agents

%% autonomous_knowledge_ingestion.erl
%% Autonomous knowledge discovery, ingestion, and curation system
-module(autonomous_knowledge_ingestion).
-behaviour(gen_server).

-export([
    start_link/0,
    ingest_from_sources/1,
    curate_knowledge_domain/2,
    discover_new_domains/0,
    evaluate_knowledge_quality/2,
    auto_categorize_content/1,
    schedule_ingestion_job/2,
    get_ingestion_status/0
]).

-export([init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).

-define(KB_BASE_PATH, "/Users/agent/agents.erl/knowledge_bases").
-define(INGESTION_QUEUE, knowledge_ingestion_queue).
-define(QUALITY_THRESHOLD, 0.7).
-define(INGESTION_INTERVAL, 3600000). % 1 hour

-record(state, {
    ingestion_queue = [],
    active_jobs = #{},
    quality_metrics = #{},
    domain_coverage = #{},
    last_discovery = undefined
}).

-record(knowledge_source, {
    type,           % web, api, file, database
    url,            % source URL or path
    domain,         % target knowledge domain
    priority,       % 1-10 priority
    frequency,      % ingestion frequency
    last_update,    % last successful update
    quality_score   % assessed quality 0.0-1.0
}).

-record(ingestion_job, {
    id,
    source,
    status,         % pending, running, completed, failed
    started_at,
    completed_at,
    items_processed,
    quality_assessment,
    errors = []
}).

%% Public API

start_link() ->
    gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).

%% Ingest knowledge from multiple sources
ingest_from_sources(Sources) ->
    gen_server:cast(?MODULE, {ingest_sources, Sources}).

%% Curate and organize knowledge in a specific domain
curate_knowledge_domain(Domain, CurationStrategy) ->
    gen_server:cast(?MODULE, {curate_domain, Domain, CurationStrategy}).

%% Discover new knowledge domains automatically
discover_new_domains() ->
    gen_server:call(?MODULE, discover_domains).

%% Evaluate quality of existing knowledge
evaluate_knowledge_quality(Domain, Content) ->
    gen_server:call(?MODULE, {evaluate_quality, Domain, Content}).

%% Auto-categorize new content
auto_categorize_content(Content) ->
    gen_server:call(?MODULE, {auto_categorize, Content}).

%% Schedule a new ingestion job
schedule_ingestion_job(Source, Options) ->
    gen_server:cast(?MODULE, {schedule_job, Source, Options}).

%% Get current ingestion status
get_ingestion_status() ->
    gen_server:call(?MODULE, get_status).

%% Gen_server callbacks

init([]) ->
    % Create ETS table for ingestion queue
    ets:new(?INGESTION_QUEUE, [named_table, public, ordered_set]),
    
    % Schedule periodic discovery and ingestion
    schedule_periodic_discovery(),
    schedule_periodic_ingestion(),
    
    % Initialize with default knowledge sources
    DefaultSources = get_default_sources(),
    
    {ok, #state{
        ingestion_queue = DefaultSources,
        domain_coverage = analyze_existing_coverage()
    }}.

handle_call(discover_domains, _From, State) ->
    NewDomains = perform_domain_discovery(),
    {reply, {ok, NewDomains}, State};

handle_call({evaluate_quality, Domain, Content}, _From, State) ->
    QualityScore = assess_content_quality(Domain, Content),
    {reply, {ok, QualityScore}, State};

handle_call({auto_categorize, Content}, _From, State) ->
    Category = categorize_content_automatically(Content),
    {reply, {ok, Category}, State};

handle_call(get_status, _From, State) ->
    Status = #{
        queue_length => length(State#state.ingestion_queue),
        active_jobs => maps:size(State#state.active_jobs),
        domain_coverage => State#state.domain_coverage,
        last_discovery => State#state.last_discovery
    },
    {reply, {ok, Status}, State};

handle_call(_Request, _From, State) ->
    {reply, {error, unknown_request}, State}.

handle_cast({ingest_sources, Sources}, State) ->
    NewQueue = State#state.ingestion_queue ++ Sources,
    spawn(fun() -> process_ingestion_queue(NewQueue) end),
    {noreply, State#state{ingestion_queue = NewQueue}};

handle_cast({curate_domain, Domain, Strategy}, State) ->
    spawn(fun() -> perform_domain_curation(Domain, Strategy) end),
    {noreply, State};

handle_cast({schedule_job, Source, Options}, State) ->
    JobId = generate_job_id(),
    Job = create_ingestion_job(JobId, Source, Options),
    NewActiveJobs = maps:put(JobId, Job, State#state.active_jobs),
    spawn(fun() -> execute_ingestion_job(Job) end),
    {noreply, State#state{active_jobs = NewActiveJobs}};

handle_cast(_Msg, State) ->
    {noreply, State}.

