WIP check num responses in cache for given prompt

chain-forge/src/PromptNode.js

@@ -232,11 +232,11 @@ const PromptNode = ({ data, id }) => {
             const all_same_num_queries = Object.keys(queries_per_llm).reduce((acc, llm) => acc && queries_per_llm[llm] === some_llm_num, true)
             if (num_llms_missing === num_llms && all_same_num_queries) { // Counts are the same
                 const req = some_llm_num > 1 ? 'requests' : 'request';
-                setRunTooltip(`Will send ${some_llm_num} ${req}` + (num_llms > 1 ? ' per LLM' : ''));
+                setRunTooltip(`Will send ${some_llm_num} new ${req}` + (num_llms > 1 ? ' per LLM' : ''));
             }
             else if (all_same_num_queries) {
                 const req = some_llm_num > 1 ? 'requests' : 'request';
-                setRunTooltip(`Will send ${some_llm_num} ${req}` + (num_llms > 1 ? ` to ${num_llms_missing} LLMs` : ''));
+                setRunTooltip(`Will send ${some_llm_num} new ${req}` + (num_llms > 1 ? ` to ${num_llms_missing} LLMs` : ''));
             }
             else { // Counts are different 
                 const sum_queries = Object.keys(queries_per_llm).reduce((acc, llm) => acc + queries_per_llm[llm], 0);

python-backend/flask_app.py

@@ -321,18 +321,21 @@ async def queryLLM():
         # Prompt the LLM with all permutations of the input prompt template:
         # NOTE: If the responses are already cache'd, this just loads them (no LLM is queried, saving $$$)
         resps = []
+        num_resps = 0
         try:
             print(f'Querying {llm}...')
             async for response in prompter.gen_responses(properties=data['vars'], llm=llm, **params):
                 resps.append(response)
                 print(f"collected response from {llm.name}:", str(response))
 
+                num_resps += len(extract_responses(response, llm))
+
                 # Save the number of responses collected to a temp file on disk
                 with open(tempfilepath, 'r') as f:
                     txt = f.read().strip()
                 
                 cur_data = json.loads(txt) if len(txt) > 0 else {}
-                cur_data[llm_str] = len(resps)
+                cur_data[llm_str] = num_resps
                 
                 with open(tempfilepath, 'w') as f:
                     json.dump(cur_data, f)

python-backend/promptengine/query.py

@@ -1,7 +1,7 @@
 from abc import abstractmethod
 from typing import List, Dict, Tuple, Iterator, Union
 import json, os, asyncio, random, string
-from promptengine.utils import LLM, call_chatgpt, call_dalai, call_anthropic, is_valid_filepath, is_valid_json
+from promptengine.utils import LLM, call_chatgpt, call_dalai, call_anthropic, is_valid_filepath, is_valid_json, cull_responses, extract_responses
 from promptengine.template import PromptTemplate, PromptPermutationGenerator
 
 # LLM APIs often have rate limits, which control number of requests. E.g., OpenAI: https://platform.openai.com/account/rate-limits
@@ -59,8 +59,9 @@ class PromptPipeline:
             prompt_str = str(prompt)
             
             # First check if there is already a response for this item. If so, we can save an LLM call:
-            if prompt_str in responses:
+            if prompt_str in responses and len(extract_responses(responses[prompt_str], llm)) >= n:
                 print(f"   - Found cache'd response for prompt {prompt_str}. Using...")
+                responses[prompt_str] = cull_responses(responses[prompt_str], llm, n)
                 yield {
                     "prompt": prompt_str,
                     "query": responses[prompt_str]["query"],

python-backend/promptengine/utils.py

@@ -174,7 +174,7 @@ def _extract_chatgpt_responses(response: dict) -> List[dict]:
     choices = response["response"]["choices"]
     return [
         c["message"]["content"]
-        for i, c in enumerate(choices)
+        for c in choices
     ]
 
 def extract_responses(response: Union[list, dict], llm: Union[LLM, str]) -> List[dict]:
@@ -191,6 +191,22 @@ def extract_responses(response: Union[list, dict], llm: Union[LLM, str]) -> List
     else:
         raise ValueError(f"LLM {llm} is unsupported.")
 
+def cull_responses(response: Union[list, dict], llm: Union[LLM, str], n: int) -> Union[list, dict]:
+    """
+        Returns the same 'response' but with only 'n' responses.
+    """
+    if llm is LLM.ChatGPT or llm == LLM.ChatGPT.value or llm is LLM.GPT4 or llm == LLM.GPT4.value:
+        response["response"]["choices"] = response["response"]["choices"][:n]
+        return response
+    elif llm is LLM.Alpaca7B or llm == LLM.Alpaca7B.value:
+        response["response"] = response["response"][:n]
+        return response
+    elif (isinstance(llm, LLM) and llm.value[:6] == 'claude') or (isinstance(llm, str) and llm[:6] == 'claude'):
+        response["response"] = response["response"][:n]
+        return response
+    else:
+        raise ValueError(f"LLM {llm} is unsupported.")
+
 def create_dir_if_not_exists(path: str) -> None:
     if not os.path.exists(path):
         os.makedirs(path)