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2025-07-21 22:30:03 -07:00
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README.md
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@@ -69,7 +69,7 @@ uv sync
uv sync --extra diskann
```
**Ollama Setup (Optional for Local LLM):**
**Ollama Setup (Recommended for full privacy):**
*We support both hf-transformers and Ollama for local LLMs. Ollama is recommended for faster performance.*
@@ -101,21 +101,27 @@ You can also replace `llama3.2:1b` to `deepseek-r1:1.5b` or `qwen3:4b` for bette
Just 3 lines of code. Our declarative API makes RAG as easy as writing a config file:
```python
from leann.api import LeannBuilder, LeannSearcher
from leann.api import LeannBuilder, LeannSearcher, LeannChat
# 1. Build index (no embeddings stored!)
builder = LeannBuilder(backend_name="hnsw")
builder.add_text("C# is a powerful programming language")
builder.add_text("Python is a powerful programming language")
builder.add_text("C# is a powerful programming language but it is not very popular")
builder.add_text("Python is a powerful programming language and it is very popular")
builder.add_text("Machine learning transforms industries")
builder.add_text("Neural networks process complex data")
builder.add_text("Leann is a great storage saving engine for RAG on your macbook")
builder.build_index("knowledge.leann")
# 2. Search with real-time embeddings
searcher = LeannSearcher("knowledge.leann")
results = searcher.search("C++ programming languages", top_k=2, recompute_beighbor_embeddings=True)
print(results)
results = searcher.search("programming languages", top_k=2, recompute_beighbor_embeddings=True,complexity=2)
print("LEANN Search results: ", results)
# 3. Chat with LEANN
chat = LeannChat(index_path="knowledge.leann", llm_config={"type": "ollama", "model": "llama3.2:1b"})
response = chat.ask(
"Compare the two retrieved programming languages and say which one is more popular today. Respond in a single well-formed sentence.",
top_k=2,
recompute_beighbor_embeddings=True,
)
print("LEANN Chat response: ", response)
```
**That's it.** No cloud setup, no API keys, no "fine-tuning". Just your data, your questions, your laptop.