101 lines
16 KiB
Plaintext
101 lines
16 KiB
Plaintext
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„ Zedkr„eƒ dS )u‚
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Semantic Graph — Connects Reality Signals into a knowledge graph
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Enables AI to discover relationships and build custom indexes
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é )ÚDictÚListÚOptionalÚSetÚTuple)Ú dataclass)ÚdatetimeNc @ sh e Zd ZU dZeed< eed< eed< eed< eed< ee ed< eed< e e ed < ed
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SignalNodez A node in the semantic graphÚ signal_idÚ signal_typeÚcategoryÚvalueÚunitÚlocationÚ timestampÚ
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r r Úroundr
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r r r r ©Úself© r úE/home/bernt/.openclaw/workspace/iom/reality_signals/semantic_graph.pyÚto_dict s
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|j ¡}|| j vrºg | j |< | j | |¡ |S ) zAdd a signal to the graphr Ú_r r r
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zSemanticGraph.add_signal)Ú source_idÚ target_idÚ
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relation_typeÚstrengthc C sX || j v rT|| j v rT| j |||t|dƒdœ¡ | j | j |¡ | j | j |¡ dS )z&Add a relationship between two signalsr )ÚsourceÚtargetÚtyper7 N)r% r& r/ r r )r r4 r5 r6 r7 r r r Úadd_relationshipM s üz SemanticGraph.add_relationshipN)r
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r6 r c C s˜ || j vrg S g }| jD ]z}|d |ks4|d |kr|du sH|d |kr|d |kr\|d n|d }|| j v r| | j | ¡ |d |d dœ¡ q|S )z&Find signals related to a given signalr8 r9 Nr: r7 )r* Z relationshipr7 )r% r& r/ r )r r
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zSemanticGraph.find_related)r r c s ˆ j |g ¡}‡ fdd„|D ƒS )z Find all signals of a given typec s$ g | ] }|ˆ j v rˆ j | ¡ ‘qS r ©r% r ©Ú.0Zsidr r r Ú
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<listcomp>q ó z.SemanticGraph.find_by_type.<locals>.<listcomp>)r' r, )r r Ú
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signal_idsr r r Ú find_by_typen s zSemanticGraph.find_by_typeéd )r Ú
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radius_metersr c s* ˆ |¡}ˆ j |g ¡}‡ fdd„|D ƒS )z Find signals near a locationc s$ g | ] }|ˆ j v rˆ j | ¡ ‘qS r r? r@ r r r rB y rC z2SemanticGraph.find_by_location.<locals>.<listcomp>)r0 r( r, )r r rG r2 rD r r r Úfind_by_locations s
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|ƒdœS )z5Get a subgraph containing only specified signal typesr8 r9 )r% r& Ú
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„ dd� |S )z*Discover correlations between signal typesé Nc s g | ]}|ˆ j v rˆ j | ‘qS r ©r% r@ r r r rB œ rC z7SemanticGraph.discover_correlations.<locals>.<listcomp>c s g | ]}|ˆ j v rˆ j | ‘qS r rR r@ r r r rB � rC c s s | ] }|j V qd S r$ ©r
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z#SemanticGraph.discover_correlations)r r c C sB | d| dd¡¡}| d| dd¡¡}t|dƒ› dt|dƒ› �S )zHash location for indexingÚlatÚlatituder ÚlngÚ longitudeé r+ )r, r )r r rl rn r r r r0 ¶ s z SemanticGraph._hash_locationc C s8 t | jƒt | jƒt | jƒt | jƒdd„ | j ¡ D ƒdœS )zGet graph statisticsc S s i | ]\}}|t |ƒ“qS r )rO )rA ÚstypeZsidsr r r Ú
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<dictcomp>Ä s ÿz+SemanticGraph.get_stats.<locals>.<dictcomp>)Ú total_nodesÚ total_edgesrI Ú locationsZtype_distribution)rO r% r& r' r( Úitemsr r r r Ú get_stats½ s þûzSemanticGraph.get_stats)N)rF )r r r r r) r r r3 r" r; r r r> rE rH rP rk r0 rw r r r r r# % s $r# c @ s– e Zd ZdZedœdd„Zedœdd„Zeedœd d
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eedœdd„Zeeedœdd„ZdS )ÚAIReasoningLayerz-AI reasoning layer for dynamic index creation)Úsemantic_graphc C s || _ | ¡ | _d S r$ )ÚgraphÚ_load_index_templatesÚindex_templates)r ry r r r r) Î s zAIReasoningLayer.__init__r c C s` g d¢g d¢ddœg d¢g d¢ddœg d¢g d ¢d
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dœg d¢g d¢ddœdœS )z'Load index templates for common queries)
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Útraffic_intensityÚsidewalk_widthÚ noise_levelZ air_qualityÚ
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playgroundÚgreeneryZschoolsÚlightingZ
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accessibility)
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ç333333Ã?çš™™™™™¹?r„ r„ rƒ r„ r„ r„ çš™™™™™©?r… z2Measures attractiveness for families with children)ÚsignalsÚweightsÚ description)Úpedestrian_flowÚevening_lightingZshade_coverageZpublic_transitÚ
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dwell_timeZcompetition_densityZ rent_burdenÚparking)çš™™™™™É?rƒ r„ rƒ rƒ r„ r„ r… z+Predicts commercial success for restaurants)Zinfrastructure_reliabilityZmaintenance_qualityZurban_contradictionZinformal_economyZmobile_connectivityZroad_integrityZstreet_lighting_reliability)r� rƒ rƒ r„ rƒ rƒ r„ z0Measures readiness for infrastructure investment)Z public_lifeZnight_activityZ walkabilityÚ shade_indexZpublic_toilet_indexZwifi_availabilityZ safety_indexZvisual_maintenance)rƒ rƒ rƒ r„ r„ r„ rƒ r„ z Measures tourism attractiveness)Z heat_stressrŽ Zdrainage_performanceZ
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tree_coverageZsurface_temperatureZ green_spaceZwater_retention)r� rƒ r� rƒ rƒ r„ r… z!Measures urban climate resilience)Úfamily_friendlyÚrestaurant_successÚinvestment_readinessZtourism_potentialZclimate_resiliencer r r r r r{ Ò s* ýýýýýëz&AIReasoningLayer._load_index_templates)Úqueryr c sh | ¡ ‰ d}| j ¡ D ],\}}t‡ fdd„| d¡D ƒƒr|} qDq|sR| |¡S | j| }| ||¡S )a
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Answer a natural language query by building a custom index
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Examples:
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- "How attractive is this area for families?"
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- "What signals correlate with restaurant success?"
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- "Show me a climate resilience index"
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Nc 3 s | ]}|ˆ v V qd S r$ r )rA Úkeyword©Z query_lowerr r rV ÿ rC z0AIReasoningLayer.answer_query.<locals>.<genexpr>r+ )Úlowerr| rv ÚanyÚsplitÚ_infer_index_from_queryÚ_build_index_from_template)r r’ Zmatched_templateZ
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template_nameÚtemplater r” r Ú answer_queryð s
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z AIReasoningLayer.answer_query)Únamerš r c
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