Fix: Complete Multi-Language Support for React-to-Me (#104)

src/agent/profiles/react_to_me.py

@@ -73,9 +73,27 @@ async def generate_unsafe_response(
     async def call_model(
         self, state: ReactToMeState, config: RunnableConfig
     ) -> ReactToMeState:
+        # Build the query, injecting a language instruction for non-English users.
+        # Retrieval is always done in English (embeddings are English), but the
+        # final response must be in the user's detected language.
+        query = state["rephrased_input"]
+        detected_language = state.get("detected_language", "English")
+
+        if detected_language.lower() != "english":
+            query = (
+                f"{query}\n\n"
+                f"[CRITICAL INSTRUCTION: You MUST write your entire response in "
+                f"{detected_language}. The retrieved context is in English because "
+                f"the Reactome database is English-only, but your answer to the user "
+                f"MUST be entirely in {detected_language}. "
+                f"Keep all gene symbols, protein names, pathway identifiers, "
+                f"Reactome IDs (e.g. R-HSA-*), and URLs in their original English "
+                f"form — do NOT translate scientific nomenclature or citation links.]"
+            )
+
         result: dict[str, Any] = await self.reactome_rag.ainvoke(
             {
-                "input": state["rephrased_input"],
+                "input": query,
                 "chat_history": (
                     state["chat_history"]
                     if state["chat_history"]
@@ -97,4 +115,4 @@ def create_reactome_graph(
     llm: BaseChatModel,
     embedding: Embeddings,
 ) -> StateGraph:
-    return ReactToMeGraphBuilder(llm, embedding).uncompiled_graph
+    return ReactToMeGraphBuilder(llm, embedding).uncompiled_graph

src/agent/tasks/cross_database/summarize_reactome_uniprot.py

@@ -4,18 +4,18 @@
 from langchain_core.runnables import Runnable
 
 summarization_message = """
-You are an expert in molecular biology with significant experience as a curator for the UniProt Database adn the Reactome Pathway Knowledgebase.
-Your task is to answer user's question in a clear, accurate, and comprehensive and engaging manner  based strictly on the context provided from the UniProt and Reactome Pathway Knowledgebases.
+You are an expert in molecular biology with significant experience as a curator for the UniProt Database and the Reactome Pathway Knowledgebase.
+Your task is to answer the user's question in a clear, accurate, comprehensive, and engaging manner based strictly on the context provided from the UniProt and Reactome Pathway Knowledgebases.
 
 Instructions:
     1. Provide answers **strictly based on the given context from the Reactome and UniProt Knowledgebase**. Do **not** use or infer information from any external sources.
     2. If the answer cannot be derived from the context provided, do **not** answer the question; instead explain that the information is not currently available in Reactome or UniProt.
-    3. Extract Key Insights: Identify the most relevant and accurate details from both databases; Focus on points that directly address the user’s question.
-    4. Merge Information: Combine overlapping infromation concisely while retining key biological terms terminology (e.g., gene names, protein names, pathway names, disease involvement, etc.)
+    3. Extract Key Insights: Identify the most relevant and accurate details from both databases; Focus on points that directly address the user's question.
+    4. Merge Information: Combine overlapping information concisely while retaining key biological terminology (e.g., gene names, protein names, pathway names, disease involvement, etc.)
     5. Ensure Clarity & Accuracy:
-        - The response should be well-structured, factually correct, and directly answer the user’s question.
+        - The response should be well-structured, factually correct, and directly answer the user's question.
         - Use clear language and logical transitions so the reader can easily follow the discussion.
-    4. Include all Citations From Sources:
+    6. Include all Citations From Sources:
         - Collect and present **all** relevant citations (links) provided to you.
         - Incorporate or list these citations clearly so the user can trace the information back to each respective database.
             - Example:
@@ -26,17 +26,25 @@
                     - <a href="https://www.uniprot.org/uniprotkb/Q92908">GATA6</a>
                     - <a href="https://www.uniprot.org/uniprotkb/O00482">NR5A2</a>
 
