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Cookbook

In this notebook we'll take a look at a few common types of sequences to create.

PromptTemplate + LLM​

A PromptTemplate -> LLM is a core chain that is used in most other larger chains/systems.

from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI

API Reference:

    /Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.14) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.
      warnings.warn(
model = ChatOpenAI()
prompt = ChatPromptTemplate.from_template("tell me a joke about {foo}")
chain = prompt | model
chain.invoke({"foo": "bears"})
    AIMessage(content='Why don\'t bears use cell phones? \n\nBecause they always get terrible "grizzly" reception!', additional_kwargs={}, example=False)

Often times we want to attach kwargs to the model that's passed in. Here's a few examples of that:

Attaching Stop Sequences​

chain = prompt | model.bind(stop=["\n"])
chain.invoke({"foo": "bears"})
    AIMessage(content="Why don't bears use cell phones?", additional_kwargs={}, example=False)

Attaching Function Call information​

functions = [
    {
      "name": "joke",
      "description": "A joke",
      "parameters": {
        "type": "object",
        "properties": {
          "setup": {
            "type": "string",
            "description": "The setup for the joke"
          },
          "punchline": {
            "type": "string",
            "description": "The punchline for the joke"
          }
        },
        "required": ["setup", "punchline"]
      }
    }
  ]
chain = prompt | model.bind(function_call= {"name": "joke"}, functions= functions)
chain.invoke({"foo": "bears"}, config={})
    AIMessage(content='', additional_kwargs={'function_call': {'name': 'joke', 'arguments': '{\n  "setup": "Why don\'t bears wear shoes?",\n  "punchline": "Because they have bear feet!"\n}'}}, example=False)

PromptTemplate + LLM + OutputParser​

We can also add in an output parser to easily trasform the raw LLM/ChatModel output into a more workable format

from langchain.schema.output_parser import StrOutputParser

API Reference:

chain = prompt | model | StrOutputParser()

Notice that this now returns a string - a much more workable format for downstream tasks

chain.invoke({"foo": "bears"})
    "Why don't bears wear shoes?\n\nBecause they have bear feet!"

Functions Output Parser​

When you specify the function to return, you may just want to parse that directly

from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
chain = (
    prompt 
    | model.bind(function_call= {"name": "joke"}, functions= functions) 
    | JsonOutputFunctionsParser()
)

API Reference:

chain.invoke({"foo": "bears"})
    {'setup': "Why don't bears wear shoes?",
     'punchline': 'Because they have bear feet!'}
from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParser
chain = (
    prompt 
    | model.bind(function_call= {"name": "joke"}, functions= functions) 
    | JsonKeyOutputFunctionsParser(key_name="setup")
)

API Reference:

chain.invoke({"foo": "bears"})
    "Why don't bears like fast food?"

Passthroughs and itemgetter​

Often times when constructing a chain you may want to pass along original input variables to future steps in the chain. How exactly you do this depends on what exactly the input is:

  • If the original input was a string, then you likely just want to pass along the string. This can be done with RunnablePassthrough. For an example of this, see LLMChain + Retriever
  • If the original input was a dictionary, then you likely want to pass along specific keys. This can be done with itemgetter. For an example of this see Multiple LLM Chains
from langchain.schema.runnable import RunnablePassthrough
from operator import itemgetter

API Reference:

LLMChain + Retriever​

Let's now look at adding in a retrieval step, which adds up to a "retrieval-augmented generation" chain

from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.schema.runnable import RunnablePassthrough

API Reference:

# Create the retriever
vectorstore = Chroma.from_texts(["harrison worked at kensho"], embedding=OpenAIEmbeddings())
retriever = vectorstore.as_retriever()
template = """Answer the question based only on the following context:
{context}

Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
chain = (
    {"context": retriever, "question": RunnablePassthrough()} 
    | prompt 
    | model 
    | StrOutputParser()
)
chain.invoke("where did harrison work?")
    Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1





    'Harrison worked at Kensho.'
template = """Answer the question based only on the following context:
{context}

Question: {question}

Answer in the following language: {language}
"""
prompt = ChatPromptTemplate.from_template(template)

chain = {
    "context": itemgetter("question") | retriever, 
    "question": itemgetter("question"), 
    "language": itemgetter("language")
} | prompt | model | StrOutputParser()
chain.invoke({"question": "where did harrison work", "language": "italian"})
    Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1





    'Harrison ha lavorato a Kensho.'

