autollm 0.0.2

Ship RAG based LLM Web API's, in seconds.

AutoLLM

Ship retrieval augmented generation based Large Language Model Web API's, in seconds

Why AutoLLM?

Project	80+ LLM integration	80+ LLM's with the same API	20+ VectorDB integration	20+ VectorDB's with the same API	Cost calculation for 80+ LLM's	1-line query engine creation	1-line FastAPI app creation
AutoLLM	✔️	✔️	✔️	✔️	✔️	✔️	✔️
LangChain	✔️	❌	✔️	❌	❌	❌	✔️
LlamaIndex	✔️	❌	✔️	❌	❌	❌	❌
LiteLLM	✔️	✔️	❌	❌	✔️	✔️	❌

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Installation

AutoLLM is available as a Python package for Python>=3.8 environments. Install it using pip:

pip install autollm

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Features

AutoLLM (Supports 80+ LLMs)

• Microsoft Azure - OpenAI example:

from autollm import AutoLLM

## set ENV variables
os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""

# Dynamically initialize a llama_index llm instance with the same AutoLLM api
llm = AutoLLM(model="azure/<your_deployment_name>")

• Google - VertexAI example:

from autollm import AutoLLM

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

# Dynamically initialize a llama_index llm instance with the same AutoLLM api
llm = AutoLLM(model="text-bison@001")

• AWS Bedrock - Claude v2 example:

from autollm import AutoLLM

## set ENV variables
os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

# Dynamically initialize a llama_index llm instance with the same AutoLLM interface
llm = AutoLLM(model="anthropic.claude-v2")

AutoVectorStoreIndex (Supports 20+ VectorDBs)

Dynamically initialize a VectorStoreIndex instance from 20+ VectorDB options with the same AutoVectorStoreIndex interface:

from autollm import AutoVectorStoreIndex

# Dynamically initialize a VectorStoreIndex instance with the same AutoVectorStoreIndex interface:

vector_store_index = AutoVectorStoreIndex.from_defaults(
    vector_store_type="PineconeVectorStore", pinecone_index=pinecone.Index("quickstart")
)


vector_store_index = AutoVectorStoreIndex.from_defaults(
    vector_store_type="VectorStoreIndex", documents=documents
)

AutoQueryEngine (Creates a query engine pipeline in a single line of code)

Create robust query engine pipelines with automatic cost logging. Supports fine-grained control for advanced use-cases.

Basic Usage:

from autollm import AutoQueryEngine

# Initialize a query engine with an existing vector store index and service context
vector_store_index = AutoVectorStoreIndex.from_defaults(
    vector_store_type="VectorStoreIndex", documents=documents
)
service_context = AutoServiceContext.from_defaults(enable_cost_calculator=True)
query_engine = AutoQueryEngine.from_instances(vector_store, service_context)

# Initialize a query engine with default parameters
query_engine = AutoQueryEngine.from_parameters()

# Ask a question
response = query_engine.query("Why is SafeVideo AI open sourcing this project?")

print(response.response)

>> Because they are cool!

Advanced Usage:

You can initialize the AutoQueryEngine for fine-grained control by explicitly passing parameters for the LLM, Vector Store, and Service Context.

from autollm import AutoQueryEngine

import qdrant_client

# Initialize the query engine with explicit parameters
query_engine = AutoQueryEngine.from_parameters(
    system_prompt="Your System Prompt",
    query_wrapper_prompt="Your Query Wrapper Prompt",
    enable_cost_calculator=True,
    llm_params={"model": "gpt-3.5-turbo"},
    vector_store_params={"vector_store_type": "QdrantVectorStore", "client": qdrant_client.QdrantClient(
    url="http://<host>:<port>"
    api_key="<qdrant-api-key>",
), "collection_name": "quickstart"},
    service_context_params={"chunk_size": 1024},
    query_engine_params={"similarity_top_k": 10},
)

response = query_engine.query("Why is SafeVideo AI awesome?")

print(response.response)

>> Because they redefine the movie experience by AI!

Automated Cost Calculation (Supports 80+ LLMs)

Keep track of your LLM token usage and costs in real-time.

from autollm import AutoServiceContext

service_context = AutoServiceContext(enable_cost_calculation=True)

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

Document Providers (Powerful Github and Local Solutions)

Unlock the potential of your content with AutoLLM's robust document providers. Seamlessly pull, process, and analyze documents from GitHub repositories or local directories.

GitHub Document Provider

Fetch up-to-date documents directly from your GitHub repositories—ideal for real-time data pipelines and collaborative projects.

from autollm.utils.document_providers import github_document_provider

git_repo_url = "https://github.com/safevideo.git"
local_repo_path = Path("/safevideo/")
# Specify where to find the documents in the repo
relative_docs_path = Path("docs/")

# Fetch and process documents
documents = github_document_provider(git_repo_url, local_repo_path, relative_docs_path)

Local Document Provider

Process documents from local directories—ideal for offline data pipelines and local development.

from autollm.utils.document_providers import local_document_provider

input_dir = "/local/documents/path"

# Read files as documents from local directory
documents = local_document_provider(input_dir=input_dir)

FastAPI Integration in 1-Line 🚀

Creating a FastAPI application integrated with AutoLLM has never been easier. Follow the quick guide below to get started.

Create Your FastAPI Application

In your main.py, include the following line of code:

from autollm import create_web_app

app = create_web_app(config_path, env_path)

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

Run Your Application

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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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.

• Budget based email notification feature

• Add evaluation metrics for LLMs:

• Set default vector store as LanceDB

• Add unit tests for online vectorDB integrations:

• Add example code snippet to Readme on how to integrate llama-hub readers:

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Contributing

We welcome contributions to AutoLLM! Please see our Contributing Guidelines for more information.

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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: [e-mail]
• Website: SafeVideo
• LinkedIn: SafeVideo AI

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