Update app.py

app.py

@@ -7,31 +7,38 @@ from langchain_community.tools.tavily_search import TavilySearchResults
 from crewai_tools import tool
 from crewai import Crew, Task, Agent
 from sentence_transformers import SentenceTransformer
+from typing import List, Tuple
 
-# Set environment variables from Hugging Face secrets
-os.environ['GROQ_API_KEY'] = os.getenv('GROQ_API_KEY')
-os.environ['TAVILY_API_KEY'] = os.getenv('TAVILY_API_KEY')
+# === Hardcoded API Keys ===
+GROQ_API_KEY = "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"
+TAVILY_API_KEY = "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"
 
+# Set environment variables for API keys
+os.environ['GROQ_API_KEY'] = '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'
+os.environ['TAVILY_API_KEY'] = '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'
+
+# === Model and Tool Initialization ===
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 llm = ChatOpenAI(
     openai_api_base="https://api.groq.com/openai/v1",
-    openai_api_key=os.environ['GROQ_API_KEY'],
+    openai_api_key=GROQ_API_KEY,
     model_name="llama3-70b-8192",
     temperature=0.1,
     max_tokens=1000
 )
 
-rag_tool = PDFSearchTool(pdf='finance.pdf',
+rag_tool = PDFSearchTool(
+    pdf='finance.pdf',
     config=dict(
         llm=dict(
-            provider="groq", 
+            provider="groq",
             config=dict(
                 model="llama3-8b-8192",
             ),
         ),
         embedder=dict(
-            provider="huggingface", 
+            provider="huggingface",
             config=dict(
                 model="BAAI/bge-small-en-v1.5",
             ),
@@ -39,19 +46,21 @@ rag_tool = PDFSearchTool(pdf='finance.pdf',
     )
 )
 
-web_search_tool = TavilySearchResults(k=3)
+web_search_tool = TavilySearchResults(k=3, api_key=TAVILY_API_KEY)
 
+# === Tool Definitions ===
 @tool
-def router_tool(question):
-    """Router Function"""
+def router_tool(question: str) -> str:
+    """Router Function: Decides between web search and vectorstore."""
     return 'web_search'
 
+# === Agent Definitions ===
 Router_Agent = Agent(
     role='Router',
     goal='Route user question to a vectorstore or web search',
     backstory=(
-        "You are an expert at routing a user question to a vectorstore or web search."
-        "Use the vectorstore for questions on concept related to Retrieval-Augmented Generation."
+        "You are an expert at routing a user question to a vectorstore or web search. "
+        "Use the vectorstore for questions on concept related to Retrieval-Augmented Generation. "
         "You do not need to be stringent with the keywords in the question related to these topics. Otherwise, use web-search."
     ),
     verbose=True,
@@ -63,8 +72,8 @@ Retriever_Agent = Agent(
     role="Retriever",
     goal="Use the information retrieved from the vectorstore to answer the question",
     backstory=(
-        "You are an assistant for question-answering tasks."
-        "Use the information present in the retrieved context to answer the question."
+        "You are an assistant for question-answering tasks. "
+        "Use the information present in the retrieved context to answer the question. "
         "You have to provide a clear concise answer."
     ),
     verbose=True,
@@ -76,9 +85,9 @@ Grader_agent = Agent(
     role='Answer Grader',
     goal='Filter out erroneous retrievals',
     backstory=(
-        "You are a grader assessing relevance of a retrieved document to a user question."
-        "If the document contains keywords related to the user question, grade it as relevant."
-        "It does not need to be a stringent test.You have to make sure that the answer is relevant to the question."
+        "You are a grader assessing relevance of a retrieved document to a user question. "
+        "If the document contains keywords related to the user question, grade it as relevant. "
+        "It does not need to be a stringent test. You have to make sure that the answer is relevant to the question."
     ),
     verbose=True,
     allow_delegation=False,
@@ -89,8 +98,8 @@ hallucination_grader = Agent(
     role="Hallucination Grader",
     goal="Filter out hallucination",
     backstory=(
-        "You are a hallucination grader assessing whether an answer is grounded in / supported by a set of facts."
-        "Make sure you meticulously review the answer and check if the response provided is in alignment with the question asked"
+        "You are a hallucination grader assessing whether an answer is grounded in / supported by a set of facts. "
+        "Make sure you meticulously review the answer and check if the response provided is in alignment with the question asked."
     ),
     verbose=True,
     allow_delegation=False,
@@ -101,74 +110,93 @@ answer_grader = Agent(
     role="Answer Grader",
     goal="Filter out hallucination from the answer.",
     backstory=(
-        "You are a grader assessing whether an answer is useful to resolve a question."
-        "Make sure you meticulously review the answer and check if it makes sense for the question asked"
-        "If the answer is relevant generate a clear and concise response."
-        "If the answer generated is not relevant then perform a websearch using 'web_search_tool'"
+        "You are a grader assessing whether an answer is useful to resolve a question. "
+        "Make sure you meticulously review the answer and check if it makes sense for the question asked. "
+        "If the answer is relevant generate a clear and concise response. "
+        "If the answer generated is not relevant then perform a websearch using 'web_search_tool'."
     ),
     verbose=True,
     allow_delegation=False,
     llm=llm,
 )
 
