wehub-resource-sync
79 lines
3.7 KiB
Markdown
79 lines
3.7 KiB
Markdown
# Running RAG-Anything in an Offline Environment
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This document explains a critical consideration for running the RAG-Anything project in an environment with no internet access.
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## The Network Dependency: `LightRAG` and `tiktoken`
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The `RAGAnything` core engine relies on the `LightRAG` library for its primary functionality. `LightRAG`, in turn, uses OpenAI's `tiktoken` library for text tokenization.
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By default, the `tiktoken` library has a network dependency. On its first use, it attempts to download tokenizer models from OpenAI's public servers (`openaipublic.blob.core.windows.net`). If the application is running in an offline or network-restricted environment, this download will fail, causing the `LightRAG` instance to fail to initialize.
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This results in an error similar to the following:
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```
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Failed to initialize LightRAG instance: HTTPSConnectionPool(host='openaipublic.blob.core.windows.net', port=443): Max retries exceeded with url: /encodings/o200k_ba
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```
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This dependency is indirect. The `RAG-Anything` codebase itself does not directly import or call `tiktoken`. The call is made from within the `lightrag` library.
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## The Solution: Using a Local `tiktoken` Cache
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To resolve this issue and enable fully offline operation, you must provide a local cache for the `tiktoken` models. This is achieved by setting the `TIKTOKEN_CACHE_DIR` environment variable **before** the application starts.
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When this environment variable is set, `tiktoken` will look for its model files in the specified local directory instead of attempting to download them from the internet.
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### Steps to Implement the Solution:
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1. **Create a Model Cache:** In an environment *with* internet access, run the provided script to download and cache the necessary `tiktoken` models.
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```bash
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# Run the cache creation script
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uv run scripts/create_tiktoken_cache.py
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```
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This will create a `tiktoken_cache` directory in your project root containing the required model files.
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2. **Configure the Environment Variable:** Add the following line to your `.env` file:
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```bash
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TIKTOKEN_CACHE_DIR=./tiktoken_cache
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```
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**Important:** You should ensure that the `.env` file is loaded **before** `LightRAG` imports `tiktoken`, making this configuration effective.
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```python
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import os
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from typing import Dict, Any, Optional, Callable
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import sys
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import asyncio
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import atexit
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from dataclasses import dataclass, field
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from pathlib import Path
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from dotenv import load_dotenv
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# Add project root directory to Python path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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# Load environment variables FIRST - before any imports that use tiktoken
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load_dotenv(dotenv_path=".env", override=False)
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# Now import LightRAG (which will import tiktoken with the correct env var set)
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from lightrag import LightRAG
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from lightrag.utils import logger
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# Rest of the code...
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```
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### Testing the Offline Setup
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1. **Create a `tiktoken_cache` directory:** If you don't have one already, create a directory named `tiktoken_cache` in the project root.
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2. **Populate the cache:** Run the `scripts/create_tiktoken_cache.py` script to download the necessary tiktoken models into the `tiktoken_cache` directory.
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3. **Set the `TIKTOKEN_CACHE_DIR` environment variable:** Add the line `TIKTOKEN_CACHE_DIR=./tiktoken_cache` to your `.env` file.
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4. **Disconnect from the internet:** Disable your internet connection or put your machine in airplane mode.
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5. **Run the application:** Start the `RAG-Anything` application. For example:
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```
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uv run examples/raganything_example.py requirements.txt
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```
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By following these steps, you can eliminate the network dependency and run the `RAG-Anything` project successfully in a fully offline environment.
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