From dcb6271c2398beacd30229d2d0da102e0a61779c Mon Sep 17 00:00:00 2001 From: Jonathan Muszkat Date: Tue, 20 May 2025 13:53:20 -0300 Subject: [PATCH] feat: LiteLLM integration --- Makefile | 53 ++ README.md | 14 +- activities/tool_activities.py | 455 ++-------- poetry.lock | 1572 ++++++++++++++++++++++++--------- pyproject.toml | 7 +- scripts/run_ollama.py | 23 - scripts/run_worker.py | 12 +- setup.md | 69 +- 8 files changed, 1337 insertions(+), 868 deletions(-) create mode 100644 Makefile delete mode 100644 scripts/run_ollama.py diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..06a85f1 --- /dev/null +++ b/Makefile @@ -0,0 +1,53 @@ +.PHONY: setup install run-worker run-api run-frontend run-train-api run-legacy-worker run-enterprise setup-venv check-python + +# Setup commands +setup: check-python setup-venv install + +check-python: + @which python3 >/dev/null 2>&1 || (echo "Python 3 is required. Please install it first." && exit 1) + +setup-venv: + python3 -m venv venv + @echo "Virtual environment created. Don't forget to activate it with 'source venv/bin/activate'" + +install: + poetry install + cd frontend && npm install + +# Run commands +run-worker: + poetry run python scripts/run_worker.py + +run-api: + poetry run uvicorn api.main:app --reload + +run-frontend: + cd frontend && npx vite + +run-train-api: + poetry run python thirdparty/train_api.py + +run-legacy-worker: + poetry run python scripts/run_legacy_worker.py + +run-enterprise: + cd enterprise && dotnet build && dotnet run + +# Development environment setup +setup-temporal-mac: + brew install temporal + temporal server start-dev + +# Help command +help: + @echo "Available commands:" + @echo " make setup - Create virtual environment and install dependencies" + @echo " make setup-venv - Create virtual environment only" + @echo " make install - Install all dependencies" + @echo " make run-worker - Start the Temporal worker" + @echo " make run-api - Start the API server" + @echo " make run-frontend - Start the frontend development server" + @echo " make run-train-api - Start the train API server" + @echo " make run-legacy-worker - Start the legacy worker" + @echo " make run-enterprise - Build and run the enterprise .NET worker" + @echo " make setup-temporal-mac - Install and start Temporal server on Mac" \ No newline at end of file diff --git a/README.md b/README.md index 9683746..3b15b83 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,13 @@ This demo shows a multi-turn conversation with an AI agent running inside a Temporal workflow. The purpose of the agent is to collect information towards a goal, running tools along the way. There's a simple DSL input for collecting information (currently set up to use mock functions to search for public events, search for flights around those events, then create a test Stripe invoice for the trip). -The AI will respond with clarifications and ask for any missing information to that goal. You can configure it to use [ChatGPT 4o](https://openai.com/index/hello-gpt-4o/), [Anthropic Claude](https://www.anthropic.com/claude), [Google Gemini](https://gemini.google.com), [Deepseek-V3](https://www.deepseek.com/), [Grok](https://docs.x.ai/docs/overview) or a local LLM of your choice using [Ollama](https://ollama.com). +The AI will respond with clarifications and ask for any missing information to that goal. You can configure it to use any LLM supported by [LiteLLM](https://docs.litellm.ai/docs/providers), including: +- OpenAI models (GPT-4, GPT-3.5) +- Anthropic Claude models +- Google Gemini models +- Deepseek models +- Ollama models (local) +- And many more! It's really helpful to [watch the demo (5 minute YouTube video)](https://www.youtube.com/watch?v=GEXllEH2XiQ) to understand how interaction works. @@ -28,7 +34,11 @@ These are the key elements of an agentic framework: For a deeper dive into this, check