Added `presidio` and `OneNote` references to `microsoft.mdx`; added link
and description to the `presidio` notebook
---------
Co-authored-by: Erick Friis <erickfriis@gmail.com>
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Keeping it consistent with everywhere else in the docs and adding the
missing imports to be able to copy paste and run the code example.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
**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>
Updated the MongoDB Atlas Vector Search docs to indicate the service is
Generally Available, updated the example to use the new index
definition, and added an example that uses metadata pre-filtering for
semantic search
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