Multiple LLM Chains​

This can also be used to string together multiple LLMChains

from operator import itemgetter

prompt1 = ChatPromptTemplate.from_template("what is the city {person} is from?")
prompt2 = ChatPromptTemplate.from_template("what country is the city {city} in? respond in {language}")

chain1 = prompt1 | model | StrOutputParser()

chain2 = {"city": chain1, "language": itemgetter("language")} | prompt2 | model | StrOutputParser()

chain2.invoke({"person": "obama", "language": "spanish"})
    'El país en el que nació la ciudad de Honolulu, Hawái, donde nació Barack Obama, el 44º presidente de los Estados Unidos, es Estados Unidos.'
from langchain.schema.runnable import RunnableMap
prompt1 = ChatPromptTemplate.from_template("generate a random color")
prompt2 = ChatPromptTemplate.from_template("what is a fruit of color: {color}")
prompt3 = ChatPromptTemplate.from_template("what is countries flag that has the color: {color}")
prompt4 = ChatPromptTemplate.from_template("What is the color of {fruit} and {country}")
chain1 = prompt1 | model | StrOutputParser()
chain2 = RunnableMap(steps={"color": chain1}) | {
    "fruit": prompt2 | model | StrOutputParser(),
    "country": prompt3 | model | StrOutputParser(),
} | prompt4

API Reference:

chain2.invoke({})
    ChatPromptValue(messages=[HumanMessage(content="What is the color of A fruit that has a color similar to #7E7DE6 is the Peruvian Apple Cactus (Cereus repandus). It is a tropical fruit with a vibrant purple or violet exterior. and The country's flag that has the color #7E7DE6 is North Macedonia.", additional_kwargs={}, example=False)])

Router​

You can also use the router runnable to conditionally route inputs to different runnables.

from langchain.chains import create_tagging_chain_pydantic
from pydantic import BaseModel, Field

class PromptToUse(BaseModel):
    """Used to determine which prompt to use to answer the user's input."""
    
    name: str = Field(description="Should be one of `math` or `english`")

API Reference:

tagger = create_tagging_chain_pydantic(PromptToUse, ChatOpenAI(temperature=0))
chain1 = ChatPromptTemplate.from_template("You are a math genius. Answer the question: {question}") | ChatOpenAI()
chain2 = ChatPromptTemplate.from_template("You are an english major. Answer the question: {question}") | ChatOpenAI()
from langchain.schema.runnable import RouterRunnable
router = RouterRunnable({"math": chain1, "english": chain2})

API Reference:

chain = {
    "key": {"input": lambda x: x["question"]} | tagger | (lambda x: x['text'].name),
    "input": {"question": lambda x: x["question"]}
} | router
chain.invoke({"question": "whats 2 + 2"})
    AIMessage(content='Thank you for the compliment! The sum of 2 + 2 is equal to 4.', additional_kwargs={}, example=False)

Tools​

You can use any LangChain tool easily

from langchain.tools import DuckDuckGoSearchRun

API Reference:

    /Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.14) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.
      warnings.warn(
search = DuckDuckGoSearchRun()
template = """turn the following user input into a search query for a search engine:

{input}"""
prompt = ChatPromptTemplate.from_template(template)
chain = prompt | model | StrOutputParser() | search
chain.invoke({"input": "I'd like to figure out what games are tonight"})
    "What sports games are on TV today & tonight? Watch and stream live sports on TV today, tonight, tomorrow. Today's 2023 sports TV schedule includes football, basketball, baseball, hockey, motorsports, soccer and more. Watch on TV or stream online on ESPN, FOX, FS1, CBS, NBC, ABC, Peacock, Paramount+, fuboTV, local channels and many other networks. Weather Alerts Alerts Bar. Not all offers available in all states, please visit BetMGM for the latest promotions for your area. Must be 21+ to gamble, please wager responsibly. If you or someone ... Speak of the Devils. Good Morning Arizona. Happy Hour Spots. Jaime's Local Love. Surprise Squad. Silver Apple. Field Trip Friday. Seen on TV. Arizona Highways TV. MLB Games Tonight: How to Watch on TV, Streaming & Odds - Friday, July 28. San Diego Padres' Juan Soto plays during the first baseball game in a doubleheader, Saturday, July 15, 2023, in Philadelphia. (AP Photo/Matt Slocum) (APMedia) Today's MLB schedule features top teams in action. Among those games is the Texas Rangers playing the San Diego ... TV. Cleveland at Chi. White Sox. 1:10pm. Bally Sports. NBCS-CHI. Cleveland Guardians (50-51) are second place in AL Central and Chicago White Sox (41-61) are fourth place in AL Central. The Guardians are 23-27 on the road this season and White Sox are 21-26 at home. Chi. Cubs at St. Louis."