out the [architecture guide](./architecture.md). ## Setup and Configuration -See [the Setup guide](./setup.md). +See [the Setup guide](./setup.md) for detailed instructions. The basic configuration requires just two environment variables: +```bash +LLM_MODEL=openai/gpt-4o # or any other model supported by LiteLLM +LLM_KEY=your-api-key-here +``` ## Customizing Interaction & Tools See [the guide to adding goals and tools](./adding-goals-and-tools.md). diff --git a/activities/tool_activities.py b/activities/tool_activities.py index 1b8d0b3..aef7ec0 100644 --- a/activities/tool_activities.py +++ b/activities/tool_activities.py @@ -1,142 +1,28 @@ import inspect from temporalio import activity -from ollama import chat, ChatResponse -from openai import OpenAI import json -from typing import Sequence, Optional +from typing import Optional, Sequence from temporalio.common import RawValue import os from datetime import datetime -import google.generativeai as genai -import anthropic -import deepseek from dotenv import load_dotenv from models.data_types import EnvLookupOutput, ValidationInput, ValidationResult, ToolPromptInput, EnvLookupInput +from litellm import completion load_dotenv(override=True) -print( - "Using LLM provider: " - + os.environ.get("LLM_PROVIDER", "openai") - + " (set LLM_PROVIDER in .env to change)" -) - -if os.environ.get("LLM_PROVIDER") == "ollama": - print( - "Using Ollama (local) model: " - + os.environ.get("OLLAMA_MODEL_NAME", "qwen2.5:14b") - ) - class ToolActivities: def __init__(self): - """Initialize LLM clients based on environment configuration.""" - self.llm_provider = os.environ.get("LLM_PROVIDER", "openai").lower() - print(f"Initializing ToolActivities with LLM provider: {self.llm_provider}") - - # Initialize client variables (all set to None initially) - self.openai_client: Optional[OpenAI] = None - self.grok_client: Optional[OpenAI] = None - self.anthropic_client: Optional[anthropic.Anthropic] = None - self.genai_configured: bool = False - self.deepseek_client: Optional[deepseek.DeepSeekAPI] = None - self.ollama_model_name: Optional[str] = None - self.ollama_initialized: bool = False - - # Only initialize the client specified by LLM_PROVIDER - if self.llm_provider == "openai": - if os.environ.get("OPENAI_API_KEY"): - self.openai_client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY")) - print("Initialized OpenAI client") - else: - print("Warning: OPENAI_API_KEY not set but LLM_PROVIDER is 'openai'") - - elif self.llm_provider == "grok": - if os.environ.get("GROK_API_KEY"): - self.grok_client = OpenAI(api_key=os.environ.get("GROK_API_KEY"), base_url="https://api.x.ai/v1") - print("Initialized grok client") - else: - print("Warning: GROK_API_KEY not set but LLM_PROVIDER is 'grok'") - - elif self.llm_provider == "anthropic": - if os.environ.get("ANTHROPIC_API_KEY"): - self.anthropic_client = anthropic.Anthropic( - api_key=os.environ.get("ANTHROPIC_API_KEY") - ) - print("Initialized Anthropic client") - else: - print( - "Warning: ANTHROPIC_API_KEY not set but LLM_PROVIDER is 'anthropic'" - ) - - elif self.llm_provider == "google": - api_key = os.environ.get("GOOGLE_API_KEY") - if api_key: - genai.configure(api_key=api_key) - self.genai_configured = True - print("Configured Google Generative AI") - else: - print("Warning: GOOGLE_API_KEY not set but LLM_PROVIDER is 'google'") - - elif self.llm_provider == "deepseek": - if os.environ.get("DEEPSEEK_API_KEY"): - self.deepseek_client = deepseek.DeepSeekAPI( - api_key=os.environ.get("DEEPSEEK_API_KEY") - ) - print("Initialized DeepSeek client") - else: - print( - "Warning: DEEPSEEK_API_KEY not set but LLM_PROVIDER is 'deepseek'" - ) - - # For Ollama, we store the model name but actual initialization happens in warm_up_ollama - elif self.llm_provider == "ollama": - self.ollama_model_name = os.environ.get("OLLAMA_MODEL_NAME", "qwen2.5:14b") - print( - f"Using