sciphi-synthesizer 1.0.5

Synthesizer: A Framework for LLM Powered Data.

Synthesizer[ΨΦ]: A multi-purpose LLM framework 💡

With Synthesizer, users can:

• Custom Data Creation: Generate datasets via LLMs that are tailored to your needs.
  • Anthropic, OpenAI, vLLM, and HuggingFace.
• Retrieval-Augmented Generation (RAG) on Demand: Built-in RAG Provider Interface to anchor generated data to real-world sources.
  • Turnkey integration with Agent Search API.
• Custom Data Creation: Generate datasets via LLMs that are tailored to your needs, for LLM training, RAG, and more.

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Fast Setup

pip install sciphi-synthesizer

Using Synthesizer

1. 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 ..."
   

2. 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
   

Documentation

For more detailed information, tutorials, and API references, please visit the official Synthesizer Documentation.

Community & Support

• Engage with our vibrant community on Discord.
• For tailored inquiries or feedback, please email us.

Developing with Synthesizer

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)