The LLM Platform or Inference Hub is an open-source product designed to simplify the development and deployment of large language model (LLM) applications at scale. It provides a unified framework that allows developers to integrate and manage multiple LLM vendors, models, and related services through a standardized but highly flexible approach.
The platform integrates with a wide range of LLM providers, including but not limited to
- OpenAI Platform and Azure OpenAI Service to access models such as GPT, DALL-E and Whisper
- Anthropic, Cohere, ElevenLabs, Google, Groq, Jina, Mistral and Replicate for various specialised models.
- Local deployments such as Ollama, LLAMA.CPP, WHISPER.CPP and Mistral.RS for running models locally.
- Community models via Hugging Face
- Custom models via gRPC plugins
Developers can define providers, models, credentials, vector databases, tools, document extractors or advanced chains using YAML configuration files. This approach streamlines the integration process and makes it easier to manage multiple services and models.
The platform includes routing capabilities such as a round-robin load balancer to efficiently distribute requests across multiple models or providers. This increases scalability and ensures high availability.
Supports integration with various vector databases and indexing services for efficient data retrieval and storage.
Supported systems include
- SaaS offerings such as Azure Search
- Self-hosting solutions such as ChromaDB, Qdrant, Weaviate, Postgres or Elasticsearch
- Custom retrievers via gRPC plugins
- In-memory and temporary indexes
The platform is fully traceable using OpenTelemetry, which provides comprehensive observability and monitoring of the entire system and its components. This increases transparency and reliability, enabling proactive maintenance and smoother operation of LLM applications at scale.
The architecture is designed to be modular and extensible, allowing developers to plug in different providers and services as needed. It consists of a number of key components:
- Providers: Interface to various AI / LLM services.
- Indexes: Handle data storage and retrieval
- Extractors: Process and extract data from documents or web pages
- Segmenters: Semantically split text into chunks for RAG
- Summarisers: Compress large texts or prompts
- Translate: Translate prompt input or output or entire documents
- Routers & Rate Limiters: Manage how requests are distributed across models
- Tools: Pre-built or custom tools for translating, retrieving documents or searching the web using function calls.
- Unified enterprise chat using multiple sources and specialised agents
- Scalable LLM applications: Ideal for building applications that need to scale horizontally and handle high volumes of requests
- Multi-model deployment: Useful for applications that require access to different models from different vendors
- Custom workflows: Enables the creation of custom NLP workflows by combining different services and models
https://platform.openai.com/docs/api-reference
providers:
- type: openai
token: sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
models:
- gpt-4o
- gpt-4o-mini
- text-embedding-3-small
- text-embedding-3-large
- whisper-1
- dall-e-3
- tts-1
- tts-1-hd
https://azure.microsoft.com/en-us/products/ai-services/openai-service
providers:
- type: openai
url: https://xxxxxxxx.openai.azure.com
token: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
models:
# https://docs.anthropic.com/en/docs/models-overview
#
# {alias}:
# - id: {azure oai deployment name}
gpt-3.5-turbo:
id: gpt-35-turbo-16k
gpt-4:
id: gpt-4-32k
text-embedding-ada-002:
id: text-embedding-ada-002
providers:
- type: anthropic
token: sk-ant-apixx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# https://docs.anthropic.com/en/docs/models-overview
#
# {alias}:
# - id: {anthropic api model name}
models:
claude-3.5-sonnet:
id: claude-3-5-sonnet-20240620
providers:
- type: cohere
token: ${COHERE_API_KEY}
# https://docs.cohere.com/docs/models
#
# {alias}:
# - id: {cohere api model name}
models:
cohere-command-r-plus:
id: command-r-plus
cohere-embed-multilingual-v3:
id: embed-multilingual-v3.0
providers:
- type: groq
token: ${GROQ_API_KEY}
# https://console.groq.com/docs/models
#
# {alias}:
# - id: {groq api model name}
models:
groq-llama-3-8b:
id: llama3-8b-8192
groq-whisper-1:
id: whisper-large-v3
providers:
- type: mistral
token: ${MISTRAL_API_KEY}
# https://docs.mistral.ai/getting-started/models/
#
# {alias}:
# - id: {mistral api model name}
models:
mistral-large:
id: mistral-large-latest
providers:
- type: replicate
token: ${REPLICATE_API_KEY}
#
# {alias}:
# - id: {cohere api model name}
models:
replicate-flux-pro:
id: black-forest-labs/flux-pro
$ ollama start
$ ollama run mistral
providers:
- type: ollama
url: http://localhost:11434
# https://ollama.com/library
#
# {alias}:
# - id: {ollama model name with optional version}
models:
mistral-7b-instruct:
id: mistral:latest
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
providers:
- type: llama
url: http://localhost:9081
models:
- mistral-7b-instruct
https://github.com/EricLBuehler/mistral.rs
$ mistralrs-server --port 1234 --isq Q4K plain -m meta-llama/Meta-Llama-3.1-8B-Instruct -a llama
providers:
- type: mistralrs
url: http://localhost:1234
models:
mistralrs-llama-3.1-8b:
id: llama
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-large-v3-turbo.bin
providers:
- type: whisper
url: http://localhost:9083
models:
- whisper
providers:
- type: huggingface
token: hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
models:
mistral-7B-instruct:
id: mistralai/Mistral-7B-Instruct-v0.1
huggingface-minilm-l6-2:
id: sentence-transformers/all-MiniLM-L6-v2
providers:
- type: elevenlabs
token: ${ELEVENLABS_API_KEY}
models:
elevenlabs-sarah:
id: EXAVITQu4vr4xnSDxMaL
elevenlabs-charlie:
id: IKne3meq5aSn9XLyUdCD
routers:
llama-lb:
type: roundrobin
models:
- llama-3-8b
- groq-llama-3-8b
- huggingface-llama-3-8b
# 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
embedder: text-embedding-3-large
# 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
embedder: text-embedding-3-large
$ docker run -p 6333:6333 qdrant/qdrant:v1.11.4
indexes:
docs:
type: qdrant
url: http://localhost:6333
namespace: docs
embedder: text-embedding-3-large
indexes:
docs:
type: memory
embedder: text-embedding-3-large
# 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
# using Docker
docker run -it --rm -p 9998:9998 apache/tika:3.0.0.0-BETA2-full
extractors:
tika:
type: tika
url: http://localhost:9998
chunkSize: 4000
chunkOverlap: 200
# using taskfile.dev
task unstructured:server
# using Docker
docker run -it --rm -p 9085:8000 quay.io/unstructured-io/unstructured-api:0.0.80 --port 8000 --host 0.0.0.0
extractors:
unstructured:
type: unstructured
url: http://localhost:9085/general/v0/general