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### Summary Closes #15423 `rag/llm/embedding_model.py` hosts about 40 embedding providers that shared several defects affecting indexing reliability, cost accounting, and error visibility. This PR fixes four concrete bugs. **Masked, inconsistent errors (27 sites).** Nearly every provider ran `log_exception(_e, res)` followed by `raise Exception(f"Error: {res}")`. Because `log_exception` always raises, the second line was dead code, and the surfaced exception varied with whether the SDK response exposed a `.text` attribute. Every failure path now raises a single `EmbeddingError` that includes the underlying response detail, so the cause of a failed embedding is consistent and visible. **Fabricated token counts.** `LocalAIEmbed` returned a hardcoded `1024` and `OllamaEmbed` added `128` per text. These values feed `used_tokens` and therefore billing and usage tracking. Both now report the real count from the API (Ollama `prompt_eval_count`, LocalAI `usage`) and fall back to a local token count only when the server omits it. **Truncation overshoot.** The `8196` limit used by Mistral and Bedrock exceeded the standard `8192` ceiling and could push boundary sized inputs past the model limit. Limits are corrected to `8192` and made intentional per provider, and providers that rely on server side truncation now request it explicitly (Ollama `truncate=True`, Cohere `truncate="END"`). **Missing batching on Zhipu and Ollama.** Both issued one request per text. They now batch like the other OpenAI compatible providers, turning N round trips into `ceil(N / batch_size)`. Batched results are realigned by response `index` so a chunk always keeps its own vector. A shared `Base._batched_encode` helper owns the batch loop, optional truncation, result accumulation, and the single error path. It is the mechanism that lets these fixes live in one place instead of across 27 duplicated sites. The public `encode()` and `encode_queries()` contract stays the same, so existing callers are unaffected. Tests covering all four fixes are added under `test/unit_test/rag/llm/test_embedding_model.py`. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)
(1). Deploy RAGFlow services and images
https://ragflow.io/docs/build_docker_image
(2). Configure the required environment for testing
Install Python dependencies (including test dependencies):
uv sync --python 3.13 --only-group test --no-default-groups --frozen
Activate the environment:
source .venv/bin/activate
Install SDK:
uv pip install sdk/python
Modify the .env file: Add the following code:
COMPOSE_PROFILES=${COMPOSE_PROFILES},tei-cpu
TEI_MODEL=BAAI/bge-small-en-v1.5
RAGFLOW_IMAGE=infiniflow/ragflow:v0.26.0 #Replace with the image you are using
Start the container（wait two minutes）:
docker compose -f docker/docker-compose.yml up -d
(3). Test Elasticsearch
a) Run sdk tests against Elasticsearch:
export HTTP_API_TEST_LEVEL=p2
export HOST_ADDRESS=http://127.0.0.1:9380 # Ensure that this port is the API port mapped to your localhost
pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_sdk_api
b) Run http api tests against Elasticsearch:
pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_http_api
(4). Test Infinity
Modify the .env file:
DOC_ENGINE=${DOC_ENGINE:-infinity}
Start the container:
docker compose -f docker/docker-compose.yml down -v
docker compose -f docker/docker-compose.yml up -d
a) Run sdk tests against Infinity:
DOC_ENGINE=infinity pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_sdk_api
b) Run http api tests against Infinity:
DOC_ENGINE=infinity pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_http_api