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app/rag_chain.py

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+# rag_chain.py
+import os
+import weaviate
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Weaviate
+from langchain.chains import RetrievalQA
+
+def build_rag_chain():
+    client = weaviate.Client(
+        url=os.getenv("WEAVIATE_URL"),
+        auth_client_secret=weaviate.AuthApiKey(os.getenv("WEAVIATE_API_KEY")),
+        additional_headers={"X-OpenAI-Api-Key": os.getenv("OPENAI_API_KEY")}
+    )
+
+    vectorstore = Weaviate(
+        client=client,
+        index_name="LangDocs",
+        text_key="text",
+        embedding=OpenAIEmbeddings()
+    )
+
+    retriever = vectorstore.as_retriever()
+    llm = ChatOpenAI(temperature=0)
+
+    return RetrievalQA.from_chain_type(llm=llm, retriever=retriever)