### What problem does this PR solve?
Feat:
- Allow upsert model_type for instance model
Fix:
- Allow create instance with duplicate api_key
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
## Summary
`get_model_config_from_provider_instance()` was not including
`max_tokens` in its returned dict, causing all downstream consumers
(dialog truncation, message fitting, knowledge base trimming, embedding,
graphrag, RAPTOR) to fall back to the hardcoded default of **8192
tokens** regardless of the actual model context window size (e.g.,
GPT-4o 128K, Claude 200K).
Closes#15944
## Root Cause
The function builds `model_config` with only: `llm_factory`, `api_key`,
`llm_name`, `api_base`, `model_type`, `is_tools`. `max_tokens` is never
included.
Yet the data exists in four independent sources:
1. `TenantModel.extra` JSON field — written by
`provider_api_service.py:659`
2. `conf/llm_factories.json` — every model entry has `max_tokens`
3. `rag/llm/model_meta.py` — 9 provider classes fetch real context
windows from APIs
4. `TenantLLM.max_tokens` database column
None of them are read by this function.
## Fix
Two lines added, one per return path:
- **Path B** (model_obj exists → provider-instance model): reads
`max_tokens` from `model_obj.extra` JSON
- **Path C** (fallback → factory config): reads `max_tokens` from
`llm_info` (sourced from `llm_factories.json`)
Both fall back to 8192 when the value is absent, preserving backward
compatibility.
## Impact
This single 5-line change fixes the context window budget for all **78+
call sites** across **20 files** that construct `LLMBundle` or read
`max_tokens` from the config dict, including:
| Consumer | File | Effect |
|---|---|---|
| Dialog chat truncation | `dialog_service.py:562` |
`message_fit_in(msg, max_tokens * 0.95)` now uses real context window |
| Knowledge base trimming | `dialog_service.py:752` |
`kb_prompt(kbinfos, max_tokens)` now fits more retrieved content |
| Agent message fitting | `agent/component/llm.py:322` | Agent prompts
no longer truncated at 7946 tokens |
| Embedding truncation | `task_executor.py:704` | Embedding input uses
actual model limit |
| GraphRAG extraction | `graphrag/*/extractor.py` | Entity extraction
gets full context budget |
| LLM4Tenant.max_length | `tenant_llm_service.py:513` | Chat model
wrapper exposes real context window |
### What problem does this PR solve?
Support factory models with multiple model types, so visual chat models
can be exposed as both image2text and chat while preserving the database
model-type-per-record design.
This also updates the SILICONFLOW model list and adds a helper script to
refresh SiliconFlow models from the provider API.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Propagate `tenant_id` from memory task messages into task collection so
refactored task execution can build a valid context.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
### Problem
On the Model Providers page, the Embedding Model dropdown in System
Model Settings shows empty (no default selected), even though a default
embedding model is configured in `service_conf.yaml`.
### Root Cause
Two issues were identified:
1. **Backend: `_get_model_info` fails for unregistered providers**
The tenant's `embd_id` is set to `bge-m3@xxxx` during initialization
(from the placeholder config `factory: 'xxxx'`). The `_get_model_info`
function requires the provider to exist in `tenant_model_provider`
table, but `xxxx` is never a real provider. Even after the user adds a
real provider (e.g., ZHIPU-AI), the stale `embd_id` still references the
non-existent one, causing the function to return `None`.
2. **Frontend: default models cache not invalidated after adding
provider**
`useAddProviderInstance` only invalidates `addedProviders` and
`allModels` caches after adding a provider instance, but does **not**
invalidate the `defaultModels` cache. This means the default model list
is not re-fetched until the user manually refreshes the page.
### Fix
**`api/apps/services/models_api_service.py`**
- Added `_resolve_model_from_tenant_providers()` helper: when the
default model's provider doesn't exist (e.g., placeholder `xxxx`), it
searches through the tenant's actually registered providers for a model
of the same type and returns the first match.
- When an instance name doesn't match (e.g., `"default"` vs actual name
`"1"`), the function now auto-resolves to the first real instance under
that provider.
- Falls back to `FACTORY_LLM_INFOS` validation when neither provider nor
instance exists.
**`web/src/hooks/use-llm-request.tsx`**
- Added `queryClient.invalidateQueries({ queryKey:
LlmKeys.defaultModels() })` to `useAddProviderInstance` so that the
default model list is re-fetched immediately after a provider instance
is added, eliminating the need for a manual page refresh.
