diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 794e0606..2e9ec2f4 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,5 +1,6 @@ # Contribution +We would love feedback on our [Roadmap](https://github.com/orgs/katanemo/projects/1) and we welcome contributions to **Plano**! We would love feedback on our [Roadmap](https://github.com/orgs/katanemo/projects/1) and we welcome contributions to **Plano**! Whether you're fixing bugs, adding new features, improving documentation, or creating tutorials, your help is much appreciated. @@ -7,22 +8,83 @@ Whether you're fixing bugs, adding new features, improving documentation, or cre ### 1. Fork the Repository +Fork the repository to create your own version of **Plano**: Fork the repository to create your own version of **Plano**: +- Navigate to the [Plano GitHub repository](https://github.com/katanemo/plano). - Navigate to the [Plano GitHub repository](https://github.com/katanemo/plano). - Click the "Fork" button in the upper right corner. - This will create a copy of the repository under your GitHub account. ### 2. Clone Your Fork -Once you've forked the repository, clone it to your local machine: +Once you've forked the repository, clone it to your local machine (replace `` with your GitHub username): + +```bash +$ git clone git@github.com:/plano.git +$ cd plano +``` + +### 3. Add Upstream Remote + +Add the original repository as an upstream remote so you can keep your fork in sync: + +```bash +$ git remote add upstream git@github.com:katanemo/plano.git +``` + +To sync your fork with the latest changes from the main repository: + +```bash +$ git fetch upstream +$ git checkout main +$ git merge upstream/main +``` + +### 4. Install Prerequisites + +**Install uv** (Python package manager for the planoai CLI): + +```bash +$ curl -LsSf https://astral.sh/uv/install.sh | sh +``` + +**Install pre-commit hooks:** + +Pre-commit hooks help maintain code quality by running automated checks before each commit. Install them with: + +```bash +$ pip install pre-commit +$ pre-commit install +``` + +The pre-commit hooks will automatically run: +- YAML validation +- Code formatting checks (Rust with `cargo fmt`, Python with `black`) +- Linting checks (Rust with `cargo clippy`) +- Rust unit tests + +### 5. Setup the planoai CLI + +The planoai CLI is used to build, run, and manage Plano locally: + +```bash +$ cd cli +$ uv sync +``` + +This creates a virtual environment in `.venv` and installs all dependencies. + +Optionally, install planoai globally in editable mode: ```bash $ git clone https://github.com/katanemo/plano.git $ cd plano ``` -### 3. Create a branch +Now you can use `planoai` commands from anywhere, or use `uv run planoai` from the `cli` directory. + +### 6. Create a Branch Use a descriptive name for your branch (e.g., fix-bug-123, add-feature-x). @@ -30,11 +92,39 @@ Use a descriptive name for your branch (e.g., fix-bug-123, add-feature-x). $ git checkout -b ``` -### 4. Make Your changes +### 7. Make Your Changes Make your changes in the relevant files. If you're adding new features or fixing bugs, please include tests where applicable. -### 5. Test your changes +### 8. Test Your Changes Locally + +**Run Rust tests:** + +```bash +$ cd crates +$ cargo test +``` + +For library tests only: +```bash +$ cargo test --lib +``` + +**Run Python CLI tests:** + +```bash +$ cd cli +$ uv run pytest +``` + +Or with verbose output: +```bash +$ uv run pytest -v +``` + +**Run pre-commit checks manually:** + +Before committing, you can run all pre-commit checks manually: ```bash cd plano @@ -43,16 +133,18 @@ cargo test ### 6. Push changes, and create a Pull request +Go back to the original Plano repository, and you should see a "Compare & pull request" button. Click that to submit a Pull Request (PR). In your PR description, clearly explain the changes you made and why they are necessary. Go back to the original Plano repository, and you should see a "Compare & pull request" button. Click that to submit a Pull Request (PR). In your PR description, clearly explain the changes you made and why they are necessary. We will review your pull request and provide feedback. Once approved, your contribution will be merged into the main repository! -Contribution Guidelines +## Contribution Guidelines - Ensure that all existing tests pass. - Write clear commit messages. - Add tests for any new functionality. - Follow the existing coding style. - Update documentation as needed. +- Ensure that all existing tests pass. +- Write clear commit messages. +- Add tests for any new functionality. +- Follow the existing coding style (enforced by pre-commit hooks). +- Update documentation as needed. +- Pre-commit hooks must pass before committing. To get in touch with us, please join our [discord server](https://discord.gg/pGZf2gcwEc). We will be monitoring that actively and offering support there. diff --git a/cli/planoai/config_generator.py b/cli/planoai/config_generator.py index e82febb4..3b920181 100644 --- a/cli/planoai/config_generator.py +++ b/cli/planoai/config_generator.py @@ -310,6 +310,7 @@ def validate_and_render_schema(): "name": "arch-router", "provider_interface": "arch", "model": config_yaml.get("routing", {}).get("model", "Arch-Router"), + "internal": True, } ) @@ -320,6 +321,7 @@ def validate_and_render_schema(): "name": "arch-function", "provider_interface": "arch", "model": "Arch-Function", + "internal": True, } ) @@ -329,6 +331,7 @@ def validate_and_render_schema(): "name": "plano-orchestrator", "provider_interface": "arch", "model": "Plano-Orchestrator", + "internal": True, } ) diff --git a/crates/common/src/configuration.rs b/crates/common/src/configuration.rs index 07d3311b..58ea1e3e 100644 --- a/crates/common/src/configuration.rs +++ b/crates/common/src/configuration.rs @@ -323,6 +323,7 @@ pub struct LlmProvider { pub routing_preferences: Option>, pub cluster_name: Option, pub base_url_path_prefix: Option, + pub internal: Option, } pub trait IntoModels { @@ -333,6 +334,7 @@ impl IntoModels for Vec { fn into_models(self) -> Models { let data = self .iter() + .filter(|provider| provider.internal != Some(true)) .map(|provider| ModelDetail { id: provider.name.clone(), object: Some("model".to_string()), @@ -364,6 +366,7 @@ impl Default for LlmProvider { routing_preferences: None, cluster_name: None, base_url_path_prefix: None, + internal: None, } } } @@ -479,6 +482,7 @@ mod test { use pretty_assertions::assert_eq; use std::fs; + use super::{IntoModels, LlmProvider, LlmProviderType}; use crate::api::open_ai::ToolType; #[test] @@ -561,4 +565,42 @@ mod test { } } } + + #[test] + fn test_into_models_filters_internal_providers() { + let providers = vec![ + LlmProvider { + name: "openai-gpt4".to_string(), + provider_interface: LlmProviderType::OpenAI, + model: Some("gpt-4".to_string()), + internal: None, + ..Default::default() + }, + LlmProvider { + name: "arch-router".to_string(), + provider_interface: LlmProviderType::Arch, + model: Some("Arch-Router".to_string()), + internal: Some(true), + ..Default::default() + }, + LlmProvider { + name: "plano-orchestrator".to_string(), + provider_interface: LlmProviderType::Arch, + model: Some("Plano-Orchestrator".to_string()), + internal: Some(true), + ..Default::default() + }, + ]; + + let models = providers.into_models(); + + // Should only have 1 model: openai-gpt4 + assert_eq!(models.data.len(), 1); + + // Verify internal models are excluded from /v1/models + let model_ids: Vec = models.data.iter().map(|m| m.id.clone()).collect(); + assert!(model_ids.contains(&"openai-gpt4".to_string())); + assert!(!model_ids.contains(&"arch-router".to_string())); + assert!