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LLM Integration

FluidGrids provides sophisticated capabilities for integrating Large Language Models (LLMs) into your workflows. Our platform supports various LLM providers and offers advanced features for optimizing model performance and cost.

Supported Models​

OpenAI Integration Integrate OpenAI models:

FluidGrids supports various OpenAI models:

  • GPT-4 and variants
  • GPT-3.5-turbo series
  • Embeddings models
  • Fine-tuned models
  • DALL-E models

Anthropic Integration Leverage Anthropic models:

Available Anthropic models:

  • Claude-2 and variants
  • Claude-instant
  • Custom-trained models
  • Specialized versions
  • Research models

Integration Setup​

Configuration Set up LLM integration:

llm:
  provider: openai
  model: gpt-4
  config:
    temperature: 0.7
    max_tokens: 2000
    top_p: 0.95

API Authentication Configure API access:

from fluidgrids.llm import LLMClient

client = LLMClient(
    provider="openai",
    api_key="YOUR_API_KEY",
    organization="YOUR_ORG_ID"
)

Advanced Features​

Prompt Engineering Optimize prompts:

from fluidgrids.llm import PromptTemplate

template = PromptTemplate(
    template="Analyze the following {text}",
    input_variables=["text"]
)

response = client.generate(
    prompt=template,
    parameters={
        "text": input_text
    }
)

Context Management Handle conversation context:

from fluidgrids.llm import ConversationManager

manager = ConversationManager()
manager.add_message("user", "Initial query")
manager.add_message("assistant", "Initial response")

response = client.generate(
    conversation=manager,
    new_message="Follow-up question"
)

Performance Optimization​

Caching System Implement response caching:

from fluidgrids.llm import ResponseCache

cache = ResponseCache(
    ttl=3600,  # 1 hour
    max_size=1000
)

response = client.generate(
    prompt="Query",
    cache=cache
)

Batch Processing Handle multiple requests:

responses = client.batch_generate(
    prompts=["Query 1", "Query 2"],
    max_concurrent=5
)

Cost Management​

Budget Controls Implement cost limits:

cost_control:
  daily_limit: 100.0
  alert_threshold: 80
  action: pause  # or warn

Usage Tracking Monitor API usage:

usage_stats = client.get_usage_stats(
    timeframe="daily",
    include_costs=True
)

Error Handling​

Retry Logic Handle API failures:

from fluidgrids.llm import RetryStrategy

strategy = RetryStrategy(
    max_retries=3,
    backoff_factor=2
)

response = client.generate(
    prompt="Query",
    retry_strategy=strategy
)

Fallback Models Configure backup options:

fallback:
  models:
    - provider: anthropic
      model: claude-instant
    - provider: openai
      model: gpt-3.5-turbo

Best Practices​

Integration Guidelines Follow these practices:

  • Implement proper error handling
  • Use appropriate model selection
  • Optimize prompt design
  • Monitor performance metrics
  • Manage API costs

Getting Started​

Begin LLM integration:

For LLM integration support, contact our AI Team.