Merge branch 'two_edges' of https://github.com/langflow-ai/langflow into two_edges

src/backend/base/langflow/api/v1/chat.py

@@ -149,7 +149,6 @@ async def build_vertex(
 
     next_runnable_vertices = []
     top_level_vertices = []
-    logs = {}
     try:
         start_time = time.perf_counter()
         cache = await chat_service.get_cache(flow_id_str)
@@ -189,15 +188,13 @@ async def build_vertex(
             valid = False
             output_label = vertex.outputs[0]["name"] if vertex.outputs else "output"
             logs = {output_label: [Log(message=params, type="error")]}
-            result_data_response = ResultDataResponse(results={})
+            result_data_response = ResultDataResponse(results={}, logs=logs)
             artifacts = {}
             # If there's an error building the vertex
             # we need to clear the cache
             await chat_service.clear_cache(flow_id_str)
 
         result_data_response.message = artifacts
-        if logs:
-            result_data_response.logs = logs
 
         # Log the vertex build
         if not vertex.will_stream:

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

@@ -3,7 +3,6 @@ from copy import deepcopy
 from langchain_core.documents import Document
 
 from langflow.schema import Data
-from langflow.schema.message import Message
 
 
 def data_to_string(record: Data) -> str:
@@ -29,6 +28,8 @@ def dict_values_to_string(d: dict) -> dict:
     Returns:
         dict: The dictionary with values converted to strings.
     """
+    from langflow.schema.message import Message
+
     # Do something similar to the above
     d_copy = deepcopy(d)
     for key, value in d_copy.items():

src/backend/base/langflow/graph/vertex/base.py

@@ -13,7 +13,7 @@ from langflow.graph.utils import UnbuiltObject, UnbuiltResult
 from langflow.interface.initialize import loading
 from langflow.interface.listing import lazy_load_dict
 from langflow.schema.artifact import ArtifactType
-from langflow.schema.schema import INPUT_FIELD_NAME, Log, build_log_from_raw_and_type
+from langflow.schema.schema import INPUT_FIELD_NAME, Log, build_logs
 from langflow.services.deps import get_storage_service
 from langflow.services.monitor.utils import log_transaction
 from langflow.utils.constants import DIRECT_TYPES
@@ -113,6 +113,8 @@ class Vertex:
         self.build_times.append(time)
 
     def set_result(self, result: ResultData) -> None:
+        from pprint import pprint
+        pprint(result.model_dump())
         self.result = result
 
     def get_built_result(self):
@@ -658,13 +660,11 @@ class Vertex:
             if len(result) == 2:
                 self._built_object, self.artifacts = result
             elif len(result) == 3:
+                import pdb; pdb.set_trace()
                 self._custom_component, self._built_object, self.artifacts = result
                 self.artifacts_raw = self.artifacts.get("raw", None)
                 self.artifacts_type = self.artifacts.get("type", None) or ArtifactType.UNKNOWN.value
-                self.logs[self.outputs[0]["name"]] = build_log_from_raw_and_type(
-                    self.artifacts_raw, self.artifacts_type
-                )
-
+                self.logs = build_log(self)
         else:
             self._built_object = result
 

src/backend/base/langflow/graph/vertex/types.py

@@ -10,7 +10,7 @@ from langflow.graph.utils import UnbuiltObject, serialize_field
 from langflow.graph.vertex.base import Vertex
 from langflow.schema import Data
 from langflow.schema.artifact import ArtifactType
-from langflow.schema.schema import INPUT_FIELD_NAME, build_logs_from_artifacts
+from langflow.schema.schema import INPUT_FIELD_NAME, build_logs
 from langflow.services.monitor.utils import log_transaction, log_vertex_build
 from langflow.utils.schemas import ChatOutputResponse, DataOutputResponse
 from langflow.utils.util import unescape_string
@@ -48,7 +48,7 @@ class ComponentVertex(Vertex):
                 for key in self.artifacts:
                     self.artifacts_raw[key] = self.artifacts[key].get("raw", None)
                     self.artifacts_type[key] = self.artifacts[key].get("type", None) or ArtifactType.UNKNOWN.value
-                self.logs = build_logs_from_artifacts(self.artifacts)
+                self.logs = build_logs(self)
         else:
             self._built_object = result
 

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

@@ -1,11 +1,14 @@
 from datetime import datetime, timezone
 from typing import Annotated, Any, AsyncIterator, Iterator, Optional
 
+from langchain_core.load import load
 from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
 from langchain_core.prompt_values import ImagePromptValue
+from langchain_core.prompts import BaseChatPromptTemplate, ChatPromptTemplate, PromptTemplate
 from langchain_core.prompts.image import ImagePromptTemplate
 from pydantic import BeforeValidator, ConfigDict, Field, field_serializer
 
+from langflow.base.prompts.utils import dict_values_to_string
 from langflow.schema.data import Data
 from langflow.schema.image import Image, get_file_paths, is_image_file
 
@@ -110,3 +113,37 @@ class Message(Data):
                 image_prompt_value: ImagePromptValue = image_template.invoke(input={"path": file})
                 content_dicts.append({"type": "image_url", "image_url": image_prompt_value.image_url})
         return content_dicts
+
+    def load_lc_prompt(self):
+        if "prompt" not in self:
+            raise ValueError("Prompt is required.")
+        return load(self.prompt)
+
+    @classmethod
+    def from_lc_prompt(
+        cls,
+        prompt: BaseChatPromptTemplate,
+    ):
+        prompt_json = prompt.to_json()
+        return cls(prompt=prompt_json)
+
+    def format_text(self):
+        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
+    async def from_template_and_variables(cls, template: str, variables: dict):
+        instance = cls(template=template, variables=variables)
+        contents = [{"type": "text", "text": instance.format_text()}]
+        # Get all Message instances from the kwargs
+        for value in variables.values():
+            if isinstance(value, cls):
+                content_dicts = await value.get_file_content_dicts()
+                contents.extend(content_dicts)
+        prompt_template = ChatPromptTemplate.from_messages([HumanMessage(content=contents)])
+        instance.messages = prompt_template.messages
+        instance.prompt = prompt_template.to_json()
+        return instance

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

@@ -17,11 +17,11 @@ class Log(TypedDict):
     type: str
 
 
-def build_logs_from_artifacts(artifacts: dict) -> dict:
+def build_logs(vertex) -> dict:
     logs = {}
-    for key in artifacts:
-        message = artifacts[key]["raw"]
-        _type = artifacts[key]["type"]
+    for key in vertex.artifacts:
+        message = vertex.artifacts[key]["raw"]
+        _type = vertex.artifacts[key]["type"]
 
         if "stream_url" in message and "type" in message:
             stream_url = StreamURL(location=message["stream_url"])
@@ -31,7 +31,3 @@ def build_logs_from_artifacts(artifacts: dict) -> dict:
 
         logs[key] = [log]
     return logs
-
-
-def build_log_from_raw_and_type(raw: Any, log_type: str) -> Log:
-    return Log(message=raw, type=log_type)