Preprocessing enhancements: Safety assessment, Language detection

.github/workflows/ci.yml

@@ -9,6 +9,7 @@ on:
   push:
     branches:
       - main
+      - pre-release
 
 permissions:
   id-token: write
@@ -35,7 +36,7 @@ jobs:
         runs-on: ${{ matrix.os }}
         strategy:
             matrix:
-                os: [ubuntu-latest, macos-13]
+                os: [ubuntu-latest, macos-15-intel]
 
         steps:
             - uses: actions/checkout@v4
@@ -81,7 +82,7 @@ jobs:
                 path: /tmp/image.tar
 
     docker-push:
-        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' }}
+        if: ${{ github.event_name == 'push' && (github.ref == 'refs/heads/main' || github.ref == 'ref/heads/pre-release') }}
         needs: docker-build
         runs-on: ubuntu-latest
 

src/agent/profiles/base.py

@@ -1,12 +1,14 @@
-from typing import Annotated, TypedDict
+from typing import Annotated, Literal, TypedDict
 
 from langchain_core.embeddings import Embeddings
 from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.messages import BaseMessage
 from langchain_core.runnables import Runnable, RunnableConfig
 from langgraph.graph.message import add_messages
 
+from agent.tasks.detect_language import create_language_detector
 from agent.tasks.rephrase import create_rephrase_chain
+from agent.tasks.safety_checker import SafetyCheck, create_safety_checker
 from tools.external_search.state import SearchState, WebSearchResult
 from tools.external_search.workflow import create_search_workflow
 
@@ -28,6 +30,11 @@ class BaseState(InputState, OutputState, total=False):
     rephrased_input: str  # LLM-generated query from user input
     chat_history: Annotated[list[BaseMessage], add_messages]
 
+    # Preprocessing results
+    safety: str  # "true" or "false" from safety check
+    reason_unsafe: str  # Reason if unsafe
+    detected_language: str  # Detected language
+
 
 class BaseGraphBuilder:
     # NOTE: Anything that is common to all graph builders goes here
@@ -38,21 +45,40 @@ def __init__(
         embedding: Embeddings,
     ) -> None:
         self.rephrase_chain: Runnable = create_rephrase_chain(llm)
+        self.safety_checker: Runnable = create_safety_checker(llm)
+        self.language_detector: Runnable = create_language_detector(llm)
         self.search_workflow: Runnable = create_search_workflow(llm)
 
     async def preprocess(self, state: BaseState, config: RunnableConfig) -> BaseState:
         rephrased_input: str = await self.rephrase_chain.ainvoke(
             {
                 "user_input": state["user_input"],
-                "chat_history": state["chat_history"],
+                "chat_history": state.get("chat_history", []),
             },
             config,
         )
-        return BaseState(rephrased_input=rephrased_input)
+        safety_check: SafetyCheck = await self.safety_checker.ainvoke(
+            {"rephrased_input": rephrased_input}, config
+        )
+        detected_language: str = await self.language_detector.ainvoke(
+            {"user_input": state["user_input"]}, config
+        )
+        return BaseState(
+            rephrased_input=rephrased_input,
+            safety=safety_check.safety,
+            reason_unsafe=safety_check.reason_unsafe,
+            detected_language=detected_language,
+        )
+
+    def proceed_with_research(self, state: BaseState) -> Literal["Continue", "Finish"]:
+        return "Continue" if state["safety"] == "true" else "Finish"
 
     async def postprocess(self, state: BaseState, config: RunnableConfig) -> BaseState:
         search_results: list[WebSearchResult] = []
-        if config["configurable"]["enable_postprocess"]:
+        if (
+            config["configurable"].get("enable_postprocess")
+            and state["safety"] == "true"
+        ):
             result: SearchState = await self.search_workflow.ainvoke(
                 SearchState(
                     input=state["rephrased_input"],

src/agent/profiles/cross_database.py

@@ -15,16 +15,11 @@
     create_uniprot_rewriter_w_reactome
 from agent.tasks.cross_database.summarize_reactome_uniprot import \
     create_reactome_uniprot_summarizer
-from agent.tasks.detect_language import create_language_detector
-from agent.tasks.safety_checker import SafetyCheck, create_safety_checker
 from retrievers.reactome.rag import create_reactome_rag
 from retrievers.uniprot.rag import create_uniprot_rag
 
