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app.py

@@ -31,8 +31,6 @@ from langchain_core.prompts import ChatPromptTemplate
 # LangChain OpenAI imports
 from langchain_openai import AzureOpenAIEmbeddings, AzureChatOpenAI  # OpenAI embeddings and models
 from langchain.embeddings.openai import OpenAIEmbeddings  # OpenAI embeddings for text vectors
-from langchain_openai import OpenAIEmbeddings, ChatOpenAI
-from langchain_openai import ChatOpenAI
 
 # LlamaParse & LlamaIndex imports
 from llama_parse import LlamaParse  # Document parsing library
@@ -143,7 +141,7 @@ def expand_query(state):
 # Initialize the Chroma vector store for retrieving documents
 vector_store = Chroma(
     collection_name="nutritional_hypotheticals",
-    persist_directory="/nutritional_db",
+    persist_directory="./nutritional_db",
     embedding_function=embedding_model
 
 )
@@ -246,9 +244,9 @@ def score_groundedness(state: Dict) -> Dict:
         Dict: The updated state with the groundedness score.
     """
     print("---------check_groundedness---------")
-    system_message = '''Given the context and the response, score the response's groundedness, 
-        which represents its factual alignment with the context. 
-        A score of 1.0 represents perfect alignment with the context, 
+    system_message = '''Given the context and the response, score the response's groundedness,
+        which represents its factual alignment with the context.
+        A score of 1.0 represents perfect alignment with the context,
         while a score of 0.0 represents a complete lack of alignment.
 
         Context: {context}
@@ -257,15 +255,15 @@ def score_groundedness(state: Dict) -> Dict:
 
         ## Instructions:
         - Please provide a groundedness score.
-        - The groundedness score should be a floating point number between 0.0 and 1.0 inclusive. 
-        - Please focus on evaluating the factual consistency and support for the claims 
-          in the response with information provided in the context.  
-        - If the response introduces or hallucinates information not found in the context, 
+        - The groundedness score should be a floating point number between 0.0 and 1.0 inclusive.
+        - Please focus on evaluating the factual consistency and support for the claims
+          in the response with information provided in the context.
+        - If the response introduces or hallucinates information not found in the context,
           consider it as evidence for a lower score.
-        - If any facts presented in the response are contradicted or unsupported by the context, 
+        - If any facts presented in the response are contradicted or unsupported by the context,
           consider it as evidence for a lower score.
 
-        Groundedness Score: 
+        Groundedness Score:
         '''
 
     groundedness_prompt = ChatPromptTemplate.from_messages([
@@ -301,16 +299,16 @@ def check_precision(state: Dict) -> Dict:
     system_message = '''Given question, answer and context verify if the context was useful in arriving at the given answer.
                        ## Instructions:
                   - Please provide a precision score.
-                  - The precision score should be a floating point number between 0.0 and 1.0 inclusive. 
-                  - Please focus on evaluating the factual consistency and support for the claims 
-                    in the response with information provided in the context.  
-                  - If the response introduces or hallucinates information not found in the context, 
+                  - The precision score should be a floating point number between 0.0 and 1.0 inclusive.
+                  - Please focus on evaluating the factual consistency and support for the claims
+                    in the response with information provided in the context.
+                  - If the response introduces or hallucinates information not found in the context,
                     consider it as evidence for a lower score.
-                  - If any facts presented in the response are contradicted or unsupported by the context, 
+                  - If any facts presented in the response are contradicted or unsupported by the context,
                     consider it as evidence for a lower score.
 
                     Precision score:
-                    
+
                     '''
 
     precision_prompt = ChatPromptTemplate.from_messages([