diff --git a/agent/component/llm.py b/agent/component/llm.py
index e9d8770684..7538e0d736 100644
--- a/agent/component/llm.py
+++ b/agent/component/llm.py
@@ -125,23 +125,118 @@ class LLM(ComponentBase):
             msg.append(p)
         return msg, self.string_format(self._param.sys_prompt, args)
 
-    def _prepare_prompt_variables(self):
-        if self._param.visual_files_var:
-            self.imgs = self._canvas.get_variable_value(self._param.visual_files_var)
-            if not self.imgs:
-                self.imgs = []
-            self.imgs = [img for img in self.imgs if img[:len("data:image/")] == "data:image/"]
-            if self.imgs and TenantLLMService.llm_id2llm_type(self._param.llm_id) == LLMType.CHAT.value:
-                self.chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.IMAGE2TEXT.value,
-                                          self._param.llm_id, max_retries=self._param.max_retries,
-                                          retry_interval=self._param.delay_after_error
-                                          )
+    @staticmethod
+    def _extract_data_images(value) -> list[str]:
+        imgs = []
 
+        def walk(v):
+            if v is None:
+                return
+            if isinstance(v, str):
+                v = v.strip()
+                if v.startswith("data:image/"):
+                    imgs.append(v)
+                return
+            if isinstance(v, (list, tuple, set)):
+                for item in v:
+                    walk(item)
+                return
+            if isinstance(v, dict):
+                if "content" in v:
+                    walk(v.get("content"))
+                else:
+                    for item in v.values():
+                        walk(item)
+
+        walk(value)
+        return imgs
+
+    @staticmethod
+    def _uniq_images(images: list[str]) -> list[str]:
+        seen = set()
+        uniq = []
+        for img in images:
+            if not isinstance(img, str):
+                continue
+            if not img.startswith("data:image/"):
+                continue
+            if img in seen:
+                continue
+            seen.add(img)
+            uniq.append(img)
+        return uniq
+
+    @classmethod
+    def _remove_data_images(cls, value):
+        if value is None:
+            return None
+
+        if isinstance(value, str):
+            return None if value.strip().startswith("data:image/") else value
+
+        if isinstance(value, list):
+            cleaned = []
+            for item in value:
+                v = cls._remove_data_images(item)
+                if v is None:
+                    continue
+                if isinstance(v, (list, tuple, set, dict)) and not v:
+                    continue
+                cleaned.append(v)
+            return cleaned
+
+        if isinstance(value, tuple):
+            cleaned = []
+            for item in value:
+                v = cls._remove_data_images(item)
+                if v is None:
+                    continue
+                if isinstance(v, (list, tuple, set, dict)) and not v:
+                    continue
+                cleaned.append(v)
+            return tuple(cleaned)
+
+        if isinstance(value, set):
+            cleaned = []
+            for item in value:
+                v = cls._remove_data_images(item)
+                if v is None:
+                    continue
+                if isinstance(v, (list, tuple, set, dict)) and not v:
+                    continue
+                cleaned.append(v)
+            return cleaned
+
+        if isinstance(value, dict):
+            if value.get("type") in {"image_url", "input_image", "image"} and cls._extract_data_images(value):
+                return None
+
+            cleaned = {}
+            for k, item in value.items():
+                v = cls._remove_data_images(item)
+                if v is None:
+                    continue
+                if isinstance(v, (list, tuple, set, dict)) and not v:
+                    continue
+                cleaned[k] = v
+            return cleaned
+
+        return value
+
+    def _prepare_prompt_variables(self):
+        self.imgs = []
+        if self._param.visual_files_var:
+            self.imgs.extend(self._extract_data_images(self._canvas.get_variable_value(self._param.visual_files_var)))
 
         args = {}
         vars = self.get_input_elements() if not self._param.debug_inputs else self._param.debug_inputs
+        extracted_imgs = []
         for k, o in vars.items():
-            args[k] = o["value"]
+            raw_value = o["value"]
+            extracted_imgs.extend(self._extract_data_images(raw_value))
+            args[k] = self._remove_data_images(raw_value)
+            if args[k] is None:
+                args[k] = ""
             if not isinstance(args[k], str):
                 try:
                     args[k] = json.dumps(args[k], ensure_ascii=False)
@@ -149,6 +244,13 @@ class LLM(ComponentBase):
                     args[k] = str(args[k])
             self.set_input_value(k, args[k])
 
