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Repository: mark-watson/langchain-book-examples
Branch: main
Commit: e01426392468
Files: 159
Total size: 1.5 MB
Directory structure:
gitextract_du4dxcr1/
├── .github/
│ └── FUNDING.yml
├── .gitignore
├── CHAPTERS_and_CODE_no_longer_in-book/
│ ├── README.md
│ └── zapier_integrations/
│ ├── README.md
│ ├── README_code.md
│ ├── get_google_calendar.py
│ ├── requirements.txt
│ └── send_gmail.py
├── Makefile
├── README.md
├── cooking_recipes/
│ ├── README.md
│ ├── data/
│ │ ├── .gitignore
│ │ ├── correlated-ingredients.json
│ │ ├── desert.json
│ │ ├── fish.json
│ │ ├── ingredients.json
│ │ ├── meat.json
│ │ ├── misc.json
│ │ ├── recipes.json
│ │ ├── tiny.json
│ │ └── vegetarian.json
│ ├── data.py
│ ├── recipe_generator.py
│ └── text_data/
│ ├── Aioli Sauce.txt
│ ├── Apple Muffins.txt
│ ├── Balsamic Mushrooms.txt
│ ├── Barbecued Chicken.txt
│ ├── Barbecued Eggplant.txt
│ ├── Barbecued Salmon with Basil.txt
│ ├── Basil Vinaigrette Salad Dressing.txt
│ ├── Black Bean Dip.txt
│ ├── Borscht.txt
│ ├── Brined Barbecued Chicken.txt
│ ├── Cauliflower and Broccoli Pizza Crust.txt
│ ├── Chicken Cacciatore.txt
│ ├── Chicken Schnitzel.txt
│ ├── Chicken with Cream of Mushroom Soup.txt
│ ├── Chinese Shrimp with Zucchini Squash.txt
│ ├── Crock Pot Lemon Honey Chicken.txt
│ ├── Curried Chicken Legs.txt
│ ├── Curried Yogurt Dip.txt
│ ├── Dijon Sour Cream Dip.txt
│ ├── Dijon Yogurt Dip.txt
│ ├── Easy Cake.txt
│ ├── English Scones.txt
│ ├── Fresh Tomato Sauce.txt
│ ├── Fried Chicken.txt
│ ├── Homemade Salad Dressing.txt
│ ├── Honey Chinese Shrimp.txt
│ ├── Honey-Curried Chicken.txt
│ ├── Indonesian Barbecue Sauce.txt
│ ├── Indonesian Chicken Sate.txt
│ ├── Irene's Snickerdoodle Cookies.txt
│ ├── Japanese Ginger Salad Dressing.txt
│ ├── Lemon Sesame Broccoli.txt
│ ├── Poached Salmon with Wine and Lemon.txt
│ ├── Quick Sweet and Sour Sauce.txt
│ ├── Scottish Shortbread.txt
│ ├── Simple Italian Chicken.txt
│ ├── Spicy Barbecued Chicken.txt
│ ├── Spicy Braised Chicken.txt
│ ├── Tasty Black Bean Dip.txt
│ └── Whole Wheat Bread.txt
├── data/
│ ├── chemistry.txt
│ ├── economics.txt
│ ├── health.txt
│ └── sports.txt
├── data_small/
│ └── sports.txt
├── embedchain_test/
│ ├── .gitignore
│ ├── README.md
│ ├── app.py
│ ├── data/
│ │ ├── haskell-cookbook.txt
│ │ └── lovinglisp-2.txt
│ ├── process_pdfs.py
│ └── requirements.txt
├── extraction/
│ ├── README.md
│ ├── person_data.py
│ └── prompt.txt
├── from_langchain_docs/
│ ├── README.md
│ ├── gpt_4o_test.py
│ ├── memory_langchain_test.py
│ ├── requirements.txt
│ └── search_simple.py
├── google_drive_llm/
│ ├── .gitignore
│ ├── README.md
│ ├── data/
│ │ └── sports.txt
│ ├── fetch_txt_files.py
│ ├── index_and_QA.py
│ └── requirements.txt
├── hugging_face/
│ ├── README.md
│ ├── hf_transformer_local.py
│ ├── requirements.txt
│ ├── simple_example.py
│ ├── test1.py
│ └── throwaway_test.py
├── kg_search/
│ ├── Google_KG_helper.py
│ ├── Google_Knowledge_Graph_Search.py
│ └── README.md
├── kor/
│ ├── README.md
│ └── dates.py
├── langchain_dbpedia_agent/
│ ├── QA.py
│ ├── README.md
│ ├── custom_func_dbpedia.py
│ └── requirements.txt
├── langchain_getting_started/
│ ├── .gitignore
│ ├── README.md
│ ├── agent_test.py
│ ├── chroma_persist_index.py
│ ├── chroma_use_disk_index_for_query.py
│ ├── country_information.py
│ ├── directions_template.py
│ ├── doc_search.py
│ ├── read_text_files.py
│ └── test.py
├── llama-index_case_study/
│ ├── .gitignore
│ ├── README.md
│ ├── requirements.txt
│ └── web_page_QA.py
├── llama.cpp/
│ ├── README.md
│ └── test.py
├── llm_enhanced_search_ddg_ollama/
│ ├── README.md
│ └── llm_enhanced_search.py
├── neo4j/
│ ├── README.md
│ ├── credentials.json
│ └── test_example.py
├── ollama_langchain/
│ ├── README.md
│ ├── llama3-instruct-8b.py
│ ├── mistral-7b.py
│ ├── rag_test.py
│ └── requirements.txt
├── prompt_examples/
│ ├── two-shot-2-var.txt
│ └── two-shot-2.txt
├── rag/
│ ├── README.md
│ ├── requirements.txt
│ ├── reranking_rag_llama_index.py
│ ├── simple_llama_index_retrieve_docs.py
│ └── simple_rag_llama_index.py
├── requirements.txt
├── sqlite/
│ ├── README.md
│ ├── requirements.txt
│ └── sqlite_chat_test.py
├── summarization/
│ ├── README.md
│ ├── prompt.txt
│ ├── requirements.txt
│ └── summarization_example.py
├── tavily_search/
│ ├── READMS.md
│ ├── langchain_test.py
│ └── llamaindex_test.py
├── text_db_llm/
│ ├── README.md
│ ├── data/
│ │ ├── chemistry.txt
│ │ ├── economics.txt
│ │ ├── health.txt
│ │ └── sports.txt
│ ├── requirements.txt
│ └── text_db_llm.py
├── tool_search_math_example/
│ ├── requirements.txt
│ └── tool_search_math_example.py
└── tools_langchain/
├── doc_example.py
└── loop_collection_with_function.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/FUNDING.yml
================================================
# These are supported funding model platforms
github: mark-watson
custom: [“https://buy.stripe.com/aFa5kudvI3mTfv5c5F8N200” stripe.com]
================================================
FILE: .gitignore
================================================
*~
.DS_Store
venv
.idea
*.pyc
target
client_secrets.json
cl*.json
index.json
*.log
cache
================================================
FILE: CHAPTERS_and_CODE_no_longer_in-book/README.md
================================================
# Examples for older 2023 version of my book
Look in sub-diurecotries for original book content that has been deprecated. The old book chapter is now a README.md file. For example, I deprecated the Zapier example so that book chapter is now available at [https://github.com/mark-watson/langchain-book-examples/blob/main/CHAPTERS_and_CODE_no_longer_in-book/zapier_integrations/README.md](https://github.com/mark-watson/langchain-book-examples/blob/main/CHAPTERS_and_CODE_no_longer_in-book/zapier_integrations/README.md) and the original Python code examples are in the same directory as the book chapter.
================================================
FILE: CHAPTERS_and_CODE_no_longer_in-book/zapier_integrations/README.md
================================================
# Using Zapier Integrations With GMail and Google Calendar
Zapier is a service for writing integrations with hundreds of cloud services. Here we will write some demos for writing automatic integrations with Gmail and Google Calendar.
Using the Zapier service is simple. You need to register the services you want to interact with on the Zapier developer web site and then you can express how you want to interact with services using natural language prompts.
## Set Up Development Environment
You will need a developer key for [Zapier Natural Language Actions API](https://nla.zapier.com/get-started/). Go to this linked web page and look for "Dev App" in the "Provider Name" column. If a key does not exist, you'll need to set up an action to create a key. Click "Set up Actions" and follow the instructions. Your key will be in the Personal API Key column for the "Dev App." Click to reveal and copy your key. You can [read the documentation](https://nla.zapier.com/api/v1/dynamic/docs).
When I set up my Zapier account I set up three Zapier Natural Language Actions:
- Gmail: Find Email
- Gmail: Send Email
- Google Calendar: Find Event
If you do the same then you will see the Zapier registered actions:
## Sending a Test GMail
In the following example replace **TEST_EMAIL_ADDRESS** with an email address that you can use for testing.
```python
from langchain.llms import OpenAI
from langchain.agents import initialize_agent
from langchain.agents.agent_toolkits import ZapierToolkit
from langchain.utilities.zapier import ZapierNLAWrapper
llm = OpenAI(temperature=0)
zapier = ZapierNLAWrapper()
toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)
agent = initialize_agent(toolkit.get_tools(), llm, agent="zero-shot-react-description", verbose=True)
agent.run("Send an Email to TEST_EMAIL_ADDRESS via gmail that is a pitch for hiring Mark Watson as a consultant for deep learning and large language models")
```
Here is the sample output:
```console
$ python send_gmail.py
> Entering new AgentExecutor chain...
