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* Password reset back button also after confirmation Signed-off-by: Juan José Mata <juanjo.mata@gmail.com> * Implement a filter for category (#215) - Also implement an is empty/is null condition. * Implement an LLM cost estimation page Track costs across all the cost categories: auto categorization, auto merchant detection and chat. Show warning with estimated cost when running a rule that contains AI. * Update pricing * Add google pricing and fix inferred model everywhere. * Update app/models/llm_usage.rb Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Signed-off-by: soky srm <sokysrm@gmail.com> * FIX address review * Linter * Address review - Lowered log level - extracted the duplicated record_usage method into a shared concern * Update app/controllers/settings/llm_usages_controller.rb Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Signed-off-by: soky srm <sokysrm@gmail.com> * Moved attr_reader out of private --------- Signed-off-by: Juan José Mata <juanjo.mata@gmail.com> Signed-off-by: soky srm <sokysrm@gmail.com> Co-authored-by: Juan José Mata <juanjo.mata@gmail.com> Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
217 lines
7.3 KiB
Ruby
217 lines
7.3 KiB
Ruby
class Provider::Openai::AutoCategorizer
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include Provider::Openai::Concerns::UsageRecorder
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attr_reader :client, :model, :transactions, :user_categories, :custom_provider, :langfuse_trace, :family
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def initialize(client, model: "", transactions: [], user_categories: [], custom_provider: false, langfuse_trace: nil, family: nil)
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@client = client
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@model = model
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@transactions = transactions
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@user_categories = user_categories
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@custom_provider = custom_provider
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@langfuse_trace = langfuse_trace
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@family = family
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end
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def auto_categorize
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if custom_provider
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auto_categorize_openai_generic
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else
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auto_categorize_openai_native
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end
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end
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def instructions
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<<~INSTRUCTIONS.strip_heredoc
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You are an assistant to a consumer personal finance app. You will be provided a list
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of the user's transactions and a list of the user's categories. Your job is to auto-categorize
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each transaction.
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Closely follow ALL the rules below while auto-categorizing:
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- Return 1 result per transaction
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- Correlate each transaction by ID (transaction_id)
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- Attempt to match the most specific category possible (i.e. subcategory over parent category)
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- Category and transaction classifications should match (i.e. if transaction is an "expense", the category must have classification of "expense")
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- If you don't know the category, return "null"
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- You should always favor "null" over false positives
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- Be slightly pessimistic. Only match a category if you're 60%+ confident it is the correct one.
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- Each transaction has varying metadata that can be used to determine the category
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- Note: "hint" comes from 3rd party aggregators and typically represents a category name that
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may or may not match any of the user-supplied categories
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INSTRUCTIONS
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end
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private
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def auto_categorize_openai_native
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span = langfuse_trace&.span(name: "auto_categorize_api_call", input: {
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model: model.presence || Provider::Openai::DEFAULT_MODEL,
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transactions: transactions,
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user_categories: user_categories
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})
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response = client.responses.create(parameters: {
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model: model.presence || Provider::Openai::DEFAULT_MODEL,
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input: [ { role: "developer", content: developer_message } ],
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text: {
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format: {
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type: "json_schema",
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name: "auto_categorize_personal_finance_transactions",
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strict: true,
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schema: json_schema
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}
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},
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instructions: instructions
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})
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Rails.logger.info("Tokens used to auto-categorize transactions: #{response.dig("usage", "total_tokens")}")
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categorizations = extract_categorizations_native(response)
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result = build_response(categorizations)
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record_usage(
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model.presence || Provider::Openai::DEFAULT_MODEL,
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response.dig("usage"),
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operation: "auto_categorize",
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metadata: {
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transaction_count: transactions.size,
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category_count: user_categories.size
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}
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)
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span&.end(output: result.map(&:to_h), usage: response.dig("usage"))
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result
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rescue => e
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span&.end(output: { error: e.message }, level: "ERROR")
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raise
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end
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def auto_categorize_openai_generic
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span = langfuse_trace&.span(name: "auto_categorize_api_call", input: {
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model: model.presence || Provider::Openai::DEFAULT_MODEL,
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transactions: transactions,
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user_categories: user_categories
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})
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response = client.chat(parameters: {
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model: model.presence || Provider::Openai::DEFAULT_MODEL,
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messages: [
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{ role: "system", content: instructions },
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{ role: "user", content: developer_message }
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],
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response_format: {
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type: "json_schema",
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json_schema: {
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name: "auto_categorize_personal_finance_transactions",
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strict: true,
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schema: json_schema
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}
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}
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})
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Rails.logger.info("Tokens used to auto-categorize transactions: #{response.dig("usage", "total_tokens")}")
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categorizations = extract_categorizations_generic(response)
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result = build_response(categorizations)
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record_usage(
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model.presence || Provider::Openai::DEFAULT_MODEL,
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response.dig("usage"),
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operation: "auto_categorize",
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metadata: {
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transaction_count: transactions.size,
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category_count: user_categories.size
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}
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)
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span&.end(output: result.map(&:to_h), usage: response.dig("usage"))
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result
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rescue => e
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span&.end(output: { error: e.message }, level: "ERROR")
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raise
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end
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AutoCategorization = Provider::LlmConcept::AutoCategorization
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def build_response(categorizations)
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categorizations.map do |categorization|
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AutoCategorization.new(
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transaction_id: categorization.dig("transaction_id"),
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category_name: normalize_category_name(categorization.dig("category_name")),
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)
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end
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end
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def normalize_category_name(category_name)
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return nil if category_name == "null"
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category_name
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end
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def extract_categorizations_native(response)
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# Find the message output (not reasoning output)
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message_output = response["output"]&.find { |o| o["type"] == "message" }
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raw = message_output&.dig("content", 0, "text")
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raise Provider::Openai::Error, "No message content found in response" if raw.nil?
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JSON.parse(raw).dig("categorizations")
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rescue JSON::ParserError => e
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raise Provider::Openai::Error, "Invalid JSON in native categorization: #{e.message}"
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end
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def extract_categorizations_generic(response)
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raw = response.dig("choices", 0, "message", "content")
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JSON.parse(raw).dig("categorizations")
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rescue JSON::ParserError => e
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raise Provider::Openai::Error, "Invalid JSON in generic categorization: #{e.message}"
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end
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def json_schema
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{
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type: "object",
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properties: {
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categorizations: {
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type: "array",
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description: "An array of auto-categorizations for each transaction",
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items: {
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type: "object",
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properties: {
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transaction_id: {
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type: "string",
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description: "The internal ID of the original transaction",
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enum: transactions.map { |t| t[:id] }
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},
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category_name: {
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type: "string",
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description: "The matched category name of the transaction, or null if no match",
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enum: [ *user_categories.map { |c| c[:name] }, "null" ]
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}
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},
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required: [ "transaction_id", "category_name" ],
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additionalProperties: false
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}
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}
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},
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required: [ "categorizations" ],
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additionalProperties: false
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}
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end
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def developer_message
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<<~MESSAGE.strip_heredoc
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Here are the user's available categories in JSON format:
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```json
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#{user_categories.to_json}
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```
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Use the available categories to auto-categorize the following transactions:
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```json
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#{transactions.to_json}
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```
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MESSAGE
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end
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end
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