LlamaFarm: a development framework for rapid local deployment of AI models and applications

LlamaFarm is a full-featured modular AI development framework with a core design philosophy of "local-first development, deploy anywhere". The framework allows developers to build, test, and run complete AI systems on their own computers, and then seamlessly deploy the same code to production environments, whether on company intranet servers or in the cloud. It provides a stable set of real-world application-tested components for handling tasks such as retrieval augmentation generation (RAG), vector databases, model management, and cue word engineering. These components can be used independently or combined to build powerful AI applications.LlamaFarm simplifies the initial setup of a project with predefined policy configurations, while retaining the flexibility for deep customization to meet the varying needs of individual developers to large enterprises. LlamaFarm offers a solid solution for teams looking for complete control over their AI technology stack, with a focus on data privacy and cost-effectiveness.

Function List

• Local Priority Development: Support for complete building and testing of AI applications on PCs without relying on cloud services.
• Production-grade components: Provides several battle-tested modules including Data Pipeline (RAG), Model Management, Cue Word Engineering, and more.
• Wide range of compatibility: Data Pipeline supports over 15 file formats (e.g. PDF, Word, Markdown) and over 8 vector databases (e.g. Chroma, Pinecone).
• Multi-model unified management: The modeling component integrates with over 25 modeling vendors (e.g., OpenAI, Anthropic, Google, Ollama) and provides a unified interface for operations.
• Enterprise-level modeling capabilities:: Advanced features such as automatic model failover, cost-optimized routing (sending requests to the most cost-effective model), load balancing and response caching.
• Advanced cue word engineering: Provide prompt word template library , version control and A/B testing features , support Jinja2 template syntax .
• Strategic Allocation: Users can be identified by a simple policy (e.g.researchmaybecustomer_support) to quickly configure the behavior of the entire system and achieve rapid switching between different scenarios.
• command-line tool: Provides an easy-to-use command line interface for each core component, allowing developers to interact and debug.
• Flexible deployment: Support multiple deployment options from local development environments, Docker containers to mainstream cloud platforms such as AWS and GCP.
• Modular Architecture: Each core component of the framework can be used independently for easy integration into existing projects.

Using Help

LlamaFarm is designed with the goal of simplifying the process of developing and deploying AI applications. Below you will find details on how to install and use the framework.

Installation process

LlamaFarm provides a convenient installation script to quickly complete the environment setup.

1. automatic installation:
   Open your terminal (on Linux or macOS) and execute the following command to download and run the installation script. This script will automatically handle the installation of dependencies.

   curl -fsSL https://raw.githubusercontent.com/llama-farm/llamafarm/main/install.sh | bash
   

2. manual installation:
   If you prefer to control the installation process manually, you can follow the steps below:
   • First, clone the project's code repository to your local computer.

     git clone https://github.com/llama-farm/llamafarm.git
     

   • Go to the project catalog.

     cd llamafarm
     

   • LlamaFarm Usageuvas a Python package management tool for faster dependency installation. You need to install and synchronize your project's dependencies first. Each major functional module (such asrag, models) all have their own dependency configurations. For example, to set up the environment for a RAG system, you can execute:

     cd rag
     uv sync
     

Core Function Operation

LlamaFarm's core functionality revolves around its AI components, which you can interact with via the command line.

1. Use of the RAG data pipeline

The RAG (Retrieval Augmented Generation) system is responsible for processing documents, extracting information and building a knowledge base so that the AI model can answer questions based on specific knowledge.

• Data entry (Ingest):
  This is the first step in building a knowledge base. You need to prepare a folder where you will store your documents (e.g.samples/), and then run the following command. This command reads all the documents in the folder and uses the specified extractor (such as thekeywordsExtract keywords.entitiesextracting entities) and chunking strategies (researchstrategies are suitable for handling research papers) to handle them.

  uv run python rag/cli.py ingest samples/ --extractors keywords entities statistics --strategy research
  

• Knowledge retrieval (Search):
  Once the data is in the database, you can ask it questions like a search engine. The following command will look for the 5 most relevant findings in the knowledge base based on your question "What are the key findings about climate change?--top-k 5) information fragments and reorder the results (--rerank) to improve accuracy.

  uv run python rag/cli.py search "What are the key findings about climate change?" --top-k 5 --rerank
  

2. Management and use of AI models

The model component gives you the flexibility to call different large language models and take advantage of advanced features such as automatic failover.

