test retriever post requests

app.py

@@ -4,6 +4,7 @@ import time
 import hmac
 import os
 import json
+import requests
 from llm_reasoner import LLMReasoner
 from prompts import templates
 from typing import Any
@@ -58,7 +59,7 @@ def select_models():
     """Returns only when a valid option is selected from both dropdowns."""
 
     #placeholders
-    retriever_options = ["Choose one...", "Simple", "Trained", "No Retriever"]
+    retriever_options = ["Choose one...", "BM25 Retriever", "Off-the-shelf Retriever", "Finetuned Retriever", "No Retriever"]
     reasoner_options = ["Choose one...", "Claude Sonnet", "GPT-4o", "o3-mini"]
 
     #selectboxes
@@ -134,12 +135,36 @@ def retriever(query: str, selected_retriever: str):
         placeholder = st.empty()
         text=""
         
-        if selected_retriever == "Simple":
-            message = "Using the simple retriever to search for documents related to your query..."
-        elif selected_retriever == "Trained":
-            message = "Using the trained retriever to fetch detailed documents relevant to your query..."
+        if selected_retriever == "BM25 Retriever":
+            message = "Using the BM25 retriever to search for documents related to your query..."
+            retriever_endpoint = "bm25"
+        elif selected_retriever == "Off-the-shelf Retriever":
+            message = "Using the off-the-shelf retriever to fetch detailed documents relevant to your query..."
+            retriever_endpoint = "ots"
+        elif selected_retriever == "Finetuned Retriever":
+            message = "Using the finetuned retriever to fetch detailed documents relevant to your query..."
+            retriever_endpoint = "ft"
         else:
             message = "No retriever selected. Skipping document retrieval."
+            return ""
+
+        headers = {
+            'Content-Type': 'application/json',
+        }
+
+        json_data = {
+            'claim': query,
+        }
+
+        url = "http://130.245.163.20"
+        port = "80"
+        response = requests.post(f'{url}:{port}/{retriever_endpoint}', headers=headers, json=json_data)
+        documents = response.json()["Documents"]
+
+        k = 3
+        topk_documents = documents[:k]
+
+        corpus = '\n\n'.join(topk_documents)
 
         for chunk in message.split():
             text += chunk + " "
@@ -148,7 +173,7 @@ def retriever(query: str, selected_retriever: str):
             placeholder.markdown(text + "▌")
         placeholder.markdown(text)
     # You could return retrieved info here.
-    return message
+    return corpus
 
 def reasoner(query: str, documents: list[str], llm_client: Any):
     """Simulate a 'reasoner' step, thinking about how to answer."""
@@ -166,23 +191,20 @@ def reasoner(query: str, documents: list[str], llm_client: Any):
         if not documents or len(documents) == 0:
             prompt_template = Template(templates["no_evidence"])
             prompt = prompt_template.substitute(claim=query)
+            print(prompt)
             # prompt = templates["no_evidence"].format(claim=query)
         else:
             # TODO: fix prompt call to include retrieved documents
-            prompt_template = Template(templates["no_evidence"])
-            prompt = prompt_template.substitute(claim=query)
-            # prompt = templates["no_evidence"].format(claim=query)
+            prompt_template = Template(templates["with_evidence"])
+            prompt = prompt_template.substitute(claim=query, corpus=documents)
+            # prompt = templates["no_evidence"].format(claim=query, corpus=documents)
         
         llm_response = llm_client.run_inference(prompt)
 
-        # message = message + '\n' + llm_response
-
         answer_dict = safe_parse_json(llm_response)
         decision = answer_dict.get("decision", "")
         reasoning = answer_dict.get("reasoning", "")
 
-        # message = message + '\n' + decision
-
         for chunk in message.split():
             text += chunk + " "
             time.sleep(0.05)
@@ -221,7 +243,6 @@ if prompt := st.chat_input("Type here"):
         options["model_name"] = "o3-mini-2025-01-31"
 
     options["API_KEY"] = api_key
-    print(f'Tester: {options["API_KEY"]}')
 
     llm_client = LLMReasoner(options)
 

prompts.py

@@ -13,5 +13,21 @@ templates = {
 "Do not add markdown formatting, code fences, or additional text. The output must start with { and end with }." \
 'Example Output: {"reasoning": "Your brief explanation here (one or two sentences).", "decision": "SUPPORT or CONTRADICT"}' \
 "Now, please evaluate the claim above." \
+"",
+	"with_evidence": "You are an AI model tasked with verifying claims related to medical and health topics using zero-shot learning. Your job is to analyze a given claim and decide whether the available evidence and your general medical knowledge would likely SUPPORT or CONTRADICT the claim." \
+"Claim to Evaluate: <claim> $claim </claim>" \
+"Relevant Documents: <corpus> $corpus </corpus>" \
+"Guidelines:" \
+"Evaluate the claim's plausibility based on general medical knowledge." \
+"Consider the specificity and credibility of any numbers or percentages." \
+"Analyze the context and scope of the claim." \
+"Assess any potential biases or limitations." \
+"Output Format:" \
+"After your analysis, output exactly one JSON object with two keys:" \
+'"reasoning": A brief explanation (one or two sentences).' \
+'"decision": Either "SUPPORT" or "CONTRADICT" (uppercase, no additional text).' \
+"Do not add markdown formatting, code fences, or additional text. The output must start with { and end with }." \
+'Example Output: {"reasoning": "Your brief explanation here (one or two sentences).", "decision": "SUPPORT or CONTRADICT"}' \
+"Now, please evaluate the claim above." \
 ""
 }

requirements.txt

@@ -1,2 +1,3 @@
 anthropic==0.45.2
-openai==1.62.0
+openai==1.62.0
+requests