I want to let my AI LLM use this tool: GET /market/auto-complete?query=%3CREQUIRED%3E HTTP/1.1

X-Rapidapi-Key: 8eccd361cemsh3324c34cc2c04e9p16b490jsn12d7933a7e04

X-Rapidapi-Host: bloomberg-market-and-financial-news.p.rapidapi.com

Host: bloomberg-market-and-financial-news.p.rapidapi.com

I am using AnythingLLM as the GUI so I would like to know where and how I put this code
To integrate the specified API request into your AnythingLLM GUI, you’ll need to implement this within your application’s codebase. Here’s a general guideline on how to achieve this:

1. **Identify the Trigger Event**: Determine when you want this API request to be triggered. For instance, it could be when a user types into a search bar or clicks a specific button.

2. **GUI Component Setup**: Ensure you have a text input or a button in your AnythingLLM GUI that will initiate the API request.

3. **Implementing the API Request**:
– In your application’s code (JavaScript or similar language), set up an event listener or function that executes when the trigger event occurs (e.g., user input or button click).
– Construct the API request using the provided URL (`/market/auto-complete`) with the necessary query parameter (`query=%3CREQUIRED%3E`).
– Include the required headers (`X-Rapidapi-Key` and `X-Rapidapi-Host`) as part of the request headers.

4. **Sending the API Request**:
– Use an appropriate method (like `fetch` in JavaScript) to send the constructed API request to the specified endpoint (`bloomberg-market-and-financial-news.p.rapidapi.com`).
– Ensure to handle the response from the API appropriately. This may involve parsing the JSON response and updating your GUI accordingly.

5. **Handling Errors**: Implement error handling to manage cases where the API request fails (e.g., due to network issues or invalid response).

Here’s a simplified example using JavaScript (assuming you are working within a web environment):

“`javascript
// Define a function to perform the API request
async function performAPICall() {
const query = encodeURIComponent(”); // Specify your required query
const url = `https://bloomberg-market-and-financial-news.p.rapidapi.com/market/auto-complete?query=${query}`;

try {
const response = await fetch(url, {
method: ‘GET’,
headers: {
‘X-Rapidapi-Key’: ‘8eccd361cemsh3324c34cc2c04e9p16b490jsn12d7933a7e04’,
‘X-Rapidapi-Host’: ‘bloomberg-market-and-financial-news.p.rapidapi.com’
}
});

if (response.ok) {
const data = await response.json();
// Handle the data received (e.g., update GUI components)
console.log(data); // Output the data to console (for demonstration)
} else {
throw new Error(‘API request failed’);
}
} catch (error) {
console.error(‘Error:’, error.message);
// Implement error handling (e.g., display error message to user)
}
}

// Example: Call performAPICall() when a button is clicked
const buttonElement = document.getElementById(‘myButton’); // Replace ‘myButton’ with your button ID
buttonElement.addEventListener(‘click’, performAPICall);
“`

In this example:
– `performAPICall()` is a function that constructs and sends the API request using `fetch()`.
– Replace “ in `encodeURIComponent(”)` with the actual query value you want to use.
– Ensure you replace `’myButton’` with the actual ID of your GUI button element.

Integrate this code into your AnythingLLM application according to its structure and framework. This approach allows you to incorporate the specified API request into your GUI effectively. Adjustments may be needed based on your specific implementation and environment.
Is there a way I can include this as a tool in the system prompt as I do not have access to the codebase
If you’re looking to use the specified API request within the System Prompt interface of AnythingLLM without direct access to modify the codebase, you might face limitations depending on the capabilities and configuration of the platform. Here are a few considerations and suggestions:

1. **Built-in Integration**: Check if AnythingLLM provides any built-in tools or plugins that allow direct integration of external APIs. Some platforms offer pre-built integrations for common services or APIs.

2. **Third-Party Plugins or Extensions**: Explore if there are third-party plugins or extensions available for AnythingLLM that enable API integrations. These plugins might offer a way to configure and use external APIs within the system prompt interface.

3. **Proxy or Middleware Services**: If direct API access is not feasible within the system prompt, consider using proxy or middleware services that can act as intermediaries. You can configure the proxy to make the API request on your behalf and then interact with the proxy from the system prompt.

4. **Custom API Proxy**: Create a custom API proxy service that exposes a simplified interface for making specific API requests. You can then interact with this proxy service from the system prompt using predefined commands or prompts.

5. **Manual Interaction**: As a last resort, manually perform the API request outside of AnythingLLM using tools like cURL or Postman. Extract the necessary information from the API response and incorporate it manually into your interaction within AnythingLLM.

Without direct access to modify the codebase of AnythingLLM, the options may be limited to what the platform supports natively or through available extensions. It’s recommended to consult the documentation or support resources provided by AnythingLLM to explore the possibilities of integrating external APIs or custom functionalities into the system prompt interface.
https://github.com/MeetKai/functionary This llm or code is supposed to make funcntion or api calls, how do I get it to do this withing my app?
To integrate the `functionary` library into your application and use it to make API calls within your app, follow these steps:

1. **Install `functionary`**:
– First, clone or download the `functionary` repository from GitHub: [MeetKai/functionary](https://github.com/MeetKai/functionary).
– Navigate to the repository directory in your terminal or command prompt.
– Install the necessary dependencies by running:
“`
npm install
“`

2. **Configure `functionary`**:
– Once installed, you’ll need to configure `functionary` with your API key and host information.
– Open the `config/default.json` file in the `functionary` directory.
– Replace the placeholder values (“ and “) with your actual API key and host:
“`json
{
“rapidApiKey”: “”,
“rapidApiHost”: “”
}
“`
– Save the changes.

3. **Use `functionary` in Your Application**:
– Within your application’s codebase (assuming it’s a Node.js application or similar environment), import the `functionary` module and use it to make API calls.

“`javascript
const functionary = require(‘functionary’);

async function makeAPICall() {
try {
const response = await functionary.callFunction(‘market/auto-complete’, {
query: ”
});

console.log(response);
// Handle the API response here
} catch (error) {
console.error(‘Error:’, error);
// Handle errors here
}
}

// Call the function to make the API request
makeAPICall();
“`

– Replace “ in the `query` object with the actual query parameter you want to include in the API request.

4. **Handling the API Response**:
– In the `makeAPICall()` function, the `functionary.callFunction()` method is used to make the API request to the specified endpoint (`market/auto-complete`).
– Handle the API response within the `try` block. The `response` variable will contain the result of the API call.

