memoryinvestguide 2025.12.21135045

A new package designed to help users navigate the volatile memory market by providing structured, long-term investment strategies. Users input their current financial situation, risk tolerance, and in

memoryinvestguide

memoryinvestguide is a Python package that helps users navigate the volatile memory market by generating personalized, long‑term investment strategies. By providing your current financial situation, risk tolerance, and investment goals as plain text, the package uses a language model to return a structured response containing:

• A personalized investment plan
• Market insights specific to memory technology
• Risk‑management recommendations

The goal is to give clear, actionable advice so you can make informed decisions despite short‑term market fluctuations.

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Installation

pip install memoryinvestguide

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Quick Start

from memoryinvestguide import memoryinvestguide

# Simple usage with the default LLM (ChatLLM7)
user_input = """
I have $20,000 to invest, moderate risk tolerance, and I aim to grow my portfolio over the next 5 years.
I am interested in memory technologies like DRAM and NAND flash.
"""
response = memoryinvestguide(user_input)

print("\n".join(response))

The function returns a list of strings that together form the structured investment plan.

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Function Signature

def memoryinvestguide(
    user_input: str,
    api_key: Optional[str] = None,
    llm: Optional[BaseChatModel] = None,
) -> List[str]:

Parameter	Type	Description
user_input	str	The user’s free‑form text describing their financial situation, risk tolerance, and investment goals.
api_key	Optional[str]	API key for ChatLLM7. If omitted, the package looks for the environment variable LLM7_API_KEY.
llm	Optional[BaseChatModel]	A custom LangChain LLM instance. When supplied, it supersedes the default ChatLLM7.

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Default Language Model (ChatLLM7)

If you do not provide an llm instance, memoryinvestguide automatically creates a ChatLLM7 client (from the langchain_llm7 package) using the supplied api_key or the LLM7_API_KEY environment variable.

pip install langchain_llm7

The free tier of LLM7 provides generous rate limits that are sufficient for most personal and prototype use cases.

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Using a Custom LLM

You can plug any LangChain‑compatible chat model instead of the default ChatLLM7. Below are examples for popular providers.

OpenAI

from langchain_openai import ChatOpenAI
from memoryinvestguide import memoryinvestguide

llm = ChatOpenAI(model="gpt-4o-mini")  # adjust model as needed
response = memoryinvestguide(
    user_input="...", 
    llm=llm
)

Anthropic

from langchain_anthropic import ChatAnthropic
from memoryinvestguide import memoryinvestguide

llm = ChatAnthropic(model="claude-3-haiku-20240307")
response = memoryinvestguide(
    user_input="...", 
    llm=llm
)

Google Gemini

from langchain_google_genai import ChatGoogleGenerativeAI
from memoryinvestguide import memoryinvestguide

llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
response = memoryinvestguide(
    user_input="...", 
    llm=llm
)

All of the above examples require the corresponding LangChain provider package to be installed.

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API Key & Rate Limits

• LLM7: Register for a free API key at https://token.llm7.io/.
• Set the key via an environment variable:

  export LLM7_API_KEY="your_api_key_here"
  

  or pass it directly:

  response = memoryinvestguide(user_input, api_key="your_api_key_here")
  

The default free tier rate limits are ample for typical usage. If you need higher throughput, upgrade your LLM7 plan and provide the new key as shown above.

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Contributing & Support

• Issue Tracker: https://github....
• Source Code: (add your repository link here)

Feel free to open an issue for bug reports, feature requests, or general questions.

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Author

Eugene Evstafev
📧 Email: [e-mail]
🐙 GitHub: chigwell

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License

This project is licensed under the MIT License – see the LICENSE file for details.