diff --git a/.coderabbit.yaml b/.coderabbit.yaml new file mode 100644 index 0000000000..a542f28d64 --- /dev/null +++ b/.coderabbit.yaml @@ -0,0 +1,70 @@ +# yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json +# https://docs.coderabbit.ai/getting-started/configure-coderabbit/ +# Validator https://docs.coderabbit.ai/configuration/yaml-validator#yaml-validator +# In PR, comment "@coderabbitai configuration" to get the full config including defaults +# Set the language for reviews by using the corresponding ISO language code. +# Default: "en-US" +language: "en-US" +# Settings related to reviews. +# Default: {} +reviews: + # Set the profile for reviews. Assertive profile yields more feedback, that may be considered nitpicky. + # Options: chill, assertive + # Default: "chill" + profile: chill + # Add this keyword in the PR/MR title to auto-generate the title. + # Default: "@coderabbitai" + auto_title_placeholder: "@coderabbitai title" + # Auto Title Instructions - Custom instructions for auto-generating the PR/MR title. + # Default: "" + auto_title_instructions: 'Format: ": ". Category must be one of: feat, fix, docs, style, refactor, perf, test, build, ci, chore, revert, cp. The category must be followed by a colon. Title should be concise (<= 80 chars). Example: "feat: Add logit_bias support".' # current: '' + # Set the commit status to 'pending' when the review is in progress and 'success' when it is complete. + # Default: true + commit_status: false + # Generate walkthrough in a markdown collapsible section. + # Default: false + collapse_walkthrough: true + # Generate an assessment of how well the changes address the linked issues in the walkthrough. + # Default: true + assess_linked_issues: true + # Include possibly related issues in the walkthrough. + # Default: true + related_issues: true + # Related PRs - Include possibly related pull requests in the walkthrough. + # Default: true + related_prs: true + # Suggest labels based on the changes in the pull request in the walkthrough. + # Default: true + suggested_labels: true + # Suggest reviewers based on the changes in the pull request in the walkthrough. + # Default: true + suggested_reviewers: true + # Generate a poem in the walkthrough comment. + # Default: true + poem: false # current: true + # Post review details on each review. Additionally, post a review status when a review is skipped in certain cases. + # Default: true + review_status: false # current: true + # Configuration for pre merge checks + # Default: {} + pre_merge_checks: + # Custom Pre-merge Checks - Add unique checks to enforce your team's standards before merging a pull request. Each check must have a unique name (up to 50 characters) and clear instructions (up to 10000 characters). Use these to automatically verify coding, security, documentation, or business rules and maintain code quality. + # Default: [] + custom_checks: + - name: "Test Results for Major Changes" + mode: "warning" # or "error" to block merges + instructions: | + If this PR contains major changes (such as new features, breaking changes, or significant refactoring), verify that the PR description includes test results or testing information. + If a change could affect numerics or convergence, the PR description should include information demonstrating that there is no regression. + If a change could affect performance, the PR description should include before-and-after performance numbers, as well as the configuration and context in which they apply. + Pass if test results are documented or if the changes are minor. + auto_review: + # Configuration for auto review + # Default: {} + # Automatic Incremental Review - Automatic incremental code review on each push + # Default: true + auto_incremental_review: false # current: true + # Review draft PRs/MRs. + # Default: false + drafts: false + # Base branches (other than the default branch) to review. Accepts regex patterns. Use '.*' to match all branches. diff --git a/CHANGELOG.md b/CHANGELOG.md index a6e31ab7a8..aaeda007c3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,6 +9,56 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm > > The changes related to the Colang language and runtime have moved to [CHANGELOG-Colang](./CHANGELOG-Colang.md) file. +## [0.21.0] - 2026-03-12 + +### 🚀 Features + +- *(library)* Update Trend Micro Vision One AI Guard official endpoint ([#1546](https://github.com/NVIDIA-NeMo/Guardrails/issues/1546)) +- *(llmrails)* Add check_async method for input/output rails validation ([#1605](https://github.com/NVIDIA-NeMo/Guardrails/issues/1605)) +- *(server)* Make guardrails server OpenAI compatible ([#1340](https://github.com/NVIDIA-NeMo/Guardrails/issues/1340)) +- New top-level scaffold ([#1613](https://github.com/NVIDIA-NeMo/Guardrails/issues/1613)) +- Add Async work queue ([#1620](https://github.com/NVIDIA-NeMo/Guardrails/issues/1620)) +- *(integration)* Add GuardrailsMiddleware for LangChain agent ([#1606](https://github.com/NVIDIA-NeMo/Guardrails/issues/1606)) +- *(library)* Update Fiddler Guardrails API to match new specification ([#1619](https://github.com/NVIDIA-NeMo/Guardrails/issues/1619)) +- *(library)* Add CrowdStrike AIDR community integration ([#1601](https://github.com/NVIDIA-NeMo/Guardrails/issues/1601)) +- *(iorails)* Introduce IORails optimized Input/Output rail engine. Supports non-streaming parallel nemoguard input/output rails (content-safety, topic-safety, jailbreak detection) ([#1638](https://github.com/NVIDIA-NeMo/Guardrails/issues/1638), [#1649](https://github.com/NVIDIA-NeMo/Guardrails/issues/1649), [#1654](https://github.com/NVIDIA-NeMo/Guardrails/issues/1654), [#1656](https://github.com/NVIDIA-NeMo/Guardrails/issues/1656), [#1658](https://github.com/NVIDIA-NeMo/Guardrails/issues/1658), [#1660](https://github.com/NVIDIA-NeMo/Guardrails/issues/1660), [#1661](https://github.com/NVIDIA-NeMo/Guardrails/issues/1661), [#1674](https://github.com/NVIDIA-NeMo/Guardrails/issues/1674)) +- *(server)* Add OpenAI compatible v1/models endpoint ([#1637](https://github.com/NVIDIA-NeMo/Guardrails/issues/1637)) +- *(benchmark)* Add Locust stress-test ([#1629](https://github.com/NVIDIA-NeMo/Guardrails/issues/1629)) +- *(jailbreak)* Validate Jailbreak Detection config at create-time ([#1675](https://github.com/NVIDIA-NeMo/Guardrails/issues/1675)) +- *(library)* Add PolicyAI Integration for Content Moderation ([#1576](https://github.com/NVIDIA-NeMo/Guardrails/issues/1576)) + +### 🐛 Bug Fixes + +- *(server)* Make openai an optional server-only dependency ([#1623](https://github.com/NVIDIA-NeMo/Guardrails/issues/1623)) +- *(actions)* Rename generate_next_step to generate_next_steps for task-specific LLM support ([#1603](https://github.com/NVIDIA-NeMo/Guardrails/issues/1603)) +- *(library)* Add `valid` alias to action results in GuardrailsAI integration ([#1578](https://github.com/NVIDIA-NeMo/Guardrails/issues/1578)) ([#1611](https://github.com/NVIDIA-NeMo/Guardrails/issues/1611)) +- *(llm)* Filter stop parameter for OpenAI reasoning models ([#1653](https://github.com/NVIDIA-NeMo/Guardrails/issues/1653)) +- *(logging)* Show cache hits in Stats log and fix duplicate metadata restore ([#1666](https://github.com/NVIDIA-NeMo/Guardrails/issues/1666)) +- *(cache)* Make cache stats log visible in verbose mode ([#1667](https://github.com/NVIDIA-NeMo/Guardrails/issues/1667)) +- *(library)* Use bot refuse to respond in gliner PII detection flows ([#1671](https://github.com/NVIDIA-NeMo/Guardrails/issues/1671)) +- *(streaming)* Handle None stop tokens in streaming handler ([#1685](https://github.com/NVIDIA-NeMo/Guardrails/issues/1685)) +- *(streaming)* Handle dict chunks in RollingBuffer.format_chunks ([#1687](https://github.com/NVIDIA-NeMo/Guardrails/issues/1687)) +- *(middleware)* Handle MODIFIED status in GuardrailsMiddleware instead of silently dropping it ([#1714](https://github.com/NVIDIA-NeMo/Guardrails/issues/1714)) + +### 🚜 Refactor + +- *(streaming)* Remove LangChain callback dependencies from StreamingHandler ([#1547](https://github.com/NVIDIA-NeMo/Guardrails/issues/1547)) +- *(streaming)* Remove ChatNVIDIA streaming patch ([#1607](https://github.com/NVIDIA-NeMo/Guardrails/issues/1607)) +- *(streaming)* [**breaking**] Remove stream_usage and fix streaming metadata capture ([#1624](https://github.com/NVIDIA-NeMo/Guardrails/issues/1624)) + +### ⚡ Performance + +- *(actions)* Lazy initialization of embedding indexes ([#1572](https://github.com/NVIDIA-NeMo/Guardrails/issues/1572)) + +### ⚙️ Miscellaneous Tasks + +- Update Pangea User-Agent repo URL ([#1595](https://github.com/NVIDIA-NeMo/Guardrails/issues/1595)) ([#1610](https://github.com/NVIDIA-NeMo/Guardrails/issues/1610)) +- *(jailbreak)* Update dependencies for jailbreak detection docker container. ([#1596](https://github.com/NVIDIA-NeMo/Guardrails/issues/1596)) +- Remove multi_kb example ([#1673](https://github.com/NVIDIA-NeMo/Guardrails/issues/1673)) +- *(iorails)* Increase work queue concurrency and depth ([#1674](https://github.com/NVIDIA-NeMo/Guardrails/issues/1674)) +- *(docs)* Remove AI Virtual Assistant Blueprint notebook ([#1682](https://github.com/NVIDIA-NeMo/Guardrails/issues/1682)) +- Update dependencies ahead of v0.21 release ([#1617](https://github.com/NVIDIA-NeMo/Guardrails/issues/1617)) + ## [0.20.0] - 2026-01-22 ### 🚀 Features diff --git a/README.md b/README.md index df2ccd2131..97b38f5064 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ [![Downloads](https://static.pepy.tech/badge/nemoguardrails)](https://pepy.tech/project/nemoguardrails) [![Downloads](https://static.pepy.tech/badge/nemoguardrails/month)](https://pepy.tech/project/nemoguardrails) -> **LATEST RELEASE / DEVELOPMENT VERSION**: The [main](https://github.com/NVIDIA-NeMo/Guardrails/tree/main) branch tracks the latest released beta version: [0.20.0](https://github.com/NVIDIA-NeMo/Guardrails/tree/v0.20.0). For the latest development version, checkout the [develop](https://github.com/NVIDIA-NeMo/Guardrails/tree/develop) branch. +> **LATEST RELEASE / DEVELOPMENT VERSION**: The [main](https://github.com/NVIDIA-NeMo/Guardrails/tree/main) branch tracks the latest released beta version: [0.21.0](https://github.com/NVIDIA-NeMo/Guardrails/tree/v0.21.0). For the latest development version, checkout the [develop](https://github.com/NVIDIA-NeMo/Guardrails/tree/develop) branch. ✨✨✨ @@ -295,9 +295,9 @@ Evaluating the safety of a LLM-based conversational application is a complex tas ## How is this different? -There are many ways guardrails can be added to an LLM-based conversational application. For example: explicit moderation endpoints (e.g., OpenAI, ActiveFence), critique chains (e.g. constitutional chain), parsing the output (e.g. guardrails.ai), individual guardrails (e.g., LLM-Guard), hallucination detection for RAG applications (e.g., Got It AI, Patronus Lynx). +There are many ways guardrails can be added to an LLM-based conversational application. For example: explicit moderation endpoints (e.g., OpenAI, ActiveFence, PolicyAI), critique chains (e.g. constitutional chain), parsing the output (e.g. guardrails.ai), individual guardrails (e.g., LLM-Guard), hallucination detection for RAG applications (e.g., Got It AI, Patronus Lynx). -NeMo Guardrails aims to provide a flexible toolkit that can integrate all these complementary approaches into a cohesive LLM guardrails layer. For example, the toolkit provides out-of-the-box integration with ActiveFence, AlignScore and LangChain chains. +NeMo Guardrails aims to provide a flexible toolkit that can integrate all these complementary approaches into a cohesive LLM guardrails layer. For example, the toolkit provides out-of-the-box integration with ActiveFence, PolicyAI, AlignScore and LangChain chains. To the best of our knowledge, NeMo Guardrails is the only guardrails toolkit that also offers a solution for modeling the dialog between the user and the LLM. This enables on one hand the ability to guide the dialog in a precise way. On the other hand it enables fine-grained control for when certain guardrails should be used, e.g., use fact-checking only for certain types of questions. diff --git a/benchmark/aiperf/README.md b/benchmark/aiperf/README.md index 5c39fa78df..32c1228181 100644 --- a/benchmark/aiperf/README.md +++ b/benchmark/aiperf/README.md @@ -27,14 +27,15 @@ To use the provided configurations, you need to create accounts at <https://buil 1. **Create a virtual environment in which to install AIPerf** ```bash - $ mkdir ~/env - $ python -m venv ~/env/aiperf + mkdir ~/env + python -m venv ~/env/aiperf + source ~/env/aiperf/bin/activate ``` 2. **Install dependencies in the virtual environment** ```bash - $ pip install aiperf huggingface_hub typer + pip install aiperf huggingface_hub typer httpx ``` 3. **Login to Hugging Face:** @@ -50,22 +51,22 @@ To use the provided configurations, you need to create accounts at <https://buil After creating a Personal API key, set the `NVIDIA_API_KEY` variable as below. ```bash - $ export NVIDIA_API_KEY="your-api-key-here" + export NVIDIA_API_KEY="your-api-key-here" ``` ## Running Benchmarks Each benchmark is configured using the `AIPerfConfig` Pydantic model in [aiperf_models.py](aiperf_models.py). The configs are stored in YAML files, and converted to an `AIPerfConfig` object. -There are two example configs included which can be extended for your use-cases. These both use Nvidia-hosted models, : +There are two example configs included which can be extended for your use-cases. These both use Nvidia-hosted models: -- [`single_concurrency.yaml`](aiperf_configs/single_concurrency.yaml): Example single-run benchmark with a single concurrency value. -- [`sweep_concurrency.yaml`](aiperf_configs/sweep_concurrency.yaml): Example multiple-run benchmark to sweep concurency values and run a new benchmark for each. +- [`single_concurrency.yaml`](configs/single_concurrency.yaml): Example single-run benchmark with a single concurrency value. +- [`sweep_concurrency.yaml`](configs/sweep_concurrency.yaml): Example multiple-run benchmark to sweep concurrency values and run a new benchmark for each. To run a benchmark, use the following command: ```bash -$ python -m benchmark.aiperf --config-file <path-to-config.yaml> +python -m benchmark.aiperf --config-file <path-to-config.yaml> ``` ### Running a Single Benchmark @@ -73,16 +74,16 @@ $ python -m benchmark.aiperf --config-file <path-to-config.yaml> To run a single benchmark with fixed parameters, use the `single_concurrency.yaml` configuration: ```bash -$ python -m benchmark.aiperf --config-file aiperf/configs/single_concurrency.yaml +python -m benchmark.aiperf --config-file benchmark/aiperf/configs/single_concurrency.yaml ``` **Example output:** -```text -2025-12-01 10:35:17 INFO: Running AIPerf with configuration: aiperf/configs/single_concurrency.yaml +```terminaloutput +2025-12-01 10:35:17 INFO: Running AIPerf with configuration: benchmark/aiperf/configs/single_concurrency.yaml 2025-12-01 10:35:17 INFO: Results root directory: aiperf_results/single_concurrency/20251201_103517 2025-12-01 10:35:17 INFO: Sweeping parameters: None -2025-12-01 10:35:17 INFO: Running AIPerf with configuration: aiperf/configs/single_concurrency.yaml +2025-12-01 10:35:17 INFO: Running AIPerf with configuration: benchmark/aiperf/configs/single_concurrency.yaml 2025-12-01 10:35:17 INFO: Output directory: aiperf_results/single_concurrency/20251201_103517 2025-12-01 10:35:17 INFO: Single Run 2025-12-01 10:36:54 INFO: Run completed successfully @@ -97,13 +98,13 @@ $ python -m benchmark.aiperf --config-file aiperf/configs/single_concurrency.yam To run multiple benchmarks with different concurrency levels, use the `sweep_concurrency.yaml` configuration as below: ```bash -$ python -m benchmark.aiperf --config-file aiperf/configs/sweep_concurrency.yaml +python -m benchmark.aiperf --config-file benchmark/aiperf/configs/sweep_concurrency.yaml ``` **Example output:** -```text -2025-11-14 14:02:54 INFO: Running AIPerf with configuration: nemoguardrails/benchmark/aiperf/aiperf_configs/sweep_concurrency.yaml +```terminaloutput +2025-11-14 14:02:54 INFO: Running AIPerf with configuration: benchmark/aiperf/configs/sweep_concurrency.yaml 2025-11-14 14:02:54 INFO: Results root directory: aiperf_results/sweep_concurrency/20251114_140254 2025-11-14 14:02:54 INFO: Sweeping parameters: {'concurrency': [1, 2, 4]} 2025-11-14 14:02:54 INFO: Running 3 benchmarks @@ -134,7 +135,7 @@ The `--dry-run` option allows you to preview all benchmark commands without exec - Debugging configuration issues ```bash -$ python -m benchmark.aiperf --config-file aiperf/configs/sweep_concurrency.yaml --dry-run +python -m benchmark.aiperf --config-file benchmark/aiperf/configs/sweep_concurrency.yaml --dry-run ``` When in dry-run mode, the script will: @@ -150,7 +151,7 @@ When in dry-run mode, the script will: The `--verbose` option outputs more detailed debugging information to understand each step of the benchmarking process. ```bash -$ python -m benchmark.aiperf --config-file <config.yaml> --verbose +python -m benchmark.aiperf --config-file <config.yaml> --verbose ``` Verbose mode provides: @@ -165,14 +166,14 @@ Verbose mode provides: ## Configuration Files -Configuration files are YAML files located in [aiperf_configs](aiperf_configs). The configuration is validated using Pydantic models to catch errors early. +Configuration files are YAML files located in [configs](configs). The configuration is validated using Pydantic models to catch errors early. ### Top-Level Configuration Fields | Field | Type | Required | Description | |-------|------|----------|-------------| -| `batch_name` | string | Yes | Name for this batch of benchmarks. Used in output directory naming (e.g., `aiperf_results/batch_name/timestamp/`) | -| `output_base_dir` | string | Yes | Base directory where all benchmark results will be stored | +| `batch_name` | string | No | Name for this batch of benchmarks. Used in output directory naming (e.g., `aiperf_results/batch_name/timestamp/`). Default: `benchmark` | +| `output_base_dir` | string | No | Base directory where all benchmark results will be stored. Default: `aiperf_results` | | `base_config` | object | Yes | Base configuration parameters applied to all benchmark runs (see below) | | `sweeps` | object | No | Optional parameter sweeps for running multiple benchmarks with different values | @@ -355,11 +356,11 @@ Each run directory contains multiple files with benchmark results and metadata. - **`inputs.json`**: Synthetic prompt data generated for the benchmark. - **`profile_export_aiperf.json`**: Main metrics file in JSON format containing aggregated statistics. - **`profile_export_aiperf.csv`**: Same metrics as the JSON file, but in CSV format for easy import into spreadsheet tools or data analysis libraries. -- **`profile_export.jsonl`**: JSON Lines format file containing per-request metrics. Each line is a complete JSON object for one request with: -- **`logs/aiperf.log`**: Detailed log file from AIPerf execution containing: +- **`profile_export.jsonl`**: JSON Lines format file containing per-request metrics. Each line is a complete JSON object for one request. +- **`logs/aiperf.log`**: Detailed log file from AIPerf execution. ## Resources -- [AIPerf GitHub Repository](https://github.com/triton-inference-server/perf_analyzer/tree/main/genai-perf) -- [AIPerf Documentation](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/client/src/c%2B%2B/perf_analyzer/genai-perf/README.html) +- [AIPerf GitHub Repository](https://github.com/ai-dynamo/aiperf) +- [AIPerf Documentation](https://docs.nvidia.com/nim/benchmarking/llm/latest/step-by-step.html) - [NVIDIA API Catalog](https://build.nvidia.com/) diff --git a/docs/about/release-notes.md b/docs/about/release-notes.md index 7e0bb7f70a..45491dd6ea 100644 --- a/docs/about/release-notes.md +++ b/docs/about/release-notes.md @@ -25,104 +25,132 @@ For a complete record of changes in a release, refer to the --- -(v0-20-0)= +(v0-21-0)= -## 0.20.0 +## 0.21.0 -(v0-20-0-features)= +(v0-21-0-features)= ### Key Features -- Added support for multilingual content safety models such as [NVIDIA Nemotron Safety Guard 8B v3](https://build.nvidia.com/nvidia/llama-3_1-nemotron-safety-guard-8b-v3). This feature uses the [fast-langdetect package](https://github.com/LlmKira/fast-langdetect) to detect the user's input language and return refusal messages in the appropriate language. To use this feature, install the NeMo Guardrails library with the `multilingual` extra. +- Added the `IORails` class, a new optimized execution engine that runs NemoGuard input and output rails, such as + content-safety, topic-safety, and jailbreak detection, in parallel. The engine is opt-in: + set `NEMO_GUARDRAILS_IORAILS_ENGINE=1` to enable it. When enabled, the configuration is + validated for compatibility and falls back to LLMRails if unsupported flows are detected. + For more information, refer to [](../configure-rails/yaml-schema/guardrails-configuration/parallel-rails.md#iorails-engine). - ```bash - pip install nemoguardrails[multilingual] - ``` +- Added the `check_async()` and `check()` methods on `LLMRails` to enable validating messages against input and output rails without triggering full LLM generation. + Returns a `RailsResult` with `PASSED`, `MODIFIED`, or `BLOCKED` status. + For more information, refer to [](../run-rails/using-python-apis/check-messages.md). -- Added support for configuring custom refusal messages per language to complement multilingual content safety models. You can enable multilingual refusal messages and specify custom refusal messages in the `rails.config.content_safety` section of the `config.yml` file. - - ```yaml - rails: - config: - content_safety: - multilingual: - enabled: true - refusal_messages: - en: "Sorry, I cannot help with that request." - es: "Lo siento, no puedo ayudar con esa solicitud." - zh: "抱歉，我无法处理该请求。" - # Add other languages as needed - ``` +- The guardrails server now exposes a fully OpenAI-compatible + REST API. The `/v1/chat/completions` endpoint accepts standard `ChatCompletion` requests with a + `guardrails` field for config selection. A new `/v1/models` endpoint lists available models from the + configured provider. The `openai` package is now a required component of the optional `server` extra ([#1623](https://github.com/NVIDIA-NeMo/Guardrails/pull/1623)). + For more information, refer to [](../run-rails/using-fastapi-server/overview.md). + +- Added the `GuardrailsMiddleware` class, a new middleware that integrates with + LangChain's Agent Middleware protocol, applying input and output rail checks before and after + every model call in the agent loop. It includes the `InputRailsMiddleware` and `OutputRailsMiddleware` + convenience subclasses. + For more information, refer to [](../integration/langchain/agent-middleware.md). + +- Added three new community rails: + [PolicyAI](../configure-rails/guardrail-catalog/community/policyai.md) for policy-based content moderation, + [CrowdStrike AIDR](../configure-rails/guardrail-catalog/community/crowdstrike-aidr.md) for AI-powered detection and response, and + [Regex Detection](../configure-rails/guardrail-catalog/community/regex.md) for pattern-based content filtering on input, output, and retrieval. + +- Jailbreak detection configuration is now validated at + create-time. Invalid thresholds and malformed URLs raise errors immediately. + For more information, refer to [](../configure-rails/guardrail-catalog/jailbreak-protection.md#configuration-validation). - For more information, refer to [](../configure-rails/guardrail-catalog/content-safety.md#multilingual-refusal-messages). -- Added support