Knowledge-Discovery-Agents is an open-source Python project offering specialized AI agents that automate retrieval, parsing, and semantic understanding of documents and web data. Leveraging LangChain and OpenAI, it enables users to build agents for document QA, data extraction, and knowledge graph compilation, all orchestrated with minimal code. Ideal for developers aiming to implement advanced knowledge discovery pipelines quickly and flexibly.
AI Platforms & Frameworks
Tool Libraries
#AI Agents
#data extraction
#Knowledge Discovery
#document automation
#PDF Insights
#CSV analysis
#Web Scraping Tools
#semantic search
#knowledge graph
#LangChain-Integration
#custom workflows
#Open-source AI
#prototyping solutions
#analytics pipelines
#QA bots
#structured data
#enterprise research
#data loaders
#modular architecture
...
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Knowledge-Discovery-Agents
Knowledge-Discovery-Agents is an open-source Python project offering specialized AI agents that automate retrieval, parsing, and semantic understanding of documents and web data. Leveraging LangChain and OpenAI, it enables users to build agents for document QA, data extraction, and knowledge graph compilation, all orchestrated with minimal code. Ideal for developers aiming to implement advanced knowledge discovery pipelines quickly and flexibly.
AI Platforms & Frameworks
Tool Libraries
#AI Agents
#data extraction
#Knowledge Discovery
#document automation
#PDF Insights
#CSV analysis
#Web Scraping Tools
#semantic search
#knowledge graph
#LangChain-Integration
#custom workflows
#Open-source AI
#prototyping solutions
#analytics pipelines
#QA bots
#structured data
#enterprise research
#data loaders
#modular architecture
...
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What is Knowledge-Discovery-Agents?
Knowledge-Discovery-Agents provides a modular set of pre-built and customizable AI agents designed to extract structured insights from PDFs, CSVs, websites, and other sources. It integrates with LangChain to manage tool usage, supports chaining of tasks like web scraping, embedding generation, semantic search, and knowledge graph creation. Users can define agent workflows, incorporate new data loaders, and deploy QA bots or analytics pipelines. With minimal boilerplate code, it accelerates prototyping, data exploration, and automated report generation in research and enterprise contexts.
Who will use Knowledge-Discovery-Agents?
- Data scientists
- AI researchers
- Software developers
- Knowledge engineers
How to use the Knowledge-Discovery-Agents?
- Step1: Clone the repository from GitHub.
- Step2: Install dependencies with pip install -r requirements.txt.
- Step3: Configure your API keys (e.g., OpenAI) in a .env file.
- Step4: Run example scripts under the examples folder.
- Step5: Customize or extend agent workflows by editing agent definitions.
- Step6: Deploy your agent as a service or integrate into your application.
Platform
- mac
- windows
- linux
Knowledge-Discovery-Agents's Core Features & Benefits
The Core Features
- Pre-built document QA agents
- Support for PDF, CSV, and web data loaders
- Knowledge graph construction
- LangChain-based tool orchestration
- Customizable agent workflows
The Benefits
- Accelerates prototyping of knowledge discovery pipelines
- Modular and extensible architecture
- Open-source and MIT licensed
- Minimal code required to deploy agents
- Seamless integration with popular LLMs
Knowledge-Discovery-Agents's Main Use Cases & Applications
- Automated analysis of research papers and reports
- Semantic search over enterprise document repositories
- Interactive chatbot for technical documentation
- Generation of structured knowledge graphs from raw data
FAQs of Knowledge-Discovery-Agents
Knowledge-Discovery-Agents Company Information
Knowledge-Discovery-Agents Reviews
5/5
Knowledge-Discovery-Agents's Main Competitors and alternatives?
- LangChain
- Haystack
- LlamaIndex
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