(!model_ids.contains(&"plano-orchestrator".to_string())); + } } diff --git a/demos/use_cases/http_filter/Dockerfile b/demos/use_cases/http_filter/Dockerfile new file mode 100644 index 00000000..5882714a --- /dev/null +++ b/demos/use_cases/http_filter/Dockerfile @@ -0,0 +1,26 @@ +FROM python:3.13-slim + +WORKDIR /app + +# Install bash and uv +RUN apt-get update && apt-get install -y bash && rm -rf /var/lib/apt/lists/* +RUN pip install --no-cache-dir uv + +# Copy dependency files +COPY pyproject.toml README.md ./ + +# Copy source code +COPY src/ ./src/ +COPY start_agents.sh ./ + +# Install dependencies using uv +RUN uv pip install --system --no-cache click fastmcp pydantic fastapi uvicorn openai + +# Make start script executable +RUN chmod +x start_agents.sh + +# Expose ports for all agents +EXPOSE 10500 10501 10502 10505 + +# Run the start script with bash +CMD ["bash", "./start_agents.sh"] diff --git a/demos/use_cases/http_filter/README.md b/demos/use_cases/http_filter/README.md new file mode 100644 index 00000000..89a5bd1a --- /dev/null +++ b/demos/use_cases/http_filter/README.md @@ -0,0 +1,128 @@ +# RAG Agent Demo + +A multi-agent RAG system demonstrating plano's agent filter chain with MCP protocol. + +## Architecture + +This demo consists of four components: +1. **Input Guards** (MCP filter) - Validates queries are within TechCorp's domain +2. **Query Rewriter** (MCP filter) - Rewrites user queries for better retrieval +3. **Context Builder** (MCP filter) - Retrieves relevant context from knowledge base +4. **RAG Agent** (REST) - Generates final responses based on augmented context + +## Components + +### Input Guards Filter (MCP) +- **Port**: 10500 +- **Tool**: `input_guards` +- Validates queries are within TechCorp's domain +- Rejects queries about other companies or unrelated topics + +### Query Rewrit3r Filter (MCP) +- **Port**: 10501 +- **Tool**: `query_rewriter` +- Improves queries using LLM before retrieval + +### Context Builder Filter (MCP) +- **Port**: 10502 +- **Tool**: `context_builder` +- Augments queries with relevant passages from knowledge base + +### RAG Agent (REST/OpenAI) +- **Port**: 10505 +- **Endpoint**: `/v1/chat/completions` +- Generates responses using OpenAI-compatible API + +## Quick Start + +### 1. Start everything with Docker Compose +```bash +docker compose up --build +``` + +This brings up: +- Input Guards MCP server on port 10500 +- Query Rewriter MCP server on port 10501 +- Context Builder MCP server on port 10502 +- RAG Agent REST server on port 10505 +- Plano listener on port 8001 (and gateway on 12000) +- Jaeger UI for viewing traces at http://localhost:16686 +- Open WebUI at http://localhost:8080 for interactive queries + +> Set `OPENAI_API_KEY` in your environment before running; `LLM_GATEWAY_ENDPOINT` defaults to `http://host.docker.internal:12000/v1`. + +### 2. Test the system + +**Option A: Using Open WebUI (recommended)** + +Navigate to http://localhost:8080 and send queries through the chat interface. + +**Option B: Using curl** +```bash +curl -X POST http://localhost:8001/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4o", + "messages": [{"role": "user", "content": "What is the guaranteed uptime for TechCorp?"}] + }' +``` + +## Configuration + +The `config.yaml` defines how agents are connected: + +```yaml +filters: + - id: input_guards + url: http://host.docker.internal:10500 + # type: mcp (default) + # tool: input_guards (default - same as filter id) + + - id: query_rewriter + url: http://host.docker.internal:10501 + # type: mcp (default) + + - id: context_builder + url: http://host.docker.internal:10502 +``` + +## How It Works + +1. User sends request to plano listener on port 8001 +2. Request passes through MCP filter chain: + - **Input Guards** validates the query is within TechCorp's domain + - **Query Rewriter** rewrites the query for better retrieval + - **Context Builder** augments query with relevant knowledge base passages +3. Augmented request is forwarded to **RAG Agent** REST endpoint +4. RAG Agent generates final response using LLM + +## Additional Configuration + +See `config.yaml` for the complete filter chain setup. The MCP filters use default settings: +- `type: mcp` (default) +- `transport: streamable-http` (default) +- Tool name defaults to filter ID + +See `sample_queries.md` for example queries to test the RAG system. + +Example request: +```bash +curl -X POST http://localhost:8001/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4o", + "messages": [ + { + "role": "user", + "content": "What is the guaranteed uptime for TechCorp?" + } + ] + }' +``` +- `LLM_GATEWAY_ENDPOINT` - lpano endpoint (default: `http://localhost:12000/v1`) +- `OPENAI_API_KEY` - OpenAI API key for model providers + +## Additional Resources + +- See `sample_queries.md` for more example queries +- See `config.yaml` for complete configuration details diff --git a/demos/use_cases/http_filter/config.yaml b/demos/use_cases/http_filter/config.yaml new file mode 100644 index 00000000..117931e2 --- /dev/null +++ b/demos/use_cases/http_filter/config.yaml @@ -0,0 +1,50 @@ +version: v0.3.0 + +agents: + - id: rag_agent + url: http://rag-agents:10505 + +filters: + - id: input_guards + url: http://rag-agents:10500 + type: http + # type: mcp (default) + # transport: streamable-http (default) + # tool: input_guards (default - same as filter id) + - id: query_rewriter + url: http://rag-agents:10501 + type: http + # type: mcp (default) + # transport: streamable-http (default) + # tool: query_rewriter (default - same as filter id) + - id: context_builder + url: http://rag-agents:10502 + type: http + +model_providers: + - model: openai/gpt-4o-mini + access_key: $OPENAI_API_KEY + default: true + - model: openai/gpt-4o + access_key: $OPENAI_API_KEY + +model_aliases: + fast-llm: + target: gpt-4o-mini + smart-llm: + target: gpt-4o + +listeners: + - type: agent + name: agent_1 + port: 8001 + router: plano_orchestrator_v1 + agents: + - id: rag_agent + description: virtual assistant for retrieval augmented generation tasks + filter_chain: + - input_guards + - query_rewriter + - context_builder +tracing: + random_sampling: 100 diff --git a/demos/use_cases/http_filter/docker-compose.yaml b/demos/use_cases/http_filter/docker-compose.yaml new file mode 100644 index 00000000..469bf0d5 --- /dev/null +++ b/demos/use_cases/http_filter/docker-compose.yaml @@ -0,0 +1,42 @@ +services: + rag-agents: + build: + context: . + dockerfile: Dockerfile + ports: + - "10500:10500" + - "10501:10501" + - "10502:10502" + - "10505:10505" + environment: + - LLM_GATEWAY_ENDPOINT=${LLM_GATEWAY_ENDPOINT:-http://host.docker.internal:12000/v1} + - OPENAI_API_KEY=${OPENAI_API_KEY:?OPENAI_API_KEY environment variable is required but not set} + plano: + build: + context: ../../../ + dockerfile: Dockerfile + ports: + - "12000:12000" + - "8001:8001" + environment: + - ARCH_CONFIG_PATH=/config/config.yaml + - OPENAI_API_KEY=${OPENAI_API_KEY:?OPENAI_API_KEY environment variable is required but not set} + volumes: + - ./config.yaml:/app/arch_config.yaml + - /etc/ssl/cert.pem:/etc/ssl/cert.pem + jaeger: + build: + context: ../../shared/jaeger + ports: + - "16686:16686" + - "4317:4317" + - "4318:4318" + open-web-ui: + image: dyrnq/open-webui:main + restart: always + ports: + - "8080:8080" + environment: + - DEFAULT_MODEL=gpt-4o-mini + - ENABLE_OPENAI_API=true + - OPENAI_API_BASE_URL=http://host.docker.internal:8001/v1 diff --git a/demos/use_cases/http_filter/http.rest b/demos/use_cases/http_filter/http.rest new file mode 100644 index 00000000..467208e2 --- /dev/null +++ b/demos/use_cases/http_filter/http.rest @@ -0,0 +1,77 @@ +# http.rest (replacement for mcp_query.rest) + +@host = http://localhost +@model = gpt-4o-mini + +# Filter endpoints (HTTP-only) +@inputGuards = {{host}}:10500 +@queryRewriter = {{host}}:10501 +@contextBuilder = {{host}}:10502 + +# Plano agent listener (the thing Open WebUI calls) +@planoAgent = {{host}}:8001 + + +POST {{queryRewriter}}/ +Content-Type: application/json + +[ + { + "role": "user", + "content": "What is the guaranteed uptime percentage for TechCorp's cloud services?" + } +] + + + +POST {{inputGuards}}/ +Content-Type: application/json + +[ + { + "role": "user", + "content": "What is the guaranteed uptime percentage for TechCorp's cloud services?" + } +] + + + +POST {{contextBuilder}}/ +Content-Type: application/json + +[ + { + "role": "user", + "content": "What is TechCorp's uptime SLA?" + } +] + + +POST {{planoAgent}}/v1/chat/completions +Content-Type: application/json + +{ + "model": "fast-llm", + "messages": [ + { + "role": "user", + "content": "What is the guaranteed uptime percentage for TechCorp's cloud services?" + } + ], + "stream": false +} + + +POST {{planoAgent}}/v1/chat/completions +Content-Type: application/json + +{ + "model": "fast-llm", + "messages": [ + { + "role": "user", + "content": "What is the guaranteed uptime percentage for TechCorp's cloud services?" + } + ], + "stream": true +} diff --git a/demos/use_cases/http_filter/mcp_query.rest b/demos/use_cases/http_filter/mcp_query.rest new file mode 100644 index 00000000..c08dd884 --- /dev/null +++ b/demos/use_cases/http_filter/mcp_query.rest @@ -0,0 +1,86 @@ +### Initialize MCP Session (SSE) +POST http://localhost:10501/mcp +Content-Type: application/json +Accept: application/json, text/event-stream + +{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"capabilities":{},"protocolVersion":"2024-11-05","clientInfo":{"name":"test","version":"1.0.0"}}} + +### Send Initialized Notification +POST http://localhost:10501/mcp +Content-Type: application/json +Accept: application/json, text/event-stream +mcp-session-id: 35d455dc07b8400887f86668590f12bb + +{ + "jsonrpc": "2.0", + "method": "notifications/initialized" +} + +### List