├── .env.example
├── .gitignore
├── Pipfile
├── Pipfile.lock
├── README.md
├── embed.py
├── main.py
├── requirements.txt
├── scrape.py
└── search.py


/.env.example:
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1 | OPENAI_API_KEY=YOUR_OPENAI_API_KEY_HERE


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/.gitignore:
--------------------------------------------------------------------------------
1 | scrape/
2 | chroma/
3 | .env
4 | 
5 | __pycache__/


--------------------------------------------------------------------------------
/Pipfile:
--------------------------------------------------------------------------------
 1 | [[source]]
 2 | url = "https://pypi.org/simple"
 3 | verify_ssl = true
 4 | name = "pypi"
 5 | 
 6 | [packages]
 7 | chromadb = "*"
 8 | langchain = "*"
 9 | openai = "*"
10 | beautifulsoup4 = "*"
11 | lxml = "*"
12 | unstructured = "*"
13 | tiktoken = "*"
14 | gradio = "*"
15 | 
16 | [dev-packages]
17 | black = "*"
18 | 
19 | [requires]
20 | python_version = "3.6"
21 | 


--------------------------------------------------------------------------------
/Pipfile.lock:
--------------------------------------------------------------------------------
   1 | {
   2 |     "_meta": {
   3 |         "hash": {
   4 |             "sha256": "89644d5cb24662bb9df05863b251d1253ac56e72cdc1a21424e2a20259397562"
   5 |         },
   6 |         "pipfile-spec": 6,
   7 |         "requires": {
   8 |             "python_version": "3.6"
   9 |         },
  10 |         "sources": [
  11 |             {
  12 |                 "name": "pypi",
  13 |                 "url": "https://pypi.org/simple",
  14 |                 "verify_ssl": true
  15 |             }
  16 |         ]
  17 |     },
  18 |     "default": {
  19 |         "aiofiles": {
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  24 |             "markers": "python_version >= '3.7' and python_version < '4.0'",
  25 |             "version": "==23.1.0"
  26 |         },
  27 |         "aiohttp": {
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2530 |                 "sha256:be6b3fdec5c62f2a67cb3f8c6dbf56bbf3f61c0f046f84645cd1ca73532ea051",
2531 |                 "sha256:bf74d08542c3a9ea97bb8f343d4fcbd4d8f91bba5ec9d5d7f792dbe727f88938",
2532 |                 "sha256:c027a6e96ef77d401d8d5a5c8d6bc478e8042f1e448272e8d9752cb0aff8b5c8",
2533 |                 "sha256:c0c77533b5ed4bcc38e943178ccae29b9bcf48ffd1063f5821192f23a1bd27b9",
2534 |                 "sha256:c1012fa63eb6c032f3ce5d2171c267992ae0c00b9e164efe4d73db818465fac3",
2535 |                 "sha256:c3a53ba34a636a256d767c086ceb111358876e1fb6b50dfc4d3f4951d40133d5",
2536 |                 "sha256:d4e2c6d555e77b37288eaf45b8f60f0737c9efa3452c6c44626a5455aeb250b9",
2537 |                 "sha256:de119f56f3c5f0e2fb4dee508531a32b069a5f2c6e827b272d1e0ff5ac040333",
2538 |                 "sha256:e65610c5792870d45d7b68c677681376fcf9cc1c289f23e8e8b39c1485384185",
2539 |                 "sha256:e9fdc7ac0d42bc3ea78818557fab03af6181e076a2944f43c38684b4b6bed8e3",
2540 |                 "sha256:ee4afac41415d52d53a9833ebae7e32b344be72835bbb589018c9e938045a560",
2541 |                 "sha256:f364d3480bffd3aa566e886587eaca7c8c04d74f6e8933f3f2c996b7f09bee1b",
2542 |                 "sha256:f3b078dbe227f79be488ffcfc7a9edb3409d018e0952cf13f15fd6512847f3f7",
2543 |                 "sha256:f4e2d08f07a3d7d3e12549052eb5ad3eab1c349c53ac51c209a0e5991bbada78",
2544 |                 "sha256:f7a3d8146575e08c29ed1cd287068e6d02f1c7bdff8970db96683b9591b86ee7"
2545 |             ],
2546 |             "markers": "python_version >= '3.7'",
