Getting Started With LangChain

1 pip install -U langchain langchain-openai faiss-cpu tiktoken
2 pip install -U langchain llama_index langchain-openai
3 pip install -U langchain-community langchainhub
4 pip install -U kor pydrive pandas rdflib 
5 pip install -U google-search-results SPARQLWrapper

1 export OPENAI_API_KEY="YOUR KEY GOES HERE"

 1 $ python
 2 >>> from langchain_openai import OpenAI
 3 >>> llm = OpenAI(temperature=0.8)
 4 >>> s = llm("John got into his new sports car, and he drove it")
 5 >>> s
 6 ' to work\n\nJohn started up his new sports car and drove it to work. He had a huge \
 7 smile on his face as he drove, excited to show off his new car to his colleagues. Th
 8 e wind blowing through his hair, and the sun on his skin, he felt a sense of freedom
 9  and joy as he cruised along the road. He arrived at work in no time, feeling refres
10 hed and energized.'
11 >>> s = llm("John got into his new sports car, and he drove it")
12 >>> s
13 " around town\n\nJohn drove his new sports car around town, enjoying the feeling of \
14 the wind in his hair. He stopped to admire the view from a scenic lookout, and then 
15 sped off to the mall to do some shopping. On the way home, he took a detour down a w
16 inding country road, admiring the scenery and enjoying the feel of the car's powerfu
17 l engine. By the time he arrived back home, he had a huge smile on his face."

 1 from langchain.prompts import PromptTemplate
 2 from langchain_openai import OpenAI
 3 llm = OpenAI(temperature=0.9)
 4 
 5 def get_directions(thing_to_do):
 6     prompt = PromptTemplate(
 7         input_variables=["thing_to_do"],
 8         template="How do I {thing_to_do}?",
 9     )
10     prompt_text = prompt.format(thing_to_do=thing_to_do)
11     print(f"\n{prompt_text}:")
12     return llm(prompt_text)
13 
14 print(get_directions("get to the store"))
15 print(get_directions("hang a picture on the wall"))

 1 $ python directions_template.py
 2 
 3 How do I get to the store?:
 4 
 5 To get to the store, you will need to use a mode of transportation such as walking, \
 6 driving, biking, or taking public transportation. Depending on the location of the s
 7 tore, you may need to look up directions or maps to determine the best route to take
 8 .
 9 
10 How do I hang a picture on the wall?:
11 
12 1. Find a stud in the wall, or use two or three wall anchors for heavier pictures.
13 2. Measure and mark the wall where the picture hanger will go. 
14 3. Pre-drill the holes and place wall anchors if needed.
15 4. Hammer the picture hanger into the holes.
16 5. Hang the picture on the picture hanger.

 1 from langchain.prompts import PromptTemplate
 2 from langchain_openai import OpenAI
 3 llm = OpenAI(temperature=0.9)
 4 
 5 def get_country_information(country_name):
 6     print(f"\nProcessing {country_name}:")
 7     global prompt
 8     if "prompt" not in globals():
 9         print("Creating prompt...")
10         prompt = PromptTemplate(
11             input_variables=["country_name"],
12             template = """
13 Predict the capital and population of a country.
14 
15 Country: {country_name}
16 Capital:
17 Population:""",
18         )
19     prompt_text = prompt.format(country_name=country_name)
20     print(prompt_text)
21     return llm(prompt_text)
22 
23 print(get_country_information("Canada"))
24 print(get_country_information("Germany"))

 1  $ python country_information.py
 2 
 3 Processing Canada:
 4 Creating prompt...
 5 
 6 Predict the capital and population of a country.
 7 
 8 Country: Canada
 9 Capital:
10 Population:
11 
12 
13 Capital: Ottawa
14 Population: 37,058,856 (as of July 2020)
15 
16 Processing Germany:
17 
18 Predict the capital and population of a country.
19 
20 Country: Germany
21 Capital:
22 Population:
23 
24 
25 Capital: Berlin
26 Population: 83,02 million (est. 2019)

 1 $ python
 2 Python 3.10.8 (main, Nov 24 2022, 08:08:27) [Clang 14.0.6 ] on darwin
 3 Type "help", "copyright", "credits" or "license" for more information.
 4 >>> from langchain_openai import OpenAIEmbeddings
 5 >>> embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
 6 >>> text = "Mary has blond hair and John has brown hair. Mary lives in town and John\
 7  lives in the country."
 8 >>> doc_embeddings = embeddings.embed_documents([text])
 9 >>> doc_embeddings
10 [[0.007727328687906265, 0.0009025644976645708, -0.0033224383369088173, -0.0179449208\
11 0807686, -0.017969949170947075, 0.028506645932793617, -0.013414892368018627, 0.00466
12 76816418766975, -0.0024965214543044567, -0.02662956342101097,
13 ...]]
14 >>> query_embedding = embeddings.embed_query("Does John live in the city?")
15 >>> query_embedding
16 [0.028048301115632057, 0.011499025858938694, -0.00944007933139801, -0.02080961130559\
17 4444, -0.023904507979750633, 0.018750663846731186, -0.01626438833773136, 0.018129095
18 435142517,
19 ...]
20 >>>

