diff --git a/config/config2.example.yaml b/config/config2.example.yaml
index 3249f5ae3..6d1148a85 100644
--- a/config/config2.example.yaml
+++ b/config/config2.example.yaml
@@ -18,6 +18,7 @@ embedding:
   model: ""
   api_version: ""
   embed_batch_size: 100
+  dimensions: 
 
 repair_llm_output: true  # when the output is not a valid json, try to repair it
 
diff --git a/metagpt/configs/embedding_config.py b/metagpt/configs/embedding_config.py
index 20de47999..f9b41b9dc 100644
--- a/metagpt/configs/embedding_config.py
+++ b/metagpt/configs/embedding_config.py
@@ -20,11 +20,13 @@ class EmbeddingConfig(YamlModel):
     ---------
     api_type: "openai"
     api_key: "YOU_API_KEY"
+    dimensions: "YOUR_MODEL_DIMENSIONS"
 
     api_type: "azure"
     api_key: "YOU_API_KEY"
     base_url: "YOU_BASE_URL"
     api_version: "YOU_API_VERSION"
+    dimensions: "YOUR_MODEL_DIMENSIONS"
 
     api_type: "gemini"
     api_key: "YOU_API_KEY"
@@ -32,6 +34,7 @@ class EmbeddingConfig(YamlModel):
     api_type: "ollama"
     base_url: "YOU_BASE_URL"
     model: "YOU_MODEL"
+    dimensions: "YOUR_MODEL_DIMENSIONS"
     """
 
     api_type: Optional[EmbeddingType] = None
@@ -41,6 +44,7 @@ class EmbeddingConfig(YamlModel):
 
     model: Optional[str] = None
     embed_batch_size: Optional[int] = None
+    dimensions: Optional[int] = None  # output dimension of embedding model
 
     @field_validator("api_type", mode="before")
     @classmethod
diff --git a/metagpt/rag/schema.py b/metagpt/rag/schema.py
index e7b2e5ce9..bedba164c 100644
--- a/metagpt/rag/schema.py
+++ b/metagpt/rag/schema.py
@@ -12,6 +12,7 @@ from pydantic import BaseModel, ConfigDict, Field, PrivateAttr, model_validator
 
 from metagpt.config2 import config
 from metagpt.configs.embedding_config import EmbeddingType
+from metagpt.logs import logger
 from metagpt.rag.interface import RAGObject
 
 
@@ -34,16 +35,21 @@ class IndexRetrieverConfig(BaseRetrieverConfig):
 class FAISSRetrieverConfig(IndexRetrieverConfig):
     """Config for FAISS-based retrievers."""
 
-    dimensions: int = Field(default=0, description="Dimensionality of the vectors for FAISS index construction.")
+    dimensions: int = Field(
+        default=config.embedding.dimensions, description="Dimensionality of the vectors for FAISS index construction."
+    )
 
     _embedding_type_to_dimensions: ClassVar[dict[EmbeddingType, int]] = {
         EmbeddingType.GEMINI: 768,
-        EmbeddingType.OLLAMA: 4096,
     }
 
     @model_validator(mode="after")
     def check_dimensions(self):
-        if self.dimensions == 0:
+        if self.dimensions is None:
+            if config.embedding.api_type not in self._embedding_type_to_dimensions:
+                logger.info(
+                    f"You didn't set the dimensions in config when using {config.embedding.api_type}, default to 1536"
+                )
             self.dimensions = self._embedding_type_to_dimensions.get(config.embedding.api_type, 1536)
 
         return self