A multi-purpose LLM framework for RAG and data creation.
With Synthesizer, users can:
pip install sciphi-synthesizer
Generate synthetic question-answer pairs
export SCIPHI_API_KEY=MY_SCIPHI_API_KEY
python -m synthesizer.scripts.data_augmenter run --dataset="wiki_qa"
tail augmented_output/config_name_eq_answer_question__dataset_name_eq_wiki_qa.jsonl
{ "formatted_prompt": "... ### Question:\nwhat country did wine originate in\n\n### Input:\n1. URL: https://en.wikipedia.org/wiki/History%20of%20wine (Score: 0.85)\nTitle:History of wine....",
{ "completion": "Wine originated in the South Caucasus, which is now part of modern-day Armenia ..."
Evaluate RAG pipeline performance
export SCIPHI_API_KEY=MY_SCIPHI_API_KEY
python -m synthesizer.scripts.rag_harness --rag_provider="agent-search" --llm_provider_name="sciphi" --n_samples=25
For more detailed information, tutorials, and API references, please visit the official Synthesizer Documentation.
Quickly set up RAG augmented generation with your choice of provider, from OpenAI, Anhtropic, vLLM, and SciPhi:
# Requires SCIPHI_API_KEY in env
from synthesizer.core import LLMProviderName, RAGProviderName
from synthesizer.interface import LLMInterfaceManager, RAGInterfaceManager
from synthesizer.llm import GenerationConfig
# RAG Provider Settings
rag_interface = RAGInterfaceManager.get_interface_from_args(
RAGProviderName("agent-search"),
limit_hierarchical_url_results=rag_limit_hierarchical_url_results,
limit_final_pagerank_results=rag_limit_final_pagerank_results,
)
rag_context = rag_interface.get_rag_context(query)
# LLM Provider Settings
llm_interface = LLMInterfaceManager.get_interface_from_args(
LLMProviderName("openai"),
)
generation_config = GenerationConfig(
model_name=llm_model_name,
max_tokens_to_sample=llm_max_tokens_to_sample,
temperature=llm_temperature,
top_p=llm_top_p,
# other generation params here ...
)
formatted_prompt = raw_prompt.format(rag_context=rag_context)
completion = llm_interface.get_completion(formatted_prompt, generation_config)