for [NVIDIA GLiNER-PII](https://huggingface.co/nvidia/gliner-PII) for detecting entities such as names, email addresses, phone numbers, social security numbers, and more. For more information, refer to [](../configure-rails/guardrail-catalog/community/gliner.md). +- Embedding indexes are now initialized lazily. + FastEmbed models are only downloaded when semantic search is needed, reducing startup time for + configurations that use only input and output rails. + +(v0-21-0-breaking-changes)= ### Breaking Changes -- A breaking change removes redundant streaming configuration for output rails. Prior to the change, streaming had to be enabled in two places: `streaming` and `rails.output.streaming.enabled`. This change removes the top-level `streaming` configuration. - - Example `config.yml` before the change: - - ```{code-block} yaml - :emphasize-lines: 21 - - models: - - type: main - engine: nvidia_ai_endpoints - model: meta/llama-3.3-70b-instruct - - type: content_safety - engine: nvidia_ai_endpoints - model: nvidia/llama-3.1-nemoguard-8b-content-safety - - rails: - input: - flows: - - content safety check input $model=content_safety - output: - flows: - - content safety check output $model=content_safety - streaming: - enabled: True - chunk_size: 200 - context_size: 50 - - streaming: True # No longer needed starting from v0.20.0 - ``` - - - Example `config.yml` after the change: - - ```yaml - models: - - type: main - engine: nvidia_ai_endpoints - model: meta/llama-3.3-70b-instruct - - - type: content_safety - engine: nvidia_ai_endpoints - model: nvidia/llama-3.1-nemoguard-8b-content-safety - - rails: - input: - flows: - - content safety check input $model=content_safety - output: - flows: - - content safety check output $model=content_safety - streaming: - enabled: True - chunk_size: 200 - context_size: 50 - ``` - - For more information, refer to [](../run-rails/using-python-apis/streaming.md). +- Streaming metadata parameter renamed. The `include_generation_metadata` parameter on + `LLMRails.stream_async()` and `StreamingHandler` is deprecated in favor of `include_metadata`. + The `generation_info` field in streaming chunk dicts is renamed to `metadata`. + The deprecated parameter still works and emits a `DeprecationWarning`. + + ```python + # Before (deprecated) + async for chunk in rails.stream_async(messages=messages, include_generation_metadata=True): + info = chunk["generation_info"] + + # After + async for chunk in rails.stream_async(messages=messages, include_metadata=True): + info = chunk["metadata"] + ``` + +- `StreamingHandler` no longer inherits from LangChain `AsyncCallbackHandler`. + Streaming now uses `llm.astream()` with direct `push_chunk()` calls. + If your code depends on `StreamingHandler` as a LangChain callback, update it to use the + new `push_chunk()` interface. + +- Removed the `stream_usage` parameter. The `stream_usage=True` parameter is no longer + automatically added to LLM call kwargs. Streaming metadata is now captured through + `response_metadata` and `usage_metadata` on final chunks. + +- Server request and response format changed. The `/v1/chat/completions` endpoint now uses + OpenAI-compatible request and response schemas. The previous `RequestBody` and `ResponseBody` + classes are removed. For the new format, refer to + [](../run-rails/using-fastapi-server/overview.md). + +- ChatNVIDIA streaming patch removed. The custom + `_langchain_nvidia_ai_endpoints_patch.py` module is removed. + The standard `ChatNVIDIA` from `langchain_nvidia_ai_endpoints` is used directly. + +(v0-21-0-bug-fixes)= + +### Bug Fixes + +- Fixed a naming mismatch where the `generate_next_step` action did not match the + `generate_next_steps` task enum value, which prevented task-specific LLM configuration + from working correctly ([#1603](https://github.com/NVIDIA-NeMo/Guardrails/pull/1603)). +- Added the `valid` alias to action results in the GuardrailsAI integration so that + Colang flows checking `$result["valid"]` work as expected ([#1611](https://github.com/NVIDIA-NeMo/Guardrails/pull/1611)). +- Filtered the `stop` parameter for OpenAI reasoning models (such as GPT-5) that do not + accept it, preventing `400` errors during dialogue rail execution ([#1653](https://github.com/NVIDIA-NeMo/Guardrails/pull/1653)). +- Fixed GLiNER PII detection to use "bot refuse to respond" instead of + "bot inform answer unknown", which returned a misleading "I don't know" message ([#1671](https://github.com/NVIDIA-NeMo/Guardrails/pull/1671)). +- Fixed a `TypeError` when `stop=None` is passed to `StreamingHandler` by coercing + `None` to an empty list ([#1685](https://github.com/NVIDIA-NeMo/Guardrails/pull/1685)). +- Fixed a `TypeError` in `RollingBuffer.format_chunks` when `include_metadata=True` is used + with output rail streaming enabled. Dict chunks are now normalized to strings at the + input boundary ([#1687](https://github.com/NVIDIA-NeMo/Guardrails/pull/1687)). +- Fixed `GuardrailsMiddleware` silently dropping content when rails return `MODIFIED` status. + Input rails now replace the last user message and output rails replace the last AI + message with the sanitized content ([#1714](https://github.com/NVIDIA-NeMo/Guardrails/pull/1714)). +- Cache hit statistics are now visible in the Stats log line. Cache stats are also + visible in verbose mode ([#1666](https://github.com/NVIDIA-NeMo/Guardrails/pull/1666), [#1667](https://github.com/NVIDIA-NeMo/Guardrails/pull/1667)). + +(v0-21-0-other-changes)= ### Other Changes -- Restructured the documentation with improved navigation, clearer content organization, and updated configuration reference and user guides. +- Updated the Fiddler Guardrails API to match the new specification: the `prompt` field is + renamed to `input`, faithfulness uses strings instead of lists, and a new `fdl_roleplaying` + category is added ([#1619](https://github.com/NVIDIA-NeMo/Guardrails/pull/1619)). +- Updated the Trend Micro Vision One AI Guard integration from the beta endpoint to the + officially released GA endpoint. A required `TMV1-Application-Name` header is added and the + request key is changed from `guard` to `prompt` ([#1546](https://github.com/NVIDIA-NeMo/Guardrails/pull/1546)). +- Added a Locust stress-test benchmark for load testing ([#1629](https://github.com/NVIDIA-NeMo/Guardrails/pull/1629)). +- Removed the `multi_kb` example ([#1673](https://github.com/NVIDIA-NeMo/Guardrails/pull/1673)). +- Removed the AI Virtual Assistant Blueprint notebook ([#1682](https://github.com/NVIDIA-NeMo/Guardrails/pull/1682)). +- Updated the Pangea User-Agent repo URL ([#1610](https://github.com/NVIDIA-NeMo/Guardrails/pull/1610)). +- Updated dependencies for the jailbreak detection Docker container ([#1596](https://github.com/NVIDIA-NeMo/Guardrails/pull/1596)). +- Major documentation revamp with improved structure and navigation. --- ## Previous Release Notes +- [0.20.0](https://docs.nvidia.com/nemo/guardrails/0.20.0/release-notes.html) - [0.19.0](https://docs.nvidia.com/nemo/guardrails/0.19.0/release-notes.html) - [0.18.0](https://docs.nvidia.com/nemo/guardrails/0.18.0/release-notes.html) - [0.17.0](https://docs.nvidia.com/nemo/guardrails/0.17.0/release-notes.html) diff --git a/docs/conf.py b/docs/conf.py index b8b8d3e273..572fa223ea 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -91,8 +91,8 @@ # Top-level pages "architecture": "about/how-it-works.html", "architecture/readme": "reference/colang-architecture-guide.html", - "faqs": "resources/faqs.html", - "glossary": "resources/glossary.html", + "faqs": "index.html", + "glossary": "index.html", "release-notes": "about/release-notes.html", "security/guidelines": "resources/security/guidelines.html", # Getting started diff --git a/docs/configure-rails/caching/index.md b/docs/configure-rails/caching/index.md index b404e2dfe5..35a60f2007 100644 --- a/docs/configure-rails/caching/index.md +++ b/docs/configure-rails/caching/index.md @@ -14,6 +14,11 @@ content: # Caching Instructions and Prompts +The NVIDIA NeMo Guardrails library provides two caching strategies to reduce inference latency. +The in-memory model cache stores LLM responses and returns them for repeated prompts without calling the LLM again. +KV cache reuse is a NIM-level optimization that avoids computation of the system prompt on each NemoGuard NIM call. +You can enable either or both strategies independently. + ::::{grid} 1 2 2 2 :gutter: 3 diff --git a/docs/configure-rails/caching/model-memory-cache.md b/docs/configure-rails/caching/model-memory-cache.md index 239a085980..054f81bcf8 100644 --- a/docs/configure-rails/caching/model-memory-cache.md +++ b/docs/configure-rails/caching/model-memory-cache.md @@ -16,15 +16,15 @@ content: # Memory Model Cache -Guardrails supports an in-memory cache that avoids making LLM calls for repeated prompts. The cache stores user prompts and their corresponding LLM responses. Prior to making an LLM call, Guardrails checks if the prompt already exists in the cache. If found, the stored response is returned instead of calling the LLM, improving latency. +The NVIDIA NeMo Guardrails library supports an in-memory cache that avoids making LLM calls for repeated prompts. The cache stores user prompts and their corresponding LLM responses. Before making an LLM call, the library checks if the prompt already exists in the cache. If found, the stored response is returned instead of calling the LLM, which improves latency. -In-memory caches are supported for all Nemoguard models: [Content-Safety](https://build.nvidia.com/nvidia/llama-3_1-nemoguard-8b-content-safety), [Topic-Control](https://build.nvidia.com/nvidia/llama-3_1-nemoguard-8b-topic-control), and [Jailbreak Detection](https://build.nvidia.com/nvidia/nemoguard-jailbreak-detect). Each model can be configured independently. +In-memory caches are supported for all NemoGuard models: [Content-Safety](https://build.nvidia.com/nvidia/llama-3_1-nemoguard-8b-content-safety), [Topic-Control](https://build.nvidia.com/nvidia/llama-3_1-nemoguard-8b-topic-control), and [Jailbreak Detection](https://build.nvidia.com/nvidia/nemoguard-jailbreak-detect). You can configure each model independently. -The cache uses exact matching (after removing whitespace) on LLM prompts with a Least-Frequently-Used (LFU) algorithm for cache evictions. +The cache uses exact matching (after normalizing whitespace) on LLM prompts with a Least-Frequently-Used (LFU) algorithm for cache evictions. Whitespace normalization collapses consecutive whitespace characters into a single space and trims leading/trailing whitespace. -For observability, cache hits and misses are visible in OpenTelemetry (OTEL) telemetry and stored in logs on a configurable cadence. +For observability, cache hits and misses are visible in OpenTelemetry (OTEL) telemetry and stored in logs at a configurable interval. -To get started with caching, refer to the example configurations below. The rest of this page provides a deep dive into how the cache works, telemetry, and considerations when enabling caching in a horizontally scalable service. +To get started with caching, refer to the example configurations below. The rest of this page provides details about how the cache works, telemetry, and considerations for enabling caching in a horizontally scalable service. --- @@ -76,7 +76,7 @@ rails: ### With Caching -The following configuration file shows the same configuration with caching enabled on the Content-Safety, Topic-Control, and Jailbreak Detection Nemoguard NIM microservices. +The following configuration file shows the same configuration with caching enabled on the Content Safety, Topic Control, and Jailbreak Detection NemoGuard NIM microservices. All three caches have a size of 10,000 records and log their statistics every 60 seconds. ```yaml @@ -137,43 +137,36 @@ rails: ## How the Cache Works -When the cache is enabled, Guardrails checks whether a prompt was already sent to the LLM before making each call. This uses an exact-match lookup after removing whitespace. +When the cache is enabled, the library checks whether a prompt was already sent to the LLM before making each call. This check uses an exact-match lookup after removing whitespace. -If there is a cache hit (that is, the same prompt was sent to the same LLM earlier and the response was stored in the cache), the response is returned without calling the LLM. +If there is a cache hit (that is, the same prompt was sent to the same LLM earlier and the response was stored in the cache), the library returns the response without calling the LLM. -If there is a cache miss (that is, there is no stored LLM response for this prompt in the cache), the LLM is called as usual. When the response is received, it is stored in the cache. +If there is a cache miss (that is, there is no stored LLM response for this prompt in the cache), the library calls the LLM as usual. After the response is received, the library stores it in the cache. -For security reasons, user prompts are not stored directly. After removing whitespace, the user prompt is hashed using SHA256 and then used as a cache key. +For security reasons, user prompts are not stored directly. After removing whitespace, the library hashes the user prompt using SHA256 and uses the hash as a cache key. -If a new cache record needs to be added and the cache already has `maxsize` entries, the Least-Frequently Used (LFU) algorithm is used to decide which cache record to evict. +If a new cache record needs to be added and the cache already has `maxsize` entries, the Least-Frequently Used (LFU) algorithm decides which cache record to evict. The LFU algorithm ensures that the most frequently accessed cache entries remain in the cache, improving the probability of a cache hit. --- ## Telemetry and Logging -Guardrails supports OTEL telemetry to trace client requests through Guardrails and any calls to LLMs or APIs. The cache operation is reflected in these traces: +The NVIDIA NeMo Guardrails library supports OTEL telemetry to trace client requests through the library and any calls to LLMs or APIs. The cache operation is reflected in these traces: - **Cache hits** have a far shorter duration with no LLM call - **Cache misses** include an LLM call This OTEL telemetry is suited for operational dashboards. -The cache statistics are also logged on a configurable cadence if `cache.stats.enabled` is set to `true`. Every `log_interval` seconds, the cache statistics are logged with the format shown below. +The cache statistics are also logged at a configurable interval if `cache.stats.enabled` is set to `true`. Every `log_interval` seconds, the cache statistics are logged with the format shown below. The most important metric is the *Hit Rate*, which represents the proportion of LLM calls returned from the cache. If this value remains low, the exact-match approach might not be a good fit for your use case. -These statistics accumulate while Guardrails is running. +These statistics accumulate while the library is running. ```text -"LFU Cache Statistics - " -"Size: 23/10000 | " -"Hits: 20 | " -"Misses: 3 | " -"Hit Rate: 87% | " -"Evictions: 0 | " -"Puts: 21 | " -"Updates: 4" +Cache Stats :: Size: 23/10000 | Hits: 20 | Misses: 3 | Hit Rate: 87.00% | Evictions: 0 | Puts: 21 | Updates: 4 ``` The following list describes the metrics included in the cache statistics: @@ -190,11 +183,59 @@ The following list describes the metrics included in the cache statistics: ## Horizontal Scaling and Caching -This cache is implemented in-memory on each Guardrails node. When operating as a horizontally-scaled backend service, multiple Guardrails nodes run behind an API Gateway and load balancer to distribute traffic and meet availability and performance targets. +The cache is implemented in-memory by the NVIDIA NeMo Guardrails library. +If a Guardrails library instance is restarted, the contents of the cache are lost. +This causes high miss rates due to compulsory or cold-start cache misses. +This section describes techniques that are out of scope for the library but can improve caching performance in a horizontally scaled backend. -The current cache implementation maintains a separate cache on each node without sharing cache entries between nodes. For a cache hit to occur, the following conditions must be met: +You can operate the library as a horizontally scalable service to meet availability and performance Service Level Objectives (SLOs). +A typical deployment has multiple Guardrails instances running in parallel behind an API gateway and load balancer. +The API gateway implements authentication and authorization, rate limiting and throttling, and any required protocol translation. +The load balancer distributes load evenly over nodes in the cluster. +As a result, highly requested prompts can spread across nodes over time rather than concentrate on a single node. -1. The request must have been previously sent and stored in a cache. -2. The load balancer must direct the subsequent request to the same node. +### Cache Fragmentation -In practice, the load balancer spreads traffic across all Guardrails nodes, distributing frequently-requested user prompts across multiple nodes. This reduces cache hit rates in horizontally-scaled deployments compared to single-node deployments. +With a default round-robin load balancing strategy, incoming traffic routes to each node in turn. +The nodes build their own partial view of traffic, which reduces cache hit rates compared to a single-node deployment. +This effect is called *cache fragmentation* and becomes more pronounced as the number of nodes increases. + +You can address cache fragmentation in one of two ways: + +1. Use a stateful load balancer to inspect the incoming request and route it to the same backend node on every request. +2. Use a cluster-wide in-memory store to store and read cache entries from all compute nodes in the cluster. This approach also helps when nodes restart because they can pull the cache state on startup. + +#### Improving Cache Hit Rates with Consistent Hashing + +Stateful load balancing strategies route repeated identical requests to the same backend node rather than spreading them evenly. +This approach increases cache hit rates and does not require any modifications to the library code. +In consistent hashing, a property of the request, such as the request body or a header value, is hashed and used to select a backend node. +Requests with identical properties always route to the same node, which improves cache hit rates. +However, over time, nodes are added or removed due to scaling, deployments, or node failures. +This causes some requests to remap to different nodes, and hit rates drop until the incoming traffic repopulates cache entries. + +- [Consistent Hashing and Random Trees: Distributed Caching Protocols for Relieving Hot Spots on the World Wide Web](https://www.cs.princeton.edu/courses/archive/fall09/cos518/papers/chash.pdf) +- [Web Caching with Consistent Hashing](https://cs.brown.edu/courses/csci2950-u/f09/papers/chash99www.pdf) +- [A Fast, Minimal Memory, Consistent Hash Algorithm](https://arxiv.org/pdf/1406.2294) +- [Maglev: A Fast and Reliable Software Network Load Balancer](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/44824.pdf) + +#### Improving Cache Hit Rates with Cluster Storage + +An alternative to stateful load balancing is to use a cluster-wide in-memory store such as [Redis](https://redis.io/). +Supporting a cluster-wide in-memory store requires modifications to the library to read and write to the cluster storage. +Instead of each node maintaining its own isolated cache, all nodes read from and write to a shared store. +This approach eliminates cache fragmentation because a prompt cached by any node is available to all nodes regardless of how the load balancer routes requests. +A cluster-wide store also improves resilience. +When a node restarts, it does not start with an empty cache. +Instead, the node can load previously cached entries from the shared store and benefits from cache hits immediately. + +The trade-off is added infrastructure complexity and a network hop for each cache lookup. +The shared store must be highly available and sized to handle the throughput of all nodes. +Refer to the documentation for your chosen in-memory store for guidance on clustering, replication, and sizing. + +For general background on cluster-wide caching, refer to: + +- [Redis: Clustering](https://redis.io/docs/latest/operate/oss_and_stack/management/scaling/) +- [Memcached: Configuration](https://github.com/memcached/memcached/wiki/ConfiguringServer) +- [AWS ElastiCache: Choosing a Cache](https://docs.aws.amazon.com/AmazonElastiCache/latest/dg/SelectEngine.html) +- [Google Cloud Memorystore](https://docs.cloud.google.com/memorystore/docs/redis/memorystore-for-redis-overview) diff --git a/docs/configure-rails/custom-initialization/index.md b/docs/configure-rails/custom-initialization/index.md index 31398832d0..d471571460 100644 --- a/docs/configure-rails/custom-initialization/index.md +++ b/docs/configure-rails/custom-initialization/index.md @@ -22,7 +22,7 @@ content: # Configuring Custom Initialization -The `config.py` file contains initialization code that runs **once at startup**, before the `LLMRails` instance is fully initialized. Use it to register custom providers and set up shared resources. +The `config.py` file contains initialization code that runs once at startup, before the `LLMRails` instance is fully initialized. Use it to register custom providers and set up shared resources. ## When to Use config.py vs actions.py @@ -53,7 +53,7 @@ Define the init() function to initialize resources and register action parameter :link: custom-llm-providers :link-type: doc -Register custom text completion (BaseLLM) and chat models (BaseChatModel) for use with NeMo Guardrails. +Register custom text completion (BaseLLM) and chat models (BaseChatModel) for use with the NVIDIA NeMo Guardrails library. +++ {bdg-secondary}`How To` ::: @@ -62,7 +62,7 @@ Register custom text completion (BaseLLM) and chat models (BaseChatModel) for us :link: custom-embedding-providers :link-type: doc -Register custom embedding providers for vector similarity search in NeMo Guardrails. +Register custom embedding providers for vector similarity search in the NVIDIA NeMo Guardrails library. +++ {bdg-secondary}`How To` ::: diff --git a/docs/configure-rails/custom-initialization/init-function.md b/docs/configure-rails/custom-initialization/init-function.md index 3c88e89c8f..f633fa1857 100644 --- a/docs/configure-rails/custom-initialization/init-function.md +++ b/docs/configure-rails/custom-initialization/init-function.md @@ -24,6 +24,12 @@ content: If `config.py` contains an `init` function, it is called during `LLMRails` initialization. Use it to set up shared resources and register action parameters. +```{important} +The `init` function **must be synchronous** (`def init`, not `async def init`). The framework calls it without `await`, so an async function would silently do nothing. +``` + +Any top-level code in `config.py` runs at import time, before `init()` is called. This can be used for provider registration that does not require the `LLMRails` instance. + ## Basic Usage ```python @@ -39,17 +45,19 @@ def init(app: LLMRails): ## Registering Action Parameters -Action parameters registered in `config.py` are automatically injected into actions that declare them: +Action parameters registered in `config.py` are automatically injected into actions that declare them. The runtime matches parameters by name, i.e., the parameter name in the action must match the name used during registration. **config.py:** ```python +import os + from nemoguardrails import LLMRails def init(app: LLMRails): # Initialize shared resources db = DatabaseConnection(host="localhost", port=5432) - api_client = ExternalAPIClient(api_key="...") + api_client = ExternalAPIClient(api_key=os.environ.get("API_KEY")) # Register as action parameters app.register_action_param("db", db) @@ -72,6 +80,20 @@ async def call_external_service(query: str, api_client=None): return await api_client.search(query) ``` +## Built-in Action Parameters + +In addition to parameters you register, the runtime automatically injects these built-in parameters into any action that declares them: + +| Parameter | Type | Description | +|-----------|------|-------------| +| `config` | `RailsConfig` | The full rails configuration object | +| `context` | `dict` | The current conversation context | +| `events` | `list` | The event history | +| `llm` | LLM instance | The main LLM (auto-registered during initialization) | +| `llm_task_manager` | `LLMTaskManager` | Manages LLM task execution | + +See [Custom Data](custom-data.md) for details on accessing `config.custom_data` inside actions. + ## Accessing the Configuration The `app` parameter provides access to the full configuration: @@ -91,37 +113,32 @@ def init(app: LLMRails): ## Example: Database Connection ```python -import asyncpg +import os +import psycopg2 from nemoguardrails import LLMRails -async def create_db_pool(): - return await asyncpg.create_pool( - host="localhost", - database="mydb", - user="user", - password="password" - ) - def init(app: LLMRails): - import asyncio - # Create connection pool - loop = asyncio.get_event_loop() - db_pool = loop.run_until_complete(create_db_pool()) + conn = psycopg2.connect( + host=os.environ.get("DB_HOST", "localhost"), + database=os.environ.get("DB_NAME", "mydb"), + user=os.environ.get("DB_USER", "user"), + password=os.environ.get("DB_PASSWORD"), + ) - # Register for use in actions - app.register_action_param("db_pool", db_pool) + app.register_action_param("db_conn", conn) ``` ## Example: API Client Initialization ```python +import os import httpx from nemoguardrails import LLMRails def init(app: LLMRails): # Get API key from custom_data in config.yml - api_key = app.config.custom_data.get("api_key") + api_key = os.environ.get("API_KEY") or app.config.custom_data.get("api_key") # Create HTTP client with authentication client = httpx.AsyncClient( diff --git a/docs/configure-rails/guardrail-catalog/community/policyai.md b/docs/configure-rails/guardrail-catalog/community/policyai.md new file mode 100644 index 0000000000..684f6891ae --- /dev/null +++ b/docs/configure-rails/guardrail-catalog/community/policyai.md @@ -0,0 +1,116 @@ +# PolicyAI Integration + +NeMo Guardrails supports using the [PolicyAI](https://musubilabs.ai) content moderation API as an input and output rail out-of-the-box (you need to have the `POLICYAI_API_KEY` environment variable set). + +PolicyAI provides flexible policy-based content moderation, allowing you to define custom policies for your specific use cases and manage them through tags. + +## Setup + +1. Sign up for a PolicyAI account at [musubilabs.ai](https://musubilabs.ai) +2. Create your policies and organize them with tags +3. Set the required environment variables: + +```bash +export POLICYAI_API_KEY="your-api-key" +export POLICYAI_BASE_URL="https://api.musubilabs.ai" # Optional, this is the default +export POLICYAI_TAG_NAME="prod" # Optional, defaults to "prod" +``` + +## Usage + +### Basic Input Moderation + +```yaml +rails: + input: + flows: + - policyai moderation on input +``` + +### Basic Output Moderation + +```yaml +rails: + output: + flows: + - policyai moderation on output +``` + +### Using Different Tags + +To use different policy tags for different environments, set the `POLICYAI_TAG_NAME` environment variable: + +```bash +# For staging environment +export POLICYAI_TAG_NAME="staging" + +# For production environment +export POLICYAI_TAG_NAME="prod" +``` + +## Complete Example + +```yaml +models: + - type: main + engine: openai + model: gpt-4 + +rails: + input: + flows: + - policyai moderation on input + + output: + flows: + - policyai moderation on output +``` + +## How It Works + +1. **Input Rails**: When a user sends a message, PolicyAI evaluates it against all policies attached to the configured tag. If any policy returns `UNSAFE`, the message is blocked. + +2. **Output Rails**: Before the bot's response is sent to the user, PolicyAI evaluates it. If the content violates any policy, the response is replaced with a refusal message. + +## Response Format + +PolicyAI returns the following information for each evaluation: + +- `assessment`: `"SAFE"` or `"UNSAFE"` +- `category`: The category of violation (if UNSAFE) +- `severity`: Severity level from 0 (safe) to 3 (high severity) +- `reason`: Human-readable explanation + +## Customizing Behavior + +To customize the behavior when content is flagged, you can override the default flows in your config: + +```text +define subflow policyai moderation on input + """Custom PolicyAI input moderation.""" + $result = execute call_policyai_api(text=$user_message) + + if $result.assessment == "UNSAFE" + bot inform content policy violation + stop + +define bot inform content policy violation + "I'm sorry, but I cannot process that request. Please rephrase your message." +``` + +## Environment Variables + +| Variable | Required | Default | Description | +|----------|----------|---------|-------------| +| `POLICYAI_API_KEY` | Yes | - | Your PolicyAI API key | +| `POLICYAI_BASE_URL` | No | `https://api.musubilabs.ai` | PolicyAI API base URL | +| `POLICYAI_TAG_NAME` | No | `prod` | Default policy tag to use | + +## Error Handling + +If the PolicyAI API is unavailable or returns an error, the action will raise an exception. To implement fail-open or fail-closed behavior, you can wrap the action in a try-catch block in your custom flows. + +## Learn More + +- [PolicyAI Documentation](https://docs.musubilabs.ai) +- [Musubi Labs](https://musubilabs.ai) diff --git a/docs/configure-rails/guardrail-catalog/jailbreak-protection.md b/docs/configure-rails/guardrail-catalog/jailbreak-protection.md index 79461fb4ad..9243b14dd1 100644 --- a/docs/configure-rails/guardrail-catalog/jailbreak-protection.md +++ b/docs/configure-rails/guardrail-catalog/jailbreak-protection.md @@ -48,6 +48,18 @@ rails: If the `server_endpoint` parameter is not set, the checks will run in-process. This is useful for TESTING PURPOSES ONLY and **IS NOT RECOMMENDED FOR PRODUCTION DEPLOYMENTS**. ``` +### Configuration Validation + +The jailbreak detection configuration is validated at create-time. Invalid values raise errors +immediately instead of failing silently at runtime. The following validation rules apply: + +| Parameter | Rule | +|-----------|------| +| `length_per_perplexity_threshold` | Must be greater than 0 | +| `prefix_suffix_perplexity_threshold` | Must be greater than 0 | +| `nim_base_url` | Must start with `http://` or `https://` | +| `server_endpoint` | Must start with `http://` or `https://` | + ### Heuristics #### Length per Perplexity diff --git a/docs/configure-rails/guardrail-catalog/third-party.md b/docs/configure-rails/guardrail-catalog/third-party.md index 4fc9175f13..ba3b829756 100644 --- a/docs/configure-rails/guardrail-catalog/third-party.md +++ b/docs/configure-rails/guardrail-catalog/third-party.md @@ -34,6 +34,26 @@ rails: For more details, check out the [ActiveFence Integration](community/active-fence.md) page. +## PolicyAI + +The NeMo Guardrails library supports using [PolicyAI](https://musubilabs.ai) by Musubi Labs as an input and output rail out-of-the-box (you need to have the `POLICYAI_API_KEY` environment variable set). + +PolicyAI provides policy-based content moderation, allowing you to define custom policies and organize them with tags for environment-based management. + +### Example usage + +```yaml +rails: + input: + flows: + - policyai moderation on input + output: + flows: + - policyai moderation on output +``` + +For more details, check out the [PolicyAI Integration](community/policyai.md) page. + ## AutoAlign The NeMo Guardrails library supports using the AutoAlign's guardrails API (you need to have the `AUTOALIGN_API_KEY` environment variable set). @@ -283,6 +303,7 @@ Llama Guard <community/llama-guard> Pangea AI Guard <community/pangea> Patronus Evaluate API <community/patronus-evaluate-api> Patronus Lynx <community/patronus-lynx> +PolicyAI <community/policyai> Presidio <community/presidio> Private AI <community/privateai> Prompt Security <community/prompt-security> diff --git a/docs/configure-rails/other-configurations/knowledge-base.md b/docs/configure-rails/other-configurations/knowledge-base.md index 453701e14b..b08dd54d66 100644 --- a/docs/configure-rails/other-configurations/knowledge-base.md +++ b/docs/configure-rails/other-configurations/knowledge-base.md @@ -40,7 +40,7 @@ Currently, only the Markdown format is supported. Documents in the knowledge base `kb` folder are automatically processed and indexed for retrieval. The system: -1. Splits documents into topic chunks based on markdown headers. +1. Splits documents into topic chunks based on markdown headers. Large chunks are further split at blank lines to stay within a maximum chunk size. 2. Uses the configured embedding model to create vector representations of each chunk. 3. Stores the embeddings for efficient similarity search. @@ -126,18 +126,28 @@ Place markdown files in the `kb` folder as described above. This is the simplest Implement a custom action to retrieve chunks from external sources: ```python +from typing import Optional + from nemoguardrails.actions import action +from nemoguardrails.actions.actions import ActionResult +from nemoguardrails.kb.kb import KnowledgeBase -@action() -async def retrieve_relevant_chunks(context: dict, llm: BaseLLM): - """Custom retrieval from external knowledge base.""" - user_message = context.get("last_user_message") +@action(is_system_action=True) +async def retrieve_relevant_chunks( + context: Optional[dict] = None, + kb: Optional[KnowledgeBase] = None, +): + user_message = context.get("last_user_message") if context else None # Implement custom retrieval logic # For example, query an external vector database chunks = await query_external_kb(user_message) + relevant_chunks = "\n".join(chunks) - return chunks + return ActionResult( + return_value=relevant_chunks, + context_updates={"relevant_chunks": relevant_chunks}, + ) ``` ### 3. Using Custom EmbeddingSearchProvider @@ -145,7 +155,8 @@ async def retrieve_relevant_chunks(context: dict, llm: BaseLLM): For advanced use cases, implement a custom embedding search provider: ```python -from nemoguardrails.embeddings.index import EmbeddingsIndex +from typing import List, Optional +from nemoguardrails.embeddings.index import EmbeddingsIndex, IndexItem class CustomEmbeddingSearchProvider(EmbeddingsIndex): """Custom embedding search provider.""" @@ -154,8 +165,7 @@ class CustomEmbeddingSearchProvider(EmbeddingsIndex): # Custom indexing logic pass - async def search(self, text: str, max_results: int) -> List[IndexItem]: - # Custom search logic + async def search(self, text: str, max_results: int, threshold: Optional[float] = None) -> List[IndexItem]: pass ``` diff --git a/docs/configure-rails/yaml-schema/guardrails-configuration/parallel-rails.md b/docs/configure-rails/yaml-schema/guardrails-configuration/parallel-rails.md index d3150fdebd..13e1c49cd6 100644 --- a/docs/configure-rails/yaml-schema/guardrails-configuration/parallel-rails.md +++ b/docs/configure-rails/yaml-schema/guardrails-configuration/parallel-rails.md @@ -7,24 +7,70 @@ description: Configure input and output rails to run in parallel for improved la You can configure input and output rails to run in parallel. This can improve latency and throughput. -## When to Use Parallel Rails Execution +## IORails Engine -Use parallel execution: +The IORails engine is an optimized execution engine that runs NemoGuard input and output rails in +parallel with dedicated model management. The IORails engine is an opt-in feature. By default, the +NeMo Guardrails library uses the LLMRails engine. + +:::{note} +IORails is an early-release feature and currently does not support streaming, reasoning models, and telemetry as in LLMRails. +::: + +### Supported Flows + +The IORails engine supports the following flows: + +- `content safety check input` / `content safety check output` +- `topic safety check input` +- `jailbreak detection model` + +When IORails is enabled and the configuration uses only these flows, the engine runs them in parallel. +Configurations that include custom flows, dialog rails, or other unsupported flows +raise an error at initialization. + +### Enabling IORails + +To enable the IORails engine, set the `NEMO_GUARDRAILS_IORAILS_ENGINE` environment variable to `1`: + +```bash +NEMO_GUARDRAILS_IORAILS_ENGINE=1 nemoguardrails chat --config examples/configs/content_safety +``` + +When using the Python API, import the `Guardrails` class directly and pass `use_iorails=True`: + +```python +from nemoguardrails import RailsConfig +from nemoguardrails.guardrails.guardrails import Guardrails + +config = RailsConfig.from_path("./config") +guardrails = Guardrails(config, use_iorails=True) +``` + +## YAML-Based Parallel Execution + +You can also configure existing LLMRails flows to run in parallel using the `parallel: True` +option in the `config.yml` file. This approach works with any flow type and does not require +the IORails engine. + +### When to Use + +Use YAML-based parallel execution: - For I/O-bound rails such as external API calls to LLMs or third-party integrations. - If you have two or more independent input or output rails without shared state dependencies. - In production environments where response latency affects user experience and business metrics. -## When Not to Use Parallel Rails Execution +### When Not to Use Avoid parallel execution: - For CPU-bound rails; it might not improve performance and can introduce overhead. - During development and testing for debugging and simpler workflows. -## Configuration Example +### Configuration Example -To enable parallel execution, set `parallel: True` in the `rails.input` and `rails.output` sections in the `config.yml` file. The following configuration example is tested by NVIDIA and shows how to enable parallel execution for input and output rails. +To enable parallel execution, set `parallel: True` in the `rails.input` and `rails.output` sections in the `config.yml` file. ```{note} Input rail mutations can lead to erroneous results during parallel execution because of race conditions arising from the execution order and timing of parallel operations. This can result in output divergence compared to sequential execution. For such cases, use sequential mode. @@ -60,5 +106,4 @@ rails: chunk_size: 200 context_size: 50 stream_first: True -streaming: True ``` diff --git a/docs/getting-started/installation-guide.md b/docs/getting-started/installation-guide.md index 706b622ef1..d922c4e430 100644 --- a/docs/getting-started/installation-guide.md +++ b/docs/getting-started/installation-guide.md @@ -122,6 +122,7 @@ You can install the NeMo Guardrails library with optional extra packages to add |-------|-------------| | `nvidia` | NVIDIA-hosted model integration through [build.nvidia.com](https://build.nvidia.com/) | | `openai` | OpenAI-hosted model integration | +| `server` | [Guardrails API server](../run-rails/using-fastapi-server/overview.md) dependencies (aiofiles for async file handling, openai for API schemas). FastAPI is a core dependency. Required to run `nemoguardrails server`. | | `sdd` | [Sensitive data detection](../configure-rails/guardrail-catalog/pii-detection.md#presidio-based-sensitive-data-detection) using Presidio | | `eval` | [Evaluation tools](../evaluation/evaluate-guardrails.md) for testing guardrails | | `tracing` | OpenTelemetry tracing support | diff --git a/docs/index.md b/docs/index.md index 8ce73a2733..e99627b736 100644 --- a/docs/index.md +++ b/docs/index.md @@ -267,7 +267,5 @@ Troubleshooting <troubleshooting> :name: Resources :hidden: -FAQs <resources/faqs.md> -Glossary <resources/glossary.md> Security <resources/security/guidelines.md> ``` diff --git a/docs/integration/langchain/agent-middleware.md b/docs/integration/langchain/agent-middleware.md index 3900a0bf8c..4cdaba0eb6 100644 --- a/docs/integration/langchain/agent-middleware.md +++ b/docs/integration/langchain/agent-middleware.md @@ -309,11 +309,15 @@ result2 = agent.invoke( Be aware of the following constraints when using `GuardrailsMiddleware` with tool-calling agents. -### Output Rails and Tool-Calling Responses +### Security Considerations for Tool-Calling Agents -LLM-based output rails (such as `self_check_output`) evaluate the `content` field of the model's response. Intermediate tool-calling responses often have **empty content** (the actual instructions are in the `tool_calls` field). Depending on the LLM used for the self-check, an empty content field may be flagged as a violation. +Rails evaluate the `content` field of messages only. This has two implications for tool-calling agents: -To work around this, disable output rails and rely on input rails for tool-calling agents: +**Tool call arguments are not inspected.** When the LLM generates a tool call, the arguments (e.g., `send_email(body="SSN: 123-45-6789")`) are in the `tool_calls` field, not `content`. Input and output rails do not see or validate these arguments. + +**Tool results bypass input rails.** When a tool returns its result as a `ToolMessage`, that message is not subject to input rail validation. Malicious or unexpected tool outputs can influence subsequent model responses without being checked. + +To mitigate these risks, enable output rails to validate the final LLM response before it reaches the user. This ensures that even if unsafe content enters through tool calls or tool results, the model's response is still checked. However, note that intermediate tool-calling responses often have **empty content** (the instructions are in the `tool_calls` field), and some LLM-based output rails (such as `self_check_output`) may flag empty content as a false positive. If you encounter this, you can disable output rails as a workaround — but be aware this also removes the safety net for tool result content: ```python guardrails = GuardrailsMiddleware( @@ -322,13 +326,11 @@ guardrails = GuardrailsMiddleware( ) ``` -### Tool Call Arguments Are Not Inspected - -Rails evaluate the `content` field of messages, not the `tool_calls` arguments. Content-based rails do not inspect PII or harmful content passed through tool call arguments (e.g., `send_email(body="SSN: 123-45-6789")`). +For more details, see [Security Considerations](https://docs.nvidia.com/nemo/guardrails/latest/integration/tools-integration.html#security-considerations) in the tools integration guide. -### MODIFIED Status Is Ignored +### MODIFIED Status Replaces Message Content -When a rail modifies content (returns `RailStatus.MODIFIED`), the middleware treats it as a pass-through and the agent uses the original, unmodified content. This is by design — applying modifications to the agent's internal state could cause inconsistencies. +When a rail modifies content (returns `RailStatus.MODIFIED`), the middleware replaces the relevant message with the modified content. For input rails, the last user message is replaced. For output rails, the last AI message is replaced. This enables use cases like PII redaction and content sanitization. --- diff --git a/docs/project.json b/docs/project.json index 9dd5b56106..108734850a 100644 --- a/docs/project.json +++ b/docs/project.json @@ -1 +1 @@ -{ "name": "nemo-guardrails-toolkit", "version": "0.20.0" } +{ "name": "nemo-guardrails-toolkit", "version": "0.21.0" } diff --git a/docs/resources/faqs.md b/docs/resources/faqs.md deleted file mode 100644 index 606c016ac7..0000000000 --- a/docs/resources/faqs.md +++ /dev/null @@ -1,45 +0,0 @@ -# Frequently Asked Questions (FAQ) - -This is an FAQ document. If your question isn't answered here, feel free to open a GitHub issue or ask a question using GitHub Discussions. - -## Table of Contents - -1. [Can I deploy NeMo Guardrails in a production?](#can-i-deploy-nemo-guardrails-in-production) -2. [How robust are the examples provided in the repo?](#how-robust-are-the-examples-provided-in-the-repo) -3. [What type of information can I add to the knowledge base?](#what-type-of-information-can-i-add-to-the-knowledge-base) -4. [What LLMs are supported by NeMo Guardrails?](#what-llms-are-supported-by-nemo-guardrails) -5. [How well does this work?](#how-well-does-this-work) - ---- - -### Can I deploy NeMo Guardrails in production? - -The current alpha release is undergoing active development and may be subject to changes and improvements, which could potentially cause instability and unexpected behavior. We currently do not recommend deploying this alpha version in a production setting. We appreciate your understanding and contribution during this stage. - -[Back to top](#table-of-contents) - -### How robust are the examples provided in the repo? - -The example configurations are meant to be educational. Their purpose is to showcase the core behavior of the toolkit. To achieve a high degree of robustness, the guardrails configurations should be extended through careful application design along with iterative testing and refinement. - -[Back to top](#table-of-contents) - -### What type of information can I add to the knowledge base? - -The knowledge base is designed for question answering on non-sensitive information (e.g., not including PII, PHI). The knowledge base's content is chunked, and any part of it can end up in the prompt(s) sent to the LLM. In any responsible security architecture, sensitive information should not be included in any source that would be exposed to the LLM. - -[Back to top](#table-of-contents) - -### What LLMs are supported by NeMo Guardrails? - -Technically, you can connect a guardrails configuration to any LLM provider that is supported by LangChain (e.g., `ai21`, `aleph_alpha`, `anthropic`, `anyscale`, `azure`, `cohere`, `huggingface_endpoint`, `huggingface_hub`, `openai`, `self_hosted`, `self_hosted_hugging_face` - check out the LangChain official documentation for the full list) or to any [custom LLM](../configure-rails/custom-initialization/custom-llm-providers.md). Depending on the capabilities of the LLM, some will work better than others. We are performing evaluations, and we will share more details soon. - -Changes to some configuration elements can help improve compatibility with a given LLM provider or custom LLM, including the general instructions or prompt templates. This is essentially prompt engineering, and it is an imperfect process. As the capabilities of various LLMs evolve in the future, we expect this process to get easier. - -[Back to top](#table-of-contents) - -### How well does this work? - -We'll be putting out a more fulsome evaluation soon, breaking down the components like canonical form generation, flow generation, safety rail accuracy, and so forth. - -[Back to top](#table-of-contents) diff --git a/docs/resources/glossary.md b/docs/resources/glossary.md deleted file mode 100644 index 7c970659fb..0000000000 --- a/docs/resources/glossary.md +++ /dev/null @@ -1,20 +0,0 @@ -# Glossary - -Below are the main concepts used in NeMo Guardrails: - -- **LLM-based Application**: a software application that uses an LLM to drive -- **Bot**: synonym for *LLM-based application*. -- **Utterance**: