Aelph Alpha Aleph Alpha PhariaAI API
★ Only Publicly Available OpenAPI DocumentAI/MLLanguage Modelsbearer11 EndpointsREST
For Agents
Run text completion, chat, embeddings, summarisation, Q&A, and tokenisation against Aleph Alpha's Luminous LLMs from a single European-hosted inference API.
Use for: Generate a text completion with a Luminous model, Get a chat completion for a conversation history, Create dense embeddings for a list of documents, Compute semantic embeddings for a search index
Not supported: Does not handle model fine-tuning, training data uploads, or non-text modalities — use for inference against pre-trained Luminous models only.
Jentic publishes the only available OpenAPI specification for Aleph Alpha PhariaAI API, keeping it validated and agent-ready. PhariaAI is the inference platform behind Aleph Alpha's Luminous family of large language models, exposing endpoints for text completion, chat, embeddings, semantic similarity, evaluation, question answering, summarisation, and tokenisation. The API is European-hosted and aimed at enterprise teams that need sovereign LLM infrastructure for retrieval, classification, and generation workloads. Authentication uses a bearer token issued from the Aleph Alpha account portal.
Install Jentic One Beta
Connect the Aleph Alpha PhariaAI API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Aleph Alpha PhariaAI API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with Aleph Alpha PhariaAI API.
Generate text completions and chat responses with Luminous-family models
Produce dense and semantic embeddings for retrieval and similarity workflows
Run reference-based answer evaluation for grading and guardrails
Answer questions over supplied context documents
Summarise long documents into structured outputs
Tokenise and detokenise text to manage prompt budgets and audit token usage
Use Cases
Patterns agents use Aleph Alpha PhariaAI API for, with concrete tasks.
★ Sovereign Enterprise RAG Pipeline
Build retrieval-augmented generation on infrastructure that stays inside the EU. PhariaAI's /semantic_embed endpoint produces embeddings for indexing, /qa answers questions with supplied context, and /complete or /chat/completions handle final generation. Suitable for regulated industries that need an LLM stack outside US-controlled clouds while still operating in English, German, French, Spanish, and Italian.
Call POST /semantic_embed for 500 policy documents, store the vectors in pgvector, then call POST /qa with the top-k passages to answer a user question
Document Summarisation at Scale
Summarise contracts, research papers, or customer transcripts using the dedicated /summarize endpoint. The endpoint accepts a document and returns a structured summary, removing the need to hand-craft summarisation prompts for the /complete endpoint. Pair with /tokenize to split documents that exceed the model context window before summarising chunk-by-chunk.
Call POST /tokenize to chunk a 50-page report, then call POST /summarize on each chunk and concatenate the bullet outputs
Classification and Grading with Evaluate
Use /evaluate to score how well a candidate completion matches a reference, which is the basis for classification, grading, and answer-verification workflows. Combined with /qa for retrieval and /complete for free-form generation, this covers the core enterprise NLP loop on a single European inference platform.
Call POST /evaluate with a candidate answer and a list of reference answers and return the log-probability score for each reference
AI Agent with Sovereign LLM Backend
Route an AI agent's reasoning through Luminous instead of a US-hosted model when data residency matters. Through Jentic, the agent searches by intent for the right PhariaAI operation, loads its schema, and executes with the bearer token isolated in the vault. The Jentic value chain stays the same — search, load, execute — but the model call lands on European infrastructure.
Search Jentic for 'generate a chat completion with luminous', load the /chat/completions schema, and execute with the user's messages and model=luminous-supreme-control
Key Endpoints
11 endpoints — jentic publishes the only available openapi specification for aleph alpha phariaai api, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
POST
/complete
Generate a text completion
POST
/chat/completions
Generate a chat completion from a message history
POST
/embed
Create dense embeddings for one or more texts
POST
/semantic_embed
Create semantic embeddings tuned for similarity search
POST
/qa
Answer a question using supplied context
POST
/summarize
Summarise a document
POST
/evaluate
Score a completion against reference answers
GET
/models_available
List Luminous models available to the account
POST
/complete
Generate a text completion
POST
/chat/completions
Generate a chat completion from a message history
POST
/embed
Create dense embeddings for one or more texts
POST
/semantic_embed
Create semantic embeddings tuned for similarity search
POST
/qa
Answer a question using supplied context
POST
/summarize
Summarise a document
POST
/evaluate
Score a completion against reference answers
GET
/models_available
List Luminous models available to the account
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
Your Aleph Alpha bearer token is stored encrypted in the Jentic vault. Agents receive scoped execution rights — Jentic injects the Authorization header at call time and the raw token never enters the agent's context.
Intent-based discovery
Agents search by intent (e.g. 'summarise a long document with luminous') and Jentic returns the matching PhariaAI operation with its input schema, so the agent picks /summarize over /complete without reading the docs.
Time to first call
Direct PhariaAI integration: 1-3 days for auth, model selection, retries, and chunking logic. Through Jentic: under an hour — search, load schema, execute.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Aleph Alpha PhariaAI API through Jentic.
Why is there no official OpenAPI spec for Aleph Alpha PhariaAI API?
Aleph Alpha does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Aleph Alpha PhariaAI API via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.
What authentication does the Aleph Alpha PhariaAI API use?
PhariaAI uses HTTP bearer authentication; pass an Authorization: Bearer <token> header with a token created in the Aleph Alpha account portal. Through Jentic the token is stored in the vault and applied at execution time.
Which models are available through the PhariaAI API?
Call GET /models_available to list the Luminous variants — base, extended, supreme, supreme-control, and any newer models — provisioned for your account. The same model identifier is then passed in the body of /complete, /chat/completions, /embed, and other endpoints.
Can I generate embeddings for semantic search with PhariaAI?
Yes. POST /embed returns dense embeddings suitable for general use, and POST /semantic_embed returns embeddings tuned for semantic similarity. Both accept arrays of texts and return float vectors that can be stored in any vector database.
What are the rate limits for the PhariaAI API?
Rate limits are tied to account tier and are not declared in the spec. Check your Aleph Alpha account dashboard or call GET /users/me/tokens to see remaining credit; HTTP 429 responses are surfaced through Jentic so agents can back off.
How do I run a chat completion through Jentic?
Run pip install jentic, search Jentic with 'generate a chat completion with luminous', load the /chat/completions schema, and execute with model and messages in the request body. Jentic injects the bearer token at execution time.
GET STARTED