 1 import os
 2 
 3 # Generated by Perplexity
 4 
 5 def read_text_files(directory_path):
 6     """
 7     Reads all .txt files in the specified directory and returns their contents as a \
 8 list of strings.
 9 
10     :param directory_path: The path to the directory containing .txt files
11     :return: A list of strings where each string is the content of a .txt file
12     """
13     txt_contents = []
14     # Check if the directory exists
15     if not os.path.isdir(directory_path):
16         print(f"The directory {directory_path} does not exist.")
17         return txt_contents
18 
19     # Iterate over all files in the directory
20     for filename in os.listdir(directory_path):
21         # Check for .txt extension
22         if filename.endswith(".txt"):
23             # Construct full file path
24             file_path = os.path.join(directory_path, filename)
25             # Open and read the contents of the file
26             try:
27                 with open(file_path, 'r') as file:
28                     txt_contents.append(file.read())
29             except IOError as e:
30                 print(f"Failed to read file {filename}: {e}")
31 
32     return txt_contents

 1 from langchain_community.vectorstores import FAISS
 2 from langchain_core.output_parsers import StrOutputParser
 3 from langchain_core.prompts import ChatPromptTemplate
 4 from langchain_core.runnables import RunnablePassthrough
 5 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 6 from langchain_community.document_loaders import DirectoryLoader
 7 
 8 model = ChatOpenAI()
 9 
10 from read_text_files import read_text_files
11 
12 vectorstore = FAISS.from_texts(read_text_files("../data/"), embedding=OpenAIEmbeddin\
13 gs())
14 
15 retriever = vectorstore.as_retriever()
16 
17 template = """Answer the question based only on the following context:
18 {context}
19 
20 Question: {question}
21 """
22 prompt = ChatPromptTemplate.from_template(template)
23 
24 chain = (
25     {"context": retriever, "question": RunnablePassthrough()}
26     | prompt
27     | model
28     | StrOutputParser()
29 )
30 
31 print(chain.invoke("who tried to define what Chemistry is?"))
32 
33 print(chain.invoke("What kinds of equipment are in a chemistry laboratory?"))
34 print(chain.invoke("What is Austrian School of Economics?"))
35 print(chain.invoke("Why do people engage in sports?"))
36 print(chain.invoke("What is the effect of body chemistry on exercise?"))

 1 $ python doc_search.py      
 2 Georg Ernst Stahl, Jean-Baptiste Dumas, Linus Pauling, and Professor Raymond Chang t\
 3 ried to define what Chemistry is.
 4 Various forms of laboratory glassware are typically used in a chemistry laboratory. \
 5 However, it is mentioned that a great deal of experimental and applied/industrial ch
 6 emistry can also be done without the use of glassware.
 7 The Austrian School of Economics is a school of economic thought that emphasizes the\
 8  spontaneous organizing power of the price mechanism, advocates a laissez-faire appr
 9 oach to the economy, and holds that commercial transactions should be subject to min
10 imal government intervention.
11 People engage in sports for physical athleticism, physical dexterity, and for leisur\
12 e activities that they find amusing or entertaining.
13 Body chemistry can affect exercise in terms of how energy is transferred from one su\
14 bstance to another, with heat being transferred more easily than other forms of ener
15 gy. Additionally, the body's ability to learn to fight specific infections after exp
16 osure to germs, known as adaptive immunity, can impact exercise performance and reco
17 very.

 1 # make sure SERPER_API_KEY is set in your environment
 2 
 3 from langchain_community.utilities import GoogleSerperAPIWrapper
 4 search_helper = GoogleSerperAPIWrapper()
 5 
 6 def search(query):
 7     return search_helper.run(query)
 8 
 9 print(search("What is the capital of Arizona?"))
10 #print(search("Sedona Arizona?"))

 1 from langchain_core.messages import HumanMessage, SystemMessage
 2 from langchain_openai import ChatOpenAI
 3 
 4 llm = ChatOpenAI(model="gpt-4o")
 5 
 6 messages = [
 7     SystemMessage(content="You're a helpful assistant"),
 8     HumanMessage(content="What is the purpose of model regularization? Be concise."),
 9 ]
10 
11 results = llm.invoke(messages)
12 print(results.content)
13 print("\n")
14 print(results)

 1 $ python gpt_4o_test.py 
 2 The purpose of model regularization is to prevent overfitting by adding a penalty fo\
 3 r larger coefficients in the model, thereby improving its generalization to new, uns
 4 een data.
 5 
 6 content='The purpose of model regularization is to prevent overfitting by adding a p\
 7 enalty for larger coefficients in the model, thereby improving its generalization to
 8  new, unseen data.' response_metadata={'token_usage': {'completion_tokens': 34, 'pro
 9 mpt_tokens': 27, 'total_tokens': 61}, 'model_name': 'gpt-4o', 'system_fingerprint': 
10 'fp_729ea513f7', 'finish_reason': 'stop', 'logprobs': None} id='run-f2fa3e57-eeff-44
11 96-b6e4-d06af8f230d0-0'