2547 |             "version": "==1.9.2"
2548 |         }
2549 |     },
2550 |     "develop": {
2551 |         "black": {
2552 |             "hashes": [
2553 |                 "sha256:01ede61aac8c154b55f35301fac3e730baf0c9cf8120f65a9cd61a81cfb4a0c3",
2554 |                 "sha256:022a582720b0d9480ed82576c920a8c1dde97cc38ff11d8d8859b3bd6ca9eedb",
2555 |                 "sha256:25cc308838fe71f7065df53aedd20327969d05671bac95b38fdf37ebe70ac087",
2556 |                 "sha256:27eb7a0c71604d5de083757fbdb245b1a4fae60e9596514c6ec497eb63f95320",
2557 |                 "sha256:327a8c2550ddc573b51e2c352adb88143464bb9d92c10416feb86b0f5aee5ff6",
2558 |                 "sha256:47e56d83aad53ca140da0af87678fb38e44fd6bc0af71eebab2d1f59b1acf1d3",
2559 |                 "sha256:501387a9edcb75d7ae8a4412bb8749900386eaef258f1aefab18adddea1936bc",
2560 |                 "sha256:552513d5cd5694590d7ef6f46e1767a4df9af168d449ff767b13b084c020e63f",
2561 |                 "sha256:5c4bc552ab52f6c1c506ccae05681fab58c3f72d59ae6e6639e8885e94fe2587",
2562 |                 "sha256:642496b675095d423f9b8448243336f8ec71c9d4d57ec17bf795b67f08132a91",
2563 |                 "sha256:6d1c6022b86f83b632d06f2b02774134def5d4d4f1dac8bef16d90cda18ba28a",
2564 |                 "sha256:7f3bf2dec7d541b4619b8ce526bda74a6b0bffc480a163fed32eb8b3c9aed8ad",
2565 |                 "sha256:831d8f54c3a8c8cf55f64d0422ee875eecac26f5f649fb6c1df65316b67c8926",
2566 |                 "sha256:8417dbd2f57b5701492cd46edcecc4f9208dc75529bcf76c514864e48da867d9",
2567 |                 "sha256:86cee259349b4448adb4ef9b204bb4467aae74a386bce85d56ba4f5dc0da27be",
2568 |                 "sha256:893695a76b140881531062d48476ebe4a48f5d1e9388177e175d76234ca247cd",
2569 |                 "sha256:9fd59d418c60c0348505f2ddf9609c1e1de8e7493eab96198fc89d9f865e7a96",
2570 |                 "sha256:ad0014efc7acf0bd745792bd0d8857413652979200ab924fbf239062adc12491",
2571 |                 "sha256:b5b0ee6d96b345a8b420100b7d71ebfdd19fab5e8301aff48ec270042cd40ac2",
2572 |                 "sha256:c333286dc3ddca6fdff74670b911cccedacb4ef0a60b34e491b8a67c833b343a",
2573 |                 "sha256:f9062af71c59c004cd519e2fb8f5d25d39e46d3af011b41ab43b9c74e27e236f",
2574 |                 "sha256:fb074d8b213749fa1d077d630db0d5f8cc3b2ae63587ad4116e8a436e9bbe995"
2575 |             ],
2576 |             "index": "pypi",
2577 |             "version": "==23.7.0"
2578 |         },
2579 |         "click": {
2580 |             "hashes": [
2581 |                 "sha256:48ee849951919527a045bfe3bf7baa8a959c423134e1a5b98c05c20ba75a1cbd",
2582 |                 "sha256:fa244bb30b3b5ee2cae3da8f55c9e5e0c0e86093306301fb418eb9dc40fbded5"
2583 |             ],
2584 |             "markers": "python_version >= '3.7'",
2585 |             "version": "==8.1.6"
2586 |         },
2587 |         "mypy-extensions": {
2588 |             "hashes": [
2589 |                 "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d",
2590 |                 "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"
2591 |             ],
2592 |             "markers": "python_version >= '3.5'",
2593 |             "version": "==1.0.0"
2594 |         },
2595 |         "packaging": {
2596 |             "hashes": [
2597 |                 "sha256:994793af429502c4ea2ebf6bf664629d07c1a9fe974af92966e4b8d2df7edc61",
2598 |                 "sha256:a392980d2b6cffa644431898be54b0045151319d1e7ec34f0cfed48767dd334f"
2599 |             ],
2600 |             "markers": "python_version >= '3.7'",
2601 |             "version": "==23.1"
2602 |         },