Tools +POST http://localhost:10501/mcp +Content-Type: application/json +Accept: application/json, text/event-stream +mcp-session-id: eb10a691b36e4547b6c93c5dc5b47e11 + +{ + "jsonrpc": "2.0", + "id": "list-tools-1", + "method": "tools/list" +} + +### Call Query Rewriter Tool +POST http://localhost:10501/mcp +Content-Type: application/json +Accept: application/json, text/event-stream +mcp-session-id: 6b95ff75825a402b90eb3ea07e23fbce + +{ + "jsonrpc": "2.0", + "id": "3d3b886a-6216-4a26-a422-7a972529c0e7", + "method": "tools/call", + "params": { + "arguments": { + "messages": [ + { + "content": "What is the guaranteed uptime percentage for TechCorp's cloud services?", + "role": "user" + } + ] + }, + "name": "query_rewriter" + } +} + +### another test + +# Content-Type: application/json +# Accept: application/json, text/event-stream +# mcp-session-id: ed7a81a1d39549ecaadb867a6b2daf1e + +POST http://localhost:10501/mcp +content-type: application/json +mcp-session-id: e4ec1ae904e14e06b7d194da10e5f74c +accept: application/json, text/event-stream + +{"jsonrpc":"2.0","id":"4bb1043a-2953-4bcd-b801-f270b0ae8c39","method":"tools/call","params":{"arguments":{"messages":[{"content":"What is the guaranteed uptime percentage for TechCorp's cloud services?","role":"user"}]},"name":"query_rewriter"}} + + + +### stream test + +POST http://localhost:10501/mcp +content-type: application/json +mcp-session-id: 35d455dc07b8400887f86668590f12bb +accept: application/json, text/event-stream + +{ + "jsonrpc": "2.0", + "id": 1, + "method": "tools/call", + "params": { + "name": "long_job", + "arguments": { + "n": 3 + } + } +} diff --git a/demos/use_cases/http_filter/pyproject.toml b/demos/use_cases/http_filter/pyproject.toml new file mode 100644 index 00000000..7cd9411b --- /dev/null +++ b/demos/use_cases/http_filter/pyproject.toml @@ -0,0 +1,22 @@ +[project] +name = "rag_agent" +version = "0.1.0" +description = "RAG Agent" +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "click>=8.2.1", + "mcp>=1.13.1", + "fastmcp>=2.14", + "pydantic>=2.11.7", + "fastapi>=0.104.1", + "uvicorn>=0.24.0", + "openai==2.13.0", +] + +[project.scripts] +rag_agent = "rag_agent:main" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" diff --git a/demos/use_cases/http_filter/sample_queries.md b/demos/use_cases/http_filter/sample_queries.md new file mode 100644 index 00000000..ba98cdfd --- /dev/null +++ b/demos/use_cases/http_filter/sample_queries.md @@ -0,0 +1,64 @@ +# Sample Queries for Knowledge Base RAG Agent + +## Service Level Agreement Queries +- What is the guaranteed uptime percentage for TechCorp's cloud services? +- What remedies are available if the API response time exceeds the agreed threshold? +- How quickly must TechCorp respond to critical support issues? +- What monitoring and reporting requirements are specified in the SLA? +- When was the TechCorp service agreement signed and by whom? + +## Privacy Policy Queries +- What encryption methods does DataSecure use to protect data? +- How long does DataSecure retain personal data after account deletion? +- What rights do users have regarding their personal information? +- Can DataSecure sell user data to third parties for marketing? +- Who should be contacted for privacy-related concerns at DataSecure? + +## Supply Chain Agreement Queries +- What types of automotive components does PrecisionParts supply? +- What are the payment terms and volume discount structure? +- What quality standards must the supplied components meet? +- What are the penalties for late delivery? +- What insurance coverage requirements apply to the supplier? + +## Student Data Management Queries +- What federal laws must EduTech comply with regarding student data? +- What security measures are in place to protect student information? +- How long are student records retained after graduation? +- What consent is required for students under 13 years old? +- Who can access student educational records? + +## Investment Advisory Queries +- What is FinanceFirst's management fee structure? +- What types of investments are included in the advisory services? +- What regulatory body oversees FinanceFirst Advisors? +- How often are portfolio reviews conducted? +- What are the client's responsibilities under this agreement? + +## Healthcare Standards Queries +- What is the target response time for emergency code teams? +- What hand hygiene compliance rate is required? +- How quickly must medical records be completed after patient encounters? +- What continuing education requirements apply to nursing staff? +- What patient safety protocols are mandatory upon admission? + +## Cross-Document Queries +- Which agreements include confidentiality or data protection provisions? +- What are the common termination notice periods across different contract types? +- Which documents specify insurance or liability coverage requirements? +- What compliance and regulatory requirements are mentioned across agreements? +- Which contracts include performance metrics or service level commitments? + +## Complex Analysis Queries +- Compare the data retention policies across the privacy policy and student data management documents. +- What are the different approaches to risk management across the supply chain and investment advisory agreements? +- How do the security measures in the healthcare standards compare to those in the privacy policy? +- Which agreements provide the most detailed compliance and regulatory frameworks? +- What common themes exist in the quality assurance requirements across different industries? + +## Document-Specific Detail Queries +- List all the specific percentages, timeframes, and numerical requirements mentioned in the SLA. +- What are all the contact persons and their roles mentioned across the documents? +- Identify all the compliance standards and certifications referenced in the supply chain agreement. +- What are the specific consequences or penalties mentioned for non-compliance across agreements? +- List all the third-party systems, tools, or services mentioned in the documents. diff --git a/demos/use_cases/http_filter/src/rag_agent/__init__.py b/demos/use_cases/http_filter/src/rag_agent/__init__.py new file mode 100644 index 00000000..6589e775 --- /dev/null +++ b/demos/use_cases/http_filter/src/rag_agent/__init__.py @@ -0,0 +1,109 @@ +import click +from fastmcp import FastMCP + +mcp = None + + +@click.command() +@click.option( + "--transport", + "transport", + default="streamable-http", + help="Transport type: stdio or sse", +) +@click.option("--host", "host", default="localhost", help="Host to bind MCP server to") +@click.option("--port", "port", type=int, default=10500, help="Port for MCP server") +@click.option( + "--agent", + "agent", + required=True, + help="Agent name: query_rewriter, context_builder, or response_generator", +) +@click.option( + "--name", + "agent_name", + default=None, + help="Custom MCP server name (defaults to agent type)", +) +@click.option( + "--rest-server", + "rest_server", + is_flag=True, + help="Start REST server instead of MCP server", +) +@click.option("--rest-port", "rest_port", default=8000, help="Port for REST server") +def main(host, port, agent, transport, agent_name, rest_server, rest_port): + """Start a RAG agent as an MCP server or REST server.""" + + # Map friendly names to agent modules + agent_map = { + "input_guards": ("rag_agent.input_guards", "Input Guards Agent"), + "query_rewriter": ("rag_agent.query_rewriter", "Query Rewriter Agent"), + "context_builder": ("rag_agent.context_builder", "Context Builder Agent"), + "response_generator": ( + "rag_agent.rag_agent", + "Response Generator Agent", + ), + } + + if agent not in agent_map: + print(f"Error: Unknown agent '{agent}'") + print(f"Available agents: {', '.join(agent_map.keys())}") + return + + module_name, default_name = agent_map[agent] + mcp_name = agent_name or default_name + + if rest_server: + # REST server mode - supported for query_rewriter and response_generator + if agent == "response_generator": + print(f"Starting REST server on {host}:{rest_port} for agent: {agent}") + from rag_agent.rag_agent import start_server + + start_server(host=host, port=rest_port) + return + elif agent == "query_rewriter": + print(f"Starting REST server on {host}:{rest_port} for agent: {agent}") + from rag_agent.query_rewriter import start_server + + start_server(host=host, port=rest_port) + return + else: + print(f"Error: Agent '{agent}' does not support REST server mode.") + print( + f"REST server is only supported for: query_rewriter, response_generator" + ) + print(f"Remove --rest-server flag to start {agent} as an MCP server.") + return + else: + # Only input_guards, query_rewriter and context_builder support MCP + if agent not in ["input_guards", "query_rewriter", "context_builder"]: + print(f"Error: Agent '{agent}' does not support MCP mode.") + print( + f"MCP is only supported for: input_guards, query_rewriter, context_builder" + ) + print(f"Use --rest-server flag to start {agent} as a REST server.") + return + + global mcp + mcp = FastMCP(mcp_name, host=host, port=port) + + print(f"Starting MCP server: {mcp_name}") + print(f" Agent: {agent}") + print(f" Transport: {transport}") + print(f" Host: {host}") + print(f" Port: {port}") + + # Import the agent module to register its tools + import importlib + + importlib.import_module(module_name) + + print(f"Agent '{agent}' loaded successfully") + print(f"MCP server ready on {transport}://{host}:{port}") + + mcp.run(transport=transport) + + +if __name__ == "__main__": + main() diff --git a/demos/use_cases/http_filter/src/rag_agent/__main__.py b/demos/use_cases/http_filter/src/rag_agent/__main__.py new file mode 100644 index 00000000..868d99ef --- /dev/null +++ b/demos/use_cases/http_filter/src/rag_agent/__main__.py @@ -0,0 +1,4 @@ +from . import main + +if __name__ == "__main__": + main() diff --git a/demos/use_cases/http_filter/src/rag_agent/api.py b/demos/use_cases/http_filter/src/rag_agent/api.py new file mode 100644 index 00000000..eb63ea99 --- /dev/null +++ b/demos/use_cases/http_filter/src/rag_agent/api.py @@ -0,0 +1,36 @@ +from pydantic import BaseModel +from typing import List, Optional, Dict, Any + + +class ChatMessage(BaseModel): + role: str + content: str + + +class ChatCompletionRequest(BaseModel): + model: str + messages: List[ChatMessage] + temperature: Optional[float] = 1.0 + max_tokens: Optional[int] = None + top_p: Optional[float] = 1.0 + frequency_penalty: Optional[float] = 0.0 + presence_penalty: Optional[float] = 0.0 + stream: Optional[bool] = False + stop: Optional[List[str]] = None + + +class ChatCompletionResponse(BaseModel): + id: str + object: str = "chat.completion" + created: int + model: str + choices: List[Dict[str, Any]] + usage: Dict[str, int] + + +class ChatCompletionStreamResponse(BaseModel): + id: str + object: str = "chat.completion.chunk" + created: int + model: str + choices: List[Dict[str, Any]] diff --git a/demos/use_cases/http_filter/src/rag_agent/context_builder.py b/demos/use_cases/http_filter/src/rag_agent/context_builder.py new file mode 100644 index 00000000..1f0fc748 --- /dev/null +++ b/demos/use_cases/http_filter/src/rag_agent/context_builder.py @@ -0,0 +1,228 @@ +import json +from typing import List, Optional, Dict, Any +from openai import AsyncOpenAI +import os +import logging +import csv +from pathlib import Path + +from .api import ChatMessage +from fastapi import Request, FastAPI + +# from . import mcp +# from fastmcp.server.dependencies import get_http_headers + +# Set up logging +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - [CONTEXT_BUILDER] - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + +### add new setup +app = FastAPI(title="RAG Agent Context Builder", version="1.0.0") + +# Configuration for archgw LLM gateway +LLM_GATEWAY_ENDPOINT = os.getenv("LLM_GATEWAY_ENDPOINT", "http://localhost:12000/v1") +RAG_MODEL = "gpt-4o-mini" + +# Initialize OpenAI client for archgw +archgw_client = AsyncOpenAI( + base_url=LLM_GATEWAY_ENDPOINT, + api_key="EMPTY", # archgw doesn't require a real API key +) + +# Global variable to store the knowledge base +knowledge_base = [] + + +def load_knowledge_base(): + """Load the sample_knowledge_base.csv file into memory on startup.""" + global knowledge_base + + # Get the path to the CSV file relative to this script + current_dir = Path(__file__).parent + csv_path = current_dir / "sample_knowledge_base.csv" + + print(f"Loading knowledge base from {csv_path}") + + try: + knowledge_base = [] + with open(csv_path, "r", encoding="utf-8-sig") as file: + csv_reader = csv.DictReader(file) + for row in csv_reader: + knowledge_base.append({"path": row["path"], "content": row["content"]}) + + logger.info(f"Loaded {len(knowledge_base)} documents from knowledge base") + + except Exception as e: + logger.error(f"Error loading knowledge base: {e}") + knowledge_base = [] + + +async def find_relevant_passages( + query: str, + traceparent: Optional[str] = None, + request_id: Optional[str] = None, + top_k: int = 3, +) -> List[Dict[str, str]]: + """Use the LLM to find the most relevant passages from the knowledge base.""" + + if not knowledge_base: + logger.warning("Knowledge base is empty") + return [] + + # Create a system prompt for passage selection + system_prompt = f"""You are a retrieval assistant that selects the most relevant document passages for a given query. + + Given a user query and a list of document passages, identify the {top_k} most relevant passages that would help answer the query. + + Query: {query} + + Available passages: + """ + + # Add all passages with indices + for i, doc in enumerate(knowledge_base): + system_prompt += ( + f"\n[{i}] Path: {doc['path']}\nContent: {doc['content'][:500]}...\n" + ) + + system_prompt += f""" + + Please respond with ONLY the indices of the {top_k} most relevant passages, separated by commas (e.g., "0,3,7"). + If fewer than {top_k} passages are relevant, return only the relevant ones. + If no passages are relevant, return "NONE".""" + + try: + # Call archgw to select relevant passages + logger.info(f"Calling archgw to find relevant passages for query: '{query}'") + + # Prepare extra headers if traceparent is provided + extra_headers = {"x-envoy-max-retries": "3", "x-request-id": request_id} + if traceparent: + extra_headers["traceparent"] = traceparent + + response = await archgw_client.chat.completions.create( + model=RAG_MODEL, + messages=[{"role": "system", "content": system_prompt}], + temperature=0.1, + max_tokens=50, + extra_headers=extra_headers, + ) + + result = response.choices[0].message.content.strip() + logger.info(f"LLM selected passages: {result}") + + # Parse the indices + if result.upper() == "NONE": + return [] + + selected_passages = [] + indices = [ + int(idx.strip()) for idx in result.split(",") if idx.strip().isdigit() + ] + + for idx in indices: + if 0 <= idx < len(knowledge_base): + selected_passages.append(knowledge_base[idx]) + + logger.info(f"Selected {len(selected_passages)} relevant passages") + return selected_passages + + except Exception as e: + logger.error(f"Error finding relevant passages: {e}") + return [] + + +async def augment_query_with_context( + messages: List[ChatMessage], + traceparent: Optional[str] = None, + request_id: Optional[str] = None, +) -> List[ChatMessage]: + """Extract user query, find relevant context, and augment the messages.""" + + # Find the last user message + last_user_message = None + last_user_index = -1 + + for i in range(len(messages) - 1, -1, -1): + if messages[i].role == "user": + last_user_message = messages[i].content + last_user_index = i + break + + if not last_user_message: + logger.warning("No user message found in conversation") + return messages + + logger.info(f"Processing user query: '{last_user_message}'") + + # Find relevant passages + relevant_passages = await find_relevant_passages( + last_user_message, traceparent, request_id + ) + + if not relevant_passages: + logger.info("No relevant passages found, returning original messages") + return messages + + # Build context from relevant passages + context_parts = [] + for i, passage in enumerate(relevant_passages): + context_parts.append( + f"Document {i+1} ({passage['path']}):\n{passage['content']}" + ) + + context = "\n\n".join(context_parts) + + # Create augmented content with original query and context + augmented_content = f"""{last_user_message} RELEVANT CONTEXT: + {context}""" + + # Create updated messages with the augmented query + updated_messages = messages.copy() + updated_messages[last_user_index] = ChatMessage( + role="user", content=augmented_content + ) + + logger.info(f"Augmented user query with {len(relevant_passages)} relevant passages") + + return updated_messages + + +# Load knowledge base on module import +load_knowledge_base() + + +@app.post("/") +async def context_builder( + messages: List[ChatMessage], request: Request +) -> List[ChatMessage]: + """MCP tool that augments user queries with relevant context from the knowledge base.""" + logger.info(f"Received chat completion request with {len(messages)} messages") + + # Get traceparent header from MCP request + # headers = get_http_headers() + # traceparent_header = headers.get("traceparent") + + traceparent_header = request.headers.get("traceparent") + request_id = request.headers.get("x-request-id") + + if