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Created Ollama Langchain agent #31

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merged 4 commits into from
Mar 12, 2024
Merged

Created Ollama Langchain agent #31

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cc93194
Added Ollama integrations to Langchain and LLamaIndex
gabrielfior Mar 12, 2024
7e1d599
Removed Llama index integration
gabrielfior Mar 12, 2024
1e662f6
Fixed mypy and black
gabrielfior Mar 12, 2024
8f6f904
Added Ollama is running check
gabrielfior Mar 12, 2024
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4 changes: 2 additions & 2 deletions prediction_market_agent/agents/langchain_agent.py
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Expand Up @@ -7,9 +7,9 @@


class LangChainAgent(AbstractAgent):
def __init__(self) -> None:
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:

  1. Default Behavior: The current implementation overwrites the llm parameter with a new OpenAI instance if it's None. This behavior is not clear from the method signature. Consider explicitly checking if llm is None before initializing a new OpenAI instance to make the logic more transparent.
  2. Error Handling: There's no explicit error handling for issues that might arise during the initialization of the OpenAI instance, such as invalid API keys. Adding error handling and logging could improve the robustness of the agent initialization.
  3. Documentation: The method could benefit from documentation comments explaining the purpose of the llm parameter and the expected type. This would help future developers understand the intended use and constraints of the parameter.

# Can use pre-defined search tool
# TODO: Tavily tool could give better results
# https://docs.tavily.com/docs/tavily-api/langchain
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10 changes: 10 additions & 0 deletions prediction_market_agent/agents/ollama_langchain_agent.py
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@@ -0,0 +1,10 @@
from langchain_community.llms.ollama import Ollama

from prediction_market_agent.agents.langchain_agent import LangChainAgent


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:

  1. 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.
  2. 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.
  3. 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.

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