feature: research agent support no knowledge db mode

python/configs/providers/openrouter.yaml

@@ -1,50 +1,62 @@
-  # ============================================
-  # OpenRouter Provider Configuration
-  # ============================================
-  name: "OpenRouter"
-  provider_type: "openrouter"
-
-  enabled: true # Default is true if not specified
-
-  # Connection Configuration
-  connection:
-    base_url: "https://openrouter.ai/api/v1"
-    api_key_env: "OPENROUTER_API_KEY"
-
-  # Default model if none specified
-  default_model: "anthropic/claude-haiku-4.5"
-
-  # Model Parameters Defaults
+# ============================================
+# OpenRouter Provider Configuration
+# ============================================
+name: "OpenRouter"
+provider_type: "openrouter"
+
+enabled: true # Default is true if not specified
+
+# Connection Configuration
+connection:
+  base_url: "https://openrouter.ai/api/v1"
+  api_key_env: "OPENROUTER_API_KEY"
+
+# Default model if none specified
+default_model: "anthropic/claude-haiku-4.5"
+
+# Model Parameters Defaults
+defaults:
+  temperature: 0.5
+
+# Extra headers for OpenRouter API
+extra_headers:
+  HTTP-Referer: "https://valuecell.ai"
+  X-Title: "ValueCell"
+
+# Available Models (commonly used)
+models:
+  - id: "anthropic/claude-haiku-4.5"
+    name: "Claude Haiku 4.5"
+  - id: "x-ai/grok-4"
+    name: "Grok 4"
+  - id: "qwen/qwen-max"
+    name: "Qwen Max"
+  - id: "openai/gpt-5"
+    name: "GPT-5"
+  - id: "google/gemini-2.5-flash"
+    name: "Gemini 2.5 Flash"
+  - id: "google/gemini-2.5-pro"
+    name: "Gemini 2.5 Pro"
+
+# ============================================
+# Embedding Models Configuration
+# ============================================
+# OpenRouter provides embedding models
+embedding:
+  # Default embedding model
+  default_model: "qwen/qwen3-embedding-4b"
+
+  # Default parameters
   defaults:
-    temperature: 0.5
-
-  # Extra headers for OpenRouter API
-  extra_headers:
-    HTTP-Referer: "https://valuecell.ai"
-    X-Title: "ValueCell"
+    dimensions: 2560
+    encoding_format: "float"
 
-  # Available Models (commonly used)
+  # Available embedding models
   models:
-
-    - id: "anthropic/claude-haiku-4.5"
-      name: "Claude Haiku 4.5"
-
-    - id: "x-ai/grok-4"
-      name: "Grok 4"
-
-    - id: "qwen/qwen-max"
-      name: "Qwen Max"
-
-    - id: "openai/gpt-5"
-      name: "GPT-5"
-
-    - id: "google/gemini-2.5-flash"
-      name: "Gemini 2.5 Flash"
-
-    - id: "google/gemini-2.5-pro"
-      name: "Gemini 2.5 Pro"
-
-  # Note: OpenRouter does not support embedding models
-  # For embedding, use other providers like OpenAI or SiliconFlow etc
+  - id: "qwen/qwen3-embedding-4b"
+    name: "Qwen3 Embedding 4B"
+    dimensions: 2560
+    max_input: 32768
+    description: "Qwen3 Embedding Model"
 
 

python/valuecell/agents/research_agent/core.py

@@ -7,7 +7,7 @@
 from loguru import logger
 
 import valuecell.utils.model as model_utils_mod
-from valuecell.agents.research_agent.knowledge import knowledge
+from valuecell.agents.research_agent.knowledge import get_knowledge
 from valuecell.agents.research_agent.prompts import (
     KNOWLEDGE_AGENT_EXPECTED_OUTPUT,
     KNOWLEDGE_AGENT_INSTRUCTION,
@@ -37,6 +37,8 @@ def __init__(self, **kwargs):
             # search_crypto_vcs,
             # search_crypto_people,
         ]
+        # Lazily obtain knowledge; disable search if unavailable
+        knowledge = get_knowledge()
         self.knowledge_research_agent = Agent(
             model=model_utils_mod.get_model_for_agent("research_agent"),
             instructions=[KNOWLEDGE_AGENT_INSTRUCTION],
@@ -45,7 +47,7 @@ def __init__(self, **kwargs):
             knowledge=knowledge,
             db=InMemoryDb(),
             # context
-            search_knowledge=True,
+            search_knowledge=knowledge is not None,
             add_datetime_to_context=True,
             add_history_to_context=True,
             num_history_runs=3,
@@ -54,7 +56,14 @@ def __init__(self, **kwargs):
             # configuration
             debug_mode=agent_debug_mode_enabled(),
         )
-        set_identity(os.getenv("SEC_EMAIL"))
+        # Configure EDGAR identity only when SEC_EMAIL is present
+        sec_email = os.getenv("SEC_EMAIL")
+        if sec_email:
+            set_identity(sec_email)
+        else:
+            logger.warning(
+                "SEC_EMAIL not set; EDGAR identity is not configured for ResearchAgent."
+            )
 
     async def stream(
         self,

python/valuecell/agents/research_agent/knowledge.py

@@ -5,20 +5,57 @@
 from agno.knowledge.knowledge import Knowledge
 from agno.knowledge.reader.markdown_reader import MarkdownReader
 from agno.knowledge.reader.pdf_reader import PDFReader
+from loguru import logger
+
+from .vdb import get_vector_db
+
+_knowledge_cache: Optional[Knowledge] = None
+
+
+def get_knowledge() -> Optional[Knowledge]:
+    """Lazily create and cache the Knowledge instance.
+
+    Returns None when embeddings/vector DB are unavailable, enabling a
+    tools-only mode without knowledge search.
+    """
+    global _knowledge_cache
+    if _knowledge_cache is not None:
+        return _knowledge_cache
+
+    vdb = get_vector_db()
+    if vdb is None:
+        logger.warning(
+            "ResearchAgent Knowledge disabled: vector DB unavailable (no embeddings)."
+        )
+        return None
+
+    try:
+        _knowledge_cache = Knowledge(
+            vector_db=vdb,
+            max_results=10,
+        )
+        return _knowledge_cache
+    except Exception as e:
+        logger.warning(
+            "Failed to create Knowledge for ResearchAgent; disabling. Error: {}",
+            e,
+        )
+        return None
 
-from .vdb import vector_db
 
-knowledge = Knowledge(
-    vector_db=vector_db,
-    max_results=10,
-)
 md_reader = MarkdownReader(chunking_strategy=MarkdownChunking())
 pdf_reader = PDFReader(chunking_strategy=MarkdownChunking())
 
 
 async def insert_md_file_to_knowledge(
     name: str, path: Path, metadata: Optional[dict] = None
 ):
+    knowledge = get_knowledge()
+    if knowledge is None:
+        logger.warning(
+            "Skipping markdown insertion: Knowledge disabled (no embeddings configured)."
+        )
+        return
     await knowledge.add_content_async(
         name=name,
         path=path,
@@ -28,6 +65,12 @@ async def insert_md_file_to_knowledge(
 
 
 async def insert_pdf_file_to_knowledge(url: str, metadata: Optional[dict] = None):
+    knowledge = get_knowledge()
+    if knowledge is None:
+        logger.warning(
+            "Skipping PDF insertion: Knowledge disabled (no embeddings configured)."
+        )
+        return
     await knowledge.add_content_async(
         url=url,
         metadata=metadata,