willnco/autollm

Ship RAG based LLM web apps in seconds.

🤔 why autollm?

Simplify. Unify. Amplify.

Feature	AutoLLM	LangChain	LlamaIndex	LiteLLM
100+ LLMs	✅	✅	✅	✅
Unified API	✅	❌	❌	✅
20+ Vector Databases	✅	✅	✅	❌
Cost Calculation (100+ LLMs)	✅	❌	❌	✅
1-Line RAG LLM Engine	✅	❌	❌	❌
1-Line FastAPI	✅	❌	❌	❌

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📦 installation

easily install autollm package with pip in Python>=3.8 environment.

pip install autollm

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🎯 quickstart

create a query engine in seconds

>>> from autollm import AutoQueryEngine

>>> query_engine = AutoQueryEngine.from_parameters(
>>>   documents: List[llama_index.Documents]
>>> )

>>> response = query_engine.query(
>>>   "Why did SafeVideo AI develop this project?"
>>> )

>>> response.response
"Because they wanted to deploy rag based llm apis in no time!"

convert it to a FastAPI app in 1-line

>>> import uvicorn

>>> from autollm import AutoFastAPI

>>> app = AutoFastAPI.from_query_engine(query_engine)

>>> uvicorn.run(app, host="0.0.0.0", port=8000)
INFO:     Started server process [12345]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://http://0.0.0.0:8000/

👉 advanced usage

>>> from autollm import AutoQueryEngine

>>> query_engine = AutoQueryEngine.from_parameters(
>>>   documents=documents,
>>>   system_prompt= ...
>>>   query_wrapper_prompt= ...
>>>   enable_cost_calculator=True,
>>>   llm_params={"model": "gpt-3.5-turbo"},
>>>   vector_store_params={
>>>     "vector_store_type": "LanceDBVectorStore",
>>>     "uri": "/tmp/lancedb",
>>>     "table_name": "lancedb",
>>>     "nprobs": 20
>>>   },
>>>   service_context_params={"chunk_size": 1024},
>>>   query_engine_params={"similarity_top_k": 10},
>>> )

>>> response = query_engine.query("Who is SafeVideo AI?")

>>> print(response.response)
"A startup that provides self hosted AI API's for companies!"

>>> from autollm import AutoFastAPI

>>> app = AutoFastAPI.from_query_engine(
      query_engine,
      api_title= ...,
      api_description= ...,
      api_version= ...,
      api_term_of_service= ...,
    )

>>> uvicorn.run(app, host="0.0.0.0", port=8000)
INFO:     Started server process [12345]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://http://0.0.0.0:8000/

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🌟 features

supports 100+ LLMs

👉 microsoft azure - openai example:

>>> from autollm import AutoLLM

>>> os.environ["AZURE_API_KEY"] = ""
>>> os.environ["AZURE_API_BASE"] = ""
>>> os.environ["AZURE_API_VERSION"] = ""

>>> llm = AutoLLM(model="azure/<your_deployment_name>")

👉 google - vertexai example

>>> from autollm import AutoLLM

>>> os.environ["VERTEXAI_PROJECT"] = "hardy-device-38811"  # Your Project ID`
>>> os.environ["VERTEXAI_LOCATION"] = "us-central1"  # Your Location

>>> llm = AutoLLM(model="text-bison@001")

👉 aws bedrock - claude v2 example

>>> from autollm import AutoLLM

>>> os.environ["AWS_ACCESS_KEY_ID"] = ""
>>> os.environ["AWS_SECRET_ACCESS_KEY"] = ""
>>> os.environ["AWS_REGION_NAME"] = ""

>>> llm = AutoLLM(model="anthropic.claude-v2")

supports 20+ VectorDBs

🌟Pro Tip: autollm defaults to lancedb as the vector store: it's setup-free, serverless, and 100x more cost-effective!

👉 default - lancedb example

>>> from autollm import AutoVectorStoreIndex

>>> vector_store_index = AutoVectorStoreIndex.from_defaults(
>>>     documents=documents
>>> )

automated cost calculation for 100+ LLMs

>>> from autollm import AutoServiceContext

>>> service_context = AutoServiceContext(enable_cost_calculation=True)

# Example verbose output after query
Embedding Token Usage: 7
LLM Prompt Token Usage: 1482
LLM Completion Token Usage: 47
LLM Total Token Cost: $0.002317

create FastAPI App in 1-Line

👉 example

>>> from autollm import AutoFastAPI

>>> app = AutoFastAPI.from_config(config_path, env_path)

Here, config and env should be replaced by your configuration and environment file paths.

After creating your FastAPI app, run the following command in your terminal to get it up and running:

uvicorn main:app

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🔄 migration from llama-index

switching from Llama-Index? We've got you covered.

👉 easy migration

>>> from llama_index import StorageContext, ServiceContext, VectorStoreIndex
>>> from llama_index.vectorstores import LanceDBVectorStore

>>> from autollm import AutoQueryEngine

>>> vector_store = LanceDBVectorStore(uri="/tmp/lancedb")
>>> storage_context = StorageContext.from_defaults(vector_store=vector_store)
>>> index = VectorStoreIndex.from_documents(documents=documents)
>>> service_context = ServiceContext.from_defaults()

>>> query_engine = AutoQueryEngine.from_instances(index, service_context)

❓ FAQ

Q: Can I use this for commercial projects?

A: Yes, AutoLLM is licensed under GNU Affero General Public License (AGPL 3.0), which allows for commercial use under certain conditions. Contact us for more information.

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roadmap

our roadmap outlines upcoming features and integrations to make autollm the most extensible and powerful base package for large language model applications.

• 1-line Gradio app creation and deployment

• Budget based email notification

• Automated LLM evaluation

• Add more quickstart apps on pdf-chat, documentation-chat, academic-paper-analysis, patent-analysis and more!

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📜 license

autollm is available under the GNU Affero General Public License (AGPL 3.0).

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📞 contact

for more information, support, or questions, please contact:

• Email: [email protected]
• Website: SafeVideo
• LinkedIn: SafeVideo AI

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🌟 contributing

love autollm? star the repo or contribute and help us make it even better! see our contributing guidelines for more information.

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