traceparent_header: + logger.info(f"Received traceparent header: {traceparent_header}") + else: + logger.info("No traceparent header found") + + # Augment the user query with relevant context + updated_messages = await augment_query_with_context( + messages, traceparent_header, request_id + ) + + # Return as dict to minimize text serialization + return [{"role": msg.role, "content": msg.content} for msg in updated_messages] + + +# Register MCP tool only if mcp is available +# if mcp is not None: +# mcp.tool()(context_builder) diff --git a/demos/use_cases/http_filter/src/rag_agent/input_guards.py b/demos/use_cases/http_filter/src/rag_agent/input_guards.py new file mode 100644 index 00000000..bdfdc09e --- /dev/null +++ b/demos/use_cases/http_filter/src/rag_agent/input_guards.py @@ -0,0 +1,172 @@ +import asyncio +import json +import time +from typing import List, Optional, Dict, Any +import uuid +from fastapi import FastAPI, Depends, Request, HTTPException + +# from fastmcp.exceptions import ToolError +from openai import AsyncOpenAI +import os +import logging + +from .api import ChatCompletionRequest, ChatCompletionResponse, ChatMessage +from . import mcp + +# from fastmcp.server.dependencies import get_http_headers + +# Set up logging +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - [INPUT_GUARDS] - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + +# Configuration for archgw LLM gateway +LLM_GATEWAY_ENDPOINT = os.getenv("LLM_GATEWAY_ENDPOINT", "http://localhost:12000/v1") +GUARD_MODEL = "gpt-4o-mini" + +# Initialize OpenAI client for archgw +archgw_client = AsyncOpenAI( + base_url=LLM_GATEWAY_ENDPOINT, + api_key="EMPTY", # archgw doesn't require a real API key +) + +app = FastAPI(title="RAG Agent Input Guards", version="1.0.0") + + +async def validate_query_scope( + messages: List[ChatMessage], + traceparent_header: Optional[str] = None, + request_id: Optional[str] = None, +) -> Dict[str, Any]: + """Validate that the user query is within TechCorp's domain. + + Returns a dict with: + - is_valid: bool indicating if query is within scope + - reason: str explaining why query is out of scope (if applicable) + """ + system_prompt = """You are an input validation guard for TechCorp's customer support system. + +Your job is to determine if a user's query is related to TechCorp and its services/products. + +TechCorp is a technology company that provides: +- Cloud services and infrastructure +- SaaS products +- Technical support +- Service level agreements (SLAs) +- Uptime guarantees +- Enterprise solutions + +ALLOW queries about: +- TechCorp's services, products, or offerings +- TechCorp's pricing, SLAs, uptime, or policies +- Technical support for TechCorp products +- General questions about TechCorp as a company + +REJECT queries about: +- Other companies or their products +- General knowledge questions unrelated to TechCorp +- Personal advice or topics outside TechCorp's domain +- Anything that doesn't relate to TechCorp's business + +Respond in JSON format: +{ + "is_valid": true/false, + "reason": "brief explanation if invalid" +}""" + + # Get the last user message for validation + last_user_message = None + for msg in reversed(messages): + if msg.role == "user": + last_user_message = msg.content + break + + if not last_user_message: + return {"is_valid": True, "reason": ""} + + # Prepare messages for the guard + guard_messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": f"Query to validate: {last_user_message}"}, + ] + + try: + # Call archgw using OpenAI client + extra_headers = {"x-envoy-max-retries": "3", "x-request-id": request_id} + if traceparent_header: + extra_headers["traceparent"] = traceparent_header + + logger.info(f"Validating query scope: '{last_user_message}'") + response = await archgw_client.chat.completions.create( + model=GUARD_MODEL, + messages=guard_messages, + temperature=0.1, + max_tokens=150, + extra_headers=extra_headers, + ) + + result_text = response.choices[0].message.content.strip() + + # Parse JSON response + try: + result = json.loads(result_text) + logger.info(f"Validation result: {result}") + return result + except json.JSONDecodeError: + logger.error(f"Failed to parse validation response: {result_text}") + # Default to allowing if parsing fails + return {"is_valid": True, "reason": ""} + + except Exception as e: + logger.error(f"Error validating query: {e}") + # Default to allowing if validation fails + return {"is_valid": True, "reason": ""} + + +# @mcp.tool +@app.post("/") +async def input_guards( + messages: List[ChatMessage], request: Request +) -> List[ChatMessage]: + """Input guard that validates queries are within TechCorp's domain. + + If the query is out of scope, replaces the user message with a rejection notice. + """ + logger.info(f"Received request with {len(messages)} messages") + + # Get traceparent header from HTTP request using FastMCP's dependency function + # headers = get_http_headers() + # traceparent_header = headers.get("traceparent") + traceparent_header = request.headers.get("traceparent") + request_id = request.headers.get("x-request-id") + + if traceparent_header: + logger.info(f"Received traceparent header: {traceparent_header}") + else: + logger.info("No traceparent header found") + + # Validate the query scope + validation_result = await validate_query_scope( + messages, traceparent_header, request_id + ) + + if not validation_result.get("is_valid", True): + reason = validation_result.get("reason", "Query is outside TechCorp's domain") + logger.warning(f"Query rejected: {reason}") + + # Throw ToolError + error_message = f"I apologize, but I can only assist with questions related to TechCorp and its services. Your query appears to be outside this scope. {reason}\n\nPlease ask me about TechCorp's products, services, pricing, SLAs, or technical support." + # raise ToolError(error_message) + raise HTTPException( + status_code=400, detail={"error": "out_of_scope", "message": error_message} + ) + + logger.info("Query validation passed - forwarding to next filter") + return messages + + +@app.get("/health") +async def health(): + return {"status": "healthy"} diff --git a/demos/use_cases/http_filter/src/rag_agent/query_rewriter.py b/demos/use_cases/http_filter/src/rag_agent/query_rewriter.py new file mode 100644 index 00000000..debacc36 --- /dev/null +++ b/demos/use_cases/http_filter/src/rag_agent/query_rewriter.py @@ -0,0 +1,133 @@ +import asyncio +import json +import time +from typing import List, Optional, Dict, Any +import uuid +from fastapi import FastAPI, Depends, Request +from openai import AsyncOpenAI +import os +import logging + +from .api import ChatCompletionRequest, ChatCompletionResponse, ChatMessage + +# from . import mcp +# from fastmcp.server.dependencies import get_http_headers + +# Set up logging +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - [QUERY_REWRITER] - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + +# Configuration for archgw LLM gateway +LLM_GATEWAY_ENDPOINT = os.getenv("LLM_GATEWAY_ENDPOINT", "http://localhost:12000/v1") +QUERY_REWRITE_MODEL = "gpt-4o-mini" + +# Initialize OpenAI client for archgw +archgw_client = AsyncOpenAI( + base_url=LLM_GATEWAY_ENDPOINT, + api_key="EMPTY", # archgw doesn't require a real API key +) + +app = FastAPI(title="RAG Agent Query Rewriter", version="1.0.0") + + +async def rewrite_query_with_archgw( + messages: List[ChatMessage], + traceparent_header: Optional[str] = None, + request_id: Optional[str] = None, +) -> str: + """Rewrite the last user message for better retrieval. Returns the rewritten query.""" + system_prompt = """You are a query rewriter that improves user queries for better retrieval. + +Given a conversation history, rewrite the last user message to be more specific and context-aware. +The rewritten query should: +1. Include relevant context from previous messages +2. Be clear and specific for information retrieval +3. Maintain the user's intent +4. Be concise but comprehensive + +Return only the rewritten query, nothing else.""" + + rewrite_messages = [{"role": "system", "content": system_prompt}] + for msg in messages: + rewrite_messages.append({"role": msg.role, "content": msg.content}) + + extra_headers = {"x-envoy-max-retries": "3", "x-request-id": request_id} + if traceparent_header: + extra_headers["traceparent"] = traceparent_header + + try: + logger.info(f"Calling archgw at {LLM_GATEWAY_ENDPOINT} to rewrite query") + resp = await archgw_client.chat.completions.create( + model=QUERY_REWRITE_MODEL, + messages=rewrite_messages, + temperature=0.3, + max_tokens=200, + extra_headers=extra_headers, + ) + rewritten = resp.choices[0].message.content.strip() + logger.info(f"Query rewritten successfully: '{rewritten}'") + return rewritten + except Exception as e: + logger.error(f"Error rewriting query: {e}") + + # Fallback: return the original last user message + for m in reversed(messages): + if m.role == "user": + logger.info("Falling back to original user message") + return m.content + return "" + + +@app.post("/") +async def query_rewriter_http( + messages: List[ChatMessage], request: Request +) -> List[ChatMessage]: + """HTTP filter endpoint used by Plano (type: http).""" + logger.info(f"Received request with {len(messages)} messages") + + traceparent_header = request.headers.get("traceparent") + request_id = request.headers.get("x-request-id") + + if traceparent_header: + logger.info(f"Received traceparent header: {traceparent_header}") + else: + logger.info("No traceparent header found") + + rewritten_query = await rewrite_query_with_archgw( + messages, traceparent_header, request_id + ) + # Create updated messages with the rewritten query + updated_messages = messages.copy() + + # Find and update the last user message with the rewritten query + for i in range(len(updated_messages) - 1, -1, -1): + if updated_messages[i].role == "user": + original_query = updated_messages[i].content + updated_messages[i] = ChatMessage(role="user", content=rewritten_query) + logger.info( + f"Updated user query from '{original_query}' to '{rewritten_query}'" + ) + break + updated_messages_data = [ + {"role": msg.role, "content": msg.content} for msg in updated_messages + ] + updated_messages = [ChatMessage(**msg) for msg in updated_messages_data] + + logger.info("Returning rewritten chat completion response") + return updated_messages + + +@app.get("/health") +async def health(): + return {"status": "healthy"} + + +def start_server(host: str = "0.0.0.0", port: int = 10501): + """Start the FastAPI server for query rewriter.""" + import uvicorn + + logger.info(f"Starting Query Rewriter REST server on {host}:{port}") + uvicorn.run(app, host=host, port=port) diff --git a/demos/use_cases/http_filter/src/rag_agent/rag_agent.py b/demos/use_cases/http_filter/src/rag_agent/rag_agent.py new file mode 100644 index 00000000..3f1c9855 --- /dev/null +++ b/demos/use_cases/http_filter/src/rag_agent/rag_agent.py @@ -0,0 +1,221 @@ +import json +from fastapi import FastAPI, Request +from fastapi.responses import StreamingResponse +from openai import AsyncOpenAI +import os +import logging +import time +import uuid +import uvicorn +import asyncio + +from .api import ( + ChatCompletionRequest, + ChatCompletionResponse, + ChatCompletionStreamResponse, +) + +# Set up logging +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - [RESPONSE_GENERATOR] - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + +# Configuration for archgw LLM gateway +LLM_GATEWAY_ENDPOINT = os.getenv("LLM_GATEWAY_ENDPOINT", "http://localhost:12000/v1") +RESPONSE_MODEL = "gpt-4o" + +# System prompt for response generation +SYSTEM_PROMPT = """You are a helpful assistant that generates coherent, contextual responses. + +Given a conversation history, generate a helpful and relevant response based on all the context available in the messages. +Your response should: +1. Be contextually aware of the entire conversation +2. Address the user's needs appropriately +3. Be helpful and informative +4. Maintain a natural conversational tone + +Generate a complete response to assist the user.""" + +# Initialize OpenAI client for archgw +archgw_client = AsyncOpenAI( + base_url=LLM_GATEWAY_ENDPOINT, + api_key="EMPTY", # archgw doesn't require a real API key +) + +# FastAPI app for REST server +app = FastAPI(title="RAG Agent Response Generator", version="1.0.0") + + +def prepare_response_messages(request_body: ChatCompletionRequest): + """Prepare messages for response generation by adding system prompt.""" + response_messages = [{"role": "system", "content": SYSTEM_PROMPT}] + + # Add conversation history + for msg in request_body.messages: + response_messages.append({"role": msg.role, "content": msg.content}) + + return response_messages + + +@app.post("/v1/chat/completions") +async def chat_completion_http(request: Request, request_body: ChatCompletionRequest): + """HTTP endpoint for chat completions with streaming support.""" + logger.info( + f"Received chat completion request with {len(request_body.messages)} messages" + ) + + # Get traceparent header from HTTP request + traceparent_header = request.headers.get("traceparent") + request_id = request.headers.get("x-request-id") or f"req-{uuid.uuid4().hex}" + + if traceparent_header: + logger.info(f"Received traceparent header: {traceparent_header}") + else: + logger.info("No traceparent header found") + + return StreamingResponse( + stream_chat_completions(request_body, traceparent_header, request_id), + media_type="text/plain", + headers={ + "content-type": "text/event-stream", + "x-request-id": request_id, + }, + ) + + +async def stream_chat_completions( + request_body: ChatCompletionRequest, + traceparent_header: str = None, + request_id: str = None, +): + """Generate streaming chat completions.""" + # Prepare messages for response generation + response_messages = prepare_response_messages(request_body) + + try: + # Call archgw using OpenAI client for streaming + logger.info( + f"Calling archgw at {LLM_GATEWAY_ENDPOINT} to generate streaming response" + ) + + # Prepare extra headers if traceparent is provided + extra_headers = {"x-envoy-max-retries": "3", "x-request-id": request_id} + if traceparent_header: + extra_headers["traceparent"] = traceparent_header + + response_stream = await archgw_client.chat.completions.create( + model=RESPONSE_MODEL, + messages=response_messages, + temperature=request_body.temperature or 0.7, + max_tokens=request_body.max_tokens or 1000, + stream=True, + extra_headers=extra_headers, + ) + + completion_id = f"chatcmpl-{uuid.uuid4().hex[:8]}" + created_time = int(time.time()) + collected_content = [] + + async for chunk in response_stream: + if chunk.choices and chunk.choices[0].delta.content: + content = chunk.choices[0].delta.content + collected_content.append(content) + + # Create streaming response chunk + stream_chunk = ChatCompletionStreamResponse( + id=completion_id, + created=created_time, + model=request_body.model, + choices=[ + { + "index": 0, + "delta": {"content": content}, + "finish_reason": None, + } + ], + ) + + yield f"data: {stream_chunk.model_dump_json()}\n\n" + + # Send final chunk with complete response in expected format + full_response = "".join(collected_content) + updated_history = [{"role": "assistant", "content": full_response}] + + final_chunk = ChatCompletionStreamResponse( + id=completion_id, + created=created_time, + model=request_body.model, + choices=[ + { + "index": 0, + "delta": {}, + "finish_reason": "stop", + "message": { + "role": "assistant", + "content": json.dumps(updated_history), + }, + } + ], + ) + + yield f"data: {final_chunk.model_dump_json()}\n\n" + yield "data: [DONE]\n\n" + + except Exception as e: + logger.error(f"Error generating streaming response: {e}") + + # Send error as streaming response + error_chunk = ChatCompletionStreamResponse( + id=f"chatcmpl-{uuid.uuid4().hex[:8]}", + created=int(time.time()), + model=request_body.model, + choices=[ + { + "index": 0, + "delta": { + "content": "I apologize, but I'm having trouble generating a response right now. Please try again." + }, + "finish_reason": "stop", + } + ], + ) + + yield f"data: {error_chunk.model_dump_json()}\n\n" + yield "data: [DONE]\n\n" + + +@app.get("/health") +async def health_check(): + """Health check endpoint.""" + return {"status": "healthy"} + + +def start_server(host: str = "localhost", port: int = 8000): + """Start the REST server.""" + uvicorn.run( + app, + host=host, + port=port, + log_config={ + "version": 1, + "disable_existing_loggers": False, + "formatters": { + "default": { + "format": "%(asctime)s - [RESPONSE_GENERATOR] - %(levelname)s - %(message)s", + }, + }, + "handlers": { + "default": { + "formatter": "default", + "class": "logging.StreamHandler", + "stream": "ext://sys.stdout", + }, + }, + "root": { + "level": "INFO", + "handlers": ["default"], + }, + }, + ) diff --git a/demos/use_cases/http_filter/src/rag_agent/sample_knowledge_base.csv b/demos/use_cases/http_filter/src/rag_agent/sample_knowledge_base.csv new file mode 100644 index 00000000..b9e6e8f0 --- /dev/null +++ b/demos/use_cases/http_filter/src/rag_agent/sample_knowledge_base.csv @@ -0,0 +1,257 @@ +path,content +TechCorp_CloudServices_SLA_Agreement_2024,"SERVICE LEVEL AGREEMENT +This Service Level Agreement (""SLA"") is entered into on March 15, 2024, between TechCorp Solutions Inc., a Delaware corporation (""Provider""), and CloudFirst