rag chat and readme updated

README.md

@@ -11,10 +11,60 @@ license: apache-2.0
 ---
 # AutogenMultiAgent
  Autogen Multiagent 
+ AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. AutoGen aims to provide an easy-to-use and flexible framework for accelerating development and research on agentic AI, like PyTorch for Deep Learning. It offers features such as agents that can converse with other agents, LLM and tool use support, autonomous and human-in-the-loop workflows, and multi-agent conversation patterns.
+
+## AutoGen Overview
+![alt text](image-2.png)
 
 
-## Code execution 
 
+## Code execution 
  ![alt text](image.png)
+ 
+ ![alt text](image-3.png)
+
+## RAG Chat
+ ![alt text](image-1.png)
+
+ Qdrant is a high-performance vector search engine/database.
+
+This notebook demonstrates the usage of QdrantRetrieveUserProxyAgent for RAG, based on agentchat_RetrieveChat.ipynb.
+
+RetrieveChat is a conversational system for retrieve augmented code generation and question answering. In this notebook, we demonstrate how to utilize RetrieveChat to generate code and answer questions based on customized documentations that are not present in the LLM's training dataset. RetrieveChat uses the RetrieveAssistantAgent and QdrantRetrieveUserProxyAgent, which is similar to the usage of AssistantAgent and UserProxyAgent in other notebooks (e.g., Automated Task Solving with Code Generation, Execution & Debugging)
+
+:::info Requirements
+Some extra dependencies are needed for this notebook, which can be installed via pip:
+
+```bash
+pip install "pyautogen[retrievechat-qdrant]" "flaml[automl]"
+```
+
+For more information, please refer to the [installation guide](/docs/installation/).
+:::
+
+
+## Groupchat with Llamaindex agents
+Llamaindex agents have the ability to use planning strategies to answer user questions. They can be integrated in Autogen in easy ways
+
+Requirements
+%pip install pyautogen llama-index llama-index-tools-wikipedia llama-index-readers-wikipedia wikipedia
 
+## Defaults
+### LLM_OPTIONS
+    Groq
+### USECASE_OPTIONS
+    MultiAgent Code Execution
+    MultiAgent Chat
+    RAG Chat
+    With LLamaIndex Tool
+    AgentChat Sql Spider
+### GROQ_MODEL_OPTIONS
+    mixtral-8x7b-32768
+    llama3-8b-8192
+    llama3-70b-8192
+    gemma-7b-i
 
+## Important links
+https://microsoft.github.io/autogen/docs/notebooks
+https://microsoft.github.io/autogen/docs/tutorial/code-executors
+https://microsoft.github.io/autogen/docs/tutorial/tool-use

app.py

@@ -7,6 +7,7 @@ from src.usecases.multiagentcodeexecution import MultiAgentCodeExecution
 from src.usecases.withllamaIndex import WithLlamaIndexMultiAgentChat
 from src.usecases.agentchatsqlspider import AgentChatSqlSpider
 from src.LLMS.groqllm import GroqLLM
+from src.usecases.multiagentragchat import MultiAgentRAGChat
 
 
 # MAIN Function START
@@ -30,14 +31,19 @@ if __name__ == "__main__":
     if problem:
         # start multichat
         if user_input['selected_usecase'] == "MultiAgent Code Execution":
-            obj_usecases_multichat = MultiAgentCodeExecution(assistant_name=['Assistant',"Product_Manager"], user_proxy_name='Userproxy', llm_config=llm_config,
+            obj_usecases_multichatexec = MultiAgentCodeExecution(assistant_name=['Assistant',"Product_Manager"], user_proxy_name='Userproxy', llm_config=llm_config,
                                           problem=problem)
-            obj_usecases_multichat.run()
+            obj_usecases_multichatexec.run()
             