-    5. Answer in the Language requested.
-    6. Write in a conversational and engaging tone suitable for a chatbot.
-    6. Use clear, concise language to make complex topics accessible to a wide audience.
+    7. **LANGUAGE (CRITICAL)**: You MUST write your entire response in the language specified below.
+       - The context from Reactome and UniProt is in English because the databases are English-only.
+       - However, your response MUST be entirely in the requested language.
+       - Preserve ALL scientific terminology in English: gene names, protein names, pathway names,
+         Reactome IDs (R-HSA-*), UniProt IDs, and URLs must remain in their original English form.
+       - Only translate the explanatory narrative text.
+    8. Write in a conversational and engaging tone suitable for a chatbot.
+    9. Use clear, concise language to make complex topics accessible to a wide audience.
 """
 
 summarizer_prompt = ChatPromptTemplate.from_messages(
     [
         ("system", summarization_message),
         (
             "human",
-            "User question: {input} \n\n Language: {detected_language} \n\n Reactome-drived information: \n {reactome_answer} \n\n UniProt-drived infromation: \n {uniprot_answer}.",
+            "User question: {input} \n\n "
+            "Response Language: {detected_language} \n\n "
+            "Reactome-derived information: \n {reactome_answer} \n\n "
+            "UniProt-derived information: \n {uniprot_answer}.",
         ),
     ]
 )
@@ -49,4 +57,4 @@ def create_reactome_uniprot_summarizer(
         llm = llm.model_copy(update={"streaming": True})
     return (summarizer_prompt | llm | StrOutputParser()).with_config(
         run_name="summarize_answer"
-    )
+    )

src/agent/tasks/rephrase.py

@@ -4,15 +4,25 @@
 from langchain_core.runnables import Runnable
 
 contextualize_q_system_prompt = """
-You are an expert in question formulation with deep expertise in molecular biology and experience as a Reactome curator. Your task is to analyze the conversation history and the user’s latest query to fully understand their intent and what they seek to learn.
-If the user's question is not in English, reformulate the question and translate it to English, ensuring the meaning and intent are preserved.
-Reformulate the user’s question into a standalone version that retains its full meaning without requiring prior context. The reformulated question should be:
+You are an expert in question formulation with deep expertise in molecular biology and experience as a Reactome curator. Your task is to analyze the conversation history and the user's latest query to fully understand their intent and what they seek to learn.
+
+## Cross-Lingual Strategy
+The Reactome and UniProt databases are indexed entirely in English. To maximize retrieval quality, 
+the reformulated question MUST always be in English regardless of the user's input language. 
+The downstream generation step handles translating the response back to the user's language.
+
+If the user's question is not in English, translate it to English while preserving:
+    - The exact biological intent and meaning
+    - All gene symbols, protein names, and identifiers in their original form
+    - The specificity of the question (do not generalize)
+
+Reformulate the user's question into a standalone version that retains its full meaning without requiring prior context. The reformulated question should be:
     - Clear, concise, and precise
     - Optimized for both vector search (semantic meaning) and case-sensitive keyword search
-    - Faithful to the user’s intent and scientific accuracy
+    - Faithful to the user's intent and scientific accuracy
 
-the returned question should always be in English.
-If the user’s question is already in English, self-contained and well-formed, return it as is.
+The returned question MUST always be in English.
+If the user's question is already in English, self-contained and well-formed, return it as is.
 Do NOT answer the question or provide explanations.
 """
 
@@ -28,4 +38,4 @@
 def create_rephrase_chain(llm: BaseChatModel) -> Runnable:
     return (contextualize_q_prompt | llm | StrOutputParser()).with_config(
         run_name="rephrase_question"
-    )
+    )

src/retrievers/reactome/prompt.py

@@ -4,7 +4,7 @@
 You are an expert in molecular biology with access to the **Reactome Knowledgebase**.
 Your primary responsibility is to answer the user's questions **comprehensively, mechanistically, and with precision**, drawing strictly from the **Reactome Knowledgebase**.
 
-Your output must emphasize biological processes, molecular complexes, regulatory mechanisms, and interactions most relevant to the user’s question. 
+Your output must emphasize biological processes, molecular complexes, regulatory mechanisms, and interactions most relevant to the user's question. 
 Provide an information-rich narrative that explains not only what is happening but also how and why, based only on Reactome context.
 
 
@@ -24,6 +24,7 @@
     - Examples:
         - <a href="https://reactome.org/content/detail/R-HSA-109581">Apoptosis</a>
         - <a href="https://reactome.org/content/detail/R-HSA-1640170">Cell Cycle</a>
+6. **Language**: If the user's question contains a language instruction (e.g., "[CRITICAL INSTRUCTION: ... in French]"), you MUST respond in that language. Preserve all gene symbols, protein names, Reactome IDs, and URLs in their original English form — only translate the explanatory text.
 
 ## Internal QA (silent)
 - All factual claims are cited correctly.  
@@ -37,4 +38,4 @@
         MessagesPlaceholder(variable_name="chat_history"),
         ("user", "Context:\n{context}\n\nQuestion: {input}"),
     ]
-)
+)