Noema 1.0.9

Description of Noema

Noema is a new way of programming, using seamless integration between python and llm's generations.

Background:

Noema is an application of the declarative programming paradigm to a langage model.

With Noema, you can control the model and choose the path it will follow. This framework aims to enable developpers to use LLM as a though interpretor, not as a source of truth.

Noema is built on llamacpp and guidance's shoulders.

• Concept
• Installation
• Features

Concept

• Noesis: can be seen as the description of a function
• Noema: is the representation (step by step) of this description
• Constitution: is the process of transformation Noesis->Noema.
• Subject: the object producing the Noema via the constitution of the noesis. Here, the LLM.

Noema/Noesis, Subject, and Constitution are a pedantic and naive application of concept borrowed from Husserl's phenomenology.

ReAct prompting:

We can use ReAct prompting with LLM.

ReAct prompting is a powerful way for guiding a LLM.

ReAct example:

Question: Here is the question 
Reflexion: Thinking about the question
Observation: Providing observation about the Reflexion
Analysis: Formulating an analysis about your current reflexion
Conclusion: Conclude by a synthesis of the reflexion.

Question: {user_input}
Reflexion:

In that case, the LLM will follow the provided steps: Reflexion,Observation,Analysis,Conclusion

Thinking about the quesion is the Noesis of Reflexion

The content generated by the LLM corresponding to Reflexion is the Noema.

Noema let you write python code that automagically:

1. Build the ReAct prompt
2. Let you intercepts (constrained) generations
3. Use it in standard python code

Full examples:

Comment classification

Code:

from Noema import *

# Create a new Subject
subject = Subject("/path/to/your/model.gguf")

# Create a way of thinking
class CommentClassifier(Noesis):
    
    def __init__(self, comments, labels):
        super().__init__()
        self.comments = comments
        self.labels = labels

    def description(self):
        """
        You are a specialist in classifying comments. You have a list of comments and a list of labels.
        You need to provide an analysis for each comment and select the most appropriate label.
        """
        comments_analysis = []
        for c in self.comments:
            comment:Information = f"This is the comment: '{c}'."
            comment_analysis:Sentence = "Providing an analysis of the comment."
            possible_labels:Information = f"Possible labels are: {self.labels}."
            task:Information = "I will provide an analysis for each label."
            reflexions = ""
            for l in self.labels:
                label:Information = f"Thinking about the label: {l}."
                reflexion:Sentence = "Providing a deep reflexion about it."
                consequence:Sentence = "Providing the consequence of the reflexion."
                reflexions += "\n"+reflexion.value
            selected_label:Word = "Providing the label name."
            comment_analysis = {"comment": c, 
                                "selected_label": selected_label.value,
                                "analysis": reflexions}
            comments_analysis.append(comment_analysis)
            
        return comments_analysis

comment_list = ["I love this product", "I hate this product", "I am not sure about this product"]
labels = ["positive", "negative", "neutral"]
comment_analysis = CommentClassifier(comment_list, 
                                     labels).constitute(subject, verbose=True)

# Print the result
for comment in comment_analysis:
    print(comment["comment"])
    print(comment["analysis"])
    print(comment["selected_label"])
    print("-"*50)

LLM output:

[INST]You are a specialist in classifying comments. You have a list of comments and a list of labels.
You need to provide an analysis for each comment and select the most appropriate label.
You are functionning in a loop of though. Your response can be multiline. Here is the reasonning you are currently executing:

#COMMENT: This is the comment: '<blank>'.
#COMMENT_ANALYSIS: Providing an analysis of the comment.
#POSSIBLE_LABELS: Possible labels are: <blank>.
#TASK: I will provide an analysis for each label.
#LABEL: Thinking about the label: <blank>.
#REFLEXION: Providing a deep reflexion about it.
#CONSEQUENCE: Providing the consequence of the reflexion.
#SELECTED_LABEL: Providing the label name.
[/INST]

