Merge pull request #2 from Emerald33/submain

general code updates

langchain_folder/agent.py

@@ -3,42 +3,77 @@
 import tempfile
 
 
+from langchain_core.tools import Tool, tool
+from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper
+from langchain.pydantic_v1 import BaseModel, Field
+from langchain_community.document_loaders import PyPDFLoader
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores.docarray import DocArrayInMemorySearch
+from langchain.chains import RetrievalQA
+from langchain_openai import ChatOpenAI
 from langchain_community.llms import ollama
 from langchain_community.chat_models import ChatOllama
 from langchain.prompts import ChatPromptTemplate
 from langchain_core.utils.function_calling import convert_to_openai_function
 from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
 from langchain.prompts import MessagesPlaceholder
-from langchain.schema.runnable.config import RunnableConfig
-from langchain.schema.runnable import RunnablePassthrough, Runnable
+from langchain.schema.runnable import RunnablePassthrough
 from langchain.agents import AgentExecutor
 from langchain.memory import ConversationBufferMemory
 from langchain.agents.format_scratchpad import format_to_openai_functions
-from langchain.pydantic_v1 import BaseModel, Field
-from langchain_core.tools import tool
-from langchain_community.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores.docarray import DocArrayInMemorySearch
-from langchain.chains import RetrievalQA
+from langchain.agents.format_scratchpad.openai_tools import (
+    format_to_openai_tool_messages,
+)
+from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser
 
 from dotenv import load_dotenv
 load_dotenv()
 
-from langchain_folder.tools.serper_tool import serper_tool
+from tools.serper_tool import serper_tool
 api_key = os.getenv('OPENAI_API_KEY')
 os.environ["SERPER_API_KEY"] = os.getenv('SERPER_API_KEY')
 
 
 
 
-file_path = []
+file_path = ['/workspaces/Full_Agent_build/langchain_folder/data/cover_docs.pdf', '/workspaces/Full_Agent_build/langchain_folder/data/data_two.pdf']
+
+
+def main():
+    st.title("Multi-Agent Chatbot")
+    st.write("Ask questions based on the uploaded document and get a response")
+
+    texts = st.text_area("Enter your questions here")
+
+    file_uploaded = st.sidebar.file_uploader(
+        "Upload two PDF files containing any document", key="file_upload",
+        accept_multiple_files = True,
+        type=['pdf']
+    )
+
+    temp_dir = tempfile.mkdtemp()
+
+    if st.sidebar.button("Upload PDF File"):
+        if file_uploaded:
+           for file in file_uploaded:
+            file_dest = os.path.join(temp_dir, file.name)
+            bytes_data = file.read()
+            with open(file_dest, "wb") as f:
+                f.write(bytes_data)
+            file_path.append(file_dest)
+
+    if st.button("Ask Question"):
+        with st.spinner(text="Generating"):
+            response = generate(texts)
+            st.markdown(response)
+
 
-class RagToolOne(BaseModel):
+class RagTool(BaseModel):
     query: str = Field(description = "This should be a search query")
 
 
-@tool("rag-tool-one", args_schema=RagToolOne, return_direct=False)
+@tool("rag-tool-one", args_schema=RagTool, return_direct=False)
 def rag_one(query: str) -> str:
     """A tool that retrieves contents that are semantically relevant to the input query from the provided document.
 
@@ -49,8 +84,8 @@ def rag_one(query: str) -> str:
         str: top k amount of retrieved content from the uploaded document. content that are semantically similar to the input query.
     """
     try:
-        pdf_file_path = file_path[0]
-  
+        pdf_file_path = file_path[-2]
+
         loader = PyPDFLoader(pdf_file_path)
         pages = loader.load_and_split()
 
@@ -63,7 +98,7 @@ def rag_one(query: str) -> str:
 
         results = vectordb.similarity_search(query, k = 4 )
         result_string = "\n\n".join(str(result) for result in results)
-      
+
     except FileNotFoundError:
       result_string = ""
 
@@ -73,11 +108,8 @@ def rag_one(query: str) -> str:
 
 
 """The second RAG tool"""
-class RagToolTwo(BaseModel):
-    query: str = Field(description = "This should be a search query")
 
-
-@tool("rag-tool-two", args_schema=RagToolTwo, return_direct=False)
+@tool("rag-tool-two", args_schema=RagTool, return_direct=False)
 def rag_two(query: str) -> str:
     """A tool that retrieves contents that are semantically relevant to the input query from the provided document.
 
