Update pinecone.py

backend/open_webui/retrieval/vector/dbs/pinecone.py

@@ -3,47 +3,6 @@ import logging
 import asyncio
 from pinecone import Pinecone, ServerlessSpec
 
-# Helper for building consistent metadata
-def build_metadata(
-    *,
-    source: str,
-    type_: str,
-    user_id: str,
-    chat_id: Optional[str] = None,
-    filename: Optional[str] = None,
-    text: Optional[str] = None,
-    topic: Optional[str] = None,
-    model: Optional[str] = None,
-    vector_dim: Optional[int] = None,
-    extra: Optional[Dict[str, Any]] = None,
-    collection_name: Optional[str] = None,
-) -> Dict[str, Any]:
-    from datetime import datetime
-
-    metadata = {
-        "source": source,
-        "type": type_,
-        "user_id": user_id,
-        "timestamp": datetime.utcnow().isoformat() + "Z",
-    }
-    if chat_id:
-        metadata["chat_id"] = chat_id
-    if filename:
-        metadata["filename"] = filename
-    if text:
-        metadata["text"] = text
-    if topic:
-        metadata["topic"] = topic
-    if model:
-        metadata["model"] = model
-    if vector_dim:
-        metadata["vector_dim"] = vector_dim
-    if collection_name:
-        metadata["collection_name"] = collection_name
-    if extra:
-        metadata.update(extra)
-    return metadata
-
 from open_webui.retrieval.vector.main import (
     VectorDBBase,
     VectorItem,
@@ -61,7 +20,7 @@ from open_webui.config import (
 from open_webui.env import SRC_LOG_LEVELS
 
 NO_LIMIT = 10000  # Reasonable limit to avoid overwhelming the system
-BATCH_SIZE = 100  # Recommended batch size for Pinecone operations
+BATCH_SIZE = 200  # Recommended batch size for Pinecone operations
 
 log = logging.getLogger(__name__)
 log.setLevel(SRC_LOG_LEVELS["RAG"])
@@ -69,8 +28,7 @@ log.setLevel(SRC_LOG_LEVELS["RAG"])
 
 class PineconeClient(VectorDBBase):
     def __init__(self):
-        from open_webui.config import PINECONE_NAMESPACE
+        self.collection_prefix = "open-webui"
-        self.namespace = PINECONE_NAMESPACE
 
         # Validate required configuration
         self._validate_config()
@@ -137,32 +95,15 @@ class PineconeClient(VectorDBBase):
         """Convert VectorItem objects to Pinecone point format."""
         points = []
         for item in items:
-            user_id = item.get("metadata", {}).get("created_by", "unknown")
+            # Start with any existing metadata or an empty dict
-            chat_id = item.get("metadata", {}).get("chat_id")
+            metadata = item.get("metadata", {}).copy() if item.get("metadata") else {}
-            filename = item.get("metadata", {}).get("name")
-            text = item.get("text")
-            model = item.get("metadata", {}).get("model")
-            topic = item.get("metadata", {}).get("topic")
 
-            # Infer source from filename or fallback
+            # Add text to metadata if available
-            raw_source = item.get("metadata", {}).get("source", "")
+            if "text" in item:
-            inferred_source = "knowledge"
+                metadata["text"] = item["text"]
-            if raw_source == filename or (isinstance(raw_source, str) and raw_source.endswith((".pdf", ".txt", ".docx"))):
-                inferred_source = "chat" if item.get("metadata", {}).get("created_by") else "knowledge"
-            else:
-                inferred_source = raw_source or "knowledge"
 
-            metadata = build_metadata(
+            # Always add collection_name to metadata for filtering
-                source=inferred_source,
+            metadata["collection_name"] = collection_name_with_prefix
-                type_="upload",
-                user_id=user_id,
-                chat_id=chat_id,
-                filename=filename,
-                text=text,
-                model=model,
-                topic=topic,
-                collection_name=collection_name_with_prefix,
-            )
 
             point = {
                 "id": item["id"],
@@ -172,9 +113,9 @@ class PineconeClient(VectorDBBase):
             points.append(point)
         return points
 
-    def _get_namespace(self) -> str:
+    def _get_collection_name_with_prefix(self, collection_name: str) -> str:
-        """Get the namespace from the environment variable."""
+        """Get the collection name with prefix."""
-        return self.namespace
+        return f"{self.collection_prefix}_{collection_name}"
 
     def _normalize_distance(self, score: float) -> float:
         """Normalize distance score based on the metric used."""
@@ -210,7 +151,9 @@ class PineconeClient(VectorDBBase):
 
     def has_collection(self, collection_name: str) -> bool:
         """Check if a collection exists by searching for at least one item."""
-        collection_name_with_prefix = self._get_namespace()
+        collection_name_with_prefix = self._get_collection_name_with_prefix(
+            collection_name
+        )
 
         try:
             # Search for at least 1 item with this collection name in metadata
@@ -229,7 +172,9 @@ class PineconeClient(VectorDBBase):
 
     def delete_collection(self, collection_name: str) -> None:
         """Delete a collection by removing all vectors with the collection name in metadata."""
-        collection_name_with_prefix = self._get_namespace()
+        collection_name_with_prefix = self._get_collection_name_with_prefix(
+            collection_name
+        )
         try:
             self.index.delete(filter={"collection_name": collection_name_with_prefix})
             log.info(
@@ -248,7 +193,9 @@ class PineconeClient(VectorDBBase):
             log.warning("No items to insert")
             return
 
-        collection_name_with_prefix = self._get_namespace()
+        collection_name_with_prefix = self._get_collection_name_with_prefix(
+            collection_name
+        )
         points = self._create_points(items, collection_name_with_prefix)
 
         # Insert in batches for better performance and reliability
@@ -276,7 +223,9 @@ class PineconeClient(VectorDBBase):
             log.warning("No items to upsert")
             return
 
-        collection_name_with_prefix = self._get_namespace()
+        collection_name_with_prefix = self._get_collection_name_with_prefix(
+            collection_name
+        )
         points = self._create_points(items, collection_name_with_prefix)
 
         # Upsert in batches
@@ -305,7 +254,9 @@ class PineconeClient(VectorDBBase):
             log.warning("No vectors provided for search")
             return None
 
-        collection_name_with_prefix = self._get_namespace()
+        collection_name_with_prefix = self._get_collection_name_with_prefix(
+            collection_name
+        )
 
         if limit is None or limit <= 0:
             limit = NO_LIMIT
@@ -356,7 +307,9 @@ class PineconeClient(VectorDBBase):
         self, collection_name: str, filter: Dict, limit: Optional[int] = None
     ) -> Optional[GetResult]:
         """Query vectors by metadata filter."""
-        collection_name_with_prefix = self._get_namespace()
+        collection_name_with_prefix = self._get_collection_name_with_prefix(
+            collection_name
+        )
 
         if limit is None or limit <= 0:
             limit = NO_LIMIT
@@ -386,7 +339,9 @@ class PineconeClient(VectorDBBase):
 
     def get(self, collection_name: str) -> Optional[GetResult]:
         """Get all vectors in a collection."""
-        collection_name_with_prefix = self._get_namespace()
+        collection_name_with_prefix = self._get_collection_name_with_prefix(
+            collection_name
+        )
 
         try:
             # Use a zero vector for fetching all entries
@@ -414,7 +369,9 @@ class PineconeClient(VectorDBBase):
     ) -> None:
         """Delete vectors by IDs or filter."""
         import time
-        collection_name_with_prefix = self._get_namespace()
+        collection_name_with_prefix = self._get_collection_name_with_prefix(
+            collection_name
+        )
 
         try:
             if ids: