langchain memory

1 ActiveLoop Courses/1 LangChain & Vector Databases in Production/6 Giving Memory to LLMs/1 Optimizing Your Communication: The Importance of Monitoring Message History/0_conversation_chain_simple.py

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+
+#from langchain import OpenAI, ConversationChain
+from langchain_openai import OpenAI
+from langchain.chains import ConversationChain
+
+
+llm = OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0)
+conversation = ConversationChain(llm=llm, verbose=True)
+
+output = conversation.predict(input="Hi there!")
+
+print(output)

1 ActiveLoop Courses/1 LangChain & Vector Databases in Production/6 Giving Memory to LLMs/1 Optimizing Your Communication: The Importance of Monitoring Message History/1_conversation_chain_concatenating_buffermemory.py

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+#from langchain import OpenAI, ConversationChain
+from langchain_openai import OpenAI
+from langchain.chains import ConversationChain
+
+llm = OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0)
+conversation = ConversationChain(llm=llm, verbose=True)
+
+from langchain.memory import ConversationBufferMemory
+
+memory = ConversationBufferMemory(return_messages=True)
+memory.save_context({"input": "hi there!"}, {"output": "Hi there! It's nice to meet you. How can I help you today?"})
+
+print( memory.load_memory_variables({}) )
+
+output = conversation.predict(input="In what scenarios extra memory should be used?")
+output = conversation.predict(input="There are various types of memory in Langchain. When to use which type?")
+output = conversation.predict(input="Do you remember what was our first message?")
+
+
+print(output)
+print("------------------")
+print( memory.load_memory_variables({}) )

1 ActiveLoop Courses/1 LangChain & Vector Databases in Production/6 Giving Memory to LLMs/1 Optimizing Your Communication: The Importance of Monitoring Message History/1_conversation_chain_concatenating_dontwork.py

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+from langchain import OpenAI, ConversationChain
+#from langchain_openai import OpenAI
+#from langchain.chains import ConversationChain
+
+llm = OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0)
+conversation = ConversationChain(llm=llm, verbose=True)
+
+output = conversation.predict(input="In what scenarios extra memory should be used?")
+output = conversation.predict(input="There are various types of memory in Langchain. When to use which type?")
+output = conversation.predict(input="Do you remember what was our first message?")
+
+print ("---- Output ----")  
+print(output)

1 ActiveLoop Courses/1 LangChain & Vector Databases in Production/6 Giving Memory to LLMs/1 Optimizing Your Communication: The Importance of Monitoring Message History/1_conversation_chain_concatenating_memory.py

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+from langchain_openai import OpenAI
+from langchain.chains import ConversationChain
+from langchain.memory import ConversationBufferMemory
+
+# Initialize the language model
+llm = OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0)
+
+# Initialize the memory
+memory = ConversationBufferMemory()
+
+# Initialize the conversation chain with the language model and memory
+conversation = ConversationChain(llm=llm, memory=memory, verbose=True)
+
+# Simulate a conversation
+output1 = conversation.predict(input="In what scenarios extra memory should be used?")
+print("--- Output 1 ---")
+print(output1)
+
+# Check memory after first message
+print("--- Memory after Output 1 ---")
+print(memory.buffer)
+
+output2 = conversation.predict(input="There are various types of memory in Langchain. When to use which type?")
+print("--- Output 2 ---")
+print(output2)
+
+# Check memory after second message
+print("--- Memory after Output 2 ---")
+print(memory.buffer)
+
+output3 = conversation.predict(input="Do you remember what was our first message?")
+print("--- Output 3 ---")
+print(output3)
+
+# Check memory after third message
+print("--- Memory after Output 3 ---")
+print(memory.buffer)
+
+

1 ActiveLoop Courses/1 LangChain & Vector Databases in Production/6 Giving Memory to LLMs/1 Optimizing Your Communication: The Importance of Monitoring Message History/1_conversation_chain_concatenating_memory_alternative.py

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+from langchain_openai import OpenAI
+from langchain.chains import ConversationChain
+from langchain.memory import ConversationBufferMemory
+
+# Initialize the language model
+llm = OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0)
+
+# Initialize the memory
+memory = ConversationBufferMemory()
+
+# Initialize the conversation chain with the language model and memory
+conversation = ConversationChain(llm=llm, memory=memory, verbose=True)
+
+# Simulate a conversation
+input_messages = [
+    "In what scenarios extra memory should be used?",
+    "There are various types of memory in Langchain. When to use which type?",
+    "Do you remember what was our first message?"
+]
+output_messages = []
+
+# Iterate over input messages
+for input_message in input_messages:
+    # Predict response
+    output = conversation.predict(input=input_message)
+    print("--- Output ---")
+    print(output)
+    output_messages.append(output)
+
+    # Ensure memory buffer is initialized as a list
+    if not isinstance(memory.buffer, list):
+        memory.buffer = []
+
+    # Add input-output pair to memory buffer
+    memory.buffer.append((input_message, output))
+
+# Check memory after the conversation
+print("--- Memory ---")
+print(memory.buffer)