2603 |         "pathspec": {
2604 |             "hashes": [
2605 |                 "sha256:1d6ed233af05e679efb96b1851550ea95bbb64b7c490b0f5aa52996c11e92a20",
2606 |                 "sha256:e0d8d0ac2f12da61956eb2306b69f9469b42f4deb0f3cb6ed47b9cce9996ced3"
2607 |             ],
2608 |             "markers": "python_version >= '3.7'",
2609 |             "version": "==0.11.2"
2610 |         },
2611 |         "platformdirs": {
2612 |             "hashes": [
2613 |                 "sha256:b45696dab2d7cc691a3226759c0d3b00c47c8b6e293d96f6436f733303f77f6d",
2614 |                 "sha256:d7c24979f292f916dc9cbf8648319032f551ea8c49a4c9bf2fb556a02070ec1d"
2615 |             ],
2616 |             "markers": "python_version >= '3.7'",
2617 |             "version": "==3.10.0"
2618 |         },
2619 |         "tomli": {
2620 |             "hashes": [
2621 |                 "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc",
2622 |                 "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"
2623 |             ],
2624 |             "markers": "python_version < '3.11'",
2625 |             "version": "==2.0.1"
2626 |         }
2627 |     }
2628 | }
2629 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # langchain-webscraper-demo
 2 | 
 3 | This is a small demo project illustrating how to create a chatbot that can query a scraped website. It uses LangChain to manage the chatbot's framework, Gradio for a user friendly interface, OpenAI's `gpt-3.5-turbo` LLM model, and ChromaDB for as a vector store. 
 4 | 
 5 | This project accompanies a blog post on my website, and can be read [here](https://jasonwebster.dev/blog/chatting-to-a-website-with-langchain-openai-and-chromadb).
 6 | 
 7 | ## Getting started
 8 | 
 9 | This project supports both `pip` and `pipenv`. I recommend using `pipenv` for the best (and least error prone) experience.
10 | 
11 | ### Installation
12 | 
13 | #### Pip
14 | Run 
15 | 
16 | ```bash
17 | pip install -r requirements.txt
18 | ```
19 | 
20 | if using `pip`.
21 | 
22 | #### Pipenv
23 | 
24 | Run
25 | 
26 | ```bash
27 | pipenv install
28 | ```
29 | 
30 | if using `pipenv`, followed by `pipenv shell` to start a shell with the installed packages.
31 | 
32 | ### Environment variables
33 | 
34 | You need to create a new `.env` file from the `.env.example` file with your `OPENAI_API_KEY`. You can create one of these on OpenAI's [platform](https://platform.openai.com/account/api-keys). This will require an OpenAI developer account.
35 | 
36 | ### Web scraping
37 | 
38 | To scrape a site, run 
39 | 
40 | ```
41 | python scrape.py --site <site_url> --depth <int>
42 | ```
43 | 
44 | This will scrape a url and all links found at that url recursively up to the specified `depth`. This will only scrape sites with the same origin as the given `<site_url>`, so for example scraping `https://python.langchain.com/docs` will only scrape sites at `https://python.langchain.com`.
45 | 
46 | The data will be stored in a new `scrape/` directory.
47 | 
48 | ### Data embeddings
49 | 
50 | To generate and persist the embeddings and create a vector store, run
51 | 
52 | ```bash
53 | python embed.py
54 | ```
55 | 
56 | A new persisted vector store will be created in the `chroma/` directory.
57 | 
58 | ### Launching the chatbot
59 | 
60 | To launch the chatbot, you can run
61 | 
62 | ```bash
63 | python main.py
64 | ```
65 | 
66 | This will start a Gradio server at http://127.0.0.1:7860, allowing you to chat to the scraped website and data store.
67 | 
68 | NOTE: you must both first scrape a site and persist a vector store in order for this to work.
