### What problem does this PR solve?
[https://github.com/infiniflow/ragflow/issues/8324](url)

docker image version: v0.19.1

The `_clean_conf` function was not implemented in the `_chat` and
`chat_streamly` methods of the `GeminiChat` class, causing the error
"Unknown field for GenerationConfig: max_tokens" when the default LLM
config includes the "max_tokens" parameter.

**Buggy Code(ragflow/rag/llm/chat_model.py)**
```python
class GeminiChat(Base):
    def __init__(self, key, model_name, base_url=None, **kwargs):
        super().__init__(key, model_name, base_url=base_url, **kwargs)

        from google.generativeai import GenerativeModel, client

        client.configure(api_key=key)
        _client = client.get_default_generative_client()
        self.model_name = "models/" + model_name
        self.model = GenerativeModel(model_name=self.model_name)
        self.model._client = _client

    def _clean_conf(self, gen_conf):
        for k in list(gen_conf.keys()):
            if k not in ["temperature", "top_p"]:
                del gen_conf[k]
        return gen_conf

    def _chat(self, history, gen_conf):
        from google.generativeai.types import content_types

        system = history[0]["content"] if history and history[0]["role"] == "system" else ""
        hist = []
        for item in history:
            if item["role"] == "system":
                continue
            hist.append(deepcopy(item))
            item = hist[-1]
            if "role" in item and item["role"] == "assistant":
                item["role"] = "model"
            if "role" in item and item["role"] == "system":
                item["role"] = "user"
            if "content" in item:
                item["parts"] = item.pop("content")

        if system:
            self.model._system_instruction = content_types.to_content(system)
        response = self.model.generate_content(hist, generation_config=gen_conf)
        ans = response.text
        return ans, response.usage_metadata.total_token_count

    def chat_streamly(self, system, history, gen_conf):
        from google.generativeai.types import content_types

        if system:
            self.model._system_instruction = content_types.to_content(system)
        #❌_clean_conf was not implemented 
        for k in list(gen_conf.keys()):
            if k not in ["temperature", "top_p", "max_tokens"]:
                del gen_conf[k]
        for item in history:
            if "role" in item and item["role"] == "assistant":
                item["role"] = "model"
            if "content" in item:
                item["parts"] = item.pop("content")
        ans = ""
        try:
            response = self.model.generate_content(history, generation_config=gen_conf, stream=True)
            for resp in response:
                ans = resp.text
                yield ans

            yield response._chunks[-1].usage_metadata.total_token_count
        except Exception as e:
            yield ans + "\n**ERROR**: " + str(e)

        yield 0
```
**Implement the _clean_conf function**
```python
class GeminiChat(Base):
    def __init__(self, key, model_name, base_url=None, **kwargs):
        super().__init__(key, model_name, base_url=base_url, **kwargs)

        from google.generativeai import GenerativeModel, client

        client.configure(api_key=key)
        _client = client.get_default_generative_client()
        self.model_name = "models/" + model_name
        self.model = GenerativeModel(model_name=self.model_name)
        self.model._client = _client

    def _clean_conf(self, gen_conf):
        for k in list(gen_conf.keys()):
            if k not in ["temperature", "top_p"]:
                del gen_conf[k]
        return gen_conf

    def _chat(self, history, gen_conf):
        from google.generativeai.types import content_types
        #✅ implement _clean_conf to remove the wrong parameters
        gen_conf = self._clean_conf(gen_conf)

        system = history[0]["content"] if history and history[0]["role"] == "system" else ""
        hist = []
        for item in history:
            if item["role"] == "system":
                continue
            hist.append(deepcopy(item))
            item = hist[-1]
            if "role" in item and item["role"] == "assistant":
                item["role"] = "model"
            if "role" in item and item["role"] == "system":
                item["role"] = "user"
            if "content" in item:
                item["parts"] = item.pop("content")

        if system:
            self.model._system_instruction = content_types.to_content(system)
        response = self.model.generate_content(hist, generation_config=gen_conf)
        ans = response.text
        return ans, response.usage_metadata.total_token_count

    def chat_streamly(self, system, history, gen_conf):
        from google.generativeai.types import content_types
        #✅ implement _clean_conf to remove the wrong parameters
        gen_conf = self._clean_conf(gen_conf)

        if system:
            self.model._system_instruction = content_types.to_content(system)
        #✅Removed duplicate parameter filtering logic "for k in list(gen_conf.keys()):"
        for item in history:
            if "role" in item and item["role"] == "assistant":
                item["role"] = "model"
            if "content" in item:
                item["parts"] = item.pop("content")
        ans = ""
        try:
            response = self.model.generate_content(history, generation_config=gen_conf, stream=True)
            for resp in response:
                ans = resp.text
                yield ans

            yield response._chunks[-1].usage_metadata.total_token_count
        except Exception as e:
            yield ans + "\n**ERROR**: " + str(e)

        yield 0
```