I need to use the Gmail: Send Email tool
Action: Gmail: Send Email
Action Input: Send an email to TEST_EMAIL_ADDRESS with the subject "Pitch for Hiring Mark Watson as a Consultant for Deep Learning and Large Language Models" and the body "Dear Mark Watson, I am writing to you to pitch the idea of hiring you as a consultant for deep learning and large language models. I believe you have the expertise and experience to help us achieve our goals. Please let me know if you are interested in discussing further. Thank you for your time."
Cc: not enough information provided in the instruction, missing Cc
Observation: {"labelIds": "SENT"}
Thought: I now know the final answer
Final Answer: An email has been sent to TEST_EMAIL_ADDRESS with the subject "Pitch for Hiring Mark Watson as a Consultant for Deep Learning and Large Language Models" and the body "Dear Mark Watson, I am writing to you to pitch the idea of hiring you as a consultant for deep learning and large language models. I believe you have the expertise and experience to help us achieve our goals. Please let me know if you are interested in discussing further. Thank you for your time."
> Finished chain.
```
## Google Calendar Integration Example
Assuming that you configured the Zapier Natural Language Action "Google Calendar: Find Event" then the same code we used to send an email in the last section works for checking calendar entries, you just need to change the natural language prompt:
```python
from langchain.llms import OpenAI
from langchain.agents import initialize_agent
from langchain.agents.agent_toolkits import ZapierToolkit
from langchain.utilities.zapier import ZapierNLAWrapper
llm = OpenAI(temperature=0)
zapier = ZapierNLAWrapper()
toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)
agent = initialize_agent(toolkit.get_tools(), llm,
agent="zero-shot-react-description", verbose=True)
agent.run("Get my Google Calendar entries for tomorrow")
```
And the output looks like:
```console
$ python get_google_calendar.py
> Entering new AgentExecutor chain...
I need to find events in my Google Calendar
Action: Google Calendar: Find Event
Action Input: Find events in my Google Calendar tomorrow
Observation: {"location": "Greg to call Mark on (928) XXX-ZZZZ", "kind": "calendar#event", "end__dateTime": "2023-03-23T10:00:00-07:00", "status": "confirmed", "end__dateTime_pretty": "Mar 23, 2023 10:00AM", "htmlLink": "https://zpr.io/WWWWWWWW"}
Thought: I now know the final answer
Final Answer: I have an event in my Google Calendar tomorrow at 10:00AM.
> Finished chain.
```
I edited this output to remove some private information.
================================================
FILE: CHAPTERS_and_CODE_no_longer_in-book/zapier_integrations/README_code.md
================================================
# Using Zapier Integrations with GMail and Google Calendar
Zapier is a service for writing integrations with hundreds of cloud services. Here we will write some demos for writing automatic integrations with GMail and Google Calendar. I will also provide you, dear reader, with links for integrationg with Microsoft and other services.
## Setup
You will need a developer key for [Zapier Natural Language Actions API](https://nla.zapier.com/get-started/). Go to this linked web page and look for "Dev App" in the "Provider Name" column. If a key does not exist, you'll need to setup an action to create a key. Click "Setup Actions" and follow the instructions. Your key will be in the Personal API Key column for the "Dev App." Click to reveal and copy your key. You can [read the documentation](https://nla.zapier.com/api/v1/dynamic/docs).
================================================
FILE: CHAPTERS_and_CODE_no_longer_in-book/zapier_integrations/get_google_calendar.py
================================================
from langchain.llms import OpenAI
from langchain.agents import initialize_agent
from langchain.agents.agent_toolkits import ZapierToolkit
from langchain.utilities.zapier import ZapierNLAWrapper
llm = OpenAI(temperature=0)
zapier = ZapierNLAWrapper()
toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)
agent = initialize_agent(toolkit.get_tools(), llm,
agent="zero-shot-react-description", verbose=True)
agent.run("Get my Google Calendar entries for tomorrow")
================================================
FILE: CHAPTERS_and_CODE_no_longer_in-book/zapier_integrations/requirements.txt
================================================
langchain
langchain_community
openai
================================================
FILE: CHAPTERS_and_CODE_no_longer_in-book/zapier_integrations/send_gmail.py
================================================
from langchain.llms import OpenAI
from langchain.agents import initialize_agent
from langchain.agents.agent_toolkits import ZapierToolkit
from langchain.utilities.zapier import ZapierNLAWrapper
llm = OpenAI(temperature=0)
zapier = ZapierNLAWrapper()
toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)
agent = initialize_agent(toolkit.get_tools(), llm,
agent="zero-shot-react-description", verbose=True)
agent.run("Send an Email to mark_watson99test.com via gmail that is a pitch for hiring Mark Watson as a consultant for deep learning and large language models")
================================================
FILE: Makefile
================================================
install:
pip install llama_index langchain trafilatura openai SPARQLWrapper rdflib rdflib-jsonld pydrive kor
clean:
rm -r -f */*~ */#*
rm -r -f */venv */*/venv
rm -r -f */__pycache__ */*/__pycache__
================================================
FILE: README.md
================================================
# Example code for my book "LangChain Project Lab Book: Hooking Large Language Models Up to the Real World"
You can purchase this book on LeanPub and get free updates as new versions are released.
This book can be purchased or read free online at [https://leanpub.com/langchain](https://leanpub.com/langchain).
I would like to thank readers who have purchased this book! I very much appreciate your support.
## Older, non-supported, book chapters and code for these chapters now in directory CHAPTERS_and_CODE_no_longer_in-book
### Starting in October 2024 I am removing older material from my book and archiving it. See the following index for older material:
[CHAPTERS_and_CODE_no_longer_in-book/README.md](CHAPTERS_and_CODE_no_longer_in-book/README.md)
================================================
FILE: cooking_recipes/README.md
================================================
# Using LLMs to Generate Recipes
Updated 1/26/2024 for langchain-0.1.4 and using the new OpenAI embedding models
See the chapter in my book for information.
So far, this directory contain an initial experiment indexing my cookingspace.com recipe data and using LangChain and a prediction model to generate new recipes.
## Recipe Copyrights
My JSON recipe files are Copyright 2010-2023 Mark Watson, as are the generated text file versions.
What does copyright even mean for recipes? To the best of my knowledge, the only thing that can be copyright in recipes is the written cooking directions.
================================================
FILE: cooking_recipes/data/.gitignore
================================================
*~
================================================
FILE: cooking_recipes/data/correlated-ingredients.json
================================================
{"chives":
{"elbow macaroni":2,
"red wine":3,
"bacon":18,
"cayenne pepper":8,
"lemon juice":48,
"honey":8,
"swiss cheese":7,
"paprika":26,
"ground cinnamon":2,
"bread":3,
"yeast":6,
"egg yolks":14,
"green pepper":5,
"baking powder":10,
"saffron":2,
"sesame oil":3,
"cream of tartar":2,
"pine nuts":8,
"cinnamon":3,
"avocado":3,
"cheddar cheese":12,
"tomatoes":23,
"white pepper":18,
"brown rice":2,
"cauliflower":3,
"frozen peas":2,
"butter":120,
"salmon":5,
"orange juice":3,
"lemon":11,
"black pepper":19,
"black olives":4,
"buttermilk":5,
"capers":7,
"cornmeal":2,
"cloves":6,
"oil":18,
"chicken":5,
"blue cheese":6,
"cucumber":7,
"canola oil":4,
"dried tarragon":5,
"celery":15,
"walnuts":5,
"nutmeg":13,
"bread crumbs":10,
"baking soda":4,
"tabasco sauce":6,
"ginger":5,
"balsamic vinegar":8,
"fresh rosemary":2,
"lime":3,
"pecans":3,
"peanut butter":2,
"green onions":9,
"cumin":4,
"skim milk":2,
"beets":2,