the raw text coming from the user or the bot. -- **Intent**: the canonical form (i.e. structured representation) of a user/bot utterance. -- **Event**: something that has happened and is relevant to the conversation e.g. user is silent, user clicked something, user made a gesture, etc. -- **Action**: a custom code that the bot can invoke; usually for connecting to third-party API. -- **Context**: any data relevant to the conversation (i.e. a key-value dictionary). -- **Flow**: a sequence of messages and events, potentially with additional branching logic. -- **Rails**: specific ways of controlling the behavior of a conversational system (a.k.a. bot) e.g. not talk about politics, respond in a specific way to certain user requests, follow a predefined dialog path, use a specific language style, extract data etc. - -## Recommended naming conventions - -User messages: -- the first word should be a verb; "ask", "respond", "inform", "provide", "express", "comment", "confirm", "deny", "request" -- the rest of the words should be nouns -- should read naturally (e.g. not `user credit card problem` vs. `user inform credit card problem`) diff --git a/docs/run-rails/using-fastapi-server/overview.md b/docs/run-rails/using-fastapi-server/overview.md index 48afcfa846..19ad3f478f 100644 --- a/docs/run-rails/using-fastapi-server/overview.md +++ b/docs/run-rails/using-fastapi-server/overview.md @@ -88,3 +88,4 @@ Open `http://localhost:8000` in your browser to access the built-in Chat UI for ## Related Topics - [](../../reference/api-server-endpoints/index.md) +- [Introduction to LLM Benchmarking](https://docs.nvidia.com/nim/benchmarking/llm/latest/overview.html) diff --git a/docs/user-guides/advanced/index.rst b/docs/user-guides/advanced/index.rst index 629321fc16..0155849889 100644 --- a/docs/user-guides/advanced/index.rst +++ b/docs/user-guides/advanced/index.rst @@ -20,4 +20,3 @@ Advanced vertexai-setup nemotron-safety-guard-deployment nemoguard-topiccontrol-deployment - Blueprint Integration <safeguarding-ai-virtual-assistant-blueprint> diff --git a/docs/user-guides/advanced/safeguarding-ai-virtual-assistant-blueprint.md b/docs/user-guides/advanced/safeguarding-ai-virtual-assistant-blueprint.md deleted file mode 100644 index 819cbd8772..0000000000 --- a/docs/user-guides/advanced/safeguarding-ai-virtual-assistant-blueprint.md +++ /dev/null @@ -1,76 +0,0 @@ ---- -title: - page: Safeguard AI Virtual Assistant Blueprint with the NeMo Guardrails library - nav: Blueprint Integration -description: Integrate NeMo Guardrails into NVIDIA AI Virtual Assistant Blueprint for customer service. -topics: -- Integration -- AI Safety -tags: -- Blueprint -- NIM -- Virtual Assistant -- Customer Service -content: - type: how_to - difficulty: technical_advanced - audience: - - engineer - - AI Engineer ---- - -# Safeguard AI Virtual Assistant Blueprint with the NeMo Guardrails library - -```{note} -This documentation is intended for developers that want to integrate the NeMo Guardrails library into [NVIDIA Blueprints](https://build.nvidia.com/blueprints) -``` - -NVIDIA Blueprints are comprehensive reference workflows that accelerate AI application development and deployment. They make it easy to start building and setting up virtual assistants, offering ready-made workflows and tools. Whether you need a simple AI-powered chatbot or a fully animated digital human interface, NVIDIA provides resources to help you create an AI assistant that’s scalable and aligned with your brand. For example, developers can use the **[NVIDIA AI Blueprint for AI virtual assistants](https://build.nvidia.com/nvidia/ai-virtual-assistant-for-customer-service)** to build an AI assistant for customer service for delivering a responsive, efficient customer support experience. - -## Prerequisites and Setup - -### NVIDIA Blueprint - -The NVIDIA Blueprint for AI virtual assistants can be deployed either with the NVIDIA-hosted endpoints or with locally hosted NIM microservices. To get started with the deployment: - -- **[Using the NVIDIA-hosted endpoint](https://build.nvidia.com/nvidia/ai-virtual-assistant-for-customer-service)**: The launchable opens to a jupyter notebook - `ai-virtual-assistant-notebook.ipynb`. Make sure that the [compute requirements](https://github.com/NVIDIA-AI-Blueprints/ai-virtual-assistant?tab=readme-ov-file#hardware-requirements) are satisfied - -### Integrating NemoGuard - -First create a `nemoguardrails.yaml` file at `ai-virtual-assistant/deploy/compose` directory to download and initialize the NeMo Guardrails library. Make sure it has the following content in it. You can change the port as per your requirement. - -```yaml -services: - nemoguardrails: - build: - context: ../../../nemoguardrails # Path to the cloned repository - dockerfile: Dockerfile - container_name: nemoguardrails - runtime: nvidia - deploy: - resources: - reservations: - devices: - - driver: nvidia - count: 1 - capabilities: [gpu] - environment: - NGC_API_KEY: ${NGC_API_KEY} # NVIDIA GPU Cloud API Key - ports: - - "8650:8650" # Map container port 8000 to host port 8000 - - "8000:8000" - user: "${USER_ID:-1000}:${GROUP_ID:-1000}" # Use the current user's UID/GID - command: > - bash -c " - export OPENAI_API_KEY=${OPENAI_API_KEY} && - cd nemoguardrails - pip install --no-cache-dir -e .[all] - " - -``` - -Follow the notebook from - `examples/notebooks/safeguard_ai_virtual_assistant_notebook.ipynb` to deploy the NemoGuard NIM using downloadable containers and learn to step by step integrate it into the NIM blueprint - -### NIM Blueprint API Usage - -Use the `ai-virtual-assistant/notebooks/api_usage.ipynb` to run the blueprint api to see the agent response diff --git a/docs/versions1.json b/docs/versions1.json index 2b284b368f..d2d88c6c1c 100644 --- a/docs/versions1.json +++ b/docs/versions1.json @@ -1,31 +1,35 @@ [ { "preferred": true, + "version": "0.21.0", + "url": "https://docs.nvidia.com/nemo/guardrails/0.21.0/" + }, + { "version": "0.20.0", - "url": "../0.20.0/" + "url": "https://docs.nvidia.com/nemo/guardrails/0.20.0/" }, { "version": "0.19.0", - "url": "../0.19.0/" + "url": "https://docs.nvidia.com/nemo/guardrails/0.19.0/" }, { "version": "0.18.0", - "url": "../0.18.0/" + "url": "https://docs.nvidia.com/nemo/guardrails/0.18.0/" }, { "version": "0.17.0", - "url": "../0.17.0/" + "url": "https://docs.nvidia.com/nemo/guardrails/0.17.0/" }, { "version": "0.16.0", - "url": "../0.16.0/" + "url": "https://docs.nvidia.com/nemo/guardrails/0.16.0/" }, { "version": "0.15.0", - "url": "../0.15.0/" + "url": "https://docs.nvidia.com/nemo/guardrails/0.15.0/" }, { "version": "0.14.1", - "url": "../0.14.1/" + "url": "https://docs.nvidia.com/nemo/guardrails/0.14.1/" } ] diff --git a/examples/notebooks/safeguard_ai_virtual_assistant_notebook.ipynb b/examples/notebooks/safeguard_ai_virtual_assistant_notebook.ipynb deleted file mode 100644 index c3035f709d..0000000000 --- a/examples/notebooks/safeguard_ai_virtual_assistant_notebook.ipynb +++ /dev/null @@ -1,1753 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d89770a0", - "metadata": { - "id": "78dc1a9c-d009-407e-836d-84cf85936ade" - }, - "source": [ - "# Safeguarding AI Virtual Assistant for Customer Service with NVIDIA NeMo Guardrails\n", - "\n", - "AI agents present a significant opportunity for businesses to scale and elevate customer service and support interactions. By automating routine inquiries and enhancing response times, these agents improve efficiency and customer satisfaction, helping organizations stay competitive. \n", - "\n", - "However, alongside these benefits, AI agents come with risks. Large language models (LLMs) are vulnerable to generating inappropriate or off-topic content and can be susceptible to jailbreak attacks. To fully realize the potential of generative AI in customer service, it is essential to implement robust AI safety and security measures.\n", - "\n", - "This tutorial equips AI builders with actionable steps to integrate essential safeguards into AI agents for customer service applications. We’ll explore how to integrate AI safeguard NIM microservices using NeMo Guardrails to build guardrail configurations that ensure your AI agent can identify and mitigate unsafe interactions in real time. Then, we’ll take it a step further by connecting these capabilities to the sophisticated agentic workflows outlined in the NVIDIA AI Blueprint for AI virtual assistants. By the end, you’ll have a clear understanding of how to create a scalable and secure AI assistant tailored to your brand’s unique needs. \n", - "\n", - "Figure 1 details the architecture workflow of integrating **[NeMo Guardrails](https://docs.nvidia.com/nemo/guardrails/index.html)** and safeguarding **[NIM microservices](https://developer.nvidia.com/nim)** in the **[NVIDIA AI Blueprint for virtual assistants](https://build.nvidia.com/nvidia/ai-virtual-assistant-for-customer-service)**.\n", - "\n", - "\n", - "## Prerequisites\n", - "\n", - "### Docker compose\n", - "\n", - "#### System requirements\n", - "\n", - "Ubuntu 20.04 or 22.04 based machine, with sudo privileges\n", - "\n", - "Install software requirements\n", - "- Install Docker Engine and Docker Compose. Refer to the instructions for Ubuntu.\n", - "- Ensure the Docker Compose plugin version is 2.29.1 or higher.\n", - "- Run docker compose version to confirm.\n", - "- Refer to Install the Compose plugin in the Docker documentation for more information.\n", - "- To configure Docker for GPU-accelerated containers, install the NVIDIA Container Toolkit.\n", - "- Install git\n", - "\n", - "By default the provided configurations use GPU optimized databases such as Milvus.\n", - "\n", - "\n", - "### Safety NIM Microservices\n", - "\n", - "#### Compute Requirements\n", - "If you are going to deploy the **[Safety NIM Miccroservices](https://docs.nvidia.com/_preview?_cms.db.previewId=00000194-6b79-d6bc-a7b5-6bfb99b10000&_fields=true&_mainObjectId=&_date=#nemoguard)** using the downloadable containers from **[NVIDIA NGC](https://registry.ngc.nvidia.com/)**, then there might be a need for higher compute\n", - "- minimum of 4xH100, or 4xA100\n", - "\n", - "If the Safety NIM Microservices are used using the build.nvidia.com endpoints, there is no need for additional compute\n", - "- 1xH100, 1xA100" - ] - }, - { - "cell_type": "markdown", - "id": "95539ada", - "metadata": {}, - "source": [ - "## Getting API Keys - Very Important\n", - "\n", - "To run the pipeline you need to obtain an API key from NVIDIA. These will be needed in a later step to Set up the environment file.\n", - "\n", - "- Required API Keys: These APIs are required by the pipeline to execute LLM queries.\n", - "\n", - "- NVIDIA API Catalog\n", - " 1. Navigate to **[NVIDIA API Catalog](https://build.nvidia.com/explore/discover)**.\n", - " 2. Select any model, such as llama-3.3-70b-instruct.\n", - " 3. On the right panel above the sample code snippet, click on \"Get API Key\". This will prompt you to log in if you have not already.\n", - "\n", - "NOTE: The API key starts with nvapi- and ends with a 32-character string. You can also generate an API key from the user settings page in NGC (https://ngc.nvidia.com/)." - ] - }, - { - "cell_type": "markdown", - "id": "f1ccc43c", - "metadata": {}, - "source": [ - "Export API Keys" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8303812e", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "044596c7-801c-4bfa-b9f6-e940cab81993", - "outputId": "8f4c4700-9cc2-47f4-e122-3b1ae38b8ab3" - }, - "outputs": [], - "source": [ - "import os\n", - "\n", - "NVIDIA_API_KEY = input(\"Please enter your NVIDIA API key (nvapi-): \")\n", - "NGC_API_KEY = NVIDIA_API_KEY\n", - "os.environ[\"NVIDIA_API_KEY\"] = NVIDIA_API_KEY\n", - "os.environ[\"NGC_CLI_API_KEY\"] = NGC_API_KEY\n", - "os.environ[\"NGC_API_KEY\"] = NGC_API_KEY" - ] - }, - { - "cell_type": "markdown", - "id": "f68f3b85", - "metadata": { - "id": "35890619-980d-4176-bbcb-c696a411d83f" - }, - "source": [ - "# Step 1: Deploying the NIM Blueprint " - ] - }, - { - "cell_type": "markdown", - "id": "8088aee0", - "metadata": {}, - "source": [ - "Open the jupyter notebook **[./ai-virtual-assistant/deploy/ai_virtual_assistant_notebook.ipynb](https://github.com/NVIDIA-AI-Blueprints/ai-virtual-assistant/blob/main/deploy/ai_virtual_assistant_notebook.ipynb)** and run through the cells (Shift + Enter) to start the **[NIM blueprint](https://github.com/NVIDIA-AI-Blueprints/ai-virtual-assistant)** and all the necessary docker containers. Following the same notebook, run the **[./ai-virtual-assistant/notebooks/ingest_data.ipynb](https://github.com/NVIDIA-AI-Blueprints/ai-virtual-assistant/blob/main/notebooks/ingest_data.ipynb)** to ingest the structured and unstructured data types." - ] - }, - { - "cell_type": "markdown", - "id": "b0146a8b", - "metadata": {}, - "source": [ - "# Step 2: Download the NeMo Guardrails Toolkit \n", - "\n", - "Start by cloning the NeMo Guardrails repository:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a1578b53", - "metadata": {}, - "outputs": [], - "source": "!git clone https://github.com/NVIDIA-NeMo/Guardrails.git nemoguardrails" - }, - { - "cell_type": "markdown", - "id": "e3646875", - "metadata": {}, - "source": [ - "Make sure that the notebook is operating from the `ai-virtual-assistant` directory. If it's not, it changes to that directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4aa96750", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "current_path = os.getcwd()\n", - "last_part = os.path.basename(current_path)\n", - "\n", - "if os.path.basename(os.getcwd()) != \"ai-virtual-assistant\":\n", - " os.chdir(\"ai-virtual-assistant\")\n", - "\n", - "os.getcwd()" - ] - }, - { - "cell_type": "markdown", - "id": "dc186b81", - "metadata": { - "id": "02e1eeec" - }, - "source": [ - "We login into the NGC catalogue." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7cb79f78", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 106 - }, - "id": "ded8d982", - "outputId": "14a0124b-1ae5-4136-b4f6-8d0ddb7b9843" - }, - "outputs": [], - "source": [ - "!docker login nvcr.io -u '$oauthtoken' -p $NGC_API_KEY" - ] - }, - { - "cell_type": "markdown", - "id": "d768ffcc", - "metadata": { - "id": "cdd03972" - }, - "source": [ - "## Build the NeMo Guardrails with Docker\n", - "\n", - "First setup the `nemoguardrails.yaml` file for NeMo Guardrails and then launch the **[container](https://docs.nvidia.com/nemo/guardrails/user_guides/advanced/using-docker.html)** by using the following command:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2345910e", - "metadata": { - "id": "a7387b26" - }, - "outputs": [], - "source": [ - "%%bash\n", - "docker compose -f deploy/compose/nemoguardrails.yaml up -d" - ] - }, - { - "cell_type": "markdown", - "id": "098800af", - "metadata": {}, - "source": [ - "Before running the nemoguardrails server, we need to add the guardrails configuration. Let's deploy the safety NIMs and integrate it with NeMo Guardrails" - ] - }, - { - "cell_type": "markdown", - "id": "b0a4ba76", - "metadata": {}, - "source": [ - "## Step 3: Deploying the Safety NIMs\n", - "\n", - "This tutorial equips AI builders with actionable steps to integrate essential safeguards into AI agents for customer service applications. It demonstrates how to leverage NVIDIA NeMo Guardrails, a scalable rail orchestration platform, including the following three new AI safeguard models offered as NVIDIA NIM microservices:\n", - "\n", - "**[Llama 3.1 NemoGuard 8B ContentSafety](https://build.nvidia.com/nvidia/llama-3_1-nemoguard-8b-content-safety)** for safeguarding input prompts and output responses in AI interactions, ensuring AI systems align with ethical standards. Llama 3.1 NemoGuard 8B ContentSafety is trained on the Aegis Content Safety Dataset including 35,000 human annotated AI safety data samples. It features explicit response labels curated through an automated process using an ensemble of LLM-as-a-judge across NVIDIA-developed and open community LLMs.\n", - "\n", - "**[Llama 3.1 NemoGuard 8B TopicControl](https://build.nvidia.com/nvidia/llama-3_1-nemoguard-8b-topic-control)** for keeping conversations focused on approved topics, avoiding derailment or inappropriate content. Llama 3.1 NemoGuard 8B TopicControl is fine-tuned on synthetic data to maintain context and enforce boundaries consistently throughout entire AI conversations. \n", - "\n", - "**[NemoGuard JailbreakDetect](https://build.nvidia.com/nvidia/nemoguard-jailbreak-detect)** for protection against jailbreak attempts, helping to maintain AI integrity in adversarial scenarios. NemoGuard JailbreakDetect is an LLM jailbreak classification model trained on a dataset of 17,000 known challenging and successful jailbreaks, built in part using NVIDIA Garak, an open-source toolkit for LLM and application vulnerability scanning developed by the NVIDIA Research team.\n", - "\n", - "Each of the Safety NIMs can be deployed either as a downloadable container or via the endpoint\n", - "\n", - "Let us see how to deploy NIMs as downloadable containers\n", - "\n", - "### Llama 3.1 NemoGuard 8B ContentSafety\n", - "\n", - "The Llama 3.1 NemoGuard 8B ContentSafety NIM follows a set of 42 Safety hazard categories with data distributions including annotations for jailbreak data, diverse cultural and geographical AI content safety like Hazards and Self Harm. Custom and novel safety risk categories and policy can also be provided in the instruction for the model to categorize using the novel taxonomy and policy. The model detects if the user input and/or the LLM response are safe or unsafe, and if unsafe, gives the violated category in the response. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b067b026", - "metadata": {}, - "outputs": [], - "source": [ - "!mkdir safety-nims" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "837f23cb", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile safety-nims/content-safety.sh\n", - "export NGC_API_KEY=<your NGC personal key>\n", - "export NIM_IMAGE=<Path to latest NIM docker container>\n", - "export MODEL_NAME=\"llama-3.1-nemoguard-8b-content-safety\"\n", - "docker pull $NIM_IMAGE\n", - "\n", - "docker run -it --name=$MODEL_NAME \\\n", - " --gpus=\"device=0\" --runtime=nvidia \\\n", - " -e NGC_API_KEY=\"$NGC_API_KEY\" \\\n", - " -e NIM_SERVED_MODEL_NAME=$MODEL_NAME \\\n", - " -e NIM_CUSTOM_MODEL_NAME=$MODEL_NAME \\\n", - " -v $LOCAL_NIM_CACHE:\"/opt/nim/.cache/\" \\\n", - " -u $(id -u) \\\n", - " -p 8123:8000 \\\n", - " $NIM_IMAGE" - ] - }, - { - "cell_type": "markdown", - "id": "f81edfb6", - "metadata": {}, - "source": [ - "On your teminal (irrespective of running locally or on VM) run the `ai-virtual-assistant/safety-nims/content-safety.sh` to deploy the NIM" - ] - }, - { - "cell_type": "markdown", - "id": "d86c72d6", - "metadata": {}, - "source": [ - "### Llama 3.1 NemoGuard 8B TopicControl\n", - "\n", - "The Llama 3.1 NemoGuard 8B TopicControl NIM can be used for topical and dialogue moderation of user prompts in human-assistant interactions being designed for task-oriented dialogue agents and custom policy-based moderation. Given a system instruction (also called topical instruction, i.e. specifying which topics are allowed and disallowed) and a conversation history ending with the last user prompt, the model returns a binary response that flags if the user message respects the system instruction, (i.e. message is on-topic or a distractor/off-topic). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "67e9ec9f", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile safety-nims/topic-control.sh\n", - "export NGC_API_KEY=<your NGC personal key>\n", - "export NIM_IMAGE=<Path to latest NIM docker container>\n", - "export MODEL_NAME=\"llama-3.1-nemoguard-8b-topic-control\"\n", - "docker pull $NIM_IMAGE\n", - "\n", - "docker run -it --name=$MODEL_NAME \\\n", - " --gpus=\"device=1\" --runtime=nvidia \\\n", - " -e NGC_API_KEY=\"$NGC_API_KEY\" \\\n", - " -e NIM_SERVED_MODEL_NAME=$MODEL_NAME \\\n", - " -e NIM_CUSTOM_MODEL_NAME=$MODEL_NAME \\\n", - " -v $LOCAL_NIM_CACHE:\"/opt/nim/.cache/\" \\\n", - " -u $(id -u) \\\n", - " -p 8124:8000 \\\n", - " $NIM_IMAGE" - ] - }, - { - "cell_type": "markdown", - "id": "8dc5b5d7", - "metadata": {}, - "source": [ - "On your teminal (irrespective of running locally or on VM) run the `ai-virtual-assistant/safety-nims/topic-control.sh` to deploy the NIM" - ] - }, - { - "cell_type": "markdown", - "id": "c66f5c70", - "metadata": {}, - "source": [ - "### NemoGuard JailbreakDetect\n", - "The NemoGuard JailbreakDetect NIM was developed to detect attempts to jailbreak large language models. The Jailbreak detection model uses Snowflake-arctic-embed-m embeddings. It is trained on the combination of three open datasets, mixed together, de-duplicated, and reviewed for data quality. Jailbreak data was augmented with the use of garak." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "14423b64", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile safety-nims/jailbreak-detect.sh\n", - "export NGC_API_KEY=<your NGC personal key>\n", - "export NIM_IMAGE=<Path to latest NIM docker container>\n", - "export MODEL_NAME='ardennes-jailbreak-arctic'\n", - "docker pull $NIM_IMAGE\n", - "\n", - "docker run -it --name=$MODEL_NAME \\\n", - " --gpus=\"device=1\" --runtime=nvidia \\\n", - " -e NGC_API_KEY=\"$NGC_API_KEY\" \\\n", - " -v $LOCAL_NIM_CACHE:\"/opt/nim/.cache/\" \\\n", - " -u $(id -u) \\\n", - " -p 8125:8000 \\\n", - " $NIM_IMAGE" - ] - }, - { - "cell_type": "markdown", - "id": "e0e9572c", - "metadata": {}, - "source": [ - "On your teminal (irrespective of running locally or on VM) run the `ai-virtual-assistant/safety-nims/jailbreak-detect.sh` to deploy the NIM" - ] - }, - { - "cell_type": "markdown", - "id": "5b899ca5", - "metadata": {}, - "source": [ - "While building the guardrails configuration, integrate the three safeguard NIM microservices, start with creating the config directory: \n", - "\n", - "\n", - "```\n", - "├── config\n", - "│ ├── config.yml\n", - "│ ├── prompts.yml\n", - "\n", - "```\n", - "\n", - "Now, add each configuration option one by one, starting with the models in the config.yml file:" - ] - }, - { - "cell_type": "markdown", - "id": "db7637ad", - "metadata": {}, - "source": [ - "## Step 4: Building the NeMo Guardrails configuration with the Safety NIMs\n", - "### 1. Creating the `config.yml` file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "991bef0e", - "metadata": {}, - "outputs": [], - "source": [ - "!mkdir config" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "20df2406", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile config/config.yml\n", - "models:\n", - " - type: main\n", - " engine: nim\n", - " model: meta/llama-3.1-70b-instruct\n", - "\n", - " - type: \"llama-3.1-nemoguard-8b-content-safety\"\n", - " engine: nim\n", - " parameters:\n", - " base_url: \"http://localhost:8123/v1\"\n", - " model_name: \"llama-3.1-nemoguard-8b-content-safety\"\n", - " - type: topic_control\n", - " engine: nim\n", - " parameters:\n", - " base_url: \"http://localhost:8124/v1/\"\n", - " model_name: \"llama-3.1-nemoguard-8b-topic-control\"\n", - "\n", - "rails:\n", - " config:\n", - " jailbreak_detection:\n", - " server_endpoint: \"\"\n", - " embedding: \"Snowflake/snowflake-arctic-embed-m-long\"\n", - " input:\n", - " flows:\n", - " - content safety check input $model=llama-3.1-nemoguard-8b-content-safety\n", - " - topic safety check input $model=topic_control\n", - " - jailbreak detection model\n", - " output:\n", - " flows:\n", - " - content safety check output $model=llama-3.1-nemoguard-8b-content-safety" - ] - }, - { - "cell_type": "markdown", - "id": "eda5a456", - "metadata": {}, - "source": [ - "Both the Content safety NIM and the Topic control NIM come with a set of policy compliances which can be added to the guardrails configuration using the `prompts.yml` file\n", - "\n", - "### 2. Creating the `prompts.yml` file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "040ed668", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile config/prompts.yml\n", - "# These are the default prompts released by Meta, except for policy O7, which was added to address direct insults.