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

---------

Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>

### What problem does this PR solve?

Feat: Filter the query variable drop-down box options by type #3221

### Type of change


- [x] New Feature (non-breaking change which adds functionality)

### What problem does this PR solve?

Improve GraphRAG similarity sensitivity to numeric differences. #8444.

### Type of change

- [x] Refactoring

### What problem does this PR solve?

This PR fixes a typo in the variable name `succesfulFilenames`,
correcting it to `successfulFilenames`. This ensures consistency and
avoids potential errors due to the misspelled variable.

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

### What problem does this PR solve?
Fix: when using external components, it is impossible to specify the
port, because the variables in the `docker/.env` variable were not
referenced by `docker/service_conf.yaml.template`.

382d2d0373/docker/.env (L85)

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

### What problem does this PR solve?

Feat: Insert the node data of the bottom subagent into the tool array of
the head agent #3221

### Type of change


- [x] New Feature (non-breaking change which adds functionality)

### What problem does this PR solve?

Using the QA chunking method with a large PDF (e.g., 300+ pages) may
lead to OOM in the ragflow-worker module.


### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

### What problem does this PR solve?

Add MCP treamable-http transport.

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

### What problem does this PR solve?
https://github.com/infiniflow/ragflow/issues/8466
I go through the codes, current logic:
When do_handle_task raises an exception, handle_task will set the
progress, but for some cases do_handle_task internal will just return
but not set the right progress, at this cases the redis stream will been
acked but the task is running.

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

---------

Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>

### What problem does this PR solve?

Add MCP server dashboard operations.

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

### What problem does this PR solve?

_Briefly describe what this PR aims to solve. Include background context
that will help reviewers understand the purpose of the PR._

### Type of change

- [ ] Bug Fix (non-breaking change which fixes an issue)
- [ ] New Feature (non-breaking change which adds functionality)
- [x] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):

### What problem does this PR solve?

This PR will fix the #8271 by extending int type to float type when
there is any value out of long type range in a column.
### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

### What problem does this PR solve?

Feat: Add IterationNode component #3221
### Type of change


- [x] New Feature (non-breaking change which adds functionality)

Saving an RGBA image directly as JPEG will cause an error. If the image
is in RGBA mode, convert it to RGB mode before saving it in JPG format.

### What problem does this PR solve?

During document parsing in the knowledge base, we occasionally encounter
the error 'cannot write mode RGBA as JPEG.' This occurs because images
in RGBA mode cannot be directly saved as JPEG. They must be converted
first before saving.

### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)

### What problem does this PR solve?

- Add new test suite for document app with
create/list/parse/upload/remove tests
- Update API URLs to use version variable from config in HTTP and web
API tests

### Type of change

- [x] Add test cases

### What problem does this PR solve?

before refactor
1. create file record
2. Add to blob

if have some execption at 2 the system db will have a file record but
not have related blob, which will introduce some bug.

after refactor
1. add to blob
2. create file record.

if 1 success but 2 failed just have a dirty blob in blob system, user
will not feel that



### Type of change


- [x] Refactoring

### What problem does this PR solve?

Feat: Delete the agent and tool nodes downstream of the agent node #3221

### Type of change


- [x] New Feature (non-breaking change which adds functionality)

### What problem does this PR solve?

image_version: v0.19.1
This PR fixes a bug in the HuggingFaceEmBedding API method that was
causing AssertionError: assert len(vects) == len(docs) during the
document embedding process.