         elif user_input['selected_usecase'] == "MultiAgent Chat":
             obj_usecases_multichat = MultiAgentChat(assistant_name='Assistant', user_proxy_name='Userproxy', llm_config=llm_config,
                                           problem=problem)
             obj_usecases_multichat.run()
+            
+        elif user_input['selected_usecase'] == "RAG Chat":
+            obj_usecases_rag_multichat = MultiAgentRAGChat(assistant_name='Assistant', user_proxy_name='Userproxy', llm_config=llm_config,
+                                          problem=problem)
+            obj_usecases_rag_multichat.run()
 
         elif user_input['selected_usecase'] == "With LLamaIndex Tool":
             obj_usecases_with_llamaIndex_multichat = WithLlamaIndexMultiAgentChat(assistant_name='Assistant', user_proxy_name='Userproxy',

codegen/configfile.ini

@@ -0,0 +1,6 @@
+[DEFAULT]
+PAGE_TITLE = AUTOGEN IN ACTION
+LLM_OPTIONS = Groq, Huggingface
+USECASE_OPTIONS = MultiAgent Code Execution, MultiAgent Chat, RAG Chat, With LLamaIndex Tool, AgentChat Sql Spider
+GROQ_MODEL_OPTIONS = mixtral-8x7b-32768, llama3-8b-8192, llama3-70b-8192, gemma-7b-i
+

codegen/configfile.py

@@ -0,0 +1,20 @@
+from configparser import ConfigParser
+
+
+class Config:
+  def __init__(self, config_file="configfile.ini"):
+    self.config = ConfigParser()
+    self.config.read(config_file)
+
+  def get_llm_options(self):
+    return self.config["DEFAULT"].get("LLM_OPTIONS").split(", ")
+
+  def get_usecase_options(self):
+    return self.config["DEFAULT"].get("USECASE_OPTIONS").split(", ")
+
+  def get_groq_model_options(self):
+    return self.config["DEFAULT"].get("GROQ_MODEL_OPTIONS").split(", ")
+
+  def get_page_title(self):
+    return self.config["DEFAULT"].get("PAGE_TITLE")
+

requirements.txt

@@ -6,4 +6,8 @@ llama-index-tools-wikipedia
 llama-index-readers-wikipedia
 wikipedia
 llama-index-llms-groq
-spider-env
+spider-env
+pyautogen[retrievechat]
+pyautogen[retrievechat-qdrant]
+flaml[automl]
+sentence_transformers

src/LLMS/groqllm.py

@@ -10,9 +10,12 @@ class GroqLLM:
 
     def groq_llm_config(self):
         config_list = [
-            {"api_type": 'groq',
-             "model": self.user_controls_input['selected_groq_model'], "api_key": st.session_state["GROQ_API_KEY"],
-             "cache_seed": None}
+            {
+             "api_type": 'groq',
+             "model": self.user_controls_input['selected_groq_model'], 
+             "api_key": st.session_state["GROQ_API_KEY"],
+             "cache_seed": None
+            }
         ]
 
         llm_config = {"config_list": config_list, "request_timeout": 60}

src/agents/qdrantretrieveuserproxyagent.py

@@ -0,0 +1,10 @@
+from autogen import UserProxyAgent
+import streamlit as st
+from autogen.agentchat.contrib.qdrant_retrieve_user_proxy_agent import QdrantRetrieveUserProxyAgent
+
+
+class TrackableQdrantRetrieveUserProxyAgent(QdrantRetrieveUserProxyAgent):
+    def _process_received_message(self, message, sender, silent):
+        with st.chat_message("user"):
+            st.write(message)
+        return super()._process_received_message(message, sender, silent)

src/agents/retrieveassistantagent.py

@@ -0,0 +1,12 @@
+from autogen import AssistantAgent
+import streamlit as st
+from autogen.agentchat.contrib.retrieve_assistant_agent import RetrieveAssistantAgent
+from autogen.agentchat.contrib.qdrant_retrieve_user_proxy_agent import QdrantRetrieveUserProxyAgent
+
+
+
+class TrackableRetrieveAssistantAgent(RetrieveAssistantAgent):
+    def _process_received_message(self, message, sender, silent):
+        with st.chat_message(sender.name):
+            st.write(message)
+        return super()._process_received_message(message, sender, silent)