 
 class CrossDatabaseState(BaseState):
-    safety: str  # LLM-assessed safety level of the user input
-    query_language: str  # language of the user input
-
     reactome_query: str  # LLM-generated query for Reactome
     reactome_answer: str  # LLM-generated answer from Reactome
     reactome_completeness: str  # LLM-assessed completeness of the Reactome answer
@@ -46,21 +41,18 @@ def __init__(
         self.reactome_rag: Runnable = create_reactome_rag(llm, embedding)
         self.uniprot_rag: Runnable = create_uniprot_rag(llm, embedding)
 
-        self.safety_checker = create_safety_checker(llm)
         self.completeness_checker = create_completeness_grader(llm)
-        self.detect_language = create_language_detector(llm)
         self.write_reactome_query = create_reactome_rewriter_w_uniprot(llm)
         self.write_uniprot_query = create_uniprot_rewriter_w_reactome(llm)
         self.summarize_final_answer = create_reactome_uniprot_summarizer(
-            llm.model_copy(update={"streaming": True})
+            llm, streaming=True
         )
 
         # Create graph
         state_graph = StateGraph(CrossDatabaseState)
         # Set up nodes
         state_graph.add_node("check_question_safety", self.check_question_safety)
         state_graph.add_node("preprocess_question", self.preprocess)
-        state_graph.add_node("identify_query_language", self.identify_query_language)
         state_graph.add_node("conduct_research", self.conduct_research)
         state_graph.add_node("generate_reactome_answer", self.generate_reactome_answer)
         state_graph.add_node("rewrite_reactome_query", self.rewrite_reactome_query)
@@ -74,7 +66,6 @@ def __init__(
         state_graph.add_node("postprocess", self.postprocess)
         # Set up edges
         state_graph.set_entry_point("preprocess_question")
-        state_graph.add_edge("preprocess_question", "identify_query_language")
         state_graph.add_edge("preprocess_question", "check_question_safety")
         state_graph.add_conditional_edges(
             "check_question_safety",
@@ -104,39 +95,18 @@ def __init__(
 
         self.uncompiled_graph: StateGraph = state_graph
 
-    async def check_question_safety(
+    def check_question_safety(
         self, state: CrossDatabaseState, config: RunnableConfig
     ) -> CrossDatabaseState:
-        result: SafetyCheck = await self.safety_checker.ainvoke(
-            {"input": state["rephrased_input"]},
-            config,
-        )
-        if result.binary_score == "No":
+        if state["safety"] != "true":
             inappropriate_input = f"This is the user's question and it is NOT appropriate for you to answer: {state["user_input"]}. \n\n explain that you are unable to answer the question but you can answer questions about topics related to the Reactome Pathway Knowledgebase or UniProt Knowledgebas."
             return CrossDatabaseState(
-                safety=result.binary_score,
                 user_input=inappropriate_input,
                 reactome_answer="",
                 uniprot_answer="",
             )
         else:
-            return CrossDatabaseState(safety=result.binary_score)
-
-    async def proceed_with_research(
-        self, state: CrossDatabaseState
-    ) -> Literal["Continue", "Finish"]:
-        if state["safety"] == "Yes":
-            return "Continue"
-        else:
-            return "Finish"
-
-    async def identify_query_language(
-        self, state: CrossDatabaseState, config: RunnableConfig
-    ) -> CrossDatabaseState:
-        query_language: str = await self.detect_language.ainvoke(
-            {"user_input": state["user_input"]}, config
-        )
-        return CrossDatabaseState(query_language=query_language)
+            return CrossDatabaseState()
 
     async def conduct_research(
         self, state: CrossDatabaseState, config: RunnableConfig
@@ -256,7 +226,7 @@ async def generate_final_response(
         final_response: str = await self.summarize_final_answer.ainvoke(
             {
                 "input": state["rephrased_input"],
-                "query_language": state["query_language"],
+                "detected_language": state["detected_language"],
                 "reactome_answer": state["reactome_answer"],
                 "uniprot_answer": state["uniprot_answer"],
             },

src/agent/profiles/react_to_me.py

@@ -7,6 +7,7 @@
 from langgraph.graph.state import StateGraph
 
 from agent.profiles.base import BaseGraphBuilder, BaseState
+from agent.tasks.unsafe_question import create_unsafe_answer_generator
 from retrievers.reactome.rag import create_reactome_rag
 