handle_info({periodic_discovery}, State) ->
    spawn(fun() -> perform_autonomous_discovery() end),
    schedule_periodic_discovery(),
    {noreply, State#state{last_discovery = erlang:system_time(millisecond)}};

handle_info({periodic_ingestion}, State) ->
    spawn(fun() -> process_scheduled_ingestions() end),
    schedule_periodic_ingestion(),
    {noreply, State};

handle_info({job_completed, JobId, Result}, State) ->
    NewActiveJobs = maps:remove(JobId, State#state.active_jobs),
    update_quality_metrics(Result),
    {noreply, State#state{active_jobs = NewActiveJobs}};

handle_info(_Info, State) ->
    {noreply, State}.

terminate(_Reason, _State) ->
    ok.

code_change(_OldVsn, State, _Extra) ->
    {ok, State}.

%% Internal functions

get_default_sources() ->
    [
        #knowledge_source{
            type = web,
            url = "https://en.wikipedia.org/api/rest_v1/",
            domain = general,
            priority = 5,
            frequency = daily,
            quality_score = 0.8
        },
        #knowledge_source{
            type = api,
            url = "https://api.semanticscholar.org/",
            domain = academic,
            priority = 8,
            frequency = hourly,
            quality_score = 0.9
        },
        #knowledge_source{
            type = web,
            url = "https://pubmed.ncbi.nlm.nih.gov/",
            domain = medicine,
            priority = 9,
            frequency = daily,
            quality_score = 0.95
        }
    ].

perform_domain_discovery() ->
    % Analyze trending topics and emerging fields
    TrendingTopics = discover_trending_topics(),
    EmergingFields = identify_emerging_fields(),
    GapAnalysis = analyze_knowledge_gaps(),
    
    NewDomains = lists:append([TrendingTopics, EmergingFields, GapAnalysis]),
    
    % Create new domain directories if needed
    lists:foreach(fun(Domain) ->
        create_domain_directory(Domain)
    end, NewDomains),
    
    NewDomains.

discover_trending_topics() ->
    % Monitor academic databases, news sources, research platforms
    % Use NLP to identify frequently mentioned new concepts
    Topics = [
        "quantum_machine_learning",
        "neural_architecture_search", 
        "synthetic_biology",
        "digital_therapeutics",
        "edge_ai",
        "sustainable_computing"
    ],
    
    % Filter for genuinely new domains
    lists:filter(fun(Topic) -> 
        not domain_exists(Topic) 
    end, Topics).

identify_emerging_fields() ->
    % Cross-reference multiple academic classification systems
    % Identify interdisciplinary convergence points
    [
        "computational_social_science",
        "bio_inspired_computing", 
        "human_centered_ai",
        "climate_informatics",
        "precision_agriculture",
        "digital_archaeology"
    ].

analyze_knowledge_gaps() ->
    % Analyze current domain coverage vs. expert agent specializations
    ExistingDomains = knowledge_base_retrieval:list_available_domains(),
    AgentDomains = extract_agent_specializations(),
    
    % Find gaps where we have agents but insufficient knowledge
    Gaps = lists:subtract(AgentDomains, ExistingDomains),
    
    % Prioritize based on agent template usage frequency
    prioritize_by_usage(Gaps).

extract_agent_specializations() ->
    {ok, Templates} = agent_templates:list_templates(),
    lists:foldl(fun(Template, Acc) ->
        Domain = infer_domain_from_template(Template),
        [Domain | Acc]
    end, [], Templates).

infer_domain_from_template(Template) ->
    % Extract domain from template name and description
    Name = maps:get(name, Template, <<"">>),
    _Description = maps:get(description, Template, <<"">>),
    
    % Simple heuristic - could be enhanced with NLP
    case binary_to_list(Name) of
        "Clinical Psychologist" -> psychology;
        "Medical Advisor" -> medicine;
        "Educational Specialist" -> education;
        "Nutritionist" -> nutrition;
        "Physical Therapist" -> physical_therapy;
        "Anthropologist" -> anthropology;
        "Linguist" -> linguistics;
        "Historian" -> history;
        "Philosopher" -> philosophy;
        "Sociologist" -> sociology;
        "Quantum Developer" -> quantum_computing;
        "Bioinformatics Analyst" -> bioinformatics;
        "Robotics Engineer" -> robotics;
        "AI Ethics Advisor" -> ai_ethics;
        _ -> general
    end.

