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Eugene Yurtsev 06e524416c
power bi api wrapper integration tests & bug fix (#4983)
# Powerbi API wrapper bug fix + integration tests

- Bug fix by removing `TYPE_CHECKING` in in utilities/powerbi.py
- Added integration test for power bi api in
utilities/test_powerbi_api.py
- Added integration test for power bi agent in
agent/test_powerbi_agent.py
- Edited .env.examples to help set up power bi related environment
variables
- Updated demo notebook with working code in
docs../examples/powerbi.ipynb - AzureOpenAI -> ChatOpenAI

Notes: 

Chat models (gpt3.5, gpt4) are much more capable than davinci at writing
DAX queries, so that is important to getting the agent to work properly.
Interestingly, gpt3.5-turbo needed the examples=DEFAULT_FEWSHOT_EXAMPLES
to write consistent DAX queries, so gpt4 seems necessary as the smart
llm.

Fixes #4325

## Before submitting

Azure-core and Azure-identity are necessary dependencies

check integration tests with the following:
`pytest tests/integration_tests/utilities/test_powerbi_api.py`
`pytest tests/integration_tests/agent/test_powerbi_agent.py`

You will need a power bi account with a dataset id + table name in order
to test. See .env.examples for details.

## Who can review?
@hwchase17
@vowelparrot

---------

Co-authored-by: aditya-pethe <adityapethe1@gmail.com>
2023-05-19 11:25:52 -04:00
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🦜🔗 LangChain

Building applications with LLMs through composability

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pip install langchain or conda install langchain -c conda-forge

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Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.

This library aims to assist in the development of those types of applications. Common examples of these applications include:

Question Answering over specific documents

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Please see here for full documentation on:

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There are six main areas that LangChain is designed to help with. These are, in increasing order of complexity:

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This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.

🔗 Chains:

Chains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.

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Data Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.

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Memory refers to persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.

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For more information on these concepts, please see our full documentation.

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