#COMMENT:This is the comment: 'I love this product'.
#COMMENT_ANALYSIS: The comment is a positive statement about a product.
#POSSIBLE_LABELS:Possible labels are: ['positive', 'negative', 'neutral'].
#TASK:I will provide an analysis for each label.
#LABEL:Thinking about the label: positive.
#REFLEXION: The comment is a positive statement, which means it expresses a favorable opinion or sentiment.
#CONSEQUENCE: The consequence of this reflexion is that the comment is likely to be a positive review or endorsement of the product.
#LABEL:Thinking about the label: negative.
#REFLEXION: The comment is not a negative statement, as it does not express a unfavorable opinion or sentiment.
#CONSEQUENCE: The consequence of this reflexion is that the comment is not a negative review or criticism of the product.
#LABEL:Thinking about the label: neutral.
#REFLEXION: The comment is not a neutral statement, as it does not express a lack of opinion or sentiment.
#CONSEQUENCE: The consequence of this reflexion is that the comment is not a neutral statement.
#SELECTED_LABEL: positive
#COMMENT:This is the comment: 'I hate this product'.
#POSSIBLE_LABELS:Possible labels are: ['positive', 'negative', 'neutral'].
#TASK:I will provide an analysis for each label.
#LABEL:Thinking about the label: positive.
#REFLEXION: The comment is a negative statement, which means it expresses a unfavorable opinion or sentiment.
#CONSEQUENCE: The consequence of this reflexion is that the comment is likely to be a negative review or criticism of the product.
#LABEL:Thinking about the label: negative.
#REFLEXION: The comment is a negative statement, which means it expresses a unfavorable opinion or sentiment.
#CONSEQUENCE: The consequence of this reflexion is that the comment is likely to be a negative review or criticism of the product.
#LABEL:Thinking about the label: neutral.
#REFLEXION: The comment is not a neutral statement, as it does not express a lack of opinion or sentiment.
#CONSEQUENCE: The consequence of this reflexion is that the comment is not a neutral statement.
#SELECTED_LABEL: negative
#COMMENT:This is the comment: 'I am not sure about this product'.
#POSSIBLE_LABELS:Possible labels are: ['positive', 'negative', 'neutral'].
#TASK:I will provide an analysis for each label.
#LABEL:Thinking about the label: positive.
#REFLEXION: The comment is not a positive statement, as it does not express a favorable opinion or sentiment.
#CONSEQUENCE: The consequence of this reflexion is that the comment is not a positive review or endorsement of the product.
#LABEL:Thinking about the label: negative.
#REFLEXION: The comment is not a negative statement, as it does not express a unfavorable opinion or sentiment.
#CONSEQUENCE: The consequence of this reflexion is that the comment is not a negative review or criticism of the product.
#LABEL:Thinking about the label: neutral.
#REFLEXION: The comment is a neutral statement, which means it expresses a lack of opinion or sentiment.
#CONSEQUENCE: The consequence of this reflexion is that the comment is a neutral statement.
#SELECTED_LABEL: neutral

Verbose output:

