Update everything to langchain 0.1 (#45)

pyproject.toml

@@ -9,18 +9,22 @@ packages = [{include = "streamlit_agent"}]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<4.0"
-langchain = {version = ">=0.0.252"}
-openai = "^0.27.8"
+langchain = {version = ">=0.1.0"}
+openai = ">=1.6.1"
 duckduckgo-search = "^3.8.3"
 pypdf = "^3.12.2"
 sentence-transformers = "^2.2.2"
 torch = ">=2.0.0, !=2.0.1"
 tabulate = "^0.9.0"
 streamlit-feedback = "^0.0.9"
-langchain-experimental = "^0.0.10"
+langchain-experimental = "^0.0.49"
 streamlit = ">=1.26"
 docarray = "^0.38.0"
 hnswlib = "^0.7.0"
+langchain-community = "^0.0.12"
+langchain-openai = "^0.0.2.post1"
+numexpr = "^2.8.8"
+langchainhub = "^0.1.14"
 
 [tool.poetry.group.dev.dependencies]
 black = "^23.3.0"

streamlit_agent/basic_memory.py

@@ -1,8 +1,8 @@
 from langchain.chains import LLMChain
-from langchain.llms import OpenAI
 from langchain.memory import ConversationBufferMemory
-from langchain.memory.chat_message_histories import StreamlitChatMessageHistory
 from langchain.prompts import PromptTemplate
+from langchain_community.chat_message_histories import StreamlitChatMessageHistory
+from langchain_openai import OpenAI
 import streamlit as st
 
 st.set_page_config(page_title="StreamlitChatMessageHistory", page_icon="📖")
@@ -49,8 +49,8 @@ for msg in msgs.messages:
 if prompt := st.chat_input():
     st.chat_message("human").write(prompt)
     # Note: new messages are saved to history automatically by Langchain during run
-    response = llm_chain.run(prompt)
-    st.chat_message("ai").write(response)
+    response = llm_chain.invoke(prompt)
+    st.chat_message("ai").write(response["text"])
 
 # Draw the messages at the end, so newly generated ones show up immediately
 with view_messages:

streamlit_agent/basic_streaming.py

@@ -1,6 +1,6 @@
 from langchain.callbacks.base import BaseCallbackHandler
-from langchain.chat_models import ChatOpenAI
 from langchain.schema import ChatMessage
+from langchain_openai import ChatOpenAI
 import streamlit as st
 
 
@@ -34,5 +34,5 @@ if prompt := st.chat_input():
     with st.chat_message("assistant"):
         stream_handler = StreamHandler(st.empty())
         llm = ChatOpenAI(openai_api_key=openai_api_key, streaming=True, callbacks=[stream_handler])
-        response = llm(st.session_state.messages)
+        response = llm.invoke(st.session_state.messages)
         st.session_state.messages.append(ChatMessage(role="assistant", content=response.content))

streamlit_agent/callbacks/capturing_callback_handler.py

@@ -92,7 +92,7 @@ def playback_callbacks(
     # Return the agent's result
     for record in records:
         if record["callback_type"] == CallbackType.ON_AGENT_FINISH:
-            return record["args"][0][0]["output"]
+            return record["args"][0].return_values
 
     return "[Missing Agent Result]"
 
@@ -107,16 +107,12 @@ class CapturingCallbackHandler(BaseCallbackHandler):
         with open(path, "wb") as file:
             pickle.dump(self._records, file)
 
-    def _append_record(
-        self, type: str, args: tuple[Any, ...], kwargs: dict[str, Any]
-    ) -> None:
+    def _append_record(self, type: str, args: tuple[Any, ...], kwargs: dict[str, Any]) -> None:
         time_now = time.time()
         time_delta = time_now - self._last_time if self._last_time is not None else 0
         self._last_time = time_now
         self._records.append(
-            CallbackRecord(
-                callback_type=type, args=args, kwargs=kwargs, time_delta=time_delta
-            )
+            CallbackRecord(callback_type=type, args=args, kwargs=kwargs, time_delta=time_delta)
         )
 
     def on_llm_start(self, *args: Any, **kwargs: Any) -> None:

streamlit_agent/chat_pandas_df.py

@@ -1,5 +1,5 @@
 from langchain.agents import AgentType
-from langchain.agents import create_pandas_dataframe_agent
+from langchain_experimental.agents import create_pandas_dataframe_agent
 from langchain.callbacks import StreamlitCallbackHandler
 from langchain.chat_models import ChatOpenAI
 import streamlit as st

streamlit_agent/mrkl_demo.py

@@ -2,14 +2,14 @@ from pathlib import Path
 
 import streamlit as st
 
-from langchain import SQLDatabase
-from langchain.agents import AgentType
-from langchain.agents import initialize_agent, Tool
-from langchain.callbacks import StreamlitCallbackHandler
+from langchain import hub
+from langchain.agents import AgentExecutor, Tool, create_react_agent
 from langchain.chains import LLMMathChain
-from langchain.llms import OpenAI
-from langchain.utilities import DuckDuckGoSearchAPIWrapper
+from langchain_community.callbacks import StreamlitCallbackHandler
+from langchain_community.utilities import DuckDuckGoSearchAPIWrapper, SQLDatabase
+from langchain_core.runnables import RunnableConfig
 from langchain_experimental.sql import SQLDatabaseChain
+from langchain_openai import OpenAI
 
 from streamlit_agent.callbacks.capturing_callback_handler import playback_callbacks
 from streamlit_agent.clear_results import with_clear_container
@@ -18,7 +18,7 @@ DB_PATH = (Path(__file__).parent / "Chinook.db").absolute()
 
