Merge remote-tracking branch 'origin/dev' into feature/store

README.md

@@ -143,7 +143,7 @@ Alternatively, click the **"Open in Cloud Shell"** button below to launch Google
 
 # 🎨 Creating Flows
 
-Creating flows with Langflow is easy. Simply drag sidebar components onto the canvas and connect them together to create your pipeline. Langflow provides a range of [LangChain components](https://docs.langchain.com/docs/category/components) to choose from, including LLMs, prompt serializers, agents, and chains.
+Creating flows with Langflow is easy. Simply drag sidebar components onto the canvas and connect them together to create your pipeline. Langflow provides a range of [LangChain components](https://python.langchain.com/docs/integrations/components) to choose from, including LLMs, prompt serializers, agents, and chains.
 
 Explore by editing prompt parameters, link chains and agents, track an agent's thought process, and export your flow.
 

src/backend/langflow/api/utils.py

@@ -57,12 +57,7 @@ def update_frontend_node_with_template_values(frontend_node, raw_template_data):
     :param raw_template_data: A dict representing raw template data.
     :return: Updated frontend node.
     """
-    if (
-        not frontend_node
-        or "template" not in frontend_node
-        or not raw_template_data
-        or not raw_template_data.template
-    ):
+    if not frontend_node or "template" not in frontend_node or not raw_template_data or not raw_template_data.template:
         return frontend_node
 
     frontend_template = frontend_node.get("template", {})

src/backend/langflow/components/embeddings/AmazonBedrockEmbeddings.py

@@ -1,8 +1,8 @@
 from typing import Optional
-from langflow import CustomComponent
 
 from langchain.embeddings import BedrockEmbeddings
 from langchain.embeddings.base import Embeddings
+from langflow import CustomComponent
 
 
 class AmazonBedrockEmeddingsComponent(CustomComponent):

src/backend/langflow/components/llms/AnthropicLLM.py

@@ -0,0 +1,71 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.chat_models.anthropic import ChatAnthropic
+from langchain.llms.base import BaseLLM
+
+
+class AnthropicLLM(CustomComponent):
+    display_name: str = "AnthropicLLM"
+    description: str = "Anthropic Chat&Completion large language models."
+
+    def build_config(self):
+        return {
+            "model": {
+                "display_name": "Model Name",
+                "options": [
+                    "claude-2.1",
+                    "claude-2.0",
+                    "claude-instant-1.2",
+                    "claude-instant-1",
+                    # Add more models as needed
+                ],
+                "info": "https://python.langchain.com/docs/integrations/chat/anthropic",
+                "required": True,
+                "value": "claude-2.1",
+            },
+            "anthropic_api_key": {
+                "display_name": "Anthropic API Key",
+                "required": True,
+                "password": True,
+                "info": "Your Anthropic API key.",
+            },
+            "max_tokens": {
+                "display_name": "Max Tokens",
+                "field_type": "int",
+                "value": 256,
+            },
+            "temperature": {
+                "display_name": "Temperature",
+                "field_type": "float",
+                "value": 0.7,
+            },
+            "api_endpoint": {
+                "display_name": "API Endpoint",
+                "info": "Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.",
+            },
+            "code": {"show": False},
+        }
+
+    def build(
+        self,
+        model: str,
+        anthropic_api_key: Optional[str] = None,
+        max_tokens: Optional[int] = None,
+        temperature: Optional[float] = None,
+        api_endpoint: Optional[str] = None,
+    ) -> BaseLLM:
+        # Set default API endpoint if not provided
+        if not api_endpoint:
+            api_endpoint = "https://api.anthropic.com"
+
+        try:
+            output = ChatAnthropic(
+                model=model,
+                anthropic_api_key=anthropic_api_key,
+                max_tokens_to_sample=max_tokens,
+                temperature=temperature,
+                anthropic_api_url=api_endpoint,
+            )
+        except Exception as e:
+            raise ValueError("Could not connect to Anthropic API.") from e
+        return output

src/backend/langflow/processing/process.py

@@ -3,6 +3,7 @@ import json
 from pathlib import Path
 from typing import Any, Coroutine, Dict, List, Optional, Tuple, Union
 
+from langchain.agents import AgentExecutor
 from langchain.chains.base import Chain
 from langchain.schema import AgentAction, Document
 from langchain.vectorstores.base import VectorStore
@@ -66,7 +67,11 @@ def get_result_and_thought(langchain_object: Any, inputs: dict):
         if hasattr(langchain_object, "return_intermediate_steps"):
             langchain_object.return_intermediate_steps = False
 
-        fix_memory_inputs(langchain_object)
+        try:
+            if not isinstance(langchain_object, AgentExecutor):
+                fix_memory_inputs(langchain_object)
+        except Exception as exc:
+            logger.error(f"Error fixing memory inputs: {exc}")
 
         try:
             output = langchain_object(inputs, return_only_outputs=True)
@@ -101,18 +106,6 @@ def get_build_result(data_graph, session_id):
     return build_sorted_vertices(data_graph)
 
 
-def load_langchain_object(
-    data_graph: Dict[str, Any], session_id: str
-) -> Tuple[Union[Chain, VectorStore], Dict[str, Any], str]:
-    langchain_object, artifacts = get_build_result(data_graph, session_id)
-    logger.debug("Loaded LangChain object")
-
-    if langchain_object is None:
-        raise ValueError("There was an error loading the langchain_object. Please, check all the nodes and try again.")
-
-    return langchain_object, artifacts, session_id
-
-
 def process_inputs(inputs: Optional[dict], artifacts: Dict[str, Any]) -> dict:
     if inputs is None:
         inputs = {}
@@ -162,9 +155,12 @@ async def process_graph_cached(
     if session_id is None:
         session_id = session_service.generate_key(session_id=session_id, data_graph=data_graph)
     # Load the graph using SessionService
-    graph, artifacts = await session_service.load_session(session_id, data_graph)
+    session = await session_service.load_session(session_id, data_graph)
+    graph, artifacts = session if session else (None, None)
+    if not graph:
+        raise ValueError("Graph not found in the session")
     built_object = await graph.build()
-    processed_inputs = process_inputs(inputs, artifacts)
+    processed_inputs = process_inputs(inputs, artifacts or {})
     result = generate_result(built_object, processed_inputs)
     # langchain_object is now updated with the new memory
     # we need to update the cache with the updated langchain_object