69 | 


--------------------------------------------------------------------------------
/embed.py:
--------------------------------------------------------------------------------
 1 | import os
 2 | import json
 3 | 
 4 | from langchain.document_loaders import (
 5 |     BSHTMLLoader,
 6 |     DirectoryLoader,
 7 | )
 8 | from langchain.embeddings.openai import OpenAIEmbeddings
 9 | from langchain.text_splitter import RecursiveCharacterTextSplitter
10 | from langchain.vectorstores import Chroma
11 | 
12 | from dotenv import load_dotenv
13 | 
14 | if __name__ == "__main__":
15 |     load_dotenv()
16 |     if os.path.exists("./chroma"):
17 |         print("already embedded")
18 |         exit(0)
19 | 
20 |     loader = DirectoryLoader(
21 |         "./scrape",
22 |         glob="*.html",
23 |         loader_cls=BSHTMLLoader,
24 |         show_progress=True,
25 |         loader_kwargs={"get_text_separator": " "},
26 |     )
27 |     text_splitter = RecursiveCharacterTextSplitter(
28 |         chunk_size=1000,
29 |         chunk_overlap=200,
30 |     )
31 |     data = loader.load()
32 |     documents = text_splitter.split_documents(data)
33 | 
34 |     # map sources from file directory to web source
35 |     with open("./scrape/sitemap.json", "r") as f:
36 |         sitemap = json.loads(f.read())
37 | 
38 |     for document in documents:
39 |         document.metadata["source"] = sitemap[
40 |             document.metadata["source"].replace(".html", "").replace("scrape/", "")
41 |         ]
42 | 
43 |     embedding_model = OpenAIEmbeddings(model="text-embedding-ada-002")
44 |     db = Chroma.from_documents(documents, embedding_model, persist_directory="./chroma")
45 |     db.persist()
46 | 


--------------------------------------------------------------------------------
/main.py:
--------------------------------------------------------------------------------
 1 | import json
 2 | 
 3 | from dotenv import load_dotenv
 4 | 
 5 | from langchain.chains import ConversationalRetrievalChain
 6 | from langchain.memory import ConversationBufferMemory
 7 | from langchain.vectorstores import Chroma
 8 | from langchain.embeddings import OpenAIEmbeddings
 9 | from langchain.chat_models import ChatOpenAI
10 | from langchain.chains import create_qa_with_sources_chain, LLMChain
11 | from langchain.prompts import PromptTemplate
12 | from langchain.chains.combine_documents.stuff import StuffDocumentsChain
13 | 
14 | import gradio
15 | 
16 | load_dotenv()
17 | 
18 | db = Chroma(
19 |     persist_directory="./chroma",
20 |     embedding_function=OpenAIEmbeddings(model="text-embedding-ada-002"),
21 | )
22 | 
23 | llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo")
24 | 
25 | memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
26 | 
27 | condense_question_prompt = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\
28 | Make sure to avoid using any unclear pronouns.
29 | 
30 | Chat History:
31 | {chat_history}
32 | Follow Up Input: {question}
33 | Standalone question:"""
34 | condense_question_prompt = PromptTemplate.from_template(condense_question_prompt)
35 | condense_question_chain = LLMChain(
36 |     llm=llm,
37 |     prompt=condense_question_prompt,
38 | )
39 | 
40 | qa_chain = create_qa_with_sources_chain(llm)
41 | 
42 | doc_prompt = PromptTemplate(
43 |     template="Content: {page_content}\nSource: {source}",
44 |     input_variables=["page_content", "source"],
45 | )
46 | 
47 | final_qa_chain = StuffDocumentsChain(
48 |     llm_chain=qa_chain,
49 |     document_variable_name="context",
50 |     document_prompt=doc_prompt,
51 | )
52 | 