comment = This is the comment: 'I love this product'. (INFORMATION)
comment_analysis = The comment is a positive statement about a product. (Providing an analysis of the comment.)
possible_labels = Possible labels are: ['positive', 'negative', 'neutral']. (INFORMATION)
task = I will provide an analysis for each label. (INFORMATION)
label = Thinking about the label: positive. (INFORMATION)
reflexion = The comment is a positive statement, which means it expresses a favorable opinion or sentiment. (Providing a deep reflexion about it.)
consequence = The consequence of this reflexion is that the comment is likely to be a positive review or endorsement of the product. (Providing the consequence of the reflexion.)
label = Thinking about the label: negative. (INFORMATION)
reflexion = The comment is not a negative statement, as it does not express a unfavorable opinion or sentiment. (Providing a deep reflexion about it.)
consequence = The consequence of this reflexion is that the comment is not a negative review or criticism of the product. (Providing the consequence of the reflexion.)
label = Thinking about the label: neutral. (INFORMATION)
reflexion = The comment is not a neutral statement, as it does not express a lack of opinion or sentiment. (Providing a deep reflexion about it.)
consequence = The consequence of this reflexion is that the comment is not a neutral statement. (Providing the consequence of the reflexion.)
selected_label = positive (Providing the label name.)
comment = This is the comment: 'I hate this product'. (INFORMATION)
possible_labels = Possible labels are: ['positive', 'negative', 'neutral']. (INFORMATION)
task = I will provide an analysis for each label. (INFORMATION)
label = Thinking about the label: positive. (INFORMATION)
reflexion = The comment is a negative statement, which means it expresses a unfavorable opinion or sentiment. (Providing a deep reflexion about it.)
consequence = The consequence of this reflexion is that the comment is likely to be a negative review or criticism of the product. (Providing the consequence of the reflexion.)
label = Thinking about the label: negative. (INFORMATION)
reflexion = The comment is a negative statement, which means it expresses a unfavorable opinion or sentiment. (Providing a deep reflexion about it.)
consequence = The consequence of this reflexion is that the comment is likely to be a negative review or criticism of the product. (Providing the consequence of the reflexion.)
label = Thinking about the label: neutral. (INFORMATION)
reflexion = The comment is not a neutral statement, as it does not express a lack of opinion or sentiment. (Providing a deep reflexion about it.)
consequence = The consequence of this reflexion is that the comment is not a neutral statement. (Providing the consequence of the reflexion.)
selected_label = negative (Providing the label name.)
comment = This is the comment: 'I am not sure about this product'. (INFORMATION)
possible_labels = Possible labels are: ['positive', 'negative', 'neutral']. (INFORMATION)
task = I will provide an analysis for each label. (INFORMATION)
label = Thinking about the label: positive. (INFORMATION)
reflexion = The comment is not a positive statement, as it does not express a favorable opinion or sentiment. (Providing a deep reflexion about it.)
consequence = The consequence of this reflexion is that the comment is not a positive review or endorsement of the product. (Providing the consequence of the reflexion.)
label = Thinking about the label: negative. (INFORMATION)
reflexion = The comment is not a negative statement, as it does not express a unfavorable opinion or sentiment. (Providing a deep reflexion about it.)
consequence = The consequence of this reflexion is that the comment is not a negative review or criticism of the product. (Providing the consequence of the reflexion.)
label = Thinking about the label: neutral. (INFORMATION)
reflexion = The comment is a neutral statement, which means it expresses a lack of opinion or sentiment. (Providing a deep reflexion about it.)
consequence = The consequence of this reflexion is that the comment is a neutral statement. (Providing the consequence of the reflexion.)
selected_label = neutral (Providing the label name.)

Web search

Code:

from datetime import date
from Noema import *
from capabilities import *

class WebSearch(Noesis):
    
    def __init__(self,request):
        super().__init__()
        self.request = request
    
    def description(self):
        """
        You are a specialist in information retrieval.
        Always looking for the best information to answer a question.
        If you don't know the answer, you are able to find it by searching on the web.
        """
        task:Information = f"{self.request}"
        current_date = date.today().strftime("%d-%m-%Y")
        current_date_info:Information = f"The current date is: {current_date}"
        knowledge_reflexion:Fill = ("Thinking about the task.",
                                f"""I have to think about the task: '{task.value}'.
                                Based on the date and my knowledge, can I Know the answer? {Bool:known_answer}.
                                """)
        if knowledge_reflexion.known_answer:
            answer:Sentence = "Producing the answer."
            return answer,None
        else:
            search_results = google_search(task.value)
            results:Information = f"The search results are: {search_results}"
            manage_results:Fill = ("Managing the search results.",
                                    f"""Selecting the best result: {SubString:infos}.
                                    Extracting the best link: {SubString:link}
                                    Producing the answer based on the information: {Sentence:answer}.
                                    """)
            elaborate:Paragraph = "Using the information of the selected result, I elaborate the answer."
            return elaborate.value, manage_results.link
            
subject = Subject("path/to/your/model.gguf")
answer,source = WebSearch("What is the population of France?").constitute(subject, verbose=True)

print(answer) 
# The current population of France is 66,589,408 as of Sunday, November 24, 2024, based on Worldometer's elaboration of the latest United Nations data1.
print(source)
# 'https://www.worldometers.info/world-population/france-population/'

LLM output:

[INST]You are a specialist in information retrieval.
Always looking for the best information to answer a question.
If you don't know the answer, you are able to find it by searching on the web.
You are functionning in a loop of though. Your response can be multiline. Here is the reasonning you are currently executing:

#TASK: {self.request}.
#CURRENT_DATE: The current date is: {date}.
#KNOWLEDGE_REFLEXION: Thinking about the task.
#ANSWER: Producing the answer.
#RESULTS: The search results are: {search_results}.
#MANAGE_RESULTS: Managing the search results.
#ELABORATE: Using the information of the selected result, I elaborate the answer.
[/INST]

#TASK:What is the population of France?
#CURRENT_DATE:The current date is: 2024-11-26
#KNOWLEDGE_REFLEXION: I have to think about the task: 'What is the population of France?'.
                        Based on the date and my knowledge, can I Know the answer? no.
                        
