class Retrieve:
    """Base retrieval class."""
    def __init__(self, k=3):
        self.k = k
        
    def forward(self, query_or_queries, k=None):
        k = k if k is not None else self.k
        queries = [query_or_queries] if isinstance(query_or_queries, str) else query_or_queries
        
        # Implement retrieval logic
        passages = self._retrieve(queries, k)
        return [Passage(text=p) for p in passages]
        
    def _retrieve(self, queries, k):
        raise NotImplementedError

defmodule DSPEx.Retrieve do
  @moduledoc """
  Base retrieval behavior for all retrieval systems.
  
  Provides a common interface for retrieving relevant documents/passages
  from various knowledge sources.
  """
  
  @type query :: String.t()
  @type passage :: %{
    text: String.t(),
    title: String.t() | nil,
    score: float() | nil,
    metadata: map()
  }
  
  @type retrieval_result :: {:ok, [passage()]} | {:error, term()}
  
  @callback retrieve(query() | [query()], opts :: keyword()) :: retrieval_result()
  @callback retrieve_batch([query()], opts :: keyword()) :: {:ok, [[passage()]]} | {:error, term()}
  
  @optional_callbacks [retrieve_batch: 2]
  
  defmacro __using__(_opts) do
    quote do
      @behaviour DSPEx.Retrieve
      
      def retrieve_batch(queries, opts \\ []) do
        # Default implementation: sequential retrieval
        results = Enum.map(queries, &retrieve(&1, opts))
        
        case Enum.find(results, &match?({:error, _}, &1)) do
          nil ->
            passages_list = Enum.map(results, fn {:ok, passages} -> passages end)
            {:ok, passages_list}
          {:error, reason} ->
            {:error, reason}
        end
      end
      
      defoverridable retrieve_batch: 2
    end
  end
  
  @doc """
  Helper function to create a passage struct.
  """
  def passage(text, opts \\ []) do
    %{
      text: text,
      title: Keyword.get(opts, :title),
      score: Keyword.get(opts, :score),
      metadata: Keyword.get(opts, :metadata, %{})
    }
  end
end

defmodule DSPEx.Retrieve.ChromaDB do
  @moduledoc """
  ChromaDB retrieval integration.
  """
  
  use DSPEx.Retrieve
  
  defstruct [:client, :collection_name, :k, :distance_metric]
  
  def new(opts \\ []) do
    %__MODULE__{
      client: Keyword.get(opts, :client),
      collection_name: Keyword.get(opts, :collection_name, "default"),
      k: Keyword.get(opts, :k, 3),
      distance_metric: Keyword.get(opts, :distance_metric, "cosine")
    }
  end
  
  @impl DSPEx.Retrieve
  def retrieve(retriever, query, opts \\ []) do
    k = Keyword.get(opts, :k, retriever.k)
    
    request_body = %{
      query_texts: [query],
      n_results: k,
      include: ["documents", "metadatas", "distances"]
    }
    
    case make_chromadb_request(retriever, request_body) do
      {:ok, response} ->
        passages = parse_chromadb_response(response)
        {:ok, passages}
      {:error, reason} ->
        {:error, {:chromadb_error, reason}}
    end
  end
  
  defp make_chromadb_request(retriever, body) do
    # HTTP request to ChromaDB API
    # Implementation would use HTTPoison or similar
    :not_implemented
  end
  
  defp parse_chromadb_response(response) do
    # Parse ChromaDB response format
    response["documents"]
    |> List.first([])
    |> Enum.with_index()
    |> Enum.map(fn {doc, idx} ->
      metadata = get_in(response, ["metadatas", Access.at(0), Access.at(idx)]) || %{}
      distance = get_in(response, ["distances", Access.at(0), Access.at(idx)])
      score = if distance, do: 1.0 - distance, else: nil
      
      DSPEx.Retrieve.passage(doc, score: score, metadata: metadata)
    end)
  end
end

defmodule DSPEx.Retrieve.Pinecone do
  @moduledoc """
  Pinecone vector database retrieval integration.
  """
  
  use DSPEx.Retrieve
  
  defstruct [:api_key, :environment, :index_name, :namespace, :k, :embedding_model]
  
  def new(opts \\ []) do
    %__MODULE__{
      api_key: Keyword.get(opts, :api_key) || System.get_env("PINECONE_API_KEY"),
      environment: Keyword.get(opts, :environment) || System.get_env("PINECONE_ENVIRONMENT"),
      index_name: Keyword.get(opts, :index_name),
      namespace: Keyword.get(opts, :namespace, ""),
      k: Keyword.get(opts, :k, 3),
      embedding_model: Keyword.get(opts, :embedding_model, "text-embedding-ada-002")
    }
  end
  
