Harrier OSS v1 Text Embeddings
Multilingual text embedding using Microsoft's Harrier OSS v1 models. Supports retrieval, clustering, semantic similarity, classification, and reranking with state-of-the-art MTEB v2 scores.
api reference
about
multilingual text embedding using microsoft's harrier oss v1 models. supports retrieval, clustering, semantic similarity, classification, and reranking with state-of-the-art mteb v2 scores.
1. calling the api
install the client
the client provides a convenient way to interact with the api.
1pip install inferenceshsetup your api key
set INFERENCE_API_KEY as an environment variable. get your key from settings → api keys.
1export INFERENCE_API_KEY="inf_your_key"run and get result
submit a request and wait for the final result. best for batch processing or when you don't need progress updates.
1import os2from inferencesh import inference34client = inference(api_key=os.environ["INFERENCE_API_KEY"])567result = client.run({8 "app": "infsh/harrier-oss-v1",9 "input": {}10 })1112print(result["output"])stream live updates
get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks.
1import os2from inferencesh import inference34client = inference(api_key=os.environ["INFERENCE_API_KEY"])567# stream=True yields updates as they arrive8for update in client.run({9 "app": "infsh/harrier-oss-v1",10 "input": {}11 }, stream=True):12 if update.get("progress"):13 print(f"progress: {update['progress']}%")14 if update.get("output"):15 print(f"output: {update['output']}")2. authentication
the api uses api keys for authentication. see the authentication docs for detailed setup instructions.
3. files
file inputs are automatically handled by the sdk. you can pass local paths, urls, or base64 data.
automatic upload
the python sdk automatically detects local file paths and uploads them. urls are passed through as-is.
1# local file paths are automatically uploaded2result = client.run({3 "app": "infsh/harrier-oss-v1",4 "input": {5 "image": "/path/to/local/image.png", # detected & uploaded6 "audio": "https://example.com/audio.mp3", # url passed through7 }8})4. webhooks
get notified when a task completes by providing a webhook url. when the task reaches a terminal state (completed, failed, or cancelled), a POST request is sent to your url with the task result.
1result = client.run({2 "app": "infsh/harrier-oss-v1",3 "input": {},4 "webhook": "https://your-server.com/webhook"5}, wait=False)webhook payload
your endpoint receives a JSON POST with the task result:
1{2 "event": "task.completed",3 "timestamp": "2024-01-15T10:30:05Z",4 "data": {5 "id": "task_abc123",6 "short_id": "abc123",7 "status": 10,8 "status_text": "completed",9 "output": { ... },10 "created_at": "2024-01-15T10:30:00Z",11 "updated_at": "2024-01-15T10:30:05Z"12 }13}5. schema
input
texts to embed (one embedding per text).
files containing texts to embed. supports .txt (one text per line), .jsonl (one json string per line), or .json (array of strings).
task instruction for queries (e.g. 'given a web search query, retrieve relevant passages that answer the query'). only needed for query-side encoding, not for documents.
pre-configured prompt name (e.g. 'web_search_query', 'sts_query', 'bitext_query'). alternative to providing a custom instruction.
chunking strategy. 'fixed': split by token count. 'recursive': split at paragraph/sentence/word boundaries within token budget. none: no chunking.
target chunk size in tokens. clamped to model max (32768). only used when chunk_strategy is set.
overlap between chunks in tokens. only used when chunk_strategy is set.
output
total number of embeddings across all files
embedding dimension
json files containing embeddings, one per input text
inline embeddings when output is small enough to skip file upload
setup
setup parameters are provided once via the setup field and are persisted for a session.
model variant: 270m (640d), 0.6b (1024d), or 27b (5376d)
ready to run Harrier OSS v1 Text Embeddings?
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