Ollama model: {self.ollama_model_name} (will be loaded on worker startup)" - ) - else: - print( - f"Warning: Unknown LLM_PROVIDER '{self.llm_provider}', defaulting to OpenAI" - ) - - def warm_up_ollama(self): - """Pre-load the Ollama model to avoid cold start latency on first request""" - if self.llm_provider != "ollama" or self.ollama_initialized: - return False # No need to warm up if not using Ollama or already warmed up - - try: - print( - f"Pre-loading Ollama model '{self.ollama_model_name}' - this may take 30+ seconds..." - ) - start_time = datetime.now() - - # Make a simple request to load the model into memory - chat( - model=self.ollama_model_name, - messages=[ - {"role": "system", "content": "You are an AI assistant"}, - { - "role": "user", - "content": "Hello! This is a warm-up message to load the model.", - }, - ], - ) - - elapsed_time = (datetime.now() - start_time).total_seconds() - print(f"✅ Ollama model loaded successfully in {elapsed_time:.2f} seconds") - self.ollama_initialized = True - return True - except Exception as e: - print(f"❌ Error pre-loading Ollama model: {str(e)}") - print( - "The worker will continue, but the first actual request may experience a delay." - ) - return False + """Initialize LLM client using LiteLLM.""" + self.llm_model = os.environ.get("LLM_MODEL", "openai/gpt-4") + self.llm_key = os.environ.get("LLM_KEY") + self.llm_base_url = os.environ.get("LLM_BASE_URL") + print(f"Initializing ToolActivities with LLM model: {self.llm_model}") + if self.llm_base_url: + print(f"Using custom base URL: {self.llm_base_url}") @activity.defn - async def agent_validatePrompt( - self, validation_input: ValidationInput - ) -> ValidationResult: + async def agent_validatePrompt(self, validation_input: ValidationInput) -> ValidationResult: """ Validates the prompt in the context of the conversation history and agent goal. Returns a ValidationResult indicating if the prompt makes sense given the context. @@ -187,7 +73,7 @@ class ToolActivities: prompt=validation_prompt, context_instructions=context_instructions ) - result = self.agent_toolPlanner(prompt_input) + result = await self.agent_toolPlanner(prompt_input) return ValidationResult( validationResult=result.get("validationResult", False), @@ -195,19 +81,43 @@ class ToolActivities: ) @activity.defn - def agent_toolPlanner(self, input: ToolPromptInput) -> dict: - if self.llm_provider == "ollama": - return self.prompt_llm_ollama(input) - elif self.llm_provider == "google": - return self.prompt_llm_google(input) - elif self.llm_provider == "anthropic": - return self.prompt_llm_anthropic(input) - elif self.llm_provider == "deepseek": - return self.prompt_llm_deepseek(input) - elif self.llm_provider == "grok": - return self.prompt_llm_grok(input) - else: - return self.prompt_llm_openai(input) + async def agent_toolPlanner(self, input: ToolPromptInput) -> dict: + messages = [ + { + "role": "system", + "content": input.context_instructions + + ". The current date is " + + datetime.now().strftime("%B %d, %Y"), + }, + { + "role": "user", + "content": input.prompt, + }, + ] + + try: + completion_kwargs = { + "model": self.llm_model, + "messages": messages, + "api_key": self.llm_key + } + + # Add base_url if configured + if self.llm_base_url: + completion_kwargs["base_url"] = self.llm_base_url + + response = completion(**completion_kwargs) + + response_content = response.choices[0].message.content + activity.logger.info(f"LLM response: {response_content}") + + # Use the new sanitize function + response_content = self.sanitize_json_response(response_content) + + return self.parse_json_response(response_content) + except Exception as e: + print(f"Error in LLM completion: {str(e)}") + raise def parse_json_response(self, response_content: str) -> dict: """ @@ -220,259 +130,18 @@ class ToolActivities: print(f"Invalid JSON: {e}") raise - def prompt_llm_openai(self, input: ToolPromptInput) -> dict: - if not self.openai_client: - api_key = os.environ.get("OPENAI_API_KEY") - if not api_key: - raise ValueError( - "OPENAI_API_KEY is not set in the environment variables but LLM_PROVIDER is 'openai'" - ) - self.openai_client = OpenAI(api_key=api_key) - print("Initialized OpenAI client on demand") - - messages = [ - { - "role": "system", - "content": input.context_instructions - + ". The current date is " - + datetime.now().strftime("%B %d, %Y"), - }, - { - "role": "user", - "content": input.prompt, - }, - ] - - chat_completion = self.openai_client.chat.completions.create( - model="gpt-4o", messages=messages # was gpt-4-0613 - ) - - response_content = chat_completion.choices[0].message.content - activity.logger.info(f"ChatGPT response: {response_content}") - - # Use the new sanitize function - response_content = self.sanitize_json_response(response_content) - - return self.parse_json_response(response_content) - - def prompt_llm_grok(self, input: ToolPromptInput) -> dict: - if not self.grok_client: - api_key = os.environ.get("GROK_API_KEY") - if not api_key: - raise ValueError( - "GROK_API_KEY is not set in the environment variables but LLM_PROVIDER is 'grok'" - ) - self.grok_client = OpenAI(api_key=api_key, base_url="https://api.x.ai/v1") - print("Initialized grok client on demand") - - messages = [ - { - "role": "system", - "content": input.context_instructions - + ". The current date is " - + datetime.now().strftime("%B %d, %Y"), - }, - { - "role": "user", - "content": input.prompt, - }, - ] - - chat_completion = self.grok_client.chat.completions.create( - model="grok-2-1212", messages=messages - ) - - response_content = chat_completion.choices[0].message.content - activity.logger.info(f"Grok response: {response_content}") - - # Use the new sanitize function - response_content = self.sanitize_json_response(response_content) - - return self.parse_json_response(response_content) - def prompt_llm_ollama(self, input: ToolPromptInput) -> dict: - # If not yet initialized, try to do so now (this is a backup if warm_up_ollama wasn't called or failed) - if not self.ollama_initialized: - print( - "Ollama model not pre-loaded. Loading now (this may take 30+ seconds)..." - ) - try: - self.warm_up_ollama() - except Exception: - # We already logged the error in warm_up_ollama, continue with the actual request - pass - - model_name = self.ollama_model_name or os.environ.get( - "OLLAMA_MODEL_NAME", "qwen2.5:14b" - ) - messages = [ - { - "role": "system", - "content": input.context_instructions - + ". The current date is " - + get_current_date_human_readable(), - }, - { - "role": "user", - "content": input.prompt, - }, - ] - - try: - response: ChatResponse = chat(model=model_name, messages=messages) - print(f"Chat response: {response.message.content}") - - # Use the new sanitize function - response_content = self.sanitize_json_response(response.message.content) - return self.parse_json_response(response_content) - except (json.JSONDecodeError, ValueError) as e: - # Re-raise JSON-related exceptions to let Temporal retry the activity - print(f"JSON parsing error with Ollama response: {str(e)}") - raise - except Exception as e: - # Log and raise other exceptions that may need retrying - print(f"Error in Ollama chat: {str(e)}") - raise - - def prompt_llm_google(self, input: ToolPromptInput) -> dict: - if not self.genai_configured: - api_key = os.environ.get("GOOGLE_API_KEY") - if not api_key: - raise ValueError( - "GOOGLE_API_KEY is not set in the environment variables but LLM_PROVIDER is 'google'" - ) - genai.configure(api_key=api_key) - self.genai_configured = True - print("Configured Google Generative AI on demand") - - model = genai.GenerativeModel( - "models/gemini-1.5-flash", - system_instruction=input.context_instructions - + ". The current date is " - + datetime.now().strftime("%B %d, %Y"), - ) - response = model.generate_content(input.prompt) - response_content = response.text - print(f"Google Gemini response: {response_content}") - - # Use the new sanitize function - response_content = self.sanitize_json_response(response_content) - - return self.parse_json_response(response_content) - - def prompt_llm_anthropic(self, input: ToolPromptInput) -> dict: - if not self.anthropic_client: - api_key = os.environ.get("ANTHROPIC_API_KEY") - if not api_key: - raise ValueError( - "ANTHROPIC_API_KEY is not set in the environment variables but LLM_PROVIDER is 'anthropic'" - ) - self.anthropic_client = anthropic.Anthropic(api_key=api_key) - print("Initialized Anthropic client on demand") - - response = self.anthropic_client.messages.create( - model="claude-3-5-sonnet-20241022", - #model="claude-3-7-sonnet-20250219", # doesn't do as well - max_tokens=1024, - system=input.context_instructions - + ". The current date is " - + get_current_date_human_readable(), - messages=[ - { - "role": "user", - "content": input.prompt, - } - ], - ) - - response_content = response.content[0].text - print(f"Anthropic response: {response_content}") - - # Use the new sanitize function - response_content = self.sanitize_json_response(response_content) - - return self.parse_json_response(response_content) - - def prompt_llm_deepseek(self, input: ToolPromptInput) -> dict: - if not self.deepseek_client: - api_key = os.environ.get("DEEPSEEK_API_KEY") - if not api_key: - raise ValueError( - "DEEPSEEK_API_KEY is not set in the environment variables but LLM_PROVIDER is 'deepseek'" - ) - self.deepseek_client = deepseek.DeepSeekAPI(api_key=api_key) - print("Initialized DeepSeek client on demand") - - messages = [ - { - "role": "system", - "content": input.context_instructions - + ". The current date is " - + datetime.now().strftime("%B %d, %Y"), - }, - { - "role": "user", - "content": input.prompt, - }, - ] - - response = self.deepseek_client.chat_completion(prompt=messages) - response_content = response - print(f"DeepSeek response: {response_content}") - - # Use the new sanitize function - response_content = self.sanitize_json_response(response_content) - - return self.parse_json_response(response_content) - def sanitize_json_response(self, response_content: str) -> str: """ - Extracts the JSON block from the response content as a string. - Supports: - - JSON surrounded by ```json and ``` - - Raw JSON input - - JSON preceded or followed by extra text - Rejects invalid input that doesn't contain JSON. + Sanitizes the response content to ensure it's valid JSON. """ - try: - start_marker = "```json" - end_marker = "```" + # Remove any markdown code block markers + response_content = response_content.replace("```json", "").replace("```", "") + + # Remove any leading/trailing whitespace + response_content = response_content.strip() + + return response_content - json_str = None - - # Case 1: JSON surrounded by markers - if start_marker in response_content and end_marker in response_content: - json_start = response_content.index(start_marker) + len(start_marker) - json_end = response_content.index(end_marker, json_start) - json_str = response_content[json_start:json_end].strip() - - # Case 2: Text with valid JSON - else: - # Try to locate the JSON block by scanning for the first `{` and last `}` - json_start = response_content.find("{") - json_end = response_content.rfind("}") - - if json_start != -1 and json_end != -1 and json_start < json_end: - json_str = response_content[json_start : json_end + 1].strip() - - # Validate and ensure the extracted JSON is valid - if json_str: - json.loads(json_str) # This will raise an error if the JSON is invalid - return json_str - - # If no valid JSON found, raise an error - raise ValueError("Response does not contain valid JSON.") - - except json.JSONDecodeError: - # Invalid JSON - print(f"Invalid JSON detected in response: {response_content}") - raise ValueError("Response does not contain valid JSON.") - except Exception as e: - # Other errors - print(f"Error processing response: {str(e)}") - print(f"Full response: {response_content}") - raise - - # get env vars for workflow @activity.defn async def get_wf_env_vars(self, input: EnvLookupInput) -> EnvLookupOutput: """ gets env vars for workflow as an activity result so it's deterministic @@ -498,18 +167,6 @@ class ToolActivities: return output - -def get_current_date_human_readable(): - """ - Returns the current date in a human-readable format. - - Example: Wednesday, January 1, 2025 - """ - from datetime import datetime - - return datetime.now().strftime("%A, %B %d, %Y") - - @activity.defn(dynamic=True) async def dynamic_tool_activity(args: Sequence[RawValue]) -> dict: from tools import get_handler diff --git a/poetry.lock b/poetry.lock index 260744f..2d205bc 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,136 @@ # This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. +[[package]] +name = "aiohappyeyeballs" +version = "2.6.1" +description = "Happy Eyeballs for asyncio" +optional = false +python-versions = ">=3.9" +groups = ["main"] +files = [ + {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"}, + {file = "aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558"}, +] + +[[package]] +name = "aiohttp" +version = "3.11.18" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.9" +groups = 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"jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] + [metadata] lock-version = "2.1" python-versions = ">=3.10,<4.0" -content-hash = "ae793854c4c87fba6ddd666299e04883038ff7af65f1707af797d4d0fa1f3c67" +content-hash = "3a264d3b3dd6d953d188cb58da97a018ca0de381a62a4d146d1f453ff935503c" diff --git a/pyproject.toml b/pyproject.toml index c06792a..6f408ea 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,16 +31,11 @@ temporalio = "^1.8.0" # Standard library modules (e.g. asyncio, collections) don't need to be added # since they're built-in for Python 3.8+. -ollama = "^0.4.5" +litellm = "^1.30.7" pyyaml = "^6.0.2" fastapi = "^0.115.6" uvicorn = "^0.34.0" python-dotenv = "^1.0.1" -openai = "^1.59.2" -stripe = "^11.4.1" -google-generativeai = "^0.8.4" -anthropic = "0.47.0" -deepseek = "^1.0.0" requests = "^2.32.3" pandas = "^2.2.3" gtfs-kit = "^10.1.1" diff --git a/scripts/run_ollama.py b/scripts/run_ollama.py deleted file mode 100644 index 310d9e0..0000000 --- a/scripts/run_ollama.py +++ /dev/null @@ -1,23 +0,0 @@ -from ollama import chat, ChatResponse - - -def main(): - model_name = "mistral" - - # The messages to pass to the model - messages = [ - { - "role": "user", - "content": "Why is the sky blue?", - } - ] - - # Call ollama's chat function - response: ChatResponse = chat(model=model_name, messages=messages) - - # Print the full message content - print(response.message.content) - - -if __name__ == "__main__": - main() diff --git a/scripts/run_worker.py b/scripts/run_worker.py index c0d2a81..bfed281 100644 --- a/scripts/run_worker.py +++ b/scripts/run_worker.py @@ -17,18 +17,18 @@ async def main(): load_dotenv(override=True) # Print LLM configuration info - llm_provider = os.environ.get("LLM_PROVIDER", "openai").lower() - print(f"Worker will use LLM provider: {llm_provider}") + llm_model = os.environ.get("LLM_MODEL", "openai/gpt-4") + print(f"Worker will use LLM model: {llm_model}") # Create the client client = await get_temporal_client() - # Initialize the activities class once with the specified LLM provider + # Initialize the activities class activities = ToolActivities() - print(f"ToolActivities initialized with LLM provider: {llm_provider}") + print(f"ToolActivities initialized with LLM model: {llm_model}") # If using Ollama, pre-load the model to avoid