Redesign Assistants

[Osgood001]

I don't have access to openai api keys recently, to fully harness the power of my LLM-Agent with wolfram alpha, I registered the free developer wolfram engine license and consider use local LLM to do so.

LangChain provide auxilary tools and easy to use syntax. So get things done with it and push it to public.

[Osgood001]

basic usage:

from langchain_community.llms import Ollama
llm = Ollama(model="llama3")

data = llm.invoke("how can langsmith help with testing?")

print(data)

Result:

Langsmith can be a valuable tool in the testing process, especially when it comes to natural language processing (NLP) and machine lea
rning (ML) models. Here are some ways Langsmith can assist with testing:                                                              
1. **Test data generation**: Langsmith can generate test data based on linguistic patterns, such as grammatical structures, lexical va
riations, or semantic relationships. This helps ensure that your model is tested against a diverse set of inputs.                     2. **Evaluation metrics**: Langsmith provides pre-defined evaluation metrics for NLP and ML tasks, including accuracy, precision, reca
ll, F1-score, BLEU, ROUGE, METEOR, and more. These metrics help you measure the performance of your model on different types of input data.                                                                                                                                 3. **Automated testing**: Langsmith can automate the testing process by generating test cases, executing them against your model, and 
providing results in a human-readable format. This saves time and reduces the likelihood of errors.                                   4. **Edge case detection**: Langsmith can help identify edge cases that might not be well-covered by traditional testing methods. For 
example, it can generate tests for unusual input sequences, out-of-vocabulary words, or linguistic phenomena like sarcasm or idioms.  5. **Model debugging**: If your model is producing incorrect results, Langsmith can help you debug the issue by generating test cases 
that isolate specific errors and identifying the root cause of the problem.                                                           6. **Test data augmentation**: Langsmith can augment your existing test data with new, diverse examples, which helps ensure that your 
model is robust against variations in input data.                                                                                     
Some specific scenarios where Langsmith can be particularly helpful include:

* Testing language translation models: Langsmith can generate test cases for translating rare words, phrases, or idioms, ensuring that
 your model is accurate and consistent.                                                                                               * Evaluating sentiment analysis models: Langsmith can create test cases that cover various sentiment expressions, such as sarcasm, iro
ny, or figurative language, to help you fine-tune your model's performance.                                                           * Validating text classification models: Langsmith can generate test cases for different text categories (e.g., spam vs. non-spam emai
ls) and linguistic styles (e.g., formal vs. informal writing).                                                                        
Overall, Langsmith is an excellent tool for testing NLP and ML models, as it helps you create diverse, relevant test data and evaluate
s model performance using standardized metrics.                                                                                       

[Osgood001]

Replace thread and function call

• Thread

now we can call it once using

'Llama.invoke(message)'

then we can simply concatenate existing messages with new one.

And only save one message as a file(markdown recommended) so all file based plugins would work just fine

• Function

LLM bind tools is already out there in the market. It provide a call

Then we simply pass the query and add all to the chat

to enable automatic tool selection and trial by error we set a max iteration and a termination call

See how far has the experimental function progressed

We already have a call

The coolest part of agent is to divide into sub goals

[Osgood001]

Already some templates like babyAGI have achieved this goal.