AI Text Analyser is a powerful tool designed to process and analyze text using advanced AI technologies. This project leverages various AI-specific libraries to provide comprehensive text analysis capabilities, including natural language processing (NLP), text extraction, and more.
To start the project, you can use the following commands:
-
Development Mode: ```bash yarn dev ```
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Build: ```bash yarn build ```
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Start: ```bash yarn start ```
This project utilizes several cutting-edge AI technologies and libraries, including:
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LangChain: A library for building language models and AI applications.
- `@langchain/community`: Provides community-driven integrations and tools.
- `@langchain/core`: Core functionalities for language models.
- `@langchain/groq`: Integration with the GROQ query language.
- `@langchain/ollama`: Integrations for OLAMMA models.
- `@langchain/openai`: Integration with OpenAI's GPT models.
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OpenAI: Used for natural language processing and understanding tasks.
- `openai`: The official OpenAI API client for accessing GPT models.
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LlamaIndex: A library for creating and querying indexes for large language models.
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PDF Parsing: Extracts text from PDF documents for analysis.
- `pdf-parse`: A library to extract text from PDF files.
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CSV Handling: Reads and writes CSV files for data processing.
- `csv-parser`: Parses CSV data.
- `csv-writer`: Writes data to CSV files.
- `fast-csv`: A fast CSV parser and writer.
URL: `/text-analyser`
Method: `POST`
Content-Type: `application/json`
The request body should be a JSON object with the following structure:
- `text` (string): The text to be analyzed.
- `additionalContext` (string, optional): Additional context to be included in the analysis.
- `expand` (boolean, optional): Whether to include expanded response details. Default is `false`.
- `jsonFormat` (object, optional): The desired JSON format for the response. Default is a predefined format.
- `mode` (string, optional): The mode of analysis. Can be either `GROQ` or `OPENAI`. Default is `GROQ`.
The response will be a JSON object containing the analysis results. The structure of the response depends on the `expand` parameter:
-
If `expand` is `false` (default):
- `done` (boolean): Indicates if the analysis was successful.
- `message` (string): The analyzed result in the desired JSON format.
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If `expand` is `true`:
- `done` (boolean): Indicates if the analysis was successful.
- `message` (string): The analyzed result in the desired JSON format.
- `response` (object): The detailed response from the AI model.
{
"text": "Your text to be analyzed goes here.",
"additionalContext": "Additional context if any.",
"expand": true,
"jsonFormat": {
"summary": "High-level summary",
"persons": "List of persons",
"subject": "Subject of the text"
},
"mode": "OPENAI"
}{
"done": true,
"message": {
"summary": "Summary of the text",
"persons": ["Person 1", "Person 2"],
"subject": "Subject of the text"
},
"response": {
"message": {
"content": {
"summary": "Summary of the text",
"persons": ["Person 1", "Person 2"],
"subject": "Subject of the text"
}
}
}
}The endpoint supports two modes of analysis using different AI models:
- GROQ: Utilizes the GROQ language model for analysis.
- OpenAI: Utilizes OpenAI's GPT models for analysis.
Both models are integrated via the `llamaindex` library, which provides a consistent interface for interacting with different language models.
Before running the project, ensure you have a `.env` file in the root directory with the necessary environment variables. You can use the `.env.example` file as a template.
Contributions are welcome! Please follow these steps to contribute:
- Fork the repository.
- Create a new branch: `git checkout -b my-feature-branch`
- Make your changes and commit them: `git commit -m 'Add new feature'`
- Push to the branch: `git push origin my-feature-branch`
- Create a pull request.
This project is licensed under the MIT License.