bold"> 251</span> post_parser<span style="color: rgb(98,98,98)">=</span>parser,
<span class="ansi-green-intense-fg ansi-bold"> 252</span> ),
<span class="ansi-green-intense-fg ansi-bold"> 253</span> cast_to<span style="color: rgb(98,98,98)">=</span>CreateEmbeddingResponse,
<span class="ansi-green-intense-fg ansi-bold"> 254</span> )
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/openai/_base_client.py:1767</span>, in <span class="ansi-cyan-fg">AsyncAPIClient.post</span><span class="ansi-blue-fg">(self, path, cast_to, body, files, options, stream, stream_cls)</span>
<span class="ansi-green-intense-fg ansi-bold"> 1753</span> <span class="ansi-bold" style="color: rgb(0,135,0)">async</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span><span style="color: rgb(188,188,188)"> </span><span style="color: rgb(0,0,255)">post</span>(
<span class="ansi-green-intense-fg ansi-bold"> 1754</span> <span style="color: rgb(0,135,0)">self</span>,
<span class="ansi-green-intense-fg ansi-bold"> 1755</span> path: <span style="color: rgb(0,135,0)">str</span>,
<span class="ansi-green-fg"> (...)</span>
<span class="ansi-green-intense-fg ansi-bold"> 1762</span> stream_cls: <span style="color: rgb(0,135,0)">type</span>[_AsyncStreamT] <span style="color: rgb(98,98,98)">|</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>,
<span class="ansi-green-intense-fg ansi-bold"> 1763</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> ResponseT <span style="color: rgb(98,98,98)">|</span> _AsyncStreamT:
<span class="ansi-green-intense-fg ansi-bold"> 1764</span> opts <span style="color: rgb(98,98,98)">=</span> FinalRequestOptions<span style="color: rgb(98,98,98)">.</span>construct(
<span class="ansi-green-intense-fg ansi-bold"> 1765</span> method<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">post</span><span style="color: rgb(175,0,0)">"</span>, url<span style="color: rgb(98,98,98)">=</span>path, json_data<span style="color: rgb(98,98,98)">=</span>body, files<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">await</span> async_to_httpx_files(files), <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>options
<span class="ansi-green-intense-fg ansi-bold"> 1766</span> )
<span class="ansi-green-fg">-&gt; 1767</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>request(cast_to, opts, stream<span style="color: rgb(98,98,98)">=</span>stream, stream_cls<span style="color: rgb(98,98,98)">=</span>stream_cls)
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/openai/_base_client.py:1461</span>, in <span class="ansi-cyan-fg">AsyncAPIClient.request</span><span class="ansi-blue-fg">(self, cast_to, options, stream, stream_cls, remaining_retries)</span>
<span class="ansi-green-intense-fg ansi-bold"> 1458</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
<span class="ansi-green-intense-fg ansi-bold"> 1459</span> retries_taken <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(98,98,98)">0</span>
<span class="ansi-green-fg">-&gt; 1461</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_request(
<span class="ansi-green-intense-fg ansi-bold"> 1462</span> cast_to<span style="color: rgb(98,98,98)">=</span>cast_to,
<span class="ansi-green-intense-fg ansi-bold"> 1463</span> options<span style="color: rgb(98,98,98)">=</span>options,
<span class="ansi-green-intense-fg ansi-bold"> 1464</span> stream<span style="color: rgb(98,98,98)">=</span>stream,
<span class="ansi-green-intense-fg ansi-bold"> 1465</span> stream_cls<span style="color: rgb(98,98,98)">=</span>stream_cls,
<span class="ansi-green-intense-fg ansi-bold"> 1466</span> retries_taken<span style="color: rgb(98,98,98)">=</span>retries_taken,
<span class="ansi-green-intense-fg ansi-bold"> 1467</span> )
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/openai/_base_client.py:1562</span>, in <span class="ansi-cyan-fg">AsyncAPIClient._request</span><span class="ansi-blue-fg">(self, cast_to, options, stream, stream_cls, retries_taken)</span>
<span class="ansi-green-intense-fg ansi-bold"> 1559</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> err<span style="color: rgb(98,98,98)">.</span>response<span style="color: rgb(98,98,98)">.</span>aread()
<span class="ansi-green-intense-fg ansi-bold"> 1561</span> log<span style="color: rgb(98,98,98)">.</span>debug(<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">Re-raising status error</span><span style="color: rgb(175,0,0)">"</span>)
<span class="ansi-green-fg">-&gt; 1562</span> <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_make_status_error_from_response(err<span style="color: rgb(98,98,98)">.</span>response) <span class="ansi-bold" style="color: rgb(0,135,0)">from</span><span style="color: rgb(188,188,188)"> </span><span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
<span class="ansi-green-intense-fg ansi-bold"> 1564</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_process_response(
<span class="ansi-green-intense-fg ansi-bold"> 1565</span> cast_to<span style="color: rgb(98,98,98)">=</span>cast_to,
<span class="ansi-green-intense-fg ansi-bold"> 1566</span> options<span style="color: rgb(98,98,98)">=</span>options,
<span class="ansi-green-fg"> (...)</span>
<span class="ansi-green-intense-fg ansi-bold"> 1570</span> retries_taken<span style="color: rgb(98,98,98)">=</span>retries_taken,
<span class="ansi-green-intense-fg ansi-bold"> 1571</span> )
<span class="ansi-red-fg">AuthenticationError</span>: Error code: 401 - {'error': {'message': 'Incorrect API key provided: sk-proj-********************************************************************************************************************************************************zWYA. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}}</pre>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">question</span> <span class="o">=</span> <span class="s2">"Tell me about Dr. Jordan Hayes"</span>
<span class="n">result</span> <span class="o">=</span> <span class="k">await</span> <span class="n">search_engine</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">question</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">response</span><span class="p">)</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-14">question = "Tell me about Dr. Jordan Hayes"
result = await search_engine.search(question)
print(result.response)</div>
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<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">AuthenticationError</span> Traceback (most recent call last)
Cell <span class="ansi-green-fg">In[14], line 2</span>
<span class="ansi-green-intense-fg ansi-bold"> 1</span> question <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">Tell me about Dr. Jordan Hayes</span><span style="color: rgb(175,0,0)">"</span>
<span class="ansi-green-fg">----&gt; 2</span> result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> search_engine<span style="color: rgb(98,98,98)">.</span>search(question)
<span class="ansi-green-intense-fg ansi-bold"> 3</span> <span style="color: rgb(0,135,0)">print</span>(result<span style="color: rgb(98,98,98)">.</span>response)
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/query/structured_search/local_search/search.py:63</span>, in <span class="ansi-cyan-fg">LocalSearch.search</span><span class="ansi-blue-fg">(self, query, conversation_history, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 61</span> search_prompt <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">"</span>
<span class="ansi-green-intense-fg ansi-bold"> 62</span> llm_calls, prompt_tokens, output_tokens <span style="color: rgb(98,98,98)">=</span> {}, {}, {}
<span class="ansi-green-fg">---&gt; 63</span> context_result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">context_builder</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">build_context</span><span class="ansi-yellow-bg">(</span>
<span class="ansi-green-intense-fg ansi-bold"> 64</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">query</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">query</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 65</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">conversation_history</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">conversation_history</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 66</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 67</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">context_builder_params</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 68</span> <span class="ansi-yellow-bg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 69</span> llm_calls[<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">build_context</span><span style="color: rgb(175,0,0)">"</span>] <span style="color: rgb(98,98,98)">=</span> context_result<span style="color: rgb(98,98,98)">.</span>llm_calls
<span class="ansi-green-intense-fg ansi-bold"> 70</span> prompt_tokens[<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">build_context</span><span style="color: rgb(175,0,0)">"</span>] <span style="color: rgb(98,98,98)">=</span> context_result<span style="color: rgb(98,98,98)">.</span>prompt_tokens
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/query/structured_search/local_search/mixed_context.py:139</span>, in <span class="ansi-cyan-fg">LocalSearchMixedContext.build_context</span><span class="ansi-blue-fg">(self, query, conversation_history, include_entity_names, exclude_entity_names, conversation_history_max_turns, conversation_history_user_turns_only, max_context_tokens, text_unit_prop, community_prop, top_k_mapped_entities, top_k_relationships, include_community_rank, include_entity_rank, rank_description, include_relationship_weight, relationship_ranking_attribute, return_candidate_context, use_community_summary, min_community_rank, community_context_name, column_delimiter, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 134</span> pre_user_questions <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(175,0,0)">"</span><span class="ansi-bold" style="color: rgb(175,95,0)">\n</span><span style="color: rgb(175,0,0)">"</span><span style="color: rgb(98,98,98)">.</span>join(
<span class="ansi-green-intense-fg ansi-bold"> 135</span> conversation_history<span style="color: rgb(98,98,98)">.</span>get_user_turns(conversation_history_max_turns)
<span class="ansi-green-intense-fg ansi-bold"> 136</span> )