• Multi-model chat:
  You can specify a master model (--primary gpt-4) and one or more alternate models (--fallback claude-3). When the primary model request fails, the system automatically switches to the standby model to ensure the stability of the service. You can even specify a local model (--local-fallback llama3.2) as the ultimate safeguard.

  uv run python models/cli.py chat --primary gpt-4 --fallback claude-3 --local-fallback llama3.2 "Explain quantum entanglement"
  

3. Use of the cue word system

The cue word system helps you manage and optimize the commands sent to the model.

• Prompts to implement specific strategies:
  You can tailor the application scenarios (--strategy medical) to select the most appropriate prompt word template (--template diagnostic_analysis) for the model to give a more specialized answer.

  uv run python prompts/cli.py execute "Analyze this medical report for anomalies" --strategy medical --template diagnostic_analysis
  

Strategic Allocation

Policies are a core concept of LlamaFarm that allows you to create a policy with a simple name (e.g.research) to uniformly configure the behavior of multiple components such as RAGs, models, and cue words.

• Configuration File Example:
  You can find more information on theconfig/strategies.yamlfile to define your own policies. For example.researchStrategy Usagegpt-4modeling and require a formal writing style, whilecustomer_supportThe strategy then uses a more economicalgpt-3.5-turbomodels and a friendly conversational style.

  strategies:
  research:
  rag:
  embedder: "sentence-transformers"
  chunk_size: 512
  models:
  primary: "gpt-4"
  fallback: "claude-3-opus"
  temperature: 0.3
  prompts:
  template: "academic_research"
  style: "formal"
  customer_support:
  rag:
  embedder: "openai"
  chunk_size: 256
  models:
  primary: "gpt-3.5-turbo"
  temperature: 0.7
  prompts:
  template: "conversational"
  style: "friendly"
  

• application strategy:
  You can specify the policy to use at runtime via environment variables or command line arguments.

  # 设置全局策略
  export LLAMAFARM_STRATEGY=research
  # 在单个命令中指定策略
  uv run python models/cli.py chat --strategy customer_support "Help me with my order"
  

application scenario

1. Building an in-house knowledge base
   Organizations can hand over their internal technical documents, rules and regulations, historical project information, etc. to LlamaFarm's RAG system for processing. Once processed, employees can quickly access the information they need through a simple chat interface, such as "How do I request a project budget?" or "What is the configuration of Server A?" . This greatly improves the efficiency of internal information retrieval and knowledge sharing.
2. Development of intelligent customer support bots
   With LlamaFarm, it is possible to build an intelligent customer service bot that understands and answers common customer questions. By using product manuals, help files, and historical customer service conversations as a knowledge source, the bot can provide real-time support to customers 24/7. Meanwhile, its model failover feature ensures high service availability.
3. Accelerating Scholarly Research and Literature Analysis
   Researchers can import a large number of academic papers and research reports into LlamaFarm to build a specialized knowledge base. They can then ask in-depth questions on specific topics, such as "Summarize all the recent research progress on A-materials", and the system can quickly integrate the information and give key summaries to help researchers save a lot of time reading and sifting through the literature.
4. Create content generation and analysis tools
   Developers can utilize LlamaFarm's cue management and multi-model invocation capabilities to develop tools for marketing copy generation, code-assisted authoring, or data report analysis. It is easy to switch the "role" of the tool by defining different strategies, for example, using the "Creative Writing" strategy to generate adwords or the "Code Review" strategy to analyze code quality. Quality.

QA

1. What is the difference between LlamaFarm and other AI frameworks like LangChain?
   LlamaFarm is designed with a focus on providing a complete, modular solution from local development to production deployment. It not only provides AI logic orchestration, but also includes enterprise-class features such as runtime management, deployment tools, and policy-based configuration. Its components can either work together or be independently integrated into existing projects, providing greater flexibility. LangChain, on the other hand, focuses more on the construction of AI Agents and call chains (Chain).LlamaFarm has considered the stability and scalability of the production environment from the very beginning of the project.
2. Do I need to pay to use LlamaFarm?
   The LlamaFarm project itself is open source and under the Apache-2.0 license, which means that you can use, modify and distribute it for free. However, you need to pay the appropriate fees to third-party LLM vendors (e.g., OpenAI's GPT-4) when you call their APIs during use. If you use a native model (e.g. Llama 3 running through Ollama), you will not incur API fees.
3. Do I need a very powerful computer to run LlamaFarm locally?
   It depends on your specific use case.The LlamaFarm framework itself is not very resource intensive. The resource consumption comes mainly from the AI model you are running. If you're just using it to call cloud APIs, then a regular computer will suffice. If you want to run a large language model like Llama 3 locally, then your computer should ideally be equipped with an NVIDIA graphics card (GPU) with a large amount of video memory, and a large enough amount of RAM.