A full version with db support and configurable components is open sourced here: LeetTools. A demo web site has been setup here. Please check them out!
A single Python program to implement the search-extract-summarize flow, similar to AI search engines such as Perplexity.
- You can run it on command line or with a GradIO UI.
- You can control the output behavior, e.g., extract structured data or change output language,
- You can control the search behavior, e.g., restrict to a specific site or date, or just scrape a specified list of URLs.
- You can run it in a cron job or bash script to automate complex search/data extraction tasks.
- You can ask questions against local files.
We have a running UI example in HuggingFace Spaces.
- Search like Perplexity
- Only use the latest information from a specific site
- Extract information from web search results
- Ask questions against local files
Note
- Our main goal is to illustrate the basic concepts of AI search engines with the raw constructs. Performance or scalability is not in the scope of this program.
- We are planning to open source a real search-enabled AI toolset with real DB setup, real document pipeline, and real query engine soon. Star and watch this repo for updates!
[UPDATE]
- 2024-12-20: add the full function version link
- 2024-11-20: add Docling converter and local mode to query against local files
- 2024-11-10: add Chonkie as the default chunker
- 2024-10-28: add extract function as a new output mode
- 2024-10-25: add hybrid search demo using DuckDB full-text search
- 2024-10-22: add GradIO integation
- 2024-10-21: use DuckDB for the vector search and use API for embedding
- 2024-10-20: allow to specify a list of input urls
- 2024-10-18: output-language and output-length parameters for LLM
- 2024-10-18: date-restrict and target-site parameters for seach
Given a query, the program will
- in search mode: search Google for the top 10 web pages
- in local mode: use the local files under the 'data' directory
- crawl and scape the result documents for their text content
- chunk the text content into chunks and save them into a vectordb
- perform a hybrid search (vector and BM25 FTS) with the query and find the top 10 matched chunks
- [Optional] use a reranker to re-rank the top chunks
- use the top chunks as the context to ask an LLM to generate the answer
- output the answer with the references
Of course this flow is a very simplified version of the real AI search engines, but it is a good starting point to understand the basic concepts.
One benefit is that we can manipulate the search function and output format.
For example, we can:
- search with date-restrict to only retrieve the latest information.
- search within a target-site to only create the answer from the contents from it.
- ask LLM to use a specific language to answer the question.
- ask LLM to answer with a specific length.
- crawl a specific list of urls and answer based on those contents only.
This program can serve as a playground to understand and experiment with different components in the pipeline.
# recommend to use Python 3.10 or later and use venv or conda to create a virtual environment
% pip install -r requirements.txt
# modify .env file to set the API keys or export them as environment variables as below
# right now we use Google search API
% export SEARCH_API_KEY="your-google-search-api-key"
% export SEARCH_PROJECT_KEY="your-google-cx-key"
# right now we use OpenAI API
% export LLM_API_KEY="your-openai-api-key"
# By default, the program will start a web UI. See GradIO Deployment section for more info.
# Run the program on command line with -c option
% python ask.py -c -q "What is an LLM agent?"
# You can also query your local files under the 'data' directory using the local mode
% python ask.py -i local -c -q "How does Ask.py work?"
# we can specify more parameters to control the behavior such as date_restrict and target_site
% python ask.py --help
Usage: ask.py [OPTIONS]
Search web for the query and summarize the results.
Options:
-q, --query TEXT Query to search
-i, --input-mode [search|local]
Input mode for the query, default is search.
When using local, files under 'data' folder
will be used as input.
-o, --output-mode [answer|extract]
Output mode for the answer, default is a
simple answer
-d, --date-restrict INTEGER Restrict search results to a specific date
range, default is no restriction
-s, --target-site TEXT Restrict search results to a specific site,
default is no restriction
--output-language TEXT Output language for the answer
--output-length INTEGER Output length for the answer
--url-list-file TEXT Instead of doing web search, scrape the
target URL list and answer the query based
on the content
--extract-schema-file TEXT Pydantic schema for the extract mode
--inference-model-name TEXT Model name to use for inference
--vector-search-only Do not use hybrid search mode, use vector
search only.
-c, --run-cli Run as a command line tool instead of
launching the Gradio UI
-l, --log-level [DEBUG|INFO|WARNING|ERROR]
Set the logging level [default: INFO]
--help Show this message and exit.The source code is licensed under MIT license. Thanks for these amazing open-source projects and API providers:
Note
Original GradIO app-sharing document here.
By default, the program will start a web UI and share through GradIO.
% python ask.py
* Running on local URL: http://127.0.0.1:7860
* Running on public URL: https://77c277af0330326587.gradio.live
# you can also specify SHARE_GRADIO_UI to only run locally
% export SHARE_GRADIO_UI=False
% python ask.py
* Running on local URL: http://127.0.0.1:7860- First, you need to create a free HuggingFace account.
- Then in your settings/token page, create a new token with Write permissions.
- In your terminal, run the following commands in you app directory to deploy your program to HuggingFace Spaces:
% pip install gradio
% gradio deploy
Creating new Spaces Repo in '/home/you/ask.py'. Collecting metadata, press Enter to accept default value.
Enter Spaces app title [ask.py]: ask.py
Enter Gradio app file [ask.py]:
Enter Spaces hardware (cpu-basic, cpu-upgrade, t4-small, t4-medium, l4x1, l4x4, zero-a10g, a10g-small, a10g-large, a10g-largex2, a10g-largex4, a100-large, v5e-1x1, v5e-2x2, v5e-2x4) [cpu-basic]:
Any Spaces secrets (y/n) [n]: y
Enter secret name (leave blank to end): SEARCH_API_KEY
Enter secret value for SEARCH_API_KEY: YOUR_SEARCH_API_KEY
Enter secret name (leave blank to end): SEARCH_PROJECT_KEY
Enter secret value for SEARCH_API_KEY: YOUR_SEARCH_PROJECT_KEY
Enter secret name (leave blank to end): LLM_API_KEY
Enter secret value for LLM_API_KEY: YOUR_LLM_API_KEY
Enter secret name (leave blank to end):
Create Github Action to automatically update Space on 'git push'? [n]: n
Space available at https://huggingface.co/spaces/your_user_name/ask.pyNow you can use the HuggingFace space app to run your queries.