assess_content_quality(Domain, Content) ->
    % Multi-factor quality assessment
    Factors = [
        assess_source_credibility(Content),
        assess_content_accuracy(Domain, Content),
        assess_completeness(Content),
        assess_recency(Content),
        assess_relevance(Domain, Content)
    ],
    
    % Weighted average
    Weights = [0.25, 0.3, 0.2, 0.15, 0.1],
    weighted_average(Factors, Weights).

assess_source_credibility(Content) ->
    % Check source against credibility databases
    % Analyze citation patterns, author credentials
    case extract_source_info(Content) of
        {peer_reviewed, _} -> 0.9;
        {academic_institution, _} -> 0.8;
        {government_source, _} -> 0.85;
        {commercial, _} -> 0.5;
        {unknown, _} -> 0.3
    end.

assess_content_accuracy(Domain, _Content) ->
    % Cross-reference with existing high-quality sources
    % Use fact-checking APIs where available
    % Domain-specific validation rules
    BaseScore = 0.7,
    
    % Domain-specific adjustments
    DomainMultiplier = case Domain of
        medicine -> 1.2;  % Higher standards for medical content
        psychology -> 1.1;
        education -> 1.0;
        general -> 0.9
    end,
    
    min(1.0, BaseScore * DomainMultiplier).

assess_completeness(Content) ->
    % Analyze content structure, coverage of key concepts
    Length = length(Content),
    HasReferences = has_references(Content),
    HasExamples = has_examples(Content),
    
    BaseScore = case Length of
        L when L > 2000 -> 0.9;
        L when L > 1000 -> 0.7;
        L when L > 500 -> 0.5;
        _ -> 0.3
    end,
    
    Bonus = case {HasReferences, HasExamples} of
        {true, true} -> 0.1;
        {true, false} -> 0.05;
        {false, true} -> 0.05;
        {false, false} -> 0.0
    end,
    
    min(1.0, BaseScore + Bonus).

assess_recency(Content) ->
    % Favor recent content, with domain-specific decay rates
    case extract_date(Content) of
        {ok, Date} ->
            DaysOld = days_since(Date),
            decay_by_age(DaysOld);
        error ->
            0.5  % Unknown date gets medium score
    end.

assess_relevance(Domain, Content) ->
    % Use keyword matching and semantic analysis
    DomainKeywords = get_domain_keywords(Domain),
    ContentKeywords = extract_keywords(Content),
    
    MatchScore = calculate_keyword_overlap(DomainKeywords, ContentKeywords),
    
    % Boost for domain-specific terminology
    SpecializedTerms = count_specialized_terms(Domain, Content),
    SpecializationBonus = min(0.2, SpecializedTerms * 0.02),
    
    min(1.0, MatchScore + SpecializationBonus).

categorize_content_automatically(Content) ->
    % Multi-stage categorization
    Keywords = extract_keywords(Content),
    
    % Primary categorization by dominant keywords
    PrimaryCategory = classify_by_keywords(Keywords),
    
    % Secondary categorization by content structure
    SecondaryCategory = classify_by_structure(Content),
    
    % Tertiary categorization by citation patterns
    TertiaryCategory = classify_by_citations(Content),
    
    % Weighted combination
    combine_classifications([
        {PrimaryCategory, 0.5},
        {SecondaryCategory, 0.3},
        {TertiaryCategory, 0.2}
    ]).

classify_by_keywords(Keywords) ->
    % Define keyword sets for each domain
    DomainKeywordSets = #{
        psychology => ["behavior", "cognitive", "therapy", "mental", "psychological"],
        medicine => ["treatment", "diagnosis", "clinical", "patient", "medical"],
        education => ["learning", "teaching", "curriculum", "student", "pedagogy"],
        technology => ["algorithm", "system", "software", "computer", "digital"],
        science => ["research", "experiment", "data", "analysis", "scientific"]
    },
    
    % Calculate overlap scores
    Scores = maps:map(fun(_Domain, DomainKeywords) ->
        length(lists:intersection(Keywords, DomainKeywords))
    end, DomainKeywordSets),
    
    % Return domain with highest score
    {Domain, _Score} = lists:max(maps:to_list(Scores)),
    Domain.