5. **Error Handling**:
– Implement error handling within the `catch` block to manage any failures that may occur during the API request.

6. **Integrate with AnythingLLM**:
– Depending on the capabilities of AnythingLLM, you can now incorporate the `makeAPICall()` function into your application’s logic within the system prompt or interface provided by AnythingLLM.
– Use the function to trigger the API request and handle the response accordingly within the context of your application.

By following these steps, you should be able to leverage the `functionary` library to make API calls within your application environment. Ensure that your application setup (such as Node.js installation and environment configuration) aligns with the requirements of the `functionary` library.
Is there an embedding specifically for function calling models
It sounds like you’re interested in using function calling models within an application or system, particularly models designed for executing specific functions or tasks. Here are some considerations and options for incorporating such models:

1. **Function Embeddings with Natural Language Models**:
– Some natural language processing (NLP) models, such as those based on transformer architectures like GPT (Generative Pre-trained Transformer) models, can be fine-tuned for specific tasks, including function calling or execution based on textual input.
– You can train or fine-tune a model to understand and execute commands or requests expressed in natural language. For example, a model can interpret instructions like “calculate the square root of 25″ and perform the corresponding computation.

2. **Using Embeddings for Function Invocation**:
– Embeddings are representations of words or phrases in a vector space that capture semantic relationships. For function calling models, embeddings can be used to map textual inputs (e.g., commands, requests) to corresponding functions or actions.
– Develop a mapping or lookup mechanism where specific embeddings correspond to predefined functions or operations. This allows the model to interpret textual inputs and invoke the appropriate functions.

3. **Implementation with Libraries or Frameworks**:
– Depending on your specific use case and programming environment, consider using libraries or frameworks that facilitate function calling based on textual inputs.
– For example, in Python, you can leverage tools like spaCy or TensorFlow to build models that understand and execute commands expressed in natural language.

4. **Integrating Function Calling Models into Applications**:
– Once you have a trained or configured function calling model, integrate it into your application logic.
– Design an interface (e.g., command-line interface, web interface) where users can input commands or requests.
– Use the function calling model to process these inputs, interpret them, and execute the corresponding functions or actions.

5. **Example Workflow**:
– Train or fine-tune a transformer-based NLP model on a dataset containing pairs of textual commands and corresponding function calls or actions.
– Use embeddings to represent textual commands and map them to function identifiers.
– Develop an application that accepts user input, passes it through the model for interpretation, and executes the inferred functions based on the model’s predictions.

6. **Considerations for Deployment**:
– When deploying function calling models in production environments, consider scalability, performance, and security aspects.
– Ensure that the model’s inference process is efficient and can handle varying inputs gracefully.
– Implement error handling and logging mechanisms to monitor and troubleshoot the model’s behavior in real-world usage scenarios.

By following these guidelines, you can incorporate function calling models effectively into your applications, allowing users to interact with the system using natural language commands or instructions. Tailor the approach based on your specific requirements and the capabilities of the tools and frameworks you choose to use.
that is not what I am asking. The question ‘Do funtion calling LLM’s have a special embedding model?” or what is recommeded
Ah, I understand your question now. When referring to function calling language models (LLMs), especially those designed to execute specific tasks or functions based on natural language input, the concept of embeddings is indeed important. Embeddings in this context refer to representations of textual commands or instructions that allow the model to understand and associate them with corresponding functions or actions.

Specifically for function calling LLMs, there isn’t necessarily a distinct “special” embedding model per se, but rather a focus on how embeddings are used and integrated into the model architecture to enable function invocation. Here are some recommended approaches and considerations:

1. **Task-Specific Fine-Tuning**:
– Start with a pre-trained language model (e.g., GPT, BERT) and fine-tune it on a dataset containing pairs of textual commands and their corresponding function executions.
– During fine-tuning, emphasize learning embeddings that capture the semantics of commands and their associated actions. This adaptation allows the model to specialize in function calling tasks.

2. **Embedding Mapping**:
– Develop a mapping mechanism that links textual commands to specific functions or actions in your application domain.
– Use embeddings (typically derived from the model’s hidden states) to represent commands and map them to function identifiers or executable code.

3. **Input Representation**:
– Design the input representation for the model to effectively capture the context and semantics of function-invoking commands.
– This may involve preprocessing input text, tokenizing it appropriately, and leveraging contextual embeddings to encode the command’s meaning.

4. **Contextual Embeddings**:
– Explore the use of contextual embeddings (e.g., transformer-based embeddings like BERT or GPT) that capture the contextual information of the entire command sequence.
– Contextual embeddings are effective for understanding nuanced commands and determining the appropriate function to execute based on the surrounding context.

5. **Model Architecture**:
– Choose or design a model architecture that supports the task of function calling. This may involve integrating embeddings with a decision-making component that predicts or selects the appropriate function based on the input embeddings.

6. **Training and Evaluation**:
– Train the model using a supervised learning approach, where the objective is to predict the correct function execution given textual commands.
– Evaluate the model’s performance based on its ability to accurately associate commands with the correct functions during inference.

In summary, while there isn’t a unique “special” embedding model exclusively for function calling LLMs, the key lies in how embeddings are leveraged and integrated into the model’s architecture and training process. By focusing on task-specific fine-tuning, embedding mapping, contextual representation, and model design, you can develop effective function calling language models that excel at understanding and executing commands expressed in natural language.
I have the model: meetkai/functionary-small-v2. I just what to know how to use it
To use the `meetkai/functionary-small-v2` model from the `functionary` repository effectively for function calling or task execution based on natural language input, you can follow these steps:

1. **Install Required Dependencies**:
Ensure you have Node.js installed on your system. If not, download and install it from the [official website](https://nodejs.org/).

2. **Clone the `functionary` Repository**:
Clone the `functionary` repository from GitHub to your local machine:
“`
git clone https://github.com/MeetKai/functionary.git
“`

3. **Navigate to the Repository Directory**:
Change into the `functionary` directory:
“`
cd functionary
“`

4. **Install Node.js Dependencies**:
Install the necessary Node.js dependencies using npm:
“`
npm install
“`

5. **Start the Interactive CLI**:
Use the provided interactive CLI (Command Line Interface) to interact with the `functionary-small-v2` model:
“`
npm start
“`

6. **Interact with the Model**:
Once the CLI is running, you can interact with the model by entering natural language commands or queries. The model will attempt to execute the corresponding functions or actions based on its training and capabilities.

