update cookbook examples to update to the fast api async proposal

confirm-action/main.py

@@ -4,32 +4,32 @@
 
 
 @cl.action_callback("confirm_action")
-def on_action(action: cl.Action):
+async def on_action(action: cl.Action):
     if action.value == "ok":
         content = "Confirmed!"
     elif action.value == "not_ok":
         content = "Rejected!"
     else:
-        cl.ErrorMessage(content="Invalid action").send()
+        await cl.ErrorMessage(content="Invalid action").send()
         return
 
     actions = cl.user_session.get("actions")
     if actions:
         for action in actions:
-            action.remove()
+            await action.remove()
         cl.user_session.set("actions", None)
 
-    cl.Message(content=content).send()
+    await cl.Message(content=content).send()
 
 
 @cl.on_chat_start
-def start():
+async def start():
     approve_action = cl.Action(name="confirm_action", value="ok", label="Confirm")
     reject_action = cl.Action(name="confirm_action", value="not_ok", label="Reject")
     actions = [approve_action, reject_action]
     cl.user_session.set("actions", actions)
 
-    cl.Message(
+    await cl.Message(
         content="Test message",
         actions=actions,
     ).send()

image-gen/app.py

@@ -1,5 +1,6 @@
 import chainlit as cl
 from chainlit.action import Action
+
 from tools import generate_image_tool, edit_image_tool
 from langchain.agents import initialize_agent, AgentType
 from langchain.chat_models import ChatOpenAI
@@ -8,11 +9,11 @@
 
 
 @cl.action_callback("Create variation")
-def create_variant(action: Action):
+async def create_variant(action: Action):
     agent = cl.user_session.get("agent")
     agent_input = f"Create a variation of {action.value}"
-    cl.Message(content=f"Creating a variation of `{action.value}`.").send()
-    run(agent, agent_input)
+    await cl.Message(content=f"Creating a variation of `{action.value}`.").send()
+    await run(agent, agent_input)
 
 
 @cl.langchain_rename
@@ -23,7 +24,7 @@ def rename(orig_author):
     return mapping.get(orig_author, orig_author)
 
 
-@cl.langchain_factory
+@cl.langchain_factory(use_async=False)
 def main():
     llm = ChatOpenAI(temperature=0, streaming=True)
     tools = [generate_image_tool, edit_image_tool]
@@ -45,9 +46,13 @@ def main():
 
 
 @cl.langchain_run
-def run(agent_executor, action_input):
+async def run(agent_executor, action_input):
     cl.user_session.set("generated_image", None)
-    res = agent_executor.run(input=action_input)
+
+    # No async implementation in the Stability AI client, fallback to sync
+    res = await cl.make_async(agent_executor.run)(
+        input=action_input, callbacks=[cl.ChainlitCallbackHandler()]
+    )
 
     elements = []
     actions = []
@@ -56,12 +61,12 @@ def run(agent_executor, action_input):
     generated_image = cl.user_session.get(generated_image_name)
     if generated_image:
         elements = [
-            cl.LocalImage(
+            cl.Image(
                 content=generated_image,
                 name=generated_image_name,
                 display="inline",
             )
         ]
         actions = [cl.Action(name="Create variation", value=generated_image_name)]
 
-    cl.Message(content=res, elements=elements, actions=actions).send()
+    await cl.Message(content=res, elements=elements, actions=actions).send()

langchain-aiplugins/app.py

@@ -5,7 +5,7 @@
 from chainlit import langchain_factory
 
 
-@langchain_factory
+@langchain_factory(use_async=True)
 def load():
     tool = AIPluginTool.from_plugin_url(
         "https://www.klarna.com/.well-known/ai-plugin.json"

pdf-qa/app.py

@@ -1,11 +1,13 @@
 import os
+
 from langchain.document_loaders import PyPDFLoader, TextLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.vectorstores import Pinecone
 from langchain.chains import RetrievalQAWithSourcesChain
 from langchain.chat_models import ChatOpenAI
 import pinecone
+
 import chainlit as cl
 from chainlit.types import AskFileResponse
 
