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Update README: Revise features and env vars for DeepSeek integration (#197)

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@ -20,21 +20,24 @@ https://github.com/user-attachments/assets/bd5d38b9-9309-40b9-93ca-918dfa4f3fd4
2. **Automatic Title & Tag Generation**
No more guesswork. Let the AI do the naming and categorizing. You can easily review suggestions and refine them if needed.
3. **Automatic Correspondent Generation**
3. **Supports DeepSeek reasoning models in Ollama**
Greatly enhance accuracy by using a reasoning model like `deepseek-r1:8b`. The perfect tradeoff between privacy and performance! Of course, if you got enough GPUs or NPUs, a bigger model will enhance the experience.
5. **Automatic Correspondent Generation**
Automatically identify and generate correspondents from your documents, making it easier to track and organize your communications.
4. **Extensive Customization**
6. **Extensive Customization**
- **Prompt Templates**: Tweak your AI prompts to reflect your domain, style, or preference.
- **Tagging**: Decide how documents get tagged—manually, automatically, or via OCR-based flows.
5. **Simple Docker Deployment**
7. **Simple Docker Deployment**
A few environment variables, and you’re off! Compose it alongside paperless-ngx with minimal fuss.
6. **Unified Web UI**
8. **Unified Web UI**
- **Manual Review**: Approve or tweak AI’s suggestions.
- **Auto Processing**: Focus only on edge cases while the rest is sorted for you.
7. **Opt-In LLM-based OCR**
9. **Opt-In LLM-based OCR**
If you opt in, your images get read by a Vision LLM, pushing boundaries beyond standard OCR tools.
---
@ -69,7 +72,7 @@ https://github.com/user-attachments/assets/bd5d38b9-9309-40b9-93ca-918dfa4f3fd4
- A running instance of [paperless-ngx][paperless-ngx].
- Access to an LLM provider:
- **OpenAI**: An API key with models like `gpt-4o` or `gpt-3.5-turbo`.
- **Ollama**: A running Ollama server with models like `llama2`.
- **Ollama**: A running Ollama server with models like `deepseek-r1:8b`.
### Installation
@ -93,7 +96,9 @@ services:
MANUAL_TAG: 'paperless-gpt' # Optional, default: paperless-gpt
AUTO_TAG: 'paperless-gpt-auto' # Optional, default: paperless-gpt-auto
LLM_PROVIDER: 'openai' # or 'ollama'
LLM_MODEL: 'gpt-4o' # or 'llama2'
LLM_MODEL: 'gpt-4o' # or 'deepseek-r1:8b'
# Optional, but recommended for Ollama
TOKEN_LIMIT: 1000
OPENAI_API_KEY: 'your_openai_api_key'
# Optional - OPENAI_BASE_URL: 'https://litellm.yourinstallationof.it.com/v1'
LLM_LANGUAGE: 'English' # Optional, default: English
@ -160,7 +165,7 @@ services:
| `MANUAL_TAG` | Tag for manual processing. Default: `paperless-gpt`. | No |
| `AUTO_TAG` | Tag for auto processing. Default: `paperless-gpt-auto`. | No |
| `LLM_PROVIDER` | AI backend (`openai` or `ollama`). | Yes |
| `LLM_MODEL` | AI model name, e.g. `gpt-4o`, `gpt-3.5-turbo`, `llama2`. | Yes |
| `LLM_MODEL` | AI model name, e.g. `gpt-4o`, `gpt-3.5-turbo`, `deepseek-r1:8b`. | Yes |
| `OPENAI_API_KEY` | OpenAI API key (required if using OpenAI). | Cond. |
| `OPENAI_BASE_URL` | OpenAI base URL (optional, if using a custom OpenAI compatible service like LiteLLM). | No |
| `LLM_LANGUAGE` | Likely language for documents (e.g. `English`). Default: `English`. | No |
@ -455,7 +460,7 @@ When using local LLMs (like those through Ollama), you might need to adjust cert
#### Token Management
- Use `TOKEN_LIMIT` environment variable to control the maximum number of tokens sent to the LLM
- Smaller models might truncate content unexpectedly if given too much text
- Start with a conservative limit (e.g., 2000 tokens) and adjust based on your model's capabilities
- Start with a conservative limit (e.g., 1000 tokens) and adjust based on your model's capabilities
- Set to `0` to disable the limit (use with caution)
Example configuration for smaller models:
@ -463,7 +468,7 @@ Example configuration for smaller models:
environment:
TOKEN_LIMIT: '2000' # Adjust based on your model's context window
LLM_PROVIDER: 'ollama'
LLM_MODEL: 'llama2' # Or other local model
LLM_MODEL: 'deepseek-r1:8b' # Or other local model
```
Common issues and solutions: