C# LLM Implementation
Budget: $30 – $250 USD
Our goal is to develop a C# application that can efficiently read PDF files and documents and implement a search functionality using vector representations. To achieve this, we will be utilizing an LLM (Language Model with Log-Likelihood Objective) implementation, which is designed to capture semantic similarities between texts using vector embeddings.
Key Features:
PDF and Document Parsing: The application should be capable of parsing various file formats, including PDFs and standard document types like DOCX, TXT, and RTF. It should extract the textual content from these files to build the corpus.
Vector Representation: Implement the LLM algorithm to transform the textual content into dense vector representations. These vectors will capture the semantic meaning of the text, allowing for efficient and accurate searching.
Semantic Search: Enable users to search for specific keywords or phrases within the documents. The search will be conducted based on the semantic similarity between the query vector and the vector representations of the documents.
Indexing: Optimize the search process by creating an index that maps vector representations to their corresponding documents. This index will speed up the search operation and enhance the overall performance of the application.
Ranking and Display: Return search results in a ranked manner, showing the most relevant documents first.
Configuration and Settings: Provide configurable options for adjusting parameters related to the LLM model, search, and indexing processes. This will allow the application to be fine-tuned based on specific requirements.
Performance and Efficiency: Optimize the application for speed and efficiency to ensure that the search operation is performed swiftly, even with a large number of documents.
Technical Requirements:
C# programming language.
Utilize existing libraries for PDF and document parsing, such as iTextSharp or DocX as long as they can be used commercially.
Implement LLM using appropriate libraries or custom implementations based on the latest research.
Conclusion:
This project aims to create a PDF and document search application using the power of LLM to provide users with an efficient and accurate way to access information.
Key Features:
PDF and Document Parsing: The application should be capable of parsing various file formats, including PDFs and standard document types like DOCX, TXT, and RTF. It should extract the textual content from these files to build the corpus.
Vector Representation: Implement the LLM algorithm to transform the textual content into dense vector representations. These vectors will capture the semantic meaning of the text, allowing for efficient and accurate searching.
Semantic Search: Enable users to search for specific keywords or phrases within the documents. The search will be conducted based on the semantic similarity between the query vector and the vector representations of the documents.
Indexing: Optimize the search process by creating an index that maps vector representations to their corresponding documents. This index will speed up the search operation and enhance the overall performance of the application.
Ranking and Display: Return search results in a ranked manner, showing the most relevant documents first.
Configuration and Settings: Provide configurable options for adjusting parameters related to the LLM model, search, and indexing processes. This will allow the application to be fine-tuned based on specific requirements.
Performance and Efficiency: Optimize the application for speed and efficiency to ensure that the search operation is performed swiftly, even with a large number of documents.
Technical Requirements:
C# programming language.
Utilize existing libraries for PDF and document parsing, such as iTextSharp or DocX as long as they can be used commercially.
Implement LLM using appropriate libraries or custom implementations based on the latest research.
Conclusion:
This project aims to create a PDF and document search application using the power of LLM to provide users with an efficient and accurate way to access information.