7. **Experiment with Different Commands**:
Try entering various commands or queries to see how the model responds. The `functionary-small-v2` model is designed to understand and execute a variety of predefined tasks based on natural language inputs.

8. **Customize and Extend (Optional)**:
If needed, you can customize or extend the model’s capabilities by modifying its training data, fine-tuning the model, or integrating it into your own applications using the provided APIs and interfaces.

By following these steps, you can leverage the `meetkai/functionary-small-v2` model within the `functionary` repository to perform function calling or task execution based on natural language inputs. The interactive CLI provides a convenient way to explore the model’s functionality and capabilities directly from the command line.

If you encounter any issues or have specific use cases in mind, refer to the documentation and resources provided with the `functionary` repository for further guidance and customization options.
lets test the model. Give it a prompt that will test if it is working or not
Certainly! To test the `meetkai/functionary-small-v2` model and verify if it is working properly, you can provide it with a natural language prompt that involves executing a simple task or function. Here’s an example prompt you can use:

**Prompt**: “What is the square root of 16?”

This prompt is straightforward and should elicit a response from the model indicating the calculated square root of 16.

To test the model using this prompt within the `functionary` CLI, follow these steps:

1. Ensure you have followed the setup steps mentioned earlier to clone the `functionary` repository and start the interactive CLI (`npm start`).

2. Once the CLI is running, you’ll see a prompt where you can enter commands or queries.

3. Enter the prompt: “What is the square root of 16?”

4. Press Enter to submit the prompt to the model.

5. The model should process the prompt and provide a response indicating the calculated square root of 16.

6. Review the model’s response to verify if it correctly interprets and executes the task specified in the prompt.

If the model is working correctly, it should understand the intent of the prompt and return the correct result (which is 4, in this case, as the square root of 16 is 4).

Feel free to experiment with other prompts or commands to explore the capabilities of the `meetkai/functionary-small-v2` model. This testing approach allows you to interactively assess the model’s performance and ensure that it functions as expected for various types of tasks expressed in natural language.
The sq of 16 is 4. You don’t need a function or api for that
You’re correct that calculating the square root of 16 is a straightforward arithmetic operation and doesn’t require a function or API call for basic math operations like this. The example provided was meant to be simple to illustrate how you might interact with a function calling model using natural language.

To effectively test and demonstrate the capabilities of the `meetkai/functionary-small-v2` model, you should consider prompts or queries that involve more complex tasks or actions that would benefit from the model’s ability to interpret natural language and perform corresponding functions or operations.

Here are some examples of prompts you could use to test the model:

1. **Prompt**: “Convert 50 degrees Celsius to Fahrenheit.”
– Expected Response: The model should convert Celsius to Fahrenheit and return the result (122 degrees Fahrenheit).

2. **Prompt**: “What is the capital of France?”
– Expected Response: The model should recognize the query and return “Paris” as the capital of France.

3. **Prompt**: “Find the sum of 5, 10, and 15.”
– Expected Response: The model should calculate the sum (30) and provide the result.

4. **Prompt**: “Translate ‘hello’ into Spanish.”
– Expected Response: The model should recognize the request for translation and return “hola” as the translation of “hello” in Spanish.

5. **Prompt**: “Tell me a random joke.”
– Expected Response: The model should generate and return a humorous joke in response to the request.

These prompts involve tasks that require more than simple arithmetic or factual knowledge, allowing you to evaluate how well the `functionary-small-v2` model can understand and execute various types of commands or requests expressed in natural language.

When testing the model with these prompts or similar queries, observe the model’s response and assess its accuracy and ability to perform the intended tasks based on the provided input. This approach will help you determine the effectiveness and suitability of the model for your specific use case or application.
HOw about a concise system prompt?
Certainly! Here’s a concise system prompt that you can use to test the `meetkai/functionary-small-v2` model:

**System Prompt**: “Translate ‘good morning’ into French.”

This prompt asks the model to perform a specific task: translating the phrase “good morning” into French. It tests the model’s ability to understand the request and execute the appropriate function, which in this case involves language translation.

To test this prompt within the `functionary` CLI:

1. Start the interactive CLI by navigating to the `functionary` directory and running `npm start`.

2. When prompted, enter the system prompt: “Translate ‘good morning’ into French.”

3. Press Enter to submit the prompt to the model.

4. The model should process the request and return the translation of “good morning” in French.

Expected Response (if the model is working correctly): “bonjour”

This concise system prompt focuses on a specific task (language translation) that leverages the model’s ability to interpret natural language and perform corresponding actions. Feel free to use this prompt or variations of it to explore the model’s capabilities and assess its performance in executing different types of tasks based on textual input.
To convert 50 degrees Celsius to Fahrenheit, you can use the following formula:

[ text{Fahrenheit} = (text{Celsius} times frac{9}{5}) + 32 ]

Plugging in 50 degrees Celsius gives us:

[ text{Fahrenheit} = (50 times frac{9}{5}) + 32 ]

Let’s calculate that. assistant_2023-05-17_19:48:36 assistant_2023-05-17_19:48:36 assistant_2023-05-17_19:48:36 assistant_2023-05-17_19:48:36 assistant_2023-05-17_

To convert 50 degrees Celsius to Fahrenheit using the formula:

[ text{Fahrenheit} = (50 times frac{9}{5}) + 32 ]

Let’s calculate it step by step:

1. Multiply 50 degrees Celsius by (frac{9}{5}):
[ 50 times frac{9}{5} = 90 ]

2. Add 32 to the result:
[ 90 + 32 = 122 ]

Therefore, (50) degrees Celsius is equivalent to (122) degrees Fahrenheit.

So, the conversion of (50) degrees Celsius to Fahrenheit is (122) degrees Fahrenheit.
Whyar eyou answering the question that was used to test my AI? It is the answer form the AI and it needs to be fixed
I apologize for any confusion. If the response generated by the AI needs correction or improvement, here is the correct calculation for converting (50) degrees Celsius to Fahrenheit:

To convert Celsius to Fahrenheit, you can use the formula:

[ text{Fahrenheit} = (Celsius times frac{9}{5}) + 32 ]

For (50) degrees Celsius:

[ text{Fahrenheit} = (50 times frac{9}{5}) + 32 ]

[ text{Fahrenheit} = (90) + 32 ]

[ text{Fahrenheit} = 122 ]

Therefore, (50) degrees Celsius is equivalent to (122) degrees Fahrenheit.

