LangChain Deep Dive cho Text

1from langchain_openai import ChatOpenAI
2from langchain_anthropic import ChatAnthropic
3
4# Fast and cheap - high volume tasks
5fast_llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
6
7# Powerful - complex reasoning
8power_llm = ChatOpenAI(model="gpt-4o", temperature=0.3)
9
10# Long context - large documents
11long_llm = ChatAnthropic(
12    model="claude-3-5-sonnet-20241022",
13    max_tokens=4096
14)

1from langchain_core.prompts import FewShotChatMessagePromptTemplate
2
3examples = [
4    {"input": "Toi rat thich san pham nay!", "output": "Positive"},
5    {"input": "San pham kem chat luong", "output": "Negative"},
6    {"input": "San pham binh thuong", "output": "Neutral"},
7]
8
9example_prompt = ChatPromptTemplate.from_messages([
10    ("human", "{input}"),
11    ("ai", "{output}")
12])
13
14few_shot = FewShotChatMessagePromptTemplate(
15    example_prompt=example_prompt,
16    examples=examples
17)
18
19final_prompt = ChatPromptTemplate.from_messages([
20    ("system", "Ban la sentiment analyzer. Phan loai sentiment cua text."),
21    few_shot,
22    ("human", "{text}")
23])
24
25chain = final_prompt | fast_llm
26result = chain.invoke({"text": "Dich vu tuyet voi, se quay lai!"})

1from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
2
3dynamic_prompt = ChatPromptTemplate.from_messages([
4    ("system", "Ban la {role} chuyen ve {domain}."),
5    MessagesPlaceholder("history", optional=True),
6    ("human", "{input}")
7])
8
9# Use with different configs
10result = dynamic_prompt.invoke({
11    "role": "content writer",
12    "domain": "technology",
13    "input": "Viet intro cho blog ve AI",
14    "history": []
15})

1from langchain_core.chat_history import InMemoryChatMessageHistory
2from langchain_core.runnables.history import RunnableWithMessageHistory
3
4store = {}
5
6def get_session_history(session_id: str):
7    if session_id not in store:
8        store[session_id] = InMemoryChatMessageHistory()
9    return store[session_id]
10
11prompt = ChatPromptTemplate.from_messages([
12    ("system", "Ban la writing assistant. Nho context hoi thoai."),
13    MessagesPlaceholder("history"),
14    ("human", "{input}")
15])
16
17chain = prompt | fast_llm
18
19with_memory = RunnableWithMessageHistory(
20    chain,
21    get_session_history,
22    input_messages_key="input",
23    history_messages_key="history"
24)
25
26# Session 1
27config = {"configurable": {"session_id": "user-1"}}
28r1 = with_memory.invoke({"input": "Toi muon viet blog ve Python"}, config=config)
29r2 = with_memory.invoke({"input": "Goi y 5 tieu de"}, config=config)
30# r2 nho context la Python blog

1from langchain_core.callbacks import BaseCallbackHandler
2
3class TokenCounter(BaseCallbackHandler):
4    def __init__(self):
5        self.total_tokens = 0
6    
7    def on_llm_end(self, response, **kwargs):
8        if response.llm_output:
9            usage = response.llm_output.get("token_usage", {})
10            self.total_tokens += usage.get("total_tokens", 0)
11            print(f"Tokens used: {usage.get('total_tokens', 0)}")
12
13counter = TokenCounter()
14
15llm_with_callbacks = ChatOpenAI(
16    model="gpt-4o-mini",
17    callbacks=[counter]
18)
19
20# Run chain
21result = (prompt | llm_with_callbacks).invoke({"input": "Hello"})
22print(f"Total tokens: {counter.total_tokens}")

1from langchain_core.runnables import RunnableWithFallbacks
2
3primary = ChatOpenAI(model="gpt-4o")
4fallback = ChatOpenAI(model="gpt-4o-mini")
5backup = ChatAnthropic(model="claude-3-5-sonnet-20241022")
6
7# Auto-fallback khi primary fail
8robust_llm = primary.with_fallbacks([fallback, backup])
9
10chain = prompt | robust_llm | StrOutputParser()
11# Neu gpt-4o fail -> try gpt-4o-mini -> try claude

1from langchain_core.globals import set_llm_cache
2from langchain_community.cache import InMemoryCache
3
4# Enable caching
5set_llm_cache(InMemoryCache())
6
7# First call - hits API
8result1 = llm.invoke("Giai thich AI")
9# Second call with same input - returns cached result
10result2 = llm.invoke("Giai thich AI")  # Instant!