Create app.py

app.py

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+import os
+os.environ['COHERE_API_KEY'] = ""
+# Create the Cohere chat model
+from langchain_cohere.chat_models import ChatCohere
+chat = ChatCohere(model="command-r-plus", temperature=0.3)
+from langchain_community.tools.tavily_search import TavilySearchResults
+
+os.environ['TAVILY_API_KEY'] = ""
+
+internet_search = TavilySearchResults()
+internet_search.name = "internet_search"
+internet_search.description = "Returns a list of relevant document snippets for a textual query retrieved from the internet."
+
+
+from langchain_core.pydantic_v1 import BaseModel, Field
+class TavilySearchInput(BaseModel):
+    query: str = Field(description="Query to search the internet with")
+internet_search.args_schema = TavilySearchInput
+from langchain.agents import Tool
+from langchain_experimental.utilities import PythonREPL
+
+python_repl = PythonREPL()
+repl_tool = Tool(
+    name="python_repl",
+    description="Executes python code and returns the result. The code runs in a static sandbox without interactive mode, so print output or save output to a file.",
+    func=python_repl.run,
+)
+repl_tool.name = "python_interpreter"
+
+# from langchain_core.pydantic_v1 import BaseModel, Field
+class ToolInput(BaseModel):
+    code: str = Field(description="Python code to execute.")
+repl_tool.args_schema = ToolInput
+from langchain.agents import AgentExecutor
+from langchain_cohere.react_multi_hop.agent import create_cohere_react_agent
+from langchain_core.prompts import ChatPromptTemplate
+# Create the prompt
+prompt = ChatPromptTemplate.from_template("{input}")
+
+
+# Create the ReAct agent
+agent = create_cohere_react_agent(
+    llm=chat,
+    tools=[internet_search, repl_tool],
+    prompt=prompt,
+)
+agent_executor = AgentExecutor(agent=agent, tools=[internet_search, repl_tool], verbose=True)
+from typing import List, Mapping, Any
+from langchain_cohere.common import CohereCitation
+def process_data(problem):
+   output = 'Gemini agent rewriting your query \n\n'
+   yield output
+   #rewrite = get_completion(f"Rewrite the user question: {problem} ")
+   #output += f"Here is your rewritten query: {rewrite} \n\n"
+   #yield output
+   output += f"Cohere agent gathering the data from public sources and doing analysis \n\n"
+   yield output
+   coh_output = agent_executor.invoke({
+    "input": f"{problem}"})
+   print ("Output is",coh_output['output'])
+   output += f"Final Output: \n\n"+coh_output['output']+"\n\n"
+   yield output
+   citations = coh_output['citations']
+   # Assuming 'citi' is a list of CohereCitation objects
+   urls = []
+   for item in citations:
+       if isinstance(item, CohereCitation) and item.documents:
+          for doc in item.documents:
+              if 'url' in doc:
+                  urls.append(doc['url'])
+   final_urls = list(set(urls))
+   output += f"Citations: \n\n" + '\n'.join(final_urls)
+   yield output
+   #add_conversation(problem,output)
+   return output