Added ollama and openai Fastapi api services

.gitignore

@@ -14,3 +14,4 @@ ignore_this.txt
 .ruff_cache/
 gui/
 *.log
+.vscode

api/.gitignore

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+inputs
+rag_storage

api/README_OLLAMA.md

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+# LightRAG API Server
+
+A powerful FastAPI-based server for managing and querying documents using LightRAG (Light Retrieval-Augmented Generation). This server provides a REST API interface for document management and intelligent querying using various LLM models through Ollama.
+
+## Features
+
+- 🔍 Multiple search modes (naive, local, global, hybrid)
+- 📡 Streaming and non-streaming responses
+- 📝 Document management (insert, batch upload, clear)
+- ⚙️ Highly configurable model parameters
+- 📚 Support for text and file uploads
+- 🔧 RESTful API with automatic documentation
+- 🚀 Built with FastAPI for high performance
+
+## Prerequisites
+
+- Python 3.8+
+- Ollama server running locally or remotely
+- Required Python packages:
+  - fastapi
+  - uvicorn
+  - lightrag
+  - pydantic
+
+## Installation
+If you are using windows, you will need to donwload and install visual c++ build tools from [https://visualstudio.microsoft.com/visual-cpp-build-tools/ ](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
+Make sure you install the VS 2022 C++ x64/x86 Build tools like from indivisual componants tab:
+![image](https://github.com/user-attachments/assets/3723e15b-0a2c-42ed-aebf-e595a9f9c946)
+
+This is mandatory for builmding some modules.
+
+1. Clone the repository:
+```bash
+git clone https://github.com/ParisNeo/LightRAG.git
+cd api
+```
+
+2. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+3. Make sure Ollama is running and accessible.
+
+## Configuration
+
+The server can be configured using command-line arguments:
+
+```bash
+python ollama_lightollama_lightrag_server.py --help
+```
+
+Available options:
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| --host | 0.0.0.0 | Server host |
+| --port | 9621 | Server port |
+| --model | mistral-nemo:latest | LLM model name |
+| --embedding-model | bge-m3:latest | Embedding model name |
+| --ollama-host | http://localhost:11434 | Ollama host URL |
+| --working-dir | ./rag_storage | Working directory for RAG |
+| --max-async | 4 | Maximum async operations |
+| --max-tokens | 32768 | Maximum token size |
+| --embedding-dim | 1024 | Embedding dimensions |
+| --max-embed-tokens | 8192 | Maximum embedding token size |
+| --input-file | ./book.txt | Initial input file |
+| --log-level | INFO | Logging level |
+
+## Quick Start
+
+1. Basic usage with default settings:
+```bash
+python ollama_lightrag_server.py
+```
+
+2. Custom configuration:
+```bash
+python ollama_lightrag_server.py --model llama2:13b --port 8080 --working-dir ./custom_rag
+```
+
+Make sure the models are installed in your ollama instance
+```bash
+python ollama_lightrag_server.py --model mistral-nemo:latest --embedding-model bge-m3  --embedding-dim 1024
+```
+
+## API Endpoints
+
+### Query Endpoints
+
+#### POST /query
+Query the RAG system with options for different search modes.
+
+```bash
+curl -X POST "http://localhost:9621/query" \
+    -H "Content-Type: application/json" \
+    -d '{"query": "Your question here", "mode": "hybrid"}'
+```
+
+#### POST /query/stream
+Stream responses from the RAG system.
+
+```bash
+curl -X POST "http://localhost:9621/query/stream" \
+    -H "Content-Type: application/json" \
+    -d '{"query": "Your question here", "mode": "hybrid"}'
+```
+
+### Document Management Endpoints
+
+#### POST /documents/text
+Insert text directly into the RAG system.
+
+```bash
+curl -X POST "http://localhost:9621/documents/text" \
+    -H "Content-Type: application/json" \
+    -d '{"text": "Your text content here", "description": "Optional description"}'
+```
+
+#### POST /documents/file
+Upload a single file to the RAG system.
+
+```bash
+curl -X POST "http://localhost:9621/documents/file" \
+    -F "file=@/path/to/your/document.txt" \
+    -F "description=Optional description"
+```
+
+#### POST /documents/batch
+Upload multiple files at once.
+
+```bash
+curl -X POST "http://localhost:9621/documents/batch" \
+    -F "files=@/path/to/doc1.txt" \
+    -F "files=@/path/to/doc2.txt"
+```
+
+#### DELETE /documents
+Clear all documents from the RAG system.
+
+```bash
+curl -X DELETE "http://localhost:9621/documents"
+```
+
+### Utility Endpoints
+
+#### GET /health
+Check server health and configuration.
+
+```bash
+curl "http://localhost:9621/health"
+```
+
+## Development
+
+### Running in Development Mode
+
+```bash
+uvicorn ollama_lightrag_server:app --reload --port 9621
+```
+
+### API Documentation
+
+When the server is running, visit:
+- Swagger UI: http://localhost:9621/docs
+- ReDoc: http://localhost:9621/redoc
+
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## Acknowledgments
+
+- Built with [FastAPI](https://fastapi.tiangolo.com/)
+- Uses [LightRAG](https://github.com/HKUDS/LightRAG) for document processing
+- Powered by [Ollama](https://ollama.ai/) for LLM inference

