Merge pull request #67 from holoviz-topics/fix_compat

Fix compat

docs/examples/features/feature_chained_response.py

@@ -22,8 +22,8 @@ async def callback(contents: str, user: str, instance: pn.chat.ChatInterface):
         }
         instance.respond()
     elif user == ARM_BOT:
-        user_entry = instance.value[-2]
-        user_contents = user_entry.value
+        user_message = instance.value[-2]
+        user_contents = user_message.value
         yield {
             "user": LEG_BOT,
             "avatar": "🦿",

docs/examples/langchain/langchain_llama_and_mistral.py

@@ -60,10 +60,10 @@ async def callback(contents: str, user: str, instance: pn.chat.ChatInterface):
             )
             llm_chains[model] = _get_llm_chain(model)
 
-        entry = None
+        message = None
         response = await _get_response(contents, model)
         for chunk in response:
-            entry = instance.stream(chunk, user=model.title(), entry=entry)
+            message = instance.stream(chunk, user=model.title(), message=message)
 
 
 chat_interface = pn.chat.ChatInterface(callback=callback, placeholder_threshold=0.1)

docs/examples/langchain/langchain_math_assistant.py

@@ -14,7 +14,7 @@
 
 async def callback(contents: str, user: str, instance: pn.chat.ChatInterface):
     final_answer = await llm_math.arun(question=contents)
-    instance.stream(final_answer, entry=instance.value[-1])
+    instance.stream(final_answer, message=instance.value[-1])
 
 
 chat_interface = pn.chat.ChatInterface(callback=callback, callback_user="Langchain")

docs/examples/langchain/langchain_with_memory.py

@@ -8,7 +8,7 @@
 from langchain.chat_models import ChatOpenAI
 from langchain.memory import ConversationBufferMemory
 
-pn.extension(design="material")
+pn.extension()
 
 
 async def callback(contents: str, user: str, instance: pn.chat.ChatInterface):

docs/examples/mistral/mistral_and_llama.py

@@ -0,0 +1,46 @@
+"""
+Demonstrates how to use the ChatInterface widget to create a chatbot using
+Llama2 and Mistral.
+"""
+
+import panel as pn
+from ctransformers import AutoModelForCausalLM
+
+pn.extension()
+
+MODEL_ARGUMENTS = {
+    "llama": {
+        "args": ["TheBloke/Llama-2-7b-Chat-GGUF"],
+        "kwargs": {"model_file": "llama-2-7b-chat.Q5_K_M.gguf"},
+    },
+    "mistral": {
+        "args": ["TheBloke/Mistral-7B-Instruct-v0.1-GGUF"],
+        "kwargs": {"model_file": "mistral-7b-instruct-v0.1.Q4_K_M.gguf"},
+    },
+}
+
+
+def callback(contents: str, user: str, instance: pn.chat.ChatInterface):
+    for model in MODEL_ARGUMENTS:
+        if model not in pn.state.cache:
+            pn.state.cache[model] = AutoModelForCausalLM.from_pretrained(
+                *MODEL_ARGUMENTS[model]["args"],
+                **MODEL_ARGUMENTS[model]["kwargs"],
+                gpu_layers=1,
+            )
+
+        llm = pn.state.cache[model]
+        response = llm(contents, max_new_tokens=512, stream=True)
+
+        message = None
+        for chunk in response:
+            message = instance.stream(chunk, user=model.title(), message=message)
+
+
+chat_interface = pn.chat.ChatInterface(callback=callback)
+chat_interface.send(
+    "Send a message to get a reply from both Llama 2 and Mistral (7B)!",
+    user="System",
+    respond=False,
+)
+chat_interface.servable()

docs/examples/mistral/mistral_with_memory.py

@@ -14,16 +14,16 @@
 
 
 def apply_template(history):
-    history = [entry for entry in history if entry.user != "System"]
+    history = [message for message in history if message.user != "System"]
     prompt = ""
-    for i, entry in enumerate(history):
+    for i, message in enumerate(history):
         if i == 0:
-            prompt += f"<s>[INST]{SYSTEM_INSTRUCTIONS} {entry.value}[/INST]"
+            prompt += f"<s>[INST]{SYSTEM_INSTRUCTIONS} {message.value}[/INST]"
         else:
-            if entry.user == "Mistral":
-                prompt += f"{entry.value}</s>"
+            if message.user == "Mistral":
+                prompt += f"{message.value}</s>"
             else:
-                prompt += f"""[INST]{entry.value}[/INST]"""
+                prompt += f"""[INST]{message.value}[/INST]"""
     return prompt
 
