fix(backend): Add diagnostic logging for vector type errors

When 'type vector does not exist' occurs in hybrid search, log search_path,
current_schema, and user info to help diagnose why the pgvector extension
isn't visible.

This is a debug-only change to help track down an intermittent issue
on dev-behave where the vector type occasionally fails to resolve.

autogpt_platform/backend/backend/api/features/store/hybrid_search.py

@@ -8,6 +8,7 @@ Includes BM25 reranking for improved lexical relevance.
 
 import logging
 import re
+import time
 from dataclasses import dataclass
 from typing import Any, Literal
 
@@ -362,7 +363,11 @@ async def unified_hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    results = await query_raw_with_schema(sql_query, *params)
+    try:
+        results = await query_raw_with_schema(sql_query, *params)
+    except Exception as e:
+        await _log_vector_error_diagnostics(e)
+        raise
 
     total = results[0]["total_count"] if results else 0
     # Apply BM25 reranking
@@ -686,7 +691,11 @@ async def hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    results = await query_raw_with_schema(sql_query, *params)
+    try:
+        results = await query_raw_with_schema(sql_query, *params)
+    except Exception as e:
+        await _log_vector_error_diagnostics(e)
+        raise
 
     total = results[0]["total_count"] if results else 0
 
@@ -718,6 +727,87 @@ async def hybrid_search_simple(
     return await hybrid_search(query=query, page=page, page_size=page_size)
 
 
+# ============================================================================
+# Diagnostics
+# ============================================================================
+
+# Rate limit: only log vector error diagnostics once per this interval
+_VECTOR_DIAG_INTERVAL_SECONDS = 60
+_last_vector_diag_time: float = 0
+
+
+async def _log_vector_error_diagnostics(error: Exception) -> None:
+    """Log diagnostic info when 'type vector does not exist' error occurs.
+
+    Note: Diagnostic queries use query_raw_with_schema which may run on a different
+    pooled connection than the one that failed. Session-level search_path can differ,
+    so these diagnostics show cluster-wide state, not necessarily the failed session.
+
+    Includes rate limiting to avoid log spam - only logs once per minute.
+    Caller should re-raise the error after calling this function.
+    """
+    global _last_vector_diag_time
+
+    # Check if this is the vector type error
+    error_str = str(error).lower()
+    if not (
+        "type" in error_str and "vector" in error_str and "does not exist" in error_str
+    ):
+        return
+
+    # Rate limit: only log once per interval
+    now = time.time()
+    if now - _last_vector_diag_time < _VECTOR_DIAG_INTERVAL_SECONDS:
+        return
+    _last_vector_diag_time = now
+
+    try:
+        diagnostics: dict[str, object] = {}
+
+        try:
+            search_path_result = await query_raw_with_schema("SHOW search_path")
+            diagnostics["search_path"] = search_path_result
+        except Exception as e:
+            diagnostics["search_path"] = f"Error: {e}"
+
+        try:
+            schema_result = await query_raw_with_schema("SELECT current_schema()")
+            diagnostics["current_schema"] = schema_result
+        except Exception as e:
+            diagnostics["current_schema"] = f"Error: {e}"
+
+        try:
+            user_result = await query_raw_with_schema(
+                "SELECT current_user, session_user, current_database()"
+            )
+            diagnostics["user_info"] = user_result
+        except Exception as e:
+            diagnostics["user_info"] = f"Error: {e}"
+
+        try:
+            # Check pgvector extension installation (cluster-wide, stable info)
+            ext_result = await query_raw_with_schema(
+                "SELECT extname, extversion, nspname as schema "
+                "FROM pg_extension e "
+                "JOIN pg_namespace n ON e.extnamespace = n.oid "
+                "WHERE extname = 'vector'"
+            )
+            diagnostics["pgvector_extension"] = ext_result
+        except Exception as e:
+            diagnostics["pgvector_extension"] = f"Error: {e}"
+
+        logger.error(
+            f"Vector type error diagnostics:\n"
+            f"  Error: {error}\n"
+            f"  search_path: {diagnostics.get('search_path')}\n"
+            f"  current_schema: {diagnostics.get('current_schema')}\n"
+            f"  user_info: {diagnostics.get('user_info')}\n"
+            f"  pgvector_extension: {diagnostics.get('pgvector_extension')}"
+        )
+    except Exception as diag_error:
+        logger.error(f"Failed to collect vector error diagnostics: {diag_error}")
+
+
 # Backward compatibility alias - HybridSearchWeights maps to StoreAgentSearchWeights
 # for existing code that expects the popularity parameter
 HybridSearchWeights = StoreAgentSearchWeights