 SAVED_SESSIONS = {
     "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?": "leo.pickle",
-    "What is the full name of the artist who recently released an album called "
+    "What is the full name of the female artist who recently released an album called "
     "'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs "
     "are in the FooBar database?": "alanis.pickle",
 }
@@ -66,7 +66,8 @@ tools = [
 ]
 
 # Initialize agent
-mrkl = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
+react_agent = create_react_agent(llm, tools, hub.pull("hwchase17/react"))
+mrkl = AgentExecutor(agent=react_agent, tools=tools)
 
 with st.form(key="form"):
     if not enable_custom:
@@ -87,6 +88,8 @@ if with_clear_container(submit_clicked):
 
     answer_container = output_container.chat_message("assistant", avatar="🦜")
     st_callback = StreamlitCallbackHandler(answer_container)
+    cfg = RunnableConfig()
+    cfg["callbacks"] = [st_callback]
 
     # If we've saved this question, play it back instead of actually running LangChain
     # (so that we don't exhaust our API calls unnecessarily)
@@ -96,6 +99,6 @@ if with_clear_container(submit_clicked):
         print(f"Playing saved session: {session_path}")
         answer = playback_callbacks([st_callback], str(session_path), max_pause_time=2)
     else:
-        answer = mrkl.run(user_input, callbacks=[st_callback])
+        answer = mrkl.invoke({"input": user_input}, cfg)
 
-    answer_container.write(answer)
+    answer_container.write(answer["output"])

streamlit_agent/search_and_chat.py

@@ -1,9 +1,11 @@
 from langchain.agents import ConversationalChatAgent, AgentExecutor
-from langchain.callbacks import StreamlitCallbackHandler
-from langchain.chat_models import ChatOpenAI
 from langchain.memory import ConversationBufferMemory
-from langchain.memory.chat_message_histories import StreamlitChatMessageHistory
-from langchain.tools import DuckDuckGoSearchRun
+from langchain_community.callbacks import StreamlitCallbackHandler
+from langchain_community.chat_message_histories import StreamlitChatMessageHistory
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_core.runnables import RunnableConfig
+from langchain_openai import ChatOpenAI
+
 import streamlit as st
 
 st.set_page_config(page_title="LangChain: Chat with search", page_icon="🦜")
@@ -51,6 +53,8 @@ if prompt := st.chat_input(placeholder="Who won the Women's U.S. Open in 2018?")
     )
     with st.chat_message("assistant"):
         st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
-        response = executor(prompt, callbacks=[st_cb])
+        cfg = RunnableConfig()
+        cfg["callbacks"] = [st_cb]
+        response = executor.invoke(prompt, cfg)
         st.write(response["output"])
         st.session_state.steps[str(len(msgs.messages) - 1)] = response["intermediate_steps"]

streamlit_agent/simple_feedback.py

@@ -1,11 +1,14 @@
-from langchain.llms import OpenAI
-from langchain.callbacks import LangChainTracer
 from langchain.chains import ConversationChain
 from langchain.memory import ConversationBufferMemory
-from langchain.memory.chat_message_histories import StreamlitChatMessageHistory
+from langchain_community.chat_message_histories import StreamlitChatMessageHistory
+from langchain_core.runnables import RunnableConfig
+from langchain_core.tracers import LangChainTracer
+from langchain_core.tracers.run_collector import RunCollectorCallbackHandler
+from langchain_openai import OpenAI
 from langsmith import Client
 import streamlit as st
 from streamlit_feedback import streamlit_feedback
+import time
 
 st.set_page_config(page_title="LangChain: Simple feedback", page_icon="🦜")
 st.title("🦜 LangChain: Simple feedback")
@@ -25,6 +28,9 @@ if not langchain_api_key or not openai_api_key:
 langchain_endpoint = "https://api.smith.langchain.com"
 client = Client(api_url=langchain_endpoint, api_key=langchain_api_key)
 ls_tracer = LangChainTracer(project_name=project, client=client)
+run_collector = RunCollectorCallbackHandler()
+cfg = RunnableConfig()
+cfg["callbacks"] = [ls_tracer, run_collector]
 
 msgs = StreamlitChatMessageHistory()
 memory = ConversationBufferMemory(chat_memory=msgs)
@@ -43,13 +49,14 @@ for msg in msgs.messages:
 if input := st.chat_input(placeholder="Tell me a joke about a shark?"):
     st.chat_message("user").write(input)
     with st.chat_message("assistant"):
-        response = llm_chain(input, callbacks=[ls_tracer], include_run_info=True)
+        response = llm_chain.invoke(input, cfg)
         st.write(response["response"])
-        st.session_state.last_run = response["__run"].run_id
+        st.session_state.last_run = run_collector.traced_runs[0].id
 
 
 @st.cache_data(ttl="2h", show_spinner=False)
 def get_run_url(run_id):
+    time.sleep(1)
     return client.read_run(run_id).url