#### Problem
The HuggingFaceEmbed.encode() method had an early return statement
inside the for loop, causing it to return after processing only the
first text input instead of processing all texts in the input list.

**Error Messenge**
```python
AssertionError: assert len(vects) == len(docs) # input chunks  != embedded  vectors from embedding api
File "/ragflow/rag/svr/task_executor.py", line 442, in embedding
```



**Buggy code(/ragflow/rag/llm/embedding_model.py)**
```python
class HuggingFaceEmbed(Base):
    def __init__(self, key, model_name, base_url=None):
        if not model_name:
            raise ValueError("Model name cannot be None")
        self.key = key
        self.model_name = model_name.split("___")[0]
        self.base_url = base_url or "http://127.0.0.1:8080"
        def encode(self, texts: list):
            embeddings = []
            for text in texts:
                response = requests.post(...)
                if response.status_code == 200:
                    try:
                        embedding = response.json()
                        embeddings.append(embedding[0])
                        # ❌ Early return
                        return np.array(embeddings), sum([num_tokens_from_string(text) for text in texts]) 
                    except Exception as _e:
                        log_exception(_e, response)
                else:
                    raise Exception(...)
```
**Fixed Code(I just Rollback this function to the v0.19.0 version)**
```python
Class HuggingFaceEmbed(Base):
    def __init__(self, key, model_name, base_url=None):
        if not model_name:
            raise ValueError("Model name cannot be None")
        self.key = key
        self.model_name = model_name.split("___")[0]
        self.base_url = base_url or "http://127.0.0.1:8080"
        def encode(self, texts: list):
            embeddings = []
            for text in texts:
                response = requests.post(...)
                if response.status_code == 200:
                    embedding = response.json()
                    embeddings.append(embedding[0])  # ✅ Only append, no return
                else:
                    raise Exception(...)
            return np.array(embeddings), sum([num_tokens_from_string(text) for text in texts])  # ✅ Return after processing all
```
### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

### What problem does this PR solve?
Feat: Use the message_id returned by the interface as the id of the
reply message #3221

### Type of change


- [x] New Feature (non-breaking change which adds functionality)

### What problem does this PR solve?

This is a cherry-pick from #7781 as requested.

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>

### What problem does this PR solve?


### Type of change

- [x] Documentation Update

### What problem does this PR solve?

Upgrade MCP SDK to v1.9.4 (latest).

### Type of change

- [x] Refactoring

### What problem does this PR solve?

Update .env ,Defaults to the v0.19.1-slim edition

### Type of change

- [x] Other (please describe): Update .env ,Defaults to the
v0.19.1-slim edition

### What problem does this PR solve?

- Simplify AzureChat constructor by passing base_url directly
- Clean up spacing and formatting in chat_model.py
- Remove redundant parentheses and improve code consistency
- #8423

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

### What problem does this PR solve?

_Briefly describe what this PR aims to solve. Include background context
that will help reviewers understand the purpose of the PR._

### Type of change: Documentation Update/Refactoring

#### Summary
Adds HTTPS/SSL configuration guide/example to enable secure RAGFlow
deployments with proper certificate management.

#### Changes
- New HTTPS Setup Section: Step-by-step guide for SSL certificate
configuration
- Let's Encrypt Integration: Complete Certbot setup instructions
- Docker Configuration: Volume mapping examples for certificates

#### Key Features
- Prerequisites checklist
- Docker Compose configuration examples
- Support for both Let's Encrypt and existing certificates

#### Files Modified
- `README.md`
- `ragflow.https.conf` (new file)

### What problem does this PR solve?

Feat: The delete button is displayed only when the cursor is hovered
over the connection line #3221

### Type of change


- [x] New Feature (non-breaking change which adds functionality)

**Context and Purpose:**

This PR automatically remediates a security vulnerability:
- **Description:** Detected possible formatted SQL query. Use
parameterized queries instead.
- **Rule ID:**
python.lang.security.audit.formatted-sql-query.formatted-sql-query
- **Severity:** HIGH
- **File:** rag/utils/opendal_conn.py
- **Lines Affected:** 98 - 98

This change is necessary to protect the application from potential
security risks associated with this vulnerability.

