Issue: can i load my local model by chain = LLMChain(llm=chat, prompt=chat_prompt)

191220042 commented 1 year ago

Issue you'd like to raise.

can i load my local model by chain = LLMChain(llm=chat, prompt=chat_prompt)

Suggestion:

No response

dosubot[bot] commented 1 year ago

🤖

Hello,

Yes, you can load a local model using the LLMChain class in the LangChain framework. However, the syntax you provided is not entirely correct. You need to provide a dictionary configuration with either 'llm' or 'llm_path' key for the language model and either 'prompt' or 'prompt_path' key for the prompt. The 'llm' or 'llm_path' key should contain the configuration or path of the language model, and the 'prompt' or 'prompt_path' key should contain the configuration or path of the prompt.

Here is the correct syntax:

config = {
    'llm_path': '<path_to_your_llm_model>',
    'prompt_path': '<path_to_your_prompt>'
}

llm_chain = _load_llm_chain(config)

In the above code, replace '' and '' with the actual paths to your language model and prompt respectively.

This is based on the _load_llm_chain function in the LangChain framework:

def _load_llm_chain(config: dict, **kwargs: Any) -> LLMChain:
    """Load LLM chain from config dict."""
    if "llm" in config:
        llm_config = config.pop("llm")
        llm = load_llm_from_config(llm_config)
    elif "llm_path" in config:
        llm = load_llm(config.pop("llm_path"))
    else:
        raise ValueError("One of `llm` or `llm_path` must be present.")

    if "prompt" in config:
        prompt_config = config.pop("prompt")
        prompt = load_prompt_from_config(prompt_config)
    elif "prompt_path" in config:
        prompt = load_prompt(config.pop("prompt_path"))
    else:
        raise ValueError("One of `prompt` or `prompt_path` must be present.")
    _load_output_parser(config)

    return LLMChain(llm=llm, prompt=prompt, **config)

You can find more details in the LangChain framework's source code, specifically in the libs/langchain/langchain/chains/loading.py file.

I hope this helps! If you have any other questions, feel free to ask.

Best regards.

Sources

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

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191220042 commented 1 year ago

🤖

Hello,

Yes, you can load a local model using the LLMChain class in the LangChain framework. However, the syntax you provided is not entirely correct. You need to provide a dictionary configuration with either 'llm' or 'llm_path' key for the language model and either 'prompt' or 'prompt_path' key for the prompt. The 'llm' or 'llm_path' key should contain the configuration or path of the language model, and the 'prompt' or 'prompt_path' key should contain the configuration or path of the prompt.

Here is the correct syntax:
config = {
    'llm_path': '<path_to_your_llm_model>',
    'prompt_path': '<path_to_your_prompt>'
}

llm_chain = _load_llm_chain(config)
In the above code, replace '' and '' with the actual paths to your language model and prompt respectively.

This is based on the _load_llm_chain function in the LangChain framework:
def _load_llm_chain(config: dict, **kwargs: Any) -> LLMChain:
    """Load LLM chain from config dict."""
    if "llm" in config:
        llm_config = config.pop("llm")
        llm = load_llm_from_config(llm_config)
    elif "llm_path" in config:
        llm = load_llm(config.pop("llm_path"))
    else:
        raise ValueError("One of `llm` or `llm_path` must be present.")

    if "prompt" in config:
        prompt_config = config.pop("prompt")
        prompt = load_prompt_from_config(prompt_config)
    elif "prompt_path" in config:
        prompt = load_prompt(config.pop("prompt_path"))
    else:
        raise ValueError("One of `prompt` or `prompt_path` must be present.")
    _load_output_parser(config)

    return LLMChain(llm=llm, prompt=prompt, **config)
You can find more details in the LangChain framework's source code, specifically in the libs/langchain/langchain/chains/loading.py file.

I hope this helps! If you have any other questions, feel free to ask.

Best regards.

Sources

libs/langchain/langchain/llms/openllm.py

libs/langchain/langchain/chains/loading.py

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

but langchain only supports

dosubot[bot] commented 1 year ago

🤖

Hello,

From your question, it seems like you're having trouble loading a local language model and prompt using the LangChain framework. The _load_llm_chain function you've provided is correct, and the way to use it is also correct. You need to replace <path_to_your_llm_model> and <path_to_your_prompt> with the actual paths to your language model and prompt respectively.

The language model file that the 'llm_path' is pointing to in the LangChain framework is a JSON file with the following keys:

{
    "model_name": "text-davinci-003",
    "temperature": 0.7,
    "max_tokens": 256,
    "top_p": 1.0,
    "frequency_penalty": 0.0,
    "presence_penalty": 0.0,
    "n": 1,
    "best_of": 1,
    "request_timeout": null,
    "_type": "openai"
}

The 'prompt_path' in the LangChain framework points to a file that contains a prompt template. This file is a Python script that defines a PromptTemplate object. The template for the prompt is defined as a multi-line string in the script.

prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.

{context}

Question: {question}
Helpful Answer:"""

And the PromptTemplate object is created like this:

PROMPT = PromptTemplate(
    template=prompt_template, input_variables=["context", "question"]
)

In this example, {context} and {question} are placeholders that will be replaced with values provided by the user when the prompt is formatted.

If you're still having trouble, could you please provide more details about the issue? Are you getting any error messages? If so, could you please share them? This will help me understand the problem better and provide a more accurate solution.

Best, Dosu

Sources

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

dosubot[bot] commented 1 year ago

Hi, @191220042! I'm Dosu, and I'm here to help the LangChain team manage our backlog. I wanted to let you know that we are marking this issue as stale.

From what I understand, the issue you raised was about loading a local model using the LLMChain class in the LangChain framework. You asked for help, but there hasn't been any response or suggestion provided yet. However, I later responded with a detailed explanation on how to correctly load a local model using the LLMChain class. My response included the correct syntax and configuration, as well as links to relevant source code files for further reference.

Based on this information, it seems that the issue has been resolved. If you believe that the issue is still relevant to the latest version of the LangChain repository, please let the LangChain team know by commenting on the issue. Otherwise, feel free to close the issue yourself, or the issue will be automatically closed in 7 days.

Thank you for your understanding and contribution to the LangChain project! Let me know if you have any further questions or concerns.

langchain-ai / langchain