Open Source LLM Orchestration Frameworks in Python

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Contents

1. LangChain

  • License: MIT
  • Stars (GitHub): 89.3k
  • Focus: LLM Orchestration
  • Overview: LangChain is designed to help developers orchestrate large language models (LLMs) efficiently. It enables the integration of various models and tools, creating complex workflows tailored to specific tasks.
  • Strengths:
    • Modular design for flexibility.
    • Support for multiple LLMs.
  • Limitations:
    • Documentation can be unreliable and frequently updated.
  • Ideal For: Complex applications requiring multiple LLMs, like conversational agents.
  • Usage:
pip install langchain
In [ ]:
from langchain import LLMChain
from langchain.llms import OpenAI

llm = OpenAI(api_key="YOUR_API_KEY")
chain = LLMChain(llm=llm)
response = chain.run("What is the capital of France?")
print(response)  # Output: Paris

2. LlamaIndex

  • License: MIT
  • Stars (GitHub): 33.7k
  • Focus: Search & Retrieval
  • Overview: LlamaIndex enhances search capabilities by structuring data to optimize search and retrieval, making it suitable for information-heavy applications.
  • Strengths:
    • Enhanced schema improves search efficiency.
    • Detailed metadata for better data categorization.
  • Limitations:
    • Less flexible than LangChain.
  • Ideal For: Search-driven applications like document databases.
  • Usage:
pip install llama-index
In [ ]:
from llama_index import SimpleIndexer

indexer = SimpleIndexer()
indexer.add_documents(["Document 1", "Document 2"])
results = indexer.query("What documents are available?")
print(results)

3. Haystack

  • License: Apache-2.0
  • Stars (GitHub): 14.7k
  • Focus: Search & QA
  • Overview: Haystack is a framework for building search systems that utilize question answering (QA) capabilities, allowing flexible integrations.
  • Strengths:
    • Semantic search improves relevance.
    • Extensive documentation aids developers.
  • Limitations:
    • Primarily focused on document understanding.
  • Ideal For: Enterprise-level applications requiring sophisticated search and QA.
  • Usage:
pip install farm-haystack
In [ ]:
from haystack import Document
from haystack.nodes import DensePassageRetriever
from haystack.pipelines import ExtractiveQAPipeline

retriever = DensePassageRetriever()
pipeline = ExtractiveQAPipeline(retriever=retriever)
result = pipeline.run(query="What is AI?", params={"Retriever": {"top_k": 5}})
print(result)

4. Botpress

  • License: MIT
  • Stars (GitHub): 12.3k
  • Focus: Low-Code Chatbot
  • Overview: Botpress is a platform for building chatbots with a focus on low-code solutions, allowing non-developers to create and manage conversational agents.
  • Strengths:
    • Low-code approach lowers entry barriers.
    • Highly customizable for unique bot designs.
  • Limitations:
    • Relies on LangChain.js for some functionality.
  • Ideal For: Quick deployment of chatbots, particularly in customer service.
  • Usage: (self-hosted solution)
In [ ]:
import requests

response = requests.post("http://localhost:3000/api/v1/bots/YOUR_BOT_ID/messages", json={"text": "Hello"})
print(response.json())

5. Danswer

  • License: Mixed
  • Stars (GitHub): 9.8k
  • Focus: Workplace Knowledge
  • Overview: Danswer enhances workplace knowledge management, helping retrieve and answer questions from internal documents.
  • Strengths:
    • Integrates well with workplace environments.
    • Focused on improving information accessibility.
  • Limitations:
    • Mixed licensing can complicate usage.
  • Ideal For: Enhancing internal knowledge bases in organizations.
  • Usage: (self-hosted solution)
In [ ]:
import requests

response = requests.get("http://localhost:8000/api/query", params={"question": "What is the capital of France?"})
print(response.json())

6. Flowise

  • License: Apache-2.0
  • Stars (GitHub): 27.7k
  • Focus: Low-Code LLM Apps
  • Overview: Flowise allows users to create applications using LLMs with minimal coding, focusing on user-friendly design.
  • Strengths:
    • User-friendly interface simplifies app development.
    • Drag-and-drop features enhance usability.
  • Limitations:
    • Primarily works with LangChain.
  • Ideal For: Building applications that leverage LLMs without extensive coding knowledge.
  • Usage: (web application)
In [ ]:
import requests

response = requests.post("http://localhost:3000/api/v1/flow", json={"input": "What is Python?"})
print(response.json())

7. Dify Cloud

  • License: Apache-2.0
  • Stars (GitHub): 37.9k
  • Focus: Low-Code LLM Apps
  • Overview: Dify Cloud enables the creation of robust applications powered by LLMs, focusing on cloud-based solutions.
  • Strengths:
    • Comprehensive stack for LLM application development.
    • Emphasizes security for production-level applications.
  • Limitations:
    • Requires Docker for setup.
  • Ideal For: Organizations seeking scalable, secure LLM applications.
  • Usage: (via API)
In [ ]:
import requests

response = requests.post("https://api.dify.ai/execute", json={"prompt": "Tell me a joke."})
print(response.json())

8. Typesense

  • License: GPL-3.0
  • Stars (GitHub): 18.9k
  • Focus: Search Engine
  • Overview: Typesense is a fast, open-source search engine optimized for delivering high-performance results and easy deployment.
  • Strengths:
    • Robust search features for speed and accuracy.
    • User-friendly for developers.
  • Limitations:
    • Being open-source, it may lack commercial support.
  • Ideal For: Websites and applications needing efficient search functionality.
  • Usage:
pip install typesense
In [ ]:
import typesense

client = typesense.Client({
  'nodes': [{
      'host': 'localhost',
      'port': '8108',
      'protocol': 'http'
  }],
  'api_key': 'YOUR_API_KEY',
  'connection_timeout_seconds': 2
})

client.collections.create({
  "name": "books",
  "fields": [
      {"name": "title", "type": "string"},
      {"name": "author", "type": "string"},
      {"name": "year", "type": "int32"},
  ]
})

client.collections['books'].documents.create({
  "id": "1",
  "title": "1984",
  "author": "George Orwell",
  "year": 1949
})

results = client.collections['books'].documents.search({
  'q': '1984',
  'query_by': 'title'
})
print(results)

9. Verba

  • License: BSD-3-Clause
  • Stars (GitHub): 5k
  • Focus: Personal Assistant
  • Overview: Verba is designed to provide personal assistant capabilities, enabling effective management of data and documents.
  • Strengths:
    • Adaptable to a wide range of tasks.
    • Supports local and cloud data storage

options.

  • Limitations: Specific limitations not widely documented.
  • Ideal For: Individuals looking for personal data management tools.
  • Usage: (via API)
In [ ]:
import requests

response = requests.get("http://localhost:8080/api/v1/query", params={"question": "What do I need to know?"})
print(response.json())

Last updated 2024-11-02 21:50:56.291539 IST

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