@@ -44,16 +46,7 @@ def process_file(file: AskFileResponse):
         return docs
 
 
-@cl.langchain_factory
-def langchain_factory():
-    file = None
-    while file is None:
-        file = cl.AskFileMessage(
-            content=welcome_message,
-            accept=["text/plain", "application/pdf"],
-            timeout=180,
-        ).send()
-
+def get_docsearch(file: AskFileResponse):
     docs = process_file(file)
 
     # Save data in the user session
@@ -72,20 +65,42 @@ def langchain_factory():
         )
         namespaces.add(namespace)
 
+    return docsearch
+
+
+@cl.langchain_factory(use_async=True)
+async def langchain_factory():
+    files = None
+    while files is None:
+        files = await cl.AskFileMessage(
+            content=welcome_message,
+            accept=["text/plain", "application/pdf"],
+            max_size_mb=20,
+            timeout=180,
+        ).send()
+
+    file = files[0]
+
+    msg = cl.Message(content=f"Processing `{file.name}`...")
+    await msg.send()
+
+    # No async implementation in the Pinecone client, fallback to sync
+    docsearch = await cl.make_async(get_docsearch)(file)
+
     chain = RetrievalQAWithSourcesChain.from_chain_type(
         ChatOpenAI(temperature=0, streaming=True),
         chain_type="stuff",
         retriever=docsearch.as_retriever(max_tokens_limit=4097),
     )
 
     # Let the user know that the system is ready
-    cl.Message(content=f"`{file.name}` uploaded, you can now ask questions!").send()
+    await msg.update(content=f"`{file.name}` processed. You can now ask questions!")
 
     return chain
 
 
 @cl.langchain_postprocess
-def process_response(res):
+async def process_response(res):
     answer = res["answer"]
     sources = res["sources"].strip()
     source_elements = []
@@ -109,11 +124,11 @@ def process_response(res):
             text = docs[index].page_content
             found_sources.append(source_name)
             # Create the text element referenced in the message
-            source_elements.append(cl.Text(text=text, name=source_name))
+            source_elements.append(cl.Text(content=text, name=source_name))
 
         if found_sources:
             answer += f"\nSources: {', '.join(found_sources)}"
         else:
             answer += "\nNo sources found"
 
-    cl.Message(content=answer, elements=source_elements).send()
+    await cl.Message(content=answer, elements=source_elements).send()

pinecone/main.py → pinecone/app.py

@@ -22,9 +22,9 @@
 welcome_message = "Welcome to the Chainlit Pinecone demo! Ask anything about documents you vectorized and stored in your Pinecone DB."
 
 
-@cl.langchain_factory
-def langchain_factory():
-    cl.Message(content=welcome_message).send()
+@cl.langchain_factory(use_async=True)
+async def langchain_factory():
+    await cl.Message(content=welcome_message).send()
     docsearch = Pinecone.from_existing_index(
         index_name=index_name, embedding=embeddings, namespace=namespace
     )
@@ -39,7 +39,7 @@ def langchain_factory():
 
 
 @cl.langchain_postprocess
-def process_response(res):
+async def process_response(res):
     answer = res["answer"]
     sources = res.get("sources", "").strip()  # Use the get method with a default value
     source_elements = []
@@ -67,7 +67,7 @@ def process_response(res):
                 text = docs[found_index].page_content
 
                 found_sources.append(clean_source_name)
-                source_elements.append(cl.Text(text=text, name=clean_source_name))
+                source_elements.append(cl.Text(content=text, name=clean_source_name))
 
             if found_sources:
                 # Add the sources to the answer, referencing the text elements
@@ -76,4 +76,4 @@ def process_response(res):
                 answer += "\nNo sources found"
 
     # Send the answer and the text elements to the UI
-    cl.Message(content=answer, elements=source_elements).send()
+    await cl.Message(content=answer, elements=source_elements).send()