 
@@ -42,7 +42,7 @@ async def callback(contents: str, user: str, instance: pn.chat.ChatInterface):
         )
 
     llm = llms["mistral"]
-    history = [entry for entry in instance.value]
+    history = [message for message in instance.value]
     prompt = apply_template(history)
     response = llm(prompt, stream=True)
     message = ""

docs/examples/openai/openai_hvplot.py

@@ -46,7 +46,7 @@ async def respond_with_openai(contents: Union[pd.DataFrame, str]):
     message = ""
     async for chunk in response:
         message += chunk["choices"][0]["delta"].get("content", "")
-        yield {"user": "ChatGPT", "value": message}
+        yield {"user": "ChatGPT", "object": message}
 
 
 async def respond_with_executor(code: str):

docs/features.md

@@ -41,8 +41,8 @@ async def callback(contents: str, user: str, instance: pn.widgets.ChatInterface)
         }
         instance.respond()
     elif user == ARM_BOT:
-        user_entry = instance.value[-2]
-        user_contents = user_entry.value
+        user_message = instance.value[-2]
+        user_contents = user_message.value
         yield {
             "user": LEG_BOT,
             "avatar": "🦿",
@@ -228,4 +228,4 @@ could add the object you are chatting about"""
     sidebar_width=500,
 ).servable()
 ```
-</details>
+</details>

docs/langchain.md

@@ -80,10 +80,10 @@ async def callback(contents: str, user: str, instance: pn.widgets.ChatInterface)
             )
             llm_chains[model] = _get_llm_chain(model)
 
-        entry = None
+        message = None
         response = await _get_response(contents, model)
         for chunk in response:
-            entry = instance.stream(chunk, user=model.title(), entry=entry)
+            message = instance.stream(chunk, user=model.title(), message=message)
 
 
 chat_interface = pn.widgets.ChatInterface(callback=callback, placeholder_threshold=0.1)
@@ -133,7 +133,7 @@ pn.extension(design="material")
 
 async def callback(contents: str, user: str, instance: pn.widgets.ChatInterface):
     final_answer = await llm_math.arun(question=contents)
-    instance.stream(final_answer, entry=instance.value[-1])
+    instance.stream(final_answer, message=instance.value[-1])
 
 
 chat_interface = pn.widgets.ChatInterface(callback=callback, callback_user="Langchain")
@@ -406,4 +406,4 @@ memory = ConversationBufferMemory()
 chain = ConversationChain(llm=llm, memory=memory)
 chat_interface.servable()
 ```
-</details>
+</details>

docs/mistral.md

@@ -111,16 +111,16 @@ SYSTEM_INSTRUCTIONS = "Do what the user requests."
 
 
 def apply_template(history):
-    history = [entry for entry in history if entry.user != "System"]
+    history = [message for message in history if message.user != "System"]
     prompt = ""
-    for i, entry in enumerate(history):
+    for i, message in enumerate(history):
         if i == 0:
-            prompt += f"<s>[INST]{SYSTEM_INSTRUCTIONS} {entry.value}[/INST]"
+            prompt += f"<s>[INST]{SYSTEM_INSTRUCTIONS} {message.value}[/INST]"
         else:
-            if entry.user == "Mistral":
-                prompt += f"{entry.value}</s>"
+            if message.user == "Mistral":
+                prompt += f"{message.value}</s>"
             else:
-                prompt += f"""[INST]{entry.value}[/INST]"""
+                prompt += f"""[INST]{message.value}[/INST]"""
     return prompt
 
 
@@ -139,7 +139,7 @@ async def callback(contents: str, user: str, instance: pn.widgets.ChatInterface)
         )
 
     llm = llms["mistral"]
-    history = [entry for entry in instance.value]
+    history = [message for message in instance.value]
     prompt = apply_template(history)
     response = llm(prompt, stream=True)
     message = ""
@@ -158,4 +158,4 @@ chat_interface.send(
 )
 chat_interface.servable()
 ```
-</details>
+</details>