Raglight

Fix bugs for streaming with Ollama.

Add new embeddings management.
Add class signatures into a new vector store collection.
Add streaming for Ollama provider.

Now you can config your Agentic RAG using all available parameters :

python
from raglight.rag.simple_agentic_rag_api import AgenticRAGPipeline
from raglight.config.agentic_rag_config import SimpleAgenticRAGConfig
from raglight.models.data_source_model import FolderSource, GitHubSource
from raglight.config.settings import Settings

Settings.setup_logging()

config = config = SimpleAgenticRAGConfig(
provider = Settings.OLLAMA.lower(), default "ollama"
model = Settings.DEFAULT_LLM, default "llama3"
k= 5,
max_steps = 4,
system_prompt = Settings.DEFAULT_AGENT_PROMPT
api_key="YOUR_API_KEY",
api_base: Settings.DEFAULT_OLLAMA_CLIENT
num_ctx: 8192
verbosity_level: 2
)

pipeline = AgenticRAGPipeline(knowledge_base=[
FolderSource(path="<path to your folder with pdf>/knowledge_base"),
GitHubSource(url="https://github.com/Bessouat40/RAGLight")
],
config=config)

pipeline.build()

response = pipeline.generate("How can I create an easy RAGPipeline using raglight framework ? Give me python implementation")
print(response)

You can now use Agentic RAG using RAGLight. You can setup one easily using default pipeline :

python
from raglight.rag.simple_agentic_rag_api import AgenticRAGPipeline
from raglight.models.data_source_model import FolderSource, GitHubSource
from raglight.config.settings import Settings

Settings.setup_logging()

pipeline = AgenticRAGPipeline(knowledge_base=[
FolderSource(path="<path to your folder with pdf>/knowledge_base"),
GitHubSource(url="https://github.com/Bessouat40/RAGLight")
],
model_name="llama3",
provider=Settings.OLLAMA,
k=5)

pipeline.build()

response = pipeline.generate("How can I create an easy RAGPipeline using raglight framework ? Give me python implementation")
print(response)

- Expose k for retrieval part
- Add Docker example