Unleash the Power of JSON Output with Mistral 7B LLM
Table of Contents
- Introduction
- Getting Structured JSON Output using LLM Model
- Output Parsers with Long Chain
- Using Pantic for Output Validation
- Creating Prompts for JSON Output
- Adjusting Chunk Size for Better Performance
- Loading LM using C Transformers
- Testing Information sent to LLM
- Alternative Libraries for Predefined JSON
- Performance Comparison with LAMA 2
- Conclusion
Introduction
In this video, I will discuss how to obtain a structured JSON output from the LLM (Long Language Model) model. We will focus on using a file-Based FPH Vector database and explore different techniques to achieve the desired output format. We will also compare the reliability and performance of various methods.
Getting Structured JSON Output using LLM Model
The LLM provides a way to specify inside the prompt that You want to receive JSON output and define the formatting of the output. This approach offers more reliability compared to using output parsers, as the structure of the output can sometimes vary. By explicitly instructing the LM to format the response as JSON and providing the desired keys, we can ensure consistency in the output.
Output Parsers with Long Chain
Using output parsers with Long Chain to transform the result into a JSON structure is another option. However, in practical scenarios, this method may not be as reliable. The output structure returned by the LLM can differ between requests, causing the output parsers to fail. Therefore, for now, it is recommended to focus on instructing the LM explicitly for the desired output format.
Using Pantic for Output Validation
Pantic can be used in conjunction with Long Chain to validate the output and ensure that it conforms to the required structure. This additional step can provide an extra layer of reliability for the output generated by the LLM.
Creating Prompts for JSON Output
To obtain JSON output, we can include information in the prompt itself, specifying the desired format and the keys to be included in the output. This approach simplifies the process and ensures a consistent JSON structure. Single values can be enclosed in curly brackets, while multiple values can be enclosed in square brackets.
Adjusting Chunk Size for Better Performance
The chunk size plays a crucial role in the performance of the LLM. Based on the specific data being processed, it is recommended to adjust the chunk size accordingly. By splitting the input into smaller chunks, the retrieval and extraction process becomes more efficient, especially when working with larger documents.
Loading LM using C Transformers
In this example, we use C Transformers to load the LLM. C Transformers provides a convenient way to Interact with the LLM and retrieve the desired information. The wrapper function sets up the question-answering prompt and builds the retrieval chain for interacting with the LLM.
Testing Information sent to LLM
To better understand the information being sent to the LLM, we can test and examine the retrieval chain. This allows us to inspect the retrieved data from the vector store and ensure the interaction with the LLM is as expected.
Alternative Libraries for Predefined JSON
Apart from Long Chain, there are other libraries available, such as Hast Stack, that can be used to obtain predefined JSON output. Exploring these alternatives can provide additional options for formatting and structuring the output according to specific requirements.
Performance Comparison with LAMA 2
Comparing the performance of LLM with LAMA 2, we find that using the LLM with FPH Vector storage yields faster response times. The smaller Context window of the LLM might result in partial answers for certain queries, but overall, the LLM provides comparable performance for Data Extraction tasks.
Conclusion
In conclusion, obtaining structured JSON output from the LLM involves explicitly instructing the LM to format the response and providing the desired keys. This approach proves to be more reliable than using output parsers. By adjusting the chunk size and leveraging alternative libraries, we can optimize the performance and obtain the required output format. The LLM, along with C Transformers, makes it easier to interact with the model and retrieve the desired information.
Highlights:
- Obtaining structured JSON output from the LLM model
- Comparing the reliability of output parsers and explicit instructions
- Using Pantic for output validation
- Creating prompts for specifying JSON output format
- Adjusting the chunk size for better performance
- Loading the LM using C Transformers
- Testing and inspecting information sent to the LLM
- Exploring alternative libraries for predefined JSON
- Performance comparison with LAMA 2
- Concluding remarks on LLM's efficiency and ease of use
FAQ:
Q: Can the LLM output multiple values in JSON format?
A: Yes, the LLM can output multiple values in JSON format. Simply enclose the multiple values in square brackets in the prompt.
Q: Is the performance of LLM affected by the chunk size?
A: Yes, the chunk size affects the performance of the LLM. It is advisable to adjust the chunk size based on the specific data being processed for optimal results.
Q: Are there any alternative libraries for obtaining predefined JSON output?
A: Yes, apart from Long Chain, libraries like Hast Stack can be used to obtain predefined JSON output. Exploring these alternatives can provide additional options for structuring the output.