53 | retrieval_qa = ConversationalRetrievalChain(
54 |     question_generator=condense_question_chain,
55 |     retriever=db.as_retriever(),
56 |     memory=memory,
57 |     combine_docs_chain=final_qa_chain,
58 | )
59 | 
60 | 
61 | def predict(message, history):
62 |     response = retrieval_qa.run({"question": message})
63 |     print(response)
64 | 
65 |     responseDict = json.loads(response)
66 |     answer = responseDict["answer"]
67 |     sources = responseDict["sources"]
68 | 
69 |     if type(sources) == list:
70 |         sources = "\n".join(sources)
71 | 
72 |     if sources:
73 |         return answer + "\n\nSee more:\n" + sources
74 |     return answer
75 | 
76 | 
77 | gradio.ChatInterface(predict).launch()
78 | 


--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
  1 | aiofiles==23.1.0
  2 | aiohttp==3.8.5
  3 | aiosignal==1.3.1
  4 | altair==5.0.1
  5 | anyio==3.7.1
  6 | async-timeout==4.0.2
  7 | attrs==23.1.0
  8 | backoff==2.2.1
  9 | beautifulsoup4==4.12.2
 10 | black==23.7.0
 11 | certifi==2023.7.22
 12 | cffi==1.15.1
 13 | chardet==5.1.0
 14 | charset-normalizer==3.2.0
 15 | chroma-hnswlib==0.7.1
 16 | chromadb==0.4.3
 17 | click==8.1.6
 18 | coloredlogs==15.0.1
 19 | contourpy==1.1.0
 20 | cryptography==41.0.2
 21 | cycler==0.11.0
 22 | dataclasses-json==0.5.13
 23 | et-xmlfile==1.1.0
 24 | exceptiongroup==1.1.2
 25 | fastapi==0.99.1
 26 | ffmpy==0.3.1
 27 | filelock==3.12.2
 28 | filetype==1.2.0
 29 | flatbuffers==23.5.26
 30 | fonttools==4.41.1
 31 | frozenlist==1.4.0
 32 | fsspec==2023.6.0
 33 | gradio==3.39.0
 34 | gradio_client==0.3.0
 35 | greenlet==2.0.2
 36 | h11==0.14.0
 37 | httpcore==0.17.3
 38 | httptools==0.6.0
 39 | httpx==0.24.1
 40 | huggingface-hub==0.16.4
 41 | humanfriendly==10.0
 42 | idna==3.4
 43 | importlib-resources==6.0.0
 44 | Jinja2==3.1.2
 45 | joblib==1.3.1
 46 | jsonschema==4.18.4
 47 | jsonschema-specifications==2023.7.1
 48 | kiwisolver==1.4.4
 49 | langchain==0.0.247
 50 | langsmith==0.0.15
 51 | linkify-it-py==2.0.2
 52 | lxml==4.9.3
 53 | Markdown==3.4.4
 54 | markdown-it-py==2.2.0
 55 | MarkupSafe==2.1.3
 56 | marshmallow==3.20.1
 57 | matplotlib==3.7.2
 58 | mdit-py-plugins==0.3.3
 59 | mdurl==0.1.2
 60 | monotonic==1.6
 61 | mpmath==1.3.0
 62 | msg-parser==1.2.0
 63 | multidict==6.0.4
 64 | mypy-extensions==1.0.0
 65 | nltk==3.8.1
 66 | numexpr==2.8.4
 67 | numpy==1.25.1
 68 | olefile==0.46
 69 | onnxruntime==1.15.1
 70 | openai==0.27.8
 71 | openapi-schema-pydantic==1.2.4
 72 | openpyxl==3.1.2
 73 | orjson==3.9.2
 74 | overrides==7.3.1
 75 | packaging==23.1
 76 | pandas==2.0.3
 77 | pathspec==0.11.1
 78 | pdf2image==1.16.3
 79 | pdfminer.six==20221105
 80 | Pillow==10.0.0
 81 | platformdirs==3.9.1
 82 | posthog==3.0.1
 83 | protobuf==4.23.4
 84 | pulsar-client==3.2.0
 85 | pycparser==2.21
 86 | pydantic==1.10.12
 87 | pydub==0.25.1
 88 | pypandoc==1.11
 89 | pyparsing==3.0.9
 90 | PyPika==0.48.9
 91 | python-dateutil==2.8.2
 92 | python-docx==0.8.11
 93 | python-dotenv==1.0.0
 94 | python-magic==0.4.27
 95 | python-multipart==0.0.6
 96 | python-pptx==0.6.21
 97 | pytz==2023.3
 98 | PyYAML==6.0.1
 99 | referencing==0.30.0
100 | regex==2023.6.3
101 | requests==2.31.0
102 | rpds-py==0.9.2
103 | semantic-version==2.10.0
104 | six==1.16.0
105 | sniffio==1.3.0
106 | soupsieve==2.4.1
107 | SQLAlchemy==2.0.19
108 | starlette==0.27.0
109 | sympy==1.12
110 | tabulate==0.9.0
111 | tenacity==8.2.2
112 | tiktoken==0.4.0
113 | tokenizers==0.13.3
114 | tomli==2.0.1
115 | toolz==0.12.0
116 | tqdm==4.65.0
117 | typing-inspect==0.9.0
118 | typing_extensions==4.7.1
119 | tzdata==2023.3
120 | uc-micro-py==1.0.2