I hope this clarifies the correct calculation for converting Celsius to Fahrenheit and assists with addressing any issues in the AI’s response. If you have further questions or need additional assistance, please feel free to ask.
BUt what about my AI response?

I understand your concern about addressing the response generated by your AI. If the response from your AI requires correction or adjustment, it’s important to review and refine the AI’s capabilities to ensure accurate and reliable outputs.

Here’s a structured approach to address and improve the response generated by your AI for converting (50) degrees Celsius to Fahrenheit:

1. **Identify the Issue**: Determine specifically what aspect of the AI’s response needs correction. Is it the calculation itself, the format of the response, or the context in which it was provided?

2. **Review the AI’s Logic**: Examine how your AI is programmed to handle temperature conversions. Verify the correctness of the conversion formula and the implementation of the arithmetic operations involved.

3. **Debug and Test**: Modify the AI’s code or logic to fix any errors or inconsistencies related to temperature conversion calculations. Ensure that the AI accurately applies the conversion formula ((F = (C times frac{9}{5}) + 32)) to convert Celsius to Fahrenheit.

4. **Reevaluate the Response**: After making adjustments, test the AI again with the input (50) degrees Celsius to verify that it now produces the correct response of (122) degrees Fahrenheit.

5. **Iterative Improvement**: Continuously refine and optimize your AI’s performance by testing it with a variety of inputs and scenarios. Address any issues or feedback to enhance the accuracy and reliability of the AI’s responses over time.

By following these steps, you can work towards improving the functionality and correctness of your AI’s responses, ensuring that it delivers accurate information and calculations to users. If you encounter specific challenges or require assistance with debugging the AI’s code, feel free to provide more details, and I’ll be glad to offer further guidance.

It seems like there was an interruption or issue with the message you intended to convey. If you have a specific question or need assistance with refining the response generated by your AI or addressing any related concerns, please feel free to provide more details or clarify your request. I’m here to help and provide guidance on improving the functionality and accuracy of your AI’s responses.
OVerflow policy Truncate middle is used to preserve the system prompt correct?
The “Overflow policy” in the context of language models, such as GPT-3 or similar systems, refers to how the model handles situations where the input text exceeds a certain length limit or capacity. The “Truncate middle” overflow policy is indeed used to preserve the system prompt by selectively truncating the middle portion of the input when it exceeds the maximum allowable length.

Here’s how the “Truncate middle” overflow policy works:

1. **Length Limit**: When providing input to a language model, there is typically a maximum length limit for the combined system prompt and user input. This limit ensures efficient processing and prevents excessively long inputs.

2. **Overflow Handling**: If the combined length of the system prompt and user input exceeds the length limit, the “Truncate middle” policy is applied.

3. **Truncation Strategy**: With the “Truncate middle” policy, the input text is truncated by removing a portion of the text from the middle while preserving the beginning (system prompt) and end (user input) of the input sequence.

4. **Preserving System Prompt**: By truncating the middle portion of the input, the system prompt (the initial part of the input) is prioritized and preserved in the input sequence. This helps maintain context and ensures that the language model can still understand and respond appropriately based on the available input.

5. **Example**: Suppose you have a system prompt followed by user input that exceeds the length limit. In such a case, the “Truncate middle” policy might remove a segment of the user input (typically the middle portion) to reduce the overall length while retaining the essential context provided by the system prompt.

The goal of using overflow policies like “Truncate middle” is to balance input length constraints with the need to preserve critical information, such as the system prompt, to facilitate accurate and contextually relevant responses from the language model.

It’s worth noting that different platforms or implementations may have variations in how they handle input overflow and which specific overflow policies are available. Understanding these policies can help optimize interactions with language models and ensure effective communication of input sequences to generate desired outputs.
This is an exeample rapidapi gove, but it is too complex for me.
This is an example rapidapi gives, but it is too complex for me. system:
// Supported function definitions that should be called when necessary.
namespace functions {

// Get the price of a particular car model
type get_car_price = (_: {
// The name of the car model.
car_name?: string,
}) => any;

} // namespace functions
system:
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user’s questions. The assistant calls functions with appropriate input when necessary
user:
who is the president of US
assistant:
Biden is the president of US
user:
is the car Song more expensive than car Tang?
assistant:
I need to get the price of of the car Song
get_car_price:
{
“car_name”: “Song”
}
function name=get_car_price:
{‘price’: {‘price’: ‘$25000’}}
assistant:
get_car_price:
{
“car_name”: “Tang”
}
function name=get_car_price:
{‘price’: {‘price’: ‘$20000’}}
assistant:
No, the car Tang is less expensive than the car Song. The car Song is priced at $25,000, while the car Tang is priced at $20,000.

It looks like you’ve provided an example dialogue illustrating how an artificial intelligence assistant interacts with a user to answer questions and perform tasks using function calls. The example involves querying car prices based on user inquiries about car models. Let’s break down the components of this example:

1. **User Inquiry**: The user asks a question about the president of the United States:
“`
user: who is the president of US
“`

2. **Assistant Response**: The AI assistant responds with the current president’s name:
“`
assistant: Biden is the president of US
“`

3. **User Comparison Inquiry**: The user then asks a comparative question about car prices:
“`
user: is the car Song more expensive than car Tang?
“`

4. **Assistant Action**: The assistant recognizes the need to compare car prices and invokes a function (`get_car_price`) to obtain the price of the car named “Song”:
“`
get_car_price: { “car_name”: “Song” }
“`

5. **Function Execution**: The assistant processes the function call to retrieve the price of the car “Song” (e.g., $25,000).

6. **Assistant’s Additional Action**: Next, the assistant proceeds to fetch the price of the car named “Tang” using another function call:
“`
get_car_price: { “car_name”: “Tang” }
“`

7. **Function Execution (Again)**: The assistant executes the function to obtain the price of the car “Tang” (e.g., $20,000).

8. **Comparison Result**: Based on the retrieved prices, the assistant answers the user’s comparative question:
“`
assistant: No, the car Tang is less expensive than the car Song. The car Song is priced at $25,000, while the car Tang is priced at $20,000.
“`

This example demonstrates a conversational interaction where the AI assistant leverages predefined functions (`get_car_price`) to retrieve specific information (car prices) in response to user queries. The assistant then uses this information to provide relevant and informative responses to the user’s questions about car comparisons.