1 ActiveLoop Courses/1 LangChain & Vector Databases in Production/6 Giving Memory to LLMs/1 Optimizing Your Communication: The Importance of Monitoring Message History/2_conversation_buffer_memory_under_the_hoods.py

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+from langchain_openai import OpenAI
+from langchain.chains import ConversationChain
+
+from langchain.memory import ConversationBufferMemory
+
+llm = OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0)
+conversation = ConversationChain(llm=llm, verbose=True, memory=ConversationBufferMemory())
+
+
+memory = ConversationBufferMemory(return_messages=True)
+memory.save_context({"input": "hi there!"}, {"output": "Hi there! It's nice to meet you. How can I help you today?"})
+
+print( memory.load_memory_variables({}) )

1 ActiveLoop Courses/1 LangChain & Vector Databases in Production/6 Giving Memory to LLMs/1 Optimizing Your Communication: The Importance of Monitoring Message History/3_conversation_chain_with_save_context.py

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+from langchain.chains import ConversationChain
+from langchain_openai import OpenAI
+from langchain.memory import ConversationBufferMemory
+
+llm = OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0)
+
+conversation = ConversationChain(
+    llm=llm, 
+    verbose=True, 
+    memory=ConversationBufferMemory()
+)
+
+output = conversation.predict(input="In what scenarios extra memory should be used?")
+output = conversation.predict(input="There are various types of memory in Langchain. When to use which type?")
+output = conversation.predict(input="Do you remember what was our first message?")
+

1 ActiveLoop Courses/1 LangChain & Vector Databases in Production/6 Giving Memory to LLMs/1 Optimizing Your Communication: The Importance of Monitoring Message History/4_conversation_chain_full_usage.py

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+from langchain.chains import ConversationChain
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate, HumanMessagePromptTemplate
+from langchain_openai import OpenAI
+
+llm = OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0)
+
+
+prompt = ChatPromptTemplate.from_messages([
+    SystemMessagePromptTemplate.from_template("The following is a friendly conversation between a human and an AI."),
+    MessagesPlaceholder(variable_name="history"),
+    HumanMessagePromptTemplate.from_template("{input}")
+])
+
+memory = ConversationBufferMemory(return_messages=True)
+conversation = ConversationChain(memory=memory, prompt=prompt, llm=llm, verbose=True)
+
+
+print( conversation.predict(input="Tell me a joke about elephants") )
+print( conversation.predict(input="Who is the author of the Harry Potter series?") )
+print( conversation.predict(input="What was the joke you told me earlier?") )

1 ActiveLoop Courses/1 LangChain & Vector Databases in Production/6 Giving Memory to LLMs/1 Optimizing Your Communication: The Importance of Monitoring Message History/5_conversation_chain_with_memory.py

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+from langchain.chains import ConversationChain
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate, HumanMessagePromptTemplate
+from langchain_openai import OpenAI
+
+from langchain.load.dump import dumps
+
+llm = OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0)
+
+prompt = ChatPromptTemplate.from_messages([
+    SystemMessagePromptTemplate.from_template("The following is a friendly conversation between a human and an AI."),
+    MessagesPlaceholder(variable_name="history"),
+    HumanMessagePromptTemplate.from_template("{input}")
+])
+
+memory = ConversationBufferMemory(return_messages=True)
+conversation = ConversationChain(memory=memory, prompt=prompt, llm=llm, verbose=True)
+
+user_message = "Tell me about the history of the Internet."
+response = conversation(user_message)
+
+import pprint
+print("------------")
+pp = pprint.PrettyPrinter(indent=4)
+pp.pprint(response)
+
+
+
+# User sends another message
+user_message = "Who are some important figures in its development?"
+response = conversation(user_message)
+pp.pprint(response)  # Chatbot responds with names of important figures, recalling the previous topic
+
+print("------------")
+
+user_message = "What did Tim Berners-Lee contribute?"
+response = conversation(user_message)
+print(response)
+print ("---------   ")
+
+print(response['response'])

1 ActiveLoop Courses/1 LangChain & Vector Databases in Production/6 Giving Memory to LLMs/1 Optimizing Your Communication: The Importance of Monitoring Message History/chat.json