api/README_OPENAI.md

@@ -0,0 +1,171 @@
+
+# LightRAG API Server
+
+A powerful FastAPI-based server for managing and querying documents using LightRAG (Light Retrieval-Augmented Generation). This server provides a REST API interface for document management and intelligent querying using OpenAI's language models.
+
+## Features
+
+- 🔍 Multiple search modes (naive, local, global, hybrid)
+- 📡 Streaming and non-streaming responses
+- 📝 Document management (insert, batch upload, clear)
+- ⚙️ Highly configurable model parameters
+- 📚 Support for text and file uploads
+- 🔧 RESTful API with automatic documentation
+- 🚀 Built with FastAPI for high performance
+
+## Prerequisites
+
+- Python 3.8+
+- OpenAI API key
+- Required Python packages:
+  - fastapi
+  - uvicorn
+  - lightrag
+  - pydantic
+  - openai
+  - nest-asyncio
+
+## Installation
+If you are using Windows, you will need to download and install visual c++ build tools from [https://visualstudio.microsoft.com/visual-cpp-build-tools/](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
+Make sure you install the VS 2022 C++ x64/x86 Build tools from individual components tab.
+
+1. Clone the repository:
+```bash
+git clone https://github.com/ParisNeo/LightRAG.git
+cd api
+```
+
+2. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+3. Set up your OpenAI API key:
+```bash
+export OPENAI_API_KEY='your-api-key-here'
+```
+
+## Configuration
+
+The server can be configured using command-line arguments:
+
+```bash
+python openai_lightrag_server.py --help
+```
+
+Available options:
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| --host | 0.0.0.0 | Server host |
+| --port | 9621 | Server port |
+| --model | gpt-4 | OpenAI model name |
+| --embedding-model | text-embedding-3-large | OpenAI embedding model |
+| --working-dir | ./rag_storage | Working directory for RAG |
+| --max-tokens | 32768 | Maximum token size |
+| --max-embed-tokens | 8192 | Maximum embedding token size |
+| --input-dir | ./inputs | Input directory for documents |
+| --log-level | INFO | Logging level |
+
+## Quick Start
+
+1. Basic usage with default settings:
+```bash
+python openai_lightrag_server.py
+```
+
+2. Custom configuration:
+```bash
+python openai_lightrag_server.py --model gpt-4 --port 8080 --working-dir ./custom_rag
+```
+
+## API Endpoints
+
+### Query Endpoints
+
+#### POST /query
+Query the RAG system with options for different search modes.
+
+```bash
+curl -X POST "http://localhost:9621/query" \
+    -H "Content-Type: application/json" \
+    -d '{"query": "Your question here", "mode": "hybrid"}'
+```
+
+#### POST /query/stream
+Stream responses from the RAG system.
+
+```bash
+curl -X POST "http://localhost:9621/query/stream" \
+    -H "Content-Type: application/json" \
+    -d '{"query": "Your question here", "mode": "hybrid"}'
+```
+
+### Document Management Endpoints
+
+#### POST /documents/text
+Insert text directly into the RAG system.
+
+```bash
+curl -X POST "http://localhost:9621/documents/text" \
+    -H "Content-Type: application/json" \
+    -d '{"text": "Your text content here", "description": "Optional description"}'
+```
+
+#### POST /documents/file
+Upload a single file to the RAG system.
+
+```bash
+curl -X POST "http://localhost:9621/documents/file" \
+    -F "file=@/path/to/your/document.txt" \
+    -F "description=Optional description"
+```
+
+#### POST /documents/batch
+Upload multiple files at once.
+
+```bash
+curl -X POST "http://localhost:9621/documents/batch" \
+    -F "files=@/path/to/doc1.txt" \
+    -F "files=@/path/to/doc2.txt"
+```
+
+#### DELETE /documents
+Clear all documents from the RAG system.
+
+```bash
+curl -X DELETE "http://localhost:9621/documents"
+```
+
+### Utility Endpoints
+
+#### GET /health
+Check server health and configuration.
+
+```bash
+curl "http://localhost:9621/health"
+```
+
+## Development
+
+### Running in Development Mode
+
+```bash
+uvicorn openai_lightrag_server:app --reload --port 9621
+```
+
+### API Documentation
+
+When the server is running, visit:
+- Swagger UI: http://localhost:9621/docs
+- ReDoc: http://localhost:9621/redoc
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## Acknowledgments
+
+- Built with [FastAPI](https://fastapi.tiangolo.com/)
+- Uses [LightRAG](https://github.com/HKUDS/LightRAG) for document processing
+- Powered by [OpenAI](https://openai.com/) for language model inference