Enterprises LLC (""Customer""). + +DEFINITIONS +Service Availability: The percentage of time during which the cloud services are operational and accessible. +Downtime: Any period when the services are unavailable or inaccessible to Customer. +Response Time: The time between service request submission and initial response from Provider. + +SERVICE COMMITMENTS +Provider guarantees 99.9% uptime for all cloud infrastructure services during any calendar month. +Average response time for API calls shall not exceed 200 milliseconds under normal operating conditions. +Customer support response times: Critical issues within 1 hour, Standard issues within 4 hours. + +REMEDIES +For each full percentage point below 99.9% availability, Customer receives 10% credit on monthly fees. +If response times exceed 500ms for more than 5 minutes in any hour, Customer receives 5% monthly credit. + +MONITORING AND REPORTING +Provider will maintain real-time monitoring systems and provide monthly performance reports. +All metrics will be measured from Provider's monitoring systems located in primary data centers. + +This SLA remains in effect for the duration of the underlying service agreement. + +Executed by: +TechCorp Solutions Inc. +Sarah Mitchell, VP Operations +Date: March 15, 2024 + +CloudFirst Enterprises LLC +Robert Chen, CTO +Date: March 16, 2024" + +DataSecure_Privacy_Policy_v3.2,"PRIVACY POLICY +DataSecure Analytics, Inc. (""Company"") Privacy Policy +Effective Date: January 1, 2024 +Last Updated: February 28, 2024 + +INFORMATION COLLECTION +We collect information you provide directly, such as account details, usage preferences, and communication records. +Automatically collected data includes IP addresses, browser types, device information, and service interaction logs. +Third-party integrations may provide additional user behavior and demographic information with consent. + +DATA USAGE +Personal information is used to provide services, improve user experience, and communicate service updates. +Aggregated, non-identifiable data may be used for analytics, research, and service enhancement. +We do not sell personal information to third parties for marketing purposes. + +DATA PROTECTION +All data is encrypted in transit using TLS 1.3 and at rest using AES-256 encryption. +Access controls limit data access to authorized personnel only on a need-to-know basis. +Regular security audits and penetration testing ensure ongoing protection measures. + +DATA RETENTION +Personal data is retained for the duration of active service plus 24 months. +Logs and analytics data are retained for 12 months unless legally required otherwise. +Upon account deletion, personal data is permanently removed within 30 days. + +USER RIGHTS +Users may request access to, correction of, or deletion of their personal information. +Data portability requests will be fulfilled in standard formats within 30 days. +Marketing communications can be opted out of at any time. + +CONTACT +For privacy concerns, contact: privacy@datasecure.com +Data Protection Officer: Jennifer Walsh, jwalsh@datasecure.com" + +GlobalManufacturing_SupplyChain_Contract_Q2_2024,"SUPPLY CHAIN AGREEMENT +This Supply Chain Agreement is entered into between GlobalManufacturing Corp (""Buyer"") and PrecisionParts Ltd (""Supplier"") effective April 1, 2024. + +SCOPE OF SERVICES +Supplier will provide automotive components including brake assemblies, suspension parts, and electrical harnesses. +All products must meet ISO 9001 quality standards and automotive industry specifications. +Delivery schedule: Weekly shipments every Tuesday, with 48-hour advance shipping notifications. + +PRICING AND PAYMENT +Component pricing is fixed for initial 6-month term with quarterly price review thereafter. +Payment terms: Net 45 days from invoice date via electronic transfer. +Volume discounts apply: 5% for orders exceeding 10,000 units per month, 8% for orders exceeding 25,000 units. + +QUALITY REQUIREMENTS +All components must pass incoming inspection with less than 0.1% defect rate. +Supplier maintains quality certifications including IATF 16949 and environmental compliance. +Batch tracking and traceability required for all delivered components. + +LOGISTICS AND DELIVERY +Supplier responsible for packaging, labeling, and delivery to Buyer's distribution centers. +Delivery windows: 8 AM - 4 PM, Monday through Friday, with advance appointment scheduling. +Late delivery penalties: 2% of shipment value for each day beyond scheduled delivery. + +RISK MANAGEMENT +Supplier maintains business continuity plans and alternative sourcing strategies. +Force majeure events must be reported within 24 hours with mitigation plans. +Insurance requirements: $5M general liability, $2M product liability coverage. + +INTELLECTUAL PROPERTY +All custom tooling and specifications remain property of Buyer. +Supplier grants license to use necessary patents for component manufacturing. + +This agreement shall remain in effect for 24 months with automatic renewal unless terminated. + +GlobalManufacturing Corp +Michael Rodriguez, Supply Chain Director +Date: April 1, 2024 + +PrecisionParts Ltd +Amanda Foster, VP Sales +Date: April 2, 2024" + +EduTech_StudentData_Management_Policy_2024,"STUDENT DATA MANAGEMENT POLICY +EduTech Learning Platform - Data Management and Protection Policy +Document Version: 2.1 +Effective Date: August 15, 2024 + +SCOPE AND PURPOSE +This policy governs the collection, use, storage, and protection of student educational records and personal information. +Applies to all employees, contractors, and third-party service providers accessing student data. +Compliance with FERPA, COPPA, and state student privacy laws is mandatory. + +DATA CLASSIFICATION +Educational Records: Grades, attendance, assignments, and academic progress information. +Personal Information: Names, addresses, contact details, and demographic information. +Behavioral Data: Learning patterns, platform usage, and engagement metrics. + +COLLECTION PRINCIPLES +Data collection is limited to educational purposes and service improvement only. +Parental consent required for students under 13 years of age. +Students and parents have right to review and request corrections to educational records. + +ACCESS CONTROLS +Role-based access ensures personnel see only data necessary for their functions. +Multi-factor authentication required for all system access. +Access logs maintained and reviewed monthly for unauthorized activity. + +DATA SHARING +Educational records shared only with authorized school personnel and parents/students. +No data sharing with third parties for commercial purposes without explicit consent. +Research data must be de-identified and aggregated before external sharing. + +SECURITY MEASURES +Data encrypted using industry-standard protocols during transmission and storage. +Regular security assessments and vulnerability testing conducted quarterly. +Incident response plan includes notification procedures for data breaches. + +RETENTION AND DISPOSAL +Student records retained according to school district policies, typically 5-7 years post-graduation. +Inactive accounts and associated data purged after 2 years of non-use. +Secure data destruction protocols ensure complete removal of sensitive information. + +COMPLIANCE MONITORING +Annual privacy training required for all staff handling student data. +Regular audits ensure ongoing compliance with applicable privacy regulations. +Privacy impact assessments conducted for new features or data uses. + +Contact: Dr. Lisa Thompson, Chief Privacy Officer +Email: privacy@edutech-learning.com +Phone: (555) 123-4567" + +FinanceFirst_Investment_Advisory_Agreement_2024,"INVESTMENT ADVISORY AGREEMENT +This Investment Advisory Agreement is entered into between FinanceFirst Advisors LLC (""Advisor"") and Madison Investment Group (""Client"") on May 20, 2024. + +ADVISORY SERVICES +Advisor will provide comprehensive investment management and financial planning services. +Services include portfolio construction, asset allocation, risk assessment, and performance monitoring. +Regular portfolio reviews conducted quarterly with detailed performance reporting. + +INVESTMENT AUTHORITY +Client grants Advisor discretionary authority to make investment decisions within agreed parameters. +Investment universe includes stocks, bonds, ETFs, mutual funds, and alternative investments as appropriate. +All trades executed through qualified broker-dealers with best execution practices. + +FEE STRUCTURE +Management fee: 1.25% annually on assets under management, calculated and billed quarterly. +Performance fee: 15% of returns exceeding S&P 500 benchmark, calculated