@@ -88,7 +120,7 @@ def rag_two(query: str) -> str:
         str: top k amount of retrieved content from the uploaded document. content that are semantically similar to the input query.
     """
     try:
-        pdf_file_path = file_path[1]
+        pdf_file_path = file_path[-1]
 
         loader = PyPDFLoader(pdf_file_path)
         pages = loader.load_and_split()
@@ -110,54 +142,48 @@ def rag_two(query: str) -> str:
 
 tools = [rag_one, rag_two, serper_tool]
 
-functions = [convert_to_openai_function(f) for f in tools]
-model = ChatOpenAI(temperature= 0, api_key = api_key).bind_functions(functions=functions)
-#model = ChatOllama(model="mistral").bind(functions = functions)
+
+# functions = [convert_to_openai_function(f) for f in tools]
+# model = ChatOpenAI(temperature= 0, api_key = api_key).bind_functions(functions=functions)
+model = ChatOpenAI(temperature= 0, api_key = api_key).bind_tools(tools=tools)
+
 
 def generate(query: str):
         """Function for interracting with the AI Agent"""
 
         prompt = ChatPromptTemplate.from_messages([
-            ("system", "You are an expert agent with the ability to decide if a function is needed and route queries to the right function"),
+            ("system", "You are an expert agent with the ability to decide if a function is needed and route queries to the right function. Don't ask, just route to the function"),
             MessagesPlaceholder(variable_name="chat_history"),
             ("user", "{input}"),
             MessagesPlaceholder(variable_name="agent_scratchpad")
         ])
-
-        agent_chain = RunnablePassthrough.assign(
-                agent_scratchpad = lambda x: format_to_openai_functions(x["intermediate_steps"])
-        ) | prompt | model | OpenAIFunctionsAgentOutputParser()
-
-        memory = ConversationBufferMemory(return_message = True, memory_key = "chat_history")
+
+        agent_chain = (
+    {
+        "input": lambda x: x["input"],
+        "agent_scratchpad": lambda x: format_to_openai_tool_messages(
+            x["intermediate_steps"]
+        ),
+        "chat_history": lambda x: x["chat_history"],
+    }
+    | prompt
+    | model
+    | OpenAIToolsAgentOutputParser()
+)
+
+        # agent_chain = RunnablePassthrough.assign(
+        #         agent_scratchpad = lambda x: format_to_openai_functions(x["intermediate_steps"])
+        # ) | prompt | model | OpenAIFunctionsAgentOutputParser()
+
+        memory = ConversationBufferMemory(return_messages = True, memory_key = "chat_history")
 
         agent_executor = AgentExecutor(agent = agent_chain, tools = tools, verbose= False, memory = memory)
 
         result = agent_executor.invoke({"input": query})
         return result['output']
 
 
-def main():
-    st.title("Multi-Agent Chatbot")
-    st.write("Ask questions based on the uploaded document and get a response")
 
-    texts = st.text_area("Enter your questions here")
-
-    file_uploaded = st.sidebar.file_uploader(
-        "Upload two PDF files containing any document", key="file_upload",
-        accept_multiple_files = True
-    )
-
-    if st.sidebar.button("Upload PDF File"):
-        if file_uploaded:
-           for file in file_uploaded:
-                while len(file_path) > 2:
-                    file_path.pop(0)
-                file_path.append(file.name)
-
-    if st.button("Ask Question"):
-        with st.spinner(text="Generating"):
-            response = generate(texts)
-            st.markdown(response)
 
 
 if __name__ =="__main__":

langchain_folder/tools/serper_tool.py

@@ -15,7 +15,7 @@ class SerperTool(BaseModel):
 
 @tool("serper_tool_main", args_schema=SerperTool, return_direct=False)
 def serper_tool(query:str) -> str:
-  """A useful for when you need to ask with search. Very useful when recent or specific information is needed from the web 
+  """A useful for when you need to ask with search. Very useful when recent or specific information is needed from the web
   """
   search = GoogleSerperAPIWrapper(k=4, type="search")
   initial_result = search.results(query)