autogpt_platform/backend/backend/api/features/v1.py

@@ -827,28 +827,7 @@ async def update_graph(
 
     existing_versions = await graph_db.get_graph_all_versions(graph_id, user_id=user_id)
     if not existing_versions:
-        # User doesn't own this graph -- check if they have it in their library
-        # (e.g. added from the marketplace). If so, fork it and apply their edits.
-        library_agent = await library_db.get_library_agent_by_graph_id(
-            user_id=user_id, graph_id=graph_id
-        )
-        if not library_agent:
-            raise HTTPException(404, detail=f"Graph #{graph_id} not found")
-
-        # Fork the marketplace agent to create a user-owned copy
-        forked = await graph_db.fork_graph(
-            graph_id, library_agent.graph_version, user_id
-        )
-        forked = await on_graph_activate(forked, user_id=user_id)
-        await graph_db.set_graph_active_version(
-            graph_id=forked.id, version=forked.version, user_id=user_id
-        )
-        await library_db.create_library_agent(forked, user_id)
-
-        # Apply the user's edits on top of the fork via the normal update path
-        graph_id = forked.id
-        graph.id = forked.id
-        existing_versions = [forked]
+        raise HTTPException(404, detail=f"Graph #{graph_id} not found")
 
     graph.version = max(g.version for g in existing_versions) + 1
     current_active_version = next((v for v in existing_versions if v.is_active), None)

autogpt_platform/backend/backend/blocks/llm.py

@@ -531,12 +531,12 @@ class LLMResponse(BaseModel):
 
 def convert_openai_tool_fmt_to_anthropic(
     openai_tools: list[dict] | None = None,
-) -> Iterable[ToolParam] | anthropic.NotGiven:
+) -> Iterable[ToolParam] | anthropic.Omit:
     """
     Convert OpenAI tool format to Anthropic tool format.
     """
     if not openai_tools or len(openai_tools) == 0:
-        return anthropic.NOT_GIVEN
+        return anthropic.omit
 
     anthropic_tools = []
     for tool in openai_tools:
@@ -596,10 +596,10 @@ def extract_openai_tool_calls(response) -> list[ToolContentBlock] | None:
 
 def get_parallel_tool_calls_param(
     llm_model: LlmModel, parallel_tool_calls: bool | None
-) -> bool | openai.NotGiven:
+) -> bool | openai.Omit:
     """Get the appropriate parallel_tool_calls parameter for OpenAI-compatible APIs."""
     if llm_model.startswith("o") or parallel_tool_calls is None:
-        return openai.NOT_GIVEN
+        return openai.omit
     return parallel_tool_calls
 
 
@@ -676,7 +676,7 @@ async def llm_call(
             response_format=response_format,  # type: ignore
             max_completion_tokens=max_tokens,
             tools=tools_param,  # type: ignore
-            parallel_tool_calls=parallel_tool_calls,  # type: ignore
+            parallel_tool_calls=parallel_tool_calls,
         )
 
         tool_calls = extract_openai_tool_calls(response)
@@ -722,7 +722,7 @@ async def llm_call(
                 system=sysprompt,
                 messages=messages,
                 max_tokens=max_tokens,
-                tools=an_tools,  # type: ignore
+                tools=an_tools,
                 timeout=600,
             )
 
@@ -838,7 +838,7 @@ async def llm_call(
             messages=prompt,  # type: ignore
             max_tokens=max_tokens,
             tools=tools_param,  # type: ignore
-            parallel_tool_calls=parallel_tool_calls_param,  # type: ignore
+            parallel_tool_calls=parallel_tool_calls_param,
         )
 
         # If there's no response, raise an error
@@ -880,7 +880,7 @@ async def llm_call(
             messages=prompt,  # type: ignore
             max_tokens=max_tokens,
             tools=tools_param,  # type: ignore
-            parallel_tool_calls=parallel_tool_calls_param,  # type: ignore
+            parallel_tool_calls=parallel_tool_calls_param,
         )
 
         # If there's no response, raise an error
@@ -951,7 +951,7 @@ async def llm_call(
             response_format=response_format,  # type: ignore
             max_tokens=max_tokens,
             tools=tools_param,  # type: ignore
-            parallel_tool_calls=parallel_tool_calls_param,  # type: ignore
+            parallel_tool_calls=parallel_tool_calls_param,
         )
 
         tool_calls = extract_openai_tool_calls(response)

autogpt_platform/backend/backend/data/rabbitmq.py

@@ -1,4 +1,3 @@
-import asyncio
 import logging
 from abc import ABC, abstractmethod
 from enum import Enum
@@ -226,10 +225,6 @@ class SyncRabbitMQ(RabbitMQBase):
 class AsyncRabbitMQ(RabbitMQBase):
     """Asynchronous RabbitMQ client"""
 