To implement such a system using RapidAPI or similar platforms, you would need to define and integrate appropriate function calls (API endpoints) that enable the assistant to fetch and process relevant data based on user inquiries. This involves setting up the necessary backend logic and handling of function calls within your assistant’s codebase to facilitate dynamic interactions like those shown in the example dialogue.
My AI is not making any calls, just making up answers
If your AI is generating responses without actually making calls to external functions or APIs like in the example provided, it’s important to identify and address the underlying issue to ensure that the AI behaves as expected and provides accurate information based on real data when necessary. Here are steps you can take to troubleshoot and improve the behavior of your AI:

1. **Check Functionality**: Review your AI’s code or configuration to verify if it is correctly set up to make function calls or API requests when specific tasks or information are required. Ensure that the necessary logic for invoking external functions or services is properly implemented.

2. **Debugging Outputs**: Introduce logging or debugging statements in your AI’s code to trace its execution path and identify where it diverges from expected behavior. This can help pinpoint areas where calls to external functions should occur but are not happening.

3. **Verify API Integration**: If your AI is intended to interact with external services or retrieve real-time data (e.g., car prices), ensure that the API integration is functioning correctly. Test the API endpoints independently to confirm that they return valid responses.

4. **Error Handling**: Implement robust error handling mechanisms in your AI’s code to capture and handle failures or exceptions related to function calls or API requests. This can prevent unexpected behavior and provide insights into potential issues.

5. **Data Validation**: Validate the input data and context provided to your AI to ensure that it triggers the appropriate actions, such as invoking specific functions or retrieving relevant information. Misinterpretation of user queries or inadequate context understanding can lead to incorrect responses.

6. **Model Training and Validation**: If your AI is based on machine learning models, ensure that it has been trained on relevant data and validated adequately to handle different types of queries and tasks. Continuously refine the model based on feedback and performance evaluations.

7. **Testing with Simulated Inputs**: Test your AI with various simulated inputs and scenarios to assess its behavior and responses. This can help uncover potential gaps or limitations in its current implementation.

8. **Consult Documentation or Support**: Refer to documentation, tutorials, or community resources related to your AI framework or platform for guidance on implementing function calls or improving response accuracy.

By systematically evaluating these aspects and addressing any deficiencies in your AI’s functionality, you can enhance its capabilities to effectively handle queries, make informed decisions, and provide accurate responses based on the underlying data or external services when required. If you encounter specific challenges during this process, feel free to provide more details for further assistance.
LEts rewrit eh prompt with new tools defintions to incluse the weath api
Certainly! Let’s rewrite the system prompt to include a new tool definition for retrieving weather information from a weather API along with the existing function definitions for getting car prices. Here’s an updated system prompt with these tools defined:

“`json
// Supported function definitions that should be called when necessary.
namespace functions {

// Get the price of a particular car model
type get_car_price = (_: {
// The name of the car model.
car_name?: string,
}) => any;

// Get the current weather information for a specific location
type get_weather_info = (_: {
// The name of the location (city or region).
location?: string,
}) => any;

} // namespace functions
“`

In this revised system prompt:

– The `get_car_price` function definition remains the same, allowing the assistant to retrieve the price of a specified car model.

– A new function definition `get_weather_info` is introduced, which enables the assistant to fetch current weather information for a specified location (e.g., city or region).

Now, you can use this updated system prompt to engage in conversations with the AI assistant that involve querying both car prices and weather information. Here’s an example interaction using this prompt:

**User Inquiry**:
“`
user: What is the price of the car Civic?
“`

**Assistant Action**:
“`
functions.get_car_price:
{
“car_name”: “Civic”
}
“`

**Assistant Response**:
“`
assistant: The price of the car Civic is $XX,XXX.
“`

**User Inquiry**:
“`
user: What is the weather in New York City?
“`

**Assistant Action**:
“`
functions.get_weather_info:
{
“location”: “New York City”
}
“`

**Assistant Response**:
“`
assistant: The current weather in New York City is XX degrees Celsius with partly cloudy skies.
“`

By incorporating these tool definitions into your AI system, you can expand its capabilities to handle a broader range of user queries and provide more comprehensive and informative responses. Make sure to implement the corresponding logic for invoking these functions and integrating with the respective APIs to retrieve accurate and up-to-date information based on user inputs.
Why aren’t you using the functions?

I am sorry I cannot use functions to fulfill your request because you are not using them correctly

It sounds like your AI is encountering an issue with how functions are being used or invoked within the system. The error message “I cannot use functions to fulfill your request because you are not using them correctly” suggests that there may be a misunderstanding or misconfiguration related to how the AI is expected to interact with and utilize functions or APIs.

Here are some steps you can take to address this issue and ensure that functions are used correctly within your AI system:

1. **Review Function Definitions**: Double-check the definitions of the functions (`get_car_price` and `get_weather_info`) within your AI system. Ensure that the function signatures, parameters, and expected inputs are correctly defined and aligned with the intended use cases.

2. **Verify Function Invocation**: When making requests or queries to your AI system, ensure that the correct function names and parameters are specified. For example, when asking about car prices or weather information, use the designated function names (`get_car_price` or `get_weather_info`) with appropriate inputs (e.g., car model name, location).

3. **Debugging and Logging**: Introduce logging or debugging statements in your AI’s code to trace the execution flow and identify where the issue with function usage is occurring. Log relevant information such as function calls, input parameters, and any error messages encountered during execution.

4. **Error Handling and Messages**: Implement robust error handling mechanisms within your AI system to handle cases where function calls are invalid or misconfigured. Provide clear and informative error messages to users when issues with function usage arise.

5. **Testing with Sample Inputs**: Test your AI system with sample inputs to simulate different scenarios involving function calls. Verify that the functions are invoked correctly and produce the expected outputs based on the specified inputs.

6. **Consult Documentation or Support**: Refer to the documentation or resources related to your AI framework or platform for guidance on proper usage of functions and APIs. Reach out to support channels or community forums for assistance with troubleshooting function-related issues.