"kidney beans":2,
"parmesan cheese":15,
"carrots":16,
"mushrooms":22,
"sour cream":32,
"yogurt":3,
"cream cheese":29,
"tofu":3,
"cherry tomatoes":2,
"salsa":3,
"parsley":76,
"green olives":2,
"rice vinegar":2,
"chili powder":6,
"spaghetti":2,
"red wine vinegar":5,
"cornstarch":4,
"orange":4,
"dried basil":2,
"radishes":2,
"catsup":3,
"ground beef":4,
"mozzarella cheese":2,
"almonds":5,
"eggs":40,
"margarine":14,
"fresh ginger":2,
"kale":2,
"garlic powder":15,
"dried thyme":8,
"whole wheat flour":6,
"salt":167,
"fresh basil":5,
"bean sprouts":4,
"potatoes":20,
"allspice":2,
"dijon mustard":17,
"soy sauce":8,
"broccoli":4,
"white rice":2,
"egg whites":6,
"worcestershire sauce":9,
"brown sugar":2,
"lime juice":5,
"dry mustard":10,
"white flour":2,
"mayonnaise":19,
"fresh thyme":2,
"vegetable oil":14,
"olive oil":58,
"curry powder":9,
"flour":37,
"vinegar":7,
"white wine":11,
"sherry":3,
"oats":2,
"whipping cream":5,
"sugar":22},
"cumin":
{"elbow macaroni":5,
"canned beef broth":3,
"red wine":16,
"canned tomato sauce":5,
"bacon":16,
"cayenne pepper":133,
"lemon juice":120,
"honey":25,
"swiss cheese":3,
"paprika":177,
"bread":2,
"ground cinnamon":6,
"yeast":2,
"green pepper":109,
"baking powder":10,
"saffron":3,
"sesame oil":9,
"pine nuts":2,
"cinnamon":73,
"avocado":11,
"cheddar cheese":33,
"corn tortilla":2,
"tomatoes":177,
"white pepper":34,
"brown rice":17,
"black beans":43,
"fresh garlic":3,
"pinto beans":38,
"cauliflower":12,
"frozen peas":4,
"butter":63,
"salmon":2,
"orange juice":13,
"black pepper":171,
"lemon":23,
"tumeric":21,
"black olives":13,
"buttermilk":2,
"powdered sugar":2,
"hoisin sauce":2,
"dried oregano":28,
"eggplant":19,
"capers":8,
"cornmeal":14,
"cloves":49,
"oil":136,
"chicken":25,
"cucumber":12,
"canola oil":13,
"dried tarragon":2,
"celery":45,
"alfalfa sprouts":2,
"fresh peaches":3,
"walnuts":10,
"nutmeg":34,
"fresh cilantro":24,
"baking soda":6,
"bread crumbs":14,
"corn syrup":2,
"tabasco sauce":45,
"ginger":68,
"balsamic vinegar":8,
"lime":17,
"pecans":2,
"peanut butter":9,
"green onions":36,
"frozen corn":13,
"banana":2,
"beets":3,
"skim milk":11,
"barley":5,
"kidney beans":41,
"shortening":6,
"applesauce":2,
"parmesan cheese":5,
"carrots":65,
"mushrooms":25,
"sour cream":50,
"yogurt":25,
"cream cheese":12,
"tofu":10,
"cherry tomatoes":3,
"salsa":22,
"parsley":79,
"green olives":4,
"rice vinegar":3,
"molasses":11,
"beer":41,
"chili powder":403,
"spaghetti":3,
"raisins":29,
"red wine vinegar":15,
"chives":4,
"garam masala":19,
"cornstarch":14,
"orange":6,
"dried basil":6,
"radishes":2,
"catsup":11,
"ground beef":68,
"mozzarella cheese":8,
"romaine lettuce":2,
"sweet red pepper":4,
"cumin seeds":3,
"barbecue sauce":3,
"red pepper flakes":16,
"almonds":13,
"eggs":26,
"margarine":15,
"fresh ginger":7,
"kale":3,
"garlic powder":134,
"dried thyme":7,
"cream of mushroom soup":4,
"whole wheat flour":2,
"salt":625,
"fresh basil":3,
"bean sprouts":2,
"potatoes":29,
"frozen spinach":4,
"celery seeds":2,
"allspice":39,
"dijon mustard":13,
"soy sauce":38,
"broccoli":3,
"apples":4,
"maple syrup":4,
"sweet potatoes":6,
"white rice":5,
"egg whites":4,
"worcestershire sauce":35,
"dried cilantro":5,
"brown sugar":40,
"lime juice":52,
"dry mustard":32,
"mayonnaise":5,
"chicken wings":5,
"vegetable oil":123,
"fresh thyme":2,
"olive oil":257,
"flour":71,
"curry powder":50,
"vinegar":48,
"white wine":8,
"sherry":5,
"oats":4,
"whipping cream":2,
"chicken breast":11,
"sugar":93},
"white wine":
{"red wine":7,
"bacon":27,
"cayenne pepper":28,
"lemon juice":92,
"honey":29,
"zucchini squash":2,
"swiss cheese":17,
"paprika":54,
"ground cinnamon":5,
"bread":4,
"yeast":5,
"egg yolks":22,
"green pepper":21,
"baking powder":7,
"saffron":9,
"sesame oil":15,
"cinnamon":11,
"pine nuts":6,
"cream of tartar":2,
"avocado":3,
"cheddar cheese":4,
"tomatoes":49,
"white pepper":35,
"brown rice":4,
"black beans":6,
"fresh garlic":3,
"cauliflower":2,
"frozen peas":2,
"butter":241,
"salmon":5,
"orange juice":19,
"lemon":40,
"black pepper":58,
"black olives":8,
"buttermilk":3,
"powdered sugar":8,
"eggplant":2,
"dried oregano":16,
"hoisin sauce":3,
"capers":21,
"cloves":11,
"oil":60,
"chicken":31,
"blue cheese":2,
"cucumber":4,
"canola oil":3,
"dried tarragon":4,
"strawberries":8,
"celery":38,
"fresh cilantro":5,
"nutmeg":31,
"bread crumbs":23,
"baking soda":2,
"corn syrup":3,
"tabasco sauce":10,
"ginger":14,
"balsamic vinegar":11,
"fresh rosemary":9,
"lime":5,
"peanut butter":2,
"green onions":40,
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"ginger":3,
"cheddar cheese":2,
"tomatoes":2,
"cumin":3,
"olive oil":15,
"fresh thyme":2,
"vegetable oil":5,
"skim milk":2,
"red pepper flakes":2,
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"eggs":3,
"pinto beans":2,
"vinegar":2,
"butter":7,
"dried thyme":3,
"parmesan cheese":5,
"carrots":3,
"sherry":2,
"sugar":5,
"salt":20,
"black pepper":4},
"white flour":
{"sour cream":6,
"yogurt":4,
"tofu":3,
"cream cheese":3,
"salsa":2,
"parsley":3,
"cayenne pepper":4,
"lemon juice":8,
"honey":36,
"molasses":14,
"paprika":2,
"chili powder":4,
"beer":2,
"ground cinnamon":2,
"raisins":16,
"yeast":51,
"egg yolks":2,
"chives":2,
"baking powder":44,
"cornstarch":4,
"dried basil":3,
"catsup":2,
"cinnamon":26,
"cream of tartar":3,
"tomatoes":3,
"cumin seeds":2,
"white pepper":2,
"eggs":33,
"margarine":15,
"butter":30,
"garlic powder":6,
"orange juice":3,
"vanilla":18,
"whole wheat flour":70,
"salt":164,
"black pepper":8,
"tumeric":2,
"black olives":2,
"buttermilk":9,
"powdered sugar":2,
"dried oregano":3,
"cornmeal":16,
"cloves":7,
"oil":20,
"chicken":2,
"allspice":6,
"canola oil":3,
"dijon mustard":2,
"soy sauce":2,
"apples":2,
"maple syrup":3,
"nutmeg":11,
"walnuts":5,
"egg whites":4,
"baking soda":48,
"worcestershire sauce":2,
"tabasco sauce":2,
"brown sugar":35,
"ginger":10,
"lime juice":3,
"dry mustard":2,
"fresh rosemary":2,
"blueberries":2,
"peanut butter":3,
"green onions":4,
"olive oil":17,
"vegetable oil":21,
"skim milk":5,
"flour":4,
"curry powder":2,
"shortening":17,
"applesauce":9,
"parmesan cheese":4,
"carrots":3,
"oats":5,
"whipping cream":2,
"sugar":93},
"cauliflower":
{"sour cream":2,
"yogurt":2,
"cream cheese":3,
"tofu":4,
"cherry tomatoes":3,
"bacon":2,
"coconut oil":3,
"cayenne pepper":8,
"parsley":9,
"green olives":3,
"honey":2,
"lemon juice":15,
"zucchini squash":2,
"paprika":6,
"chili powder":3,
"ground cinnamon":2,
"bread":2,
"raisins":2,
"garam masala":10,
"chives":3,
"egg yolks":2,
"green pepper":10,
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"dried basil":3,
"catsup":2,
"radishes":2,
"sesame oil":3,
"cinnamon":3,
"pine nuts":2,
"cheddar cheese":8,
"sweet red pepper":4,
"tomatoes":23,
"cumin seeds":3,
"white pepper":5,
"brown rice":2,
"red pepper flakes":3,
"eggs":12,
"margarine":2,
"fresh ginger":2,
"frozen peas":2,
"butter":15,
"garlic powder":7,
"dried thyme":3,
"lemon":4,
"salt":89,
"black pepper":9,
"tumeric":4,
"fresh basil":2,
"bean sprouts":5,
"black olives":3,
"potatoes":24,
"eggplant":5,
"capers":4,
"cloves":2,
"frozen spinach":2,
"oil":29,
"chicken":3,
"cucumber":2,
"canola oil":3,
"celery seeds":3,
"dijon mustard":4,
"soy sauce":12,
"celery":20,
"broccoli":30,
"alfalfa sprouts":2,
"fresh cilantro":2,
"nutmeg":6,
"egg whites":2,
"bread crumbs":3,
"worcestershire sauce":4,
"brown sugar":3,
"ginger":11,
"balsamic vinegar":2,
"dry mustard":3,
"mayonnaise":4,
"cumin":12,
"green onions":6,
"olive oil":18,
"vegetable oil":14,
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"kidney beans":2,
"vinegar":12,
"white wine":2,
"parmesan cheese":10,
"sherry":4,
"carrots":52,
"whipping cream":3,
"chicken breast":2,
"mushrooms":16,
"sugar":26},
"flour tortilla":
{"fresh basil":2,
"sour cream":7,
"yogurt":2,
"potatoes":3,
"buttermilk":2,
"bacon":4,
"dried oregano":4,
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"applesauce":
{"sour cream":5,
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"eggs":81,
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gitextract_du4dxcr1/
├── .github/
│ └── FUNDING.yml
├── .gitignore
├── CHAPTERS_and_CODE_no_longer_in-book/
│ ├── README.md
│ └── zapier_integrations/
│ ├── README.md
│ ├── README_code.md
│ ├── get_google_calendar.py
│ ├── requirements.txt
│ └── send_gmail.py
├── Makefile
├── README.md
├── cooking_recipes/
│ ├── README.md
│ ├── data/
│ │ ├── .gitignore
│ │ ├── correlated-ingredients.json
│ │ ├── desert.json
