added validation using tonic validate

api/Pipfile

@@ -6,7 +6,7 @@ name = "pypi"
 [packages]
 stampy-chat = {editable = true, path = "."}
 # ---- <flask stuff> ----
-flask = "==1.1.2"
+flask = ">=2.2.2"
 gunicorn = "==20.0.4"
 jinja2 = "==2.11.3"
 markupsafe = "==1.1.1"
@@ -25,7 +25,7 @@ requests = "*"
 alembic = "*"
 sqlalchemy = "*"
 mysql-connector-python = "*"
-langchain = "*"
+langchain = "==0.1.20"
 transformers = "*"
 langchain-anthropic = "*"
 

api/src/stampy_chat/chat.py

@@ -1,5 +1,8 @@
 from typing import Any, Callable, Dict, List
 
+from tonic_validate import BenchmarkItem, ValidateScorer, LLMResponse
+from tonic_validate.metrics import AnswerConsistencyMetric, RetrievalPrecisionMetric, AugmentationAccuracyMetric
+
 from langchain.chains import LLMChain, OpenAIModerationChain
 from langchain_community.chat_models import ChatOpenAI
 from langchain_anthropic import ChatAnthropic
@@ -17,7 +20,7 @@
 from stampy_chat.settings import Settings, MODELS, OPENAI, ANTRHROPIC
 from stampy_chat.callbacks import StampyCallbackHandler, BroadcastCallbackHandler, LoggerCallbackHandler
 from stampy_chat.followups import StampyChain
-from stampy_chat.citations import make_example_selector
+from stampy_chat.citations import make_example_selector, get_top_k_blocks
 
 from langsmith import Client
 
@@ -322,6 +325,7 @@ def run_query(session_id: str, query: str, history: List[Dict], settings: Settin
     :param Callable[[Any], None] callback: an optional callback that will be called at various key parts of the chain
     :returns: the result of the chain
     """
+
     callbacks = [LoggerCallbackHandler(session_id=session_id, query=query, history=history)]
     if callback:
         callbacks += [BroadcastCallbackHandler(callback)]
@@ -340,9 +344,38 @@ def run_query(session_id: str, query: str, history: List[Dict], settings: Settin
         prompt=make_prompt(settings, chat_model, callbacks),
         memory=make_memory(settings, history, callbacks)
     )
+
     if followups:
         chain = chain | StampyChain(callbacks=callbacks)
+
     result = chain.invoke({"query": query, 'history': history}, {'callbacks': []})
+
+    #Validate results
+    contexts=[c['text'] for c in get_top_k_blocks(query, 5)]
+    rag_response={
+            "llm_answer": result['text'],
+            "llm_context_list": contexts
+            }
+
+    benchmark = BenchmarkItem(question=query)
+
+    llm_response = LLMResponse(
+            llm_answer=result['text'],
+            llm_context_list=contexts,
+            benchmark_item=benchmark
+        )
+
+    # Score the responses
+    scorer = ValidateScorer([
+        AnswerConsistencyMetric(),
+        AugmentationAccuracyMetric(),
+        RetrievalPrecisionMetric()
+    ])
+    run = scorer.score_responses([llm_response])
+    print(run.overall_scores)
+
+
+
     if callback:
         callback({'state': 'done'})
         callback(None)  # make sure the callback handler know that things have ended