#RESULTS:The search results are: [{'title': 'France Population (2024)', 'link': 'https://www.worldometers.info/world-population/france-population/', 'description': "The current population of France is 66,589,968 as of Tuesday, November 26, 2024, based on Worldometer's elaboration of the latest United Nations data1."}, {'title': 'Demographics of France', 'link': 'https://en.wikipedia.org/wiki/Demographics_of_France', 'description': 'As of 1 January 2021, 66,142,961 people lived in Metropolitan France, while 2,230,472 lived in overseas France, for a total of 68,373,433 inhabitants in the\xa0...'}, {'title': 'Total population in France 1982-2024 - Demographics', 'link': 'https://www.statista.com/statistics/459939/population-france/', 'description': '12 sept. 2024 — The total population of France has been increasing for years now, exceeding 68 million inhabitants in 2024. France is the second most populous\xa0...'}, {'title': 'Population Clock - France', 'link': 'https://www.census.gov/popclock/world/fr', 'description': 'France · 68.4M · 106.8 · 96.4 · 1.9 · Annual Population Estimates · Annual Population Estimates · Annual Population Estimates. A Closer Look.'}, {'title': 'France Population 1950-2024', 'link': 'https://www.macrotrends.net/global-metrics/countries/fra/france/population', 'description': 'The current population of France in 2024 is 64,881,830, a 0.19% increase from 2023. The population of France in 2023 was 64,756,584, a 0.2% increase from 2022.\xa0...'}, {'title': 'France Population', 'link': 'https://tradingeconomics.com/france/population', 'description': 'The total population in France was estimated at 68.1 million people in 2023, according to the latest census figures and projections from Trading Economics.'}, {'title': 'Population estimates', 'link': 'https://www.insee.fr/en/metadonnees/source/serie/s1169', 'description': 'On 1st January 2024, the population of France was 68.4 million. In 2023, the population increased by 0.3%.In 2023, 678,000 babies were born in France.'}, {'title': 'France - total population 2019-2029', 'link': 'https://www.statista.com/statistics/263743/total-population-of-france/', 'description': '4 juil. 2024 — In 2022, the total population of France amounted to 65.72 million people. See the population of Italy for comparison.'}, {'title': 'France', 'link': 'https://en.wikipedia.org/wiki/France', 'description': 'Its eighteen integral regions (five of which are overseas) span a combined area of 643,801 km2 (248,573 sq mi) and have a total population of 68.4 million as of\xa0...'}, {'title': 'Demography - Population at the beginning of the month - ...', 'link': 'https://www.insee.fr/en/statistiques/serie/000436387', 'description': '31 oct. 2024 — tableauDemography - Population at the beginning of the month - Metropolitan France ; 2020, December, 65,497 ; 2020, November, 65,490 ; 2020\xa0...'}]
#MANAGE_RESULTS: Selecting the best result: {'title': 'France Population (2024)', 'link': 'https://www.worldometers.info/world-population/france-population/', 'description': "The current population of France is 66,589,968 as of Tuesday, November 26, 2024, based on Worldometer's elaboration of the latest United Nations data1."},.
                            Extracting the best link: 'https://www.worldometers.info/world-population/france-population/'
                            Producing the answer based on the information: The current population of France is 66,589,968 as of Tuesday, November 26, 2024, based on Worldometer's elaboration of the latest United Nations data1..
                            
#ELABORATE: The current population of France is 66,589,968 as of Tuesday, November 26, 2024, based on Worldometer's elaboration of the latest United Nations data1.

