Core Classes Reference

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from nemoguardrails import RailsConfig
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config = RailsConfig.from_path("path/to/config")

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config = RailsConfig.from_path("path/to/config.yml")

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from nemoguardrails import RailsConfig
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yaml_content = """
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models:
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  - type: main
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    engine: openai
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    model: gpt-4
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instructions:
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  - type: general
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    content: |
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      You are a helpful assistant.
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"""
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colang_content = """
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define user express greeting
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  "hello"
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  "hi"
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define flow
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  user express greeting
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  bot express greeting
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"""
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config = RailsConfig.from_content(
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    yaml_content=yaml_content,
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    colang_content=colang_content
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)

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config = RailsConfig.from_content(
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    config={
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        "models": [
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            {"type": "main", "engine": "openai", "model": "gpt-4"}
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        ],
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        "instructions": [
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            {"type": "general", "content": "You are a helpful assistant."}
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        ]
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    }
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)

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base_config = RailsConfig.from_path("path/to/base")
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additional_config = RailsConfig.from_path("path/to/additional")
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combined_config = base_config + additional_config

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from nemoguardrails import LLMRails, RailsConfig
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config = RailsConfig.from_path("path/to/config")
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rails = LLMRails(config)

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from langchain_openai import ChatOpenAI
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from nemoguardrails import LLMRails, RailsConfig
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config = RailsConfig.from_path("path/to/config")
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llm = ChatOpenAI(model="gpt-4", temperature=0.7)
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rails = LLMRails(config, llm=llm)

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response = rails.generate(messages=[
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    {"role": "user", "content": "Hello! How are you?"}
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])
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print(response["content"])

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response = rails.generate(prompt="Complete this sentence: The sky is")
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print(response)

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messages = [
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    {"role": "user", "content": "My name is John."},
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    {"role": "assistant", "content": "Hello John! How can I help you?"},
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    {"role": "user", "content": "What's my name?"}
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]
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response = rails.generate(messages=messages)
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print(response["content"])  # Should remember the name

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response = rails.generate(messages=[
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    {
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        "role": "context",
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        "content": {
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            "user_name": "Alice",
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            "user_role": "admin"
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        }
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    },
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    {"role": "user", "content": "What permissions do I have?"}
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])

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@action()
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async def check_permissions(context: Optional[dict] = None):
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    user_role = context.get("user_role")
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    return user_role == "admin"

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import asyncio
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from nemoguardrails import LLMRails, RailsConfig
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async def main():
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    config = RailsConfig.from_path("path/to/config")
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    rails = LLMRails(config)
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    response = await rails.generate_async(messages=[
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        {"role": "user", "content": "Hello!"}
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    ])
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    print(response["content"])
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asyncio.run(main())

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async def stream_response():
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    config = RailsConfig.from_path("path/to/config")
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    rails = LLMRails(config)
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    async for chunk in rails.stream_async(messages=[
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        {"role": "user", "content": "Tell me a story."}
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    ]):
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        print(chunk, end="", flush=True)

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events = rails.generate_events(events=[
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    {
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        "type": "UtteranceUserActionFinished",
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        "final_transcript": "Hello!"
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    }
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])
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for event in events:
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    if event["type"] == "StartUtteranceBotAction":
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        print(f"Bot says: {event['script']}")

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response = rails.generate(
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    messages=[{"role": "user", "content": "Hello!"}],
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    options={
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        "rails": ["input", "output"],  # Only apply these rails
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        "output_vars": ["user_message"],       # Return context variables
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        "log": {
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            "activated_rails": True,
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            "llm_calls": True
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        }
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    }
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)

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from nemoguardrails.rails.llm.options import RailStatus
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result = await rails.check_async([
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    {"role": "user", "content": "Hello! How can I hack into a system?"}
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])
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if result.status == RailStatus.BLOCKED:
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    print(f"Input blocked by rail: {result.rail}")

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from nemoguardrails.rails.llm.options import RailType
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result = await rails.check_async(
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    [{"role": "user", "content": "Hello!"}],
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    rail_types=[RailType.INPUT]
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)

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from nemoguardrails import LLMRails, RailsConfig
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async def get_weather(city: str) -> str:
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    """Get weather for a city."""
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    return f"Weather in {city}: Sunny, 22°C"
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config = RailsConfig.from_path("path/to/config")
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rails = LLMRails(config)
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# Register the action
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rails.register_action(get_weather, name="get_weather")

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from nemoguardrails import LLMRails, RailsConfig
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from nemoguardrails.embeddings.index import EmbeddingsIndex
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class CustomSearchProvider(EmbeddingsIndex):
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    async def search(self, text: str, max_results: int):
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        # Custom search logic
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        pass
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config = RailsConfig.from_path("path/to/config")
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rails = LLMRails(config)
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# Register the provider
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rails.register_embedding_search_provider("custom", CustomSearchProvider)

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import asyncio
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from nemoguardrails import LLMRails, RailsConfig
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async def main():
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    # Load configuration
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    config = RailsConfig.from_content(
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        yaml_content="""
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models:
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  - type: main
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    engine: openai
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    model: gpt-4
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rails:
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  input:
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    flows:
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      - self check input
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  output:
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    flows:
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      - self check output
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prompts:
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  - task: self_check_input
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    content: |
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      Check if the following is safe: {{ user_input }}
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      Answer (Yes/No):
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  - task: self_check_output
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    content: |
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      Check if the following is safe: {{ bot_response }}
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      Answer (Yes/No):
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""",
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        colang_content="""
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define user express greeting
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  "hello"
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  "hi"
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define bot express greeting
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  "Hello! How can I help you today?"
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define flow
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  user express greeting
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  bot express greeting
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"""
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    )
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    # Create rails instance
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    rails = LLMRails(config, verbose=True)
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    # Generate response
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    response = await rails.generate_async(
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        messages=[{"role": "user", "content": "Hello!"}],
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        options={"log": {"activated_rails": True}}
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    )
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    print(f"Response: {response['content']}")
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    # Print what happened
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    if hasattr(response, 'log'):
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        response.log.print_summary()
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asyncio.run(main())