<span class="ansi-green-intense-fg ansi-bold"> 137</span> query <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(175,0,0)">f</span><span style="color: rgb(175,0,0)">"</span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>query<span class="ansi-bold" style="color: rgb(175,95,135)">}</span><span class="ansi-bold" style="color: rgb(175,95,0)">\n</span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>pre_user_questions<span class="ansi-bold" style="color: rgb(175,95,135)">}</span><span style="color: rgb(175,0,0)">"</span>
<span class="ansi-green-fg">--&gt; 139</span> selected_entities <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">map_query_to_entities</span><span class="ansi-yellow-bg">(</span>
<span class="ansi-green-intense-fg ansi-bold"> 140</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">query</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">query</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 141</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">text_embedding_vectorstore</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">entity_text_embeddings</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 142</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 143</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">all_entities_dict</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">entities</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 144</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">embedding_vectorstore_key</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">embedding_vectorstore_key</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 145</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">include_entity_names</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">include_entity_names</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 146</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">exclude_entity_names</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">exclude_entity_names</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 147</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">k</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">top_k_mapped_entities</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 148</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">oversample_scaler</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">2</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 149</span> <span class="ansi-yellow-bg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 151</span> <span style="color: rgb(95,135,135)"># build context</span>
<span class="ansi-green-intense-fg ansi-bold"> 152</span> final_context <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">list</span>[<span style="color: rgb(0,135,0)">str</span>]()
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/query/context_builder/entity_extraction.py:58</span>, in <span class="ansi-cyan-fg">map_query_to_entities</span><span class="ansi-blue-fg">(query, text_embedding_vectorstore, text_embedder, all_entities_dict, embedding_vectorstore_key, include_entity_names, exclude_entity_names, k, oversample_scaler)</span>
<span class="ansi-green-intense-fg ansi-bold"> 54</span> matched_entities <span style="color: rgb(98,98,98)">=</span> []
<span class="ansi-green-intense-fg ansi-bold"> 55</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> query <span style="color: rgb(98,98,98)">!=</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">"</span>:
<span class="ansi-green-intense-fg ansi-bold"> 56</span> <span style="color: rgb(95,135,135)"># get entities with highest semantic similarity to query</span>
<span class="ansi-green-intense-fg ansi-bold"> 57</span> <span style="color: rgb(95,135,135)"># oversample to account for excluded entities</span>
<span class="ansi-green-fg">---&gt; 58</span> search_results <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">text_embedding_vectorstore</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">similarity_search_by_text</span><span class="ansi-yellow-bg">(</span>
<span class="ansi-green-intense-fg ansi-bold"> 59</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">text</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">query</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 60</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">lambda</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">:</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">embed</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 61</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">k</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">k</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">oversample_scaler</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 62</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 63</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> result <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> search_results:
<span class="ansi-green-intense-fg ansi-bold"> 64</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> embedding_vectorstore_key <span style="color: rgb(98,98,98)">==</span> EntityVectorStoreKey<span style="color: rgb(98,98,98)">.</span>ID <span class="ansi-bold" style="color: rgb(175,0,255)">and</span> <span style="color: rgb(0,135,0)">isinstance</span>(
<span class="ansi-green-intense-fg ansi-bold"> 65</span> result<span style="color: rgb(98,98,98)">.</span>document<span style="color: rgb(98,98,98)">.</span>id, <span style="color: rgb(0,135,0)">str</span>
<span class="ansi-green-intense-fg ansi-bold"> 66</span> ):
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/vector_stores/lancedb.py:134</span>, in <span class="ansi-cyan-fg">LanceDBVectorStore.similarity_search_by_text</span><span class="ansi-blue-fg">(self, text, text_embedder, k, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 130</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span><span style="color: rgb(188,188,188)"> </span><span style="color: rgb(0,0,255)">similarity_search_by_text</span>(
<span class="ansi-green-intense-fg ansi-bold"> 131</span> <span style="color: rgb(0,135,0)">self</span>, text: <span style="color: rgb(0,135,0)">str</span>, text_embedder: TextEmbedder, k: <span style="color: rgb(0,135,0)">int</span> <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(98,98,98)">10</span>, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs: Any
<span class="ansi-green-intense-fg ansi-bold"> 132</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> <span style="color: rgb(0,135,0)">list</span>[VectorStoreSearchResult]:
<span class="ansi-green-intense-fg ansi-bold"> 133</span> <span style="color: rgb(188,188,188)"> </span><span style="color: rgb(175,0,0)">"""Perform a similarity search using a given input text."""</span>
<span class="ansi-green-fg">--&gt; 134</span> query_embedding <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">text</span><span class="ansi-yellow-bg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 135</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> query_embedding:
<span class="ansi-green-intense-fg ansi-bold"> 136</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>similarity_search_by_vector(query_embedding, k)
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/query/context_builder/entity_extraction.py:60</span>, in <span class="ansi-cyan-fg">map_query_to_entities.&lt;locals&gt;.&lt;lambda&gt;</span><span class="ansi-blue-fg">(t)</span>
<span class="ansi-green-intense-fg ansi-bold"> 54</span> matched_entities <span style="color: rgb(98,98,98)">=</span> []
<span class="ansi-green-intense-fg ansi-bold"> 55</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> query <span style="color: rgb(98,98,98)">!=</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">"</span>:
<span class="ansi-green-intense-fg ansi-bold"> 56</span> <span style="color: rgb(95,135,135)"># get entities with highest semantic similarity to query</span>
<span class="ansi-green-intense-fg ansi-bold"> 57</span> <span style="color: rgb(95,135,135)"># oversample to account for excluded entities</span>
<span class="ansi-green-intense-fg ansi-bold"> 58</span> search_results <span style="color: rgb(98,98,98)">=</span> text_embedding_vectorstore<span style="color: rgb(98,98,98)">.</span>similarity_search_by_text(
<span class="ansi-green-intense-fg ansi-bold"> 59</span> text<span style="color: rgb(98,98,98)">=</span>query,
<span class="ansi-green-fg">---&gt; 60</span> text_embedder<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">lambda</span> t: <span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">embed</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">)</span>,
<span class="ansi-green-intense-fg ansi-bold"> 61</span> k<span style="color: rgb(98,98,98)">=</span>k <span style="color: rgb(98,98,98)">*</span> oversample_scaler,
<span class="ansi-green-intense-fg ansi-bold"> 62</span> )
<span class="ansi-green-intense-fg ansi-bold"> 63</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> result <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> search_results:
<span class="ansi-green-intense-fg ansi-bold"> 64</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> embedding_vectorstore_key <span style="color: rgb(98,98,98)">==</span> EntityVectorStoreKey<span style="color: rgb(98,98,98)">.</span>ID <span class="ansi-bold" style="color: rgb(175,0,255)">and</span> <span style="color: rgb(0,135,0)">isinstance</span>(
<span class="ansi-green-intense-fg ansi-bold"> 65</span> result<span style="color: rgb(98,98,98)">.</span>document<span style="color: rgb(98,98,98)">.</span>id, <span style="color: rgb(0,135,0)">str</span>
<span class="ansi-green-intense-fg ansi-bold"> 66</span> ):
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/language_model/providers/fnllm/models.py:237</span>, in <span class="ansi-cyan-fg">OpenAIEmbeddingFNLLM.embed</span><span class="ansi-blue-fg">(self, text, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 225</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span><span style="color: rgb(188,188,188)"> </span><span style="color: rgb(0,0,255)">embed</span>(<span style="color: rgb(0,135,0)">self</span>, text: <span style="color: rgb(0,135,0)">str</span>, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> <span style="color: rgb(0,135,0)">list</span>[<span style="color: rgb(0,135,0)">float</span>]:
