Merge branch 'cz/mergeAll' into fix/minor_ui_adjustments

src/backend/base/langflow/api/utils.py

@@ -219,7 +219,7 @@ async def build_and_cache_graph_from_db(flow_id: str, session: Session, chat_ser
         if vertex is None:
             raise ValueError(f"Vertex {vertex_id} not found")
         if not vertex._raw_params.get("session_id"):
-            vertex.update_raw_params({"session_id": flow_id})
+            vertex.update_raw_params({"session_id": flow_id}, overwrite=True)
     await chat_service.set_cache(flow_id, graph)
     return graph
 

src/backend/base/langflow/components/outputs/RecordsOutput.py

@@ -16,4 +16,5 @@ class RecordOutput(CustomComponent):
         }
 
     def build(self, input_value: Record) -> Record:
+        self.status = input_value
         return input_value

src/backend/base/langflow/components/retrievers/SelfQueryRetriever.py

@@ -0,0 +1,39 @@
+# from langflow.field_typing import Data
+from langchain.chains.query_constructor.base import AttributeInfo
+from langchain.retrievers.self_query.base import SelfQueryRetriever
+from langchain_core.vectorstores import VectorStore
+
+from langflow.custom import CustomComponent
+from langflow.field_typing import BaseLanguageModel
+from langflow.schema import Record
+from langflow.schema.message import Message
+
+
+class SelfQueryRetrieverComponent(CustomComponent):
+    display_name: str = "Self Query Retriever"
+    description: str = "Retriever that uses a vector store and an LLM to generate the vector store queries."
+    icon = "LangChain"
+
+    def build(
+        self,
+        query: Message,
+        vectorstore: VectorStore,
+        metadata_field_info: list[AttributeInfo],
+        document_content_description: str,
+        llm: BaseLanguageModel,
+    ) -> Record:
+        metadata_field_info = [i[0] for i in metadata_field_info]
+
+        self_query_retriever = SelfQueryRetriever.from_llm(
+            llm,
+            vectorstore,
+            document_content_description,
+            metadata_field_info,
+            enable_limit=True,
+        )
+
+        input_text = query.text
+        documents = self_query_retriever.invoke(input=input_text)
+        records = [Record.from_document(document) for document in documents]
+        self.status = records
+        return records

src/backend/base/langflow/custom/custom_component/custom_component.py

@@ -126,8 +126,11 @@ class CustomComponent(Component):
     @staticmethod
     def resolve_path(path: str) -> str:
         """Resolves the path to an absolute path."""
+        if not path:
+            return path
         path_object = Path(path)
-        if path_object.parts[0] == "~":
+
+        if path_object.parts and path_object.parts[0] == "~":
             path_object = path_object.expanduser()
         elif path_object.is_relative_to("."):
             path_object = path_object.resolve()

src/backend/base/langflow/field_typing/prompt.py

@@ -25,6 +25,7 @@ class Prompt(Record):
         prompt_template = PromptTemplate.from_template(self.template)
         variables_with_str_values = dict_values_to_string(self.variables)
         formatted_prompt = prompt_template.format(**variables_with_str_values)
+        self.text = formatted_prompt
         return formatted_prompt
 
     @classmethod

src/backend/base/langflow/schema/record.py

@@ -1,12 +1,12 @@
 import copy
 import json
-from typing import cast, Optional
+from typing import Optional, cast
 
 from langchain_core.documents import Document
 from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
+from langchain_core.prompt_values import ImagePromptValue
 from langchain_core.prompts.image import ImagePromptTemplate
 from pydantic import BaseModel, model_serializer, model_validator
-from langchain_core.prompt_values import ImagePromptValue
 
 
 class Record(BaseModel):
@@ -200,3 +200,6 @@ class Record(BaseModel):
 
     def __contains__(self, key):
         return key in self.data
+
+    def __eq__(self, other):
+        return isinstance(other, Record) and self.data == other.data