Verbose output:

task = What is the population of France? (INFORMATION)

current_date = The current date is: 2024-11-26 (INFORMATION)

knowledge_reflexion = I have to think about the task: 'What is the population of France?'.
                        Based on the date and my knowledge, can I Know the answer? no.
                         (Thinking about the task.)

results = The search results are: [{'title': 'France Population (2024)', 'link': 'https://www.worldometers.info/world-population/france-population/', 'description': "The current population of France is 66,589,968 as of Tuesday, November 26, 2024, based on Worldometer's elaboration of the latest United Nations data1."}, {'title': 'Demographics of France', 'link': 'https://en.wikipedia.org/wiki/Demographics_of_France', 'description': 'As of 1 January 2021, 66,142,961 people lived in Metropolitan France, while 2,230,472 lived in overseas France, for a total of 68,373,433 inhabitants in the\xa0...'}, {'title': 'Total population in France 1982-2024 - Demographics', 'link': 'https://www.statista.com/statistics/459939/population-france/', 'description': '12 sept. 2024 — The total population of France has been increasing for years now, exceeding 68 million inhabitants in 2024. France is the second most populous\xa0...'}, {'title': 'Population Clock - France', 'link': 'https://www.census.gov/popclock/world/fr', 'description': 'France · 68.4M · 106.8 · 96.4 · 1.9 · Annual Population Estimates · Annual Population Estimates · Annual Population Estimates. A Closer Look.'}, {'title': 'France Population 1950-2024', 'link': 'https://www.macrotrends.net/global-metrics/countries/fra/france/population', 'description': 'The current population of France in 2024 is 64,881,830, a 0.19% increase from 2023. The population of France in 2023 was 64,756,584, a 0.2% increase from 2022.\xa0...'}, {'title': 'France Population', 'link': 'https://tradingeconomics.com/france/population', 'description': 'The total population in France was estimated at 68.1 million people in 2023, according to the latest census figures and projections from Trading Economics.'}, {'title': 'Population estimates', 'link': 'https://www.insee.fr/en/metadonnees/source/serie/s1169', 'description': 'On 1st January 2024, the population of France was 68.4 million. In 2023, the population increased by 0.3%.In 2023, 678,000 babies were born in France.'}, {'title': 'France - total population 2019-2029', 'link': 'https://www.statista.com/statistics/263743/total-population-of-france/', 'description': '4 juil. 2024 — In 2022, the total population of France amounted to 65.72 million people. See the population of Italy for comparison.'}, {'title': 'France', 'link': 'https://en.wikipedia.org/wiki/France', 'description': 'Its eighteen integral regions (five of which are overseas) span a combined area of 643,801 km2 (248,573 sq mi) and have a total population of 68.4 million as of\xa0...'}, {'title': 'Demography - Population at the beginning of the month - ...', 'link': 'https://www.insee.fr/en/statistiques/serie/000436387', 'description': '31 oct. 2024 — tableauDemography - Population at the beginning of the month - Metropolitan France ; 2020, December, 65,497 ; 2020, November, 65,490 ; 2020\xa0...'}] (INFORMATION)

manage_results = Selecting the best result: {'title': 'France Population (2024)', 'link': 'https://www.worldometers.info/world-population/france-population/', 'description': "The current population of France is 66,589,968 as of Tuesday, November 26, 2024, based on Worldometer's elaboration of the latest United Nations data1."},.
                            Extracting the best link: 'https://www.worldometers.info/world-population/france-population/'
                            Producing the answer based on the information: The current population of France is 66,589,968 as of Tuesday, November 26, 2024, based on Worldometer's elaboration of the latest United Nations data1..
                             (Managing the search results.)

elaborate = The current population of France is 66,589,968 as of Tuesday, November 26, 2024, based on Worldometer's elaboration of the latest United Nations data1.(Using the information of the selected result, I elaborate the answer.)

Usage:

Installation

Requires python <11.0,>= 3.6

pip install Noema

Features

Create the Subject

from Noema import *

subject = Subject("path/to/your/model.gguf") # Full Compatibiliy with LLamaCPP.