 
@@ -23,6 +24,9 @@ def __init__(
         super().__init__(llm, embedding)
 
         # Create runnables (tasks & tools)
+        self.unsafe_answer_generator: Runnable = create_unsafe_answer_generator(
+            llm, streaming=True
+        )
         self.reactome_rag: Runnable = create_reactome_rag(
             llm, embedding, streaming=True
         )
@@ -32,15 +36,40 @@ def __init__(
         # Set up nodes
         state_graph.add_node("preprocess", self.preprocess)
         state_graph.add_node("model", self.call_model)
+        state_graph.add_node("generate_unsafe_response", self.generate_unsafe_response)
         state_graph.add_node("postprocess", self.postprocess)
         # Set up edges
         state_graph.set_entry_point("preprocess")
-        state_graph.add_edge("preprocess", "model")
+        state_graph.add_conditional_edges(
+            "preprocess",
+            self.proceed_with_research,
+            {"Continue": "model", "Finish": "generate_unsafe_response"},
+        )
         state_graph.add_edge("model", "postprocess")
+        state_graph.add_edge("generate_unsafe_response", "postprocess")
         state_graph.set_finish_point("postprocess")
 
         self.uncompiled_graph: StateGraph = state_graph
 
+    async def generate_unsafe_response(
+        self, state: ReactToMeState, config: RunnableConfig
+    ) -> ReactToMeState:
+        answer: str = await self.unsafe_answer_generator.ainvoke(
+            {
+                "language": state["detected_language"],
+                "user_input": state["rephrased_input"],
+                "reason_unsafe": state["reason_unsafe"],
+            },
+            config,
+        )
+        return ReactToMeState(
+            chat_history=[
+                HumanMessage(state["user_input"]),
+                AIMessage(answer),
+            ],
+            answer=answer,
+        )
+
     async def call_model(
         self, state: ReactToMeState, config: RunnableConfig
     ) -> ReactToMeState:

src/agent/tasks/cross_database/summarize_reactome_uniprot.py

@@ -36,13 +36,17 @@
         ("system", summarization_message),
         (
             "human",
-            "User question: {input} \n\n Language: {query_language} \n\n Reactome-drived information: \n {reactome_answer} \n\n UniProt-drived infromation: \n {uniprot_answer}.",
+            "User question: {input} \n\n Language: {detected_language} \n\n Reactome-drived information: \n {reactome_answer} \n\n UniProt-drived infromation: \n {uniprot_answer}.",
         ),
     ]
 )
 
 
-def create_reactome_uniprot_summarizer(llm: BaseChatModel) -> Runnable:
+def create_reactome_uniprot_summarizer(
+    llm: BaseChatModel, streaming: bool = False
+) -> Runnable:
+    if streaming:
+        llm = llm.model_copy(update={"streaming": True})
     return (summarizer_prompt | llm | StrOutputParser()).with_config(
         run_name="summarize_answer"
     )

src/agent/tasks/safety_checker.py

@@ -4,25 +4,62 @@
 from pydantic import BaseModel, Field
 
 safety_check_message = """
-You are an expert grader with a strong understanding of ethical guidelines and responsible AI usage.
+You are an expert scientific assistant. You have advanced training in scientific ethics, dual-use assessment, and responsible AI.
 