<span class="ansi-green-intense-fg ansi-bold"> 226</span> <span style="color: rgb(188,188,188)"> </span><span style="color: rgb(175,0,0)">"""</span>
<span class="ansi-green-intense-fg ansi-bold"> 227</span> <span style="color: rgb(175,0,0)"> Embed the given text using the Model.</span>
<span class="ansi-green-intense-fg ansi-bold"> 228</span>
<span class="ansi-green-fg"> (...)</span>
<span class="ansi-green-intense-fg ansi-bold"> 235</span> <span style="color: rgb(175,0,0)"> The embeddings of the text.</span>
<span class="ansi-green-intense-fg ansi-bold"> 236</span> <span style="color: rgb(175,0,0)"> """</span>
<span class="ansi-green-fg">--&gt; 237</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">run_coroutine_sync</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">aembed</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">text</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg">)</span>
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/language_model/providers/fnllm/utils.py:127</span>, in <span class="ansi-cyan-fg">run_coroutine_sync</span><span class="ansi-blue-fg">(coroutine)</span>
<span class="ansi-green-intense-fg ansi-bold"> 125</span> _thr<span style="color: rgb(98,98,98)">.</span>start()
<span class="ansi-green-intense-fg ansi-bold"> 126</span> future <span style="color: rgb(98,98,98)">=</span> asyncio<span style="color: rgb(98,98,98)">.</span>run_coroutine_threadsafe(coroutine, _loop)
<span class="ansi-green-fg">--&gt; 127</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">future</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">result</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">)</span>
File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.11.12/x64/lib/python3.11/concurrent/futures/_base.py:456</span>, in <span class="ansi-cyan-fg">Future.result</span><span class="ansi-blue-fg">(self, timeout)</span>
<span class="ansi-green-intense-fg ansi-bold"> 454</span> <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> CancelledError()
<span class="ansi-green-intense-fg ansi-bold"> 455</span> <span class="ansi-bold" style="color: rgb(0,135,0)">elif</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_state <span style="color: rgb(98,98,98)">==</span> FINISHED:
<span class="ansi-green-fg">--&gt; 456</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">__get_result</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 457</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
<span class="ansi-green-intense-fg ansi-bold"> 458</span> <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> <span class="ansi-bold" style="color: rgb(215,95,95)">TimeoutError</span>()
File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.11.12/x64/lib/python3.11/concurrent/futures/_base.py:401</span>, in <span class="ansi-cyan-fg">Future.__get_result</span><span class="ansi-blue-fg">(self)</span>
<span class="ansi-green-intense-fg ansi-bold"> 399</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_exception:
<span class="ansi-green-intense-fg ansi-bold"> 400</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
<span class="ansi-green-fg">--&gt; 401</span> <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_exception
<span class="ansi-green-intense-fg ansi-bold"> 402</span> <span class="ansi-bold" style="color: rgb(0,135,0)">finally</span>:
<span class="ansi-green-intense-fg ansi-bold"> 403</span> <span style="color: rgb(95,135,135)"># Break a reference cycle with the exception in self._exception</span>
<span class="ansi-green-intense-fg ansi-bold"> 404</span> <span style="color: rgb(0,135,0)">self</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/language_model/providers/fnllm/models.py:204</span>, in <span class="ansi-cyan-fg">OpenAIEmbeddingFNLLM.aembed</span><span class="ansi-blue-fg">(self, text, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 192</span> <span class="ansi-bold" style="color: rgb(0,135,0)">async</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span><span style="color: rgb(188,188,188)"> </span><span style="color: rgb(0,0,255)">aembed</span>(<span style="color: rgb(0,135,0)">self</span>, text: <span style="color: rgb(0,135,0)">str</span>, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> <span style="color: rgb(0,135,0)">list</span>[<span style="color: rgb(0,135,0)">float</span>]:
<span class="ansi-green-intense-fg ansi-bold"> 193</span> <span style="color: rgb(188,188,188)"> </span><span style="color: rgb(175,0,0)">"""</span>
<span class="ansi-green-intense-fg ansi-bold"> 194</span> <span style="color: rgb(175,0,0)"> Embed the given text using the Model.</span>
<span class="ansi-green-intense-fg ansi-bold"> 195</span>
<span class="ansi-green-fg"> (...)</span>
<span class="ansi-green-intense-fg ansi-bold"> 202</span> <span style="color: rgb(175,0,0)"> The embeddings of the text.</span>
<span class="ansi-green-intense-fg ansi-bold"> 203</span> <span style="color: rgb(175,0,0)"> """</span>
<span class="ansi-green-fg">--&gt; 204</span> response <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>model([text], <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)
<span class="ansi-green-intense-fg ansi-bold"> 205</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> response<span style="color: rgb(98,98,98)">.</span>output<span style="color: rgb(98,98,98)">.</span>embeddings <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>:
<span class="ansi-green-intense-fg ansi-bold"> 206</span> msg <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">No embeddings found in response</span><span style="color: rgb(175,0,0)">"</span>
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/fnllm/base/base_llm.py:144</span>, in <span class="ansi-cyan-fg">BaseLLM.__call__</span><span class="ansi-blue-fg">(self, prompt, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 142</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
<span class="ansi-green-intense-fg ansi-bold"> 143</span> prompt, kwargs <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_rewrite_input(prompt, kwargs)
<span class="ansi-green-fg">--&gt; 144</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_decorated_target(prompt, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)
<span class="ansi-green-intense-fg ansi-bold"> 145</span> <span class="ansi-bold" style="color: rgb(0,135,0)">except</span> <span class="ansi-bold" style="color: rgb(215,95,95)">BaseException</span> <span class="ansi-bold" style="color: rgb(0,135,0)">as</span> e:
<span class="ansi-green-intense-fg ansi-bold"> 146</span> stack_trace <span style="color: rgb(98,98,98)">=</span> traceback<span style="color: rgb(98,98,98)">.</span>format_exc()
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/fnllm/base/services/rate_limiter.py:75</span>, in <span class="ansi-cyan-fg">RateLimiter.decorate.&lt;locals&gt;.invoke</span><span class="ansi-blue-fg">(prompt, **args)</span>
<span class="ansi-green-intense-fg ansi-bold"> 73</span> <span class="ansi-bold" style="color: rgb(0,135,0)">async</span> <span class="ansi-bold" style="color: rgb(0,135,0)">with</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_limiter<span style="color: rgb(98,98,98)">.</span>use(manifest):
<span class="ansi-green-intense-fg ansi-bold"> 74</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_events<span style="color: rgb(98,98,98)">.</span>on_limit_acquired(manifest)
<span class="ansi-green-fg">---&gt; 75</span> result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> delegate(prompt, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>args)
<span class="ansi-green-intense-fg ansi-bold"> 76</span> <span class="ansi-bold" style="color: rgb(0,135,0)">finally</span>:
<span class="ansi-green-intense-fg ansi-bold"> 77</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_events<span style="color: rgb(98,98,98)">.</span>on_limit_released(manifest)
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/fnllm/base/base_llm.py:126</span>, in <span class="ansi-cyan-fg">BaseLLM._decorator_target</span><span class="ansi-blue-fg">(self, prompt, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 121</span> <span style="color: rgb(175,0,0)">"""Target for the decorator chain.</span>
<span class="ansi-green-intense-fg ansi-bold"> 122</span>
<span class="ansi-green-intense-fg ansi-bold"> 123</span> <span style="color: rgb(175,0,0)">Leave signature alone as prompt, kwargs.</span>
<span class="ansi-green-intense-fg ansi-bold"> 124</span> <span style="color: rgb(175,0,0)">"""</span>
<span class="ansi-green-intense-fg ansi-bold"> 125</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_events<span style="color: rgb(98,98,98)">.</span>on_execute_llm()
<span class="ansi-green-fg">--&gt; 126</span> output <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_execute_llm(prompt, kwargs)
<span class="ansi-green-intense-fg ansi-bold"> 127</span> result <span style="color: rgb(98,98,98)">=</span> LLMOutput(output<span style="color: rgb(98,98,98)">=</span>output)
<span class="ansi-green-intense-fg ansi-bold"> 128</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_inject_usage(result)
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/fnllm/openai/llm/openai_embeddings_llm.py:126</span>, in <span class="ansi-cyan-fg">OpenAIEmbeddingsLLMImpl._execute_llm</span><span class="ansi-blue-fg">(self, prompt, kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 121</span> local_model_parameters <span style="color: rgb(98,98,98)">=</span> kwargs<span style="color: rgb(98,98,98)">.</span>get(<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">model_parameters</span><span style="color: rgb(175,0,0)">"</span>)
<span class="ansi-green-intense-fg ansi-bold"> 122</span> embeddings_parameters <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_build_embeddings_parameters(
<span class="ansi-green-intense-fg ansi-bold"> 123</span> local_model_parameters
<span class="ansi-green-intense-fg ansi-bold"> 124</span> )
<span class="ansi-green-fg">--&gt; 126</span> result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_client<span style="color: rgb(98,98,98)">.</span>embeddings<span style="color: rgb(98,98,98)">.</span>create(
<span class="ansi-green-intense-fg ansi-bold"> 127</span> <span style="color: rgb(0,135,0)">input</span><span style="color: rgb(98,98,98)">=</span>prompt,