\n", - "prompts:\n", - "\n", - " - task: content_safety_check_input $model=llama_3.1_aegis_guard_2.0\n", - " content: |\n", - " Task: Check if there is unsafe content in the user or agent messages in the below conversation in accordance with our safety policy with the below categories. Provide a 'safe' or 'unsafe' rating for both the user message and the agent response. If there is unsafe content, please also provide a list of violated categories according to our safety policy below.\n", - "\n", - " <BEGIN UNSAFE CONTENT CATEGORIES>\n", - " S1: Violence.\n", - " S2: Sexual.\n", - " S3: Criminal Planning/Confessions.\n", - " S4: Guns and Illegal Weapons.\n", - " S5: Controlled/Regulated Substances.\n", - " S6: Suicide and Self Harm.\n", - " S7: Sexual (minor).\n", - " S8: Hate/Identity Hate.\n", - " S9: PII/Privacy.\n", - " S10: Harassment.\n", - " S11: Threat.\n", - " S12: Profanity.\n", - " S13: Needs Caution.\n", - " S14: Other.\n", - " S15: Manipulation.\n", - " S16: Fraud/Deception.\n", - " S17: Malware.\n", - " S18: High Risk Gov Decision Making.\n", - " S19: Political/Misinformation/Conspiracy.\n", - " S20: Copyright/Trademark/Plagiarism.\n", - " S21: Unauthorized Advice.\n", - " S22: Illegal Activity.\n", - " S23: Immoral/Unethical.\n", - " <END UNSAFE CONTENT CATEGORIES>\n", - "\n", - " <BEGIN CONVERSATION>\n", - "\n", - " user: {{ user_input }}\n", - "\n", - " <END CONVERSATION>\n", - "\n", - " Provide your safety assessment for the above conversation in the below output JSON format:\n", - " {\"User Safety\": safe or unsafe rating for the user message, \"Response Safety\": safe or unsafe rating for the agent response. Omit if no agent response present. \"Safety Categories\": a comma-separated list of applicable safety categories from the provided taxonomy. Omit if all safe.}\n", - "\n", - " Do not include anything other than the output JSON in your response.\n", - " Output JSON:\n", - " output_parser: is_content_safe_aegis_input\n", - " max_tokens: 50\n", - "\n", - " - task: content_safety_check_output $model=llama_3.1_aegis_guard_2.0\n", - " content: |\n", - " Task: Check if there is unsafe content in the user or agent messages in the below conversation in accordance with our safety policy with the below categories. Provide a 'safe' or 'unsafe' rating for both the user message and the agent response. If there is unsafe content, please also provide a list of violated categories according to our safety policy below.\n", - "\n", - " <BEGIN UNSAFE CONTENT CATEGORIES>\n", - " S1: Violence.\n", - " S2: Sexual.\n", - " S3: Criminal Planning/Confessions.\n", - " S4: Guns and Illegal Weapons.\n", - " S5: Controlled/Regulated Substances.\n", - " S6: Suicide and Self Harm.\n", - " S7: Sexual (minor).\n", - " S8: Hate/Identity Hate.\n", - " S9: PII/Privacy.\n", - " S10: Harassment.\n", - " S11: Threat.\n", - " S12: Profanity.\n", - " S13: Needs Caution.\n", - " S14: Other.\n", - " S15: Manipulation.\n", - " S16: Fraud/Deception.\n", - " S17: Malware.\n", - " S18: High Risk Gov Decision Making.\n", - " S19: Political/Misinformation/Conspiracy.\n", - " S20: Copyright/Trademark/Plagiarism.\n", - " S21: Unauthorized Advice.\n", - " S22: Illegal Activity.\n", - " S23: Immoral/Unethical.\n", - " <END UNSAFE CONTENT CATEGORIES>\n", - "\n", - " <BEGIN CONVERSATION>\n", - "\n", - " user: {{ user_input }}\n", - "\n", - " response: agent: {{ bot_response }}\n", - "\n", - " <END CONVERSATION>\n", - "\n", - " Provide your safety assessment for the above conversation in the below output JSON format:\n", - " {\"User Safety\": safe or unsafe rating for the user message, \"Response Safety\": safe or unsafe rating for the agent response. Omit if no agent response present. \"Safety Categories\": a comma-separated list of applicable safety categories from the provided taxonomy. Omit if all safe.}\n", - "\n", - " Do not include anything other than the output JSON in your response.\n", - " Output JSON:\n", - " output_parser: is_content_safe_aegis_output\n", - " max_tokens: 50\n", - "\n", - " - task: content_safety_check_input $model=llama_guard\n", - " content: |\n", - " <s>[INST] Task: Check if there is unsafe content in 'User' messages in conversations according our safety policy with the below categories.\n", - " \n", - " - task: topic_safety_check_input $model=topic_control\n", - " content: |\n", - " You are to act as a customer service agent, providing users with factual information in accordance to the knowledge base. Your role is to ensure that you respond only to relevant queries and adhere to the following guidelines\n", - "\n", - " Guidelines for the user messages:\n", - " - Do not answer questions related to personal opinions or advice on user's order, future recommendations\n", - " - Do not provide any information on non-company products or services.\n", - " - Do not answer enquiries unrelated to the company policies.\n", - " - Do not answer questions asking for personal details about the agent or its creators.\n", - " - Do not answer questions about sensitive topics related to politics, religion, or other sensitive subjects.\n", - " - If a user asks topics irrelevant to the company's customer service relations, politely redirect the conversation or end the interaction.\n", - " - Your responses should be professional, accurate, and compliant with customer relations guidelines, focusing solely on providing transparent, up-to-date information about the company that is already publicly available." - ] - }, - { - "cell_type": "markdown", - "id": "fdaf1f39", - "metadata": {}, - "source": [ - "## Step5: Wrapping the guardrails configuration around the agentic system" - ] - }, - { - "cell_type": "markdown", - "id": "be30a152", - "metadata": {}, - "source": [ - "With the configuration complete, you could use it as is to apply guardrails to a general-purpose conversational AI by interfacing with the NeMo Guardrails server through its API. The assistant or agent from the NIM Blueprint performs multiple tasks, a few including RAG, checking if the user is compliant with the return policy, and thereby updating the return option, getting the user's purchase history. \n", - "\n", - "Start with adding chains to the following agent components\n", - "- `src/analytics/main.py`\n", - "- `src/agent/utils.py`\n", - "- `src/agent/main.py`\n", - "\n", - "#### 1. Analytics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "24cbef9f", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile src/analytics/main.py\n", - "# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n", - "# SPDX-License-Identifier: Apache-2.0\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# http://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License.\n", - "import os\n", - "import logging\n", - "from datetime import datetime\n", - "from enum import Enum\n", - "from typing import Annotated, Generator, Literal, Sequence, TypedDict\n", - "\n", - "from langchain_core.messages import BaseMessage\n", - "from langchain_core.output_parsers import StrOutputParser\n", - "from langchain_core.prompts.chat import ChatPromptTemplate\n", - "from langchain_core.runnables import RunnableConfig\n", - "from pydantic import BaseModel, Field\n", - "from src.analytics.datastore.session_manager import SessionManager\n", - "from src.common.utils import get_config, get_llm, get_prompts\n", - "from nemoguardrails import RailsConfig\n", - "from nemoguardrails.integrations.langchain.runnable_rails import RunnableRails\n", - "\n", - "logger = logging.getLogger(__name__)\n", - "prompts = get_prompts()\n", - "\n", - "# TODO get the default_kwargs from the Agent Server API\n", - "default_llm_kwargs = {\"temperature\": 0, \"top_p\": 0.7, \"max_tokens\": 1024}\n", - "\n", - "# Initialize persist_data to determine whether data should be stored in the database.\n", - "persist_data = os.environ.get(\"PERSIST_DATA\", \"true\").lower() == \"true\"\n", - "\n", - "# Initialize session manager during startup\n", - "session_manager = None\n", - "try:\n", - " session_manager = SessionManager()\n", - "except Exception as e:\n", - " logger.info(f\"Failed to connect to DB during init, due to exception {e}\")\n", - " \n", - "# Initialize guardrails configuration\n", - "rail_config = RailsConfig.from_path(\"./config\")\n", - "guardrails = RunnableRails(rail_config, input_key=\"query\", output_key='content')\n", - " \n", - " \n", - "def get_database():\n", - " \"\"\"\n", - " Connect to the database.\n", - " \"\"\"\n", - " global session_manager\n", - " try:\n", - " if not session_manager:\n", - " session_manager = SessionManager()\n", - "\n", - " return session_manager\n", - " except Exception as e:\n", - " logger.info(f\"Error connecting to database: {e}\")\n", - " return None\n", - "\n", - "\n", - "def generate_summary(conversation_history):\n", - " \"\"\"\n", - " Generate a summary of the conversation.\n", - "\n", - " Parameters:\n", - " conversation_history (List): The conversation text.\n", - "\n", - " Returns:\n", - " str: A summary of the conversation.\n", - " \"\"\"\n", - " logger.info(f\"conversation history: {conversation_history}\")\n", - " llm = get_llm(**default_llm_kwargs)\n", - " prompt = prompts.get(\"summary_prompt\", \"\")\n", - " for turn in conversation_history:\n", - " prompt += f\"{turn['role']}: {turn['content']}\\n\"\n", - "\n", - " prompt += \"\\n\\nSummary: \"\n", - " \n", - " # Apply guardrails to the chain\n", - " chain_with_guardrails = guardrails | llm\n", - " response = chain_with_guardrails.invoke({\"query\": prompt})\n", - "\n", - " return response.content\n", - "\n", - "\n", - "def generate_session_summary(session_id):\n", - " # TODO: Check for corner cases like when session_id does not exist\n", - " session_manager = get_database()\n", - "\n", - " # Check if summary already exists in database\n", - " session_info = session_manager.get_session_summary_and_sentiment(session_id)\n", - " if session_info and session_info.get(\"summary\", None):\n", - " return session_info\n", - "\n", - " # Generate summary and session info\n", - " conversation_history = session_manager.get_conversation(session_id)\n", - " summary = generate_summary(conversation_history)\n", - " sentiment = generate_sentiment(conversation_history)\n", - "\n", - " if persist_data:\n", - " # Save the summary and sentiment in database\n", - " session_manager.save_summary_and_sentiment(\n", - " session_id,\n", - " {\n", - " \"summary\": summary,\n", - " \"sentiment\": sentiment,\n", - " \"start_time\": conversation_history[0].get(\"timestamp\", 0),\n", - " \"end_time\": conversation_history[-1].get(\"timestamp\", 0),\n", - " }\n", - " )\n", - " return {\n", - " \"summary\": summary,\n", - " \"sentiment\": sentiment,\n", - " \"start_time\": datetime.fromtimestamp(\n", - " float(conversation_history[0].get(\"timestamp\", 0))\n", - " ),\n", - " \"end_time\": datetime.fromtimestamp(\n", - " float(conversation_history[-1].get(\"timestamp\", 0))\n", - " ),\n", - " }\n", - "\n", - "\n", - "def fetch_user_conversation(user_id, start_time=None, end_time=None):\n", - " \"\"\"\n", - " Fetch a user's conversation from the database.\n", - " \"\"\"\n", - " try:\n", - " # TODO: Use start time and end time to filter the data\n", - " session_manager = get_database()\n", - " conversations = session_manager.list_sessions_for_user(user_id)\n", - " logger.info(f\"Conversation: {conversations}\")\n", - " return conversations\n", - " except Exception as e:\n", - " logger.error(f\"Error fetching conversation: {e}\")\n", - " return None\n", - "\n", - "\n", - "def generate_sentiment(conversation_history):\n", - " # Define an Enum for the sentiment values\n", - " class SentimentEnum(str, Enum):\n", - " POSITIVE = \"positive\"\n", - " NEUTRAL = \"neutral\"\n", - " NEGATIVE = \"negative\"\n", - "\n", - " # Define the Pydantic model using the Enum\n", - " class Sentiment(BaseModel):\n", - " \"\"\"Sentiment for conversation.\"\"\"\n", - "\n", - " sentiment: SentimentEnum = Field(\n", - " description=\"Relevant value 'positive', 'neutral' or 'negative'\"\n", - " )\n", - "\n", - " logger.info(\"Finding sentiment for conversation\")\n", - " llm = get_llm(**default_llm_kwargs)\n", - " prompt = prompts.get(\"sentiment_prompt\", \"\")\n", - " for turn in conversation_history:\n", - " prompt += f\"{turn['role']}: {turn['content']}\\n\"\n", - "\n", - " llm_with_tool = llm.with_structured_output(Sentiment)\n", - "\n", - " # Apply guardrails to the chain\n", - " chain_with_guardrails = guardrails | llm_with_tool\n", - " response = chain_with_guardrails.invoke({\"query\": prompt})\n", - " \n", - " sentiment = response.content.sentiment.value\n", - " logger.info(f\"Conversation classified as {sentiment}\")\n", - " return sentiment\n", - "\n", - "\n", - "def generate_sentiment_for_query(session_id):\n", - " \"\"\"Generate sentiment for user query and assistant response\n", - " \"\"\"\n", - "\n", - " logger.info(\"Fetching sentiment for queries\")\n", - " # Check if the sentiment is already identified in database, if yes return that\n", - " session_manager = get_database()\n", - "\n", - " session_info = session_manager.get_query_sentiment(session_id)\n", - "\n", - " if session_info and session_info.get(\"messages\", None):\n", - " return {\n", - " \"messages\": session_info.get(\"messages\"),\n", - " \"session_info\": {\n", - " \"session_id\": session_id,\n", - " \"start_time\": session_info.get(\"start_time\"),\n", - " \"end_time\": session_info.get(\"start_time\"),\n", - " },\n", - " }\n", - "\n", - " class SentimentEnum(str, Enum):\n", - " POSITIVE = \"positive\"\n", - " NEUTRAL = \"neutral\"\n", - " NEGATIVE = \"negative\"\n", - "\n", - " # Define the Pydantic model using the Enum\n", - " class Sentiment(BaseModel):\n", - " \"\"\"Sentiment for conversation.\"\"\"\n", - "\n", - " sentiment: SentimentEnum = Field(\n", - " description=\"Relevant value 'positive', 'neutral' or 'negative'\"\n", - " )\n", - "\n", - "\n", - " # Generate summary and session info\n", - " conversation_history = session_manager.get_conversation(session_id)\n", - " logger.info(f\"Conversation history: {conversation_history}\")\n", - "\n", - " logger.info(\"Finding sentiment for conversation\")\n", - " llm = get_llm(**default_llm_kwargs)\n", - "\n", - " llm_with_tool = llm.with_structured_output(Sentiment)\n", - " \n", - " # Apply guardrails to the chain\n", - " chain_with_guardrails = guardrails | llm_with_tool\n", - "\n", - " messages = []\n", - " # TODO: parallize this operation for faster response\n", - " # Find sentiment for individual query and assistant response\n", - " for turn in conversation_history:\n", - " prompt = prompts.get(\"query_sentiment_prompt\", \"\")\n", - " prompt += f\"{turn['role']}: {turn['content']}\\n\"\n", - "\n", - " response = chain_with_guardrails.invoke({\"query\": prompt})\n", - " sentiment = response.content.sentiment.value\n", - " messages.append({\n", - " \"role\": turn[\"role\"],\n", - " \"content\": turn[\"content\"],\n", - " \"sentiment\": sentiment,\n", - " })\n", - "\n", - " session_info = {\n", - " \"messages\": messages,\n", - " \"start_time\": conversation_history[0].get(\"timestamp\", 0),\n", - " \"end_time\": conversation_history[-1].get(\"timestamp\", 0),\n", - " }\n", - " if persist_data:\n", - " # Save information before sending it to user\n", - " session_manager.save_query_sentiment(session_id, session_info)\n", - " return {\n", - " \"messages\": messages,\n", - " \"session_info\": {\n", - " \"session_id\": session_id,\n", - " \"start_time\": datetime.fromtimestamp(\n", - " float(conversation_history[0].get(\"timestamp\", 0))\n", - " ),\n", - " \"end_time\": datetime.fromtimestamp(\n", - " float(conversation_history[-1].get(\"timestamp\", 0))\n", - " ),\n", - " },\n", - " }\n" - ] - }, - { - "cell_type": "markdown", - "id": "53cec8f7", - "metadata": {}, - "source": [ - "#### 2. Agent" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4b45f256", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile src/agent/utils.py\n", - "# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n", - "# SPDX-License-Identifier: Apache-2.0\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# http://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License.\n", - "import re\n", - "import os\n", - "import logging\n", - "from typing import Dict\n", - "from pydantic import BaseModel, Field\n", - "from urllib.parse import urlparse\n", - "\n", - "import requests\n", - "\n", - "from psycopg_pool import AsyncConnectionPool\n", - "from psycopg.rows import dict_row\n", - "import psycopg2\n", - "\n", - "from src.common.utils import get_llm, get_prompts, get_config\n", - "from langchain_core.prompts.chat import ChatPromptTemplate\n", - "from langchain_core.messages import HumanMessage\n", - "from langchain_core.messages import ToolMessage\n", - "from langchain_core.runnables import RunnableLambda\n", - "from langgraph.checkpoint.memory import MemorySaver\n", - "from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver\n", - "from langgraph.prebuilt import ToolNode\n", - "from nemoguardrails import RailsConfig\n", - "from nemoguardrails.integrations.langchain.runnable_rails import RunnableRails\n", - "\n", - "prompts = get_prompts()\n", - "logger = logging.getLogger(__name__)\n", - "\n", - "# TODO get the default_kwargs from the Agent Server API\n", - "default_llm_kwargs = {\"temperature\": 0, \"top_p\": 0.7, \"max_tokens\": 1024}\n", - "\n", - "canonical_rag_url = os.getenv('CANONICAL_RAG_URL', 'http://unstructured-retriever:8081')\n", - "canonical_rag_search = f\"{canonical_rag_url}/search\"\n", - "\n", - "# Initialize guardrails configuration\n", - "rail_config = RailsConfig.from_path(\"./config\")\n", - "guardrails = RunnableRails(rail_config, input_key=\"query\", output_key='content')\n", - "\n", - "def get_product_name(messages, product_list) -> Dict:\n", - " \"\"\"Given the user message and list of product find list of items which user might be talking about\"\"\"\n", - "\n", - " # First check product name in query\n", - " # If it's not in query, check in conversation\n", - " # Once the product name is known we will search for product name from database\n", - " # We will return product name from list and actual name detected.\n", - "\n", - " llm = get_llm(**default_llm_kwargs)\n", - "\n", - " class Product(BaseModel):\n", - " name: str = Field(..., description=\"Name of the product talked about.\")\n", - "\n", - " prompt_text = prompts.get(\"get_product_name\")[\"base_prompt\"]\n", - " prompt = ChatPromptTemplate.from_messages(\n", - " [\n", - " (\"system\", prompt_text),\n", - " ]\n", - " )\n", - " llm = llm.with_structured_output(Product)\n", - "\n", - " chain = prompt | llm\n", - " # Adding guardrails to the chain\n", - " chain_with_guardrails = guardrails | chain\n", - " # query to be used for document retrieval\n", - " # Get the last human message instead of messages[-2]\n", - " last_human_message = next((m.content for m in reversed(messages) if isinstance(m, HumanMessage)), None)\n", - " response = chain_with_guardrails.invoke({\"query\": last_human_message})\n", - "\n", - " product_name = response.content.name\n", - "\n", - " # Check if product name is in query\n", - " if product_name == 'null':\n", - "\n", - " # Check for produt name in user conversation\n", - " fallback_prompt_text = prompts.get(\"get_product_name\")[\"fallback_prompt\"]\n", - " prompt = ChatPromptTemplate.from_messages(\n", - " [\n", - " (\"system\", fallback_prompt_text),\n", - " ]\n", - " )\n", - "\n", - " llm = get_llm(**default_llm_kwargs)\n", - " llm = llm.with_structured_output(Product)\n", - "\n", - " chain = prompt | llm\n", - " # Adding guardrails to the chain\n", - " chain_with_guardrails = guardrails | chain\n", - " # query to be used for document retrieval\n", - " response = chain.invoke({\"query\": messages})\n", - "\n", - " product_name = response.content.name\n", - " # Check if it's partial name exists or not\n", - " if product_name == 'null':\n", - " return {}\n", - "\n", - " def filter_products_by_name(name, products):\n", - " # TODO: Replace this by llm call to check if that can take care of cases like\n", - " # spelling mistakes or words which are seperated\n", - " # TODO: Directly make sql query with wildcard\n", - " name_lower = name.lower()\n", - "\n", - " # Check for exact match first\n", - " exact_match = [product for product in products if product.lower() == name_lower]\n", - " if exact_match:\n", - " return exact_match\n", - "\n", - " # If no exact match, fall back to partial matches\n", - " name_parts = [part for part in re.split(r'\\s+', name_lower) if part.lower() != 'nvidia']\n", - " # Match only if all parts of the search term are found in the product name\n", - " matching_products = [\n", - " product for product in products\n", - " if all(part in product.lower() for part in name_parts if part)\n", - " ]\n", - "\n", - " return matching_products\n", - "\n", - " matching_products = filter_products_by_name(product_name, product_list)\n", - "\n", - " return {\n", - " \"product_in_query\": product_name,\n", - " \"products_from_purchase\": list(set([product for product in matching_products]))\n", - " }\n", - "\n", - "\n", - "def handle_tool_error(state) -> dict:\n", - " error = state.get(\"error\")\n", - " tool_calls = state[\"messages\"][-1].tool_calls\n", - " return {\n", - " \"messages\": [\n", - " ToolMessage(\n", - " content=f\"Error: {repr(error)}\\n please fix your mistakes.\",\n", - " tool_call_id=tc[\"id\"],\n", - " )\n", - " for tc in tool_calls\n", - " ]\n", - " }\n", - "\n", - "\n", - "def create_tool_node_with_fallback(tools: list) -> dict:\n", - " return ToolNode(tools).with_fallbacks(\n", - " [RunnableLambda(handle_tool_error)], exception_key=\"error\"\n", - " )\n", - "\n", - "\n", - "async def get_checkpointer() -> tuple:\n", - " settings = get_config()\n", - "\n", - " if settings.checkpointer.name == \"postgres\":\n", - " print(f\"Using {settings.checkpointer.name} hosted on {settings.checkpointer.url} for checkpointer\")\n", - " db_user = os.environ.get(\"POSTGRES_USER\")\n", - " db_password = os.environ.get(\"POSTGRES_PASSWORD\")\n", - " db_name = os.environ.get(\"POSTGRES_DB\")\n", - " db_uri = f\"postgresql://{db_user}:{db_password}@{settings.checkpointer.url}/{db_name}?sslmode=disable\"\n", - " connection_kwargs = {\n", - " \"autocommit\": True,\n", - " \"prepare_threshold\": 0,\n", - " \"row_factory\": dict_row,\n", - " }\n", - "\n", - " # Initialize PostgreSQL checkpointer\n", - " pool = AsyncConnectionPool(\n", - " conninfo=db_uri,\n", - " min_size=2,\n", - " kwargs=connection_kwargs,\n", - " )\n", - " checkpointer = AsyncPostgresSaver(pool)\n", - " await checkpointer.setup()\n", - " return checkpointer, pool\n", - " elif settings.checkpointer.name == \"inmemory\":\n", - " print(f\"Using MemorySaver as checkpointer\")\n", - " return MemorySaver(), None\n", - " else:\n", - " raise ValueError(f\"Only inmemory and postgres is supported chckpointer type\")\n", - "\n", - "\n", - "def remove_state_from_checkpointer(session_id):\n", - "\n", - " settings = get_config()\n", - " if settings.checkpointer.name == \"postgres\":\n", - " # Handle cleanup for PostgreSQL checkpointer\n", - " # Currently, there is no langgraph checkpointer API to remove data directly.