fix: correct issue with handling float32 (#4312)

* Update pinecone.py

Error building Component Pinecone: Invalid type for variable '0'. Required value type is float and passed type was float64 at ['values'][0]

Fixed the issue by forcing float32

* [autofix.ci] apply automated fixes

* Linit Recommendation Handled

Linit Recommendation Handled

* [autofix.ci] apply automated fixes

* refactor: Simplify Float32Embeddings implementation

Streamline the Float32Embeddings class by removing unnecessary parent linkage and consolidating the embedding logic, ensuring consistent float32 output for embeddings.

* refactor: Re-arranging the code.

Co-Authored-By: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>

* [autofix.ci] apply automated fixes

---------

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>

src/backend/base/langflow/components/vectorstores/pinecone.py

@@ -1,16 +1,9 @@
+import numpy as np
 from langchain_pinecone import Pinecone
 
 from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
 from langflow.helpers.data import docs_to_data
-from langflow.io import (
+from langflow.io import DataInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput
-    DataInput,
-    DropdownInput,
-    HandleInput,
-    IntInput,
-    MultilineInput,
-    SecretStrInput,
-    StrInput,
-)
 from langflow.schema import Data
 
 
@@ -20,7 +13,6 @@ class PineconeVectorStoreComponent(LCVectorStoreComponent):
     documentation = "https://python.langchain.com/v0.2/docs/integrations/vectorstores/pinecone/"
     name = "Pinecone"
     icon = "Pinecone"
-
     inputs = [
         StrInput(name="index_name", display_name="Index Name", required=True),
         StrInput(name="namespace", display_name="Namespace", info="Namespace for the index."),
@@ -57,42 +49,82 @@ class PineconeVectorStoreComponent(LCVectorStoreComponent):
 
     @check_cached_vector_store
     def build_vector_store(self) -> Pinecone:
+        """Build and return a Pinecone vector store instance."""
+        try:
             from langchain_pinecone._utilities import DistanceStrategy
-        from langchain_pinecone.vectorstores import Pinecone
 
+            # Wrap the embedding model to ensure float32 output
+            wrapped_embeddings = Float32Embeddings(self.embedding)
+
+            # Convert distance strategy
             distance_strategy = self.distance_strategy.replace(" ", "_").upper()
-        _distance_strategy = DistanceStrategy[distance_strategy]
+            distance_strategy = DistanceStrategy[distance_strategy]
 
+            # Initialize Pinecone instance with wrapped embeddings
             pinecone = Pinecone(
                 index_name=self.index_name,
-            embedding=self.embedding,
+                embedding=wrapped_embeddings,  # Use wrapped embeddings
                 text_key=self.text_key,
                 namespace=self.namespace,
-            distance_strategy=_distance_strategy,
+                distance_strategy=distance_strategy,
                 pinecone_api_key=self.pinecone_api_key,
             )
-
+        except Exception as e:
-        documents = []
+            error_msg = "Error building Pinecone vector store"
-        for _input in self.ingest_data or []:
+            raise ValueError(error_msg) from e
-            if isinstance(_input, Data):
-                documents.append(_input.to_lc_document())
         else:
-                documents.append(_input)
+            # Process documents if any
+            documents = []
+            if self.ingest_data:
+                for doc in self.ingest_data:
+                    if isinstance(doc, Data):
+                        documents.append(doc.to_lc_document())
+                    else:
+                        documents.append(doc)
 
                 if documents:
                     pinecone.add_documents(documents)
+
             return pinecone
 
     def search_documents(self) -> list[Data]:
-        vector_store = self.build_vector_store()
+        """Search documents in the vector store."""
+        try:
+            if not self.search_query or not isinstance(self.search_query, str) or not self.search_query.strip():
+                return []
 
-        if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():
+            vector_store = self.build_vector_store()
             docs = vector_store.similarity_search(
                 query=self.search_query,
                 k=self.number_of_results,
             )
-
+        except Exception as e:
+            error_msg = "Error searching documents"
+            raise ValueError(error_msg) from e
+        else:
             data = docs_to_data(docs)
             self.status = data
             return data
-        return []
+
+
+class Float32Embeddings:
+    """Wrapper class to ensure float32 embeddings."""
+
+    def __init__(self, base_embeddings):
+        self.base_embeddings = base_embeddings
+
+    def embed_documents(self, texts):
+        embeddings = self.base_embeddings.embed_documents(texts)
+        if isinstance(embeddings, np.ndarray):
+            return [[self._force_float32(x) for x in vec] for vec in embeddings]
+        return [[self._force_float32(x) for x in vec] for vec in embeddings]
+
+    def embed_query(self, text):
+        embedding = self.base_embeddings.embed_query(text)
+        if isinstance(embedding, np.ndarray):
+            return [self._force_float32(x) for x in embedding]
+        return [self._force_float32(x) for x in embedding]
+
+    def _force_float32(self, value):
+        """Convert any numeric type to Python float."""
+        return float(np.float32(value))