<span class="ansi-green-intense-fg ansi-bold"> 128</span> <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>embeddings_parameters,
<span class="ansi-green-intense-fg ansi-bold"> 129</span> )
<span class="ansi-green-intense-fg ansi-bold"> 130</span> usage: LLMUsageMetrics <span style="color: rgb(98,98,98)">|</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
<span class="ansi-green-intense-fg ansi-bold"> 131</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> result<span style="color: rgb(98,98,98)">.</span>usage:
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/openai/resources/embeddings.py:243</span>, in <span class="ansi-cyan-fg">AsyncEmbeddings.create</span><span class="ansi-blue-fg">(self, input, model, dimensions, encoding_format, user, extra_headers, extra_query, extra_body, timeout)</span>
<span class="ansi-green-intense-fg ansi-bold"> 237</span> embedding<span style="color: rgb(98,98,98)">.</span>embedding <span style="color: rgb(98,98,98)">=</span> np<span style="color: rgb(98,98,98)">.</span>frombuffer( <span style="color: rgb(95,135,135)"># type: ignore[no-untyped-call]</span>
<span class="ansi-green-intense-fg ansi-bold"> 238</span> base64<span style="color: rgb(98,98,98)">.</span>b64decode(data), dtype<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">float32</span><span style="color: rgb(175,0,0)">"</span>
<span class="ansi-green-intense-fg ansi-bold"> 239</span> )<span style="color: rgb(98,98,98)">.</span>tolist()
<span class="ansi-green-intense-fg ansi-bold"> 241</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> obj
<span class="ansi-green-fg">--&gt; 243</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_post(
<span class="ansi-green-intense-fg ansi-bold"> 244</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">/embeddings</span><span style="color: rgb(175,0,0)">"</span>,
<span class="ansi-green-intense-fg ansi-bold"> 245</span> body<span style="color: rgb(98,98,98)">=</span>maybe_transform(params, embedding_create_params<span style="color: rgb(98,98,98)">.</span>EmbeddingCreateParams),
<span class="ansi-green-intense-fg ansi-bold"> 246</span> options<span style="color: rgb(98,98,98)">=</span>make_request_options(
<span class="ansi-green-intense-fg ansi-bold"> 247</span> extra_headers<span style="color: rgb(98,98,98)">=</span>extra_headers,
<span class="ansi-green-intense-fg ansi-bold"> 248</span> extra_query<span style="color: rgb(98,98,98)">=</span>extra_query,
<span class="ansi-green-intense-fg ansi-bold"> 249</span> extra_body<span style="color: rgb(98,98,98)">=</span>extra_body,
<span class="ansi-green-intense-fg ansi-bold"> 250</span> timeout<span style="color: rgb(98,98,98)">=</span>timeout,
<span class="ansi-green-intense-fg ansi-bold"> 251</span> post_parser<span style="color: rgb(98,98,98)">=</span>parser,
<span class="ansi-green-intense-fg ansi-bold"> 252</span> ),
<span class="ansi-green-intense-fg ansi-bold"> 253</span> cast_to<span style="color: rgb(98,98,98)">=</span>CreateEmbeddingResponse,
<span class="ansi-green-intense-fg ansi-bold"> 254</span> )
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/openai/_base_client.py:1767</span>, in <span class="ansi-cyan-fg">AsyncAPIClient.post</span><span class="ansi-blue-fg">(self, path, cast_to, body, files, options, stream, stream_cls)</span>
<span class="ansi-green-intense-fg ansi-bold"> 1753</span> <span class="ansi-bold" style="color: rgb(0,135,0)">async</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span><span style="color: rgb(188,188,188)"> </span><span style="color: rgb(0,0,255)">post</span>(
<span class="ansi-green-intense-fg ansi-bold"> 1754</span> <span style="color: rgb(0,135,0)">self</span>,
<span class="ansi-green-intense-fg ansi-bold"> 1755</span> path: <span style="color: rgb(0,135,0)">str</span>,
<span class="ansi-green-fg"> (...)</span>
<span class="ansi-green-intense-fg ansi-bold"> 1762</span> stream_cls: <span style="color: rgb(0,135,0)">type</span>[_AsyncStreamT] <span style="color: rgb(98,98,98)">|</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>,
<span class="ansi-green-intense-fg ansi-bold"> 1763</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> ResponseT <span style="color: rgb(98,98,98)">|</span> _AsyncStreamT:
<span class="ansi-green-intense-fg ansi-bold"> 1764</span> opts <span style="color: rgb(98,98,98)">=</span> FinalRequestOptions<span style="color: rgb(98,98,98)">.</span>construct(
<span class="ansi-green-intense-fg ansi-bold"> 1765</span> method<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">post</span><span style="color: rgb(175,0,0)">"</span>, url<span style="color: rgb(98,98,98)">=</span>path, json_data<span style="color: rgb(98,98,98)">=</span>body, files<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">await</span> async_to_httpx_files(files), <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>options
<span class="ansi-green-intense-fg ansi-bold"> 1766</span> )
<span class="ansi-green-fg">-&gt; 1767</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>request(cast_to, opts, stream<span style="color: rgb(98,98,98)">=</span>stream, stream_cls<span style="color: rgb(98,98,98)">=</span>stream_cls)
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/openai/_base_client.py:1461</span>, in <span class="ansi-cyan-fg">AsyncAPIClient.request</span><span class="ansi-blue-fg">(self, cast_to, options, stream, stream_cls, remaining_retries)</span>
<span class="ansi-green-intense-fg ansi-bold"> 1458</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
<span class="ansi-green-intense-fg ansi-bold"> 1459</span> retries_taken <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(98,98,98)">0</span>
<span class="ansi-green-fg">-&gt; 1461</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_request(
<span class="ansi-green-intense-fg ansi-bold"> 1462</span> cast_to<span style="color: rgb(98,98,98)">=</span>cast_to,
<span class="ansi-green-intense-fg ansi-bold"> 1463</span> options<span style="color: rgb(98,98,98)">=</span>options,
<span class="ansi-green-intense-fg ansi-bold"> 1464</span> stream<span style="color: rgb(98,98,98)">=</span>stream,
<span class="ansi-green-intense-fg ansi-bold"> 1465</span> stream_cls<span style="color: rgb(98,98,98)">=</span>stream_cls,
<span class="ansi-green-intense-fg ansi-bold"> 1466</span> retries_taken<span style="color: rgb(98,98,98)">=</span>retries_taken,
<span class="ansi-green-intense-fg ansi-bold"> 1467</span> )
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/openai/_base_client.py:1562</span>, in <span class="ansi-cyan-fg">AsyncAPIClient._request</span><span class="ansi-blue-fg">(self, cast_to, options, stream, stream_cls, retries_taken)</span>
<span class="ansi-green-intense-fg ansi-bold"> 1559</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> err<span style="color: rgb(98,98,98)">.</span>response<span style="color: rgb(98,98,98)">.</span>aread()
<span class="ansi-green-intense-fg ansi-bold"> 1561</span> log<span style="color: rgb(98,98,98)">.</span>debug(<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">Re-raising status error</span><span style="color: rgb(175,0,0)">"</span>)
<span class="ansi-green-fg">-&gt; 1562</span> <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_make_status_error_from_response(err<span style="color: rgb(98,98,98)">.</span>response) <span class="ansi-bold" style="color: rgb(0,135,0)">from</span><span style="color: rgb(188,188,188)"> </span><span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
<span class="ansi-green-intense-fg ansi-bold"> 1564</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_process_response(
<span class="ansi-green-intense-fg ansi-bold"> 1565</span> cast_to<span style="color: rgb(98,98,98)">=</span>cast_to,
<span class="ansi-green-intense-fg ansi-bold"> 1566</span> options<span style="color: rgb(98,98,98)">=</span>options,
<span class="ansi-green-fg"> (...)</span>
<span class="ansi-green-intense-fg ansi-bold"> 1570</span> retries_taken<span style="color: rgb(98,98,98)">=</span>retries_taken,
<span class="ansi-green-intense-fg ansi-bold"> 1571</span> )
<span class="ansi-red-fg">AuthenticationError</span>: Error code: 401 - {'error': {'message': 'Incorrect API key provided: sk-proj-********************************************************************************************************************************************************zWYA. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}}</pre>
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<h4 id="inspecting-the-context-data-used-to-generate-the-response">Inspecting the context data used to generate the response<a class="anchor-link" href="#inspecting-the-context-data-used-to-generate-the-response"></a></h4>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">result</span><span class="o">.</span><span class="n">context_data</span><span class="p">[</span><span class="s2">"entities"</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-15">result.context_data["entities"].head()</div>
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<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">NameError</span> Traceback (most recent call last)
Cell <span class="ansi-green-fg">In[15], line 1</span>
<span class="ansi-green-fg">----&gt; 1</span> <span class="ansi-yellow-bg">result</span><span style="color: rgb(98,98,98)">.</span>context_data[<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">entities</span><span style="color: rgb(175,0,0)">"</span>]<span style="color: rgb(98,98,98)">.</span>head()
<span class="ansi-red-fg">NameError</span>: name 'result' is not defined</pre>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">result</span><span class="o">.</span><span class="n">context_data</span><span class="p">[</span><span class="s2">"relationships"</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-16">result.context_data["relationships"].head()</div>
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<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">NameError</span> Traceback (most recent call last)
Cell <span class="ansi-green-fg">In[16], line 1</span>
<span class="ansi-green-fg">----&gt; 1</span> <span class="ansi-yellow-bg">result</span><span style="color: rgb(98,98,98)">.</span>context_data[<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">relationships</span><span style="color: rgb(175,0,0)">"</span>]<span style="color: rgb(98,98,98)">.</span>head()