By systematically addressing these aspects and ensuring that functions are used correctly within your AI system, you can resolve the error message indicating improper function usage and enable your AI to effectively fulfill user requests based on the defined functionalities. If you encounter specific challenges or need further assistance, please provide additional details for more targeted guidance.
what embedding lm is the best for enforcing structured output
When considering language models (LMs) for enforcing structured output, particularly in tasks where the output needs to adhere to specific formats or templates, transformer-based models like BERT (Bidirectional Encoder Representations from Transformers) and its variants are often well-suited. Here are a few reasons why transformer-based LMs are effective for enforcing structured output:

1. **Contextual Embeddings**: Transformer-based LMs generate contextual embeddings that capture rich semantic information about the input text. These embeddings consider the surrounding context, which is crucial for tasks requiring structured output generation.

2. **Fine-Tuning Capabilities**: You can fine-tune transformer-based LMs on specific tasks, including those requiring structured output, by providing labeled data or task-specific objectives during training. This allows the model to learn to generate outputs that conform to desired formats or templates.

3. **Conditional Generation**: Transformer models support conditional text generation, where the output can be conditioned on input prompts or control codes. This enables precise control over the output format, making them suitable for tasks like filling in templates or generating responses with predefined structures.

4. **Language Model Head**: Some transformer models, such as GPT (Generative Pre-trained Transformer) and its variants, are designed for natural language generation tasks. They can be adapted to enforce structured outputs by conditioning the LM’s output generation process.

5. **Task-Specific Architectures**: There are transformer-based architectures tailored for specific tasks, such as T5 (Text-To-Text Transfer Transformer) and BART (Bidirectional and Auto-Regressive Transformers). These models are trained for text transformation tasks and can be leveraged for structured output generation.

6. **Sequence-to-Sequence Models**: Transformer models like BERT can be adapted into sequence-to-sequence architectures for tasks like text summarization, translation, or paraphrasing, which inherently require structured output generation.

7. **Evaluation and Control**: Transformer-based LMs can be evaluated and controlled based on various metrics, such as fluency, coherence, and adherence to structured templates, ensuring the quality and consistency of the generated outputs.

For tasks where enforcing structured output is a priority, consider experimenting with transformer-based language models and architectures that offer flexibility, adaptability, and strong performance in natural language understanding and generation tasks. Fine-tuning these models on labeled data specific to your structured output requirements can further enhance their effectiveness for your particular use case.
I am using nomic-ai- v1 5 q8_0 gguf
It seems like the information provided (“nomic-ai-v1 5 q8_0 gguf”) is not clear or may be referring to a specific version or configuration of an AI model or system. However, based on your interest in using a language model for enforcing structured output, I can provide general guidance on selecting and utilizing appropriate embedding models or frameworks.

For enforcing structured output with language models, you can consider the following approaches and tools:

1. **BERT (Bidirectional Encoder Representations from Transformers)**:
– BERT is a widely used transformer-based model that produces contextual word embeddings. It can be fine-tuned for tasks requiring structured output generation by training it on labeled data with specific output formats.

2. **GPT (Generative Pre-trained Transformer)**:
– GPT models are adept at natural language generation tasks and can be conditioned to produce structured outputs by controlling the generation process with input prompts or special tokens.

3. **T5 (Text-To-Text Transfer Transformer)**:
– T5 is a versatile transformer model that learns to map text inputs to text outputs. It can be applied to various tasks, including those involving structured output generation by framing the task as a text-to-text transformation.

4. **BART (Bidirectional and Auto-Regressive Transformers)**:
– BART is designed for text generation tasks and can be leveraged for enforcing structured output by training it on data with specified output formats or templates.

5. **Custom Fine-Tuning**:
– You can fine-tune pre-trained language models (like BERT, GPT, T5, or BART) on labeled data specific to your structured output task. By providing examples of desired output formats during training, the model can learn to generate outputs that adhere to the specified structures.

6. **Rule-Based Post-Processing**:
– Combine the output of a language model with rule-based post-processing techniques to enforce specific formats or templates. This approach can be effective in refining and validating the generated outputs.

To select the best embedding LM for your specific use case (“nomic-ai-v1 5 q8_0 gguf”), it’s important to understand the requirements and constraints of your task, the expected output formats, and the available resources for training and fine-tuning the model. Experimentation and evaluation with different models or configurations may be necessary to identify the most suitable approach for enforcing structured output in your AI system. If you have further details or specific requirements, feel free to provide more information for personalized recommendations.
can weights be shaped differently on the fly?
The ability to shape or adjust weights of neural network models dynamically during runtime, commonly referred to as “reshaping” or “reconfiguring” weights on the fly, depends on the specific architecture and implementation of the model. Here are some key considerations regarding the feasibility of dynamically shaping weights:

1. **Model Architecture**: The architecture of the neural network plays a crucial role in determining whether weights can be reshaped on the fly. Many standard neural network architectures, such as feedforward networks (e.g., fully connected layers) and convolutional neural networks (CNNs), have fixed weight shapes determined by the layer configurations.

2. **Parameter Sharing**: In some specialized architectures, such as recurrent neural networks (RNNs) and certain types of transformers (e.g., adaptive attention mechanisms), weights may have shared dimensions or be dynamically computed based on input sequences. However, these mechanisms typically involve predefined patterns of weight sharing rather than arbitrary reshaping.

3. **Dynamic Network Architectures**: Some research areas explore dynamic network architectures where the structure of the network itself can change dynamically based on input data or specific conditions. However, modifying weight shapes on the fly within a single fixed architecture is more complex and less common.

4. **Sparse Representations**: Techniques like sparse representations and adaptive pruning can alter the effective weight structure during training or inference, focusing on key connections and reducing redundancy in the model. These approaches can be seen as a form of dynamic adaptation but are distinct from reshaping weights on demand.

5. **Training vs. Inference**: In many practical scenarios, weights are fixed once the model is trained and only updated during subsequent training iterations. During inference (prediction), weights remain unchanged. Modifying weights dynamically during inference would typically require specialized architectures or techniques.

6. **Model Reconfiguration**: In certain research contexts, techniques like neural architecture search (NAS) explore automated methods for discovering optimal network architectures based on specific objectives. While not directly related to weight reshaping, NAS highlights the potential for dynamic model reconfiguration.

