llama

LLM Platform

Open Source LLM Platform to build and deploy applications at scale

Integrations & Configuration

LLM Providers

OpenAI Platform

https://platform.openai.com/docs/api-reference

providers:
  - type: openai
    token: sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
    models:
      gpt-3.5-turbo:
        id: gpt-3.5-turbo-1106

      gpt-4:
        id: gpt-4-1106-preview
        
      text-embedding-ada-002:
        id: text-embedding-ada-002

Azure OpenAI Service

https://azure.microsoft.com/en-us/products/ai-services/openai-service

providers:
  - type: openai
    url: https://xxxxxxxx.openai.azure.com
    token: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

    models:
      gpt-3.5-turbo:
        id: gpt-35-turbo-16k

      gpt-4:
        id: gpt-4-32k
        
      text-embedding-ada-002:
        id: text-embedding-ada-002

Anthropic

https://www.anthropic.com/api

providers:
  - type: anthropic
    token: sk-ant-apixx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
    models:
      claude-3-opus:
        id: claude-3-opus-20240229

Ollama

https://ollama.ai

$ ollama start
$ ollama run mistral

providers:
  - type: ollama
    url: http://localhost:11434

    models:
      mistral-7b-instruct:
        id: mistral

LLAMA.CPP

https://github.com/ggerganov/llama.cpp/tree/master/examples/server

# using taskfile.dev
$ task llama:server

# LLAMA.CPP Server
$ llama-server --port 9081 --log-disable --model ./models/mistral-7b-instruct-v0.2.Q4_K_M.gguf

# LLAMA.CPP Server (Multimodal Model)
$ llama-server --port 9081 --log-disable --model ./models/llava-v1.5-7b-Q4_K.gguf --mmproj ./models/llava-v1.5-7b-mmproj-Q4_0.gguf

# using Docker (might be slow)
$ docker run -it --rm -p 9081:9081 -v ./models/:/models/ ghcr.io/ggerganov/llama.cpp:server --host 0.0.0.0 --port 9081 --model /models/mistral-7b-instruct-v0.2.Q4_K_M.gguf

providers:
  - type: llama
    url: http://localhost:9081

    models:
      mistral-7b-instruct:
        id: /models/mistral-7b-instruct-v0.2.Q4_K_M.gguf

WHISPER.CPP

https://github.com/ggerganov/whisper.cpp/tree/master/examples/server

# using taskfile.dev
$ task whisper:server

# WHISPER.CPP Server
$ whisper-server --port 9083 --convert --model ./models/whisper-ggml-medium.bin

providers:
  - type: whisper
    url: http://localhost:9085

    models:
      whisper:
        id: whisper

Hugging Face

https://huggingface.co/

providers:
  - type: huggingface
    url: https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.1

    models:
      mistral-7B-instruct:
        id: tgi

Mimic 3

mkdir -p mimic3
chmod 777 mimic3
docker run -it -p 59125:59125 -v $(pwd)/models/mimic3:/home/mimic3/.local/share/mycroft/mimic3 mycroftai/mimic3

providers:
  - type: mimic
      url: http://localhost:59125
  
      models:
        tts-1:
          id: mimic-3

LangChain / LangServe

https://python.langchain.com/docs/langserve

providers:
  - type: langchain
    url: http://your-langchain-server:8000

    models:
      langchain:
        id: default

Vector Databses / Indexes

Chroma

https://www.trychroma.com

# using Docker
$ docker run -it --rm -p 9083:8000 -v chroma-data:/chroma/chroma ghcr.io/chroma-core/chroma

indexes:
  docs:
    type: chroma
    url: http://localhost:9083
    namespace: docs
    embedding: text-embedding-ada-002

Weaviate

https://weaviate.io

# using Docker
$ docker run -it --rm -p 9084:8080 -v weaviate-data:/var/lib/weaviate -e AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true -e PERSISTENCE_DATA_PATH=/var/lib/weaviate semitechnologies/weaviate

indexes:
  docs:
    type: weaviate
    url: http://localhost:9084
    namespace: Document
    embedding: text-embedding-ada-002

In-Memory

indexes:
  docs:
    type: memory   
    embedding: text-embedding-ada-002

# using Docker
docker run -it --rm -p 9200:9200 -v opensearch-data:/usr/share/opensearch/data -e "discovery.type=single-node" -e DISABLE_SECURITY_PLUGIN=true opensearchproject/opensearch:latest

indexes:
  docs:
    type: elasticsearch
    url: http://localhost:9200
    namespace: docs

Extractors

Text

extractors:
  text:
    type: text

Code

Supported Languages:

• C#
• C++
• Go
• Java
• Kotlin
• Java Script
• Type Script
• Python
• Ruby
• Rust
• Scala
• Swfit

extractors:
  code:
    type: code

Tesseract

https://tesseract-ocr.github.io

# using Docker
docker run -it --rm -p 9086:8884 hertzg/tesseract-server:latest

extractors:
  tesseract:
    type: tesseract
    url: http://localhost:9086

Unstructured

https://unstructured.io

# using Docker
docker run -it --rm -p 9085:8000 quay.io/unstructured-io/unstructured-api:0.0.64 --port 8000 --host 0.0.0.0

extractors:
  unstructured:
    type: unstructured
    url: http://localhost:9085

Classifications

LLM Classifier

classifiers:
  {classifier-id}:
    type: llm
    model: mistral-7b-instruct
    classes:
      class-1: "...Description when to use Class 1..."
      class-2: "...Description when to use Class 2..."

Use Cases

Retrieval Augmented Generation (RAG)

Configuration

chains:
  qa:
    type: rag
    index: docs
    model: mistral-7b-instruct

    # limit: 10
    # distance: 1

    # filters:
    #  {metadata-key}:
    #    classifier: {classifier-id}

Index Documents

Using Extractor

POST http://localhost:8080/v1/index/{index-name}/{extractor}
Content-Type: application/pdf
Content-Disposition: attachment; filename="filename.pdf"

Using Documents

POST http://localhost:8080/v1/index/{index-name}

[
    {
        "id": "id1",
        "content": "content of document...",
        "metadata": {
          "key1": "value1",
          "key2": "value2"
        }
    },
    {
        "id": "id2",
        "content": "content of document...",
        "metadata": {
          "key1": "value1",
          "key2": "value2"
        }
    }
]

Function Calling

Hermes Function Calling

providers:
  - type: llama
    url: http://localhost:9081

    models:
      hermes-2-pro:
        id: /models/Hermes-2-Pro-Mistral-7B.Q4_K_M.gguf
        adapter: hermesfn