Create a way of thinking:

1. Create a class that inherits from Noesis

2. Add a method named description

3. Add a system prompt using the python docstring

4. Write python code

from Noema import *

subject = Subject("path/to/your/model.gguf")

class WayOfThinking(Noesis):
    
    def description(self):
        """
        Here write the system prompt, describing the role/task of the system.
        In the same way as for a classical prompt, you can use multiline.
        """
        task_list = ["Task description 1", "Task description 2"]
        reflexions = []
        for task_description in task_list:
            task:Information = f"{task_description}" # Insert description
            thought:Fill = ("""Thinking about the task.
                            Here you can write the different steps of the thought process.
                            1. Step 1: I do this.
                            2. Step 2: I do that.
                            3. Step 3: I do this other thing.
                            """,
                            f"""Step 1: {Sentence:step1}
                            Step 2: {Sentence:step2}
                            Step 3: {Sentence:step3}
                            """)
            print(thought.step1) # easy access to the though variables (step1,step2,step3)
            reflexion:Paragraph = "Here you can write a reflexion about the thought process."
            reflexions.append(reflexion.value)
        return reflexions

wot = WayOfThinking()
reflexions = wot.constitute(subject)
print(reflexions) # contains the reflexions produced by the LLM

Generators

Generators are used to generate content from the subject (LLM) through the noesis (the task description).

They always produce the corresponding python type.

Simple Generators

Noema Type	Python Type	Usage
Int	int	number:Int = "Give me a number between 0 and 10"
Float	float	number:Float = "Give me a number between 0.1 and 0.7"
Bool	bool	truth:Bool = "Are local LLMs better than online LLMs?"
Word	str	better:Word = "Which instruct LLM is the best?"
Sentence	str	explaination:Sentence = "Explain why"
Paragraph	str	long_explaination:Paragraph = "Give mode details"

Example:

class WayOfThinking(Noesis):
    
    def description(self):
        """
        You are a nice assistant.
        """
        found = False
        hello:Word = "Say 'hello' in French"
        while(not found):
            nb_letter:Int = f"How many letter in {hello.value}"
            verification:Bool = f"Does {hello.value} really contains {nb_letter.value} letters?"
            if verification.value:
                print("Verification done!")
                found = True

        return hello.value, nb_letter.value

wot = WayOfThinking()
reflexions = wot.constitute(subject, verbose=True)
print(reflexions)

Composed Generators

List of simple Generators can be built.

Noema Type	Python Type	Usage
IntList	[int]	number:IntList = "Give me a list of number between 0 and 10"
FloatList	[float]	number:FloatList = "Give me a list of number between 0.1 and 0.7"
BoolList	[bool]	truth:BoolList = "Are local LLMs better than online LLMs, and Mistral better than LLama?"
WordList	[str]	better:WordList = "List the best instruct LLM"
SentenceList	[str]	explaination:SentenceList = "Explain step by step why"

Fill-in-the-blanks Generator

Noema Type	Python Type
Fill	var_name.sub_var_name

though:Fill = ("Categorizing user comment",
                """The user language is {Word:language}
                From a psychologist point of view, this comment is {Sentence:psycho}"""

# Property are added to though
# though.language 
# thought.psycho

Reflexion Generator:

Reflexion generators provide a simple way to make the LLM think about something.

Noema Type	Python Type	Usage
Reflexion	str	builder:Reflexion = "How to build a house in the forest?"

It will follow an abstract reflection prompt:

[INST]How to build a house in the forest?
 Follow these steps of reasoning, using a loop to determine whether the process should continue or if the reflection is complete:
1. Initial Hypothesis: Provide a first answer or explanation based on your current knowledge.
2. Critical Analysis: Evaluate the initial hypothesis. Look for contradictions, weaknesses, or areas of uncertainty.
3. Conceptual Revision: Revise or improve the hypothesis based on the critiques from the Critical Analysis.
4. Extended Synthesis: Develop a more complete and nuanced response by incorporating additional perspectives or knowledge.
5. Loop or Conclusion: Return to the Decision Point. If the answer is now coherent and well-justified, you repond 'satisfying' and move to the Conclusion. If further refinement is needed, respond 'loop again' and go to the Critical Analysis. 
6. Final Conclusion: Once the reflection is considered complete, provide a final answer, clearly explaining why this response is coherent and well-justified, summarizing the key steps of the reasoning process.
7. Quality of the reflection: Provide a quality assessment of the reflection process. 
Done.
[/INST]

Code Generator

The LanguageName type provide a way to generate LanguageName code

Noema Type	Python Type	Usage
Python	str	interface:Python = "With pyqt5, genereate a window with a text field and a OK button."