+        self.imgs = self._uniq_images(self.imgs + extracted_imgs)
+        if self.imgs and TenantLLMService.llm_id2llm_type(self._param.llm_id) == LLMType.CHAT.value:
+            self.chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.IMAGE2TEXT.value,
+                                      self._param.llm_id, max_retries=self._param.max_retries,
+                                      retry_interval=self._param.delay_after_error
+                                      )
+
         msg, sys_prompt = self._sys_prompt_and_msg(self._canvas.get_history(self._param.message_history_window_size)[:-1], args)
         user_defined_prompt, sys_prompt = self._extract_prompts(sys_prompt)
         if self._param.cite and self._canvas.get_reference()["chunks"]:
diff --git a/api/db/services/dialog_service.py b/api/db/services/dialog_service.py
index 66025d13ef..0ed5d830b3 100644
--- a/api/db/services/dialog_service.py
+++ b/api/db/services/dialog_service.py
@@ -180,10 +180,24 @@ class DialogService(CommonService):
 
 
 async def async_chat_solo(dialog, messages, stream=True):
+    llm_type = TenantLLMService.llm_id2llm_type(dialog.llm_id)
     attachments = ""
+    image_attachments = []
+    image_files = []
     if "files" in messages[-1]:
-        attachments = "\n\n".join(FileService.get_files(messages[-1]["files"]))
-    if TenantLLMService.llm_id2llm_type(dialog.llm_id) == "image2text":
+        if llm_type == "chat":
+            text_attachments, image_attachments = split_file_attachments(messages[-1]["files"])
+        else:
+            text_attachments, image_files = split_file_attachments(messages[-1]["files"], raw=True)
+        attachments = "\n\n".join(text_attachments)
+
+    if llm_type == "image2text":
+        llm_model_config = TenantLLMService.get_model_config(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id)
+    else:
+        llm_model_config = TenantLLMService.get_model_config(dialog.tenant_id, LLMType.CHAT, dialog.llm_id)
+    factory = llm_model_config.get("llm_factory", "") if llm_model_config else ""
+
+    if llm_type == "image2text":
         chat_mdl = LLMBundle(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id)
     else:
         chat_mdl = LLMBundle(dialog.tenant_id, LLMType.CHAT, dialog.llm_id)
@@ -195,8 +209,13 @@ async def async_chat_solo(dialog, messages, stream=True):
     msg = [{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])} for m in messages if m["role"] != "system"]
     if attachments and msg:
         msg[-1]["content"] += attachments
+    if llm_type == "chat" and image_attachments:
+        convert_last_user_msg_to_multimodal(msg, image_attachments, factory)
     if stream:
-        stream_iter = chat_mdl.async_chat_streamly_delta(prompt_config.get("system", ""), msg, dialog.llm_setting)
+        if llm_type == "chat":
+            stream_iter = chat_mdl.async_chat_streamly_delta(prompt_config.get("system", ""), msg, dialog.llm_setting)
+        else:
+            stream_iter = chat_mdl.async_chat_streamly_delta(prompt_config.get("system", ""), msg, dialog.llm_setting, images=image_files)
         async for kind, value, state in _stream_with_think_delta(stream_iter):
             if kind == "marker":
                 flags = {"start_to_think": True} if value == "<think>" else {"end_to_think": True}
@@ -204,7 +223,10 @@ async def async_chat_solo(dialog, messages, stream=True):
                 continue
             yield {"answer": value, "reference": {}, "audio_binary": tts(tts_mdl, value), "prompt": "", "created_at": time.time(), "final": False}
     else:
-        answer = await chat_mdl.async_chat(prompt_config.get("system", ""), msg, dialog.llm_setting)
+        if llm_type == "chat":
+            answer = await chat_mdl.async_chat(prompt_config.get("system", ""), msg, dialog.llm_setting)
+        else:
+            answer = await chat_mdl.async_chat(prompt_config.get("system", ""), msg, dialog.llm_setting, images=image_files)
         user_content = msg[-1].get("content", "[content not available]")
         logging.debug("User: {}|Assistant: {}".format(user_content, answer))
         yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, answer), "prompt": "", "created_at": time.time()}
@@ -235,6 +257,120 @@ def get_models(dialog):
     return kbs, embd_mdl, rerank_mdl, chat_mdl, tts_mdl
 