│ │ ├── fish.json
│ │ ├── ingredients.json
│ │ ├── meat.json
│ │ ├── misc.json
│ │ ├── recipes.json
│ │ ├── tiny.json
│ │ └── vegetarian.json
│ ├── data.py
│ ├── recipe_generator.py
│ └── text_data/
│ ├── Aioli Sauce.txt
│ ├── Apple Muffins.txt
│ ├── Balsamic Mushrooms.txt
│ ├── Barbecued Chicken.txt
│ ├── Barbecued Eggplant.txt
│ ├── Barbecued Salmon with Basil.txt
│ ├── Basil Vinaigrette Salad Dressing.txt
│ ├── Black Bean Dip.txt
│ ├── Borscht.txt
│ ├── Brined Barbecued Chicken.txt
│ ├── Cauliflower and Broccoli Pizza Crust.txt
│ ├── Chicken Cacciatore.txt
│ ├── Chicken Schnitzel.txt
│ ├── Chicken with Cream of Mushroom Soup.txt
│ ├── Chinese Shrimp with Zucchini Squash.txt
│ ├── Crock Pot Lemon Honey Chicken.txt
│ ├── Curried Chicken Legs.txt
│ ├── Curried Yogurt Dip.txt
│ ├── Dijon Sour Cream Dip.txt
│ ├── Dijon Yogurt Dip.txt
│ ├── Easy Cake.txt
│ ├── English Scones.txt
│ ├── Fresh Tomato Sauce.txt
│ ├── Fried Chicken.txt
│ ├── Homemade Salad Dressing.txt
│ ├── Honey Chinese Shrimp.txt
│ ├── Honey-Curried Chicken.txt
│ ├── Indonesian Barbecue Sauce.txt
│ ├── Indonesian Chicken Sate.txt
│ ├── Irene's Snickerdoodle Cookies.txt
│ ├── Japanese Ginger Salad Dressing.txt
│ ├── Lemon Sesame Broccoli.txt
│ ├── Poached Salmon with Wine and Lemon.txt
│ ├── Quick Sweet and Sour Sauce.txt
│ ├── Scottish Shortbread.txt
│ ├── Simple Italian Chicken.txt
│ ├── Spicy Barbecued Chicken.txt
│ ├── Spicy Braised Chicken.txt
│ ├── Tasty Black Bean Dip.txt
│ └── Whole Wheat Bread.txt
├── data/
│ ├── chemistry.txt
│ ├── economics.txt
│ ├── health.txt
│ └── sports.txt
├── data_small/
│ └── sports.txt
├── embedchain_test/
│ ├── .gitignore
│ ├── README.md
│ ├── app.py
│ ├── data/
│ │ ├── haskell-cookbook.txt
│ │ └── lovinglisp-2.txt
│ ├── process_pdfs.py
│ └── requirements.txt
├── extraction/
│ ├── README.md
│ ├── person_data.py
│ └── prompt.txt
├── from_langchain_docs/
│ ├── README.md
│ ├── gpt_4o_test.py
│ ├── memory_langchain_test.py
│ ├── requirements.txt
│ └── search_simple.py
├── google_drive_llm/
│ ├── .gitignore
│ ├── README.md
│ ├── data/
│ │ └── sports.txt
│ ├── fetch_txt_files.py
│ ├── index_and_QA.py
│ └── requirements.txt
├── hugging_face/
│ ├── README.md
│ ├── hf_transformer_local.py
│ ├── requirements.txt
│ ├── simple_example.py
│ ├── test1.py
│ └── throwaway_test.py
├── kg_search/
│ ├── Google_KG_helper.py
│ ├── Google_Knowledge_Graph_Search.py
│ └── README.md
├── kor/
│ ├── README.md
│ └── dates.py
├── langchain_dbpedia_agent/
│ ├── QA.py
│ ├── README.md
│ ├── custom_func_dbpedia.py
│ └── requirements.txt
├── langchain_getting_started/
│ ├── .gitignore
│ ├── README.md
│ ├── agent_test.py
│ ├── chroma_persist_index.py
│ ├── chroma_use_disk_index_for_query.py
│ ├── country_information.py
│ ├── directions_template.py
│ ├── doc_search.py
│ ├── read_text_files.py
│ └── test.py
├── llama-index_case_study/
│ ├── .gitignore
│ ├── README.md
│ ├── requirements.txt
│ └── web_page_QA.py
├── llama.cpp/
│ ├── README.md
│ └── test.py
├── llm_enhanced_search_ddg_ollama/
│ ├── README.md
│ └── llm_enhanced_search.py
├── neo4j/
│ ├── README.md
│ ├── credentials.json
│ └── test_example.py
├── ollama_langchain/
│ ├── README.md
│ ├── llama3-instruct-8b.py
│ ├── mistral-7b.py
│ ├── rag_test.py
│ └── requirements.txt
├── prompt_examples/
│ ├── two-shot-2-var.txt
│ └── two-shot-2.txt
├── rag/
│ ├── README.md
│ ├── requirements.txt
│ ├── reranking_rag_llama_index.py
│ ├── simple_llama_index_retrieve_docs.py
│ └── simple_rag_llama_index.py
├── requirements.txt
├── sqlite/
│ ├── README.md
│ ├── requirements.txt
│ └── sqlite_chat_test.py
├── summarization/
│ ├── README.md
│ ├── prompt.txt
│ ├── requirements.txt
│ └── summarization_example.py
├── tavily_search/
│ ├── READMS.md
│ ├── langchain_test.py
│ └── llamaindex_test.py
├── text_db_llm/
│ ├── README.md
│ ├── data/
│ │ ├── chemistry.txt
│ │ ├── economics.txt
│ │ ├── health.txt
│ │ └── sports.txt
│ ├── requirements.txt
│ └── text_db_llm.py
├── tool_search_math_example/
│ ├── requirements.txt
│ └── tool_search_math_example.py
└── tools_langchain/
├── doc_example.py
└── loop_collection_with_function.py
SYMBOL INDEX (54 symbols across 20 files)
FILE: cooking_recipes/data.py
function process_json (line 3) | def process_json(fpath):
FILE: embedchain_test/app.py
function test (line 9) | def test(q):
FILE: from_langchain_docs/search_simple.py
function search (line 6) | def search(query):
FILE: google_drive_llm/fetch_txt_files.py
function get_txt_files (line 14) | def get_txt_files(dir_id='root'):
function create_test_file (line 23) | def create_test_file():
function test (line 31) | def test():
FILE: hugging_face/hf_transformer_local.py
class CustomLLM (line 17) | class CustomLLM(LLM):
method _call (line 25) | def _call(self, prompt, stop = None):
method _identifying_params (line 34) | def _identifying_params(self):
method _llm_type (line 38) | def _llm_type(self):
FILE: hugging_face/test1.py
class CustomLLM (line 15) | class CustomLLM(LLM):
method _call (line 23) | def _call(self, prompt, stop = None):
method _identifying_params (line 32) | def _identifying_params(self):
method _llm_type (line 36) | def _llm_type(self):
function query (line 67) | def query(query_string):
FILE: kg_search/Google_KG_helper.py
function get_entity_info (line 15) | def get_entity_info(entity_name):
function tree_traverse (line 27) | def tree_traverse(a_dict):
function get_context_text (line 42) | def get_context_text(entity_name):
FILE: kg_search/Google_Knowledge_Graph_Search.py
function kg_search (line 7) | def kg_search(entity_name, *questions):
FILE: langchain_dbpedia_agent/QA.py
function query (line 25) | def query(query):
function entities_in_text (line 31) | def entities_in_text(s):
function dbpedia_get_entities_by_name (line 55) | def dbpedia_get_entities_by_name(name, dbpedia_type):
function get_context_text (line 70) | def get_context_text(query_text):
FILE: langchain_dbpedia_agent/custom_func_dbpedia.py
function get_context_data (line 3) | def get_context_data(query_text):
class GetContextTextFromDbPediaInput (line 17) | class GetContextTextFromDbPediaInput(BaseModel):
class GetContextTextFromDbPediaTool (line 23) | class GetContextTextFromDbPediaTool(BaseTool):
method _run (line 31) | def _run(self, query_text: str):
method _arun (line 35) | def _arun(self, query_text: str):
FILE: langchain_getting_started/agent_test.py
function add (line 6) | def add(input: str) -> str:
function is_prime (line 10) | def is_prime(input: str) -> str:
FILE: langchain_getting_started/country_information.py
function get_country_information (line 5) | def get_country_information(country_name):
FILE: langchain_getting_started/directions_template.py
function get_directions (line 5) | def get_directions(thing_to_do):
FILE: langchain_getting_started/read_text_files.py
function read_text_files (line 5) | def read_text_files(directory_path):
FILE: langchain_getting_started/test.py
function query (line 73) | def query(q):
FILE: llama-index_case_study/web_page_QA.py
function query_website (line 11) | def query_website(url, *questions):
FILE: llm_enhanced_search_ddg_ollama/llm_enhanced_search.py
function llm_search (line 22) | def llm_search(query):
function test1 (line 45) | def test1():
function test2 (line 59) | def test2():
FILE: tool_search_math_example/tool_search_math_example.py
class DuckDuckGoSearchAPIWrapper (line 13) | class DuckDuckGoSearchAPIWrapper:
method __call__ (line 14) | def __call__(self, query):
class SimpleCalculator (line 19) | class SimpleCalculator:
method __call__ (line 20) | def __call__(self, expression):
FILE: tools_langchain/doc_example.py
function add (line 10) | def add(input: str) -> str:
function is_prime (line 14) | def is_prime(input: str) -> str:
function test1 (line 49) | def test1():
function test2 (line 52) | def test2():
FILE: tools_langchain/loop_collection_with_function.py
function loop (line 13) | def loop(a_function: Callable, a_collection: list) -> list: # needs Pyth...
function loop2 (line 20) | def loop2(input: str) -> str:
function add (line 32) | def add(input: str) -> str:
function is_prime (line 36) | def is_prime(input: str) -> str:
function test1 (line 76) | def test1():
function test2 (line 79) | def test2():
function test3 (line 82) | def test3():
function test4 (line 87) | def test4():
Condensed preview — 159 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (1,689K chars).
