import os os.environ["FI_API_KEY"] = "your_api_key" os.environ["FI_SECRET_KEY"] = "your_secret_key"

from fi.datasets import Dataset from fi.datasets.types import ( Cell, Column, DatasetConfig, DataTypeChoices, ModelTypes, Row, SourceChoices ) # Create a new dataset config = DatasetConfig(name="qa_dataset", model_type=ModelTypes.GENERATIVE_LLM) dataset = Dataset(dataset_config=config) dataset = dataset.create() # Define columns columns = [ Column(name="user_query", data_type=DataTypeChoices.TEXT, source=SourceChoices.OTHERS), Column(name="ai_response", data_type=DataTypeChoices.TEXT, source=SourceChoices.OTHERS), Column(name="quality_score", data_type=DataTypeChoices.INTEGER, source=SourceChoices.OTHERS), ] # Add data rows = [ Row(order=1, cells=[ Cell(column_name="user_query", value="What is machine learning?"), Cell(column_name="ai_response", value="Machine learning is a subset of AI..."), Cell(column_name="quality_score", value=9), ]), Row(order=2, cells=[ Cell(column_name="user_query", value="Explain quantum computing"), Cell(column_name="ai_response", value="Quantum computing uses quantum bits..."), Cell(column_name="quality_score", value=8), ]), ] # Push data and run evaluations dataset = dataset.add_columns(columns=columns) dataset = dataset.add_rows(rows=rows) # Add automated evaluation dataset.add_evaluation( name="factual_accuracy", eval_template="is_factually_consistent", required_keys_to_column_names={ "input": "user_query", "output": "ai_response", "context": "user_query", }, run=True ) print("✓ Dataset created with automated evaluations")

from fi.prompt import Prompt, PromptTemplate, ModelConfig # Create a versioned prompt template template = PromptTemplate( name="customer_support", messages=[ {"role": "system", "content": "You are a helpful customer support agent."}, {"role": "user", "content": "Help {{customer_name}} with {{issue_type}}."}, ], variable_names={"customer_name": ["Alice"], "issue_type": ["billing"]}, model_configuration=ModelConfig(model_name="gpt-4o-mini", temperature=0.7) ) # Create and version the template client = Prompt(template) client.create() # Create v1 client.commit_current_version("Initial version", set_as_default=True) # Assign deployment labels client.labels().assign("Production", "v1") # Compile with variables compiled = client.compile({"customer_name": "Bob", "issue_type": "refund"}) print(compiled) # Output: [ # {"role": "system", "content": "You are a helpful customer support agent."}, # {"role": "user", "content": "Help Bob with refund."} # ]

import random from openai import OpenAI from fi.prompt import Prompt # Fetch different variants variant_a = Prompt.get_template_by_name("customer_support", label="variant-a") variant_b = Prompt.get_template_by_name("customer_support", label="variant-b") # Randomly select and use selected = random.choice([variant_a, variant_b]) client = Prompt(selected) compiled = client.compile({"customer_name": "Alice", "issue_type": "refund"}) # Send to your LLM provider openai = OpenAI(api_key="your_openai_key") response = openai.chat.completions.create(model="gpt-4o", messages=compiled) print(f"Using variant: {selected.version}") print(f"Response: {response.choices[0].message.content}")

from fi.kb import KnowledgeBase # Initialize client kb_client = KnowledgeBase( fi_api_key="your_api_key", fi_secret_key="your_secret_key" ) # Create a knowledge base with documents kb = kb_client.create_kb( name="product_docs", file_paths=["manual.pdf", "faq.txt", "guide.md"] ) print(f"✓ Knowledge base created: {kb.kb.name}") print(f" Files uploaded: {len(kb.kb.file_names)}") # Update with more files updated_kb = kb_client.update_kb( kb_id=kb.kb.id, file_paths=["updates.pdf"] ) # Delete specific files kb_client.delete_files_from_kb(file_ids=["file_id_here"]) # Clean up kb_client.delete_kb(kb_ids=[kb.kb.id])