 
+def split_file_attachments(files: list[dict] | None, raw: bool = False) -> tuple[list[str], list[str] | list[dict]]:
+    if not files:
+        return [], []
+
+    text_attachments = []
+    if raw:
+        file_contents, image_files = FileService.get_files(files, raw=True)
+        for content in file_contents:
+            if not isinstance(content, str):
+                content = str(content)
+            text_attachments.append(content)
+        return text_attachments, image_files
+
+    image_attachments = []
+    for content in FileService.get_files(files, raw=False):
+        if not isinstance(content, str):
+            content = str(content)
+        if content.strip().startswith("data:"):
+            image_attachments.append(content.strip())
+            continue
+        text_attachments.append(content)
+    return text_attachments, image_attachments
+
+
+_DATA_URI_RE = re.compile(r"^data:(?P<mime>[^;]+);base64,(?P<b64>[A-Za-z0-9+/=\s]+)$")
+
+
+def _parse_data_uri_or_b64(s: str, default_mime: str = "image/png") -> tuple[str, str]:
+    s = (s or "").strip()
+    match = _DATA_URI_RE.match(s)
+    if match:
+        mime = match.group("mime").strip()
+        b64 = match.group("b64").strip()
+        return mime, b64
+    return default_mime, s
+
+
+def _normalize_text_from_content(content) -> str:
+    if content is None:
+        return ""
+    if isinstance(content, str):
+        return content
+    if isinstance(content, list):
+        texts = []
+        for blk in content:
+            if isinstance(blk, dict):
+                if blk.get("type") in {"text", "input_text"}:
+                    txt = blk.get("text")
+                    if txt:
+                        texts.append(str(txt))
+                elif "text" in blk and isinstance(blk.get("text"), (str, int, float)):
+                    texts.append(str(blk["text"]))
+        return "\n".join(texts).strip()
+    return str(content)
+
+
+def convert_last_user_msg_to_multimodal(msg: list[dict], image_data_uris: list[str], factory: str) -> None:
+    if not msg or not image_data_uris:
+        return
+
+    factory_norm = (factory or "").strip().lower()
+
+    for idx in range(len(msg) - 1, -1, -1):
+        if msg[idx].get("role") != "user":
+            continue
+
+        original_content = msg[idx].get("content", "")
+        text = _normalize_text_from_content(original_content)
+
+        if factory_norm == "gemini":
+            parts = []
+            if text:
+                parts.append({"text": text})
+            for image in image_data_uris:
+                mime, b64 = _parse_data_uri_or_b64(str(image), default_mime="image/png")
+                parts.append({"inline_data": {"mime_type": mime, "data": b64}})
+            msg[idx]["content"] = parts
+            return
+
+        if factory_norm == "anthropic":
+            blocks = []
+            if text:
+                blocks.append({"type": "text", "text": text})
+            for image in image_data_uris:
+                mime, b64 = _parse_data_uri_or_b64(str(image), default_mime="image/png")
+                blocks.append(
+                    {
+                        "type": "image",
+                        "source": {"type": "base64", "media_type": mime, "data": b64},
+                    }
+                )
+            msg[idx]["content"] = blocks
+            return
+
+        multimodal_content = []
+        if isinstance(original_content, list):
+            multimodal_content = deepcopy(original_content)
+        else:
+            text_content = "" if original_content is None else str(original_content)
+            if text_content:
+                multimodal_content.append({"type": "text", "text": text_content})
+
+        for data_uri in image_data_uris:
+            image_url = data_uri
+            if not isinstance(image_url, str):
+                image_url = str(image_url)
+            if not image_url.startswith("data:"):
+                image_url = f"data:image/png;base64,{image_url}"
+            multimodal_content.append({"type": "image_url", "image_url": {"url": image_url}})
+
+        msg[idx]["content"] = multimodal_content
+        return
+
+
 BAD_CITATION_PATTERNS = [
     re.compile(r"\(\s*ID\s*[: ]*\s*(\d+)\s*\)"),  # (ID: 12)
     re.compile(r"\[\s*ID\s*[: ]*\s*(\d+)\s*\]"),  # [ID: 12]
@@ -281,12 +417,13 @@ async def async_chat(dialog, messages, stream=True, **kwargs):
         return
 