[
{
"path": ".github/FUNDING.yml",
"chars": 137,
"preview": "# These are supported funding model platforms\n\ngithub: mark-watson\ncustom: [“https://buy.stripe.com/aFa5kudvI3mTfv5c5F8N"
},
{
"path": ".gitignore",
"chars": 89,
"preview": "*~\n.DS_Store\nvenv\n.idea\n*.pyc\ntarget\nclient_secrets.json\ncl*.json\nindex.json\n*.log\ncache\n"
},
{
"path": "CHAPTERS_and_CODE_no_longer_in-book/README.md",
"chars": 606,
"preview": "# Examples for older 2023 version of my book\n\nLook in sub-diurecotries for original book content that has been deprecate"
},
{
"path": "CHAPTERS_and_CODE_no_longer_in-book/zapier_integrations/README.md",
"chars": 4719,
"preview": "# Using Zapier Integrations With GMail and Google Calendar\n\nZapier is a service for writing integrations with hundreds o"
},
{
"path": "CHAPTERS_and_CODE_no_longer_in-book/zapier_integrations/README_code.md",
"chars": 843,
"preview": "# Using Zapier Integrations with GMail and Google Calendar\n\nZapier is a service for writing integrations with hundreds o"
},
{
"path": "CHAPTERS_and_CODE_no_longer_in-book/zapier_integrations/get_google_calendar.py",
"chars": 493,
"preview": "from langchain.llms import OpenAI\nfrom langchain.agents import initialize_agent\nfrom langchain.agents.agent_toolkits imp"
},
{
"path": "CHAPTERS_and_CODE_no_longer_in-book/zapier_integrations/requirements.txt",
"chars": 36,
"preview": "langchain\nlangchain_community\nopenai"
},
{
"path": "CHAPTERS_and_CODE_no_longer_in-book/zapier_integrations/send_gmail.py",
"chars": 597,
"preview": "from langchain.llms import OpenAI\nfrom langchain.agents import initialize_agent\nfrom langchain.agents.agent_toolkits imp"
},
{
"path": "Makefile",
"chars": 205,
"preview": "install:\n\tpip install llama_index langchain trafilatura openai SPARQLWrapper rdflib rdflib-jsonld pydrive kor\n\nclean:\n\tr"
},
{
"path": "README.md",
"chars": 763,
"preview": "# Example code for my book \"LangChain Project Lab Book: Hooking Large Language Models Up to the Real World\"\n\nYou can pur"
},
{
"path": "cooking_recipes/README.md",
"chars": 600,
"preview": "# Using LLMs to Generate Recipes\n\nUpdated 1/26/2024 for langchain-0.1.4 and using the new OpenAI embedding models\n\nSee t"
},
{
"path": "cooking_recipes/data/.gitignore",
"chars": 4,
"preview": "*~\n\n"
},
{
"path": "cooking_recipes/data/correlated-ingredients.json",
"chars": 352258,
"preview": "{\"chives\":\n {\"elbow macaroni\":2,\n \"red wine\":3,\n \"bacon\":18,\n \"cayenne pepper\":8,\n \"lemon juice\":48,\n \"honey\":8,\n "
},
{
"path": "cooking_recipes/data/desert.json",
"chars": 6865,
"preview": "[\n {\n \"name\": \"Easy Cake\",\n \"num_served\": 8,\n \"ingredients\": [\n {\n \"na"
},
{
"path": "cooking_recipes/data/fish.json",
"chars": 4407,
"preview": "[\n {\"name\":\"Honey Chinese Shrimp\",\n \"num_served\":4,\n \"ingredients\":\n [{\"name\":\"honey\",\n "
},
{
"path": "cooking_recipes/data/ingredients.json",
"chars": 162611,
"preview": "{\"101010101\":\n {\"beta_carotene\":0.0,\n \"lycopene\":0.0,\n \"vitamin_a_rae\":0.0,\n \"pantothenic_acid\":0.0,\n \"phosphorus\":0"
},
{
"path": "cooking_recipes/data/meat.json",
"chars": 26707,
"preview": "[\n {\"name\":\"Fried Chicken\",\n \"num_served\":3,\n \"ingredients\":\n [{\"name\":\"chicken (whole fryer"
},
{
"path": "cooking_recipes/data/misc.json",
"chars": 22660,
"preview": "[\n {\n \"name\": \"Whole Wheat Bread\",\n \"num_served\":10,\n \"ingredients\": [\n {\n "
},
{
"path": "cooking_recipes/data/recipes.json",
"chars": 49665,
"preview": "[\n {\n \"items\": [\n [\"onions\", [\"2 \", \"onion\"], 2.0, \"onion\"],\n [\"fresh garlic (peeled and"
},
{
"path": "cooking_recipes/data/tiny.json",
"chars": 861,
"preview": " {\"name\":\"Balsamic Mushrooms\",\n \"num_served\":3,\n \"ingredients\":\n [{\"name\":\"mushrooms\",\n "
},
{
"path": "cooking_recipes/data/vegetarian.json",
"chars": 8505,
"preview": "[\n {\"name\":\"Balsamic Mushrooms\",\n \"num_served\":3,\n \"ingredients\":\n [{\"name\":\"mushrooms\",\n "
},
{
"path": "cooking_recipes/data.py",
"chars": 818,
"preview": "import json\n\ndef process_json(fpath):\n with open(fpath, 'r') as f:\n data = json.load(f)\n\n for d in data:\n "
},
{
"path": "cooking_recipes/recipe_generator.py",
"chars": 1489,
"preview": "# This example uses code from https://python.langchain.com/docs/get_started\n\n# on macOS: pip install faiss-cpu\n\nfrom lan"
},
{
"path": "cooking_recipes/text_data/Aioli Sauce.txt",
"chars": 643,
"preview": "Recipe name: Aioli Sauce\n\nNumber of servings: 8\n\nIngredients:\n 3 unit Egg yolks\n 4 clove Cloves garlic\n 1 tablespoon "
},
{
"path": "cooking_recipes/text_data/Apple Muffins.txt",
"chars": 590,
"preview": "Recipe name: Apple Muffins\n\nNumber of servings: 10\n\nIngredients:\n 1 Egg\n 1/2 cup Milk\n 1/4 cup Vegetable oil\n 1/2 c"
},
{
"path": "cooking_recipes/text_data/Balsamic Mushrooms.txt",
"chars": 385,
"preview": "Recipe name: Balsamic Mushrooms\n\nNumber of servings: 3\n\nIngredients:\n 12 medium Mushrooms\n 1/4 cup Balsamic vinegar\n "
},
{
"path": "cooking_recipes/text_data/Barbecued Chicken.txt",
"chars": 689,
"preview": "Recipe name: Barbecued Chicken\n\nNumber of servings: 2\n\nIngredients:\n 2 unit Boneless chicken breasts\n 2 tablespoon Soy"
},
{
"path": "cooking_recipes/text_data/Barbecued Eggplant.txt",
"chars": 556,
"preview": "Recipe name: Barbecued Eggplant\n\nNumber of servings: 2\n\nIngredients:\n 1 eggplant\n 1/4 cup Eggplant Barbecue Sauce sal"
},
{
"path": "cooking_recipes/text_data/Barbecued Salmon with Basil.txt",
"chars": 484,
"preview": "Recipe name: Barbecued Salmon with Basil\n\nNumber of servings: 4\n\nIngredients:\n 4 unit Salmon steaks\n 2 tablespoon Oliv"
},
{
"path": "cooking_recipes/text_data/Basil Vinaigrette Salad Dressing.txt",
"chars": 374,
"preview": "Recipe name: Basil Vinaigrette Salad Dressing\n\nNumber of servings: 12\n\nIngredients:\n 1/4 cup balsamic vinegar\n 1/4 tea"
},
{
"path": "cooking_recipes/text_data/Black Bean Dip.txt",
"chars": 450,
"preview": "Recipe name: Black Bean Dip\n\nNumber of servings: 6\n\nIngredients:\n 2 cup Refried Black Beans (see rec.)\n 1/4 cup Sour c"
},
{
"path": "cooking_recipes/text_data/Borscht.txt",
"chars": 721,
"preview": "Recipe name: Borscht\n\nNumber of servings: 4\n\nIngredients:\n 2 cup Beets\n 1 Onion\n 2 clove Garlic clove\n 2 quart Wate"
},
{
"path": "cooking_recipes/text_data/Brined Barbecued Chicken.txt",
"chars": 1021,
"preview": "Recipe name: Brined Barbecued Chicken\n\nNumber of servings: 2\n\nIngredients:\n 2 unit Boneless chicken breasts\n 4 tablesp"
},
{
"path": "cooking_recipes/text_data/Cauliflower and Broccoli Pizza Crust.txt",
"chars": 3277,
"preview": "Recipe name: Cauliflower and Broccoli Pizza Crust\n\nNumber of servings: 4\n\nIngredients:\n 5 cup Broccoli\n 1 cup Broccoli"
},
{
"path": "cooking_recipes/text_data/Chicken Cacciatore.txt",
"chars": 599,
"preview": "Recipe name: Chicken Cacciatore\n\nNumber of servings: 4\n\nIngredients:\n 2 Pieces chicken\n 2 Crushed tomatoes\n 2 teasp"
},
{
"path": "cooking_recipes/text_data/Chicken Schnitzel.txt",
"chars": 593,
"preview": "Recipe name: Chicken Schnitzel\n\nNumber of servings: 6\n\nIngredients:\n 2 pounds Chicken Breasts\n 4 unit Eggs\n 1.5 cup F"
},
{
"path": "cooking_recipes/text_data/Chicken with Cream of Mushroom Soup.txt",
"chars": 585,
"preview": "Recipe name: Chicken with Cream of Mushroom Soup\n\nNumber of servings: 2\n\nIngredients:\n 2 Pieces chicken\n 1 tablespoon"
},
{
"path": "cooking_recipes/text_data/Chinese Shrimp with Zucchini Squash.txt",
"chars": 940,
"preview": "Recipe name: Chinese Shrimp with Zucchini Squash\n\nNumber of servings: 2\n\nIngredients:\n 14 medium-large medium shrimp\n "
