Part 2: Mastering Prompts and Language Models with LangChain
In the previous part of our LangChain tutorial series, we introduced the core components of the library. Now, let's dive deeper into two essential aspects of building LangChain applications: prompts and language models (LLMs). You'll learn how to create effective prompts, integrate various LLMs, and customize them for your specific use cases.
Introduction to Prompt Engineering
Prompt engineering is the art of designing and crafting input prompts that guide the behavior of LLMs to generate desired outputs. Well-crafted prompts are crucial for obtaining high-quality and relevant responses from LLMs.
LangChain provides the PromptTemplate class to create structured and dynamic prompts. With PromptTemplate, you can define a template string that includes input variables, which can be dynamically populated with specific values when the prompt is executed.
Example: Creating a Basic Prompt Template
from langchain import PromptTemplate
template = "Translate the following English text to {target_language}: {text}"
prompt = PromptTemplate(template=template, input_variables=["target_language", "text"])
# Using the prompt with input values
formatted_prompt = prompt.format(target_language="Spanish", text="Hello, how are you?")
print(formatted_prompt)
This would output:
Translate the following English text to Spanish: Hello, how are you?
Designing Effective Prompts
To create effective prompts, consider the following best practices and principles:
- Clarity and Specificity: Be clear and specific about the task or question you want the LLM to address.
- Dynamic and Reusable: Use input variables to make your prompts dynamic and reusable across different inputs.
- Context and Examples: Provide sufficient context and examples to guide the LLM towards the desired output format and style.
- Experimentation: Experiment with different prompt variations and assess their impact on the generated responses.
Example: Summarization Prompt
from langchain import PromptTemplate
summarization_template = "Summarize the following text in {num_sentences} sentences: {text}"
summarization_prompt = PromptTemplate(template=summarization_template, input_variables=["num_sentences", "text"])
# Using the summarization prompt
formatted_prompt = summarization_prompt.format(num_sentences=3, text="Artificial intelligence is transforming the world in various sectors including healthcare, finance, and transportation...")
print(formatted_prompt)
Example: Question-Answering Prompt
from langchain import PromptTemplate
qa_template = "Answer the following question based on the provided context:\nContext: {context}\nQuestion: {question}\nAnswer:"
qa_prompt = PromptTemplate(template=qa_template, input_variables=["context", "question"])
# Using the question-answering prompt
formatted_prompt = qa_prompt.format(context="The Eiffel Tower is located in Paris, France.", question="Where is the Eiffel Tower located?")
print(formatted_prompt)
Example: Translation Prompt
from langchain import PromptTemplate
translation_template = "Translate the following text from {source_language} to {target_language}: {text}"
translation_prompt = PromptTemplate(template=translation_template, input_variables=["source_language", "target_language", "text"])
# Using the translation prompt
formatted_prompt = translation_prompt.format(source_language="English", target_language="French", text="Good morning!")
print(formatted_prompt)
Integrating Language Models
LangChain supports various LLMs, including OpenAI, Hugging Face, Cohere, and more. To integrate an LLM into your LangChain application, you need to create an instance of the corresponding LLM class.
Example: Integrating OpenAI
from langchain.llms import OpenAI
# Creating an instance of the OpenAI LLM
llm = OpenAI(model_name="text-davinci-002", temperature=0.7)
# Using the LLM with a prompt
response = llm("Translate the following English text to Spanish: Hello, how are you?")
print(response)
When selecting an LLM, consider factors such as the specific task requirements, model capabilities, performance, and cost. LangChain provides a consistent interface across different LLMs, making it easy to switch between them as needed.
Example: Integrating Hugging Face
from langchain.llms import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
# Creating an instance of the Hugging Face LLM
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
llm = HuggingFacePipeline(model=model, tokenizer=tokenizer)
# Using the LLM with a prompt
response = llm("Translate the following English text to French: Good morning!")
print(response)
Customizing and Fine-Tuning LLMs
In some cases, you may want to fine-tune an LLM to improve its performance on a specific task or domain. Fine-tuning involves training the LLM on a smaller dataset relevant to your use case, allowing it to adapt to the specific language patterns and knowledge required.
Example: Fine-Tuning with Hugging Face
from langchain.llms import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
from datasets import load_dataset
# Load a dataset
dataset = load_dataset('text', data_files={'train': 'path/to/your/train.txt', 'test': 'path/to/your/test.txt'})
# Fine-tuning configurations
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=4,
num_train_epochs=3,
weight_decay=0.01,
)
# Fine-tune the model
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset['train'],
eval_dataset=dataset['test'],
)
trainer.train()
# Integrate the fine-tuned model
llm = HuggingFacePipeline(model=model, tokenizer=tokenizer)
# Using the fine-tuned model
response = llm("Translate the following English text to German: Good morning!")
print(response)
Advanced Prompting Techniques
LangChain offers advanced prompting techniques to enhance the capabilities of your LLMs. One such technique is incorporating external knowledge using document loaders and vector stores.
Document Loaders and Vector Stores
Document loaders allow you to load and process external data sources, such as web pages, PDF files, or databases. Vector stores enable efficient similarity search over the loaded documents, helping you retrieve relevant information based on the input query.
from langchain.document_loaders import WebBaseLoader
from langchain.indexes import VectorstoreIndexCreator
# Loading documents from a webpage
loader = WebBaseLoader("https://example.com")
index = VectorstoreIndexCreator().from_loaders([loader])
# Querying the index
query = "What are the key features of the product?"
docs = index.similarity_search(query)
for doc in docs:
print(doc)
Controlling Output Style and Structure
Another advanced technique is controlling the output style, structure, and length of the generated responses. You can achieve this by providing examples or templates within your prompts that demonstrate the desired output format.
from langchain import PromptTemplate
product_description_template = """
Generate a product description based on the following information:
Product: {product_name}
Key features: {features}
Competitor analysis: {competitor_info}
Product Description:
"""
prompt = PromptTemplate(template=product_description_template, input_variables=["product_name", "features", "competitor_info"])
# Using the prompt with detailed input
formatted_prompt = prompt.format(
product_name="Super Blender",
features="High-speed motor, Multiple blending modes",
competitor_info="Similar products in the market lack advanced features."
)
print(formatted_prompt)
Managing Prompts and Responses
When working with LLMs, it's important to handle prompts and responses effectively. LangChain provides methods to send prompts to LLMs and retrieve the generated responses.
Example: Sending Prompts and Handling Responses
# Using the OpenAI LLM with a detailed prompt
response = llm(formatted_prompt)
print(response)
To ensure robustness, implement error-handling mechanisms to handle scenarios such as timeouts or invalid responses. LangChain provides utilities for parsing and processing LLM output, making it easier to extract relevant information for downstream tasks.
Example: Parsing Responses with Regex
from langchain.output_parsers import RegexParser
# Define a regex parser to extract the product description
parser = RegexParser(regex=r"Product Description:\s*(.*)")
parsed_output = parser.parse(response)
print(parsed_output)
In the next part of this tutorial series, we'll explore how to combine prompts and LLMs with chains and agents to build more complex and interactive applications.
Stay tuned for Part 3, where we'll dive into the world of chains and agents in LangChain!
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