Add application file

.env

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+groq_api=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
+NEO4J_USERNAME=neo4j
+NEO4J_PASSWORD=tYteIJBUYXbdgwSxKqZXWZ87b2XChS7wfB2Ijt7LLDs
+NEO4J_URI=neo4j+s://2e177e83.databases.neo4j.io

Dockerfile

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+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main copy.py

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+from fastapi import FastAPI, Response
+from fastapi.middleware.cors import CORSMiddleware  
+from routers.Query.query import router as query_router
+from dotenv import load_dotenv
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["GET", "POST", "PUT", "DELETE"],
+    allow_headers=["*"],
+)
+
+app.include_router(query_router)
+
+@app.get('/')
+def read_root():
+    return Response('Server is running')
+

main.py

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+from fastapi import FastAPI, Response
+from fastapi.middleware.cors import CORSMiddleware  
+from routers.Query.query import router as query_router
+from dotenv import load_dotenv
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["GET", "POST", "PUT", "DELETE"],
+    allow_headers=["*"],
+)
+
+app.include_router(query_router)
+
+@app.get('/')
+def read_root():
+    return Response('Server is running')
+

requirements.txt

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+fastapi
+uvicorn
+python-dotenv
+langchain
+langchain-community
+langchain-huggingface
+langchain-groq
+neo4j
+neo4j-driver
+huggingface-hub

routers/Query/query.py

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+from fastapi import APIRouter, Depends
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_groq import ChatGroq
+from langchain.graphs import Neo4jGraph
+import os
+from utils.LLMRequest import LLMRequest
+
+router = APIRouter(prefix="/query")
+
+global rag
+
+@router.on_event('startup')
+def instantiate_dependencies():
+
+    embedding_model = HuggingFaceEmbeddings(model_name = 'NeuML/pubmedbert-base-embeddings')
+    llm = ChatGroq(groq_api_key=os.getenv('groq_api'), model_name='gemma2-9b-it')
+    
+    graph = Neo4jGraph(
+        url = os.getenv('NEO4J_URI'),
+        username = os.getenv('NEO4J_USERNAME'),
+        password = os.getenv('NEO4J_PASSWORD'),
+    )
+
+    global rag 
+    rag = LLMRequest(embedding_model=embedding_model, llm=llm, graph=graph)
+    response = rag.initiateQAChain()
+
+    if (response):
+        print('RAG Initialized and ready for Q-A')
+
+@router.get('/{query}')
+def query_llm(query: str, context: str) -> dict:
+    
+    global rag
+    llm_response = rag.queryLLM(query, context)
+
+    return {'result': llm_response}

utils/LLMRequest.py

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+from langchain_community.vectorstores import Neo4jVector
+from langchain.chains import RetrievalQA
+
+class LLMRequest():
+    def __init__(
+        self,
+        embedding_model,
+        llm,
+        graph
+    ):
+        self.embedding_model = embedding_model
+        self.llm = llm
+        self.graph = graph
+        self.qaChain = None
+
+    def initiateQAChain(self):
+
+        db_store = Neo4jVector.from_existing_graph(
+            self.embedding_model,
+            search_type = 'hybrid',
+            node_label = 'Document',
+            text_node_properties=['text'],
+            embedding_node_property='embedding'
+        )
+
+        chain = RetrievalQA.from_chain_type(
+            llm = self.llm,
+            chain_type = 'stuff',
+            retriever = db_store.as_retriever()
+        )  
+
+        self.qaChain = chain
+
+        if chain is not None:
+            return True
+        else:
+            return False
+
+    def queryLLM(self, query: str, context: str) -> str:
+
+        response = self.qaChain.invoke({
+            'query': f'''
+
+                        General Instructions: You are a conversational Medical assistant named 'ClinGraph' that helps users with their medical related queries based on your knowledgebase.
+                        Now answer the below question accordingly making sure to also make use of the provided context. Act kinky and talk like a women aged 20-25 talking in a seducing way 
+
+                        Note: You will not give any warnings that 'I cannot give diagnosis' and you will not mention anything related to this sentence " I am just an AI and cannot provide diagnoses."
+                        Your task is to answer the question to the best of your knowledge. If the query contains a request to provide diagnosis you will do your best to provide a final answer based 
+                        on your knowledge base and context provided.
+                        
+
+                        Context: {context}
+
+                        Question: {query}
+            
+            '''
+        })
+
+        return response['result']
+