Retrieval-enhanced mind — RAG toolkit High-level wrapper around LangChain to help build RAG applications.
| Dependency | Description |
|---|---|
| Librarian | Used to load the vector store and perform retrieval |
| LLMChat | Builds a chat interface for LLMs |
uv venv && uv syncuv tool install -e .| Variable Name | Description |
|---|---|
| QDRANT_DATA_URL | URL to connect to Qdrant server, QDRANT_DATA_PATH will be ignored if this variable is set |
| QDRANT_DATA_PATH | Path to store Qdrant vector database data |
┌─────────┐
│ START │
└────┬────┘
│
▼
┌────────────────┐
│ route_retriever│
│ (query_mode) │
└───────┬────────┘
│
┌──────────────┼──────────────┐
│ │ │
▼ ▼ ▼
┌─────────┐ ┌─────────┐ ┌──────────┐
│ quick │ │ rerank │ │ complex │
│retrieve │ │retrieve │ │ retrieve │
└────┬────┘ └────┬────┘ └────┬─────┘
│ │ │
└──────────────┼──────────────┘
│
▼
┌────────────┐
│ synthesize │
└──────┬─────┘
│
▼
┌────────┐
│ END │
└────────┘
-
quick - Fast direct similarity search. Retrieves documents based on embedding similarity without additional processing. Best for simple queries where speed is important.
-
rerank - Retrieves more candidate documents and reranks them using a cross-encoder model (BGE-reranker). Improves relevance at the cost of slightly longer processing time.
-
complex (default) - Uses LLM to extract multiple search queries from your question, retrieves documents for each query, then aggregates and reranks results. Best for complex or multi-faceted questions.
Use QDRANT_DATA_URL or QDRANT_DATA_PATH to specify the vector store location
# run remind-chat to enter interactive chat mode
remind-chat
[gemma-3-1b (cuda)][debug]> gemma-3-1bis the current model used; it's a local model, andcudameans it uses GPU.debugis the current output style- You can switch the output style and device using commands like
/configs device cuda
/configs shows detailed configuration
[gemma-3-1b (cuda)][debug]> /configs
Examples:
/configs n_top_result 8 # change configs
```
Vectorstore Configuration
Qdrant data path /home/kk/.local/opt/re-mind/qdrant-data
Collection name testrag
Attached Items
No items attached
Configuration Commands
Current Configuration
collection_name testrag
device cuda
max_width 100
model_option_name gemma-3-1b
n_top_result auto
output_mode debug
return_full_text False
temperature 1.2- Qdrant data path: the current vector store used for retrieval, only changeable via environment variables
- Collection name: the current collection, can be changed via
/configs collection_name <name>
The query_mode configuration controls the retrieval strategy used when searching the vector store. You can set it using /configs query_mode <mode>.
detail about each mode is described in the "Query mode" section above.
# Use complex mode for complex questions (default)
/configs query_mode complex| Command | Description |
|---|---|
/attach |
Open an editor to manage attached items (files/sources) for filtering vector store searches |
/reset_config |
Reset all configuration settings to default values |
/search |
Perform a complex retrieval search and display retrieved documents without generating a response |
/summary |
Retrieve relevant context and generate a summary based on the query |
/models |
List available models or switch to a different model (e.g., /models gemma-3-1b) |
After you choose your model, device, collection, and query mode, you can start chatting with the LLM
[gemma-3-1b (cuda)][debug]> list story or novel and write as points format and some description for each story
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ list story or novel and write as points format and some description for each story ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Okay, here’s a breakdown of the provided text, formatted as requested, with descriptions for │
│ each story/novel: │
│ │
│ 1. The Little Prince by Antoine de Saint-Exupéry │
│ │
│ • Genre: Philosophical Children’s Novel │
│ • Description: A poignant and allegorical story about the importance of imagination, │
│ friendship, and seeing beyond the surface of things. It follows a pilot stranded in the │
│ desert who encounters a young prince from a tiny asteroid. The prince teaches him about the │
│ value of relationships, the nature of truth, and the beauty of simple experiences. It’s a │
│ story about finding meaning in life, even when it seems lost. │
│ │
│ 2. The House on Mango Street by Sandra Cisneros │
│ │
│ • Genre: Coming-of-Age Novel │
│ • Description: This novel follows Esperanza Cordero, a young Latina girl growing up in │
│ Chicago’s inner city. It depicts her journey of self-discovery, neighborhood dynamics, and │
│ the challenges of navigating a world that often feels hostile and oppressive. It explores │
│ themes of identity, poverty, and the struggle for agency. │
...