### Testing
- Verified with a tenant whose `embd_id=bge-m3@xxxx` and only provider
is ZHIPU-AI (instance `1`): `_resolve_model_from_tenant_providers`
correctly resolves to `embedding-2@1@ZHIPU-AI`.
- After adding a provider via the UI, the embedding model dropdown now
immediately shows the resolved default without requiring a page refresh.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Signed-off-by: noob <yixiao121314@outlook.com>
### What problem does this PR solve?
Fix:
- Handle siliconflow and siliconflow_intl api_key
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fix:
- Use @ to avoid split by `_` in model_name.
- Verify api_key when add instance.
- Pop api_key in list intances response.
- Remove useless index.
- Sort providers, instances and models by name.
- Get `is_tools` from llm_factories.json
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Python implementation of the Go-based model_provider API suite.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: bill <yibie_jingnian@163.com>
### What problem does this PR solve?
1. Break huge function into smaller pieces
2. Add unit test for the smaller pieces function
3. Layer-ed design
a. infra layer - task_context.py, recording_context.py,
write_operation_interceptor.py, ...
b. service layer - *_service.py
c. business layer - task_handler.py
4. Default behavior: use "refactor-ed version" - can switch to original
version by change env variable
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
- [x] Performance Improvement
---------
Co-authored-by: Liu An <asiro@qq.com>
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
### Related issues
Closes#14922
### What problem does this PR solve?
`POST /memories` already resolves `tenant_llm_id` and `tenant_embd_id`
through `ensure_tenant_model_id_for_params`, but `PUT
/memories/<memory_id>` accepted client-supplied `tenant_llm_id` /
`tenant_embd_id` without checking that those `tenant_llm` rows belong to
the memory owner’s tenant. A caller could persist another tenant’s row
IDs and later trigger extraction or embedding that loaded foreign model
credentials via `get_model_config_by_id(tenant_model_id)` with no tenant
allow-list.
This change aligns the update path with create: updates that change
models must go through `llm_id` / `embd_id` and
`ensure_tenant_model_id_for_params` scoped to the **memory’s**
`tenant_id` (not only the current user, so team-access cases stay
correct). Direct `tenant_*` fields in the body without `llm_id` /
`embd_id` are rejected. As defense in depth, `memory_message_service`
passes `allowed_tenant_ids` / `requester_tenant_id` into
`get_model_config_by_id` for LLM and embedding resolution so mismatched
IDs cannot be used even if bad data existed. A regression test rejects
payloads that set only `tenant_llm_id` / `tenant_embd_id`.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: jony376 <jony376@gmail.com>
### Related issues
Closes#14781
### What problem does this PR solve?
Some retrieval endpoints accepted caller-supplied `tenant_rerank_id` and
resolved it through `get_model_config_by_id(...)`. That helper loaded
`TenantLLM` rows by global database id and returned decoded model
configuration without checking whether the model belonged to the
authenticated tenant or the dataset owner tenant.
This meant dataset access was validated, but rerank-model selection was
not. A caller who knew or could guess another tenant's
`tenant_rerank_id` could attempt retrieval with a foreign rerank model
config, creating a cross-tenant authorization gap for model usage.
This PR closes that gap by making `tenant_rerank_id` resolution
tenant-aware across the retrieval paths that accept it.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [ ] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
### Solution
- Extend `get_model_config_by_id(...)` to accept an optional
`allowed_tenant_ids` set and reject `TenantLLM` rows whose `tenant_id`
is outside that set.
- Pass the allowed tenant scope from retrieval endpoints that accept
`tenant_rerank_id`:
- `api/apps/sdk/doc.py`
- `api/apps/sdk/session.py`
- `api/apps/services/dataset_api_service.py`
- Use the authenticated tenant plus dataset-owner tenant ids already
derived by each retrieval flow as the authorization boundary for rerank
model selection.
- Add focused unit coverage to assert unauthorized `tenant_rerank_id`
values are rejected and that the allowed tenant set is propagated
correctly.