     chat_start_ts = timer()
-
-    if TenantLLMService.llm_id2llm_type(dialog.llm_id) == "image2text":
+    llm_type = TenantLLMService.llm_id2llm_type(dialog.llm_id)
+    if llm_type == "image2text":
         llm_model_config = TenantLLMService.get_model_config(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id)
     else:
         llm_model_config = TenantLLMService.get_model_config(dialog.tenant_id, LLMType.CHAT, dialog.llm_id)
 
+    factory = llm_model_config.get("llm_factory", "") if llm_model_config else ""
     max_tokens = llm_model_config.get("max_tokens", 8192)
 
     check_llm_ts = timer()
@@ -316,10 +453,16 @@ async def async_chat(dialog, messages, stream=True, **kwargs):
     questions = [m["content"] for m in messages if m["role"] == "user"][-3:]
     attachments = kwargs["doc_ids"].split(",") if "doc_ids" in kwargs else []
     attachments_= ""
+    image_attachments = []
+    image_files = []
     if "doc_ids" in messages[-1]:
         attachments = messages[-1]["doc_ids"]
     if "files" in messages[-1]:
-        attachments_ = "\n\n".join(FileService.get_files(messages[-1]["files"]))
+        if llm_type == "chat":
+            text_attachments, image_attachments = split_file_attachments(messages[-1]["files"])
+        else:
+            text_attachments, image_files = split_file_attachments(messages[-1]["files"], raw=True)
+        attachments_ = "\n\n".join(text_attachments)
 
     prompt_config = dialog.prompt_config
     field_map = KnowledgebaseService.get_field_map(dialog.kb_ids)
@@ -464,6 +607,8 @@ async def async_chat(dialog, messages, stream=True, **kwargs):
         prompt4citation = citation_prompt()
     msg.extend([{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])} for m in messages if m["role"] != "system"])
     used_token_count, msg = message_fit_in(msg, int(max_tokens * 0.95))
+    if llm_type == "chat" and image_attachments:
+        convert_last_user_msg_to_multimodal(msg, image_attachments, factory)
     assert len(msg) >= 2, f"message_fit_in has bug: {msg}"
     prompt = msg[0]["content"]
 