121 | unstructured==0.8.7
122 | urllib3==2.0.4
123 | uvicorn==0.23.1
124 | uvloop==0.17.0
125 | watchfiles==0.19.0
126 | websockets==11.0.3
127 | xlrd==2.0.1
128 | XlsxWriter==3.1.2
129 | yarl==1.9.2
130 | 


--------------------------------------------------------------------------------
/scrape.py:
--------------------------------------------------------------------------------
 1 | import argparse
 2 | import requests
 3 | import os
 4 | from urllib.parse import urlparse
 5 | from collections import defaultdict
 6 | from bs4 import BeautifulSoup
 7 | import json
 8 | 
 9 | parser = argparse.ArgumentParser()
10 | parser.add_argument("--site", type=str, required=True)
11 | parser.add_argument("--depth", type=int, default=3)
12 | 
13 | 
14 | def cleanUrl(url: str):
15 |     return url.replace("https://", "").replace("/", "-").replace(".", "_")
16 | 
17 | 
18 | def get_response_and_save(url: str):
19 |     response = requests.get(url)
20 |     if not os.path.exists("./scrape"):
21 |         os.mkdir("./scrape")
22 |     parsedUrl = cleanUrl(url)
23 |     with open("./scrape/" + parsedUrl + ".html", "wb") as f:
24 |         f.write(response.content)
25 |     return response
26 | 
27 | 
28 | def scrape_links(
29 |     scheme: str,
30 |     origin: str,
31 |     path: str,
32 |     depth=3,
33 |     sitemap: dict = defaultdict(lambda: ""),
34 | ):
35 |     siteUrl = scheme + "://" + origin + path
36 |     cleanedUrl = cleanUrl(siteUrl)
37 | 
38 |     if depth < 0:
39 |         return
40 |     if sitemap[cleanedUrl] != "":
41 |         return
42 | 
43 |     sitemap[cleanedUrl] = siteUrl
44 |     response = get_response_and_save(siteUrl)
45 |     soup = BeautifulSoup(response.content, "html.parser")
46 |     links = soup.find_all("a")
47 | 
48 |     for link in links:
49 |         href = urlparse(link.get("href"))
50 |         if (href.netloc != origin and href.netloc != "") or (
51 |             href.scheme != "" and href.scheme != "https"
52 |         ):
53 |             continue
54 |         scrape_links(
55 |             href.scheme or "https",
56 |             href.netloc or origin,
57 |             href.path,
58 |             depth=depth - 1,
59 |             sitemap=sitemap,
60 |         )
61 |     return sitemap
62 | 
63 | 
64 | if __name__ == "__main__":
65 |     args = parser.parse_args()
66 |     url = urlparse(args.site)
67 |     sitemap = scrape_links(url.scheme, url.netloc, url.path, depth=args.depth)
68 |     with open("./scrape/sitemap.json", "w") as f:
69 |         f.write(json.dumps(sitemap))
70 | 


--------------------------------------------------------------------------------
/search.py:
--------------------------------------------------------------------------------
 1 | import argparse
 2 | import os
 3 | 
 4 | from dotenv import load_dotenv
 5 | 
 6 | from langchain.vectorstores import Chroma
 7 | from langchain.embeddings import OpenAIEmbeddings
 8 | 
 9 | load_dotenv()
10 | 
11 | parser = argparse.ArgumentParser()
12 | parser.add_argument("search_string", type=str)
13 | 
14 | if __name__ == "__main__":
15 |     if not os.path.exists("./chroma"):
16 |         print("No chroma persisted, run `python embed.py` to create one")
17 |         exit(0)
18 |     args = parser.parse_args()
19 |     embeddings = OpenAIEmbeddings(model="text-embedding-ada-002")
20 |     db = Chroma(persist_directory="./chroma", embedding_function=embeddings)
21 |     docs = db.similarity_search(args.search_string)
22 |     for doc in docs[0:5]:
23 |         print(doc)
24 |         print("\n")
25 | 


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