  @impl DSPEx.Retrieve
  def retrieve(retriever, query, opts \\ []) do
    k = Keyword.get(opts, :k, retriever.k)
    
    with {:ok, query_embedding} <- get_embedding(retriever, query),
         {:ok, search_results} <- search_pinecone(retriever, query_embedding, k) do
      passages = parse_pinecone_results(search_results)
      {:ok, passages}
    else
      {:error, reason} -> {:error, {:pinecone_error, reason}}
    end
  end
  
  defp get_embedding(retriever, text) do
    # Get embedding using OpenAI or other embedding service
    # Would integrate with DSPEx.Client for embeddings
    :not_implemented
  end
  
  defp search_pinecone(retriever, embedding, k) do
    # Make Pinecone search request
    :not_implemented
  end
  
  defp parse_pinecone_results(results) do
    results["matches"]
    |> Enum.map(fn match ->
      DSPEx.Retrieve.passage(
        match["metadata"]["text"],
        score: match["score"],
        metadata: match["metadata"]
      )
    end)
  end
end

defmodule DSPEx.Retrieve.ColBERTv2 do
  @moduledoc """
  ColBERTv2 dense retrieval with late interaction.
  """
  
  use DSPEx.Retrieve
  
  defstruct [:checkpoint, :collection, :k, :ncells, :centroid_score_threshold]
  
  def new(opts \\ []) do
    %__MODULE__{
      checkpoint: Keyword.get(opts, :checkpoint),
      collection: Keyword.get(opts, :collection),
      k: Keyword.get(opts, :k, 3),
      ncells: Keyword.get(opts, :ncells, 1),
      centroid_score_threshold: Keyword.get(opts, :centroid_score_threshold, 0.5)
    }
  end
  
  @impl DSPEx.Retrieve
  def retrieve(retriever, query, opts \\ []) do
    # ColBERTv2 late interaction retrieval
    # Would require integration with ColBERT Python library or native implementation
    :not_implemented
  end
end

defmodule DSPEx.Retrieve.Embeddings do
  @moduledoc """
  Simple embeddings-based retrieval using in-memory search.
  """
  
  use DSPEx.Retrieve
  
  defstruct [:documents, :embeddings, :embedding_model, :k]
  
  def new(documents, opts \\ []) do
    %__MODULE__{
      documents: documents,
      embeddings: nil,  # Will be computed lazily
      embedding_model: Keyword.get(opts, :embedding_model, "text-embedding-ada-002"),
      k: Keyword.get(opts, :k, 3)
    }
  end
  
  @impl DSPEx.Retrieve
  def retrieve(retriever, query, opts \\ []) do
    k = Keyword.get(opts, :k, retriever.k)
    
    with {:ok, retriever} <- ensure_embeddings_computed(retriever),
         {:ok, query_embedding} <- get_query_embedding(retriever, query) do
      
      # Compute similarities
      similarities = compute_similarities(query_embedding, retriever.embeddings)
      
      # Get top-k documents
      top_indices = similarities
      |> Enum.with_index()
      |> Enum.sort_by(fn {sim, _idx} -> sim end, :desc)
      |> Enum.take(k)
      |> Enum.map(fn {_sim, idx} -> idx end)
      
      passages = top_indices
      |> Enum.map(fn idx ->
        doc = Enum.at(retriever.documents, idx)
        score = Enum.at(similarities, idx)
        DSPEx.Retrieve.passage(doc, score: score)
      end)
      
      {:ok, passages}
    else
      {:error, reason} -> {:error, reason}
    end
  end
  
  defp ensure_embeddings_computed(retriever) do
    if retriever.embeddings do
      {:ok, retriever}
    else
      # Compute embeddings for all documents
      case compute_document_embeddings(retriever.documents, retriever.embedding_model) do
        {:ok, embeddings} ->
          {:ok, %{retriever | embeddings: embeddings}}
        error -> error
      end
    end
  end
  
  defp compute_document_embeddings(documents, model) do
    # Batch compute embeddings using DSPEx.Client
    :not_implemented
  end
  
  defp get_query_embedding(retriever, query) do
    # Get single query embedding
    :not_implemented
  end
  
  defp compute_similarities(query_embedding, doc_embeddings) do
    # Compute cosine similarities
    Enum.map(doc_embeddings, fn doc_emb ->
      cosine_similarity(query_embedding, doc_emb)
    end)
  end
  
  defp cosine_similarity(vec1, vec2) do
    # Implement cosine similarity calculation
    :not_implemented
  end
end