@@ -555,7 +700,10 @@ async def async_chat(dialog, messages, stream=True, **kwargs):
         )
 
     if stream:
-        stream_iter = chat_mdl.async_chat_streamly_delta(prompt + prompt4citation, msg[1:], gen_conf)
+        if llm_type == "chat":
+            stream_iter = chat_mdl.async_chat_streamly_delta(prompt + prompt4citation, msg[1:], gen_conf)
+        else:
+            stream_iter = chat_mdl.async_chat_streamly_delta(prompt + prompt4citation, msg[1:], gen_conf, images=image_files)
         last_state = None
         async for kind, value, state in _stream_with_think_delta(stream_iter):
             last_state = state
@@ -572,7 +720,10 @@ async def async_chat(dialog, messages, stream=True, **kwargs):
             final["answer"] = ""
             yield final
     else:
-        answer = await chat_mdl.async_chat(prompt + prompt4citation, msg[1:], gen_conf)
+        if llm_type == "chat":
+            answer = await chat_mdl.async_chat(prompt + prompt4citation, msg[1:], gen_conf)
+        else:
+            answer = await chat_mdl.async_chat(prompt + prompt4citation, msg[1:], gen_conf, images=image_files)
         user_content = msg[-1].get("content", "[content not available]")
         logging.debug("User: {}|Assistant: {}".format(user_content, answer))
         res = decorate_answer(answer)
diff --git a/api/db/services/file_service.py b/api/db/services/file_service.py
index eba59a3cf2..498199393e 100644
--- a/api/db/services/file_service.py
+++ b/api/db/services/file_service.py
@@ -663,7 +663,7 @@ class FileService(CommonService):
         return structured(file.filename, filename_type(file.filename), file.read(), file.content_type)
 
     @staticmethod
-    def get_files(files: Union[None, list[dict]]) -> list[str]:
+    def get_files(files: Union[None, list[dict]], raw: bool = False) -> Union[list[str], tuple[list[str], list[dict]]]:
         if not files:
             return  []
         def image_to_base64(file):
@@ -671,10 +671,17 @@ class FileService(CommonService):
                                         base64.b64encode(FileService.get_blob(file["created_by"], file["id"])).decode("utf-8"))
         exe = ThreadPoolExecutor(max_workers=5)
         threads = []
+        imgs = []
         for file in files:
             if file["mime_type"].find("image") >=0:
-                threads.append(exe.submit(image_to_base64, file))
+                if raw:
+                    imgs.append(FileService.get_blob(file["created_by"], file["id"]))
+                else:
+                    threads.append(exe.submit(image_to_base64, file))
                 continue
             threads.append(exe.submit(FileService.parse, file["name"], FileService.get_blob(file["created_by"], file["id"]), True, file["created_by"]))
-        return [th.result() for th in threads]
-
+    
+        if raw:
+            return [th.result() for th in threads], imgs
+        else:
+            return [th.result() for th in threads]
diff --git a/rag/app/picture.py b/rag/app/picture.py
index 2ad773a3cd..67540772ab 100644
--- a/rag/app/picture.py
+++ b/rag/app/picture.py
@@ -51,8 +51,9 @@ def chunk(filename, binary, tenant_id, lang, callback=None, **kwargs):
                 }
             )
             cv_mdl = LLMBundle(tenant_id, llm_type=LLMType.IMAGE2TEXT, lang=lang)
+            video_prompt = str(parser_config.get("video_prompt", "") or "")
             ans = asyncio.run(
-                cv_mdl.async_chat(system="", history=[], gen_conf={}, video_bytes=binary, filename=filename))
+                cv_mdl.async_chat(system="", history=[], gen_conf={}, video_bytes=binary, filename=filename, video_prompt=video_prompt))
             callback(0.8, "CV LLM respond: %s ..." % ans[:32])
             ans += "\n" + ans
             tokenize(doc, ans, eng)
diff --git a/rag/flow/parser/parser.py b/rag/flow/parser/parser.py
index 7fcdde860f..9ed7d65d72 100644
--- a/rag/flow/parser/parser.py
+++ b/rag/flow/parser/parser.py
@@ -161,6 +161,7 @@ class ParserParam(ProcessParamBase):
                     "mkv",
                 ],
                 "output_format": "text",
+                "prompt": "",
             },
         }
 