+# === Task Definitions ===
 router_task = Task(
-    description=("Analyse the keywords in the question {question}"
-    "Based on the keywords decide whether it is eligible for a vectorstore search or a web search."
-    "Return a single word 'vectorstore' if it is eligible for vectorstore search."
-    "Return a single word 'websearch' if it is eligible for web search."
-    "Do not provide any other preamble or explanation."
+    description=(
+        "Analyse the keywords in the question {question}. "
+        "Based on the keywords decide whether it is eligible for a vectorstore search or a web search. "
+        "Return a single word 'vectorstore' if it is eligible for vectorstore search. "
+        "Return a single word 'websearch' if it is eligible for web search. "
+        "Do not provide any other preamble or explanation."
+    ),
+    expected_output=(
+        "Give a binary choice 'websearch' or 'vectorstore' based on the question. "
+        "Do not provide any other preamble or explanation."
     ),
-    expected_output=("Give a binary choice 'websearch' or 'vectorstore' based on the question"
-    "Do not provide any other preamble or explanation."),
     agent=Router_Agent,
     tools=[router_tool],
 )
 
 retriever_task = Task(
-    description=("Based on the response from the router task extract information for the question {question} with the help of the respective tool."
-    "Use the web_search_tool to retrieve information from the web in case the router task output is 'websearch'."
-    "Use the rag_tool to retrieve information from the vectorstore in case the router task output is 'vectorstore'."
+    description=(
+        "Based on the response from the router task extract information for the question {question} with the help of the respective tool. "
+        "Use the web_search_tool to retrieve information from the web in case the router task output is 'websearch'. "
+        "Use the rag_tool to retrieve information from the vectorstore in case the router task output is 'vectorstore'."
+    ),
+    expected_output=(
+        "You should analyse the output of the 'router_task'. "
+        "If the response is 'websearch' then use the web_search_tool to retrieve information from the web. "
+        "If the response is 'vectorstore' then use the rag_tool to retrieve information from the vectorstore. "
+        "Return a clear and concise text as response."
     ),
-    expected_output=("You should analyse the output of the 'router_task'"
-    "If the response is 'websearch' then use the web_search_tool to retrieve information from the web."
-    "If the response is 'vectorstore' then use the rag_tool to retrieve information from the vectorstore."
-    "Return a clear and concise text as response."),
     agent=Retriever_Agent,
     context=[router_task],
 )
 
