Upload 2 files

main.py

@@ -0,0 +1,78 @@
+from langchain_community.retrievers import WikipediaRetriever
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_core.runnables import RunnableParallel, RunnablePassthrough, RunnableLambda
+import google.generativeai as genai
+
+def concat_data(docs: list, search_results: str) -> str:
+    """
+    Combines Wikipedia docs and web search results into one string.
+    """
+    data = ""
+    for doc in docs:
+        data += "\n\n" + doc.page_content
+    data += "\n\n" + search_results
+    return data
+
+# Configure Gemini
+genai.configure(api_key="AIzaSyD-iwKoPUSxGerqKjKhjvAJ3KRERpy0-18")
+
+# Load Gemini model
+gemini_model = genai.GenerativeModel("gemini-2.5-flash")
+
+# Wrap Gemini in a LangChain Runnable
+model = RunnableLambda(
+    lambda x: gemini_model.generate_content(x if isinstance(x, str) else str(x)).text
+)
+
+# Prompt templates
+main_template = PromptTemplate(
+    template=(
+        "You are a historical assistant. Based on the following context, "
+        "answer the user's question or summarize the topic if it's not a question.\n\n"
+        "Context:\n{context}\n\n"
+        "User's question:\n{question}\n"
+    ),
+    input_variables=["context", "question"]
+)
+
+wiki_template = PromptTemplate(
+    template=(
+        "You are an expert at identifying the core topic of a user's historical question.\n"
+        "Extract and return only the specific topic or event (no explanation).\n\n"
+        "Query: {query}\nOutput:"
+    ),
+    input_variables=["query"]
+)
+
+# Components
+parser = StrOutputParser()
+search_tool = DuckDuckGoSearchRun()
+retriever = WikipediaRetriever(top_k_results=4, lang="en")
+
+# Topic chain → extract core topic
+topic_chain = wiki_template | model | parser
+
+# Retrieve from Wikipedia
+wiki_chain = topic_chain | retriever
+
+# Web search chain
+search_chain = RunnableLambda(lambda x: search_tool.run(x))
+
+# Combine data sources
+data_chain = RunnableParallel({
+    "docs": wiki_chain,
+    "search_results": search_chain
+}) | RunnableLambda(lambda x: concat_data(x["docs"], x["search_results"]))
+
+# Final reasoning + answer generation
+final_chain = RunnableParallel({
+    "context": data_chain,
+    "question": RunnablePassthrough()
+}) | main_template | model | parser
+
+if __name__ == "__main__":
+    query = "wars between china and india"
+    output = final_chain.invoke(query)
+    print("\n🧠 Final Answer:\n", output)

requirements.txt

@@ -0,0 +1,8 @@
+langchain==1.0.2
+langchain-core==1.0.1
+langchain-community==0.4.1
+langchain-google-genai==0.0.1
+google-generativeai==0.3.2
+wikipedia==1.4.0
+duckduckgo-search==8.1.1
+streamlit==1.50.0