Version 1 (#160)

New front-end
Launch Chat API
Manage Sources
Enable re-embedding of all contents
Sources can be added without a notebook now
Improved settings
Enable model selector on all chats
Background processing for better experience
Dark mode
Improved Notes

Improved Docs: 
- Remove all Streamlit references from documentation
- Update deployment guides with React frontend setup
- Fix Docker environment variables format (SURREAL_URL, SURREAL_PASSWORD)
- Update docker image tag from :latest to :v1-latest
- Change navigation references (Settings → Models to just Models)
- Update development setup to include frontend npm commands
- Add MIGRATION.md guide for users upgrading from Streamlit
- Update quick-start guide with correct environment variables
- Add port 5055 documentation for API access
- Update project structure to reflect frontend/ directory
- Remove outdated source-chat documentation files

open_notebook/graphs/ask.py

@@ -17,17 +17,14 @@ from open_notebook.utils import clean_thinking_content
 class SubGraphState(TypedDict):
     question: str
     term: str
-    # type: Literal["text", "vector"]
     instructions: str
     results: dict
     answer: str
+    ids: list  # Added for provide_answer function
 
 
 class Search(BaseModel):
     term: str
-    # type: Literal["text", "vector"] = Field(
-    #     description="The type of search. Use 'text' for keyword search and 'vector' for semantic search. If you are using text, search always for a single word"
-    # )
     instructions: str = Field(
         description="Tell the answeting LLM what information you need extracted from this search"
     )
@@ -50,8 +47,8 @@ class ThreadState(TypedDict):
 
 async def call_model_with_messages(state: ThreadState, config: RunnableConfig) -> dict:
     parser = PydanticOutputParser(pydantic_object=Strategy)
-    system_prompt = Prompter(prompt_template="ask/entry", parser=parser).render(
-        data=state
+    system_prompt = Prompter(prompt_template="ask/entry", parser=parser).render(  # type: ignore[arg-type]
+        data=state  # type: ignore[arg-type]
     )
     model = await provision_langchain_model(
         system_prompt,
@@ -65,7 +62,8 @@ async def call_model_with_messages(state: ThreadState, config: RunnableConfig) -
     ai_message = await model.ainvoke(system_prompt)
 
     # Clean the thinking content from the response
-    cleaned_content = clean_thinking_content(ai_message.content)
+    message_content = ai_message.content if isinstance(ai_message.content, str) else str(ai_message.content)
+    cleaned_content = clean_thinking_content(message_content)
 
     # Parse the cleaned JSON content
     strategy = parser.parse(cleaned_content)
@@ -99,7 +97,7 @@ async def provide_answer(state: SubGraphState, config: RunnableConfig) -> dict:
     payload["results"] = results
     ids = [r["id"] for r in results]
     payload["ids"] = ids
-    system_prompt = Prompter(prompt_template="ask/query_process").render(data=payload)
+    system_prompt = Prompter(prompt_template="ask/query_process").render(data=payload)  # type: ignore[arg-type]
     model = await provision_langchain_model(
         system_prompt,
         config.get("configurable", {}).get("answer_model"),
@@ -107,11 +105,12 @@ async def provide_answer(state: SubGraphState, config: RunnableConfig) -> dict:
         max_tokens=2000,
     )
     ai_message = await model.ainvoke(system_prompt)
-    return {"answers": [clean_thinking_content(ai_message.content)]}
+    ai_content = ai_message.content if isinstance(ai_message.content, str) else str(ai_message.content)
+    return {"answers": [clean_thinking_content(ai_content)]}
 
 
 async def write_final_answer(state: ThreadState, config: RunnableConfig) -> dict:
-    system_prompt = Prompter(prompt_template="ask/final_answer").render(data=state)
+    system_prompt = Prompter(prompt_template="ask/final_answer").render(data=state)  # type: ignore[arg-type]
     model = await provision_langchain_model(
         system_prompt,
         config.get("configurable", {}).get("final_answer_model"),
@@ -119,7 +118,8 @@ async def write_final_answer(state: ThreadState, config: RunnableConfig) -> dict
         max_tokens=2000,
     )
     ai_message = await model.ainvoke(system_prompt)
-    return {"final_answer": clean_thinking_content(ai_message.content)}
+    final_content = ai_message.content if isinstance(ai_message.content, str) else str(ai_message.content)
+    return {"final_answer": clean_thinking_content(final_content)}
 
 
 agent_state = StateGraph(ThreadState)