I noticed (after publication) that the getting_started link on the main
page was borked. This should fix it.
Co-authored-by: Hunter Gerlach <hunter@huntergerlach.com>
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
I was honored by the twitter mention, so used PyCharm to try and... help
docs even a little bit.
Mostly typo-s and correct spellings.
PyCharm really complains about "really good" being used all the time and
recommended alternative wordings haha
Hi! This PR adds support for the Azure OpenAI service to LangChain.
I've tried to follow the contributing guidelines.
Co-authored-by: Keiji Kanazawa <{ID}+{username}@users.noreply.github.com>
Created a generic SQLAlchemyCache class to plug any database supported
by SQAlchemy. (I am using Postgres).
I also based the class SQLiteCache class on this class SQLAlchemyCache.
As a side note, I'm questioning the need for two distinct class
LLMCache, FullLLMCache. Shouldn't we merge both ?
Nothing of substance was changed. I simply corrected a few minor errors
that could slow down the reader.
Co-authored-by: Hunter Gerlach <hunter@huntergerlach.com>
Love the project, a ton of fun!
I think the PR is pretty self-explanatory, happy to make any changes! I
am working on using it in an `LLMBashChain` and may update as that
progresses.
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
With the original prompt, the chain keeps trying to jump straight to
doing math directly, without first looking up ages. With this two-part
question, it behaves more as intended:
> Entering new ZeroShotAgent chain...
How old is Olivia Wilde's boyfriend? What is that number raised to the
0.23 power?
Thought: I need to find out how old Olivia Wilde's boyfriend is, and
then use a calculator to calculate the power.
Action: Search
Action Input: Olivia Wilde's boyfriend age
Observation: While Wilde, 37, and Styles, 27, have both kept a low
profile when it comes to talking about their relationship, Wilde did
address their ...
Thought: Olivia Wilde's boyfriend is 27 years old.
Action: Calculator
Action Input: 27^0.23
> Entering new LLMMathChain chain...
27^0.23
```python
import math
print(math.pow(27, 0.23))
```
Answer: 2.1340945944237553
> Finished LLMMathChain chain.
Observation: Answer: 2.1340945944237553
Thought: I now know the final answer.
Final Answer: 2.1340945944237553
> Finished ZeroShotAgent chain.
Add MemoryChain and ConversationChain as chains that take a docstore in
addition to the prompt, and use the docstore to stuff context into the
prompt. This can be used to have an ongoing conversation with a chatbot.
Probably needs a bit of refactoring for code quality
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Also updated docs, and noticed an issue with the add_texts method on
VectorStores that I had missed before -- the metadatas arg should be
required to match the classmethod which initializes the VectorStores
(the add_example methods break otherwise in the ExampleSelectors)
Without the print on the `llm` call, the new user sees no visible effect
when just getting started. The assumption here is the new user is
running this in a new sandbox script file or repl via copy-paste.