<span class="ansi-red-fg">NameError</span>: name 'result' is not defined</pre>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [17]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="s2">"reports"</span> <span class="ow">in</span> <span class="n">result</span><span class="o">.</span><span class="n">context_data</span><span class="p">:</span>
<span class="n">result</span><span class="o">.</span><span class="n">context_data</span><span class="p">[</span><span class="s2">"reports"</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-17">if "reports" in result.context_data:
result.context_data["reports"].head()</div>
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<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">NameError</span> Traceback (most recent call last)
Cell <span class="ansi-green-fg">In[17], line 1</span>
<span class="ansi-green-fg">----&gt; 1</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">reports</span><span style="color: rgb(175,0,0)">"</span> <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> <span class="ansi-yellow-bg">result</span><span style="color: rgb(98,98,98)">.</span>context_data:
<span class="ansi-green-intense-fg ansi-bold"> 2</span> result<span style="color: rgb(98,98,98)">.</span>context_data[<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">reports</span><span style="color: rgb(175,0,0)">"</span>]<span style="color: rgb(98,98,98)">.</span>head()
<span class="ansi-red-fg">NameError</span>: name 'result' is not defined</pre>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">result</span><span class="o">.</span><span class="n">context_data</span><span class="p">[</span><span class="s2">"sources"</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-18">result.context_data["sources"].head()</div>
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<pre>
<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">NameError</span> Traceback (most recent call last)
Cell <span class="ansi-green-fg">In[18], line 1</span>
<span class="ansi-green-fg">----&gt; 1</span> <span class="ansi-yellow-bg">result</span><span style="color: rgb(98,98,98)">.</span>context_data[<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">sources</span><span style="color: rgb(175,0,0)">"</span>]<span style="color: rgb(98,98,98)">.</span>head()
<span class="ansi-red-fg">NameError</span>: name 'result' is not defined</pre>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="s2">"claims"</span> <span class="ow">in</span> <span class="n">result</span><span class="o">.</span><span class="n">context_data</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">context_data</span><span class="p">[</span><span class="s2">"claims"</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-19">if "claims" in result.context_data:
print(result.context_data["claims"].head())</div>
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<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">NameError</span> Traceback (most recent call last)
Cell <span class="ansi-green-fg">In[19], line 1</span>
<span class="ansi-green-fg">----&gt; 1</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">claims</span><span style="color: rgb(175,0,0)">"</span> <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> <span class="ansi-yellow-bg">result</span><span style="color: rgb(98,98,98)">.</span>context_data:
<span class="ansi-green-intense-fg ansi-bold"> 2</span> <span style="color: rgb(0,135,0)">print</span>(result<span style="color: rgb(98,98,98)">.</span>context_data[<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">claims</span><span style="color: rgb(175,0,0)">"</span>]<span style="color: rgb(98,98,98)">.</span>head())
<span class="ansi-red-fg">NameError</span>: name 'result' is not defined</pre>
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<h3 id="question-generation">Question Generation<a class="anchor-link" href="#question-generation"></a></h3>
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<p>This function takes a list of user queries and generates the next candidate questions.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [20]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">question_generator</span> <span class="o">=</span> <span class="n">LocalQuestionGen</span><span class="p">(</span>
<span class="n">model</span><span class="o">=</span><span class="n">chat_model</span><span class="p">,</span>
<span class="n">context_builder</span><span class="o">=</span><span class="n">context_builder</span><span class="p">,</span>
<span class="n">token_encoder</span><span class="o">=</span><span class="n">token_encoder</span><span class="p">,</span>
<span class="n">model_params</span><span class="o">=</span><span class="n">model_params</span><span class="p">,</span>
<span class="n">context_builder_params</span><span class="o">=</span><span class="n">local_context_params</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-20">question_generator = LocalQuestionGen(
model=chat_model,
context_builder=context_builder,
token_encoder=token_encoder,
model_params=model_params,
context_builder_params=local_context_params,
)</div>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [21]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="CodeMirror cm-s-jupyter">
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">question_history</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">"Tell me about Agent Mercer"</span><span class="p">,</span>
<span class="s2">"What happens in Dulce military base?"</span><span class="p">,</span>
<span class="p">]</span>
<span class="n">candidate_questions</span> <span class="o">=</span> <span class="k">await</span> <span class="n">question_generator</span><span class="o">.</span><span class="n">agenerate</span><span class="p">(</span>
<span class="n">question_history</span><span class="o">=</span><span class="n">question_history</span><span class="p">,</span> <span class="n">context_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">question_count</span><span class="o">=</span><span class="mi">5</span>
<span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">candidate_questions</span><span class="o">.</span><span class="n">response</span><span class="p">)</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-21">question_history = [
"Tell me about Agent Mercer",
"What happens in Dulce military base?",
]
candidate_questions = await question_generator.agenerate(
question_history=question_history, context_data=None, question_count=5
)
print(candidate_questions.response)</div>
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<pre>
<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">AuthenticationError</span> Traceback (most recent call last)
Cell <span class="ansi-green-fg">In[21], line 5</span>
<span class="ansi-green-intense-fg ansi-bold"> 1</span> question_history <span style="color: rgb(98,98,98)">=</span> [
<span class="ansi-green-intense-fg ansi-bold"> 2</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">Tell me about Agent Mercer</span><span style="color: rgb(175,0,0)">"</span>,
<span class="ansi-green-intense-fg ansi-bold"> 3</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">What happens in Dulce military base?</span><span style="color: rgb(175,0,0)">"</span>,
<span class="ansi-green-intense-fg ansi-bold"> 4</span> ]
<span class="ansi-green-fg">----&gt; 5</span> candidate_questions <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> question_generator<span style="color: rgb(98,98,98)">.</span>agenerate(
<span class="ansi-green-intense-fg ansi-bold"> 6</span> question_history<span style="color: rgb(98,98,98)">=</span>question_history, context_data<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">None</span>, question_count<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(98,98,98)">5</span>
<span class="ansi-green-intense-fg ansi-bold"> 7</span> )
<span class="ansi-green-intense-fg ansi-bold"> 8</span> <span style="color: rgb(0,135,0)">print</span>(candidate_questions<span style="color: rgb(98,98,98)">.</span>response)
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/query/question_gen/local_gen.py:80</span>, in <span class="ansi-cyan-fg">LocalQuestionGen.agenerate</span><span class="ansi-blue-fg">(self, question_history, context_data, question_count, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 74</span> conversation_history <span style="color: rgb(98,98,98)">=</span> ConversationHistory<span style="color: rgb(98,98,98)">.</span>from_list(history)
<span class="ansi-green-intense-fg ansi-bold"> 76</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> context_data <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>:
<span class="ansi-green-intense-fg ansi-bold"> 77</span> <span style="color: rgb(95,135,135)"># generate context data based on the question history</span>
<span class="ansi-green-intense-fg ansi-bold"> 78</span> result <span style="color: rgb(98,98,98)">=</span> cast(
<span class="ansi-green-intense-fg ansi-bold"> 79</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">ContextBuilderResult</span><span style="color: rgb(175,0,0)">"</span>,
<span class="ansi-green-fg">---&gt; 80</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">context_builder</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">build_context</span><span class="ansi-yellow-bg">(</span>
<span class="ansi-green-intense-fg ansi-bold"> 81</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">query</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">question_text</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 82</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">conversation_history</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">conversation_history</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 83</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 84</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">context_builder_params</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 85</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">)</span>,
<span class="ansi-green-intense-fg ansi-bold"> 86</span> )
<span class="ansi-green-intense-fg ansi-bold"> 87</span> context_data <span style="color: rgb(98,98,98)">=</span> cast(<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">str</span><span style="color: rgb(175,0,0)">"</span>, result<span style="color: rgb(98,98,98)">.</span>context_chunks)