-Your task is to determine whether a user-submitted question is appropriate and not harmful.
-Provide a binary output as either:
-Yes: The question is appropriate and not harmful.
-No: The question is harmful, offensive, inappropriate, or unethical.
+Tasks:
+
+1. Safety & Appropriateness Check
+    - Assess the question and determine if it is safe and appropriate for responsible AI use.
+    - The question and any plausible answer to it must NOT be harmful, offensive, unethical, or violate scientific or ethical guidelines.
+    - The question or its likely answers must NOT request, describe, or explore how to carry out harmful, illegal, dangerous, or dual-use activities (such as modifying pathogens, increasing infectivity, or other forms of misuse)—even if the scenario is hypothetical, fictional, or indirect.
+    - The question must NOT request personal, medical, legal, or other types of advice outside the scope of scientific knowledge.
+    - If a question is ambiguous or could plausibly be used for harm, treat it as unsafe.
+    - Treat hypothetical, fictional, or made-up scenarios with the same level of scrutiny as real-world questions.
+
+2. Reactome Relevance Check
+    - Determine if the question is relevant to biology, life sciences, molecular biology, or related topics.
+    - Mark questions as not relevant if they are about unrelated topics (such as programming, math, history, trivia, etc.).
+
+IMPORTANT:
+    - If the standalone question is unsafe or not relevant return "safety": "false".
+    - If the standalone question is both safe **and** relevant, return "safety": "true".
+
+Return only a JSON object in the following format:
+    "safety": "true" or "false", // Use string, not boolean. Mark as "false" if unsafe OR not relevant.
+    "reason_unsafe": "..." // If 'safety' is false, briefly state the reason
+
+Examples:    
+  1. Q: Describe methods for bypassing cellular immune responses to viral infection
+       "safety": "false",
+       "reason_unsafe": "Could be used for harmful or unethical manipulation of immune evasion."
+
+  2. Q: What are common side effects of statins in the treatment of high cholesterol?
+       "safety": "false",
+       "reason_unsafe": "Requests medical advice, which is out of scientific scope."
+
+  3. Q: How can gene drives be used to eliminate invasive species in an ecosystem?
+       "safety": "false",
+       "reason_unsafe": "Enables the use of gene drives, which have significant ecological and dual-use risks."
+
+  4. Q: What is the role of the immune system in the treatment of cancer?
+       "safety": "true",
+       "reason_unsafe": ""
 """
 
 safety_check_prompt = ChatPromptTemplate.from_messages(
     [
         ("system", safety_check_message),
-        ("human", "User question: \n\n {input}"),
+        ("human", "User question: \n\n {rephrased_input}"),
     ]
 )
 
 
 class SafetyCheck(BaseModel):
-    binary_score: str = Field(
-        description="Indicates whether the question is appropriate and related to molecular biology. Expected values: 'Yes' or 'No'."
+    safety: str = Field(
+        description="Indicates whether the question is appropriate and related to molecular biology. Expected values: 'true' or 'false'."
+    )
+    reason_unsafe: str = Field(
+        description="If 'safety' is false, briefly state the reason; if 'safety' is true, leave this field empty."
     )
 
 

src/agent/tasks/unsafe_question.py

@@ -0,0 +1,45 @@
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import Runnable
+
+safety_check_message = """
+You are an expert scientific assistant operating under the React-to-Me platform. React-to-Me helps both experts and non-experts explore molecular biology using trusted data from the Reactome database.
+
+You have advanced training in scientific ethics, dual-use research concerns, and responsible AI use.
+
+You will receive three inputs: 
+1. The user's question.
+2. A system-generated variable called `reason_unsafe`, which explains why the question cannot be answered.
+3. The user's preferred language (as a language code or name).
+
+Your task is to clearly, respectfully, and firmly explain to the user *why* their question cannot be answered, based solely on the `reason_unsafe` input. Do **not** attempt to answer, rephrase, or guide the user toward answering the original question.
+
+You must:
+- Respond in the user’s preferred language.
+- Politely explain the refusal, grounded in the `reason_unsafe`.
+- Emphasize React-to-Me’s mission: to support responsible exploration of molecular biology through trusted databases.
+- Suggest examples of appropriate topics (e.g., protein function, pathways, gene interactions using Reactome/UniProt).
+
+You must not provide any workaround, implicit answer, or redirection toward unsafe content.
+"""
+
+safety_check_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", safety_check_message),
+        (
+            "user",
+            "Language:{language}\n\nQuestion:{user_input}\n\n Reason for unsafe or out of scope: {reason_unsafe}",
+        ),
+    ]
+)
+
+
+def create_unsafe_answer_generator(
+    llm: BaseChatModel, streaming: bool = False
+) -> Runnable:
+    if streaming:
+        llm = llm.model_copy(update={"streaming": True})
+    return (safety_check_prompt | llm | StrOutputParser()).with_config(
+        run_name="unsafe_answer_generator"
+    )