### Testing
- `python -m py_compile` on:
- `api/db/joint_services/tenant_model_service.py`
- `api/apps/services/dataset_api_service.py`
- `api/apps/sdk/doc.py`
- `api/apps/sdk/session.py`
- Added unit tests in:
-
`test/testcases/test_http_api/test_file_management_within_dataset/test_doc_sdk_routes_unit.py`
-
`test/testcases/test_http_api/test_session_management/test_session_sdk_routes_unit.py`
### Notes for reviewers
- This change is intentionally narrow: it affects only the
`tenant_rerank_id` path, not the normal `rerank_id` name-based
resolution path.
- Local lint/syntax checks passed.
- Full pytest execution could not be completed in this environment
because the local test runtime is missing `strenum`, so the route-test
files fail during collection before exercising the updated cases.
---------
Co-authored-by: jony376 <jony376@gmail.com>
## Summary
- When a model is registered as `chat` in `tenant_llm` but has the
`IMAGE2TEXT` tag in `llm_factories.json`, requesting it as `image2text`
(e.g. PDF parser) fails with `Tenant Model with name <model> and type
image2text not found`.
- After resolution via the new fallback, the returned
`config_dict["model_type"]` was still `"chat"`, causing
`tenant_llm_service.model_instance()` to instantiate `ChatModel` instead
of `CvModel` — breaking `describe_with_prompt` at ingestion time.
## What problem does this PR solve?
RAGFlow already has a `CHAT→IMAGE2TEXT` fallback: when a chat model is
not found, it retries with `image2text`. The symmetric fallback
(`IMAGE2TEXT→CHAT`) was missing.
This matters for multimodal models declared as `model_type: "chat"` with
an `IMAGE2TEXT` tag in `llm_factories.json` (e.g. models added after
tenant creation, or providers where a single model serves both
purposes). The frontend PDF parser selector correctly surfaces these
models via the `IMAGE2TEXT` tag, but the backend fails to resolve them
at runtime.
## Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
## Changes
**`api/db/joint_services/tenant_model_service.py`**
1. Add `IMAGE2TEXT→CHAT` fallback in
`get_model_config_by_type_and_name`: when an `image2text` model is not
found in `tenant_llm`, retry with `chat` — but only if the `llm` table
confirms `IMAGE2TEXT` capability via the `tags` field. This mirrors the
philosophy of the existing `CHAT→IMAGE2TEXT` fallback: substitution is
only allowed when the model has declared the required capability.
2. Normalize `config_dict["model_type"]` to `image2text` after the
fallback, so the caller (`model_instance`) correctly routes to `CvModel`
instead of `ChatModel`.
3. Extend the type validation guard to allow `(requested=image2text,
found=chat)` alongside the existing `(requested=chat, found=image2text)`
exception.
## Test plan
- [ ] Add a model with `model_type=chat` and `tags` containing
`IMAGE2TEXT` to a tenant
- [ ] Select it as PDF parser in a knowledge base
- [ ] Verify ingestion succeeds without `image2text not found` or
`describe_with_prompt` errors
- [ ] Verify the same model still works correctly in chat context
🤖 Generated with [Claude Code](https://claude.ai/claude-code)
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
### What problem does this PR solve?
Allow image2text models (multimodal) to be used as chat models.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
using builtin model when parsing gave an error because it expects
fid==builtin. split_model_name_and_factory returns id=None. pr allows
the model to be accepted wheter with or without @Builtin
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fix: model selecton rule in get_model_config_by_type_and_name
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Get user_id from canvas and record it.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Avoid getting doc in function delete_document_metadata as the doc might
have been removed.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Add id for table tenant_llm and apply in LLMBundle.
### Type of change
- [x] Refactoring
---------
Co-authored-by: Yingfeng <yingfeng.zhang@gmail.com>
Co-authored-by: Liu An <asiro@qq.com>
### What problem does this PR solve?
Put document metadata in ES/Infinity.
Index name of meta data: ragflow_doc_meta_{tenant_id}
### Type of change
- [x] Refactoring
### What problem does this PR solve?
Add tokenized content es field to query zh message.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Add task status for raw message, and move extract message as a nested
property under raw message
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Use async task to save memory.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
Handle 404 exception when init memory size from es.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: Liu An <asiro@qq.com>
### What problem does this PR solve?
Judge retrieval from in retrieval component, and fix bug in message
component
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fix several text issues.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
Manage message and use in agent.
Issue #4213
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
- Admin client support drop user.
Issue: #10241
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
- Admin client support show user and create user command.
- Admin client support alter user password and active status.
- Admin client support list user datasets.
issue: #10241
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