Arbitrary Functions​

You can use arbitrary functions in the pipeline

Note that all inputs to these functions need to be a SINGLE argument. If you have a function that accepts multiple arguments, you should write a wrapper that accepts a single input and unpacks it into multiple argument.

from langchain.schema.runnable import RunnableLambda

def length_function(text):
    return len(text)

def _multiple_length_function(text1, text2):
    return len(text1) * len(text2)

def multiple_length_function(_dict):
    return _multiple_length_function(_dict["text1"], _dict["text2"])

prompt = ChatPromptTemplate.from_template("what is {a} + {b}")

chain1 = prompt | model

chain = {
    "a": itemgetter("foo") | RunnableLambda(length_function),
    "b": {"text1": itemgetter("foo"), "text2": itemgetter("bar")} | RunnableLambda(multiple_length_function)
} | prompt | model

API Reference:

chain.invoke({"foo": "bar", "bar": "gah"})
    AIMessage(content='3 + 9 is equal to 12.', additional_kwargs={}, example=False)

SQL Database​

We can also try to replicate our SQLDatabaseChain using this style.

template = """Based on the table schema below, write a SQL query that would answer the user's question:
{schema}

Question: {question}"""
prompt = ChatPromptTemplate.from_template(template)
from langchain.utilities import SQLDatabase

API Reference:

db = SQLDatabase.from_uri("sqlite:///../../../../notebooks/Chinook.db")
def get_schema(_):
    return db.get_table_info()
def run_query(query):
    return db.run(query)
inputs = {
    "schema": RunnableLambda(get_schema),
    "question": itemgetter("question")
}
sql_response = (
        RunnableMap(inputs)
        | prompt
        | model.bind(stop=["\nSQLResult:"])
        | StrOutputParser()
    )
sql_response.invoke({"question": "How many employees are there?"})
    'SELECT COUNT(*) \nFROM Employee;'
template = """Based on the table schema below, question, sql query, and sql response, write a natural language response:
{schema}

Question: {question}
SQL Query: {query}
SQL Response: {response}"""
prompt_response = ChatPromptTemplate.from_template(template)
full_chain = (
    RunnableMap({
        "question": itemgetter("question"),
        "query": sql_response,
    }) 
    | {
        "schema": RunnableLambda(get_schema),
        "question": itemgetter("question"),
        "query": itemgetter("query"),
        "response": lambda x: db.run(x["query"])    
    } 
    | prompt_response 
    | model
)
full_chain.invoke({"question": "How many employees are there?"})
    AIMessage(content='There are 8 employees.', additional_kwargs={}, example=False)

Code Writing​

from langchain.utilities import PythonREPL
from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate

API Reference:

template = """Write some python code to solve the user's problem. 

Return only python code in Markdown format, eg:

```python
....
```"""
prompt = ChatPromptTemplate(messages=[
    SystemMessagePromptTemplate.from_template(template),
    HumanMessagePromptTemplate.from_template("{input}")
])
def _sanitize_output(text: str):
    _, after = text.split("```python")
    return after.split("```")[0]
chain = prompt | model | StrOutputParser() | _sanitize_output | PythonREPL().run
chain.invoke({"input": "whats 2 plus 2"})
    Python REPL can execute arbitrary code. Use with caution.





    '4\n'

Memory​

This shows how to add memory to an arbitrary chain. Right now, you can use the memory classes but need to hook it up manually

from langchain.memory import ConversationBufferMemory
from langchain.schema.runnable import RunnableMap
from langchain.prompts import MessagesPlaceholder
model = ChatOpenAI()
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful chatbot"),
    MessagesPlaceholder(variable_name="history"),
    ("human", "{input}")
])

API Reference:

memory = ConversationBufferMemory(return_messages=True)
memory.load_memory_variables({})
    {'history': []}
chain = RunnableMap({
    "input": lambda x: x["input"],
    "memory": memory.load_memory_variables
}) | {
    "input": lambda x: x["input"],
    "history": lambda x: x["memory"]["history"]
} | prompt | model
inputs = {"input": "hi im bob"}
response = chain.invoke(inputs)
response
    AIMessage(content='Hello Bob! How can I assist you today?', additional_kwargs={}, example=False)
memory.save_context(inputs, {"output": response.content})
memory.load_memory_variables({})
    {'history': [HumanMessage(content='hi im bob', additional_kwargs={}, example=False),
      AIMessage(content='Hello Bob! How can I assist you today?', additional_kwargs={}, example=False)]}
inputs = {"input": "whats my name"}
response = chain.invoke(inputs)
response
    AIMessage(content='Your name is Bob. You mentioned it in your previous message. Is there anything else I can help you with, Bob?', additional_kwargs={}, example=False)