ساخت چت‌ بات با استفاده از LangChain

1pip install langchain langchain-core langgraph>0.2.27 langchain_openai

1conda install langchain langchain-core langgraph>0.2.27 langchain_openai -c conda-forge

1export LANGCHAIN_TRACING_V2="true"
2export LANGCHAIN_API_KEY="..."

1import os
2
3os.environ["LANGCHAIN_TRACING_V2"] = "true"
4os.environ["LANGCHAIN_API_KEY"] = "..."

1from langchain_openai import ChatOpenAI
2
3model = ChatOpenAI(
4    api_key="GILAS_API_KEY",
5    base_url="https://api.gilas.io/v1/",
6    model="gpt-4o-mini")

1from langchain_core.messages import HumanMessage
2
3model.invoke([HumanMessage(content="Hi! I'm Bob")])

1AIMessage(content='Hi Bob! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 11, 'total_tokens': 21, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-149994c0-d958-49bb-9a9d-df911baea29f-0', usage_metadata={'input_tokens': 11, 'output_tokens': 10, 'total_tokens': 21})

1model.invoke([HumanMessage(content="What's my name?")])

1AIMessage(content="I'm sorry, but I don't have access to personal information about individuals unless you've shared it with me in this conversation. How can I assist you today?", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 30, 'prompt_tokens': 11, 'total_tokens': 41, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-0ecab57c-728d-4fd1-845c-394a62df8e13-0', usage_metadata={'input_tokens': 11, 'output_tokens': 30, 'total_tokens': 41})

1from langchain_core.messages import AIMessage
2
3model.invoke(
4    [
5        HumanMessage(content="Hi! I'm Bob"),
6        AIMessage(content="Hello Bob! How can I assist you today?"),
7        HumanMessage(content="What's my name?"),
8    ]
9)

1AIMessage(content='Your name is Bob! How can I help you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 12, 'prompt_tokens': 33, 'total_tokens': 45, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-c164c5a1-d85f-46ee-ba8a-bb511cfb0e51-0', usage_metadata={'input_tokens': 33, 'output_tokens': 12, 'total_tokens': 45})

 1from langgraph.checkpoint.memory import MemorySaver
 2from langgraph.graph import START, MessagesState, StateGraph
 3
 4# Define a new graph
 5workflow = StateGraph(state_schema=MessagesState)
 6
 7
 8# Define the function that calls the model
 9def call_model(state: MessagesState):
10    response = model.invoke(state["messages"])
11    return {"messages": response}
12
13
14# Define the (single) node in the graph
15workflow.add_edge(START, "model")
16workflow.add_node("model", call_model)
17
18# Add memory
19memory = MemorySaver()
20app = workflow.compile(checkpointer=memory)

1config = {"configurable": {"thread_id": "abc123"}}

1query = "Hi! I'm Bob."
2
3input_messages = [HumanMessage(query)]
4output = app.invoke({"messages": input_messages}, config)
5output["messages"][-1].pretty_print()  # output contains all messages in state

1Ai Message:
2
3Hi Bob! How can I assist you today?

1query = "What's my name?"
2
3input_messages = [HumanMessage(query)]
4output = app.invoke({"messages": input_messages}, config)
5output["messages"][-1].pretty_print()

1Ai Message:
2
3Your name is Bob! How can I help you today?

1config = {"configurable": {"thread_id": "abc234"}}
2
3input_messages = [HumanMessage(query)]
4output = app.invoke({"messages": input_messages}, config)
5output["messages"][-1].pretty_print()

1Ai Message:
2
3I'm sorry, but I don't have access to personal information about you unless you provide it. How can I assist you today?

1config = {"configurable": {"thread_id": "abc123"}}
2
3input_messages = [HumanMessage(query)]
4output = app.invoke({"messages": input_messages}, config)
5output["messages"][-1].pretty_print()

1Ai Message:
2
3Your name is Bob! If there's anything else you'd like to discuss or ask, feel free!

 1# Async function for node:
 2async def call_model(state: MessagesState):
 3    response = await model.ainvoke(state["messages"])
 4    return {"messages": response}
 5
 6
 7# Define graph as before:
 8workflow = StateGraph(state_schema=MessagesState)
 9workflow.add_edge(START, "model")
10workflow.add_node("model", call_model)
11app = workflow.compile(checkpointer=MemorySaver())
12
13# Async invocation:
14output = await app.ainvoke({"messages": input_messages}, config)
15output["messages"][-1].pretty_print()

 1from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 2
 3prompt = ChatPromptTemplate.from_messages(
 4    [
 5        (
 6            "system",
 7            "You talk like a pirate. Answer all questions to the best of your ability.",
 8        ),
 9        MessagesPlaceholder(variable_name="messages"),
10    ]
11)

 1workflow = StateGraph(state_schema=MessagesState)
 2
 3
 4def call_model(state: MessagesState):
 5    # change-start
 6    chain = prompt | model
 7    response = chain.invoke(state)
 8    # change-end
 9    return {"messages": response}
10
11
12workflow.add_edge(START, "model")
13workflow.add_node("model", call_model)
14
15memory = MemorySaver()
16app = workflow.compile(checkpointer=memory)

1config = {"configurable": {"thread_id": "abc345"}}
2query = "Hi! I'm Jim."
3
4input_messages = [HumanMessage(query)]
5output = app.invoke({"messages": input_messages}, config)
6output["messages"][-1].pretty_print()

1Ai Message:
2
3Ahoy there, Jim! What brings ye to these treacherous waters today? Be ye seekin’ treasure, tales, or perhaps a bit o’ knowledge? Speak up, matey!

