update

app.py

@@ -11,21 +11,22 @@ es = Elasticsearch(hosts=["https://data.neuralnoise.com:9200"],
 # Load your language model from HuggingFace Transformers
 generator = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.2")
 
-def search_es(query, index="pubmed", num_results=3):
+def search(query, index="pubmed", num_results=3):
     """
     Search the Elasticsearch index for the most relevant documents.
     """
     
     print(f'Running query: {query}')
     
-    response = es.search(index=index,
-                         request_timeout=60,
-                         body={
-                             "query": {
-                                 "match": {
-                                     "content": query  # Assuming documents have a 'content' field
-                                }
-                            }, "size": num_results})
+    es_request_body = {
+        "query": {
+            "match": {
+                "content": query  # Assuming documents have a 'content' field
+            }
+        }, "size": num_results
+    }
+
+    response = es.options(request_timeout=60).search(index=index, body=es_request_body)
     
     # Extract and return the documents
     docs = [hit["_source"]["content"] for hit in response['hits']['hits']]
@@ -39,13 +40,23 @@ def rag_pipeline(prompt, index="pubmed"):
     A simple RAG pipeline that retrieves documents and uses them to enrich the context for the LLM.
     """
     # Retrieve documents
-    docs = search_es(prompt, index=index)
-    
-    # Combine prompt with retrieved documents
-    enriched_prompt = f"{prompt}\n\n{' '.join(docs)}"
+    docs = search(prompt, index=index)
+    joined_docs = '\n\n'.join(docs)
     
+    messages = [
+        {
+            "role": "system",
+            "content": f"You are an advanced medical support assistant, designed to help clinicians by providing quick access to medical information, guidelines, and evidence-based recommendations. Alongside your built-in knowledge, you have access to a curated set of documents retrieved from trustworthy sources such as Wikipedia and PubMed. These documents include up-to-date medical guidelines, research summaries, and clinical practice information. You should use these documents as a primary source of information to ensure your responses are based on the most current and credible evidence available. Your responses should be accurate, concise, and in full compliance with medical ethics. You must always remind users that your guidance does not substitute for professional medical advice, diagnosis, or treatment. Your tone should be professional, supportive, and respectful, recognizing the complexity of healthcare decisions and the importance of personalized patient care. While you can offer information and suggestions based on the documents provided and current medical knowledge, you must emphasize the importance of clinicians' expertise and judgment in making clinical decisions.\n\nRetrieved documents from {index}:\n\n{joined_docs}"
+        },
+        {
+            "role": "user",
+            "content": prompt
+        }
+    ]
+
+
     # Generate response using the LLM
-    response = generator(enriched_prompt, max_new_tokens=256, return_full_text=False)
+    response = generator(messages, max_new_tokens=256, return_full_text=False)
     
     # Return the generated text and the documents
     return response[0]['generated_text'], "\n\n".join(docs)