Commit Graph

11 Commits

Author SHA1 Message Date
Leonid Ganeline
e512d3c6a6
langchain: callbacks imports fix (#20348)
Replaced all `from langchain.callbacks` into `from
langchain_core.callbacks` .
Changes in the `langchain` and `langchain_experimental`

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
2024-04-12 20:13:14 +00:00
Leonid Ganeline
4159a4723c
experimental[patch]: update module doc strings (#19539)
Added missed module descriptions. Fixed format.
2024-03-26 10:38:10 -04:00
Leonid Ganeline
3f6bf852ea
experimental: docstrings update (#18048)
Added missed docstrings. Formatted docsctrings to the consistent format.
2024-02-23 21:24:16 -05:00
Mattt394
7c6009b76f
experimental[patch]: Fixed typos in SmartLLMChain ideation and critique prompts (#11507)
Noticed and fixed a few typos in the SmartLLMChain default ideation and
critique prompts

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2024-02-14 13:20:10 -08:00
Anish Nag
6da0cfea0e
experimental[patch]: SmartLLMChain Output Key Customization (#14466)
**Description**
The `SmartLLMChain` was was fixed to output key "resolution".
Unfortunately, this prevents the ability to use multiple `SmartLLMChain`
in a `SequentialChain` because of colliding output keys. This change
simply gives the option the customize the output key to allow for
sequential chaining. The default behavior is the same as the current
behavior.

Now, it's possible to do the following:
```
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain_experimental.smart_llm import SmartLLMChain
from langchain.chains import SequentialChain

joke_prompt = PromptTemplate(
    input_variables=["content"],
    template="Tell me a joke about {content}.",
)
review_prompt = PromptTemplate(
    input_variables=["scale", "joke"],
    template="Rate the following joke from 1 to {scale}: {joke}"
)

llm = ChatOpenAI(temperature=0.9, model_name="gpt-4-32k")
joke_chain = SmartLLMChain(llm=llm, prompt=joke_prompt, output_key="joke")
review_chain = SmartLLMChain(llm=llm, prompt=review_prompt, output_key="review")

chain = SequentialChain(
    chains=[joke_chain, review_chain],
    input_variables=["content", "scale"],
    output_variables=["review"],
    verbose=True
)
response = chain.run({"content": "chickens", "scale": "10"})
print(response)
```

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
2023-12-08 13:55:51 -08:00
kavinraj A S
ab6b41937a
Fixed a typo in smart_llm prompt (#13052)
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2023-12-05 19:16:18 -08:00
Predrag Gruevski
7c0f1bf23f
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339)
Part of upgrading our CI to use Poetry 1.6.1.
2023-10-16 21:13:31 -04:00
Aashish Saini
f9f1340208
Fixed some grammatical and spelling errors (#10595)
Fixed some grammatical and spelling errors
2023-09-14 17:43:36 -07:00
Nuno Campos
c0d67420e5
Use a submodule for pydantic v1 compat (#9371)
<!-- Thank you for contributing to LangChain!

Replace this entire comment with:
  - Description: a description of the change, 
  - Issue: the issue # it fixes (if applicable),
  - Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
- Twitter handle: we announce bigger features on Twitter. If your PR
gets announced and you'd like a mention, we'll gladly shout you out!

Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.

See contribution guidelines for more information on how to write/run
tests, lint, etc:

https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md

If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. These live is docs/extras
directory.

If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
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2023-08-17 16:35:49 +01:00
Eugene Yurtsev
2673b3a314
Create pydantic v1 namespace in langchain (#9254)
Create pydantic v1 namespace in langchain experimental
2023-08-16 21:19:31 -07:00
UmerHA
8aab39e3ce
Added SmartGPT workflow (issue #4463) (#4816)
# Added SmartGPT workflow by providing SmartLLM wrapper around LLMs
Edit:
As @hwchase17 suggested, this should be a chain, not an LLM. I have
adapted the PR.

It is used like this:
```
from langchain.prompts import PromptTemplate
from langchain.chains import SmartLLMChain
from langchain.chat_models import ChatOpenAI

hard_question = "I have a 12 liter jug and a 6 liter jug. I want to measure 6 liters. How do I do it?"
hard_question_prompt = PromptTemplate.from_template(hard_question)

llm = ChatOpenAI(model_name="gpt-4")
prompt = PromptTemplate.from_template(hard_question)
chain = SmartLLMChain(llm=llm, prompt=prompt, verbose=True)

chain.run({})
```


Original text: 
Added SmartLLM wrapper around LLMs to allow for SmartGPT workflow (as in
https://youtu.be/wVzuvf9D9BU). SmartLLM can be used wherever LLM can be
used. E.g:

```
smart_llm = SmartLLM(llm=OpenAI())
smart_llm("What would be a good company name for a company that makes colorful socks?")
```
or
```
smart_llm = SmartLLM(llm=OpenAI())
prompt = PromptTemplate(
    input_variables=["product"],
    template="What is a good name for a company that makes {product}?",
)
chain = LLMChain(llm=smart_llm, prompt=prompt)
chain.run("colorful socks")
```

SmartGPT consists of 3 steps:

1. Ideate - generate n possible solutions ("ideas") to user prompt
2. Critique - find flaws in every idea & select best one
3. Resolve - improve upon best idea & return it

Fixes #4463

## Who can review?

Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:

- @hwchase17
- @agola11

Twitter: [@UmerHAdil](https://twitter.com/@UmerHAdil) | Discord:
RicChilligerDude#7589

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-11 15:44:27 -07:00