@@ -685,7 +686,8 @@ class Parser(ProcessBase):
         self.set_output("output_format", conf["output_format"])
 
         cv_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.IMAGE2TEXT, llm_name=conf["llm_id"])
-        txt = asyncio.run(cv_mdl.async_chat(system="", history=[], gen_conf={}, video_bytes=blob, filename=name))
+        video_prompt = str(conf.get("prompt", "") or "")
+        txt = asyncio.run(cv_mdl.async_chat(system="", history=[], gen_conf={}, video_bytes=blob, filename=name, video_prompt=video_prompt))
 
         self.set_output("text", txt)
 
diff --git a/rag/llm/cv_model.py b/rag/llm/cv_model.py
index 9fdd9680a5..e8f28f7eba 100644
--- a/rag/llm/cv_model.py
+++ b/rag/llm/cv_model.py
@@ -67,6 +67,61 @@ class Base(ABC):
             hist.append(h)
         return hist
 
+    @staticmethod
+    def _blob_to_data_url(blob, mime_type="image/png"):
+        if isinstance(blob, str):
+            blob = blob.strip()
+            if blob.startswith("data:") or blob.startswith("http://") or blob.startswith("https://") or blob.startswith("file://"):
+                return blob
+            return f"data:{mime_type};base64,{blob}"
+        if isinstance(blob, BytesIO):
+            blob = blob.getvalue()
+        if isinstance(blob, memoryview):
+            blob = blob.tobytes()
+        if isinstance(blob, bytearray):
+            blob = bytes(blob)
+        if isinstance(blob, bytes):
+            b64 = base64.b64encode(blob).decode("utf-8")
+            return f"data:{mime_type};base64,{b64}"
+        return None
+
+    def _normalize_image(self, image):
+        if isinstance(image, dict):
+            inline_data = image.get("inline_data")
+            if isinstance(inline_data, dict):
+                mime = inline_data.get("mime_type") or "image/png"
+                data_url = self._blob_to_data_url(inline_data.get("data"), mime)
+                if data_url:
+                    return data_url
+
+            image_url = image.get("image_url")
+            if isinstance(image_url, dict):
+                data_url = self._blob_to_data_url(image_url.get("url"), image.get("mime_type") or "image/png")
+                if data_url:
+                    return data_url
+            if isinstance(image_url, str):
+                data_url = self._blob_to_data_url(image_url, image.get("mime_type") or "image/png")
+                if data_url:
+                    return data_url
+
+            if "url" in image:
+                data_url = self._blob_to_data_url(image.get("url"), image.get("mime_type") or "image/png")
+                if data_url:
+                    return data_url
+
+            mime = image.get("mime_type") or image.get("media_type") or "image/png"
+            for key in ("blob", "data"):
+                if key in image:
+                    data_url = self._blob_to_data_url(image.get(key), mime)
+                    if data_url:
+                        return data_url
+
+        if isinstance(image, (bytes, bytearray, memoryview, BytesIO)):
+            return self.image2base64(image)
+        if isinstance(image, str):
+            return self._blob_to_data_url(image, "image/png")
+        return self.image2base64(image)
+
     def _image_prompt(self, text, images):
         if not images:
             return text
@@ -76,7 +131,11 @@ class Base(ABC):
 
         pmpt = [{"type": "text", "text": text}]
         for img in images:
-            pmpt.append({"type": "image_url", "image_url": {"url": img if isinstance(img, str) and img.startswith("data:") else f"data:image/png;base64,{img}"}})
+            try:
+                pmpt.append({"type": "image_url", "image_url": {"url": self._normalize_image(img)}})
+            except Exception:
+                logging.warning("[%s] Skip invalid image input in request payload.", self.__class__.__name__)
+                continue
         return pmpt
 
     async def async_chat(self, system, history, gen_conf, images=None, **kwargs):
@@ -248,51 +307,86 @@ class QWenCV(GptV4):
             base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
         super().__init__(key, model_name, lang=lang, base_url=base_url, **kwargs)
 