1query = "What is my name?"
2
3input_messages = [HumanMessage(query)]
4output = app.invoke({"messages": input_messages}, config)
5output["messages"][-1].pretty_print()

1prompt = ChatPromptTemplate.from_messages(
2    [
3        (
4            "system",
5            "You are a helpful assistant. Answer all questions to the best of your ability in {language}.",
6        ),
7        MessagesPlaceholder(variable_name="messages"),
8    ]
9)

 1from typing import Sequence
 2
 3from langchain_core.messages import BaseMessage
 4from langgraph.graph.message import add_messages
 5from typing_extensions import Annotated, TypedDict
 6
 7
 8class State(TypedDict):
 9    messages: Annotated[Sequence[BaseMessage], add_messages]
10    language: str
11
12
13workflow = StateGraph(state_schema=State)
14
15
16def call_model(state: State):
17    chain = prompt | model
18    response = chain.invoke(state)
19    return {"messages": [response]}
20
21
22workflow.add_edge(START, "model")
23workflow.add_node("model", call_model)
24
25memory = MemorySaver()
26app = workflow.compile(checkpointer=memory)

 1config = {"configurable": {"thread_id": "abc456"}}
 2query = "Hi! I'm Bob."
 3language = "Farsi"
 4
 5input_messages = [HumanMessage(query)]
 6output = app.invoke(
 7    {"messages": input_messages, "language": language},
 8    config,
 9)
10output["messages"][-1].pretty_print()

1Ai Message:
2
3سلام باب! چه کمکی میتونم بهت بکنم؟

 1from langchain_core.messages import SystemMessage, trim_messages
 2
 3trimmer = trim_messages(
 4    max_tokens=65,
 5    strategy="last",
 6    token_counter=model,
 7    include_system=True,
 8    allow_partial=False,
 9    start_on="human",
10)
11
12messages = [
13    SystemMessage(content="you're a good assistant"),
14    HumanMessage(content="hi! I'm bob"),
15    AIMessage(content="hi!"),
16    HumanMessage(content="I like vanilla ice cream"),
17    AIMessage(content="nice"),
18    HumanMessage(content="whats 2 + 2"),
19    AIMessage(content="4"),
20    HumanMessage(content="thanks"),
21    AIMessage(content="no problem!"),
22    HumanMessage(content="having fun?"),
23    AIMessage(content="yes!"),
24]
25
26trimmer.invoke(messages)

 1workflow = StateGraph(state_schema=State)
 2
 3
 4def call_model(state: State):
 5    chain = prompt | model
 6    # change-start
 7    trimmed_messages = trimmer.invoke(state["messages"])
 8    response = chain.invoke(
 9        {"messages": trimmed_messages, "language": state["language"]}
10    )
11    # change-end
12    return {"messages": [response]}
13
14
15workflow.add_edge(START, "model")
16workflow.add_node("model", call_model)
17
18memory = MemorySaver()
19app = workflow.compile(checkpointer=memory)

 1config = {"configurable": {"thread_id": "abc567"}}
 2query = "What is my name?"
 3language = "English"
 4
 5input_messages = messages + [HumanMessage(query)]
 6output = app.invoke(
 7    {"messages": input_messages, "language": language},
 8    config,
 9)
10output["messages"][-1].pretty_print()

1Ai Message:
2
3I don't know your name. If you'd like to share it, feel free!

 1config = {"configurable": {"thread_id": "abc678"}}
 2query = "What math problem did I ask?"
 3language = "English"
 4
 5input_messages = messages + [HumanMessage(query)]
 6output = app.invoke(
 7    {"messages": input_messages, "language": language},
 8    config,
 9)
10output["messages"][-1].pretty_print()

1Ai Message:
2
3You asked what 2 + 2 equals.

 1config = {"configurable": {"thread_id": "abc789"}}
 2query = "Hi I'm Todd, please tell me a joke."
 3language = "English"
 4
 5input_messages = [HumanMessage(query)]
 6for chunk, metadata in app.stream(
 7    {"messages": input_messages, "language": language},
 8    config,
 9    stream_mode="messages",
10):
11    if isinstance(chunk, AIMessage):  # Filter to just model responses
12        print(chunk.content, end="|")

1|Hi| Todd|!| Here|’s| a| joke| for| you|:
2
3|Why| did| the| scare|crow| win| an| award|?
4
5|Because| he| was| outstanding| in| his| field|!||