},
{
"path": "cooking_recipes/text_data/Crock Pot Lemon Honey Chicken.txt",
"chars": 559,
"preview": "Recipe name: Crock Pot Lemon Honey Chicken\n\nNumber of servings: 4\n\nIngredients:\n 1 Roaster Chicken\n 1/2 cup Water\n 2"
},
{
"path": "cooking_recipes/text_data/Curried Chicken Legs.txt",
"chars": 1361,
"preview": "Recipe name: Curried Chicken Legs\n\nNumber of servings: 8\n\nIngredients:\n 8 Chicken legs\n 4 teaspoon Curry powder\n 1 "
},
{
"path": "cooking_recipes/text_data/Curried Yogurt Dip.txt",
"chars": 604,
"preview": "Recipe name: Curried Yogurt Dip\n\nNumber of servings: 6\n\nIngredients:\n 1 cup Plain Yogurt\n 1.5 teaspoon Curry powder\n "
},
{
"path": "cooking_recipes/text_data/Dijon Sour Cream Dip.txt",
"chars": 469,
"preview": "Recipe name: Dijon Sour Cream Dip\n\nNumber of servings: 6\n\nIngredients:\n 1 cup Sour cream OR Plain yogurt\n 1/3 cup Mayo"
},
{
"path": "cooking_recipes/text_data/Dijon Yogurt Dip.txt",
"chars": 464,
"preview": "Recipe name: Dijon Yogurt Dip\n\nNumber of servings: 6\n\nIngredients:\n 1 cup Sour cream OR Plain yogurt\n 1/3 cup Mayonnai"
},
{
"path": "cooking_recipes/text_data/Easy Cake.txt",
"chars": 378,
"preview": "Recipe name: Easy Cake\n\nNumber of servings: 8\n\nIngredients:\n 1 cup Butter\n 2 cup Sugar\n 3 cup Flour\n 4 Eggs\n 5 tea"
},
{
"path": "cooking_recipes/text_data/English Scones.txt",
"chars": 607,
"preview": "Recipe name: English Scones\n\nNumber of servings: 6\n\nIngredients:\n 2.5 cup Flour\n 1 teaspoon Cream of tartar\n 1 teaspo"
},
{
"path": "cooking_recipes/text_data/Fresh Tomato Sauce.txt",
"chars": 567,
"preview": "Recipe name: Fresh Tomato Sauce\n\nNumber of servings: 4\n\nIngredients:\n 12 Tomatoes\n 2 tablespoon Sugar\n 1 teaspoon bl"
},
{
"path": "cooking_recipes/text_data/Fried Chicken.txt",
"chars": 545,
"preview": "Recipe name: Fried Chicken\n\nNumber of servings: 3\n\nIngredients:\n 1 Frying chickens\n 1/4 cup Flour\n 1/2 teaspoon Salt"
},
{
"path": "cooking_recipes/text_data/Homemade Salad Dressing.txt",
"chars": 397,
"preview": "Recipe name: Homemade Salad Dressing\n\nNumber of servings: 16\n\nIngredients:\n 1 teaspoon Salt\n 1/4 teaspoon Black Pepper"
},
{
"path": "cooking_recipes/text_data/Honey Chinese Shrimp.txt",
"chars": 640,
"preview": "Recipe name: Honey Chinese Shrimp\n\nNumber of servings: 4\n\nIngredients:\n 1/4 cup Honey\n 16 \n 1/4 cup Red wine\n 2 tab"
},
{
"path": "cooking_recipes/text_data/Honey-Curried Chicken.txt",
"chars": 828,
"preview": "Recipe name: Honey-Curried Chicken\n\nNumber of servings: 6\n\nIngredients:\n 2 pound Chicken breasts\n 2 teaspoon Vegetable"
},
{
"path": "cooking_recipes/text_data/Indonesian Barbecue Sauce.txt",
"chars": 459,
"preview": "Recipe name: Indonesian Barbecue Sauce\n\nNumber of servings: 12\n\nIngredients:\n 1/2 cup Corn syrup (dark)\n 1/4 cup Cream"
},
{
"path": "cooking_recipes/text_data/Indonesian Chicken Sate.txt",
"chars": 995,
"preview": "Recipe name: Indonesian Chicken Sate\n\nNumber of servings: 4\n\nIngredients:\n 4 Skinless, boneless chicken breast halves "
},
{
"path": "cooking_recipes/text_data/Irene's Snickerdoodle Cookies.txt",
"chars": 654,
"preview": "Recipe name: Irene's Snickerdoodle Cookies\n\nNumber of servings: 10\n\nIngredients:\n 1 cup Shortening\n 1 3/4 cup Sugar\n "
},
{
"path": "cooking_recipes/text_data/Japanese Ginger Salad Dressing.txt",
"chars": 404,
"preview": "Recipe name: Japanese Ginger Salad Dressing\n\nNumber of servings: 4\n\nIngredients:\n 1 tablespoon Rice vinegar\n 2 teaspoo"
},
{
"path": "cooking_recipes/text_data/Lemon Sesame Broccoli.txt",
"chars": 613,
"preview": "Recipe name: Lemon Sesame Broccoli\n\nNumber of servings: 4\n\nIngredients:\n 2 cup Broccoli\n 2 teaspoon Sesame oil\n 1/2 t"
},
{
"path": "cooking_recipes/text_data/Poached Salmon with Wine and Lemon.txt",
"chars": 570,
"preview": "Recipe name: Poached Salmon with Wine and Lemon\n\nNumber of servings: 4\n\nIngredients:\n 1/4 cup Lemon juice\n 1/4 cup Dry"
},
{
"path": "cooking_recipes/text_data/Quick Sweet and Sour Sauce.txt",
"chars": 535,
"preview": "Recipe name: Quick Sweet and Sour Sauce\n\nNumber of servings: 8\n\nIngredients:\n 1 cup White vinegar\n 2/3 cup Sugar\n 2 t"
},
{
"path": "cooking_recipes/text_data/Scottish Shortbread.txt",
"chars": 490,
"preview": "Recipe name: Scottish Shortbread\n\nNumber of servings: 10\n\nIngredients:\n 4 cup Plain flour\n 2 cup Butter (nothing else "
},
{
"path": "cooking_recipes/text_data/Simple Italian Chicken.txt",
"chars": 670,
"preview": "Recipe name: Simple Italian Chicken\n\nNumber of servings: 4\n\nIngredients:\n 2 lb Skinned chicken breasts\n 2 tablespoon O"
},
{
"path": "cooking_recipes/text_data/Spicy Barbecued Chicken.txt",
"chars": 1632,
"preview": "Recipe name: Spicy Barbecued Chicken\n\nNumber of servings: 4\n\nIngredients:\n 1 chicken (2 lb)\n 1 Juice of 1 lemon\n 2 "
},
{
"path": "cooking_recipes/text_data/Spicy Braised Chicken.txt",
"chars": 705,
"preview": "Recipe name: Spicy Braised Chicken\n\nNumber of servings: 4\n\nIngredients:\n 1 teaspoon Red peppers\n 1 tablespoon Oil\n 1 "
},
{
"path": "cooking_recipes/text_data/Tasty Black Bean Dip.txt",
"chars": 681,
"preview": "Recipe name: Tasty Black Bean Dip\n\nNumber of servings: 4\n\nIngredients:\n 1.5 cup 16-0z can black beans\n 2 tablespoon Ch"
},
{
"path": "cooking_recipes/text_data/Whole Wheat Bread.txt",
"chars": 477,
"preview": "Recipe name: Whole Wheat Bread\n\nNumber of servings: 10\n\nIngredients:\n 1 1/2 cup Water\n 1 1/3 tablespoon Vegetable oil\n"
},
{
"path": "data/chemistry.txt",
"chars": 2974,
"preview": "Amyl alcohol is an organic compound with the formula C 5 H 12 O. All eight isomers of amyl alcohol are known. The most i"
},
{
"path": "data/economics.txt",
"chars": 3757,
"preview": "The Austrian School (also known as the Vienna School or the Psychological School ) is a Schools of economic thought|scho"
},
{
"path": "data/health.txt",
"chars": 3953,
"preview": " \r\n which requires that you sit at a desk all day. ; If you hate to talk\r\n politics, don't associate with peo"
},
{
"path": "data/sports.txt",
"chars": 601,
"preview": "Sport is generally recognised as activities based in physical athleticism or physical dexterity.[3] Sports are usually g"
},
{
"path": "data_small/sports.txt",
"chars": 601,
"preview": "Sport is generally recognised as activities based in physical athleticism or physical dexterity.[3] Sports are usually g"
},
{
"path": "embedchain_test/.gitignore",
"chars": 4,
"preview": "db\n\n"
},
{
"path": "embedchain_test/README.md",
"chars": 1675,
"preview": "# Creating an index\n\n python process_pdfs.py\n\nI hardwired the directory path for PDF files for my books in the file *"
},
{
"path": "embedchain_test/app.py",
"chars": 346,
"preview": "\n# https://github.com/embedchain/embedchain\n\nfrom embedchain import App\n\ntest_chat = App()\n\n\ndef test(q):\n print(q)\n "
},
{
"path": "embedchain_test/data/haskell-cookbook.txt",
"chars": 281913,
"preview": "\nHaskell Tutorial and Cookbook\nMark Watson\nThis book is for sale at http://leanpub.com/haskell-cookbook\nThis version was"
},
{
"path": "embedchain_test/data/lovinglisp-2.txt",
"chars": 510888,
"preview": "\nLoving Common Lisp, or the Savvy\nProgrammer’s Secret Weapon\nMark Watson\nThis book is for sale at http://leanpub.com/lov"
},
{
"path": "embedchain_test/process_pdfs.py",
"chars": 313,
"preview": "\n# https://github.com/embedchain/embedchain\n\nfrom embedchain import App\nimport os\n\ntest_chat = App()\n\nmy_books_dir = \"./"
},
{
"path": "embedchain_test/requirements.txt",
"chars": 20,
"preview": "embedchain=0.1.123\n\n"
},
{
"path": "extraction/README.md",
"chars": 24,
"preview": "# Extraction from text\n\n"
