Update RAG.py

RAG.py

@@ -1,72 +1,71 @@
-import pandas as pd
-import json
-from langchain.docstore.document import Document
-from langchain.vectorstores import Chroma
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.llms import HuggingFaceHub
-from langchain.chains import RetrievalQA
-
-
-# file_path = "thyroidDF.csv"
-# df = pd.read_csv(file_path)
-
-def create_doucment(df):
-    documents = [
-        Document(
-            metadata={"id": str(i)},
-            # Serialize the dictionary to a JSON string
-            page_content=json.dumps(row.to_dict())
-        )
-        for i, row in df.iterrows()
-    ]
-    return documents
-
-
-def load_models_embedding():
-    embeddings = HuggingFaceEmbeddings(
-        model_name="sentence-transformers/all-MiniLM-L6-v2")
-    return embeddings
-
-
-def load_models_llm():
-    llm = HuggingFaceHub(
-        repo_id="Qwen/Qwen2.5-72B-Instruct",
-        # Replace with your token
-        api="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22"
-        huggingfacehub_api_token=api[:-2],
-        model_kwargs={"temperature": 0.5,
-                      "max_length": 100}  # Faster inference
-    )
-    return llm
-
-
-def create_database(embedding, documents):
-    vector_store = Chroma.from_documents(documents, embedding=embedding)
-    return vector_store
-
-# retriever = create_database().as_retriever()
-
-
-def ask_me(question, retriever, llm):
-
-    qa_chain = RetrievalQA.from_chain_type(
-        retriever=retriever,
-        chain_type="stuff",
-        llm=load_models_llm(),
-        return_source_documents=True)
-
-    response = qa_chain.invoke({"query": question})
-    print("Answer:", response["result"])
-
-
-# qa_chain = RetrievalQA.from_chain_type(
-#     retriever=retriever,
-#     chain_type="stuff",
-#     llm=llm,
-#     return_source_documents=True
-# )
-
-# question = "Can you provide the TSH, T3, and FTI values for patients aged 55?"
-# # question = "What columns are in the dataset?"
-# response = qa_chain.invoke({"query": question})
-# print("Answer:", response["result"])
+import pandas as pd
+import json
+from langchain.docstore.document import Document
+from langchain.vectorstores import Chroma
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.llms import HuggingFaceHub
+from langchain.chains import RetrievalQA
+
+
+# file_path = "thyroidDF.csv"
+# df = pd.read_csv(file_path)
+
+def create_doucment(df):
+    documents = [
+        Document(
+            metadata={"id": str(i)},
+            # Serialize the dictionary to a JSON string
+            page_content=json.dumps(row.to_dict())
+        )
+        for i, row in df.iterrows()
+    ]
+    return documents
+
+
+def load_models_embedding():
+    embeddings = HuggingFaceEmbeddings(
+        model_name="sentence-transformers/all-MiniLM-L6-v2")
+    return embeddings
+
+api="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22"
+def load_models_llm():
+    llm = HuggingFaceHub(
+        repo_id="Qwen/Qwen2.5-72B-Instruct",
+        # Replace with your token
+        huggingfacehub_api_token=api[:-2],
+        model_kwargs={"temperature": 0.5,
+                      "max_length": 100}  # Faster inference
+    )
+    return llm
+
+
+def create_database(embedding, documents):
+    vector_store = Chroma.from_documents(documents, embedding=embedding)
+    return vector_store
+
+# retriever = create_database().as_retriever()
+
+
+def ask_me(question, retriever, llm):
+
+    qa_chain = RetrievalQA.from_chain_type(
+        retriever=retriever,
+        chain_type="stuff",
+        llm=load_models_llm(),
+        return_source_documents=True)
+
+    response = qa_chain.invoke({"query": question})
+    print("Answer:", response["result"])
+
+
+# qa_chain = RetrievalQA.from_chain_type(
+#     retriever=retriever,
+#     chain_type="stuff",
+#     llm=llm,
+#     return_source_documents=True
+# )
+
+# question = "Can you provide the TSH, T3, and FTI values for patients aged 55?"
+# # question = "What columns are in the dataset?"
+# response = qa_chain.invoke({"query": question})
+# print("Answer:", response["result"])