AI Framework Comparison for Elixir

Setup

Mix.install([
  {:nx, "~> 0.5"},
  {:bumblebee, "~> 0.4"},
  {:axon, "~> 0.5"},
  {:explorer, "~> 0.7"},
  {:kino, "~> 0.11"},
  {:req, "~> 0.4"},
  {:jason, "~> 1.4"}
])

Framework Comparison

defmodule AIComparison do
  def frameworks do
    %{
      "nx_and_bumblebee" => %{
        name: "Nx + Bumblebee",
        strengths: [
          "Native Elixir implementation",
          "HuggingFace models support",
          "GPU acceleration",
          "Built-in model serving"
        ],
        weaknesses: [
          "Smaller ecosystem than Python",
          "Fewer pre-trained models"
        ],
        use_cases: [
          "Text generation",
          "Classification",
          "Embeddings",
          "Image processing"
        ],
        example: """
        {:ok, model} = Bumblebee.load_model({:hf, "microsoft/resnet-50"})
        {:ok, featurizer} = Bumblebee.load_featurizer({:hf, "microsoft/resnet-50"})
        
        serving = Bumblebee.Vision.image_classification(model, featurizer)
        
        Nx.Serving.run(serving, image_input)
        """
      },
      
      "axon" => %{
        name: "Axon",
        strengths: [
          "Neural network framework",
          "Native Elixir",
          "Good for custom models",
          "Integration with Nx"
        ],
        weaknesses: [
          "More low-level than alternatives",
          "Requires more ML expertise"
        ],
        use_cases: [
          "Custom neural networks",
          "Model training",
          "Research"
        ],
        example: """
        model = 
          Axon.input("input", shape: {nil, 784})
          |> Axon.dense(128, activation: :relu)
          |> Axon.dense(10, activation: :softmax)
        
        model
        |> Axon.Loop.trainer(:categorical_crossentropy, :adam)
        |> Axon.Loop.run(data, epochs: 10)
        """
      },
      
      "python_interop" => %{
        name: "Python Interop (LangChain)",
        strengths: [
          "Full LangChain features",
          "Large ecosystem",
          "Many pre-trained models",
          "Rich tooling"
        ],
        weaknesses: [
          "Python dependency",
          "Performance overhead",
          "More complex setup"
        ],
        use_cases: [
          "Complex LLM applications",
          "Chain operations",
          "Document processing",
          "RAG implementations"
        ],
        example: """
        # config/config.exs
        config :erlport_python,
          python: "/path/to/python",
          python_path: "/path/to/langchain"
        
        # Implementation
        defmodule PyLangChain do
          use Export.Python
          
          def create_chain do
            py_call(langchain_module, :create_chain, [])
          end
        end
        """
      }
    }
  end
end

Microsoft Fabric Integration

defmodule FabricIntegration do
  def connection_examples do
    %{
      "synapse" => %{
        name: "Synapse Analytics",
        setup: """
        # mix.exs
        def deps do
          [
            {:microsoft_fabric, "~> 0.1"},
            {:req, "~> 0.4"}
          ]
        end
        """,
        example: """
        defmodule MyApp.Fabric do
          def query_synapse(query) do
            headers = [
              {"Authorization", "Bearer " <> get_token()},
              {"Content-Type", "application/json"}
            ]
            
            body = Jason.encode!(%{
              "query" => query,
              "workspace" => workspace_id()
            })
            
            Req.post!(
              "https://#{workspace()}.fabric.microsoft.com/synapse/query",
              headers: headers,
              body: body
            )
          end
          
          defp get_token do
            # Implement Azure AD token acquisition
          end
        end
        """
      },
      
      "lakehouse" => %{
        name: "Lakehouse",
        example: """
        defmodule MyApp.Lakehouse do
          def read_data(path) do
            Explorer.DataFrame.from_parquet!(
              "abfs://#{container()}/#{path}",
              storage_options: azure_credentials()
            )
          end
          
          defp azure_credentials do
            %{
              "account_name" => System.get_env("AZURE_STORAGE_ACCOUNT"),
              "account_key" => System.get_env("AZURE_STORAGE_KEY")
            }
          end
        end
        """
      }
    }
  end
end

AI Pipeline Example

defmodule AIPipeline do
  @moduledoc """
  Example combining Elixir AI tools with Microsoft Fabric
  """
  
  def create_pipeline do
    %{
      "data_ingestion" => %{
        step: :ingest,
        implementation: """
        defmodule Pipeline.Ingestion do
          def load_training_data do
            MyApp.Fabric.query_synapse(\"\"\"
              SELECT *
              FROM training_data
              WHERE batch_id = @batch_id
            \"\"\")
            |> transform_results()
          end
          
          def load_embeddings do
            MyApp.Lakehouse.read_data("embeddings/latest.parquet")
            |> Explorer.DataFrame.filter(col("score") > 0.8)
          end
        end
        """
      },
      
      "preprocessing" => %{
        step: :preprocess,
        implementation: """
        defmodule Pipeline.Preprocessing do
          def prepare_features(data) do
            data
            |> Explorer.DataFrame.mutate(
              text_length: col("text") |> transform(&String.length/1),
              normalized_score: col("score") / col("max_score")
            )
          end
        end
        """
      },
      
      "model_inference" => %{
        step: :inference,
        implementation: """
        defmodule Pipeline.Inference do
          def run_inference(data) do
            # Load Bumblebee model
            {:ok, model} = Bumblebee.load_model({:hf, "microsoft/deberta-v3-base"})
            {:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "microsoft/deberta-v3-base"})
            
            serving = Bumblebee.Text.sequence_classification(
              model,
              tokenizer,
              top_k: 3
            )
            
            # Run inference
            data
            |> Explorer.DataFrame.to_rows()
            |> Task.async_stream(&process_row(&1, serving))
            |> Enum.to_list()
          end
        end
        """
      },
      
      "result_storage" => %{
        step: :store,
        implementation: """
        defmodule Pipeline.Storage do
          def save_results(results) do
            results
            |> Explorer.DataFrame.from_rows()
            |> Explorer.DataFrame.to_parquet!(
              "results/#{timestamp()}.parquet",
              storage_options: azure_credentials()
            )
          end
        end
        """
      }
    }
  end
end

Recommendations

For a Microsoft Fabric competition, here’s a suggested approach:

1. Core Framework Choice:

   • Use Nx + Bumblebee for model inference
   • Use Explorer for data handling
   • Consider Python interop for LangChain if needed

2. Data Pipeline:

   defmodule CompetitionPipeline do
     def run do
       Pipeline.Ingestion.load_training_data()
       |> Pipeline.Preprocessing.prepare_features()
       |> Pipeline.Inference.run_inference()
       |> Pipeline.Storage.save_results()
     end
   end

3. Microsoft Fabric Integration:

   • Use Synapse for data warehousing
   • Use Lakehouse for model artifacts
   • Use OneLake for data storage

4. Development Workflow:

   # Setup
   mix new ai_competition --sup
   cd ai_competition
   
   # Add dependencies
   mix deps.get
   
   # Configure Fabric connection
   mix fabric.setup
   
   # Run pipeline
   mix run lib/pipeline.ex

Would you like me to expand on any particular aspect or provide more specific examples?