-    def __init__(self, config: RabbitMQConfig):
-        super().__init__(config)
-        self._reconnect_lock: asyncio.Lock | None = None
-
     @property
     def is_connected(self) -> bool:
         return bool(self._connection and not self._connection.is_closed)
@@ -240,17 +235,7 @@ class AsyncRabbitMQ(RabbitMQBase):
 
     @conn_retry("AsyncRabbitMQ", "Acquiring async connection")
     async def connect(self):
-        if self.is_connected and self._channel and not self._channel.is_closed:
-            return
-
-        if (
-            self.is_connected
-            and self._connection
-            and (self._channel is None or self._channel.is_closed)
-        ):
-            self._channel = await self._connection.channel()
-            await self._channel.set_qos(prefetch_count=1)
-            await self.declare_infrastructure()
+        if self.is_connected:
             return
 
         self._connection = await aio_pika.connect_robust(
@@ -306,46 +291,24 @@ class AsyncRabbitMQ(RabbitMQBase):
                     exchange, routing_key=queue.routing_key or queue.name
                 )
 
-    @property
-    def _lock(self) -> asyncio.Lock:
-        if self._reconnect_lock is None:
-            self._reconnect_lock = asyncio.Lock()
-        return self._reconnect_lock
-
-    async def _ensure_channel(self) -> aio_pika.abc.AbstractChannel:
-        """Get a valid channel, reconnecting if the current one is stale.
-
-        Uses a lock to prevent concurrent reconnection attempts from racing.
-        """
-        if self.is_ready:
-            return self._channel  # type: ignore  # is_ready guarantees non-None
-
-        async with self._lock:
-            # Double-check after acquiring lock
-            if self.is_ready:
-                return self._channel  # type: ignore
-
-            self._channel = None
-            await self.connect()
-
-            if self._channel is None:
-                raise RuntimeError("Channel should be established after connect")
-
-            return self._channel
-
-    async def _publish_once(
+    @func_retry
+    async def publish_message(
         self,
         routing_key: str,
         message: str,
         exchange: Optional[Exchange] = None,
         persistent: bool = True,
     ) -> None:
-        channel = await self._ensure_channel()
+        if not self.is_ready:
+            await self.connect()
+
+        if self._channel is None:
+            raise RuntimeError("Channel should be established after connect")
 
         if exchange:
-            exchange_obj = await channel.get_exchange(exchange.name)
+            exchange_obj = await self._channel.get_exchange(exchange.name)
         else:
-            exchange_obj = channel.default_exchange
+            exchange_obj = self._channel.default_exchange
 
         await exchange_obj.publish(
             aio_pika.Message(
@@ -359,23 +322,9 @@ class AsyncRabbitMQ(RabbitMQBase):
             routing_key=routing_key,
         )
 
-    @func_retry
-    async def publish_message(
-        self,
-        routing_key: str,
-        message: str,
-        exchange: Optional[Exchange] = None,
-        persistent: bool = True,
-    ) -> None:
-        try:
-            await self._publish_once(routing_key, message, exchange, persistent)
-        except aio_pika.exceptions.ChannelInvalidStateError:
-            logger.warning(
-                "RabbitMQ channel invalid, forcing reconnect and retrying publish"
-            )
-            async with self._lock:
-                self._channel = None
-            await self._publish_once(routing_key, message, exchange, persistent)
-
     async def get_channel(self) -> aio_pika.abc.AbstractChannel:
-        return await self._ensure_channel()
+        if not self.is_ready:
+            await self.connect()
+        if self._channel is None:
+            raise RuntimeError("Channel should be established after connect")
+        return self._channel

autogpt_platform/frontend/src/components/atoms/FileInput/FileInput.tsx

@@ -104,31 +104,7 @@ export function FileInput(props: Props) {
     return false;
   }
 
-  const getFileLabelFromValue = (val: unknown): string => {
-    // Handle object format from external API: { name, type, size, data }
-    if (val && typeof val === "object") {
-      const obj = val as Record<string, unknown>;
-      if (typeof obj.name === "string") {
-        return getFileLabel(
-          obj.name,
-          typeof obj.type === "string" ? obj.type : "",
-        );
-      }
-      if (typeof obj.type === "string") {
-        const mimeParts = obj.type.split("/");
-        if (mimeParts.length > 1) {
-          return `${mimeParts[1].toUpperCase()} file`;
-        }
-        return `${obj.type} file`;
-      }
-      return "File";
-    }
-
-    // Handle string values (data URIs or file paths)
-    if (typeof val !== "string") {
-      return "File";
-    }
-
+  const getFileLabelFromValue = (val: string) => {
     if (val.startsWith("data:")) {
       const matches = val.match(/^data:([^;]+);/);
       if (matches?.[1]) {