perform_domain_curation(Domain, Strategy) ->
    % Implement different curation strategies
    case Strategy of
        consolidate ->
            consolidate_domain_knowledge(Domain);
        update ->
            update_stale_content(Domain);
        expand ->
            expand_domain_coverage(Domain);
        quality_improve ->
            improve_content_quality(Domain)
    end.

consolidate_domain_knowledge(Domain) ->
    % Remove duplicates, merge complementary content
    DomainFiles = get_domain_files(Domain),
    
    % Analyze content similarity
    Similarities = analyze_content_similarities(DomainFiles),
    
    % Merge highly similar content
    Merges = identify_merge_candidates(Similarities),
    execute_content_merges(Merges),
    
    % Remove redundant files
    Redundancies = identify_redundant_files(DomainFiles),
    remove_redundant_content(Redundancies).

update_stale_content(Domain) ->
    % Update content that is outdated or needs refreshing
    DomainFiles = get_domain_files(Domain),
    StaleContent = identify_stale_content(DomainFiles),
    
    % Re-fetch updated content from original sources
    lists:foreach(fun(File) ->
        case extract_source_from_file(File) of
            {ok, Source} ->
                % Re-ingest from the same source
                spawn(fun() -> ingest_from_source(Source) end);
            {error, _} ->
                % Skip if source cannot be determined
                ok
        end
    end, StaleContent).

expand_domain_coverage(Domain) ->
    % Identify gaps in domain coverage and fill them
    CurrentTopics = analyze_current_coverage(Domain),
    RequiredTopics = get_domain_template(Domain),
    Gaps = identify_coverage_gaps(CurrentTopics, RequiredTopics),
    
    % Find and ingest content for missing topics
    lists:foreach(fun(Topic) ->
        Sources = discover_sources_for_topic(Domain, Topic),
        lists:foreach(fun(Source) ->
            spawn(fun() -> ingest_from_source(Source) end)
        end, Sources)
    end, Gaps).

improve_content_quality(Domain) ->
    % Enhance existing content quality through various means
    DomainFiles = get_domain_files(Domain),
    
    % Assess current quality levels
    QualityAssessments = assess_domain_quality(DomainFiles),
    
    % Focus on low-quality content
    LowQualityContent = filter_low_quality(QualityAssessments),
    
    % Apply quality improvement strategies
    lists:foreach(fun({File, Quality}) ->
        case Quality of
            Q when Q < 0.5 ->
                % Very low quality - consider replacement
                replace_with_better_source(File);
            Q when Q < 0.7 ->
                % Medium quality - enhance with additional sources
                enhance_with_supplementary_content(File);
            _ ->
                % Acceptable quality - no action needed
                ok
        end
    end, LowQualityContent).

process_ingestion_queue(Queue) ->
    lists:foreach(fun(Source) ->
        spawn(fun() -> ingest_from_source(Source) end)
    end, Queue).

ingest_from_source(Source) ->
    try
        Content = fetch_content_from_source(Source),
        Quality = assess_content_quality(Source#knowledge_source.domain, Content),
        
        case Quality >= ?QUALITY_THRESHOLD of
            true ->
                Category = auto_categorize_content(Content),
                store_knowledge(Category, Content, Source),
                {ok, ingested};
            false ->
                {error, {quality_too_low, Quality}}
        end
    catch
        E:R:S ->
            {error, {ingestion_failed, E, R, S}}
    end.

fetch_content_from_source(#knowledge_source{type = web, url = Url}) ->
    % Implement web scraping with rate limiting
    http_request_with_rate_limit(Url);
    
fetch_content_from_source(#knowledge_source{type = api, url = Url}) ->
    % Implement API calls with authentication
    api_request_with_auth(Url);
    
fetch_content_from_source(#knowledge_source{type = file, url = Path}) ->
    % Read from file system
    file:read_file(Path).

store_knowledge(Domain, Content, Source) ->
    % Generate unique filename
    Timestamp = integer_to_list(erlang:system_time(second)),
    SourceHash = generate_source_hash(Source),
    Filename = lists:flatten([atom_to_list(Domain), "_", Timestamp, "_", SourceHash, ".json"]),
    
    % Create structured knowledge entry
    KnowledgeEntry = #{
        content => Content,
        source => Source,
        ingested_at => erlang:system_time(millisecond),
        quality_score => assess_content_quality(Domain, Content),
        category => Domain,
        metadata => extract_metadata(Content)
    },
    
    % Write to knowledge base
    FilePath = filename:join([?KB_BASE_PATH, atom_to_list(Domain), Filename]),
    file:write_file(FilePath, jsx:encode(KnowledgeEntry)).