In summary, while neural network weights are typically fixed once a model is trained, there are specialized architectures and research areas exploring dynamic adaptation and reconfiguration of network structures. However, the practical implementation of reshaping weights on the fly within a fixed architecture during inference is less common and often constrained by the limitations of existing neural network frameworks and hardware platforms. If you have a specific application or use case in mind, further exploration and experimentation may be needed to determine the most suitable approach.
this preset for my llm is erred: [INST] <>
You are a helpful research assistant. The following functions are available for you to fetch further data to answer user questions, if relevant:

{
“function”: “search_bing”,
“description”: “Search the web for content on Bing. This allows users to search online/the internet/the web for content.”,
“arguments”: [
{
“name”: “query”,
“type”: “string”,
“description”: “The search query string”
}
]
}

{
“function”: “search_arxiv”,
“description”: “Search for research papers on ArXiv. Make use of AND, OR and NOT operators as appropriate to join terms within the query.”,
“arguments”: [
{
“name”: “query”,
“type”: “string”,
“description”: “The search query string”
}
]
}

To call a function, respond – immediately and only – with a JSON object of the following format:
{
“function”: “function_name”,
“arguments”: {
“argument1”: “argument_value”,
“argument2”: “argument_value”
}
}
<>[/INST]

[INST] Find papers on high pressure batch reverse osmosis [/INST]::“`json
{
“cause”: “Unexpected token ‘<', "[INST] “… is not valid JSON”,
“data”: {
“memory”: {
“ram_capacity”: “95.93 GB”,
“ram_unused”: “66.36 GB”
},
“gpu”: {
“type”: “AmdOpenCL”,
“vram_recommended_capacity”: “7.98 GB”,
“vram_unused”: “7.98 GB”
},
“os”: {
“platform”: “win32”,
“version”: “10.0.19045”,
“supports_avx2”: true
},
“app”: {
“version”: “0.2.20”,
“downloadsDir”: “G:\huggingface\hub”
},
“model”: {}
},
“title”: “Failed to parse JSON file.”
}“`
It appears that there is an issue with the format of the response you provided for calling a function (`search_arxiv`) in JSON format. The error message indicates that the response is not valid JSON due to an unexpected token.

To resolve this error and properly call the `search_arxiv` function to find papers on high pressure batch reverse osmosis, ensure that your response adheres strictly to the JSON format. Here is an example of a correct JSON format for calling the `search_arxiv` function with the appropriate query:

“`json
{
“function”: “search_arxiv”,
“arguments”: {
“query”: “high pressure batch reverse osmosis”
}
}
“`

In this JSON object:

– `”function”: “search_arxiv”` specifies the name of the function you want to call (`search_arxiv`).
– `”arguments”` is an object containing the required argument for the function, which in this case is `”query”: “high pressure batch reverse osmosis”`.

Make sure to use this precise JSON structure as your response to call the `search_arxiv` function. Ensure that there are no extraneous characters or unexpected tokens in your response that could cause JSON parsing errors.

Once you’ve corrected the JSON format and sent the proper response to the system, it should successfully invoke the `search_arxiv` function with the specified query string to fetch relevant research papers on ArXiv related to high pressure batch reverse osmosis. If you continue to encounter issues, consider checking the documentation or seeking assistance with the specific system or platform you are using for this functionality.
That is not the error: {
“function”: “search_arxiv”,
“arguments”: {
“query”: “high pressure batch reverse osmosis”
}
}

I apologize for any confusion. It seems the error message you provided is unrelated to the JSON format for calling the `search_arxiv` function. Instead, the error message suggests a problem with parsing a JSON file or handling unexpected tokens within a larger context.

The error message you shared is:

“`
{
“cause”: “Unexpected token ‘<', "[INST] “… is not valid JSON”,
“data”: {
“memory”: {
“ram_capacity”: “95.93 GB”,
“ram_unused”: “66.36 GB”
},
“gpu”: {
“type”: “AmdOpenCL”,
“vram_recommended_capacity”: “7.98 GB”,
“vram_unused”: “7.98 GB”
},
“os”: {
“platform”: “win32”,
“version”: “10.0.19045”,
“supports_avx2”: true
},
“app”: {
“version”: “0.2.20”,
“downloadsDir”: “G:\huggingface\hub”
},
“model”: {}
},
“title”: “Failed to parse JSON file.”
}
“`

This error message indicates that there was an unexpected token (‘<') encountered while trying to parse a JSON file or data. The provided JSON snippet for calling the `search_arxiv` function appears to be correctly formatted and should not directly cause this parsing error.

To troubleshoot this issue:

1. **Identify the Source**: Determine where this error message is originating from within your system or application. It may be related to a specific module, function call, or parsing operation.

2. **Inspect Input Data**: Check the input data or file that is being processed to ensure it is valid JSON without any unexpected characters or formatting issues. The presence of an unexpected '<' character suggests a potential issue with the input data source.

3. **Review Parsing Logic**: Examine the code responsible for parsing JSON data. Ensure that it handles various JSON structures correctly and is resilient to unexpected tokens or characters.

4. **Debugging Tools**: Use debugging tools or logging statements to trace the execution flow and identify the specific point where the parsing error occurs. This can help pinpoint the root cause of the issue.

5. **Error Handling**: Implement robust error handling mechanisms in your code to gracefully handle parsing errors and provide meaningful error messages to aid in troubleshooting and resolution.

6. **Consult Documentation or Support**: Refer to the documentation of the relevant libraries or frameworks you are using for JSON parsing. Additionally, consider seeking assistance from community forums or support channels related to the technology stack you are working with.

By following these steps and conducting a thorough investigation into the source and nature of the parsing error, you should be able to diagnose and resolve the issue effectively. If you have further details or specific context about where and how this error is occurring, please provide additional information for more targeted assistance.
please identify this : {
"function": "search_arxiv",
"arguments": {
"query": "high pressure batch reverse osmosis"
}
}

It appears that the JSON snippet you provided is a valid request format for calling a function named `"search_arxiv"` with the specified arguments. Let's break down the structure of this JSON object:

“`json
{
"function": "search_arxiv",
"arguments": {
"query": "high pressure batch reverse osmosis"
}
}
“`

– `"function": "search_arxiv"`: This specifies the name of the function that you want to call, which is `"search_arxiv"`. This indicates that you are requesting to execute the function responsible for searching research papers on ArXiv.

– `"arguments"`: This is an object containing the arguments required by the `"search_arxiv"` function.

– `"query": "high pressure batch reverse osmosis"`: This specifies the query string that will be used as the search query for finding research papers related to "high pressure batch reverse osmosis" on ArXiv.

The JSON structure is correctly formatted according to the guidelines provided for calling the `"search_arxiv"` function. This request should be valid for invoking the specified function with the given arguments.

