Autollm

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questions | feature requests

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<a href="https://pepy.tech/project/autollm"><img src="https://pepy.tech/badge/autollm" alt="total autollm downloads"></a>

</div>

🤔 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 | ✅ | ❌ | ❌ | ❌ |

---

📦 installation

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

pip install autollm

for built-in data readers (github, pdf, docx, ipynb, epub, mbox, websites..), install with:

pip install autollm[readers]

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🎯 quickstart

tutorials

• video tutorials:

  • Make RAG with API Just in a few Seconds!!!
  • Autollm automagically create llm apps in seconds
  • AutoLLM: Create RAG Based LLM Web Apps in SECONDS!
  • AutoLLM: Ship RAG based LLM Apps and API in Seconds
  • 🚀 AutoLLM: Unlock the Power of 100+ Language Models! Step-by-Step Tutorial

• blog posts:

  • Introduction to AutoLLM

• colab notebooks:

  • quickstart:

create a query engine in seconds

>>> from autollm import AutoQueryEngine, read_files_as_documents

>>> documents = read_files_as_documents(input_dir="path/to/documents")
>>> query_engine = AutoQueryEngine.from_defaults(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!"

<details> <summary>👉 advanced usage </summary>

>>> from autollm import AutoQueryEngine

>>> query_engine = AutoQueryEngine.from_defaults(
...     documents=documents,
...     llm_model="gpt-3.5-turbo",
...     llm_max_tokens="256",
...     llm_temperature="0.1",
...     system_prompt='...',
...     query_wrapper_prompt='...',
...     enable_cost_calculator=True,
...     embed_model="huggingface/BAAI/bge-large-zh",
...     chunk_size=512,
...     chunk_overlap=64,
...     context_window=4096,
...     similarity_top_k=3,
...     response_mode="compact",
...     structured_answer_filtering=False,
...     vector_store_type="LanceDBVectorStore",
...     lancedb_uri="./lancedb",
...     lancedb_table_name="vectors",
...     exist_ok=True,
...     overwrite_existing=False,
... )

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

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

</details>

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/

<details> <summary>👉 advanced usage </summary>

>>> 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/

</details>

---

🌟 features

supports 100+ LLMs

>>> from autollm import AutoQueryEngine

>>> os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key"

>>> llm_model = "huggingface/WizardLM/WizardCoder-Python-34B-V1.0"
>>> llm_api_base = "https://my-endpoint.huggingface.cloud"

>>> AutoQueryEngine.from_defaults(
...     documents='...',
...     llm_model=llm_model,
...     llm_api_base=llm_api_base,
... )

<details> <summary>👉 more llms:</summary>

• huggingface - ollama example:

  >>> from autollm import AutoQueryEngine
  
  >>> llm_model = "ollama/llama2"
  >>> llm_api_base = "http://localhost:11434"
  
  >>> AutoQueryEngine.from_defaults(
  ...     documents='...',
  ...     llm_model=llm_model,
  ...     llm_api_base=llm_api_base,
  ... )
  

• microsoft azure - openai example:

  >>> from autollm import AutoQueryEngine
  
  >>> os.environ["AZURE_API_KEY"] = ""
  >>> os.environ["AZURE_API_BASE"] = ""
  >>> os.environ["AZURE_API_VERSION"] = ""
  
  >>> llm_model = "azure/<your_deployment_name>")
  
  >>> AutoQueryEngine.from_defaults(
  ...     documents='...',
  ...     llm_model=llm_model
  ... )
  

• google - vertexai example:

  >>> from autollm import AutoQueryEngine
  
  >>> os.environ["VERTEXAI_PROJECT"] = "hardy-device-38811"  # Your Project ID`
  >>> os.environ["VERTEXAI_LOCATION"] = "us-central1"  # Your Location
  
  >>> llm_model = "text-bison@001"
  
  >>> AutoQueryEngine.from_defaults(
  ...     documents='...',
  ...     llm_model=llm_model
  ... )
  

• aws bedrock - claude v2 example:

  >>> from autollm import AutoQueryEngine
  
  >>> os.environ["AWS_ACCESS_KEY_ID"] = ""
  >>> os.environ["AWS_SECRET_ACCESS_KEY"] = ""
  >>> os.environ["AWS_REGION_NAME"] = ""
  
  >>> llm_model = "anthropic.claude-v2"
  
  >>> AutoQueryEngine.from_defaults(
  ...     documents='...',
  ...     llm_model=llm_model
  ... )
  

</details>

supports 20+ VectorDBs

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

<details> <summary>👉 more vectordbs:</summary>

• QdrantVectorStore example:

  >>> from autollm import AutoQueryEngine
  >>> import qdrant_client
  
  >>> vector_store_type = "QdrantVectorStore"
  >>> client = qdrant_client.QdrantClient(
  ...     url="http://<host>:<port>",
  ...     api_key="<qdrant-api-key>"
  ... )
  >>> collection_name = "quickstart"
  
  >>> AutoQueryEngine.from_defaults(
  ...     documents='...',
  ...     vector_store_type=vector_store_type,
  ...     client=client,
  ...     collection_name=collection_name,
  ... )
  

</details>

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

<details> <summary>👉 example</summary>

>>> 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

</details>

---

🔄 migration from llama-index

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

<details> <summary>👉 easy migration </summary>

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

>>> from autollm import AutoQueryEngine

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

>>> query_engine = AutoQueryEngine.from_instances(index)

</details>

❓ FAQ

Q: Can I use this for commercial projects?

A: Yes, AutoLLM i