How can I get an LLM's output into a string variable.

[phalexo]

I would like to be able to do the following:

llm = LlamaCpp {
model = "model",
....
}

result = llm(prompt)

post process the result.

I cannot find any relevant code to do this.

Hopefully someone has a suggestion how to do this.

I found something with StrOutputParser but

llm does not have a pipe method, was the error.

[dosubot[bot]]

🤖

Hello,

To get the output of a LlamaCpp language model into a string variable for post-processing, you can use the CombiningOutputParser class from the combining.py file in the LangChain framework. This class combines multiple output parsers into one and parses the output of a language model into a dictionary. Here is an example of how you can use it:

from langchain.llms import LlamaCpp
from langchain.schema.output_parser import StrOutputParser
from langchain.output_parsers.combining import CombiningOutputParser

# Initialize the LlamaCpp model
llm = LlamaCpp(model_path="/path/to/llama/model")

# Call the model with a prompt
output = llm._call("This is a prompt.")

# Initialize the StrOutputParser and CombiningOutputParser
str_parser = StrOutputParser()
combining_parser = CombiningOutputParser(parsers=[str_parser])

# Parse the output into a dictionary
parsed_output = combining_parser.parse(output)

# Now the parsed_output variable contains the generated text as a dictionary
print(parsed_output)

In this example, "This is a prompt." is the input to the model. The _call method returns the generated text, which is then parsed into a dictionary by the CombiningOutputParser. The parsed output is stored in the parsed_output variable.

If you are encountering an error with the StrOutputParser and the lack of a pipe method in llm, it might be because these are not provided in the LlamaCpp class as per the provided context. The LlamaCpp class in the provided context does not have a pipe method. If you need these functionalities, you might need to implement them yourself or check if they are available in the llama_cpp library.

The StrOutputParser in the LangChain framework is a class that inherits from the BaseTransformOutputParser class. Its main functionality is to parse the output of a language model into a string format. The StrOutputParser class has two main methods:

1. is_lc_serializable: This method returns a boolean value indicating whether this class is serializable. In this case, it always returns True.

2. _type: This property returns the output parser type for serialization. In this case, it returns the string "default".

3. parse: This method takes a string as input and returns the same string without any changes. This means that the StrOutputParser does not perform any transformations or modifications on the input string.

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

Best,
Dosu

Sources

• libs/langchain/langchain/llms/llamacpp.py
• libs/langchain/langchain/schema/output_parser.py
• libs/langchain/langchain/output_parsers/combining.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.

[dragonskyside]

I would like to be able to do the following:

llm = LlamaCpp { model = "model", .... }

result = llm(prompt)

post process the result.

I cannot find any relevant code to do this.

Hopefully someone has a suggestion how to do this.

I found something with StrOutputParser but

llm does not have a pipe method, was the error.

I would like to be able to do the following:

llm = LlamaCpp { model = "model", .... }

result = llm(prompt)

post process the result.

I cannot find any relevant code to do this.

Hopefully someone has a suggestion how to do this.

I found something with StrOutputParser but

llm does not have a pipe method, was the error.

This is the same exact problem I have.

[phalexo]

The bot's solution has a very simple clue, you don't need to do all of the code. result = llm._call(prompt) does the trick.

…

On Sat, Sep 30, 2023 at 8:08 PM dragonskyside ***@***.***> wrote: I would like to be able to do the following: llm = LlamaCpp { model = "model", .... } result = llm(prompt) post process the result. I cannot find any relevant code to do this. Hopefully someone has a suggestion how to do this. I found something with StrOutputParser but llm does not have a pipe method, was the error. I would like to be able to do the following: llm = LlamaCpp { model = "model", .... } result = llm(prompt) post process the result. I cannot find any relevant code to do this. Hopefully someone has a suggestion how to do this. I found something with StrOutputParser but llm does not have a pipe method, was the error. This is the same exact problem I have. — Reply to this email directly, view it on GitHub <#11253 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ABDD3ZINZNIMZS5DIDNTAOTX5CYABANCNFSM6AAAAAA5NW5DGE> . You are receiving this because you authored the thread.Message ID: ***@***.*** com>

[dragonskyside]

yes.