<span class="ansi-green-intense-fg ansi-bold"> 88</span> context_records <span style="color: rgb(98,98,98)">=</span> result<span style="color: rgb(98,98,98)">.</span>context_records
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/query/structured_search/local_search/mixed_context.py:139</span>, in <span class="ansi-cyan-fg">LocalSearchMixedContext.build_context</span><span class="ansi-blue-fg">(self, query, conversation_history, include_entity_names, exclude_entity_names, conversation_history_max_turns, conversation_history_user_turns_only, max_context_tokens, text_unit_prop, community_prop, top_k_mapped_entities, top_k_relationships, include_community_rank, include_entity_rank, rank_description, include_relationship_weight, relationship_ranking_attribute, return_candidate_context, use_community_summary, min_community_rank, community_context_name, column_delimiter, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 134</span> pre_user_questions <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(175,0,0)">"</span><span class="ansi-bold" style="color: rgb(175,95,0)">\n</span><span style="color: rgb(175,0,0)">"</span><span style="color: rgb(98,98,98)">.</span>join(
<span class="ansi-green-intense-fg ansi-bold"> 135</span> conversation_history<span style="color: rgb(98,98,98)">.</span>get_user_turns(conversation_history_max_turns)
<span class="ansi-green-intense-fg ansi-bold"> 136</span> )
<span class="ansi-green-intense-fg ansi-bold"> 137</span> query <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(175,0,0)">f</span><span style="color: rgb(175,0,0)">"</span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>query<span class="ansi-bold" style="color: rgb(175,95,135)">}</span><span class="ansi-bold" style="color: rgb(175,95,0)">\n</span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>pre_user_questions<span class="ansi-bold" style="color: rgb(175,95,135)">}</span><span style="color: rgb(175,0,0)">"</span>
<span class="ansi-green-fg">--&gt; 139</span> selected_entities <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">map_query_to_entities</span><span class="ansi-yellow-bg">(</span>
<span class="ansi-green-intense-fg ansi-bold"> 140</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">query</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">query</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 141</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">text_embedding_vectorstore</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">entity_text_embeddings</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 142</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 143</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">all_entities_dict</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">entities</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 144</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">embedding_vectorstore_key</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">embedding_vectorstore_key</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 145</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">include_entity_names</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">include_entity_names</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 146</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">exclude_entity_names</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">exclude_entity_names</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 147</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">k</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">top_k_mapped_entities</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 148</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">oversample_scaler</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">2</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 149</span> <span class="ansi-yellow-bg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 151</span> <span style="color: rgb(95,135,135)"># build context</span>
<span class="ansi-green-intense-fg ansi-bold"> 152</span> final_context <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">list</span>[<span style="color: rgb(0,135,0)">str</span>]()
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/query/context_builder/entity_extraction.py:58</span>, in <span class="ansi-cyan-fg">map_query_to_entities</span><span class="ansi-blue-fg">(query, text_embedding_vectorstore, text_embedder, all_entities_dict, embedding_vectorstore_key, include_entity_names, exclude_entity_names, k, oversample_scaler)</span>
<span class="ansi-green-intense-fg ansi-bold"> 54</span> matched_entities <span style="color: rgb(98,98,98)">=</span> []
<span class="ansi-green-intense-fg ansi-bold"> 55</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> query <span style="color: rgb(98,98,98)">!=</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">"</span>:
<span class="ansi-green-intense-fg ansi-bold"> 56</span> <span style="color: rgb(95,135,135)"># get entities with highest semantic similarity to query</span>
<span class="ansi-green-intense-fg ansi-bold"> 57</span> <span style="color: rgb(95,135,135)"># oversample to account for excluded entities</span>
<span class="ansi-green-fg">---&gt; 58</span> search_results <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">text_embedding_vectorstore</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">similarity_search_by_text</span><span class="ansi-yellow-bg">(</span>
<span class="ansi-green-intense-fg ansi-bold"> 59</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">text</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">query</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 60</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">lambda</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">:</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">embed</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 61</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">k</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">k</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">oversample_scaler</span><span class="ansi-yellow-bg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 62</span> <span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 63</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> result <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> search_results:
<span class="ansi-green-intense-fg ansi-bold"> 64</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> embedding_vectorstore_key <span style="color: rgb(98,98,98)">==</span> EntityVectorStoreKey<span style="color: rgb(98,98,98)">.</span>ID <span class="ansi-bold" style="color: rgb(175,0,255)">and</span> <span style="color: rgb(0,135,0)">isinstance</span>(
<span class="ansi-green-intense-fg ansi-bold"> 65</span> result<span style="color: rgb(98,98,98)">.</span>document<span style="color: rgb(98,98,98)">.</span>id, <span style="color: rgb(0,135,0)">str</span>
<span class="ansi-green-intense-fg ansi-bold"> 66</span> ):
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/vector_stores/lancedb.py:134</span>, in <span class="ansi-cyan-fg">LanceDBVectorStore.similarity_search_by_text</span><span class="ansi-blue-fg">(self, text, text_embedder, k, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 130</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span><span style="color: rgb(188,188,188)"> </span><span style="color: rgb(0,0,255)">similarity_search_by_text</span>(
<span class="ansi-green-intense-fg ansi-bold"> 131</span> <span style="color: rgb(0,135,0)">self</span>, text: <span style="color: rgb(0,135,0)">str</span>, text_embedder: TextEmbedder, k: <span style="color: rgb(0,135,0)">int</span> <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(98,98,98)">10</span>, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs: Any
<span class="ansi-green-intense-fg ansi-bold"> 132</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> <span style="color: rgb(0,135,0)">list</span>[VectorStoreSearchResult]:
<span class="ansi-green-intense-fg ansi-bold"> 133</span> <span style="color: rgb(188,188,188)"> </span><span style="color: rgb(175,0,0)">"""Perform a similarity search using a given input text."""</span>
<span class="ansi-green-fg">--&gt; 134</span> query_embedding <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">text</span><span class="ansi-yellow-bg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 135</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> query_embedding:
<span class="ansi-green-intense-fg ansi-bold"> 136</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>similarity_search_by_vector(query_embedding, k)
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/query/context_builder/entity_extraction.py:60</span>, in <span class="ansi-cyan-fg">map_query_to_entities.&lt;locals&gt;.&lt;lambda&gt;</span><span class="ansi-blue-fg">(t)</span>
<span class="ansi-green-intense-fg ansi-bold"> 54</span> matched_entities <span style="color: rgb(98,98,98)">=</span> []
<span class="ansi-green-intense-fg ansi-bold"> 55</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> query <span style="color: rgb(98,98,98)">!=</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">"</span>:
<span class="ansi-green-intense-fg ansi-bold"> 56</span> <span style="color: rgb(95,135,135)"># get entities with highest semantic similarity to query</span>
<span class="ansi-green-intense-fg ansi-bold"> 57</span> <span style="color: rgb(95,135,135)"># oversample to account for excluded entities</span>
<span class="ansi-green-intense-fg ansi-bold"> 58</span> search_results <span style="color: rgb(98,98,98)">=</span> text_embedding_vectorstore<span style="color: rgb(98,98,98)">.</span>similarity_search_by_text(
<span class="ansi-green-intense-fg ansi-bold"> 59</span> text<span style="color: rgb(98,98,98)">=</span>query,
<span class="ansi-green-fg">---&gt; 60</span> text_embedder<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">lambda</span> t: <span class="ansi-yellow-bg">text_embedder</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">embed</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">)</span>,
