Merge 0fa7865cee into 56694a6dea

mcp_server/.env.example

@@ -20,6 +20,12 @@ FALKORDB_PASSWORD=
 OPENAI_API_KEY=your_openai_api_key_here
 MODEL_NAME=gpt-4.1-mini
 
+# Embedding Service Configuration (Optional)  
+# Use these to configure a separate embedding service (e.g., Ollama, Voyage, or custom OpenAI-compatible service)  
+# EMBEDDER_API_KEY=your_embedder_api_key_here  # Defaults to OPENAI_API_KEY if not set  
+# EMBEDDER_BASE_URL=http://localhost:11434/v1  # For Ollama or other OpenAI-compatible endpoints  
+# EMBEDDER_MODEL_NAME=nomic-embed-text          # Model name for embedding service  
+  
 # Optional: Only needed for non-standard OpenAI endpoints
 # OPENAI_BASE_URL=https://api.openai.com/v1
 

mcp_server/README.md

@@ -102,6 +102,9 @@ The server supports both Neo4j and FalkorDB as database backends. Use the `DATAB
 - `MODEL_NAME`: OpenAI model name to use for LLM operations.
 - `SMALL_MODEL_NAME`: OpenAI model name to use for smaller LLM operations.
 - `LLM_TEMPERATURE`: Temperature for LLM responses (0.0-2.0).
+- `EMBEDDER_API_KEY`: Optional API key for embedding service (defaults to OPENAI_API_KEY if not set)  
+- `EMBEDDER_BASE_URL`: Optional base URL for embedding service (e.g., http://localhost:11434/v1 for Ollama)  
+- `EMBEDDER_MODEL_NAME`: Embedding model name (default: text-embedding-3-small) 
 - `AZURE_OPENAI_ENDPOINT`: Optional Azure OpenAI LLM endpoint URL
 - `AZURE_OPENAI_DEPLOYMENT_NAME`: Optional Azure OpenAI LLM deployment name
 - `AZURE_OPENAI_API_VERSION`: Optional Azure OpenAI LLM API version

mcp_server/docker-compose.yml

@@ -39,7 +39,11 @@ services:
       - NEO4J_USER=${NEO4J_USER:-neo4j}
       - NEO4J_PASSWORD=${NEO4J_PASSWORD:-demodemo}
       - OPENAI_API_KEY=${OPENAI_API_KEY}
+      - OPENAI_BASE_URL=${OPENAI_BASE_URL}
       - MODEL_NAME=${MODEL_NAME}
+      - EMBEDDER_API_KEY=${EMBEDDER_API_KEY}          
+      - EMBEDDER_BASE_URL=${EMBEDDER_BASE_URL}       
+      - EMBEDDER_MODEL_NAME=${EMBEDDER_MODEL_NAME}     
       - PATH=/root/.local/bin:${PATH}
       - SEMAPHORE_LIMIT=${SEMAPHORE_LIMIT:-10}
     ports:

mcp_server/graphiti_mcp_server.py

@@ -356,6 +356,7 @@ class GraphitiEmbedderConfig(BaseModel):
 
     model: str = DEFAULT_EMBEDDER_MODEL
     api_key: str | None = None
+    base_url: str | None = None
     azure_openai_endpoint: str | None = None
     azure_openai_deployment_name: str | None = None
     azure_openai_api_version: str | None = None
@@ -369,6 +370,11 @@ class GraphitiEmbedderConfig(BaseModel):
         model_env = os.environ.get('EMBEDDER_MODEL_NAME', '')
         model = model_env if model_env.strip() else DEFAULT_EMBEDDER_MODEL
 
+        # Get base_url from environment
+        base_url = os.environ.get('EMBEDDER_BASE_URL', None)  
+        logger.info(f'EMBEDDER_BASE_URL from env: {base_url}')  
+        logger.info(f'EMBEDDER_MODEL_NAME from env: {model}')  
+
         azure_openai_endpoint = os.environ.get('AZURE_OPENAI_EMBEDDING_ENDPOINT', None)
         azure_openai_api_version = os.environ.get('AZURE_OPENAI_EMBEDDING_API_VERSION', None)
         azure_openai_deployment_name = os.environ.get(
@@ -407,9 +413,12 @@ class GraphitiEmbedderConfig(BaseModel):
                 azure_openai_deployment_name=azure_openai_deployment_name,
             )
         else:
+            api_key = os.environ.get('EMBEDDER_API_KEY') or os.environ.get('OPENAI_API_KEY')
+
             return cls(
                 model=model,
-                api_key=os.environ.get('OPENAI_API_KEY'),
+                api_key=api_key,
+                base_url=base_url,
             )
 
     def create_client(self) -> EmbedderClient | None:
@@ -446,7 +455,7 @@ class GraphitiEmbedderConfig(BaseModel):
             if not self.api_key:
                 return None
 
-            embedder_config = OpenAIEmbedderConfig(api_key=self.api_key, embedding_model=self.model)
+            embedder_config = OpenAIEmbedderConfig(api_key=self.api_key, embedding_model=self.model, base_url=self.base_url)
 
             return OpenAIEmbedder(config=embedder_config)