src/streamlitui/loadui.py

@@ -28,6 +28,10 @@ class LoadStreamlitUI:
                                                                                                       type="password")
             # Use case selection
             self.user_controls["selected_usecase"] = st.selectbox("Select Usecases", usecase_options)
+            if self.user_controls['selected_usecase'] == "RAG Chat":
+                st.session_state["docs_path"]  = st.text_input("Enter Docs path or filename")
+                
+            
             st.session_state["chat_with_history"] = st.sidebar.toggle("Chat With History")
 
         return self.user_controls

src/usecases/multiagentcodeexecution.py

@@ -8,7 +8,7 @@ import autogen
 class MultiAgentCodeExecution:
     def __init__(self, assistant_name, user_proxy_name, llm_config, problem):
         self.coder = TrackableAssistantAgent(name=assistant_name[0],
-                                                 system_message="""you are helpful assistant and efficient in writing code.""",
+                                                 system_message="""you are helpful assistant and efficient in writing code in python.""",
                                                  human_input_mode="NEVER",
                                                  llm_config=llm_config,
                                                  )
@@ -33,8 +33,15 @@ class MultiAgentCodeExecution:
         self.problem = problem
         self.loop = asyncio.new_event_loop()
         asyncio.set_event_loop(self.loop)
+        
+    def _reset(self):
+        self.coder.reset()
+        self.pm.reset()
+        
+        
 
     async def initiate_chat(self):
+        self._reset()
         await self.user_proxy.a_initiate_chat(self.manager, message=self.problem, clear_history=st.session_state["chat_with_history"])
 
     def run(self):

src/usecases/multiagentragchat.py

@@ -0,0 +1,60 @@
+import asyncio
+from src.agents.qdrantretrieveuserproxyagent import TrackableQdrantRetrieveUserProxyAgent
+from src.agents.retrieveassistantagent import TrackableRetrieveAssistantAgent
+import streamlit as st
+from qdrant_client import QdrantClient
+import glob
+import os
+from sentence_transformers import SentenceTransformer
+
+class MultiAgentRAGChat:
+    def __init__(self, assistant_name, user_proxy_name, llm_config, problem):
+        self.assistant = TrackableRetrieveAssistantAgent(name=assistant_name,
+                                                 system_message="""you are helpful assistant. Reply "TERMINATE" in 
+                                                 the end when everything is done """,
+                                                 human_input_mode="NEVER",
+                                                 llm_config=llm_config,
+                                                 )
+        self.user_proxy = TrackableQdrantRetrieveUserProxyAgent(name=user_proxy_name,
+                                                    human_input_mode="NEVER",
+                                                    max_consecutive_auto_reply=4,
+                                                    retrieve_config={
+                                                        "task": "code",
+                                                        "docs_path": self.list_files(st.session_state["docs_path"]),
+                                                        "chunk_token_size": 500,
+                                                        "model": llm_config["config_list"][0]["model"],
+                                                        "client": QdrantClient(":memory:"),
+                                                        "embedding_model": self.embeddings_model()
+                                                    },
+                                                    code_execution_config=False,
+                                                    is_termination_msg=lambda x: x.get("content", "").strip().endswith(
+                                                      "TERMINATE"))
+        self.problem = problem
+        self.loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(self.loop)
+        
+        
+    
+    def embeddings_model(self):
+        sentence_transformer_ef = SentenceTransformer("all-distilroberta-v1").encode    
+        return sentence_transformer_ef
+   
+
+    def list_files(self,directory):
+        # Ensure the directory path ends with a slash
+        if not directory.endswith('/'):
+            directory += '/'
+        
+        # Use glob to get the list of files
+        files = glob.glob(os.path.join(directory, '*'))
+        file_list = [path.replace('\\', '/') for path in files]
+        
+        return file_list
+
+
+
+    async def initiate_chat(self):
+        await self.user_proxy.a_initiate_chat(self.assistant,  message=self.user_proxy.message_generator, problem=self.problem, clear_history=st.session_state["chat_with_history"])
+
+    def run(self):
+        self.loop.run_until_complete(self.initiate_chat())