Merge branch 'aws-cdk-dev-dockerfile' into aws-cdk-dev2

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.
 

cdk.Dockerfile

@@ -17,4 +17,4 @@ RUN poetry config virtualenvs.create false && poetry install --no-interaction --
 
 RUN poetry add pymysql==1.0.2
 
-CMD ["uvicorn", "--factory", "src.backend.langflow.main:create_app", "--host", "0.0.0.0", "--port", "7860", "--reload", "--log-level", "debug"]
+CMD ["sh", "./container-cmd-cdk.sh"]

container-cmd-cdk.sh

@@ -0,0 +1,3 @@
+export LANGFLOW_DATABASE_URL="mysql+pymysql://${username}:${password}@${host}:3306/${dbname}"
+# echo $LANGFLOW_DATABASE_URL
+uvicorn --factory src.backend.langflow.main:create_app --host 0.0.0.0 --port 7860 --reload --log-level debug

docker-compose.yml

@@ -6,7 +6,7 @@ services:
   backend:
     build:
       context: ./
-      dockerfile: ./cdk.Dockerfile
+      dockerfile: ./dev.Dockerfile
     env_file:
       - .env
     ports:

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

@@ -1,20 +1,16 @@
 import json
 from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple, Union
+
 from langchain.agents import AgentExecutor
-from langchain.schema import AgentAction
-from langflow.interface.run import (
-    build_sorted_vertices,
-    get_memory_key,
-    update_memory_keys,
-)
+from langchain.chains.base import Chain
+from langchain.schema import AgentAction, Document
+from langchain.vectorstores.base import VectorStore
+from langflow.graph import Graph
+from langflow.interface.run import (build_sorted_vertices, get_memory_key,
+                                    update_memory_keys)
 from langflow.services.getters import get_session_service
 from loguru import logger
-from langflow.graph import Graph
-from langchain.chains.base import Chain
-from langchain.vectorstores.base import VectorStore
-from typing import Any, Dict, List, Optional, Tuple, Union
-from langchain.schema import Document
-
 from pydantic import BaseModel
 
 
@@ -129,7 +125,9 @@ def process_inputs(inputs: Optional[dict], artifacts: Dict[str, Any]) -> dict:
         inputs = {}
 
     for key, value in artifacts.items():
-        if key not in inputs or not inputs[key]:
+        if key == "repr":
+            continue
+        elif key not in inputs or not inputs[key]:
             inputs[key] = value
 
     return inputs