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+{
+    "input": "Tell me about the history of the Internet.",
+    "history": [
+        {
+            "lc": 1,
+            "type": "constructor",
+            "id": [
+                "langchain",
+                "schema",
+                "messages",
+                "HumanMessage"
+            ],
+            "kwargs": {
+                "content": "Tell me about the history of the Internet.",
+                "type": "human"
+            }
+        },
+        {
+            "lc": 1,
+            "type": "constructor",
+            "id": [
+                "langchain",
+                "schema",
+                "messages",
+                "AIMessage"
+            ],
+            "kwargs": {
+                "content": " \n\nAI: The Internet, also known as the World Wide Web, has a rich and complex history. It began as a project in the late 1960s by the United States Department of Defense, with the goal of creating a decentralized network that could withstand a nuclear attack. This project, called ARPANET, was the precursor to the modern Internet.\n\nIn the 1980s, the development of the TCP/IP protocol allowed for multiple networks to be connected, leading to the creation of the Internet as we know it today. The 1990s saw a surge in the popularity of the Internet, with the introduction of the World Wide Web and the first web browser, allowing for easy access to information and communication.\n\nThe early 2000s saw the rise of social media and e-commerce, further expanding the capabilities and reach of the Internet. Today, the Internet is an integral part of our daily lives, connecting people and information from all corners of the globe. Its history is constantly evolving as new technologies and innovations continue to shape its future.",
+                "type": "ai",
+                "tool_calls": [],
+                "invalid_tool_calls": []
+            }
+        }
+    ],
+    "response": " \n\nAI: The Internet, also known as the World Wide Web, has a rich and complex history. It began as a project in the late 1960s by the United States Department of Defense, with the goal of creating a decentralized network that could withstand a nuclear attack. This project, called ARPANET, was the precursor to the modern Internet.\n\nIn the 1980s, the development of the TCP/IP protocol allowed for multiple networks to be connected, leading to the creation of the Internet as we know it today. The 1990s saw a surge in the popularity of the Internet, with the introduction of the World Wide Web and the first web browser, allowing for easy access to information and communication.\n\nThe early 2000s saw the rise of social media and e-commerce, further expanding the capabilities and reach of the Internet. Today, the Internet is an integral part of our daily lives, connecting people and information from all corners of the globe. Its history is constantly evolving as new technologies and innovations continue to shape its future."
+}

1 ActiveLoop Courses/1 LangChain & Vector Databases in Production/6 Giving Memory to LLMs/2 Mastering Memory Types in Langchain: A Comprehensive Guide with Practical Examples/1_conversation_buffer_memory.py

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+#from langchain import OpenAI, ConversationChain
+from langchain_openai import OpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationChain
+
+# TODO: Set your OPENAI API credentials in environemnt variables.
+llm = OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0)
+
+conversation = ConversationChain(
+    llm=llm, 
+    verbose=True, 
+    memory=ConversationBufferMemory()
+)
+result = conversation.predict(input="Hello!")
+print(result)

1 ActiveLoop Courses/1 LangChain & Vector Databases in Production/6 Giving Memory to LLMs/2 Mastering Memory Types in Langchain: A Comprehensive Guide with Practical Examples/3_conversation_window_buffer_memory.py

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+from langchain.memory import ConversationBufferWindowMemory
+from langchain_openai import OpenAI
+from langchain.chains import ConversationChain, LLMChain
+from langchain_core.prompts import PromptTemplate
+
+llm = OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0)
+
+template = """You are ArtVenture, a cutting-edge virtual tour guide for
+ an art gallery that showcases masterpieces from alternate dimensions and
+ timelines. Your advanced AI capabilities allow you to perceive and understand
+ the intricacies of each artwork, as well as their origins and significance in
+ their respective dimensions. As visitors embark on their journey with you
+ through the gallery, you weave enthralling tales about the alternate histories
+ and cultures that gave birth to these otherworldly creations.
+
+{chat_history}
+Visitor: {visitor_input}
+Tour Guide:"""
+
+prompt = PromptTemplate(
+    input_variables=["chat_history", "visitor_input"], 
+    template=template
+)
+
+chat_history=""
+
+convo_buffer_win = ConversationChain(
+    llm=llm,
+    memory = ConversationBufferWindowMemory(k=3, return_messages=True)
+)
+
+convo_buffer_win("What is your name?")
+convo_buffer_win("What can you do?")
+convo_buffer_win("Do you mind give me a tour, I want to see your galery?")
+convo_buffer_win("what is your working hours?")
+result = convo_buffer_win("See you soon.")
+print(result)

1 ActiveLoop Courses/1 LangChain & Vector Databases in Production/6 Giving Memory to LLMs/2 Mastering Memory Types in Langchain: A Comprehensive Guide with Practical Examples/4_conversation_summary_memory.py

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+from langchain.memory import ConversationBufferWindowMemory
+from langchain_openai import OpenAI
+from langchain.chains import ConversationChain, LLMChain
+from langchain_core.prompts import PromptTemplate
+from langchain.memory import ConversationSummaryMemory
+
+
+
+llm = OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0)
+
+
+# Create a ConversationChain with ConversationSummaryMemory
+conversation_with_summary = ConversationChain(
+    llm=llm, 
+    memory=ConversationSummaryMemory(llm=llm),
+    verbose=True
+)
+
+# Example conversation
+response = conversation_with_summary.predict(input="Hi, what's up?")
+print(response)
+
+
+
+
+
+from langchain.memory import ConversationSummaryBufferMemory
+
+prompt = PromptTemplate(
+    input_variables=["topic"],
+    template="The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\nCurrent conversation:\n{topic}",
+)
+
+llm = OpenAI(temperature=0)
+conversation_with_summary = ConversationChain(
+    llm=llm,
+    memory=ConversationSummaryBufferMemory(llm=OpenAI(), max_token_limit=40),
+    verbose=True
+)
+conversation_with_summary.predict(input="Hi, what's up?")
+conversation_with_summary.predict(input="Just working on writing some documentation!")
+response = conversation_with_summary.predict(input="For LangChain! Have you heard of it?")
+print(response)
+