\n", - " # The following tables are involved in storing checkpoint data:\n", - " # - checkpoint_blobs\n", - " # - checkpoint_writes\n", - " # - checkpoints\n", - " # Note: checkpoint_migrations table can be skipped for deletion.\n", - " try:\n", - " app_database_url = settings.checkpointer.url\n", - "\n", - " # Parse the URL\n", - " parsed_url = urlparse(f\"//{app_database_url}\", scheme='postgres')\n", - "\n", - " # Extract host and port\n", - " host = parsed_url.hostname\n", - " port = parsed_url.port\n", - "\n", - " # Connect to your PostgreSQL database\n", - " connection = psycopg2.connect(\n", - " dbname=os.getenv('POSTGRES_DB', None),\n", - " user=os.getenv('POSTGRES_USER', None),\n", - " password=os.getenv('POSTGRES_PASSWORD', None),\n", - " host=host,\n", - " port=port\n", - " )\n", - " cursor = connection.cursor()\n", - "\n", - " # Execute delete commands\n", - " cursor.execute(\"DELETE FROM checkpoint_blobs WHERE thread_id = %s\", (session_id,))\n", - " cursor.execute(\"DELETE FROM checkpoint_writes WHERE thread_id = %s\", (session_id,))\n", - " cursor.execute(\"DELETE FROM checkpoints WHERE thread_id = %s\", (session_id,))\n", - "\n", - " # Commit the changes\n", - " connection.commit()\n", - " logger.info(f\"Deleted rows with thread_id: {session_id}\")\n", - "\n", - " except Exception as e:\n", - " logger.info(f\"Error occurred while deleting data from checkpointer: {e}\")\n", - " # Optionally rollback if needed\n", - " if connection:\n", - " connection.rollback()\n", - " finally:\n", - " # Close the cursor and connection\n", - " if cursor:\n", - " cursor.close()\n", - " if connection:\n", - " connection.close()\n", - " else:\n", - " # For other supported checkpointer(i.e. inmemory) we don't need cleanup\n", - " pass\n", - "\n", - "def canonical_rag(query: str, conv_history: list) -> str:\n", - " \"\"\"Use this for answering generic queries about products, specifications, warranties, usage, and issues.\"\"\"\n", - "\n", - " entry_doc_search = {\"query\": query, \"top_k\": 4, \"conv_history\": conv_history}\n", - " response = requests.post(canonical_rag_search, json=entry_doc_search).json()\n", - "\n", - " # Extract and aggregate the content\n", - " aggregated_content = \"\\n\".join(chunk[\"content\"] for chunk in response.get(\"chunks\", []))\n", - "\n", - " return aggregated_content" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d4499af", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile src/agent/main.py\n", - "# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n", - "# SPDX-License-Identifier: Apache-2.0\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# http://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License.\n", - "import logging\n", - "import os\n", - "from typing import Annotated, TypedDict, Dict\n", - "from langgraph.graph.message import AnyMessage, add_messages\n", - "from typing import Callable\n", - "from langchain_core.messages import ToolMessage, AIMessage, HumanMessage, SystemMessage\n", - "from typing import Annotated, Optional, Literal, TypedDict\n", - "from langchain_core.prompts.chat import ChatPromptTemplate\n", - "from langchain_core.prompts import MessagesPlaceholder\n", - "from langgraph.graph import END, StateGraph, START\n", - "from langgraph.prebuilt import tools_condition\n", - "from langchain_core.runnables import RunnableConfig\n", - "from src.agent.tools import (\n", - " structured_rag, get_purchase_history, HandleOtherTalk, ProductValidation,\n", - " return_window_validation, update_return, get_recent_return_details,\n", - " ToProductQAAssistant,\n", - " ToOrderStatusAssistant,\n", - " ToReturnProcessing)\n", - "from src.agent.utils import get_product_name, create_tool_node_with_fallback, get_checkpointer, canonical_rag\n", - "from src.common.utils import get_llm, get_prompts\n", - "from nemoguardrails import RailsConfig\n", - "from nemoguardrails.integrations.langchain.runnable_rails import RunnableRails\n", - "\n", - "logger = logging.getLogger(__name__)\n", - "prompts = get_prompts()\n", - "# TODO get the default_kwargs from the Agent Server API\n", - "default_llm_kwargs = {\"temperature\": 0.2, \"top_p\": 0.7, \"max_tokens\": 1024}\n", - "\n", - "# Initialize guardrails configuration\n", - "rail_config = RailsConfig.from_path(\"./config\")\n", - "guardrails = RunnableRails(rail_config, input_key=\"query\", output_key='content')\n", - "\n", - "# STATE OF THE AGENT\n", - "class State(TypedDict):\n", - " messages: Annotated[list[AnyMessage], add_messages]\n", - " user_id: str\n", - " user_purchase_history: Dict\n", - " current_product: str\n", - " needs_clarification: bool\n", - " clarification_type: str\n", - " reason: str\n", - "\n", - "# NODES FOR THE AGENT\n", - "def validate_product_info(state: State, config: RunnableConfig):\n", - " # This node will take user history and find product name based on query\n", - " # If there are multiple name of no name specified in the graph then it will\n", - "\n", - " # This dict is to populate the user_purchase_history and product details if required\n", - " response_dict = {\"needs_clarification\": False}\n", - " if state[\"user_id\"]:\n", - " # Update user purchase history based\n", - " response_dict.update({\"user_purchase_history\": get_purchase_history(state[\"user_id\"])})\n", - "\n", - " # Extracting product name which user is expecting\n", - " product_list = list(set([resp.get(\"product_name\") for resp in response_dict.get(\"user_purchase_history\", [])]))\n", - "\n", - " # Extract product name from query and filter from database\n", - " product_info = get_product_name(state[\"messages\"], product_list)\n", - "\n", - " product_names = product_info.get(\"products_from_purchase\", [])\n", - " product_in_query = product_info.get(\"product_in_query\", \"\")\n", - " if len(product_names) == 0:\n", - " reason = \"\"\n", - " if product_in_query:\n", - " reason = f\"{product_in_query}\"\n", - " response_dict.update({\"needs_clarification\": True, \"clarification_type\": \"no_product\", \"reason\": reason})\n", - " return response_dict\n", - " elif len(product_names) > 1:\n", - " reason = \", \".join(product_names)\n", - " response_dict.update({\"needs_clarification\": True, \"clarification_type\": \"multiple_products\", \"reason\": reason})\n", - " return response_dict\n", - " else:\n", - " response_dict.update({\"current_product\": product_names[0]})\n", - "\n", - " return response_dict\n", - "\n", - "async def handle_other_talk(state: State, config: RunnableConfig):\n", - " \"\"\"Handles greetings and queries outside order status, returns, or products, providing polite redirection and explaining chatbot limitations.\"\"\"\n", - "\n", - " prompt = prompts.get(\"other_talk_template\", \"\")\n", - "\n", - " prompt = ChatPromptTemplate.from_messages(\n", - " [\n", - " (\"system\", prompt),\n", - " (\"placeholder\", \"{messages}\"),\n", - " ]\n", - " )\n", - "\n", - " # LLM\n", - " llm_settings = config.get('configurable', {}).get(\"llm_settings\", default_llm_kwargs)\n", - " llm = get_llm(**llm_settings)\n", - " llm = llm.with_config(tags=[\"should_stream\"])\n", - "\n", - " # Chain\n", - " small_talk_chain = prompt | llm\n", - " \n", - " # Adding guardrails\n", - " small_talk_chain_guardrails = guardrails | small_talk_chain\n", - " response = await small_talk_chain_guardrails.ainvoke(state, config)\n", - "\n", - " return {\"messages\": [response.content]}\n", - "\n", - "\n", - "def create_entry_node(assistant_name: str) -> Callable:\n", - " def entry_node(state: State) -> dict:\n", - " tool_call_id = state[\"messages\"][-1].tool_calls[0][\"id\"]\n", - " return {\n", - " \"messages\": [\n", - " ToolMessage(\n", - " content=f\"The assistant is now the {assistant_name}. Reflect on the above conversation between the host assistant and the user.\"\n", - " f\" The user's intent is unsatisfied. Use the provided tools to assist the user. Remember, you are {assistant_name},\"\n", - " \" and the booking, update, other other action is not complete until after you have successfully invoked the appropriate tool.\"\n", - " \" If the user changes their mind or needs help for other tasks, let the primary host assistant take control.\"\n", - " \" Do not mention who you are - just act as the proxy for the assistant.\",\n", - " tool_call_id=tool_call_id,\n", - " )\n", - " ]\n", - " }\n", - "\n", - " return entry_node\n", - "\n", - "async def ask_clarification(state: State, config: RunnableConfig):\n", - "\n", - " # Extract the base prompt\n", - " base_prompt = prompts.get(\"ask_clarification\")[\"base_prompt\"]\n", - " previous_conversation = [m for m in state['messages'] if not isinstance(m, ToolMessage)]\n", - " base_prompt = base_prompt.format(previous_conversation=previous_conversation)\n", - "\n", - " purchase_history = state.get(\"user_purchase_history\", [])\n", - " if state[\"clarification_type\"] == \"no_product\" and state['reason'].strip():\n", - " followup_prompt = prompts.get(\"ask_clarification\")[\"followup\"][\"no_product\"].format(\n", - " reason=state['reason'],\n", - " purchase_history=purchase_history\n", - " )\n", - " elif not state['reason'].strip():\n", - " followup_prompt = prompts.get(\"ask_clarification\")[\"followup\"][\"default\"].format(reason=purchase_history)\n", - " else:\n", - " followup_prompt = prompts.get(\"ask_clarification\")[\"followup\"][\"default\"].format(reason=state['reason'])\n", - "\n", - " # Combine base prompt and followup prompt\n", - " prompt = f\"{base_prompt} {followup_prompt}\"\n", - "\n", - " # LLM\n", - " llm_settings = config.get('configurable', {}).get(\"llm_settings\", default_llm_kwargs)\n", - " llm = get_llm(**llm_settings)\n", - " llm = llm.with_config(tags=[\"should_stream\"])\n", - " \n", - " # Adding the guardrails\n", - " chain_with guardrails = guardrails | llm\n", - "\n", - " response = await chain_with_guardrails.ainvoke(prompt, config)\n", - "\n", - " return {\"messages\": [response.content]}\n", - "\n", - "async def handle_product_qa(state: State, config: RunnableConfig):\n", - "\n", - " # Extract the previous_conversation\n", - " previous_conversation = [m for m in state['messages'] if not isinstance(m, ToolMessage) and m.content]\n", - " message_type_map = {\n", - " HumanMessage: \"user\",\n", - " AIMessage: \"assistant\",\n", - " SystemMessage: \"system\"\n", - " }\n", - "\n", - " # Serialized conversation\n", - " get_role = lambda x: message_type_map.get(type(x), None)\n", - " previous_conversation_serialized = [{\"role\": get_role(m), \"content\": m.content} for m in previous_conversation if m.content]\n", - " last_message = previous_conversation_serialized[-1]['content']\n", - "\n", - " retireved_content = canonical_rag(query=last_message, conv_history=previous_conversation_serialized)\n", - "\n", - " # Use the RAG Template to generate the response\n", - " base_rag_prompt = prompts.get(\"rag_template\")\n", - " rag_prompt = ChatPromptTemplate.from_messages(\n", - " [\n", - " (\"system\", base_rag_prompt),\n", - " MessagesPlaceholder(\"chat_history\") + \"\\n\\nCONTEXT: {context}\"\n", - " ]\n", - " )\n", - " rag_prompt = rag_prompt.format(chat_history=previous_conversation, context=retireved_content)\n", - "\n", - " # LLM\n", - " llm_settings = config.get('configurable', {}).get(\"llm_settings\", default_llm_kwargs)\n", - " llm = get_llm(**llm_settings)\n", - " llm = llm.with_config(tags=[\"should_stream\"])\n", - " \n", - " # Adding guardrails\n", - " chain_with_guardrails = guardrails | llm\n", - "\n", - " response = await chain_with_guardrails.ainvoke(rag_prompt, config)\n", - "\n", - " return {\"messages\": [response.content]}\n", - "\n", - "class Assistant:\n", - " def __init__(self, prompt: str, tools: list):\n", - " self.prompt = prompt\n", - " self.tools = tools\n", - "\n", - " async def __call__(self, state: State, config: RunnableConfig):\n", - " while True:\n", - "\n", - " llm_settings = config.get('configurable', {}).get(\"llm_settings\", default_llm_kwargs)\n", - " llm = get_llm(**llm_settings)\n", - " runnable = self.prompt | llm.bind_tools(self.tools)\n", - " runnable_with_guardrails = guardrails | runnable\n", - " state = await runnable_with_guardrails.invoke(state)\n", - " last_message = state[\"messages\"][-1]\n", - " messages = []\n", - " if isinstance(last_message, ToolMessage) and last_message.name in [\"structured_rag\", \"return_window_validation\", \"update_return\", \"get_purchase_history\", \"get_recent_return_details\"]:\n", - " gen = runnable.with_config(\n", - " tags=[\"should_stream\"],\n", - " callbacks=config.get(\n", - " \"callbacks\", []\n", - " ), # <-- Propagate callbacks (Python <= 3.10)\n", - " )\n", - " async for message in gen.astream(state):\n", - " messages.append(message.content)\n", - " result = AIMessage(content=\"\".join(messages))\n", - " else:\n", - " result = runnable_with_guardrails.invoke(state)\n", - "\n", - " if not result.tool_calls and (\n", - " not result.content\n", - " or isinstance(result.content, list)\n", - " and not result.content[0].get(\"text\")\n", - " ):\n", - " messages = state[\"messages\"] + [(\"user\", \"Respond with a real output.\")]\n", - " state = {**state, \"messages\": messages}\n", - " messages = state[\"messages\"] + [(\"user\", \"Respond with a real output.\")]\n", - " state = {**state, \"messages\": messages}\n", - " else:\n", - " break\n", - " return {\"messages\": result}\n", - "\n", - "# order status Assistant\n", - "order_status_prompt_template = prompts.get(\"order_status_template\", \"\")\n", - "\n", - "order_status_prompt = ChatPromptTemplate.from_messages(\n", - " [\n", - " (\n", - " \"system\",\n", - " order_status_prompt_template\n", - " ),\n", - " (\"placeholder\", \"{messages}\"),\n", - " ]\n", - ")\n", - "\n", - "order_status_safe_tools = [structured_rag]\n", - "order_status_tools = order_status_safe_tools + [ProductValidation]\n", - "\n", - "# Return Processing Assistant\n", - "return_processing_prompt_template = prompts.get(\"return_processing_template\", \"\")\n", - "\n", - "return_processing_prompt = ChatPromptTemplate.from_messages(\n", - " [\n", - " (\n", - " \"system\",\n", - " return_processing_prompt_template\n", - " ),\n", - " (\"placeholder\", \"{messages}\"),\n", - " ]\n", - ")\n", - "\n", - "return_processing_safe_tools = [get_recent_return_details, return_window_validation]\n", - "return_processing_sensitive_tools = [update_return]\n", - "return_processing_tools = return_processing_safe_tools + return_processing_sensitive_tools + [ProductValidation]\n", - "\n", - "primary_assistant_prompt_template = prompts.get(\"primary_assistant_template\", \"\")\n", - "\n", - "primary_assistant_prompt = ChatPromptTemplate.from_messages(\n", - " [\n", - " (\n", - " \"system\",\n", - " primary_assistant_prompt_template\n", - " ),\n", - " (\"placeholder\", \"{messages}\"),\n", - " ]\n", - ")\n", - "\n", - "primary_assistant_tools = [\n", - " HandleOtherTalk,\n", - " ToProductQAAssistant,\n", - " ToOrderStatusAssistant,\n", - " ToReturnProcessing,\n", - " ]\n", - "\n", - "# BUILD THE GRAPH\n", - "builder = StateGraph(State)\n", - "\n", - "\n", - "# SUB AGENTS\n", - "# Create product_qa Assistant\n", - "builder.add_node(\n", - " \"enter_product_qa\",\n", - " handle_product_qa,\n", - ")\n", - "\n", - "builder.add_edge(\"enter_product_qa\", END)\n", - "\n", - "builder.add_node(\"order_validation\", validate_product_info)\n", - "builder.add_node(\"ask_clarification\", ask_clarification)\n", - "\n", - "# Create order_status Assistant\n", - "builder.add_node(\n", - " \"enter_order_status\", create_entry_node(\"Order Status Assistant\")\n", - ")\n", - "builder.add_node(\"order_status\", Assistant(order_status_prompt, order_status_tools))\n", - "builder.add_edge(\"enter_order_status\", \"order_status\")\n", - "builder.add_node(\n", - " \"order_status_safe_tools\",\n", - " create_tool_node_with_fallback(order_status_safe_tools),\n", - ")\n", - "\n", - "\n", - "def route_order_status(\n", - " state: State,\n", - ") -> Literal[\n", - " \"order_status_safe_tools\",\n", - " \"order_validation\",\n", - " \"__end__\"\n", - "]:\n", - " route = tools_condition(state)\n", - " if route == END:\n", - " return END\n", - " tool_calls = state[\"messages\"][-1].tool_calls\n", - " tool_names = [t.name for t in order_status_safe_tools]\n", - " do_product_validation = any(tc[\"name\"] == ProductValidation.__name__ for tc in tool_calls)\n", - " if do_product_validation:\n", - " return \"order_validation\"\n", - " if all(tc[\"name\"] in tool_names for tc in tool_calls):\n", - " return \"order_status_safe_tools\"\n", - " return \"order_status_sensitive_tools\"\n", - "\n", - "builder.add_edge(\"order_status_safe_tools\", \"order_status\")\n", - "builder.add_conditional_edges(\"order_status\", route_order_status)\n", - "\n", - "# Create return_processing Assistant\n", - "builder.add_node(\"return_validation\", validate_product_info)\n", - "\n", - "builder.add_node(\n", - " \"enter_return_processing\",\n", - " create_entry_node(\"Return Processing Assistant\"),\n", - ")\n", - "builder.add_node(\"return_processing\", Assistant(return_processing_prompt, return_processing_tools))\n", - "builder.add_edge(\"enter_return_processing\", \"return_processing\")\n", - "\n", - "builder.add_node(\n", - " \"return_processing_safe_tools\",\n", - " create_tool_node_with_fallback(return_processing_safe_tools),\n", - ")\n", - "builder.add_node(\n", - " \"return_processing_sensitive_tools\",\n", - " create_tool_node_with_fallback(return_processing_sensitive_tools),\n", - ")\n", - "\n", - "\n", - "def route_return_processing(\n", - " state: State,\n", - ") -> Literal[\n", - " \"return_processing_safe_tools\",\n", - " \"return_processing_sensitive_tools\",\n", - " \"return_validation\",\n", - " \"__end__\",\n", - "]:\n", - " route = tools_condition(state)\n", - " if route == END:\n", - " return END\n", - " tool_calls = state[\"messages\"][-1].tool_calls\n", - " do_product_validation = any(tc[\"name\"] == ProductValidation.__name__ for tc in tool_calls)\n", - " if do_product_validation:\n", - " return \"return_validation\"\n", - " tool_names = [t.name for t in return_processing_safe_tools]\n", - " if all(tc[\"name\"] in tool_names for tc in tool_calls):\n", - " return \"return_processing_safe_tools\"\n", - " return \"return_processing_sensitive_tools\"\n", - "\n", - "\n", - "builder.add_edge(\"return_processing_sensitive_tools\", \"return_processing\")\n", - "builder.add_edge(\"return_processing_safe_tools\", \"return_processing\")\n", - "builder.add_conditional_edges(\"return_processing\", route_return_processing)\n", - "\n", - "\n", - "def user_info(state: State):\n", - " return {\"user_purchase_history\": get_purchase_history(state[\"user_id\"]), \"current_product\": \"\"}\n", - "\n", - "builder.add_node(\"fetch_purchase_history\", user_info)\n", - "builder.add_edge(START, \"fetch_purchase_history\")\n", - "builder.add_edge(\"ask_clarification\", END)\n", - "\n", - "# Primary assistant\n", - "builder.add_node(\"primary_assistant\", Assistant(primary_assistant_prompt, primary_assistant_tools))\n", - "builder.add_node(\n", - " \"other_talk\", handle_other_talk\n", - ")\n", - "\n", - "# Add \"primary_assistant_tools\", if necessary\n", - "def route_primary_assistant(\n", - " state: State,\n", - ") -> Literal[\n", - " \"enter_product_qa\",\n", - " \"enter_order_status\",\n", - " \"enter_return_processing\",\n", - " \"other_talk\",\n", - " \"__end__\",\n", - "]:\n", - " route = tools_condition(state)\n", - " if route == END:\n", - " return END\n", - " tool_calls = state[\"messages\"][-1].tool_calls\n", - " if tool_calls:\n", - " if tool_calls[0][\"name\"] == ToProductQAAssistant.__name__:\n", - " return \"enter_product_qa\"\n", - " elif tool_calls[0][\"name\"] == ToOrderStatusAssistant.__name__:\n", - " return \"enter_order_status\"\n", - " elif tool_calls[0][\"name\"] == ToReturnProcessing.__name__:\n", - " return \"enter_return_processing\"\n", - " elif tool_calls[0][\"name\"] == HandleOtherTalk.__name__:\n", - " return \"other_talk\"\n", - " raise ValueError(\"Invalid route\")\n", - "\n", - "builder.add_edge(\"other_talk\", END)\n", - "\n", - "# The assistant can route to one of the delegated assistants,\n", - "# directly use a tool, or directly respond to the user\n", - "builder.add_conditional_edges(\n", - " \"primary_assistant\",\n", - " route_primary_assistant,\n", - " {\n", - " \"enter_product_qa\": \"enter_product_qa\",\n", - " \"enter_order_status\": \"enter_order_status\",\n", - " \"enter_return_processing\": \"enter_return_processing\",\n", - " \"other_talk\":\"other_talk\",\n", - " END: END,\n", - " },\n", - ")\n", - "\n", - "\n", - "def is_order_product_valid(state: State) -> Literal[\n", - " \"ask_clarification\",\n", - " \"order_status\"\n", - "]:\n", - " \"\"\"Conditional edge from validation node to decide if we should ask followup questions\"\"\"\n", - " if state[\"needs_clarification\"] == True:\n", - " return \"ask_clarification\"\n", - " return \"order_status\"\n", - "\n", - "def is_return_product_valid(state: State) -> Literal[\n", - " \"ask_clarification\",\n", - " \"return_processing\"\n", - "]:\n", - " \"\"\"Conditional edge from validation node to decide if we should ask followup questions\"\"\"\n", - " if state[\"needs_clarification\"] == True:\n", - " return \"ask_clarification\"\n", - " return \"return_processing\"\n", - "\n", - "builder.add_conditional_edges(\n", - " \"order_validation\",\n", - " is_order_product_valid\n", - ")\n", - "builder.add_conditional_edges(\n", - " \"return_validation\",\n", - " is_return_product_valid\n", - ")\n", - "\n", - "builder.add_edge(\"fetch_purchase_history\", \"primary_assistant\")\n", - "\n", - "\n", - "# Allow multiple async loop togeather\n", - "# This is needed to create checkpoint as it needs async event loop\n", - "# TODO: Move graph build into a async function and call that to remove nest_asyncio\n", - "import nest_asyncio\n", - "nest_asyncio.apply()\n", - "\n", - "# To run the async main function\n", - "import asyncio\n", - "\n", - "memory = None\n", - "pool = None\n", - "\n", - "# TODO: Remove pool as it's not getting used\n", - "# WAR: It's added so postgres does not close it's session\n", - "async def get_checkpoint():\n", - " global memory, pool\n", - " memory, pool = await get_checkpointer()\n", - "\n", - "asyncio.run(get_checkpoint())\n", - "\n", - "# Compile\n", - "graph = builder.compile(checkpointer=memory,\n", - " interrupt_before=[\"return_processing_sensitive_tools\"],\n", - " #interrupt_after=[\"ask_human\"]\n", - " )\n", - "\n", - "try:\n", - " # Generate the PNG image from the graph\n", - " png_image_data = graph.get_graph(xray=True).draw_mermaid_png()\n", - " # Save the image to a file in the current directory\n", - " with open(\"graph_image.png\", \"wb\") as f:\n", - " f.write(png_image_data)\n", - "except Exception as e:\n", - " # This requires some extra dependencies and is optional\n", - " logger.info(f\"An error occurred: {e}\")" - ] - }, - { - "cell_type": "markdown", - "id": "4a1920a3", - "metadata": {}, - "source": [ - "with the guardrails configuration built and wrapped around the agent, we will run the nemoguardrails server. Make sure to add the absolute path of the `config` directory and the `container image` and run the following cell" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c6910e96", - "metadata": { - "id": "b618cbbc-4e6c-44db-85d0-b5dbe4617f33" - }, - "outputs": [], - "source": [ - "%%bash\n", - "docker run -p 8000:8000 -v </path/to/local/config/>:/config <IMAGE_NAME>" - ] - }, - { - "cell_type": "markdown", - "id": "82ee29a0", - "metadata": { - "id": "74eeec5a" - }, - "source": [ - "## Exposing the Interface for Testing (optional)" - ] - }, - { - "cell_type": "markdown", - "id": "34a9104e", - "metadata": { - "id": "4f76c683" - }, - "source": [ - "The Blueprint comes equiped with a basic UI for testing the deployment. This interface is served at port 3001. In order to expose the port and try out the interaction, you need to follow the steps below.