vectara 0.0.2

An unofficial Python SDK and CLI

An UNofficial Python SDK and CLI for Vectara's RAG platform

It supports major features of Vectara's RAG platform for you to build your own search engine.

Additional features:

• It expands the upload function to allow you to upload a list of files or all files under a directory in one function call.
• Renders query results in beautiful Markdown printout (see demo.ipynb).
• It supports logging user feedback from the GUI in a local SQLite database for evaluating the quality of search and RAG.

If you like this SDK/CLI/GUI, give us a star on Github!

Installation

# Stable
pip install vectara

# Nightly
pip install "git+https://github.com/forrestbao/vectara-python-cli.git"

Usage

Credentials

This client supports authentication in both Vectara's Personal API key and OAuth2. In either case, you need to obtain customer ID as well.

Following the convention of OpenAI's client, we recommend setting credentials as environment variables. You can save them as a bash script and simply source it before using the SDK or CLI.

export VECTARA_CUSTOMER_ID=123 # Required regardless of the authentication method

# Set API Key if you plan to use Personal API Key to authenticate
export VECTARA_API_KEY=abc

# Set the following two variables if you plan to use OAuth2 to authenticate
export VECTARA_CLIENT_ID=abc
export VECTARA_CLIENT_SECRET=xyz

# If both API key and OAuth2 credentials are set, the SDK will use API key only.

# optional, if you are using a proxy like LlaMasterKey https://github.com/TexteaInc/LlaMasterKey/
export VECTARA_BASE_URL="http://127.0.0.1:8000/vectara"
export VECTARA_PROXY_MODE=true # Enable proxy mode

Alternatively, you can pass in your credentials as arguments when initializing the client. See it from the complete code.

Using the Python SDK

Try the Jupyter notebook demo.ipynb, the script demo.py, or read the docstring.

from vectara import vectara, post_process_query_result

client = vectara() # get credentials from environment variables 

corpus_id = client.create_corpus('my knowledge base')

client.upload(corpus_id, 'one_file.pdf', description='A scientific paper')  # add one file to the corpus 
client.upload(corpus_id, 'a_folder_of_documents') # add all files under a folder to the corpus
client.upload(corpus_id, ['README.md', 'demo.py'], description=['README', 'demo code']) # add a list of files to the corpus

r = client.query(corpus_id, 'How to set credentials? ', top_k=5) # query the corpus for top 5 answers, return a JSON string 
print (post_process_query_result(r)) # post process the query result to get a list of answers

r = client.query(corpus_id, 'How to set credentials? ', top_k=5, return_summary=False) # query the corpus for top 5 answers, no summary, just search, return a JSON string
print (post_process_query_result(r)) # post process the query result to get a list of answers

client.reset_corpus(corpus_id) # delete all documents in the corpus

Command line

To learn the command line usage, run vectara --help.

You must set up your Vectara credentials as environment variables before using the command line interface.

# create a corpus
vectara create_corpus 'my knowledge base'
# output: corpus_id = 12

# upload a file to the corpus
vectara upload 12 one_file.pdf # corpurs_id = 12

# upload a folder to the corpus
vectara upload 12 ./a_folder_of_documents # corpurs_id = 12

# query the corpus
vectara query 12 'Vectara allows me to search for anything, right?' --top_k=5  # corpurs_id = 12

# reset the corpus
vectara reset_corpus 12 # corpurs_id = 12

Web interface via Funix

The SDK can be converted into a web interface via Funix. You can drag and drop to add a file to your Vectara corpus.

pip install funix
funix src/vectara/__init__.py 

Then you can access the web interface at http://localhost:3000 (the port number maybe different if port 3000 is occupied).

Below please find the screenshots.

Known bugs: Funix seems to have some memory leakage issues that the web interface may freeze after uploading a file. If that happens, please kill the process and restart the web interface.

Stylish query results

The query results are typeset Markdown ready to be rendered. A query result includes the following info:

• A summary with citations and matching scores with respect to the query
• References cited by the summary

### Here is the answer
To rearrange objects, you can utilize the "direction" attribute in a Funix decorator [1]. Manually resizing and positioning objects can be a tedious and inefficient process [2]. Another approach is to use a collision-free algorithm for auto-layout, where scopes will be resized to fit the objects inside [4]. An example of arranging objects in a column-reverse direction can be seen in the ChatGPT multiturn app [3]. Additionally, organizing your canvas with scopes can help in rearranging objects effectively [5]. Remember to experiment with these methods to find the best arrangement for your specific needs.

### References:
    
1. From document **funix.md** (matchness=0.673933):
  _...You can change their order and orientation using the "direction" attribute in a Funix decorator...._

2. From document **codepod.md** (matchness=0.65305215):
  _...It is painful and inefficient to resize and position the pods and scopes manually...._

3. From document **funix.md** (matchness=0.6520513):
  _...The example below shall be self-explaining:
A more advanced example is our ChatGPT multiturn app where "direction = "column-reverse"" so the message you type stays at the bottom...._

4. From document **codepod.md** (matchness=0.6495899):
  _...default}alt="Example banner"width="600"/>
After auto-layout, the pods and scopes are organized by a collision-free algorithm, and the scopes will be resized to fit the pods inside...._

5. From document **codepod.md** (matchness=0.6460015):
  _...Organize your Canvas with scopes..._

Questions

Contact forrest at vectara dot com

Disclaimer

This is an UNofficial SDK and CLI for Vectara's RAG platform. Use at your own risk. Vectara does NOT provide support for this SDK or CLI.