<span class="ansi-green-intense-fg ansi-bold"> 61</span> k<span style="color: rgb(98,98,98)">=</span>k <span style="color: rgb(98,98,98)">*</span> oversample_scaler,
<span class="ansi-green-intense-fg ansi-bold"> 62</span> )
<span class="ansi-green-intense-fg ansi-bold"> 63</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> result <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> search_results:
<span class="ansi-green-intense-fg ansi-bold"> 64</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> embedding_vectorstore_key <span style="color: rgb(98,98,98)">==</span> EntityVectorStoreKey<span style="color: rgb(98,98,98)">.</span>ID <span class="ansi-bold" style="color: rgb(175,0,255)">and</span> <span style="color: rgb(0,135,0)">isinstance</span>(
<span class="ansi-green-intense-fg ansi-bold"> 65</span> result<span style="color: rgb(98,98,98)">.</span>document<span style="color: rgb(98,98,98)">.</span>id, <span style="color: rgb(0,135,0)">str</span>
<span class="ansi-green-intense-fg ansi-bold"> 66</span> ):
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/language_model/providers/fnllm/models.py:237</span>, in <span class="ansi-cyan-fg">OpenAIEmbeddingFNLLM.embed</span><span class="ansi-blue-fg">(self, text, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 225</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span><span style="color: rgb(188,188,188)"> </span><span style="color: rgb(0,0,255)">embed</span>(<span style="color: rgb(0,135,0)">self</span>, text: <span style="color: rgb(0,135,0)">str</span>, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> <span style="color: rgb(0,135,0)">list</span>[<span style="color: rgb(0,135,0)">float</span>]:
<span class="ansi-green-intense-fg ansi-bold"> 226</span> <span style="color: rgb(188,188,188)"> </span><span style="color: rgb(175,0,0)">"""</span>
<span class="ansi-green-intense-fg ansi-bold"> 227</span> <span style="color: rgb(175,0,0)"> Embed the given text using the Model.</span>
<span class="ansi-green-intense-fg ansi-bold"> 228</span>
<span class="ansi-green-fg"> (...)</span>
<span class="ansi-green-intense-fg ansi-bold"> 235</span> <span style="color: rgb(175,0,0)"> The embeddings of the text.</span>
<span class="ansi-green-intense-fg ansi-bold"> 236</span> <span style="color: rgb(175,0,0)"> """</span>
<span class="ansi-green-fg">--&gt; 237</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">run_coroutine_sync</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">aembed</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">text</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg">)</span>
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/language_model/providers/fnllm/utils.py:127</span>, in <span class="ansi-cyan-fg">run_coroutine_sync</span><span class="ansi-blue-fg">(coroutine)</span>
<span class="ansi-green-intense-fg ansi-bold"> 125</span> _thr<span style="color: rgb(98,98,98)">.</span>start()
<span class="ansi-green-intense-fg ansi-bold"> 126</span> future <span style="color: rgb(98,98,98)">=</span> asyncio<span style="color: rgb(98,98,98)">.</span>run_coroutine_threadsafe(coroutine, _loop)
<span class="ansi-green-fg">--&gt; 127</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">future</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">result</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">)</span>
File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.11.12/x64/lib/python3.11/concurrent/futures/_base.py:456</span>, in <span class="ansi-cyan-fg">Future.result</span><span class="ansi-blue-fg">(self, timeout)</span>
<span class="ansi-green-intense-fg ansi-bold"> 454</span> <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> CancelledError()
<span class="ansi-green-intense-fg ansi-bold"> 455</span> <span class="ansi-bold" style="color: rgb(0,135,0)">elif</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_state <span style="color: rgb(98,98,98)">==</span> FINISHED:
<span class="ansi-green-fg">--&gt; 456</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">__get_result</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 457</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
<span class="ansi-green-intense-fg ansi-bold"> 458</span> <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> <span class="ansi-bold" style="color: rgb(215,95,95)">TimeoutError</span>()
File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.11.12/x64/lib/python3.11/concurrent/futures/_base.py:401</span>, in <span class="ansi-cyan-fg">Future.__get_result</span><span class="ansi-blue-fg">(self)</span>
<span class="ansi-green-intense-fg ansi-bold"> 399</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_exception:
<span class="ansi-green-intense-fg ansi-bold"> 400</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
<span class="ansi-green-fg">--&gt; 401</span> <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_exception
<span class="ansi-green-intense-fg ansi-bold"> 402</span> <span class="ansi-bold" style="color: rgb(0,135,0)">finally</span>:
<span class="ansi-green-intense-fg ansi-bold"> 403</span> <span style="color: rgb(95,135,135)"># Break a reference cycle with the exception in self._exception</span>
<span class="ansi-green-intense-fg ansi-bold"> 404</span> <span style="color: rgb(0,135,0)">self</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
File <span class="ansi-green-fg">~/work/graphrag/graphrag/graphrag/language_model/providers/fnllm/models.py:204</span>, in <span class="ansi-cyan-fg">OpenAIEmbeddingFNLLM.aembed</span><span class="ansi-blue-fg">(self, text, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 192</span> <span class="ansi-bold" style="color: rgb(0,135,0)">async</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span><span style="color: rgb(188,188,188)"> </span><span style="color: rgb(0,0,255)">aembed</span>(<span style="color: rgb(0,135,0)">self</span>, text: <span style="color: rgb(0,135,0)">str</span>, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> <span style="color: rgb(0,135,0)">list</span>[<span style="color: rgb(0,135,0)">float</span>]:
<span class="ansi-green-intense-fg ansi-bold"> 193</span> <span style="color: rgb(188,188,188)"> </span><span style="color: rgb(175,0,0)">"""</span>
<span class="ansi-green-intense-fg ansi-bold"> 194</span> <span style="color: rgb(175,0,0)"> Embed the given text using the Model.</span>
<span class="ansi-green-intense-fg ansi-bold"> 195</span>
<span class="ansi-green-fg"> (...)</span>
<span class="ansi-green-intense-fg ansi-bold"> 202</span> <span style="color: rgb(175,0,0)"> The embeddings of the text.</span>
<span class="ansi-green-intense-fg ansi-bold"> 203</span> <span style="color: rgb(175,0,0)"> """</span>
<span class="ansi-green-fg">--&gt; 204</span> response <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>model([text], <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)
<span class="ansi-green-intense-fg ansi-bold"> 205</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> response<span style="color: rgb(98,98,98)">.</span>output<span style="color: rgb(98,98,98)">.</span>embeddings <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>:
<span class="ansi-green-intense-fg ansi-bold"> 206</span> msg <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">No embeddings found in response</span><span style="color: rgb(175,0,0)">"</span>
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/fnllm/base/base_llm.py:144</span>, in <span class="ansi-cyan-fg">BaseLLM.__call__</span><span class="ansi-blue-fg">(self, prompt, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 142</span> <span class="ansi-bold" style="color: rgb(0,135,0)">try</span>:
<span class="ansi-green-intense-fg ansi-bold"> 143</span> prompt, kwargs <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_rewrite_input(prompt, kwargs)
<span class="ansi-green-fg">--&gt; 144</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_decorated_target(prompt, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs)
<span class="ansi-green-intense-fg ansi-bold"> 145</span> <span class="ansi-bold" style="color: rgb(0,135,0)">except</span> <span class="ansi-bold" style="color: rgb(215,95,95)">BaseException</span> <span class="ansi-bold" style="color: rgb(0,135,0)">as</span> e:
<span class="ansi-green-intense-fg ansi-bold"> 146</span> stack_trace <span style="color: rgb(98,98,98)">=</span> traceback<span style="color: rgb(98,98,98)">.</span>format_exc()
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/fnllm/base/services/rate_limiter.py:75</span>, in <span class="ansi-cyan-fg">RateLimiter.decorate.&lt;locals&gt;.invoke</span><span class="ansi-blue-fg">(prompt, **args)</span>
<span class="ansi-green-intense-fg ansi-bold"> 73</span> <span class="ansi-bold" style="color: rgb(0,135,0)">async</span> <span class="ansi-bold" style="color: rgb(0,135,0)">with</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_limiter<span style="color: rgb(98,98,98)">.</span>use(manifest):
<span class="ansi-green-intense-fg ansi-bold"> 74</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_events<span style="color: rgb(98,98,98)">.</span>on_limit_acquired(manifest)
<span class="ansi-green-fg">---&gt; 75</span> result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> delegate(prompt, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>args)
<span class="ansi-green-intense-fg ansi-bold"> 76</span> <span class="ansi-bold" style="color: rgb(0,135,0)">finally</span>:
<span class="ansi-green-intense-fg ansi-bold"> 77</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_events<span style="color: rgb(98,98,98)">.</span>on_limit_released(manifest)
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/fnllm/base/base_llm.py:126</span>, in <span class="ansi-cyan-fg">BaseLLM._decorator_target</span><span class="ansi-blue-fg">(self, prompt, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 121</span> <span style="color: rgb(175,0,0)">"""Target for the decorator chain.</span>
<span class="ansi-green-intense-fg ansi-bold"> 122</span>
<span class="ansi-green-intense-fg ansi-bold"> 123</span> <span style="color: rgb(175,0,0)">Leave signature alone as prompt, kwargs.</span>
<span class="ansi-green-intense-fg ansi-bold"> 124</span> <span style="color: rgb(175,0,0)">"""</span>
<span class="ansi-green-intense-fg ansi-bold"> 125</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_events<span style="color: rgb(98,98,98)">.</span>on_execute_llm()