},
{
"path": "extraction/person_data.py",
"chars": 742,
"preview": "import openai\nfrom openai import OpenAI\nimport os\n\nopenai.api_key = os.getenv(\"OPENAI_API_KEY\")\nclient = OpenAI()\n\n# Rea"
},
{
"path": "extraction/prompt.txt",
"chars": 739,
"preview": "Given the two examples below, extract the names, addresses, and email addresses of individuals mentioned later as Proces"
},
{
"path": "from_langchain_docs/README.md",
"chars": 261,
"preview": "pip install -U langchain-openai\npip install -U langchain-community\npip install langchainhub\n\nNote: I no longer use Serp "
},
{
"path": "from_langchain_docs/gpt_4o_test.py",
"chars": 433,
"preview": "from langchain_core.messages import HumanMessage, SystemMessage\nfrom langchain_openai import ChatOpenAI\n\nllm = ChatOpenA"
},
{
"path": "from_langchain_docs/memory_langchain_test.py",
"chars": 878,
"preview": "# Derived from:\n# https://langchain.readthedocs.io/en/latest/modules/memory/examples/adding_memory.html\n# with slight "
},
{
"path": "from_langchain_docs/requirements.txt",
"chars": 52,
"preview": "langchain_core\nlangchain_openai\nlangchain_community\n"
},
{
"path": "from_langchain_docs/search_simple.py",
"chars": 300,
"preview": "# make sure SERPER_API_KEY is set in your environment\n\nfrom langchain_community.utilities import GoogleSerperAPIWrapper\n"
},
{
"path": "google_drive_llm/.gitignore",
"chars": 32,
"preview": "client_secrets.json\nclie*.json\n\n"
},
{
"path": "google_drive_llm/README.md",
"chars": 316,
"preview": "# Setup\n\nFollow directions in the book to set up Google app authentication.\n\n# run\n\n```\npython fetch_txt_files.py\npython"
},
{
"path": "google_drive_llm/data/sports.txt",
"chars": 601,
"preview": "Sport is generally recognised as activities based in physical athleticism or physical dexterity.[3] Sports are usually g"
},
{
"path": "google_drive_llm/fetch_txt_files.py",
"chars": 1224,
"preview": "from pydrive.auth import GoogleAuth\nfrom pydrive.drive import GoogleDrive\nfrom pathlib import Path\n\n# good GD search doc"
},
{
"path": "google_drive_llm/index_and_QA.py",
"chars": 449,
"preview": "# make sure you set the following environment variable is set:\n# OPENAI_API_KEY\n\nfrom llama_index import GPTSimpleVect"
},
{
"path": "google_drive_llm/requirements.txt",
"chars": 8,
"preview": "pydrive\n"
},
{
"path": "hugging_face/README.md",
"chars": 138,
"preview": "# Using local models\n\npip install -U torch langchain transformers llama_index\n\nFor CPU only, this speeds things up:\n\npip"
},
{
"path": "hugging_face/hf_transformer_local.py",
"chars": 2239,
"preview": "# Derived from example:\n# https://gpt-index.readthedocs.io/en/latest/how_to/custom_llms.html\n\nimport time\nimport torch"
},
{
"path": "hugging_face/requirements.txt",
"chars": 57,
"preview": "numpy==1.22.0\nllama-index==0.10.13\nsentence-transformers\n"
},
{
"path": "hugging_face/simple_example.py",
"chars": 401,
"preview": "from langchain import HuggingFaceHub, LLMChain\nfrom langchain.prompts import PromptTemplate\n\nhub_llm = HuggingFaceHub(\n "
},
{
"path": "hugging_face/test1.py",
"chars": 2457,
"preview": "# pip install xformers\n\nfrom llama_index import ListIndex, SimpleDirectoryReader, set_global_service_context\nfrom langch"
},
{
"path": "hugging_face/throwaway_test.py",
"chars": 475,
"preview": "from llama_index import ListIndex, SimpleDirectoryReader\n#from llama_index import QueryEngine\n\n# Load documents from a d"
},
{
"path": "kg_search/Google_KG_helper.py",
"chars": 1342,
"preview": "\"\"\"Python client calling for Knowledge Graph Search API.\"\"\"\n\nimport json\nimport os\nfrom urllib.parse import urlencode\nfr"
},
{
"path": "kg_search/Google_Knowledge_Graph_Search.py",
"chars": 697,
"preview": "\"\"\"Example of Python client calling Knowledge Graph Search API.\"\"\"\n\nfrom llama_index.core.schema import Document\nfrom ll"
},
{
"path": "kg_search/README.md",
"chars": 404,
"preview": "# Running the example\n\nYou will need Google and OpenAI API keys (see book text).\n\nInstall virtualenv if required and set"
},
{
"path": "kor/README.md",
"chars": 537,
"preview": "# Kor Library: a Useful Library for using LLMs to extract data from text\n\nThe Kor library was written by Eugene Yurtsev."
},
{
"path": "kor/dates.py",
"chars": 1056,
"preview": "\" From documentation: https://eyurtsev.github.io/kor/\"\n\nfrom kor.extraction import create_extraction_chain\nfrom kor.node"
},
{
"path": "langchain_dbpedia_agent/QA.py",
"chars": 2618,
"preview": "# Copyright 2021-2023 Mark Watson\n# This is an example I wriote and distributed in 2021. Here I am just\n# reusing a few "
},
{
"path": "langchain_dbpedia_agent/README.md",
"chars": 2350,
"preview": "# LangChain Agent Tool Example Using DBPedia SPARQL Queries\n\nTo run the example:\n\n python custom_func_dbpedia.py\n\nExa"
},
{
"path": "langchain_dbpedia_agent/custom_func_dbpedia.py",
"chars": 1540,
"preview": "from QA import get_context_text\n\ndef get_context_data(query_text):\n \"\"\"Method to get context text for entities from D"
},
{
"path": "langchain_dbpedia_agent/requirements.txt",
"chars": 94,
"preview": "spacy==3.7.6\nSPARQLWrapper==2.0.0\nlangchain==0.2.14\nlangchain-community==0.2.12\nopenai==1.42.0"
},
{
"path": "langchain_getting_started/.gitignore",
"chars": 15,
"preview": "*.log\ntmp\ntmp/*"
},
{
"path": "langchain_getting_started/README.md",
"chars": 431,
"preview": "# LangChain Getting Started Examples\n\nRequirements:\n\n pip install langchain langchain-openai faiss-cpu chromadb unstr"
},
{
"path": "langchain_getting_started/agent_test.py",
"chars": 993,
"preview": "from langchain.agents import initialize_agent, Tool\nfrom langchain.llms import OpenAI\n\nllm = OpenAI(temperature=0)\n\ndef "
},
{
"path": "langchain_getting_started/chroma_persist_index.py",
"chars": 755,
"preview": "from langchain.vectorstores import Chroma\nfrom langchain.embeddings.openai import OpenAIEmbeddings\n\nembeddings = OpenAIE"
},
{
"path": "langchain_getting_started/chroma_use_disk_index_for_query.py",
"chars": 424,
"preview": "from langchain.vectorstores import Chroma\nfrom langchain.embeddings.openai import OpenAIEmbeddings\n\nembeddings = OpenAIE"
},
{
"path": "langchain_getting_started/country_information.py",
"chars": 694,
"preview": "from langchain.prompts import PromptTemplate\nfrom langchain_openai import OpenAI\nllm = OpenAI(temperature=0.9)\n\ndef get_"
},
{
"path": "langchain_getting_started/directions_template.py",
"chars": 476,
"preview": "from langchain.prompts import PromptTemplate\nfrom langchain_openai import OpenAI\nllm = OpenAI(temperature=0.9)\n\ndef get_"
},
{
"path": "langchain_getting_started/doc_search.py",
"chars": 1155,
"preview": "from langchain_community.vectorstores import FAISS\nfrom langchain_core.output_parsers import StrOutputParser\nfrom langch"
},
{
"path": "langchain_getting_started/read_text_files.py",
"chars": 1104,
"preview": "import os\n\n# Generated by Perplexity\n\ndef read_text_files(directory_path):\n \"\"\"\n Reads all .txt files in the speci"
},
{
"path": "langchain_getting_started/test.py",
"chars": 3675,
"preview": "from langchain_community.vectorstores import FAISS\nfrom langchain_core.output_parsers import StrOutputParser\nfrom langch"
},
{
"path": "llama-index_case_study/.gitignore",
"chars": 18,
"preview": "index.json\ncache\n\n"
},
{
"path": "llama-index_case_study/README.md",
"chars": 124,
"preview": "# LlamaIndex Examples\n\nThe old example test_from_docs.py has been deprecated and removed. The new example is web_page_QA"
},
{
"path": "llama-index_case_study/requirements.txt",
"chars": 23,
"preview": "llama_index\ntrafilatura"
},
{
"path": "llama-index_case_study/web_page_QA.py",