annually. +Additional fees may apply for specialized services such as tax planning or estate planning. + +CLIENT RESPONSIBILITIES +Client must provide accurate financial information and promptly communicate changes in circumstances. +Investment objectives and risk tolerance should be reviewed and updated annually. +Client responsible for reviewing and approving investment policy statement. + +RISK DISCLOSURE +All investments carry risk of loss, and past performance does not guarantee future results. +Diversification does not ensure profit or protect against loss in declining markets. +Alternative investments may have limited liquidity and higher volatility. + +REGULATORY COMPLIANCE +Advisor is registered with the Securities and Exchange Commission as an investment advisor. +All activities conducted in accordance with Investment Advisers Act of 1940 and applicable regulations. +Form ADV Part 2 brochure provided annually with material updates. + +CONFIDENTIALITY +All client information treated as confidential and shared only as necessary for service provision. +Third-party service providers bound by confidentiality agreements. +Client data protected through secure systems and access controls. + +TERMINATION +Either party may terminate agreement with 30 days written notice. +Upon termination, Advisor will assist with orderly transfer of assets to new custodian or advisor. +Final fee calculation prorated to date of termination. + +FinanceFirst Advisors LLC +Thomas Anderson, Managing Partner +Date: May 20, 2024 + +Madison Investment Group +Rebecca Martinez, Chief Investment Officer +Date: May 21, 2024" + +HealthSystem_PatientCare_Standards_2024,"PATIENT CARE STANDARDS AND PROTOCOLS +Metropolitan Health System - Clinical Care Standards +Document ID: MHS-PCS-2024-001 +Effective Date: June 1, 2024 + +PATIENT SAFETY PROTOCOLS +All patients must have proper identification verification using two unique identifiers. +Medication administration requires independent double-check for high-risk medications. +Fall risk assessments completed within 4 hours of admission with appropriate interventions. + +CLINICAL DOCUMENTATION +Medical records must be completed within 24 hours of patient encounter. +All entries require electronic signature with timestamp and provider identification. +Critical values and abnormal results must be communicated and documented immediately. + +INFECTION CONTROL +Hand hygiene compliance monitored with target rate of 95% or higher. +Personal protective equipment used according to transmission-based precautions. +Isolation procedures implemented within 2 hours of identification of infectious conditions. + +EMERGENCY RESPONSE +Code team response time target: 3 minutes from activation to arrival. +Crash cart and emergency equipment checks performed daily and documented. +All staff required to maintain current CPR and emergency response certifications. + +PATIENT COMMUNICATION +Patient rights and responsibilities communicated upon admission. +Informed consent obtained and documented prior to procedures and treatments. +Family involvement encouraged with respect for patient privacy preferences. + +QUALITY MEASURES +Patient satisfaction scores monitored monthly with target of 4.5/5.0 or higher. +Medication error rates tracked with goal of less than 1 per 1000 patient days. +Hospital-acquired infection rates measured and benchmarked against national standards. + +STAFF COMPETENCY +Annual competency assessments required for all clinical staff. +Continuing education requirements: 24 hours annually for nurses, 40 hours for physicians. +Specialty certifications maintained according to department and role requirements. + +TECHNOLOGY STANDARDS +Electronic health record system used for all patient documentation. +Telemedicine capabilities available for remote consultations and monitoring. +Clinical decision support tools integrated to assist with diagnosis and treatment decisions. + +Contact: Dr. Patricia Williams, Chief Medical Officer +Email: pwilliams@metrohealthsystem.org +Phone: (555) 987-6543" diff --git a/demos/use_cases/http_filter/start_agents.sh b/demos/use_cases/http_filter/start_agents.sh new file mode 100644 index 00000000..06cabeec --- /dev/null +++ b/demos/use_cases/http_filter/start_agents.sh @@ -0,0 +1,78 @@ +# #!/bin/bash +# set -e + +# WAIT_FOR_PIDS=() + +# log() { +# timestamp=$(python3 -c 'from datetime import datetime; print(datetime.now().strftime("%Y-%m-%d %H:%M:%S,%f")[:23])') +# message="$*" +# echo "$timestamp - $message" +# } + +# cleanup() { +# log "Caught signal, terminating all user processes ..." +# for PID in "${WAIT_FOR_PIDS[@]}"; do +# if kill $PID 2> /dev/null; then +# log "killed process: $PID" +# fi +# done +# exit 1 +# } + +# trap cleanup EXIT + +# log "Starting input_guards agent on port 10500/mcp..." +# uv run python -m rag_agent --rest-server --host 0.0.0.0 --rest-port 10500 --agent input_guards & +# WAIT_FOR_PIDS+=($!) + +# log "Starting query_rewriter agent on port 10501/mcp..." +# uv run python -m rag_agent --rest-server --host 0.0.0.0 --rest-port 10501 --agent query_rewriter & +# WAIT_FOR_PIDS+=($!) + +# log "Starting context_builder agent on port 10502/mcp..." +# uv run python -m rag_agent --rest-server --host 0.0.0.0 --rest-port 10502 --agent context_builder & +# WAIT_FOR_PIDS+=($!) + +# # log "Starting response_generator agent on port 10400..." +# # uv run python -m rag_agent --host 0.0.0.0 --port 10400 --agent response_generator & +# # WAIT_FOR_PIDS+=($!) + +# log "Starting response_generator agent on port 10505..." +# uv run python -m rag_agent --rest-server --host 0.0.0.0 --rest-port 10505 --agent response_generator & +# WAIT_FOR_PIDS+=($!) + +# for PID in "${WAIT_FOR_PIDS[@]}"; do +# wait "$PID" +# done + + + + +#!/bin/bash +set -e + +export PYTHONPATH=/app/src + +pids=() + +log() { echo "$(date '+%F %T') - $*"; } + +log "Starting input_guards HTTP server on :10500" +uv run uvicorn rag_agent.input_guards:app --host 0.0.0.0 --port 10500 & +pids+=($!) + +log "Starting query_rewriter HTTP server on :10501" +uv run uvicorn rag_agent.query_rewriter:app --host 0.0.0.0 --port 10501 & +pids+=($!) + +log "Starting context_builder HTTP server on :10502" +uv run uvicorn rag_agent.context_builder:app --host 0.0.0.0 --port 10502 & +pids+=($!) + +log "Starting response_generator (OpenAI-compatible) on :10505" +uv run uvicorn rag_agent.rag_agent:app --host 0.0.0.0 --port 10505 & +pids+=($!) + +for PID in "${pids[@]}"; do + wait "$PID" +done diff --git a/demos/use_cases/http_filter/test.rest b/demos/use_cases/http_filter/test.rest new file mode 100644 index 00000000..daf93b92 --- /dev/null +++ b/demos/use_cases/http_filter/test.rest @@ -0,0 +1,92 @@ +@baseUrl = http://0.0.0.0:10502 +@model = gpt-4o + +# Health Check +GET {{baseUrl}}/health + +### + +# Test 1: Simple Non-Streaming Chat Completion +POST {{baseUrl}}/v1/chat/completions +Content-Type: application/json + +{ + "model": "{{model}}", + "messages": [ + { + "role": "user", + "content": "Hello! Can you help me understand what machine learning is?" + } + ] +} + +### + +# Test 2: Simple Streaming Chat Completion +POST {{baseUrl}}/v1/chat/completions +Content-Type: application/json + +{ + "model": "{{model}}", + "messages": [ + { + "role": "user", + "content": "Explain the concept of artificial intelligence in simple terms." + } + ], + "stream": true +} + +### Test 3 +POST http://localhost:8001/v1/chat/completions +Content-Type: application/json + +{ + "model": "{{model}}", + "messages": [ + { + "role": "user", + "content": "What is the guaranteed uptime percentage for TechCorp's cloud services?" + } + ], + "stream": true +} + +### send request to query_rewriter agent +POST http://localhost:10500/ +Content-Type: application/json + +[ + { + "role": "user", + "content": "What is the guaranteed uptime percentage for TechCorp's cloud services?" + } +] + +### test fast-llm +POST http://localhost:12000/v1/chat/completions +Content-Type: application/json + +{ + "model": "fast-llm", + "messages": [ + { + "role": "user", + "content": "hello" + } + ] +} + +### test smart-llm +POST http://localhost:12000/v1/chat/completions +Content-Type: application/json + +{ + "model": "smart-llm", + "messages": [ + { + "role": "user", + "content": "hello" + } + ] +} diff --git a/demos/use_cases/http_filter/uv.lock b/demos/use_cases/http_filter/uv.lock new file mode 100644 index 00000000..6a39c6ec --- /dev/null +++ b/demos/use_cases/http_filter/uv.lock @@ -0,0 +1,1830 @@ +version = 1 +requires-python = ">=3.10" + +[[package]] +name = "annotated-types" +version = "0.7.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081 } +wheels = [ + { url = 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