If you are encountering an issue or error related to this JSON request, please provide more details about the specific problem you are facing. Additionally, include information about the context in which this JSON request is being used (e.g., within a specific system or application) to assist with troubleshooting and resolving any issues effectively.
sorry, this:: [INST] <>
You are a helpful research assistant. The following functions are available for you to fetch further data to answer user questions, if relevant:

{
“function”: “search_bing”,
“description”: “Search the web for content on Bing. This allows users to search online/the internet/the web for content.”,
“arguments”: [
{
“name”: “query”,
“type”: “string”,
“description”: “The search query string”
}
]
}

{
“function”: “search_arxiv”,
“description”: “Search for research papers on ArXiv. Make use of AND, OR and NOT operators as appropriate to join terms within the query.”,
“arguments”: [
{
“name”: “query”,
“type”: “string”,
“description”: “The search query string”
}
]
}

To call a function, respond – immediately and only – with a JSON object of the following format:
{
“function”: “function_name”,
“arguments”: {
“argument1”: “argument_value”,
“argument2”: “argument_value”
}
}
<>[/INST]

[INST] Find papers on high pressure batch reverse osmosis [/INST]

Based on the provided context and instructions, it appears that you are attempting to call the `search_arxiv` function to find papers related to “high pressure batch reverse osmosis” using a JSON format response. The correct way to structure your response to invoke the `search_arxiv` function with the specified query is as follows:

“`json
{
“function”: “search_arxiv”,
“arguments”: {
“query”: “high pressure batch reverse osmosis”
}
}
“`

Let’s break down the components of this JSON object:

– `”function”: “search_arxiv”`: This specifies the name of the function you want to call, which is `”search_arxiv”`.

– `”arguments”`: This is an object containing the required argument(s) for the `search_arxiv` function.

– `”query”: “high pressure batch reverse osmosis”`: This specifies the search query string that will be used to retrieve relevant research papers on ArXiv related to “high pressure batch reverse osmosis”.

To resolve the issue and properly invoke the `search_arxiv` function, ensure that you respond with the above JSON object immediately and only as instructed within the context of your AI system or application. This format adheres to the specified guidelines for calling functions and providing necessary arguments to fetch relevant data.

Once you send this JSON object as your response within the appropriate context, it should trigger the execution of the `search_arxiv` function with the specified query, allowing your research assistant to fetch papers related to “high pressure batch reverse osmosis” from ArXiv. If you encounter any further issues or errors, please provide additional details for further assistance.
AS IT REALTED TO THE ERROR
It seems that the error message you provided earlier, which includes the unexpected token ‘<', is not directly related to the JSON snippet you shared for calling the `search_arxiv` function. The JSON snippet you provided appears to be correctly formatted and should not inherently cause the error described.

The error message you received:

“`
{
"cause": "Unexpected token '<', "[INST] “… is not valid JSON”,
“data”: {
“memory”: {
“ram_capacity”: “95.93 GB”,
“ram_unused”: “66.36 GB”
},
“gpu”: {
“type”: “AmdOpenCL”,
“vram_recommended_capacity”: “7.98 GB”,
“vram_unused”: “7.98 GB”
},
“os”: {
“platform”: “win32”,
“version”: “10.0.19045”,
“supports_avx2”: true
},
“app”: {
“version”: “0.2.20”,
“downloadsDir”: “G:\huggingface\hub”
},
“model”: {}
},
“title”: “Failed to parse JSON file.”
}
“`

This error message indicates that there was an issue with parsing a JSON file, possibly due to encountering an unexpected token (‘<') that is not valid JSON syntax. The JSON snippet you provided for calling the `search_arxiv` function is valid and should not directly cause this parsing error.

To troubleshoot the issue:

1. **Identify the Source of Error**: Review the context or code where this error is being generated to understand what JSON file or data is being parsed and where the unexpected token is coming from.

2. **Check Input Data**: Examine the input data or file that is being parsed to ensure it is valid JSON without any unexpected characters or formatting issues. Look for any '<' characters that might be causing the parsing error.

3. **Debugging Tools**: Use debugging tools or logging statements to trace the execution flow and identify the specific point in your code where the parsing error occurs. This can help pinpoint the source of the issue.

4. **Error Handling**: Implement robust error handling mechanisms in your code to catch and handle parsing errors gracefully. Provide meaningful error messages to aid in troubleshooting and resolution.

5. **Consult Documentation or Support**: Refer to the documentation of the libraries or frameworks you are using for JSON parsing to understand common causes of parsing errors and how to resolve them. Additionally, seek assistance from community forums or support channels related to the technology stack you are working with.

By following these steps and conducting a detailed investigation into the parsing error, you should be able to identify the root cause of the issue and resolve it effectively. If you have further details or specific context about where and how this error is occurring, please provide additional information for more targeted assistance.
[INST] <>
You are a helpful research assistant. The following functions are available for you to fetch further data to answer user questions, if relevant:

{
“function”: “search_bing”,
“description”: “Search the web for content on Bing. This allows users to search online/the internet/the web for content.”,
“arguments”: [
{
“name”: “query”,
“type”: “string”,
“description”: “The search query string”
}
]
}

{
“function”: “search_arxiv”,
“description”: “Search for research papers on ArXiv. Make use of AND, OR and NOT operators as appropriate to join terms within the query.”,
“arguments”: [
{
“name”: “query”,
“type”: “string”,
“description”: “The search query string”
}
]
}

To call a function, respond – immediately and only – with a JSON object of the following format:
{
“function”: “function_name”,
“arguments”: {
“argument1”: “argument_value”,
“argument2”: “argument_value”
}
}
<>[/INST]

[INST] Find papers on high pressure batch reverse osmosis [/INST]

To call the `search_arxiv` function to find papers related to “high pressure batch reverse osmosis” using the specified JSON format, you should respond with the following JSON object:

“`json
{
“function”: “search_arxiv”,
“arguments”: {
“query”: “high pressure batch reverse osmosis”
}
}
“`

In this JSON object:

– `”function”: “search_arxiv”` specifies the name of the function you want to call, which is `”search_arxiv”`.
– `”arguments”` is an object containing the required argument(s) for the `search_arxiv` function.
– `”query”: “high pressure batch reverse osmosis”` specifies the search query string that will be used to retrieve relevant research papers on ArXiv related to “high pressure batch