+    @staticmethod
+    def _extract_text_from_content(content):
+        if isinstance(content, str):
+            return content.strip()
+        if isinstance(content, list):
+            texts = []
+            for blk in content:
+                if not isinstance(blk, dict):
+                    continue
+                if blk.get("type") in {"text", "input_text"} and blk.get("text"):
+                    texts.append(str(blk["text"]))
+                elif "text" in blk and isinstance(blk.get("text"), (str, int, float)):
+                    texts.append(str(blk["text"]))
+            return "\n".join(texts).strip()
+        return ""
+
+    def _resolve_video_prompt(self, system, history, **kwargs):
+        prompt = kwargs.get("video_prompt") or kwargs.get("prompt")
+        if isinstance(prompt, str) and prompt.strip():
+            return prompt.strip()
+
+        for h in reversed(history or []):
+            if h.get("role") != "user":
+                continue
+            txt = self._extract_text_from_content(h.get("content"))
+            if txt:
+                return txt
+
+        if isinstance(system, str) and system.strip():
+            return system.strip()
+
+        return "Please summarize this video in proper sentences."
+
     async def async_chat(self, system, history, gen_conf, images=None, video_bytes=None, filename="", **kwargs):
         if video_bytes:
             try:
-                summary, summary_num_tokens = self._process_video(video_bytes, filename)
+                summary, summary_num_tokens = self._process_video(video_bytes, filename, self._resolve_video_prompt(system, history, **kwargs))
                 return summary, summary_num_tokens
             except Exception as e:
                 return "**ERROR**: " + str(e), 0
 
-        return "**ERROR**: Method chat not supported yet.", 0
+        return await super().async_chat(system, history, gen_conf, images=images, **kwargs)
 
-    def _process_video(self, video_bytes, filename):
+    def _process_video(self, video_bytes, filename, prompt):
         from dashscope import MultiModalConversation
 
         video_suffix = Path(filename).suffix or ".mp4"
+        tmp_path = None
         with tempfile.NamedTemporaryFile(delete=False, suffix=video_suffix) as tmp:
             tmp.write(video_bytes)
             tmp_path = tmp.name
 
-            video_path = f"file://{tmp_path}"
-            messages = [
-                {
-                    "role": "user",
-                    "content": [
-                        {
-                            "video": video_path,
-                            "fps": 2,
-                        },
-                        {
-                            "text": "Please summarize this video in proper sentences.",
-                        },
-                    ],
-                }
-            ]
+        video_path = f"file://{tmp_path}"
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "video": video_path,
+                        "fps": 2,
+                    },
+                    {
+                        "text": prompt,
+                    },
+                ],
+            }
+        ]
 
-            def call_api():
-                response = MultiModalConversation.call(
-                    api_key=self.api_key,
-                    model=self.model_name,
-                    messages=messages,
-                )
-                if response.get("message"):
-                    raise Exception(response["message"])
-                summary = response["output"]["choices"][0]["message"].content[0]["text"]
-                return summary, num_tokens_from_string(summary)
+        def call_api():
+            response = MultiModalConversation.call(
+                api_key=self.api_key,
+                model=self.model_name,
+                messages=messages,
+            )
+            if response.get("message"):
+                raise Exception(response["message"])
+            summary = response["output"]["choices"][0]["message"].content[0]["text"]
+            return summary, num_tokens_from_string(summary)
 
+        try:
             try:
                 return call_api()
             except Exception as e1:
@@ -303,6 +397,12 @@ class QWenCV(GptV4):
                     return call_api()
                 except Exception as e2:
                     raise RuntimeError(f"Both default and intl endpoint failed.\nFirst error: {e1}\nSecond error: {e2}")
+        finally:
+            if tmp_path and os.path.exists(tmp_path):
+                try:
+                    os.remove(tmp_path)
+                except Exception:
+                    logging.warning("[QWenCV] Failed to cleanup temp video file: %s", tmp_path)
 
 
 class HunyuanCV(GptV4):