Create app.py

app.py

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+# app.py
+
+import os
+import gradio as gr
+
+from langchain_community.vectorstores.neo4j_vector import remove_lucene_chars
+from langchain_community.graphs import Neo4jGraph
+from langchain_experimental.graph_transformers import LLMGraphTransformer
+from langchain.document_loaders import PyPDFLoader
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Neo4jVector
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_groq import ChatGroq
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnableParallel, RunnableLambda
+from langchain_core.pydantic_v1 import BaseModel, Field
+
+# --- API & DB Setup ---
+os.environ["GROQ_API_KEY"] = "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"
+NEO4J_URI = "neo4j+s://491b8299.databases.neo4j.io"
+NEO4J_USERNAME = "neo4j"
+NEO4J_PASSWORD = "W3i8UiePw9QyaSJxK9l_apbzUnzh10YWxZQtnpSS02I"
+
+graph = Neo4jGraph(url=NEO4J_URI, username=NEO4J_USERNAME, password=NEO4J_PASSWORD)
+llm = ChatGroq(model="llama3-8b-8192")
+embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+llm_transformer = LLMGraphTransformer(llm=llm)
+
+# --- Entity Extraction Schema ---
+class Entities(BaseModel):
+    names: list[str] = Field(..., description="All person, org, or business names")
+
+entity_prompt = ChatPromptTemplate.from_messages([
+    ("system", "you are extracting organization and person entities from the text"),
+    ("human", "Use the given format to extract entities:\ninput: {question}")
+])
+entity_chain = entity_prompt | llm.with_structured_output(Entities)
+
+# --- Helpers ---
+def generate_full_text_query(input: str) -> str:
+    words = [el for el in remove_lucene_chars(input).split() if el]
+    return " AND ".join([f"{word}~2" for word in words])
+
+def structured_retriever(question: str) -> str:
+    entities = entity_chain.invoke({"question": question})
+    result = ""
+    for entity in entities.names:
+        cypher = """
+        CALL db.index.fulltext.queryNodes('entity', $query, {limit:2})
+        YIELD node,score
+        CALL {
+            WITH node
+            MATCH (node)-[r:!MENTIONS]->(neighbor)
+            RETURN node.id + '-' + type(r) + '->' + neighbor.id AS output
+            UNION ALL
+            WITH node
+            MATCH (node)<-[r:!MENTIONS]-(neighbor)
+            RETURN neighbor.id + '-' + type(r) + '->' + node.id AS output
+        }
+        RETURN output LIMIT 50
+        """
+        response = graph.query(cypher, {"query": generate_full_text_query(entity)})
+        result += "\n".join([el['output'] for el in response])
+    return result
+
+def retriever(question: str) -> str:
+    structured = structured_retriever(question)
+    unstructured = [el.page_content for el in vector_index.similarity_search(question)]
+    return f"Structured Data:\n{structured}\n\nUnstructured Data:\n" + "\n---\n".join(unstructured)
+
+# --- RAG Chain ---
+template = """Answer the question based only on the context:
+{context}
+
+Question: {question}
+Use natural language and be concise.
+Answer:"""
+
+qa_prompt = ChatPromptTemplate.from_template(template)
+
+chain = (
+    RunnableParallel({
+        "context": RunnableLambda(lambda x: retriever(x["question"])),
+        "question": RunnableLambda(lambda x: x["question"]),
+    })
+    | qa_prompt
+    | llm
+    | StrOutputParser()
+)
+
+# --- Gradio Pipeline ---
+vector_index = None
+
+def process_pdf(pdf_file):
+    global vector_index
+    loader = PyPDFLoader(pdf_file.name)
+    docs = loader.load()
+
+    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    docs_split = splitter.split_documents(docs)
+
+    graph_docs = []
+    for i in range(0, len(docs_split), 2):
+        try:
+            graph_docs.extend(llm_transformer.convert_to_graph_documents(docs_split[i:i+2]))
+        except Exception as e:
+            print(f"Error: {e}")
+
+    graph.add_graph_documents(graph_docs, baseEntityLabel=True, include_source=True)
+    graph.query("CREATE FULLTEXT INDEX entity IF NOT EXISTS FOR (e:__Entity__) ON EACH [e.id]")
+
+    vector_index = Neo4jVector.from_existing_graph(
+        embedding_model,
+        search_type="hybrid",
+        graph=graph,
+        node_label="Document",
+        embedding_node_property="embedding",
+        text_node_properties=["text"]
+    )
+    return "PDF uploaded and processed successfully!"
+
+def chat_with_doc(question):
+    if vector_index is None:
+        return "Please upload and process a PDF first."
+    return chain.invoke({"question": question})
+
+# --- Gradio UI ---
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧠 Graph RAG PDF Q&A")
+    with gr.Row():
+        pdf_input = gr.File(label="Upload PDF")
+        upload_btn = gr.Button("Process PDF")
+    output_info = gr.Textbox(label="Status", interactive=False)
+
+    with gr.Row():
+        question_input = gr.Textbox(label="Ask a Question")
+        ask_btn = gr.Button("Get Answer")
+    answer_output = gr.Textbox(label="Answer")
+
+    upload_btn.click(process_pdf, inputs=[pdf_input], outputs=[output_info])
+    ask_btn.click(chat_with_doc, inputs=[question_input], outputs=[answer_output])
+
+demo.launch()