# Built-in metrics
dspy.evaluate.answer_exact_match(example, pred)
dspy.evaluate.answer_passage_match(example, pred)
dspy.evaluate.answer_match_f1(example, pred)

# Semantic metrics
from dspy.evaluate import SemanticF1
semantic_metric = SemanticF1()
score = semantic_metric(example, prediction)

defmodule DSPEx.Evaluate.Metrics do
  @moduledoc """
  Standard evaluation metrics for DSPEx programs.
  """
  
  @doc """
  Exact match between expected and predicted answers.
  """
  def answer_exact_match(example, prediction, opts \\ []) do
    expected = get_answer(example)
    predicted = get_answer(prediction)
    
    case_sensitive = Keyword.get(opts, :case_sensitive, false)
    
    if case_sensitive do
      if expected == predicted, do: 1.0, else: 0.0
    else
      if String.downcase(expected) == String.downcase(predicted), do: 1.0, else: 0.0
    end
  end
  
  @doc """
  F1 score between expected and predicted answers at token level.
  """
  def answer_f1(example, prediction, opts \\ []) do
    expected = get_answer(example) |> tokenize()
    predicted = get_answer(prediction) |> tokenize()
    
    calculate_f1_score(expected, predicted)
  end
  
  @doc """
  Semantic similarity using embeddings.
  """
  def semantic_similarity(example, prediction, opts \\ []) do
    model = Keyword.get(opts, :embedding_model, "text-embedding-ada-002")
    
    expected = get_answer(example)
    predicted = get_answer(prediction)
    
    case get_embeddings([expected, predicted], model) do
      {:ok, [exp_emb, pred_emb]} ->
        cosine_similarity(exp_emb, pred_emb)
      {:error, _} ->
        0.0
    end
  end
  
  @doc """
  BLEU score for text generation tasks.
  """
  def bleu_score(example, prediction, opts \\ []) do
    n_gram = Keyword.get(opts, :n_gram, 4)
    
    expected = get_answer(example) |> tokenize()
    predicted = get_answer(prediction) |> tokenize()
    
    calculate_bleu_score(expected, predicted, n_gram)
  end
  
  @doc """
  ROUGE score for summarization tasks.
  """
  def rouge_score(example, prediction, opts \\ []) do
    rouge_type = Keyword.get(opts, :rouge_type, :rouge_1)
    
    expected = get_answer(example) |> tokenize()
    predicted = get_answer(prediction) |> tokenize()
    
    calculate_rouge_score(expected, predicted, rouge_type)
  end
  
  # Helper functions
  defp get_answer(%{answer: answer}), do: answer
  defp get_answer(%{output: output}), do: output
  defp get_answer(%{"answer" => answer}), do: answer
  defp get_answer(map) when is_map(map) do
    # Try common answer keys
    map[:answer] || map[:output] || map["answer"] || map["output"] || ""
  end
  defp get_answer(string) when is_binary(string), do: string
  defp get_answer(_), do: ""
  
  defp tokenize(text) do
    text
    |> String.downcase()
    |> String.replace(~r/[^\w\s]/, "")
    |> String.split()
  end
  
  defp calculate_f1_score(expected_tokens, predicted_tokens) do
    expected_set = MapSet.new(expected_tokens)
    predicted_set = MapSet.new(predicted_tokens)
    
    intersection = MapSet.intersection(expected_set, predicted_set) |> MapSet.size()
    
    precision = if MapSet.size(predicted_set) > 0 do
      intersection / MapSet.size(predicted_set)
    else
      0.0
    end
    
    recall = if MapSet.size(expected_set) > 0 do
      intersection / MapSet.size(expected_set)
    else
      0.0
    end
    
    if precision + recall > 0 do
      2 * precision * recall / (precision + recall)
    else
      0.0
    end
  end
  