<span class="ansi-green-fg">--&gt; 126</span> output <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_execute_llm(prompt, kwargs)
<span class="ansi-green-intense-fg ansi-bold"> 127</span> result <span style="color: rgb(98,98,98)">=</span> LLMOutput(output<span style="color: rgb(98,98,98)">=</span>output)
<span class="ansi-green-intense-fg ansi-bold"> 128</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_inject_usage(result)
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/fnllm/openai/llm/openai_embeddings_llm.py:126</span>, in <span class="ansi-cyan-fg">OpenAIEmbeddingsLLMImpl._execute_llm</span><span class="ansi-blue-fg">(self, prompt, kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold"> 121</span> local_model_parameters <span style="color: rgb(98,98,98)">=</span> kwargs<span style="color: rgb(98,98,98)">.</span>get(<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">model_parameters</span><span style="color: rgb(175,0,0)">"</span>)
<span class="ansi-green-intense-fg ansi-bold"> 122</span> embeddings_parameters <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_build_embeddings_parameters(
<span class="ansi-green-intense-fg ansi-bold"> 123</span> local_model_parameters
<span class="ansi-green-intense-fg ansi-bold"> 124</span> )
<span class="ansi-green-fg">--&gt; 126</span> result <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_client<span style="color: rgb(98,98,98)">.</span>embeddings<span style="color: rgb(98,98,98)">.</span>create(
<span class="ansi-green-intense-fg ansi-bold"> 127</span> <span style="color: rgb(0,135,0)">input</span><span style="color: rgb(98,98,98)">=</span>prompt,
<span class="ansi-green-intense-fg ansi-bold"> 128</span> <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>embeddings_parameters,
<span class="ansi-green-intense-fg ansi-bold"> 129</span> )
<span class="ansi-green-intense-fg ansi-bold"> 130</span> usage: LLMUsageMetrics <span style="color: rgb(98,98,98)">|</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
<span class="ansi-green-intense-fg ansi-bold"> 131</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> result<span style="color: rgb(98,98,98)">.</span>usage:
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/openai/resources/embeddings.py:243</span>, in <span class="ansi-cyan-fg">AsyncEmbeddings.create</span><span class="ansi-blue-fg">(self, input, model, dimensions, encoding_format, user, extra_headers, extra_query, extra_body, timeout)</span>
<span class="ansi-green-intense-fg ansi-bold"> 237</span> embedding<span style="color: rgb(98,98,98)">.</span>embedding <span style="color: rgb(98,98,98)">=</span> np<span style="color: rgb(98,98,98)">.</span>frombuffer( <span style="color: rgb(95,135,135)"># type: ignore[no-untyped-call]</span>
<span class="ansi-green-intense-fg ansi-bold"> 238</span> base64<span style="color: rgb(98,98,98)">.</span>b64decode(data), dtype<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">float32</span><span style="color: rgb(175,0,0)">"</span>
<span class="ansi-green-intense-fg ansi-bold"> 239</span> )<span style="color: rgb(98,98,98)">.</span>tolist()
<span class="ansi-green-intense-fg ansi-bold"> 241</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> obj
<span class="ansi-green-fg">--&gt; 243</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_post(
<span class="ansi-green-intense-fg ansi-bold"> 244</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">/embeddings</span><span style="color: rgb(175,0,0)">"</span>,
<span class="ansi-green-intense-fg ansi-bold"> 245</span> body<span style="color: rgb(98,98,98)">=</span>maybe_transform(params, embedding_create_params<span style="color: rgb(98,98,98)">.</span>EmbeddingCreateParams),
<span class="ansi-green-intense-fg ansi-bold"> 246</span> options<span style="color: rgb(98,98,98)">=</span>make_request_options(
<span class="ansi-green-intense-fg ansi-bold"> 247</span> extra_headers<span style="color: rgb(98,98,98)">=</span>extra_headers,
<span class="ansi-green-intense-fg ansi-bold"> 248</span> extra_query<span style="color: rgb(98,98,98)">=</span>extra_query,
<span class="ansi-green-intense-fg ansi-bold"> 249</span> extra_body<span style="color: rgb(98,98,98)">=</span>extra_body,
<span class="ansi-green-intense-fg ansi-bold"> 250</span> timeout<span style="color: rgb(98,98,98)">=</span>timeout,
<span class="ansi-green-intense-fg ansi-bold"> 251</span> post_parser<span style="color: rgb(98,98,98)">=</span>parser,
<span class="ansi-green-intense-fg ansi-bold"> 252</span> ),
<span class="ansi-green-intense-fg ansi-bold"> 253</span> cast_to<span style="color: rgb(98,98,98)">=</span>CreateEmbeddingResponse,
<span class="ansi-green-intense-fg ansi-bold"> 254</span> )
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/openai/_base_client.py:1767</span>, in <span class="ansi-cyan-fg">AsyncAPIClient.post</span><span class="ansi-blue-fg">(self, path, cast_to, body, files, options, stream, stream_cls)</span>
<span class="ansi-green-intense-fg ansi-bold"> 1753</span> <span class="ansi-bold" style="color: rgb(0,135,0)">async</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span><span style="color: rgb(188,188,188)"> </span><span style="color: rgb(0,0,255)">post</span>(
<span class="ansi-green-intense-fg ansi-bold"> 1754</span> <span style="color: rgb(0,135,0)">self</span>,
<span class="ansi-green-intense-fg ansi-bold"> 1755</span> path: <span style="color: rgb(0,135,0)">str</span>,
<span class="ansi-green-fg"> (...)</span>
<span class="ansi-green-intense-fg ansi-bold"> 1762</span> stream_cls: <span style="color: rgb(0,135,0)">type</span>[_AsyncStreamT] <span style="color: rgb(98,98,98)">|</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>,
<span class="ansi-green-intense-fg ansi-bold"> 1763</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> ResponseT <span style="color: rgb(98,98,98)">|</span> _AsyncStreamT:
<span class="ansi-green-intense-fg ansi-bold"> 1764</span> opts <span style="color: rgb(98,98,98)">=</span> FinalRequestOptions<span style="color: rgb(98,98,98)">.</span>construct(
<span class="ansi-green-intense-fg ansi-bold"> 1765</span> method<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">post</span><span style="color: rgb(175,0,0)">"</span>, url<span style="color: rgb(98,98,98)">=</span>path, json_data<span style="color: rgb(98,98,98)">=</span>body, files<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">await</span> async_to_httpx_files(files), <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>options
<span class="ansi-green-intense-fg ansi-bold"> 1766</span> )
<span class="ansi-green-fg">-&gt; 1767</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>request(cast_to, opts, stream<span style="color: rgb(98,98,98)">=</span>stream, stream_cls<span style="color: rgb(98,98,98)">=</span>stream_cls)
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/openai/_base_client.py:1461</span>, in <span class="ansi-cyan-fg">AsyncAPIClient.request</span><span class="ansi-blue-fg">(self, cast_to, options, stream, stream_cls, remaining_retries)</span>
<span class="ansi-green-intense-fg ansi-bold"> 1458</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
<span class="ansi-green-intense-fg ansi-bold"> 1459</span> retries_taken <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(98,98,98)">0</span>
<span class="ansi-green-fg">-&gt; 1461</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_request(
<span class="ansi-green-intense-fg ansi-bold"> 1462</span> cast_to<span style="color: rgb(98,98,98)">=</span>cast_to,
<span class="ansi-green-intense-fg ansi-bold"> 1463</span> options<span style="color: rgb(98,98,98)">=</span>options,
<span class="ansi-green-intense-fg ansi-bold"> 1464</span> stream<span style="color: rgb(98,98,98)">=</span>stream,
<span class="ansi-green-intense-fg ansi-bold"> 1465</span> stream_cls<span style="color: rgb(98,98,98)">=</span>stream_cls,
<span class="ansi-green-intense-fg ansi-bold"> 1466</span> retries_taken<span style="color: rgb(98,98,98)">=</span>retries_taken,
<span class="ansi-green-intense-fg ansi-bold"> 1467</span> )
File <span class="ansi-green-fg">~/.cache/pypoetry/virtualenvs/graphrag-F2jvqev7-py3.11/lib/python3.11/site-packages/openai/_base_client.py:1562</span>, in <span class="ansi-cyan-fg">AsyncAPIClient._request</span><span class="ansi-blue-fg">(self, cast_to, options, stream, stream_cls, retries_taken)</span>
<span class="ansi-green-intense-fg ansi-bold"> 1559</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> err<span style="color: rgb(98,98,98)">.</span>response<span style="color: rgb(98,98,98)">.</span>aread()
<span class="ansi-green-intense-fg ansi-bold"> 1561</span> log<span style="color: rgb(98,98,98)">.</span>debug(<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">Re-raising status error</span><span style="color: rgb(175,0,0)">"</span>)
<span class="ansi-green-fg">-&gt; 1562</span> <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_make_status_error_from_response(err<span style="color: rgb(98,98,98)">.</span>response) <span class="ansi-bold" style="color: rgb(0,135,0)">from</span><span style="color: rgb(188,188,188)"> </span><span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
<span class="ansi-green-intense-fg ansi-bold"> 1564</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-bold" style="color: rgb(0,135,0)">await</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_process_response(
<span class="ansi-green-intense-fg ansi-bold"> 1565</span> cast_to<span style="color: rgb(98,98,98)">=</span>cast_to,
<span class="ansi-green-intense-fg ansi-bold"> 1566</span> options<span style="color: rgb(98,98,98)">=</span>options,
<span class="ansi-green-fg"> (...)</span>
<span class="ansi-green-intense-fg ansi-bold"> 1570</span> retries_taken<span style="color: rgb(98,98,98)">=</span>retries_taken,
<span class="ansi-green-intense-fg ansi-bold"> 1571</span> )
<span class="ansi-red-fg">AuthenticationError</span>: Error code: 401 - {'error': {'message': 'Incorrect API key provided: sk-proj-********************************************************************************************************************************************************zWYA. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}}</pre>
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