\n", - "\n", - "First, navigate back to the created Launchable instance page and click on the Access menu.\n", - "\n", - "\n", - "![Access Menu](https://github.com/NVIDIA-AI-Blueprints/ai-virtual-assistant/raw/main/docs/imgs/brev-cli-install.png)\n", - "\n", - "\n", - "Scroll down until you find \"Using Tunnels\" section and click on Share a Service button.\n", - "\n", - "\n", - "![Using Tunnels](https://github.com/NVIDIA-AI-Blueprints/ai-virtual-assistant/raw/main/docs/imgs/brev-tunnels.png)\n", - "\n", - "\n", - "Enter the port 3001, as that is where the UI service endpoint is. Confirm with Done. Then click on Edit Access and make the port public:\n", - "\n", - "\n", - "![Share Access](https://github.com/NVIDIA-AI-Blueprints/ai-virtual-assistant/raw/main/docs/imgs/brev-share-access.png)\n", - "\n", - "\n", - "Past this point, by clicking on the link, the UI should appear in your browser and you are free to interact with the assistant and to ask him about the data that was ingested.\n", - "\n", - "\n", - "![AI Virtual Assistant Interface](https://github.com/NVIDIA-AI-Blueprints/ai-virtual-assistant/raw/main/docs/imgs/ai-virtual-assistant-interface.png)" - ] - }, - { - "cell_type": "markdown", - "id": "262835e1", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/greptile.json b/greptile.json index 5630fc6c05..e603c4e80a 100644 --- a/greptile.json +++ b/greptile.json @@ -1,5 +1,5 @@ { - "triggerOnUpdates": false, + "triggerOnUpdates": true, "shouldUpdateDescription": false, "fixWithAI": true, "summarySection": { diff --git a/nemoguardrails/integrations/langchain/middleware.py b/nemoguardrails/integrations/langchain/middleware.py index 773d1342cc..2f32779bf0 100644 --- a/nemoguardrails/integrations/langchain/middleware.py +++ b/nemoguardrails/integrations/langchain/middleware.py @@ -111,6 +111,10 @@ async def abefore_model(self, state: AgentState, runtime: LangGraphRuntime) -> O if not messages: return None + last_user_message = self._get_last_user_message(messages) + if not last_user_message: + return None + rails_messages = self._convert_to_rails_messages(messages) try: @@ -125,6 +129,11 @@ async def abefore_model(self, state: AgentState, runtime: LangGraphRuntime) -> O blocked_msg = create_ai_message(self.blocked_input_message) return {"messages": messages + [blocked_msg], "jump_to": "end"} + if result.status == RailStatus.MODIFIED: + log.info("Input modified by rail '%s': content replaced", result.rail or "unknown rail") + modified_msg = last_user_message.model_copy(update={"content": result.content}) + return {"messages": self._replace_last_human_message(messages, modified_msg)} + return None except GuardrailViolation: @@ -141,6 +150,12 @@ async def abefore_model(self, state: AgentState, runtime: LangGraphRuntime) -> O blocked_msg = create_ai_message(self.blocked_input_message) return {"messages": messages + [blocked_msg], "jump_to": "end"} + def _replace_last_human_message(self, messages: list, replacement: HumanMessage) -> list: + for i in range(len(messages) - 1, -1, -1): + if is_human_message(messages[i]): + return messages[:i] + [replacement] + messages[i + 1 :] + return messages + [replacement] + def _replace_last_ai_message(self, messages: list, replacement: AIMessage) -> list: for i in range(len(messages) - 1, -1, -1): if is_ai_message(messages[i]): @@ -173,6 +188,11 @@ async def aafter_model(self, state: AgentState, runtime: LangGraphRuntime) -> Op blocked_msg = create_ai_message(self.blocked_output_message) return {"messages": self._replace_last_ai_message(messages, blocked_msg)} + if result.status == RailStatus.MODIFIED: + log.info("Output modified by rail '%s': content replaced", result.rail or "unknown rail") + modified_msg = last_ai_message.model_copy(update={"content": result.content}) + return {"messages": self._replace_last_ai_message(messages, modified_msg)} + return None except GuardrailViolation: diff --git a/nemoguardrails/library/policyai/__init__.py b/nemoguardrails/library/policyai/__init__.py new file mode 100644 index 0000000000..6c7f64065d --- /dev/null +++ b/nemoguardrails/library/policyai/__init__.py @@ -0,0 +1,14 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/nemoguardrails/library/policyai/actions.py b/nemoguardrails/library/policyai/actions.py new file mode 100644 index 0000000000..60ce3e8380 --- /dev/null +++ b/nemoguardrails/library/policyai/actions.py @@ -0,0 +1,159 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +PolicyAI Integration for NeMo Guardrails. + +PolicyAI provides content moderation and policy enforcement capabilities +for LLM applications. This integration allows using PolicyAI as an input +and output rail for content moderation. + +For more information, see: https://musubilabs.ai +""" + +import json +import logging +import os +from typing import Optional + +import aiohttp + +from nemoguardrails.actions import action + +log = logging.getLogger(__name__) + + +def call_policyai_api_mapping(result: dict) -> bool: + """ + Mapping for call_policyai_api. + + Expects result to be a dict with: + - "assessment": "SAFE" or "UNSAFE" + - "category": the violation category (if UNSAFE) + - "severity": severity level 0-3 + + Block (return True) if: + 1. Assessment is "UNSAFE" + """ + assessment = result.get("assessment", "SAFE") + return assessment == "UNSAFE" + + +@action(is_system_action=True, output_mapping=call_policyai_api_mapping) +async def call_policyai_api( + text: Optional[str] = None, + tag_name: Optional[str] = None, + **kwargs, +): + """ + Call the PolicyAI API to evaluate content. + + Args: + text: The text content to evaluate. + tag_name: Optional tag name for the PolicyAI evaluation. + If not provided, uses POLICYAI_TAG_NAME env var or "prod". + + Returns: + dict with: + - assessment: "SAFE" or "UNSAFE" + - category: the violation category (if UNSAFE) + - severity: severity level 0-3 + - reason: explanation for the decision + """ + api_key = os.environ.get("POLICYAI_API_KEY") + + if api_key is None: + raise ValueError("POLICYAI_API_KEY environment variable not set.") + + base_url = os.environ.get("POLICYAI_BASE_URL", "https://api.musubilabs.ai") + base_url = base_url.rstrip("/") + + # Get tag name from parameter, env var, or default + if tag_name is None: + tag_name = os.environ.get("POLICYAI_TAG_NAME", "prod") + + url = f"{base_url}/policyai/v1/decisions/evaluate/{tag_name}" + + headers = { + "Musubi-Api-Key": api_key, + "Content-Type": "application/json", + } + + data = { + "content": [ + { + "type": "TEXT", + "content": text, + } + ], + } + + timeout = aiohttp.ClientTimeout(total=30) + async with aiohttp.ClientSession(timeout=timeout) as session: + async with session.post( + url=url, + headers=headers, + json=data, + ) as response: + if response.status != 200: + raise ValueError( + f"PolicyAI call failed with status code {response.status}.\nDetails: {await response.text()}" + ) + response_json = await response.json() + log.info(json.dumps(response_json, indent=2)) + + # PolicyAI returns results in "data" array for tag-based evaluation + results = response_json.get("data", []) + + # Fail-closed: If no policies are attached to the tag, raise an error + # rather than silently allowing content through + if not results: + raise ValueError( + f"PolicyAI returned no policy results for tag '{tag_name}'. " + "Ensure policies are attached to this tag." + ) + + # Check if all policies failed evaluation + successful_results = [r for r in results if r.get("status") != "failed"] + if not successful_results: + raise ValueError( + f"All PolicyAI policy evaluations failed for tag '{tag_name}'. Check policy configurations." + ) + + # Aggregate results - if ANY policy returns UNSAFE, overall is UNSAFE + overall_assessment = "SAFE" + triggered_category = "Safe" + max_severity = 0 + reason = "Content passed all policy checks" + + for result in successful_results: + assessment = result.get("assessment", "SAFE") + if assessment == "UNSAFE": + overall_assessment = "UNSAFE" + triggered_category = result.get("category", "Unknown") + max_severity = max(max_severity, result.get("severity", 0)) + reason = result.get("reason", "Policy violation detected") + break # Stop at first UNSAFE result + + # Pre-format exception message for Colang 1.x compatibility + # (Colang 1.x doesn't support string concatenation in create event) + exception_message = f"PolicyAI moderation triggered. Content violated policy: {triggered_category}" + + return { + "assessment": overall_assessment, + "category": triggered_category, + "severity": max_severity, + "reason": reason, + "exception_message": exception_message, + } diff --git a/nemoguardrails/library/policyai/flows.co b/nemoguardrails/library/policyai/flows.co new file mode 100644 index 0000000000..f892cb9fe5 --- /dev/null +++ b/nemoguardrails/library/policyai/flows.co @@ -0,0 +1,33 @@ +""" +PolicyAI Integration Flows (Colang 2.x) + +PolicyAI provides content moderation and policy enforcement capabilities. +For more information, see: https://musubilabs.ai + +Supported features: +- Input moderation: Check user messages against configured policies +- Output moderation: Check bot responses against configured policies +- Tag-based evaluation: Use POLICYAI_TAG_NAME env var to specify policy tag +""" + +flow policyai moderation on input + """Guardrail based on PolicyAI assessment.""" + $result = await CallPolicyaiApiAction(text=$user_message) + + if $result.assessment == "UNSAFE" + if $system.config.enable_rails_exceptions + send PolicyAIModerationRailException(message="PolicyAI moderation triggered. Content violated policy: " + $result.category) + else + bot refuse to respond + abort + +flow policyai moderation on output + """Guardrail based on PolicyAI assessment.""" + $result = await CallPolicyaiApiAction(text=$bot_message) + + if $result.assessment == "UNSAFE" + if $system.config.enable_rails_exceptions + send PolicyAIModerationRailException(message="PolicyAI moderation triggered. Content violated policy: " + $result.category) + else + bot refuse to respond + abort diff --git a/nemoguardrails/library/policyai/flows.v1.co b/nemoguardrails/library/policyai/flows.v1.co new file mode 100644 index 0000000000..259cd4916e --- /dev/null +++ b/nemoguardrails/library/policyai/flows.v1.co @@ -0,0 +1,33 @@ +""" +PolicyAI Integration Flows (Colang 1.x) + +PolicyAI provides content moderation and policy enforcement capabilities. +For more information, see: https://musubilabs.ai + +Supported features: +- Input moderation: Check user messages against configured policies +- Output moderation: Check bot responses against configured policies +- Tag-based evaluation: Use POLICYAI_TAG_NAME env var to specify policy tag +""" + +define subflow policyai moderation on input + """Guardrail based on PolicyAI assessment.""" + $result = execute call_policyai_api(text=$user_message) + + if $result.assessment == "UNSAFE" + if $config.enable_rails_exceptions + create event PolicyAIModerationRailException(message=$result.exception_message) + else + bot refuse to respond + stop + +define subflow policyai moderation on output + """Guardrail based on PolicyAI assessment.""" + $result = execute call_policyai_api(text=$bot_message) + + if $result.assessment == "UNSAFE" + if $config.enable_rails_exceptions + create event PolicyAIModerationRailException(message=$result.exception_message) + else + bot refuse to respond + stop diff --git a/nemoguardrails/llm/providers/providers.py b/nemoguardrails/llm/providers/providers.py index bfbcce51af..beb1aa7013 100644 --- a/nemoguardrails/llm/providers/providers.py +++ b/nemoguardrails/llm/providers/providers.py @@ -86,11 +86,17 @@ def _discover_langchain_community_llm_providers(): def _discover_langchain_partner_chat_providers() -> Set[str]: - from langchain.chat_models.base import _SUPPORTED_PROVIDERS + import langchain.chat_models.base as _base - if isinstance(_SUPPORTED_PROVIDERS, dict): - return set(_SUPPORTED_PROVIDERS.keys()) | _CUSTOM_CHAT_PROVIDERS - return _SUPPORTED_PROVIDERS | _CUSTOM_CHAT_PROVIDERS + # The internal variable listing supported providers was renamed across langchain versions: + # _SUPPORTED_PROVIDERS (<=1.2.1, set) -> _SUPPORTED_PROVIDERS (1.2.1, dict) -> _BUILTIN_PROVIDERS (>=1.2.10, dict) + _PROVIDERS = getattr(_base, "_BUILTIN_PROVIDERS", None) or getattr(_base, "_SUPPORTED_PROVIDERS", None) + if _PROVIDERS is None: + return _CUSTOM_CHAT_PROVIDERS + + if isinstance(_PROVIDERS, dict): + return set(_PROVIDERS.keys()) | _CUSTOM_CHAT_PROVIDERS + return _PROVIDERS | _CUSTOM_CHAT_PROVIDERS def _discover_langchain_community_chat_providers(): diff --git a/nemoguardrails/rails/llm/buffer.py b/nemoguardrails/rails/llm/buffer.py index ecb062392e..78d7addb0f 100644 --- a/nemoguardrails/rails/llm/buffer.py +++ b/nemoguardrails/rails/llm/buffer.py @@ -292,7 +292,7 @@ async def process_stream(self, streaming_handler) -> AsyncGenerator[ChunkBatch, total_chunks = 0 async for chunk in streaming_handler: - buffer.append(chunk) + buffer.append(chunk["text"] if isinstance(chunk, dict) else chunk) total_chunks += 1 if len(buffer) >= self.buffer_chunk_size: diff --git a/nemoguardrails/streaming.py b/nemoguardrails/streaming.py index d866f66768..4dfcbde6c1 100644 --- a/nemoguardrails/streaming.py +++ b/nemoguardrails/streaming.py @@ -16,7 +16,7 @@ import asyncio import logging import warnings -from typing import Any, AsyncIterator, Dict, Optional, Union +from typing import Any, AsyncIterator, Dict, List, Optional, Union from nemoguardrails.utils import new_uuid @@ -83,12 +83,19 @@ def __init__( # If set, the chunk will be piped to the specified handler rather than added to the queue or printed self.pipe_to = None - # The stop chunks - self.stop = [] + self._stop = [] self.include_metadata = include_metadata self.current_metadata = {} + @property + def stop(self) -> List[str]: + return self._stop + + @stop.setter + def stop(self, value: Optional[List[str]]) -> None: + self._stop = value or [] + def set_pattern(self, prefix: Optional[str] = None, suffix: Optional[str] = None): """Sets the pattern that is expected. diff --git a/poetry.lock b/poetry.lock index 7a2337e623..06c9fd6726 100644 --- a/poetry.lock +++ b/poetry.lock @@ -20,13 +20,13 @@ tests = ["hypothesis", "pytest"] [[package]] name = "aiofiles" -version = "24.1.0" +version = "25.1.0" description = "File support for asyncio." optional = true -python-versions = ">=3.8" +python-versions = ">=3.9" files = [ - {file = "aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5"}, - {file = "aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c"}, + {file = "aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695"}, + {file = "aiofiles-25.1.0.tar.gz", hash = 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["cffi (>=1.17)"] +cffi = ["cffi (>=1.17,<2.0)", "cffi (>=2.0.0b)"] [extras] all = ["aiofiles", "fast-langdetect", "google-cloud-language", "langchain-nvidia-ai-endpoints", "langchain-openai", "numpy", "numpy", "numpy", "numpy", "openai", "opentelemetry-api", "presidio-analyzer", "presidio-anonymizer", "streamlit", "tqdm", "yara-python"] @@ -6914,4 +7410,4 @@ tracing = ["aiofiles", "opentelemetry-api"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.14" -content-hash = "525873b52b3cf098f1e41cbed55c75ed4ab5fcdbb6c11e872cc393791a1363bd" +content-hash = "066d34fb2e969dc7f2ff1d132afae3aa59ecd021122776c7579ad21764de8750" diff --git a/pyproject.toml b/pyproject.toml index a5c03261a0..b8ddb67e82 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ description = "NeMo Guardrails is an open-source toolkit for easily adding progr authors = ["NVIDIA <nemoguardrails@nvidia.com>"] license = "LICENSE.md" readme = "README.md" -version = "0.20.0" +version = "0.21.0" packages = [{ include = "nemoguardrails" }] @@ -51,6 +51,11 @@ aiohttp-retry = ">=2.9.0" annoy = ">=1.17.3" fastapi = ">=0.103.0," fastembed = [{ version = ">=0.2.2", python = ">=3.10,<3.14" }] +# onnxruntime 1.24+ dropped Python 3.10 wheels; cap it for that version +onnxruntime = [ + { version = ">=1.17.0,<1.24", python = ">=3.10,<3.11" }, + { version = ">=1.17.0", python = ">=3.11" }, +] httpx = ">=0.24.1" jinja2 = ">=3.1.6" langchain = ">=0.2.14,<2.0.0" diff --git a/tests/integrations/langchain/test_middleware.py b/tests/integrations/langchain/test_middleware.py index f3568fc730..5661859fad 100644 --- a/tests/integrations/langchain/test_middleware.py +++ b/tests/integrations/langchain/test_middleware.py @@ -1342,3 +1342,230 @@ async def test_error_handler_also_replaces_correctly(self, mock_rails_factory): assert result["messages"][1].content == middleware.blocked_output_message assert isinstance(result["messages"][1], AIMessage) assert result["messages"][2] is trailing_msg + + +class TestModifiedStatus: + @pytest.mark.asyncio + async def test_input_modified_replaces_last_human_message(self, mock_rails_factory): + mock_rails = mock_rails_factory(status=RailStatus.MODIFIED, content="sanitized input") + middleware = create_middleware_with_rails(mock_rails) + + state = {"messages": [HumanMessage(content="original input with PII")]} + result = await middleware.abefore_model(state, None) + + assert result is not None + assert "jump_to" not in result + assert len(result["messages"]) == 1 + assert isinstance(result["messages"][0], HumanMessage) + assert result["messages"][0].content == "sanitized input" + + @pytest.mark.asyncio + async def test_input_modified_preserves_surrounding_messages(self, mock_rails_factory): + mock_rails = mock_rails_factory(status=RailStatus.MODIFIED, content="redacted") + middleware = create_middleware_with_rails(mock_rails) + + system_msg = SystemMessage(content="Be helpful") + state = { + "messages": [ + system_msg, + HumanMessage(content="my SSN is 123-45-6789"), + ] + } + result = await middleware.abefore_model(state, None) + + assert len(result["messages"]) == 2 + assert result["messages"][0] is system_msg + assert isinstance(result["messages"][1], HumanMessage) + assert result["messages"][1].content == "redacted" + + @pytest.mark.asyncio + async def test_input_modified_with_multi_turn_history(self, mock_rails_factory): + mock_rails = mock_rails_factory(status=RailStatus.MODIFIED, content="cleaned message") + middleware = create_middleware_with_rails(mock_rails) + + first_human = HumanMessage(content="Hello") + first_ai = AIMessage(content="Hi there") + state = { + "messages": [ + first_human, + first_ai, + HumanMessage(content="my email is foo@bar.com"), + ] + } + result = await middleware.abefore_model(state, None) + + assert len(result["messages"]) == 3 + assert result["messages"][0] is first_human + assert result["messages"][1] is first_ai + assert result["messages"][2].content == "cleaned message" + + @pytest.mark.asyncio + async def test_output_modified_replaces_last_ai_message(self, mock_rails_factory): + mock_rails = mock_rails_factory(status=RailStatus.MODIFIED, content="sanitized output") + middleware = create_middleware_with_rails(mock_rails) + + state = { + "messages": [ + HumanMessage(content="Hello"), + AIMessage(content="original response with PII"), + ] + } + result = await middleware.aafter_model(state, None) + + assert result is not None + assert len(result["messages"]) == 2 + assert isinstance(result["messages"][0], HumanMessage) + assert result["messages"][0].content == "Hello" + assert isinstance(result["messages"][1], AIMessage) + assert result["messages"][1].content == "sanitized output" + + @pytest.mark.asyncio + async def test_output_modified_preserves_trailing_messages(self, mock_rails_factory): + mock_rails = mock_rails_factory(status=RailStatus.MODIFIED, content="redacted output") + middleware = create_middleware_with_rails(mock_rails) + + trailing = SystemMessage(content="trailing") + state = { + "messages": [ + HumanMessage(content="Hello"), + AIMessage(content="bad output"), + trailing, + ] + } + result = await middleware.aafter_model(state, None) + + assert len(result["messages"]) == 3 + assert isinstance(result["messages"][1], AIMessage) + assert result["messages"][1].content == "redacted output" + assert result["messages"][2] is trailing + + @pytest.mark.asyncio + async def test_output_modified_replaces_only_last_ai_message(self, mock_rails_factory): + mock_rails = mock_rails_factory(status=RailStatus.MODIFIED, content="fixed") + middleware = create_middleware_with_rails(mock_rails) + + state = { + "messages": [ + HumanMessage(content="Hello"), + AIMessage(content="First response"), + HumanMessage(content="Follow up"), + AIMessage(content="Second response with PII"), + ] + } + result = await middleware.aafter_model(state, None) + + assert len(result["messages"]) == 4 + assert result["messages"][1].content == "First response" + assert result["messages"][3].content == "fixed" + + def test_sync_before_model_handles_modified(self, mock_rails_factory): + mock_rails = mock_rails_factory(status=RailStatus.MODIFIED, content="sanitized") + middleware = create_middleware_with_rails(mock_rails) + + state = {"messages": [HumanMessage(content="PII content")]} + result = middleware.before_model(state, None) + + assert result is not None + assert len(result["messages"]) == 1 + assert isinstance(result["messages"][0], HumanMessage) + assert result["messages"][0].content == "sanitized" + + def test_sync_after_model_handles_modified(self, mock_rails_factory): + mock_rails = mock_rails_factory(status=RailStatus.MODIFIED, content="sanitized output") + middleware = create_middleware_with_rails(mock_rails) + + state = { + "messages": [ + HumanMessage(content="Hello"), + AIMessage(content="PII output"), + ] + } + result = middleware.after_model(state, None) + + assert result is not None + assert len(result["messages"]) == 2 + assert result["messages"][1].content == "sanitized output" + + @pytest.mark.asyncio + async def test_input_modified_with_empty_content(self, mock_rails_factory): + mock_rails = mock_rails_factory(status=RailStatus.MODIFIED, content="") + middleware = create_middleware_with_rails(mock_rails) + + state = {"messages": [HumanMessage(content="sensitive data")]} + result = await middleware.abefore_model(state, None) + + assert result is not None + assert len(result["messages"]) == 1 + assert isinstance(result["messages"][0], HumanMessage) + assert result["messages"][0].content == "" + + @pytest.mark.asyncio + async def test_input_modified_preserves_message_metadata(self, mock_rails_factory): + mock_rails = mock_rails_factory(status=RailStatus.MODIFIED, content="redacted") + middleware = create_middleware_with_rails(mock_rails) + + original = HumanMessage( + content="my SSN is 123-45-6789", + id="msg-123", + name="user1", + additional_kwargs={"source": "web"}, + ) + state = {"messages": [original]} + result = await middleware.abefore_model(state, None) + + modified = result["messages"][0] + assert modified.content == "redacted" + assert modified.id == "msg-123" + assert modified.name == "user1" + assert modified.additional_kwargs == {"source": "web"} + + @pytest.mark.asyncio + async def test_output_modified_preserves_message_metadata(self, mock_rails_factory): + mock_rails = mock_rails_factory(status=RailStatus.MODIFIED, content="safe output") + middleware = create_middleware_with_rails(mock_rails) + + original_ai = AIMessage( + content="PII in response", + id="ai-456", + name="assistant", + additional_kwargs={"model": "gpt-4"}, + ) + state = { + "messages": [ + HumanMessage(content="Hello"), + original_ai, + ] + } + result = await middleware.aafter_model(state, None) + + modified = result["messages"][1] + assert modified.content == "safe output" + assert modified.id == "ai-456" + assert modified.name == "assistant" + assert modified.additional_kwargs == {"model": "gpt-4"} + + @pytest.mark.asyncio + async def test_output_modified_preserves_tool_calls(self, mock_rails_factory): + mock_rails = mock_rails_factory(status=RailStatus.MODIFIED, content="sanitized") + middleware = create_middleware_with_rails(mock_rails) + + tool_call = ToolCall(name="search", args={"q": "test"}, id="tc-1") + original_ai = AIMessage( + content="PII response", + id="ai-789", + tool_calls=[tool_call], + ) + state = { + "messages": [ + HumanMessage(content="Hello"), + original_ai, + ] + } + result = await middleware.aafter_model(state, None) + + modified = result["messages"][1] + assert modified.content == "sanitized" + assert modified.id == "ai-789" + assert len(modified.tool_calls) == 1 + assert modified.tool_calls[0]["name"] == "search" + assert modified.tool_calls[0]["id"] == "tc-1" diff --git a/tests/llm/test_version_compatibility.py b/tests/llm/test_version_compatibility.py index 2487726929..11e2de563a 100644 --- a/tests/llm/test_version_compatibility.py +++ b/tests/llm/test_version_compatibility.py @@ -343,6 +343,64 @@ def test_discover_langchain_community_chat_providers(): ) +def test_discover_partner_chat_providers_no_providers_attr(monkeypatch): + """Test fallback when neither _BUILTIN_PROVIDERS nor _SUPPORTED_PROVIDERS exists.""" + import langchain.chat_models.base as _base + + monkeypatch.delattr(_base, "_BUILTIN_PROVIDERS", raising=False) + monkeypatch.delattr(_base, "_SUPPORTED_PROVIDERS", raising=False) + + from nemoguardrails.llm.providers.providers import _CUSTOM_CHAT_PROVIDERS + + result = _discover_langchain_partner_chat_providers() + assert result == _CUSTOM_CHAT_PROVIDERS + + +def test_discover_partner_chat_providers_set_type(monkeypatch): + """Test branch when _SUPPORTED_PROVIDERS is a set (older langchain versions).""" + import langchain.chat_models.base as _base + + providers_set = {"openai", "anthropic"} + monkeypatch.delattr(_base, "_BUILTIN_PROVIDERS", raising=False) + monkeypatch.setattr(_base, "_SUPPORTED_PROVIDERS", providers_set, raising=False) + + from nemoguardrails.llm.providers.providers import _CUSTOM_CHAT_PROVIDERS + + result = _discover_langchain_partner_chat_providers() + assert result == providers_set | _CUSTOM_CHAT_PROVIDERS + + +def test_discover_partner_chat_providers_supported_dict(monkeypatch): + """Test branch when _SUPPORTED_PROVIDERS is a dict (langchain ~1.2.1).""" + import langchain.chat_models.base as _base + + providers_dict = { + "openai": ("langchain_openai", "ChatOpenAI"), + "anthropic": ("langchain_anthropic", "ChatAnthropic"), + } + monkeypatch.delattr(_base, "_BUILTIN_PROVIDERS", raising=False) + monkeypatch.setattr(_base, "_SUPPORTED_PROVIDERS", providers_dict, raising=False) + + from nemoguardrails.llm.providers.providers import _CUSTOM_CHAT_PROVIDERS + + result = _discover_langchain_partner_chat_providers() + assert result == set(providers_dict.keys()) | _CUSTOM_CHAT_PROVIDERS + + +def test_discover_partner_chat_providers_builtin_set(monkeypatch): + """Test branch when _BUILTIN_PROVIDERS is a set (hypothetical).""" + import langchain.chat_models.base as _base + + providers_set = {"openai", "anthropic"} + monkeypatch.setattr(_base, "_BUILTIN_PROVIDERS", providers_set) + monkeypatch.delattr(_base, "_SUPPORTED_PROVIDERS", raising=False) + + from nemoguardrails.llm.providers.providers import _CUSTOM_CHAT_PROVIDERS + + result = _discover_langchain_partner_chat_providers() + assert result == providers_set | _CUSTOM_CHAT_PROVIDERS + + def test_dicsover_partner_chat_providers(): """Test that the function correctly discovers LangChain partner chat providers.""" diff --git a/tests/test_actions_llm_utils.py b/tests/test_actions_llm_utils.py index 570450c301..7f7bebc82e 100644 --- a/tests/test_actions_llm_utils.py +++ b/tests/test_actions_llm_utils.py @@ -29,6 +29,7 @@ _infer_provider_from_module, _store_reasoning_traces, _store_tool_calls, + _stream_llm_call, llm_call, ) from nemoguardrails.context import reasoning_trace_var, tool_calls_var @@ -712,3 +713,48 @@ async def test_llm_call_does_not_mutate_llm_params(self): original_params = {"max_tokens": 100} await llm_call(mock_llm, "prompt", stop=["User:"], llm_params=original_params) assert original_params == {"max_tokens": 100} + + +async def _empty_astream(*args, **kwargs): + return + yield + + +class _FakeLLM: + def __init__(self, stop=None, kwargs=None): + self.stop = stop + if kwargs is not None: + self.kwargs = kwargs + self.astream = _empty_astream + + +class TestStreamLlmCallStopCoercion: + @pytest.mark.asyncio + async def test_llm_stop_attr_none_coerced_to_list(self): + from nemoguardrails.streaming import StreamingHandler + + llm = _FakeLLM(stop=None) + handler = StreamingHandler() + await _stream_llm_call(llm, "prompt", handler) + + assert handler.stop == [] + + @pytest.mark.asyncio + async def test_llm_kwargs_stop_none_coerced_to_list(self): + from nemoguardrails.streaming import StreamingHandler + + llm = _FakeLLM(kwargs={"stop": None}) + handler = StreamingHandler() + await _stream_llm_call(llm, "prompt", handler) + + assert handler.stop == [] + + @pytest.mark.asyncio + async def test_llm_with_valid_stop_preserved(self): + from nemoguardrails.streaming import StreamingHandler + + llm = _FakeLLM(stop=["User:"]) + handler = StreamingHandler() + await _stream_llm_call(llm, "prompt", handler) + + assert handler.stop == ["User:"] diff --git a/tests/test_buffer_strategy.py b/tests/test_buffer_strategy.py index 73957e8953..a9ac8812ce 100644 --- a/tests/test_buffer_strategy.py +++ b/tests/test_buffer_strategy.py @@ -266,6 +266,50 @@ def test_format_chunks_realistic(): assert buffer_strategy.format_chunks(["test"]) == "test" +@pytest.mark.asyncio +async def test_process_stream_with_metadata_dicts(): + """Test that process_stream normalizes dict chunks to strings.""" + + async def metadata_streaming_handler(): + for token in ["Hello", " ", "world", "!"]: + yield {"text": token, "metadata": {"response_metadata": {"model_provider": "openai"}}} + + buffer_strategy = RollingBuffer(buffer_context_size=1, buffer_chunk_size=2) + + user_output_parts = [] + async for chunk_batch in buffer_strategy(metadata_streaming_handler()): + for chunk in chunk_batch.processing_context: + assert isinstance(chunk, str) + formatted = buffer_strategy.format_chunks(chunk_batch.processing_context) + assert isinstance(formatted, str) + user_output_parts.append(buffer_strategy.format_chunks(chunk_batch.user_output_chunks)) + + full_text = "".join(user_output_parts) + assert full_text == "Hello world!" + + +@pytest.mark.asyncio +async def test_process_stream_with_mixed_chunk_types(): + """Test that process_stream handles a mix of string and dict chunks.""" + + async def mixed_streaming_handler(): + yield "Hello" + yield {"text": " ", "metadata": {"response_metadata": {"model_provider": "openai"}}} + yield {"text": "world", "metadata": {"response_metadata": {"model_provider": "openai"}}} + yield "!" + + buffer_strategy = RollingBuffer(buffer_context_size=1, buffer_chunk_size=2) + + user_output_parts = [] + async for chunk_batch in buffer_strategy(mixed_streaming_handler()): + for chunk in chunk_batch.processing_context: + assert isinstance(chunk, str) + user_output_parts.append(buffer_strategy.format_chunks(chunk_batch.user_output_chunks)) + + full_text = "".join(user_output_parts) + assert full_text == "Hello world!" + + @pytest.mark.asyncio async def test_total_yielded_tracking(): """Test that total_yielded is correctly tracked and reset.""" diff --git a/tests/test_policyai_rail.py b/tests/test_policyai_rail.py new file mode 100644 index 0000000000..10d3dfc365 --- /dev/null +++ b/tests/test_policyai_rail.py @@ -0,0 +1,641 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from aioresponses import aioresponses + +from nemoguardrails import RailsConfig +from nemoguardrails.library.policyai.actions import call_policyai_api +from tests.utils import TestChat + + +def test_input_safe(monkeypatch): + """Test that safe input is allowed through.""" + monkeypatch.setenv("POLICYAI_API_KEY", "test-api-key") + monkeypatch.setenv("POLICYAI_TAG_NAME", "test") + + config = RailsConfig.from_content( + colang_content=""" + define user express greeting + "hi" + + define flow + user express greeting + bot express greeting + + define bot express greeting + "Hello! How can I assist you today?" + """, + yaml_content=""" + models: + - type: main + engine: openai + model: gpt-3.5-turbo-instruct + + rails: + input: + flows: + - policyai moderation on input + """, + ) + chat = TestChat( + config, + llm_completions=[ + " express greeting", + ], + ) + + with aioresponses() as m: + # PolicyAI returns SAFE assessment + m.post( + "https://api.musubilabs.ai/policyai/v1/decisions/evaluate/test", + payload={ + "data": [ + { + "status": "success", + "assessment": "SAFE", + "category": "Safe", + "severity": 0, + "reason": "Content is safe", + } + ] + }, + ) + + chat >> "Hello!" + chat << "Hello! How can I assist you today?" + + +def test_input_unsafe(monkeypatch): + """Test that unsafe input is blocked.""" + monkeypatch.setenv("POLICYAI_API_KEY", "test-api-key") + monkeypatch.setenv("POLICYAI_TAG_NAME", "test") + + config = RailsConfig.from_content( + colang_content=""" + define user express greeting + "hi" + + define flow + user express greeting + bot express greeting + + define bot express greeting + "Hello! How can I assist you today?" + """, + yaml_content=""" + models: + - type: main + engine: openai + model: gpt-3.5-turbo-instruct + + rails: + input: + flows: + - policyai moderation on input + """, + ) + chat = TestChat( + config, + llm_completions=[ + " express greeting", + ], + ) + + with aioresponses() as m: + # PolicyAI returns UNSAFE assessment + m.post( + "https://api.musubilabs.ai/policyai/v1/decisions/evaluate/test", + payload={ + "data": [ + { + "status": "success", + "assessment": "UNSAFE", + "category": "HarmfulContent", + "severity": 2, + "reason": "Content contains harmful language", + } + ] + }, + ) + + chat >> "some harmful content" + chat << "I'm sorry, I can't respond to that." + + +def test_output_safe(monkeypatch): + """Test that safe output is allowed through.""" + monkeypatch.setenv("POLICYAI_API_KEY", "test-api-key") + monkeypatch.setenv("POLICYAI_TAG_NAME", "test") + + config = RailsConfig.from_content( + yaml_content=""" + models: + - type: main + engine: openai + model: gpt-3.5-turbo-instruct + + rails: + output: + flows: + - policyai moderation on output + """, + ) + chat = TestChat( + config, + llm_completions=[ + " Hello! How can I help you today?", + ], + ) + + with aioresponses() as m: + # PolicyAI returns SAFE assessment + m.post( + "https://api.musubilabs.ai/policyai/v1/decisions/evaluate/test", + payload={ + "data": [ + { + "status": "success", + "assessment": "SAFE", + "category": "Safe", + "severity": 0, + "reason": "Content is safe", + } + ] + }, + ) + + chat >> "Hello!" + chat << "Hello! How can I help you today?" + + +def test_output_unsafe(monkeypatch): + """Test that unsafe output is blocked.""" + monkeypatch.setenv("POLICYAI_API_KEY", "test-api-key") + monkeypatch.setenv("POLICYAI_TAG_NAME", "test") + + config = RailsConfig.from_content( + yaml_content=""" + models: + - type: main + engine: openai + model: gpt-3.5-turbo-instruct + + rails: + output: + flows: + - policyai moderation on output + """, + ) + chat = TestChat( + config, + llm_completions=[ + " I promise you a full refund of $500!", + ], + ) + + with aioresponses() as m: + # PolicyAI returns UNSAFE assessment (e.g., unauthorized refund promise) + m.post( + "https://api.musubilabs.ai/policyai/v1/decisions/evaluate/test", + payload={ + "data": [ + { + "status": "success", + "assessment": "UNSAFE", + "category": "UnauthorizedCommitment", + "severity": 2, + "reason": "AI made unauthorized commitment about refund", + } + ] + }, + ) + + chat >> "Can I get a refund?" + chat << "I'm sorry, I can't respond to that." + + +def test_custom_tag_via_env(monkeypatch): + """Test using a custom policy tag via environment variable.""" + monkeypatch.setenv("POLICYAI_API_KEY", "test-api-key") + monkeypatch.setenv("POLICYAI_TAG_NAME", "custom-tag") + + config = RailsConfig.from_content( + colang_content=""" + define user express greeting + "hi" + + define flow + user express greeting + bot express greeting + + define bot express greeting + "Hello! How can I assist you today?" + """, + yaml_content=""" + models: + - type: main + engine: openai + model: gpt-3.5-turbo-instruct + + rails: + input: + flows: + - policyai moderation on input + """, + ) + chat = TestChat( + config, + llm_completions=[ + " express greeting", + ], + ) + + with aioresponses() as m: + # Note: The URL should use the custom tag from env var + m.post( + "https://api.musubilabs.ai/policyai/v1/decisions/evaluate/custom-tag", + payload={ + "data": [ + { + "status": "success", + "assessment": "SAFE", + "category": "Safe", + "severity": 0, + "reason": "Content is safe", + } + ] + }, + ) + + chat >> "Hello!" + chat << "Hello! How can I assist you today?" + + +def test_multiple_policies(monkeypatch): + """Test when multiple policies are evaluated (tag has multiple policies).""" + monkeypatch.setenv("POLICYAI_API_KEY", "test-api-key") + monkeypatch.setenv("POLICYAI_TAG_NAME", "test") + + config = RailsConfig.from_content( + yaml_content=""" + models: + - type: main + engine: openai + model: gpt-3.5-turbo-instruct + + rails: + output: + flows: + - policyai moderation on output + """, + ) + chat = TestChat( + config, + llm_completions=[ + " Here's some content", + ], + ) + + with aioresponses() as m: + # Multiple policies - first is SAFE, second is UNSAFE + m.post( + "https://api.musubilabs.ai/policyai/v1/decisions/evaluate/test", + payload={ + "data": [ + { + "status": "success", + "assessment": "SAFE", + "category": "ToxicityCheck", + "severity": 0, + "reason": "No toxic content detected", + }, + { + "status": "success", + "assessment": "UNSAFE", + "category": "PIIDetection", + "severity": 1, + "reason": "PII detected in content", + }, + ] + }, + ) + + chat >> "Hello!" + # Should be blocked because one policy returned UNSAFE + chat << "I'm sorry, I can't respond to that." + + +@pytest.mark.asyncio +async def test_empty_data_array_raises_error(monkeypatch): + """Test that empty data array (no policies attached to tag) raises an error.""" + monkeypatch.setenv("POLICYAI_API_KEY", "test-api-key") + monkeypatch.setenv("POLICYAI_TAG_NAME", "empty-tag") + + with aioresponses() as m: + # PolicyAI returns empty data array (no policies attached to tag) + m.post( + "https://api.musubilabs.ai/policyai/v1/decisions/evaluate/empty-tag", + payload={"data": []}, + ) + + with pytest.raises(ValueError) as exc_info: + await call_policyai_api(text="Hello!") + + assert "no policy results" in str(exc_info.value).lower() + assert "empty-tag" in str(exc_info.value) + + +@pytest.mark.asyncio +async def test_api_error_raises_exception(monkeypatch): + """Test that API errors (non-200 status) raise an exception.""" + monkeypatch.setenv("POLICYAI_API_KEY", "test-api-key") + monkeypatch.setenv("POLICYAI_TAG_NAME", "test") + + with aioresponses() as m: + # PolicyAI returns 500 error + m.post( + "https://api.musubilabs.ai/policyai/v1/decisions/evaluate/test", + status=500, + body="Internal Server Error", + ) + + with pytest.raises(ValueError) as exc_info: + await call_policyai_api(text="Hello!") + + assert "500" in str(exc_info.value) + + +@pytest.mark.asyncio +async def test_all_policies_failed_raises_error(monkeypatch): + """Test that all policies failing raises an error.""" + monkeypatch.setenv("POLICYAI_API_KEY", "test-api-key") + monkeypatch.setenv("POLICYAI_TAG_NAME", "failing-tag") + + with aioresponses() as m: + # All policies return failed status + m.post( + "https://api.musubilabs.ai/policyai/v1/decisions/evaluate/failing-tag", + payload={ + "data": [ + { + "status": "failed", + "error": "Policy configuration error", + }, + { + "status": "failed", + "error": "Policy timeout", + }, + ] + }, + ) + + with pytest.raises(ValueError) as exc_info: + await call_policyai_api(text="Hello!") + + assert "all" in str(exc_info.value).lower() + assert "failed" in str(exc_info.value).lower() + + +@pytest.mark.asyncio +async def test_missing_api_key_raises_error(monkeypatch): + """Test that missing API key raises an error.""" + # Ensure POLICYAI_API_KEY is not set + monkeypatch.delenv("POLICYAI_API_KEY", raising=False) + + with pytest.raises(ValueError) as exc_info: + await call_policyai_api(text="Hello!") + + assert "POLICYAI_API_KEY" in str(exc_info.value) + + +@pytest.mark.asyncio +async def test_empty_text_parameter(monkeypatch): + """Test handling of empty text parameter.""" + monkeypatch.setenv("POLICYAI_API_KEY", "test-api-key") + monkeypatch.setenv("POLICYAI_TAG_NAME", "test") + + with aioresponses() as m: + # PolicyAI should still process empty/None text + m.post( + "https://api.musubilabs.ai/policyai/v1/decisions/evaluate/test", + payload={ + "data": [ + { + "status": "success", + "assessment": "SAFE", + "category": "Safe", + "severity": 0, + "reason": "Empty content is safe", + } + ] + }, + ) + + # Test with empty string + result = await call_policyai_api(text="") + assert result["assessment"] == "SAFE" + + +@pytest.mark.asyncio +async def test_none_text_parameter(monkeypatch): + """Test handling of None text parameter.""" + monkeypatch.setenv("POLICYAI_API_KEY", "test-api-key") + monkeypatch.setenv("POLICYAI_TAG_NAME", "test") + + with aioresponses() as m: + # PolicyAI should still process None text + m.post( + "https://api.musubilabs.ai/policyai/v1/decisions/evaluate/test", + payload={ + "data": [ + { + "status": "success", + "assessment": "SAFE", + "category": "Safe", + "severity": 0, + "reason": "Null content is safe", + } + ] + }, + ) + + # Test with None + result = await call_policyai_api(text=None) + assert result["assessment"] == "SAFE" + + +@pytest.mark.asyncio +async def test_partial_policy_failures(monkeypatch): + """Test that partial policy failures still work if some succeed.""" + monkeypatch.setenv("POLICYAI_API_KEY", "test-api-key") + monkeypatch.setenv("POLICYAI_TAG_NAME", "test") + + with aioresponses() as m: + # Some policies fail, but one succeeds with SAFE + m.post( + "https://api.musubilabs.ai/policyai/v1/decisions/evaluate/test", + payload={ + "data": [ + { + "status": "failed", + "error": "Policy timeout", + }, + { + "status": "success", + "assessment": "SAFE", + "category": "ToxicityCheck", + "severity": 0, + "reason": "No toxic content", + }, + ] + }, + ) + + result = await call_policyai_api(text="Hello!") + assert result["assessment"] == "SAFE" + + +@pytest.mark.asyncio +async def test_custom_base_url_with_trailing_slash(monkeypatch): + """Test that custom base URL with trailing slash is handled correctly.""" + monkeypatch.setenv("POLICYAI_API_KEY", "test-api-key") + monkeypatch.setenv("POLICYAI_BASE_URL", "https://custom.api.example.com/") + monkeypatch.setenv("POLICYAI_TAG_NAME", "test") + + with aioresponses() as m: + # URL should have trailing slash stripped + m.post( + "https://custom.api.example.com/policyai/v1/decisions/evaluate/test", + payload={ + "data": [ + { + "status": "success", + "assessment": "SAFE", + "category": "Safe", + "severity": 0, + "reason": "Content is safe", + } + ] + }, + ) + + result = await call_policyai_api(text="Hello!") + assert result["assessment"] == "SAFE" + + +@pytest.mark.asyncio +async def test_tag_name_parameter_overrides_env(monkeypatch): + """Test that tag_name parameter overrides environment variable.""" + monkeypatch.setenv("POLICYAI_API_KEY", "test-api-key") + monkeypatch.setenv("POLICYAI_TAG_NAME", "env-tag") + + with aioresponses() as m: + # Should use parameter tag, not env var tag + m.post( + "https://api.musubilabs.ai/policyai/v1/decisions/evaluate/param-tag", + payload={ + "data": [ + { + "status": "success", + "assessment": "SAFE", + "category": "Safe", + "severity": 0, + "reason": "Content is safe", + } + ] + }, + ) + + result = await call_policyai_api(text="Hello!", tag_name="param-tag") + assert result["assessment"] == "SAFE" + + +@pytest.mark.asyncio +async def test_unsafe_with_missing_fields(monkeypatch): + """Test UNSAFE response with missing optional fields uses defaults.""" + monkeypatch.setenv("POLICYAI_API_KEY", "test-api-key") + monkeypatch.setenv("POLICYAI_TAG_NAME", "test") + + with aioresponses() as m: + # UNSAFE response without category, severity, or reason + m.post( + "https://api.musubilabs.ai/policyai/v1/decisions/evaluate/test", + payload={ + "data": [ + { + "status": "success", + "assessment": "UNSAFE", + } + ] + }, + ) + + result = await call_policyai_api(text="Bad content") + assert result["assessment"] == "UNSAFE" + assert result["category"] == "Unknown" + assert result["severity"] == 0 + assert result["reason"] == "Policy violation detected" + + +def test_mapping_function_safe(): + """Test the output mapping function returns False for SAFE.""" + from nemoguardrails.library.policyai.actions import call_policyai_api_mapping + + result = call_policyai_api_mapping({"assessment": "SAFE"}) + assert result is False + + +def test_mapping_function_unsafe(): + """Test the output mapping function returns True for UNSAFE.""" + from nemoguardrails.library.policyai.actions import call_policyai_api_mapping + + result = call_policyai_api_mapping({"assessment": "UNSAFE"}) + assert result is True + + +def test_mapping_function_missing_assessment(): + """Test the output mapping function defaults to SAFE when assessment is missing.""" + from nemoguardrails.library.policyai.actions import call_policyai_api_mapping + + result = call_policyai_api_mapping({}) + assert result is False + + +@pytest.mark.asyncio +async def test_default_tag_name_prod(monkeypatch): + """Test that default tag 'prod' is used when env var is not set.""" + monkeypatch.setenv("POLICYAI_API_KEY", "test-api-key") + monkeypatch.delenv("POLICYAI_TAG_NAME", raising=False) + + with aioresponses() as m: + # Should use default "prod" tag + m.post( + "https://api.musubilabs.ai/policyai/v1/decisions/evaluate/prod", + payload={ + "data": [ + { + "status": "success", + "assessment": "SAFE", + "category": "Safe", + "severity": 0, + "reason": "Content is safe", + } + ] + }, + ) + + result = await call_policyai_api(text="Hello!") + assert result["assessment"] == "SAFE" diff --git a/tests/test_streaming_handler.py b/tests/test_streaming_handler.py index 2573a47aa2..55b1f075c0 100644 --- a/tests/test_streaming_handler.py +++ b/tests/test_streaming_handler.py @@ -435,6 +435,40 @@ async def test_multiple_stop_tokens(): await consumer.cancel() +@pytest.mark.asyncio +async def test_stop_none_does_not_raise(): + handler = StreamingHandler() + consumer = StreamingConsumer(handler) + + try: + handler.stop = None + await handler.push_chunk("Hello world") + await handler.push_chunk(END_OF_STREAM) + + chunks = await consumer.get_chunks() + assert chunks == ["Hello world"] + assert handler.completion == "Hello world" + finally: + await consumer.cancel() + + +@pytest.mark.asyncio +async def test_stop_none_with_pattern(): + await _test_pattern_case( + prefix='Bot message: "', + suffix='"', + stop=None, + chunks=[ + "Bot", + " message: ", + '"', + "This is a message", + '"', + ], + final_chunks=["This is a message"], + ) + + @pytest.mark.asyncio async def test_enable_print_functionality(): """Test the enable_print functionality."""