  # Additional metric implementations...
  defp calculate_bleu_score(_expected, _predicted, _n_gram), do: :not_implemented
  defp calculate_rouge_score(_expected, _predicted, _rouge_type), do: :not_implemented
  defp get_embeddings(_texts, _model), do: {:error, :not_implemented}
  defp cosine_similarity(_vec1, _vec2), do: :not_implemented
end

from dspy.evaluate import Evaluate

evaluator = Evaluate(
    devset=dev_examples,
    metric=answer_exact_match,
    num_threads=10,
    display_progress=True,
    display_table=5
)

results = evaluator(program)

defmodule DSPEx.Evaluate do
  @moduledoc """
  Comprehensive evaluation framework for DSPEx programs.
  """
  
  defstruct [
    :dataset,
    :metric_fn,
    :num_threads,
    :display_progress,
    :display_table,
    :return_outputs,
    :cache_results
  ]
  
  def new(dataset, metric_fn, opts \\ []) do
    %__MODULE__{
      dataset: dataset,
      metric_fn: metric_fn,
      num_threads: Keyword.get(opts, :num_threads, 10),
      display_progress: Keyword.get(opts, :display_progress, true),
      display_table: Keyword.get(opts, :display_table, 5),
      return_outputs: Keyword.get(opts, :return_outputs, false),
      cache_results: Keyword.get(opts, :cache_results, true)
    }
  end
  
  @doc """
  Evaluate a program on the configured dataset.
  """
  def evaluate(evaluator, program, opts \\ []) do
    start_time = System.monotonic_time()
    
    # Setup progress tracking
    if evaluator.display_progress do
      IO.puts("Evaluating program on #{length(evaluator.dataset)} examples...")
    end
    
    # Parallel evaluation
    results = evaluator.dataset
    |> Task.async_stream(
      fn example -> evaluate_single_example(program, example, evaluator.metric_fn) end,
      max_concurrency: evaluator.num_threads,
      timeout: 60_000,
      on_timeout: :kill_task
    )
    |> Enum.map(fn
      {:ok, result} -> result
      {:exit, reason} -> {:error, reason}
    end)
    
    # Compile evaluation statistics
    end_time = System.monotonic_time()
    duration = System.convert_time_unit(end_time - start_time, :native, :millisecond)
    
    stats = compile_evaluation_stats(results, duration)
    
    if evaluator.display_progress do
      display_evaluation_results(stats, evaluator.display_table)
    end
    
    if evaluator.return_outputs do
      %{statistics: stats, outputs: results}
    else
      stats
    end
  end
  
  defp evaluate_single_example(program, example, metric_fn) do
    inputs = DSPEx.Example.inputs(example)
    
    case DSPEx.Program.forward(program, inputs) do
      {:ok, outputs} ->
        try do
          score = metric_fn.(example, outputs)
          {:ok, %{example: example, outputs: outputs, score: score}}
        rescue
          error ->
            {:error, {:metric_error, error}}
        end
      {:error, reason} ->
        {:error, {:program_error, reason}}
    end
  end
  
  defp compile_evaluation_stats(results, duration_ms) do
    successful_results = Enum.filter(results, &match?({:ok, _}, &1))
    error_results = Enum.filter(results, &match?({:error, _}, &1))
    
    scores = Enum.map(successful_results, fn {:ok, result} -> result.score end)
    
    %{
      total_examples: length(results),
      successful_examples: length(successful_results),
      failed_examples: length(error_results),
      success_rate: length(successful_results) / length(results),
      average_score: if Enum.empty?(scores), do: 0.0, else: Enum.sum(scores) / length(scores),
      min_score: if Enum.empty?(scores), do: 0.0, else: Enum.min(scores),
      max_score: if Enum.empty?(scores), do: 0.0, else: Enum.max(scores),
      score_distribution: calculate_score_distribution(scores),
      duration_ms: duration_ms,
      examples_per_second: length(results) / (duration_ms / 1000),
      error_breakdown: compile_error_breakdown(error_results)
    }
  end
  
  defp calculate_score_distribution(scores) when length(scores) == 0, do: %{}
  defp calculate_score_distribution(scores) do
    # Create score buckets
    buckets = 0..10
    |> Enum.map(fn i ->
      lower = i / 10.0
      upper = (i + 1) / 10.0
      count = Enum.count(scores, fn score -> 
        score >= lower and (score < upper or (i == 10 and score <= upper))
      end)
      {"#{lower}-#{upper}", count}
    end)
    |> Enum.into(%{})
    
    buckets
  end
  
  defp compile_error_breakdown(error_results) do
    error_results
    |> Enum.map(fn {:error, reason} ->
      case reason do
        {:metric_error, _} -> :metric_error
        {:program_error, _} -> :program_error
        _ -> :unknown_error
      end
    end)
    |> Enum.frequencies()
  end
  
  defp display_evaluation_results(stats, display_table) do
    IO.puts("""
    