Language List

• Python
• Java
• C
• Cpp
• CSharp
• JavaScript
• TypeScript
• HTML
• CSS
• SQL
• NoSQL
• GraphQL
• Rust
• Go
• Ruby
• PHP
• Shell
• Bash
• PowerShell
• Perl
• Lua
• R
• Scala
• Kotlin
• Dart
• Swift
• ObjectiveC
• Assembly
• VHDL
• Verilog
• SystemVerilog
• Julia
• MATLAB
• COBOL
• Fortran
• Ada
• Pascal
• Lisp
• Prolog
• Smalltalk
• APL

Information

The type Information is useful to insert some context to the LLM at the right time in the reflexion process.

Noema Type	Python Type	Usage
Information	str	tips:Information = "Here you can inject some information in the LLM"

Here we use a simple string, but we can also insert a string from a python function call, do some RAG or any other tasks.

Noesis / Noema / Value

Every generator as the following properties by default:

from Noema import *

subject = Subject("path/to/your/model.gguf")

class WayOfThinking(Noesis):
    
    def description(self):
        """
        You are a specialist in nice house building.
        """
        builder:Reflexion = "How to build a house in the forest?"
        print("Noesis:")
        print(builder.noesis)
        print("-"*50)
        print("Noema:")
        print(builder.noema)
        print("-"*50)
        print("Value:")
        print(builder.value)
        print("-"*50)

WayOfThinking().constitute(subject)

Produced output:

Noesis:
How to build a house in the forest?
--------------------------------------------------
Noema:

        ***Initial Hypothesis: To build a house in the forest, you need to find a suitable location, clear the area of trees and debris, and then construct the house using materials such as wood, stone, and metal. You may also need to consider factors such as water supply, electricity, and waste disposal.
While this initial hypothesis provides a general outline of the process, it lacks specific details and considerations. For example, it does not address the importance of selecting a location with good drainage to prevent flooding, or the need to obtain necessary permits and approvals from local authorities.
To build a house in the forest, you should first research and select a suitable location, taking into account factors such as soil type, drainage, and proximity to water sources. You should also obtain necessary permits and approvals from local authorities. Once you have a clear plan, you can begin clearing the area of trees and debris, and then construct the house using sustainable materials such as wood, stone, and metal. You should also consider installing a water filtration system, a solar-powered electricity system, and a composting toilet to minimize your impact on the environment.
Building a house in the forest requires careful planning and consideration of environmental factors. You should research and select a location that is suitable for building, taking into account factors such as soil type, drainage, and proximity to water sources. You should also obtain necessary permits and approvals from local authorities. Once you have a clear plan, you can begin clearing the area of trees and debris, and then construct the house using sustainable materials such as wood, stone, and metal. You should also consider installing a water filtration system, a solar-powered electricity system, and a composting toilet to minimize your impact on the environment. Additionally, you may want to consider building a greenhouse or garden to grow your own food and reduce your reliance on outside resources.
loop again
Building a house in the forest requires careful planning and consideration of environmental factors. You should research and select a suitable location, taking into account factors such as soil type, drainage, and proximity to water sources. You should also obtain necessary permits and approvals from local authorities. Once you have a clear plan, you can begin clearing the area of trees and debris, and then construct the house using sustainable materials such as wood, stone, and metal. You should also consider installing a water filtration system, a solar-powered electricity system, and a composting toilet to minimize your impact on the environment. Additionally, you may want to consider building a greenhouse or garden to grow your own food and reduce your reliance on outside resources. This response is coherent and well-justified because it takes into account the importance of selecting a suitable location, obtaining necessary permits and approvals, and using sustainable materials and systems to minimize the impact on the environment.
Reflexion loop completed.
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Value:
Building a house in the forest requires careful planning and consideration of environmental factors. You should research and select a suitable location, taking into account factors such as soil type, drainage, and proximity to water sources. You should also obtain necessary permits and approvals from local authorities. Once you have a clear plan, you can begin clearing the area of trees and debris, and then construct the house using sustainable materials such as wood, stone, and metal. You should also consider installing a water filtration system, a solar-powered electricity system, and a composting toilet to minimize your impact on the environment. Additionally, you may want to consider building a greenhouse or garden to grow your own food and reduce your reliance on outside resources. This response is coherent and well-justified because it takes into account the importance of selecting a suitable location, obtaining necessary permits and approvals, and using sustainable materials and systems to minimize the impact on the environment.

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