Created Ollama Langchain agent #31
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WalkthroughThe recent update enhances the flexibility of language model initialization in the Changes
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| class OllamaLangChainAgent(LangChainAgent): | ||
| def __init__(self) -> None: | ||
| # Make sure Ollama is running locally | ||
| llm = Ollama(model='mistral', base_url='http://localhost:11434') # Mistral since it supports function calling | ||
| super().__init__(llm=llm) |
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The OllamaLangChainAgent class is correctly inheriting from LangChainAgent and is initialized with an Ollama instance. However, there are a few areas that could be improved for better robustness and flexibility:
- Error Handling: There's no error handling for the case where the Ollama service is not running locally. Consider adding a try-except block around the Ollama initialization to catch connection errors and provide a meaningful error message.
- Configuration Flexibility: The model and base URL for the Ollama instance are hardcoded. It might be beneficial to allow these to be passed as parameters to the
__init__method, providing more flexibility for different environments or use cases. - Performance Considerations: Ensure that the local Ollama instance is adequately optimized for the expected workload. Depending on the deployment environment, it might be necessary to adjust the configuration or scale the service to handle the load efficiently.
| def __init__(self, llm=None) -> None: | ||
| keys = utils.APIKeys() | ||
| llm = OpenAI(openai_api_key=keys.openai_api_key) | ||
| llm = OpenAI(openai_api_key=keys.openai_api_key) if not llm else llm |
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The addition of an optional llm parameter to the LangChainAgent class is a welcome change for flexibility. However, there are a few areas that could be improved:
- Default Behavior: The current implementation overwrites the
llmparameter with a newOpenAIinstance if it'sNone. This behavior is not clear from the method signature. Consider explicitly checking ifllmisNonebefore initializing a newOpenAIinstance to make the logic more transparent. - Error Handling: There's no explicit error handling for issues that might arise during the initialization of the
OpenAIinstance, such as invalid API keys. Adding error handling and logging could improve the robustness of the agent initialization. - Documentation: The method could benefit from documentation comments explaining the purpose of the
llmparameter and the expected type. This would help future developers understand the intended use and constraints of the parameter.
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Review Status
Actionable comments generated: 1
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Files selected for processing (3)
- prediction_market_agent/agents/langchain_agent.py (1 hunks)
- prediction_market_agent/agents/ollama_langchain_agent.py (1 hunks)
- prediction_market_agent/tools/ollama_utils.py (1 hunks)
Files skipped from review as they are similar to previous changes (2)
- prediction_market_agent/agents/langchain_agent.py
- prediction_market_agent/agents/ollama_langchain_agent.py
| def is_ollama_running(base_url: str = "http://localhost:11434") -> bool: | ||
| r = requests.get(f"{base_url}/api/tags") | ||
| return r.status_code == 200 |
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The is_ollama_running function correctly checks if the Ollama service is running by making a GET request to the /api/tags endpoint. However, there are a few areas that could be improved:
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Error Handling: Currently, the function does not handle exceptions that might occur during the request, such as network errors or timeouts. It's recommended to wrap the request in a try-except block to catch exceptions like
requests.exceptions.RequestException. -
Performance: Making a synchronous HTTP request could block the execution if the Ollama service is slow to respond or if there are network issues. Consider using asynchronous requests or setting a timeout for the request to improve responsiveness.
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Security: While not directly related to the current implementation, ensure that any communication with the Ollama service, especially in production environments, is secured (e.g., using HTTPS).
Suggested improvements:
import requests
def is_ollama_running(base_url: str = "http://localhost:11434") -> bool:
try:
r = requests.get(f"{base_url}/api/tags", timeout=5) # Set a reasonable timeout
return r.status_code == 200
except requests.exceptions.RequestException:
return False
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