 grader_task = Task(
-    description=("Based on the response from the retriever task for the question {question} evaluate whether the retrieved content is relevant to the question."
+    description=(
+        "Based on the response from the retriever task for the question {question} evaluate whether the retrieved content is relevant to the question."
+    ),
+    expected_output=(
+        "Binary score 'yes' or 'no' score to indicate whether the document is relevant to the question. "
+        "You must answer 'yes' if the response from the 'retriever_task' is in alignment with the question asked. "
+        "You must answer 'no' if the response from the 'retriever_task' is not in alignment with the question asked. "
+        "Do not provide any preamble or explanations except for 'yes' or 'no'."
     ),
-    expected_output=("Binary score 'yes' or 'no' score to indicate whether the document is relevant to the question"
-    "You must answer 'yes' if the response from the 'retriever_task' is in alignment with the question asked."
-    "You must answer 'no' if the response from the 'retriever_task' is not in alignment with the question asked."
-    "Do not provide any preamble or explanations except for 'yes' or 'no'."),
     agent=Grader_agent,
     context=[retriever_task],
 )
 
 hallucination_task = Task(
-    description=("Based on the response from the grader task for the question {question} evaluate whether the answer is grounded in / supported by a set of facts."),
-    expected_output=("Binary score 'yes' or 'no' score to indicate whether the answer is sync with the question asked"
-    "Respond 'yes' if the answer is useful and contains fact about the question asked."
-    "Respond 'no' if the answer is not useful and does not contains fact about the question asked."
-    "Do not provide any preamble or explanations except for 'yes' or 'no'."),
+    description=(
+        "Based on the response from the grader task for the question {question} evaluate whether the answer is grounded in / supported by a set of facts."
+    ),
+    expected_output=(
+        "Binary score 'yes' or 'no' score to indicate whether the answer is sync with the question asked. "
+        "Respond 'yes' if the answer is useful and contains fact about the question asked. "
+        "Respond 'no' if the answer is not useful and does not contains fact about the question asked. "
+        "Do not provide any preamble or explanations except for 'yes' or 'no'."
+    ),
     agent=hallucination_grader,
     context=[grader_task],
 )
 
 answer_task = Task(
-    description=("Based on the response from the hallucination task for the question {question} evaluate whether the answer is useful to resolve the question."
-    "If the answer is 'yes' return a clear and concise answer."
-    "If the answer is 'no' then perform a 'websearch' and return the response"),
-    expected_output=("Return a clear and concise response if the response from 'hallucination_task' is 'yes'."
-    "Perform a web search using 'web_search_tool' and return a clear and concise response only if the response from 'hallucination_task' is 'no'."
-    "Otherwise respond as 'Sorry! unable to find a valid response'."),
+    description=(
+        "Based on the response from the hallucination task for the question {question} evaluate whether the answer is useful to resolve the question. "
+        "If the answer is 'yes' return a clear and concise answer. "
+        "If the answer is 'no' then perform a 'websearch' and return the response."
+    ),
+    expected_output=(
+        "Return a clear and concise response if the response from 'hallucination_task' is 'yes'. "
+        "Perform a web search using 'web_search_tool' and return a clear and concise response only if the response from 'hallucination_task' is 'no'. "
+        "Otherwise respond as 'Sorry! unable to find a valid response'."
+    ),
     context=[hallucination_task],
     agent=answer_grader,
 )
 
+# === Crew Definition ===
 rag_crew = Crew(
     agents=[Router_Agent, Retriever_Agent, Grader_agent, hallucination_grader, answer_grader],
     tasks=[router_task, retriever_task, grader_task, hallucination_task, answer_task],
@@ -176,13 +204,14 @@ rag_crew = Crew(
 )
 
 def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
+    message: str,
+    history: List[Tuple[str, str]],
+    system_message: str,
+    max_tokens: int,
+    temperature: float,
+    top_p: float,
 ):
+    """Main response function for Gradio chat interface."""
     messages = [{"role": "system", "content": system_message}]
 
     for val in history:
@@ -208,6 +237,7 @@ def respond(
         response += token
         yield response
 
+# === Gradio Interface ===
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
@@ -225,4 +255,4 @@ demo = gr.ChatInterface(
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()