    Evaluation Results:
    ==================
    Total Examples: #{stats.total_examples}
    Successful: #{stats.successful_examples}
    Failed: #{stats.failed_examples}
    Success Rate: #{Float.round(stats.success_rate * 100, 2)}%
    
    Score Statistics:
    Average: #{Float.round(stats.average_score, 4)}
    Min: #{Float.round(stats.min_score, 4)}
    Max: #{Float.round(stats.max_score, 4)}
    
    Performance:
    Duration: #{stats.duration_ms}ms
    Examples/second: #{Float.round(stats.examples_per_second, 2)}
    """)
    
    if display_table > 0 and stats.failed_examples > 0 do
      IO.puts("\nError Breakdown:")
      Enum.each(stats.error_breakdown, fn {error_type, count} ->
        IO.puts("  #{error_type}: #{count}")
      end)
    end
  end
end

import dspy

def qa_assertion(example, pred, trace=None):
    if len(pred.answer) < 10:
        dspy.Suggest(
            "The answer is too short. Please provide more detail.",
            target_module="answer_generator"
        )
    
    if "sorry" in pred.answer.lower():
        dspy.Assert(
            False,
            "Don't apologize in answers",
            target_module="answer_generator"
        )
    
    return dspy.evaluate.answer_exact_match(example, pred)

# Usage with context
with dspy.context(lm=lm):
    with dspy.settings.context(assert_=qa_assertion):
        result = program(question="What is AI?")

defmodule DSPEx.Assertions do
  @moduledoc """
  Assertion and suggestion system for runtime program validation and improvement.
  """
  
  defmodule Assert do
    @moduledoc """
    Hard assertion that causes retry when failed.
    """
    
    defexception [:message, :target_module, :context]
    
    def assert!(condition, message, opts \\ []) do
      unless condition do
        target_module = Keyword.get(opts, :target_module)
        context = Keyword.get(opts, :context, %{})
        
        raise __MODULE__, 
          message: message, 
          target_module: target_module, 
          context: context
      end
    end
  end
  
  defmodule Suggest do
    @moduledoc """
    Soft suggestion for program improvement.
    """
    
    defstruct [:message, :target_module, :context, :severity]
    
    def suggest(message, opts \\ []) do
      %__MODULE__{
        message: message,
        target_module: Keyword.get(opts, :target_module),
        context: Keyword.get(opts, :context, %{}),
        severity: Keyword.get(opts, :severity, :warning)
      }
    end
  end
  
  @doc """
  Execute a program with assertion checking.
  """
  def with_assertions(program, inputs, assertion_fn, opts \\ []) do
    max_retries = Keyword.get(opts, :max_retries, 3)
    
    execute_with_retries(program, inputs, assertion_fn, max_retries, [])
  end
  
  defp execute_with_retries(_program, _inputs, _assertion_fn, 0, suggestions) do
    {:error, {:max_retries_exceeded, suggestions}}
  end
  
  defp execute_with_retries(program, inputs, assertion_fn, retries_left, suggestions) do
    case DSPEx.Program.forward(program, inputs) do
      {:ok, outputs} ->
        try do
          # Run assertion function
          score = assertion_fn.(inputs, outputs)
          {:ok, outputs, suggestions, score}
        rescue
          %Assert{} = assertion_error ->
            # Hard assertion failed - retry
            suggestion = %Suggest{
              message: assertion_error.message,
              target_module: assertion_error.target_module,
              context: assertion_error.context,
              severity: :error
            }
            
            execute_with_retries(program, inputs, assertion_fn, retries_left - 1, 
                               [suggestion | suggestions])
        catch
          %Suggest{} = suggestion ->
            # Soft suggestion - continue but record
            case assertion_fn.(inputs, outputs) do
              score when is_number(score) ->
                {:ok, outputs, [suggestion | suggestions], score}
              _ ->
                {:ok, outputs, [suggestion | suggestions], nil}
            end
        end
      {:error, reason} ->
        {:error, reason}
    end
  end
end