BlackBoxModel

BlackBoxModel provides a standardized interface for interacting with models hosted via APIs like OpenAI, Anthropic, and Together.ai. This class handles authentication, retry logic, and batch processing for efficient model querying.

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Constructor

from generalanalysis.boiler_room import BlackBoxModel

model = BlackBoxModel(
    model_name="gpt-4o",
    system_prompt=None,  # Optional
    max_retries=5,       # Optional
    retry_delay=10.0     # Optional
)

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Parameters

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Methods

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query

Sends a prompt to the model and retrieves the generated response.

response = model.query(
    prompt="Explain quantum computing",
    system_prompt=None,  # Overrides default if provided
    temperature=0.7,     # Optional
    max_tokens=2048      # Optional
)

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Parameters

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Returns

A string containing the model’s response.

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query_parallel

Sends multiple prompts to the model in parallel for efficiency.

responses = model.query_parallel(
    prompts=["Tell me about Mars", "Tell me about Venus"],
    max_threads=50,         # Optional
    show_progress=True,     # Optional
    system_prompt=None,     # Optional
    temperature=0.7         # Optional
)

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Parameters

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Returns

A list of strings containing the model’s responses in the same order as the input prompts.

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embed

Generates embeddings (vector representations) for text.

embedding = model.embed(
    text="Text to embed",
    batch_size=100      # Optional
)

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Parameters

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Returns

A list of float values (embedding vector) if a single text is provided, or a list of embedding vectors (list of lists) if multiple texts are provided.

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embed_parallel

Generates embeddings for multiple texts in parallel.

embeddings = model.embed_parallel(
    texts=["Text one", "Text two"],
    batch_size=100,         # Optional
    max_threads=10,         # Optional
    show_progress=True      # Optional
)

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Parameters

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Returns

A list of embedding vectors (list of lists of float values), one for each input text.

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Supported Models

The BlackBoxModel class automatically determines the appropriate API to use based on the model name:

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OpenAI Models

• GPT-4o, GPT-4o-mini, GPT-4-turbo
• GPT-4.5-preview-2025-02-27
• GPT-4-0125-preview, GPT-4-0613
• GPT-3.5-turbo
• o1, o1-mini, o3-mini, o3-mini-2025-01-31
• Embedding models: text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002

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Anthropic Models

• claude-3-7-sonnet-20250219
• claude-3-5-sonnet-20241022, claude-3-5-sonnet-20240620
• claude-3-5-haiku-20241022
• claude-3-sonnet-20240229

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Together.ai Hosted Models

• meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo
• meta-llama/Llama-3.3-70B-Instruct-Turbo
• meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo
• mistralai/Mistral-Small-24B-Instruct-2501
• mistralai/Mixtral-8x22B-Instruct-v0.1
• deepseek-ai/DeepSeek-R1, deepseek-ai/DeepSeek-R1-Distill-Llama-70B
• databricks/dbrx-instruct
• Qwen/Qwen2.5-7B-Instruct-Turbo
• google/gemma-2-27b-it

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Error Handling

The BlackBoxModel implements robust error handling:

• Automatic retries for temporary API failures
• Logging of error details for debugging
• Graceful degradation for rate limiting

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Examples

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Basic Query

model = BlackBoxModel("claude-3-7-sonnet-20250219")
response = model.query("Summarize the key points of quantum computing")
print(response)

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Parallel Processing

questions = [
    "What is quantum computing?",
    "What are qubits?",
    "Explain quantum entanglement"
]

model = BlackBoxModel("meta-llama/Llama-3.3-70B-Instruct-Turbo")
answers = model.query_parallel(
    prompts=questions,
    max_threads=3,
    temperature=0.5
)

for question, answer in zip(questions, answers):
    print(f"Q: {question}")
    print(f"A: {answer}")
    print()

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Semantic Search with Embeddings

model = BlackBoxModel("text-embedding-3-small")
documents = [
    "Quantum computing uses quantum bits or qubits.",
    "Classical computing uses binary digits or bits.",
    "Superposition allows qubits to exist in multiple states simultaneously."
]

query = "How are quantum computers different?"
query_embedding = model.embed(query)
document_embeddings = model.embed_parallel(documents)

# Now you can compare embeddings to find the most relevant documents