├── app.py
└── requirements.txt


/app.py:
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  1 | from langchain.vectorstores import Chroma
  2 | from langchain.embeddings import HuggingFaceEmbeddings
  3 | from langchain.text_splitter import RecursiveCharacterTextSplitter
  4 | from langchain.document_loaders import PyPDFLoader
  5 | from langchain.memory import ConversationBufferMemory
  6 | from langchain.llms import OpenAI
  7 | from langchain.chains import ConversationalRetrievalChain
  8 | import openai
  9 | import autogen
 10 | 
 11 | 
 12 | 
 13 | 
 14 | #set llm for langchain using model from lmstudio
 15 | openai.api_type = "open_ai"
 16 | openai.api_base = "http://localhost:1234/v1"
 17 | openai.api_key = "NULL"
 18 | 
 19 | #load the pdf file from directory
 20 | loaders = [PyPDFLoader('./chat_docs.pdf')]
 21 | docs = []
 22 | for file in loaders:
 23 |     docs.extend(file.load())
 24 | #split text to chunks
 25 | text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
 26 | docs = text_splitter.split_documents(docs)
 27 | 
 28 | #create a vectorstore
 29 | vectorstore = Chroma(
 30 | collection_name="full_documents",
 31 | embedding_function=HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", 
 32 |                                            model_kwargs={'device': 'cpu'})
 33 | )
 34 | vectorstore.add_documents(docs)
 35 | 
 36 | qa = ConversationalRetrievalChain.from_llm(
 37 |     OpenAI(temperature=0),
 38 |     vectorstore.as_retriever(),
 39 |     memory=ConversationBufferMemory(memory_key="chat_history", return_messages=True)
 40 | )
 41 | 
 42 | 
 43 | #set config for autogen
 44 | config_list = [
 45 |     {
 46 |          "api_base": "http://localhost:1234/v1",
 47 |         "api_key": "NULL"
 48 |     }
 49 | ]
 50 | 
 51 | #set autogen user agent and assistant agent with function calling
 52 | llm_config={
 53 |     "request_timeout": 600,
 54 |     "seed": 42,
 55 |     "config_list": config_list,
 56 |     "temperature": 0,
 57 |     "functions": [
 58 |         {
 59 |             "name": "chat_docs",
 60 |             "description": "Answer any chat_docs related questions",
 61 |             "parameters": {
 62 |                 "type": "object",
 63 |                 "properties": {
 64 |                     "question": {
 65 |                         "type": "string",
 66 |                         "description": "The question to ask in relation to chat_docs",
 67 |                     }
 68 |                 },
 69 |                 "required": ["question"],
 70 |             },
 71 |         }
 72 |     ],
 73 | }
 74 | 
 75 | #the function takes a parameter question,calls the qa chain and answer it by returnin the answer
 76 | # from the chain
 77 | def chat_docs(question):
 78 |     response = qa({"question": question})
 79 |     return response["answer"]
 80 | 
 81 | 
 82 | # create an AssistantAgent instance "assistant"
 83 | assistant = autogen.AssistantAgent(
 84 |     name="assistant",
 85 |     llm_config=llm_config,
 86 | )
 87 | 
 88 | 
 89 | # create a UserProxyAgent instance "user_proxy"
 90 | user_proxy = autogen.UserProxyAgent(
 91 |     name="user_proxy",
 92 |     human_input_mode="NEVER",
 93 |     max_consecutive_auto_reply=1,
 94 |     code_execution_config={"work_dir": "docs"},
 95 |     llm_config=llm_config,
 96 |     system_message="""Reply TERMINATE if the task has been solved at full satisfaction.
 97 | Otherwise, reply CONTINUE, or the reason why the task is not solved yet.""",
 98 |     function_map={"chat_docs":chat_docs}
 99 | )
100 | 
101 | # the assistant receives a message from the user, which contains the task description
102 | user_proxy.initiate_chat(
103 |     assistant,
104 |     message="""
105 | Find the answers to the 3 questions below from the chat_docs.pdf and do not write any code.
106 | 
107 | 1.Who is the CEO of walmart?
108 | 2.What did Doug McMillon write about walmart.
109 | 3.Write about the Executive Shuffle?
110 | 
111 | Start the work now.
112 | """
113 | )
114 | 


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/requirements.txt:
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1 | langchain 
2 | openai 
3 | tiktoken 
4 | chromadb 
5 | pypdf 
6 | pyautogen
7 | huggingface_hub
8 | sentence-transformers


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