BlackBoxModel

API for interfacing with cloud-hosted models

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.

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
)

Parameters

Parameter	Type	Default	Description
model_name	string	(Required)	Name of the model (e.g., “gpt-4o”, “claude-3-7-sonnet-20250219”)
system_prompt	string	None	Default system prompt to use for all queries
max_retries	int	5	Maximum number of retry attempts for failed queries
retry_delay	float	10.0	Delay in seconds between retry attempts

Methods

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
)

Parameters

Parameter	Type	Default	Description
prompt	string	(Required)	The text prompt to send to the model
system_prompt	string	None	System prompt for this specific query
temperature	float	0	Controls randomness (0.0 = deterministic, 1.0 = creative)
max_tokens	int	2048	Maximum number of tokens to generate
message_history	List[Dict]	[]	Message history for chat context

Returns

A string containing the model’s response.

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
)

Parameters

Parameter	Type	Default	Description
prompts	List[string]	(Required)	List of text prompts to send to the model
max_threads	int	50	Maximum number of parallel threads to use
show_progress	bool	True	Whether to display a progress bar
system_prompt	string	None	System prompt for all queries
temperature	float	0	Controls randomness
max_tokens	int	2048	Maximum number of tokens to generate
message_history	List[Dict]	[]	Message history for chat context

Returns

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

embed

Generates embeddings (vector representations) for text.

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

Parameters

Parameter	Type	Default	Description
text	string or List[string]	(Required)	Text to generate embeddings for
batch_size	int	100	Maximum batch size for processing

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.

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
)

Parameters

Parameter	Type	Default	Description
texts	List[string]	(Required)	List of texts to generate embeddings for
batch_size	int	100	Maximum batch size for processing
max_threads	int	10	Maximum number of parallel threads to use
show_progress	bool	True	Whether to display a progress bar

Returns

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

Supported Models

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

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

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

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

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

Examples

Basic Query

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

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()

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