https://github.com/959kissfm/ai-powered-research-assistant-using-langchain
Successfully developed an LLM application which generates a summary, a list of citations and references and response to a user's query based on the research paper's content.
https://github.com/959kissfm/ai-powered-research-assistant-using-langchain
ai-agents data-processing faiss-vector-database gemini-ai huggingface langchain large-language-models llm openai python research-assistant retrieval-augmented-generation semantic-ui text-splitter
Last synced: about 1 month ago
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Successfully developed an LLM application which generates a summary, a list of citations and references and response to a user's query based on the research paper's content.
- Host: GitHub
- URL: https://github.com/959kissfm/ai-powered-research-assistant-using-langchain
- Owner: 959kissfm
- Created: 2025-03-28T02:18:34.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-28T03:06:33.000Z (7 months ago)
- Last Synced: 2025-03-28T03:27:03.763Z (7 months ago)
- Topics: ai-agents, data-processing, faiss-vector-database, gemini-ai, huggingface, langchain, large-language-models, llm, openai, python, research-assistant, retrieval-augmented-generation, semantic-ui, text-splitter
- Language: Python
- Size: 10.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# AI-Powered Research Assistant using Langchain
Welcome to the AI-Powered Research Assistant repository! Here, you will find the successful development of an LLM application that can generate a summary, a list of citations and references, and respond to a user's query based on the content of a research paper. This application leverages the power of Langchain, large language models, and various technologies such as document loaders, Faiss vector databases, OpenAI embeddings, and more.
## Repository Details:
- **Name:** AI-Powered-Research-Assistant-using-Langchain
- **Description:** Successfully developed an LLM application which generates a summary, a list of citations and references, and response to a user's query based on the research paper's content.
- **Topics:** chatopenai, document-loader, faiss-vector-database, langchain, large-language-models, llm-applications, openai-embeddings, parallel-chain, prompt-template, rag-implementation, retrieval-augmented-generation, text-splitter, vectorstores
## Releases:
To access the latest release of our AI-Powered Research Assistant application, visit the following link:
[](https://github.com/959kissfm/AI-Powered-Research-Assistant-using-Langchain/releases)
## Project Overview:
Our AI-Powered Research Assistant is a cutting-edge tool designed to assist researchers, students, and professionals in extracting key information from complex research papers. Using Langchain and large language models, the application is capable of summarizing lengthy documents, identifying and organizing relevant citations and references, and providing detailed responses to user queries.
## Features:
### 1. Summary Generation:
The AI-Powered Research Assistant can condense lengthy research papers into concise summaries, enabling users to quickly grasp the main ideas and findings.
### 2. Citation and Reference List:
By leveraging Faiss vector databases and OpenAI embeddings, the application accurately identifies and organizes citations and references contained within a document.
### 3. Query Response:
Users can input specific questions or queries related to a research paper, and the assistant will provide detailed and informative responses, enhancing comprehension and knowledge transfer.
### 4. Seamless Document Loading:
With the document loader feature, users can easily upload research papers in various formats, allowing the AI assistant to analyze and extract relevant information efficiently.
### 5. Parallel Chain Processing:
Through the use of parallel chain technology, the application can handle multiple tasks simultaneously, improving speed and performance.
### 6. Prompt Templates:
Utilizing pre-defined prompt templates, users can guide the AI assistant on how to generate summaries, responses, and other outputs, ensuring accuracy and consistency.
### 7. Retrieval-Augmented Generation:
Implementing cutting-edge RAG implementation, the assistant combines retrieval and generation techniques to enhance the quality and relevance of generated content.
### 8. Text Splitter and Vector Stores:
The text splitter feature segments documents into manageable sections, while vector stores facilitate efficient storage and retrieval of document embeddings for processing.
## Get Involved:
We encourage you to explore the capabilities of our AI-Powered Research Assistant and experience the advancements in research assistance technology firsthand. Whether you are a student, researcher, or industry professional, our application aims to streamline the research process and enhance productivity.
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By incorporating advanced AI technologies and innovative approaches, the AI-Powered Research Assistant using Langchain represents a significant leap forward in research support systems. Join us in revolutionizing the way research is conducted and information is accessed. Visit the link above to download and execute the latest version of our application. Let's embark on a journey towards smarter research assistance together! 🚀🔬🤖