**Solution Implemented:**

The automated remediation process has applied the necessary changes to
the affected code in `rag/utils/opendal_conn.py` to resolve the
identified issue.

Please review the changes to ensure they are correct and integrate as
expected.

### What problem does this PR solve?

#8406

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

### What problem does this PR solve?

Feat: Solved the conflict between the Handle click and drag events of
the canvas node #3221

### Type of change


- [x] New Feature (non-breaking change which adds functionality)

### What problem does this PR solve?

#8414

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

### What problem does this PR solve?
#8391
#8404

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

---------

Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>

### What problem does this PR solve?

### Type of change

- [x] Documentation Update

### What problem does this PR solve?

Feat: Add Tavily operator #3221

### Type of change


- [x] New Feature (non-breaking change which adds functionality)

### What problem does this PR solve?

Fix: Fixed the issue where tag content would overflow the container
#8392
### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

### What problem does this PR solve?

Refactor Pdf 2 Slices page to new style

### Type of change

- [X] Refactoring

### What problem does this PR solve?

Feat: Improve the tavily form #3221

### Type of change


- [x] New Feature (non-breaking change which adds functionality)

### What problem does this PR solve?

Fix code debug may corrupt by history answer.

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

### What problem does this PR solve?

https://github.com/infiniflow/ragflow/issues/8360

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

### What problem does this PR solve?

Add curl example for interacting with the RAGFlow MCP server. Special
thanks to @writinwaters for his expert refinement.

### Type of change

- [x] Documentation Update

---------

Co-authored-by: writinwaters <cai.keith@gmail.com>

### What problem does this PR solve?

Feat: Synchronize the data of the tavily form to the canvas node #3221

### Type of change


- [x] New Feature (non-breaking change which adds functionality)

### What problem does this PR solve?

Feat: Deleting the last tool of the agent will delete the tool node
#3221

### Type of change


- [x] New Feature (non-breaking change which adds functionality)

### What problem does this PR solve?

Refactor Datasets UI #3221.
### Type of change

- [X] New Feature (non-breaking change which adds functionality)

### What problem does this PR solve?

Feat: Save the agent tool data to the node #3221

### Type of change


- [x] New Feature (non-breaking change which adds functionality)

### What problem does this PR solve?

- Update Docker image version badges and references from v0.19.0 to
v0.19.1
- Modify version mentions in all localized README files (id, ja, ko,
pt_br, tzh, zh)
- Update version in docker/README.md and related documentation files
- Includes updates to Helm values and Python SDK dependencies

### Type of change

- [x] Documentation Update

### What problem does this PR solve?

- Correct boolean parsing for 'desc' parameter in document_app.py to
properly handle string values

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

### What problem does this PR solve?

The existing
/api/v1/datasets/{dataset_id}/documents/{document_id}/chunks endpoint
fails to accurately return a document's chunk status. Even when a chunk
is explicitly marked as unavailable, the API still returns true.

![img_v3_02nc_3458a1b7-609e-4f20-8cb7-2156a489848g](https://github.com/user-attachments/assets/ab3b8f69-1284-49c1-8af3-bdfae3416583)

![img_v3_02nc_82f1d96e-7596-4def-ba75-5a2bd10d56cg](https://github.com/user-attachments/assets/a8a4162b-b50d-4dfc-af72-e1d7812a0a93)

Co-authored-by: zhoudeyong <zhoudeyong@idr.ai>

### What problem does this PR solve?


### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

### What problem does this PR solve?

Fixes a minor grammar issue in a user-facing error message. The original
message said "large than" instead of the correct comparative form
"larger than". Just a quick fix I noticed while reading the code.

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

### What problem does this PR solve?

Fix: Fixed the issue where the initial value of the slice method was not
displayed in the dialog box #3221

### Type of change


- [x] New Feature (non-breaking change which adds functionality)

### What problem does this PR solve?

Wrap search app

### Type of change

- [x] New Feature (non-breaking change which adds functionality)