"chars": 952,
"preview": "# Derived from examples in llama_index documentation\n\n# pip install llama-index html2text trafilatura\n\nfrom pprint impor"
},
{
"path": "llama.cpp/README.md",
"chars": 114,
"preview": "# llama.cpp local modes with Langchain\n\nreference:\n\nhttps://python.langchain.com/docs/integrations/llms/llamacpp\n\n"
},
{
"path": "llama.cpp/test.py",
"chars": 1112,
"preview": "# code example from:\n# https://python.langchain.com/docs/integrations/llms/llamacpp\n\nfrom langchain.llms import LlamaC"
},
{
"path": "llm_enhanced_search_ddg_ollama/README.md",
"chars": 474,
"preview": "# Multi-prompt Search using LLMs, the Duckduckgo search API, and local Ollama models\n\n\n## install Duckduckgo search API "
},
{
"path": "llm_enhanced_search_ddg_ollama/llm_enhanced_search.py",
"chars": 2188,
"preview": "from ddg import Duckduckgo\nfrom langchain_community.llms.ollama import Ollama\n\n# pip install llama-index html2text trafi"
},
{
"path": "neo4j/README.md",
"chars": 178,
"preview": "# Neo4J example, not yet in my book, this directory just shows an example from LlamaIndex docs\n\ndocs with this example:\n"
},
{
"path": "neo4j/credentials.json",
"chars": 104,
"preview": "{\"username\": \"neo4j\", \"password\": \"demodemo\", \"database\": \"llamatest\", \"url\": \"bolt://localhost:7687\"}\n\n"
},
{
"path": "neo4j/test_example.py",
"chars": 1256,
"preview": "import json\n\nfrom llama_index.llama_pack import download_llama_pack\n\n# download and install dependencies\nNeo4jQueryEngin"
},
{
"path": "ollama_langchain/README.md",
"chars": 0,
"preview": ""
},
{
"path": "ollama_langchain/llama3-instruct-8b.py",
"chars": 331,
"preview": "# requires \"ollama serve\" to be running in another terminal\n\nfrom langchain_community.llms.ollama import Ollama\n\nllm = O"
},
{
"path": "ollama_langchain/mistral-7b.py",
"chars": 330,
"preview": "# requires \"ollama serve\" to be running in another terminal\n\nfrom langchain_community.llms.ollama import Ollama\n\nllm = O"
},
{
"path": "ollama_langchain/rag_test.py",
"chars": 1594,
"preview": "# requires \"ollama serve\" to be running in another terminal\n\n# pip install python-docx\n\n\nfrom langchain_community.llms.o"
},
{
"path": "ollama_langchain/requirements.txt",
"chars": 30,
"preview": "langchain_community\nlangchain\n"
},
{
"path": "prompt_examples/two-shot-2-var.txt",
"chars": 799,
"preview": "Given the two examples below, extract the names, addresses, and email addresses of individuals mentioned later as Proces"
},
{
"path": "prompt_examples/two-shot-2.txt",
"chars": 903,
"preview": "Given the two examples below, extract the names, addresses, and email addresses of individuals mentioned later as Proces"
},
{
"path": "rag/README.md",
"chars": 288,
"preview": "# RAG examples\n\nFor my book [LangChain and LlamaIndex Projects Lab Book: Hooking Large Language Models Up to the Real Wo"
},
{
"path": "rag/requirements.txt",
"chars": 49,
"preview": "llama-index==0.11.1\nsentence-transformers==3.0.1\n"
},
{
"path": "rag/reranking_rag_llama_index.py",
"chars": 1472,
"preview": "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\nfrom llama_index.embeddings.openai import OpenAIEmb"
},
{
"path": "rag/simple_llama_index_retrieve_docs.py",
"chars": 452,
"preview": "from llama_index.core import VectorStoreIndex\nfrom llama_index.core import Document\n\ntext_list = [\"LlamaIndex is a power"
},
{
"path": "rag/simple_rag_llama_index.py",
"chars": 692,
"preview": "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\nfrom llama_index.embeddings.openai import OpenAIEmb"
},
{
"path": "requirements.txt",
"chars": 22,
"preview": "llama-index>=0.10.13\n\n"
},
{
"path": "sqlite/README.md",
"chars": 379,
"preview": "# SqLite Chat Example\n\nWe will use the SQlite sample database from the SQLite Tutorial web site:\n\n https://www.sqlite"
},
{
"path": "sqlite/requirements.txt",
"chars": 88,
"preview": "langchain==0.3.3\nlangchain_community==0.3.2\nlangchain_experimental==0.3.3\nopenai==1.51.2"
},
{
"path": "sqlite/sqlite_chat_test.py",
"chars": 579,
"preview": "from langchain.llms import OpenAI\nfrom langchain_experimental.sql import SQLDatabaseChain\nfrom langchain_experimental.sq"
},
{
"path": "summarization/README.md",
"chars": 22,
"preview": "# Text summarization\n\n"
},
{
"path": "summarization/prompt.txt",
"chars": 53,
"preview": "Summarize the following text: \"{input_text}\"\nOutput:\n"
},
{
"path": "summarization/requirements.txt",
"chars": 7,
"preview": "openai\n"
},
{
"path": "summarization/summarization_example.py",
"chars": 694,
"preview": "import openai\nfrom openai import OpenAI\nimport os\n\nopenai.api_key = os.getenv(\"OPENAI_API_KEY\")\nclient = OpenAI()\n\n# Rea"
},
{
"path": "tavily_search/READMS.md",
"chars": 145,
"preview": "Example code from:\n\nLangChain: https://docs.tavily.com/docs/tavily-api/langchain\n\nLlamaIndex: https://docs.tavily.com/do"
},
{
"path": "tavily_search/langchain_test.py",
"chars": 699,
"preview": "import os\nfrom langchain.utilities.tavily_search import TavilySearchAPIWrapper\nfrom langchain.agents import initialize_a"
},
{
"path": "tavily_search/llamaindex_test.py",
"chars": 352,
"preview": "from llama_hub.tools.tavily_research import TavilyToolSpec\nfrom llama_index.agent import OpenAIAgent\nimport os\n\n# set TA"
},
{
"path": "text_db_llm/README.md",
"chars": 68,
"preview": "# Text Database Supporting Search and Chat-based Exploration\n\nTBD\n\n\n"
},
{
"path": "text_db_llm/data/chemistry.txt",
"chars": 2974,
"preview": "Amyl alcohol is an organic compound with the formula C 5 H 12 O. All eight isomers of amyl alcohol are known. The most i"
},
{
"path": "text_db_llm/data/economics.txt",
"chars": 3615,
"preview": "The Austrian School (also known as the Vienna School or the Psychological School ) is a Schools of economic thought|scho"
},
{
"path": "text_db_llm/data/health.txt",
"chars": 3953,
"preview": " \r\n which requires that you sit at a desk all day. ; If you hate to talk\r\n politics, don't associate with peo"
},
{
"path": "text_db_llm/data/sports.txt",
"chars": 601,
"preview": "Sport is generally recognised as activities based in physical athleticism or physical dexterity.[3] Sports are usually g"
},
{
"path": "text_db_llm/requirements.txt",
"chars": 132,
"preview": "openai==1.42.0\nchromadb==0.5.5\nllama-index-vector-stores-chroma==0.2.0\nllama-index-embeddings-huggingface==0.3.1\nllama-i"
},
{
"path": "text_db_llm/text_db_llm.py",
"chars": 1476,
"preview": "# Text Database Supporting Search and Chat-based Exploration\n\n# make sure you set the following environment variable:\n# "
},
{
"path": "tool_search_math_example/requirements.txt",
"chars": 64,
"preview": "langchain\nduckduckgo_search\nlangchain-community\nlangchain-openai"
},
{
"path": "tool_search_math_example/tool_search_math_example.py",
"chars": 2513,
"preview": "from langchain.agents import create_react_agent, Tool, AgentExecutor\nfrom langchain.agents.agent_types import AgentType\n"
},
{
"path": "tools_langchain/doc_example.py",
"chars": 1331,
"preview": "# example from documentation: https://github.com/cristobalcl/LearningLangChain/blob/master/notebooks/01%20-%20Simple%20A"
},
{
"path": "tools_langchain/loop_collection_with_function.py",
"chars": 2295,
"preview": "# example from documentation: https://github.com/cristobalcl/LearningLangChain/blob/master/notebooks/01%20-%20Simple%20A"
}
]
About this extraction
This page contains the full source code of the mark-watson/langchain-book-examples GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 159 files (1.5 MB), approximately 494.2k tokens, and a symbol index with 54 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.