cold start latency - if llm_provider == "ollama": + if llm_model.startswith("ollama"): print("\n======== OLLAMA MODEL INITIALIZATION ========") print("Ollama models need to be loaded into memory on first use.") print("This may take 30+ seconds depending on your hardware and model size.") @@ -51,8 +51,6 @@ async def main(): print("Worker ready to process tasks!") logging.basicConfig(level=logging.WARN) - - # Run the worker with concurrent.futures.ThreadPoolExecutor(max_workers=100) as activity_executor: worker = Worker( diff --git a/setup.md b/setup.md index 61cbaad..49738c2 100644 --- a/setup.md +++ b/setup.md @@ -14,6 +14,37 @@ If you want to show confirmations/enable the debugging UI that shows tool args, SHOW_CONFIRM=True ``` +### Quick Start with Makefile + +We've provided a Makefile to simplify the setup and running of the application. Here are the main commands: + +```bash +# Initial setup +make setup # Creates virtual environment and installs dependencies +make setup-venv # Creates virtual environment only +make install # Installs all dependencies + +# Running the application +make run-worker # Starts the Temporal worker +make run-api # Starts the API server +make run-frontend # Starts the frontend development server + +# Additional services +make run-train-api # Starts the train API server +make run-legacy-worker # Starts the legacy worker +make run-enterprise # Builds and runs the enterprise .NET worker + +# Development environment setup +make setup-temporal-mac # Installs and starts Temporal server on Mac + +# View all available commands +make help +``` + +### Manual Setup (Alternative to Makefile) + +If you prefer to run commands manually, follow these steps: + ### Agent Goal Configuration The agent can be configured to pursue different goals using the `AGENT_GOAL` environment variable in your `.env` file. If unset, default is `goal_choose_agent_type`. @@ -25,15 +56,39 @@ GOAL_CATEGORIES=hr,travel-flights,travel-trains,fin See the section Goal-Specific Tool Configuration below for tool configuration for specific goals. -### LLM Provider Configuration +### LLM Configuration -The agent can use OpenAI's GPT-4o, Google Gemini, Anthropic Claude, or a local LLM via Ollama. Set the `LLM_PROVIDER` environment variable in your `.env` file to choose the desired provider: +The agent uses LiteLLM to interact with various LLM providers. Configure the following environment variables in your `.env` file: -- `LLM_PROVIDER=openai` for OpenAI's GPT-4o -- `LLM_PROVIDER=google` for Google Gemini -- `LLM_PROVIDER=anthropic` for Anthropic Claude -- `LLM_PROVIDER=deepseek` for DeepSeek-V3 -- `LLM_PROVIDER=ollama` for running LLMs via [Ollama](https://ollama.ai) (not recommended for this use case) +- `LLM_MODEL`: The model to use (e.g., "openai/gpt-4o", "anthropic/claude-3-sonnet", "google/gemini-pro", etc.) +- `LLM_KEY`: Your API key for the selected provider +- `LLM_BASE_URL`: (Optional) Custom base URL for the LLM provider. Useful for: + - Using Ollama with a custom endpoint + - Using a proxy or custom API gateway + - Testing with different API versions + +LiteLLM will automatically detect the provider based on the model name. For example: +- For OpenAI models: `openai/gpt-4o` or `openai/gpt-3.5-turbo` +- For Anthropic models: `anthropic/claude-3-sonnet` +- For Google models: `google/gemini-pro` +- For Ollama models: `ollama/mistral` (requires `LLM_BASE_URL` set to your Ollama server) + +Example configurations: +```bash +# For OpenAI +LLM_MODEL=openai/gpt-4o +LLM_KEY=your-api-key-here + +# For Anthropic +LLM_MODEL=anthropic/claude-3-sonnet +LLM_KEY=your-api-key-here + +# For Ollama with custom URL +LLM_MODEL=ollama/mistral +LLM_BASE_URL=http://localhost:11434 +``` + +For a complete list of supported models and providers, visit the [LiteLLM documentation](https://docs.litellm.ai/docs/providers). ### Option 1: OpenAI