%% Utility functions

weighted_average(Values, Weights) ->
    WeightedSum = lists:sum([V * W || {V, W} <- lists:zip(Values, Weights)]),
    TotalWeight = lists:sum(Weights),
    WeightedSum / TotalWeight.

domain_exists(Domain) ->
    DomainPath = filename:join(?KB_BASE_PATH, atom_to_list(Domain)),
    filelib:is_dir(DomainPath).

create_domain_directory(Domain) ->
    DomainPath = filename:join(?KB_BASE_PATH, atom_to_list(Domain)),
    filelib:ensure_dir(DomainPath ++ "/"),
    file:make_dir(DomainPath).

prioritize_by_usage(Gaps) ->
    % For now, return as-is. Could implement usage tracking
    Gaps.

analyze_existing_coverage() ->
    case file:list_dir(?KB_BASE_PATH) of
        {ok, Dirs} ->
            lists:foldl(fun(Dir, Acc) ->
                DomainPath = filename:join(?KB_BASE_PATH, Dir),
                case filelib:is_dir(DomainPath) of
                    true ->
                        FileCount = count_files_in_domain(DomainPath),
                        maps:put(list_to_atom(Dir), FileCount, Acc);
                    false ->
                        Acc
                end
            end, #{}, Dirs);
        {error, _} ->
            #{}
    end.

count_files_in_domain(DomainPath) ->
    case file:list_dir(DomainPath) of
        {ok, Files} ->
            length([F || F <- Files, filename:extension(F) =:= ".json"]);
        {error, _} ->
            0
    end.

schedule_periodic_discovery() ->
    erlang:send_after(?INGESTION_INTERVAL, self(), {periodic_discovery}).

schedule_periodic_ingestion() ->
    erlang:send_after(?INGESTION_INTERVAL div 2, self(), {periodic_ingestion}).

perform_autonomous_discovery() ->
    % Placeholder for autonomous discovery logic
    ok.

process_scheduled_ingestions() ->
    % Placeholder for scheduled ingestion processing
    ok.

generate_job_id() ->
    list_to_binary(uuid:uuid_to_string(uuid:get_v4())).

create_ingestion_job(JobId, Source, _Options) ->
    #ingestion_job{
        id = JobId,
        source = Source,
        status = pending,
        started_at = erlang:system_time(millisecond)
    }.

execute_ingestion_job(_Job) ->
    % Placeholder for job execution
    ok.

update_quality_metrics(_Result) ->
    % Placeholder for quality metrics update
    ok.

% Placeholder implementations for helper functions
extract_source_info(_Content) -> {unknown, ""}.
has_references(_Content) -> false.
has_examples(_Content) -> false.
extract_date(_Content) -> error.
days_since(_Date) -> 30.
decay_by_age(Days) -> max(0.1, 1.0 - (Days / 365.0)).
get_domain_keywords(_Domain) -> [].
extract_keywords(_Content) -> [].
calculate_keyword_overlap(_Keywords1, _Keywords2) -> 0.5.
count_specialized_terms(_Domain, _Content) -> 0.
classify_by_structure(_Content) -> general.
classify_by_citations(_Content) -> general.
combine_classifications(Classifications) ->
    {Category, _Weight} = hd(Classifications),
    Category.
get_domain_files(_Domain) -> [].
analyze_content_similarities(_Files) -> [].
identify_merge_candidates(_Similarities) -> [].
execute_content_merges(_Merges) -> ok.
identify_redundant_files(_Files) -> [].
remove_redundant_content(_Redundancies) -> ok.
http_request_with_rate_limit(_Url) -> <<"sample content">>.
api_request_with_auth(_Url) -> <<"sample content">>.
generate_source_hash(_Source) -> "hash123".
extract_metadata(_Content) -> #{}.
identify_stale_content(_DomainFiles) -> [].
extract_source_from_file(_File) -> {ok, unknown}.
analyze_current_coverage(_Domain) -> [].
get_domain_template(_Domain) -> [].
identify_coverage_gaps(_CurrentTopics, _RequiredTopics) -> [].
discover_sources_for_topic(_Domain, _Topic) -> [].
assess_domain_quality(_DomainFiles) -> [].
filter_low_quality(_QualityAssessments) -> [].
replace_with_better_source(_File) -> ok.
enhance_with_supplementary_content(_File) -> ok.