图片知识库报错code=105000030 message=parser parse failed: parse document failed, err: [ParseImage] model generate failed: error during Chat request: 400 Bad Request: illegal base64 data at input byte 4

[parkerisme]

文本知识库无问题，图片知识库报下面错误：
code=105000030 message=parser parse failed: parse document failed, err: [ParseImage] model generate failed: error during Chat request: 400 Bad Request: illegal base64 data at input byte 4

关键配置：

Settings for Model

Model for agent & workflow

add suffix number to add different models

export MODEL_PROTOCOL_0="ollama" # protocol
export MODEL_OPENCOZE_ID_0="100001" # id for record
export MODEL_NAME_0="llava:7b" # model name for show
export MODEL_ID_0="llava:7b" # model name for connection
export MODEL_API_KEY_0="" # model api key
export MODEL_BASE_URL_0="http://host.docker.internal:11434" # model base url

Model for knowledge nl2sql, messages2query (rewrite), image annotation, workflow knowledge recall

add prefix to assign specific model, downgrade to default config when prefix is not configured:

1. nl2sql: NL2SQL_ (e.g. NL2SQL_BUILTIN_CM_TYPE)

2. messages2query: M2Q_ (e.g. M2Q_BUILTIN_CM_TYPE)

3. image annotation: IA_ (e.g. IA_BUILTIN_CM_TYPE)

4. workflow knowledge recall: WKR_ (e.g. WKR_BUILTIN_CM_TYPE)

supported chat model type: openai / ark / deepseek / ollama / qwen / gemini

export BUILTIN_CM_TYPE="ollama"

显式指定图像标注任务使用 Ollama

export IA_BUILTIN_CM_TYPE="ollama"
export IA_BUILTIN_CM_OLLAMA_BASE_URL="http://host.docker.internal:11434" # 替换为您自己的宿主机IP
export IA_BUILTIN_CM_OLLAMA_MODEL="llava:7b"

type ollama

export BUILTIN_CM_OLLAMA_BASE_URL="http://host.docker.internal:11434"
export BUILTIN_CM_OLLAMA_MODEL="llava:7b"

Settings for Embedding

The Embedding model relied on by knowledge base vectorization does not need to be configured

if the vector database comes with built-in Embedding functionality (such as VikingDB). Currently,

Coze Studio supports four access methods: openai, ark, ollama, and custom http. Users can simply choose one of them when using

embedding type: ark / openai / ollama / gemini / http

export EMBEDDING_TYPE="ollama"
export EMBEDDING_MAX_BATCH_SIZE=100

ollama embedding

export OLLAMA_EMBEDDING_BASE_URL="http://host.docker.internal:11434" # (string, required) Ollama embedding base_url
export OLLAMA_EMBEDDING_MODEL="bge-m3" # (string, required) Ollama embedding model
export OLLAMA_EMBEDDING_DIMS="1024" # (int, required) Ollama embedding dimensions
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