Update app.py

app.py

@@ -3,7 +3,7 @@ import os
 import logging
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.output_parsers import StrOutputParser
-from langchain_openai import ChatOpenAI
+#from langchain_openai import ChatOpenAI
 from langchain_community.graphs import Neo4jGraph
 from typing import List, Tuple
 from pydantic import BaseModel, Field
@@ -42,6 +42,15 @@ graph = Neo4jGraph(
     password="Z10duoPkKCtENuOukw3eIlvl0xJWKtrVSr-_hGX1LQ4"
 )
 
+os.environ["NVIDIA_API_KEY"] = "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"
+llm = ChatNVIDIA(model="meta/llama-3.1-70b-instruct")
+
+
+llm = ChatNVIDIA(
+  base_url="https://integrate.api.nvidia.com/v1", 
+  model="meta/llama-3.1-8b-instruct"
+)
+
 # Define entity extraction and retrieval functions
 class Entities(BaseModel):
     names: List[str] = Field(
@@ -53,7 +62,7 @@ entity_prompt = ChatPromptTemplate.from_messages([
     ("human", "Use the given format to extract information from the following input: {question}"),
 ])
 
-chat_model = ChatOpenAI(temperature=0, model_name="gpt-4o", api_key=os.environ['OPENAI_API_KEY'])
+#chat_model = ChatOpenAI(temperature=0, model_name="gpt-4o", api_key=os.environ['OPENAI_API_KEY'])
 entity_chain = entity_prompt | chat_model.with_structured_output(Entities)
 
 def remove_lucene_chars(input: str) -> str:
@@ -129,21 +138,35 @@ _search_query = RunnableBranch(
             chat_history=lambda x: _format_chat_history(x["chat_history"])
         )
         | CONDENSE_QUESTION_PROMPT
-        | ChatOpenAI(temperature=0, api_key=os.environ['OPENAI_API_KEY'])
+        | llm
         | StrOutputParser(),
     ),
     RunnableLambda(lambda x: x["question"]),
 )
 
 
-template = """I am a guide for Birmingham, Alabama. I can provide recommendations and insights about the city, including events and activities.
-Ask your question directly, and I'll provide a precise and quick,short and crisp response in a conversational way without any Greet.
-{context}
-Question: {question}
-Answer:"""
+#template = """I am a guide for Birmingham, Alabama. I can provide recommendations and insights about the city, including events and activities.
+#Ask your question directly, and I'll provide a precise and quick,short and crisp response in a conversational way without any Greet.
+#{context}
+#Question: {question}
+#Answer:"""
+
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+           "I am a guide for Birmingham, Alabama. I can provide recommendations and insights about the city, including events and activities.
+           Ask your question directly, and I'll provide a precise and quick,short and crisp response in a conversational way without any Greet."
+        ),
+        MessagesPlaceholder(variable_name="chat_history"),
+        ("user", "{input}"),
+        MessagesPlaceholder(variable_name="agent_scratchpad"),
+    ]
+)
 
 
-qa_prompt = ChatPromptTemplate.from_template(template)
+qa_prompt = ChatPromptTemplate.from_template(prompt)
 
 # Define the chain for Neo4j-based retrieval and response generation
 chain_neo4j = (
@@ -154,7 +177,7 @@ chain_neo4j = (
         }
     )
     | qa_prompt
-    | chat_model
+    | llm
     | StrOutputParser()
 )