# glossary.md renamed as concepts.md and moved under the Getting Started
small PR.
`Concepts` looks right to the point. It is moved under Getting Started
(typical place). Previously it was lost in the Additional Resources
section.
## Who can review?
@hwchase17
# Added Tutorials section on the top-level of documentation
**Problem Statement**: the Tutorials section in the documentation is
top-priority. Not every project has resources to make tutorials. We have
such a privilege. Community experts created several tutorials on
YouTube.
But the tutorial links are now hidden on the YouTube page and not easily
discovered by first-time visitors.
**PR**: I've created the `Tutorials` page (from the `Additional
Resources/YouTube` page) and moved it to the top level of documentation
in the `Getting Started` section.
## Who can review?
@dev2049
NOTE:
PR checks are randomly failing
3aefaafcdb258819eadf514d81b5b3
In the section `Get Message Completions from a Chat Model` of the quick
start guide, the HumanMessage doesn't need to include `Translate this
sentence from English to French.` when there is a system message.
Simplify HumanMessages in these examples can further demonstrate the
power of LLM.
Hi,
just wanted to mention that I added `langchain` to
[conda-forge](https://github.com/conda-forge/langchain-feedstock), so
that it can be installed with `conda`/`mamba` etc.
This makes it available to some corporate users with custom
conda-servers and people who like to manage their python envs with
conda.
This pull request adds an enum class for the various types of agents
used in the project, located in the `agent_types.py` file. Currently,
the project is using hardcoded strings for the initialization of these
agents, which can lead to errors and make the code harder to maintain.
With the introduction of the new enums, the code will be more readable
and less error-prone.
The new enum members include:
- ZERO_SHOT_REACT_DESCRIPTION
- REACT_DOCSTORE
- SELF_ASK_WITH_SEARCH
- CONVERSATIONAL_REACT_DESCRIPTION
- CHAT_ZERO_SHOT_REACT_DESCRIPTION
- CHAT_CONVERSATIONAL_REACT_DESCRIPTION
In this PR, I have also replaced the hardcoded strings with the
appropriate enum members throughout the codebase, ensuring a smooth
transition to the new approach.
Seems like a copy paste error. The very next example does have this
line.
Please tell me if I missed something in the process and should have
created an issue or something first!
seems linkchecker isn't catching them because it runs on generated html.
at that point the links are already missing.
the generation process seems to strip invalid references when they can't
be re-written from md to html.
I used https://github.com/tcort/markdown-link-check to check the doc
source directly.
There are a few false positives on localhost for development.
I noticed that the "getting started" guide section on agents included an
example test where the agent was getting the question wrong 😅
I guess Olivia Wilde's dating life is too tough to keep track of for
this simple agent example. Let's change it to something a little easier,
so users who are running their agent for the first time are less likely
to be confused by a result that doesn't match that which is on the docs.
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>
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>
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.
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.
