# Fix wrong class instantiation in docs MMR example
<!--
Thank you for contributing to LangChain! Your PR will appear in our
release under the title you set. Please make sure it highlights your
valuable contribution.
Replace this with a description of the change, the issue it fixes (if
applicable), and relevant context. List any dependencies required for
this change.
After you're done, someone will review your PR. They may suggest
improvements. If no one reviews your PR within a few days, feel free to
@-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd
like us to shout you out on Twitter, please also include your handle!
-->
When looking at the Maximal Marginal Relevance ExampleSelector example
at
https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/mmr.html,
I noticed that there seems to be an error. Initially, the
`MaxMarginalRelevanceExampleSelector` class is used as an
`example_selector` argument to the `FewShotPromptTemplate` class. Then,
according to the text, a comparison is made to regular similarity
search. However, the `FewShotPromptTemplate` still uses the
`MaxMarginalRelevanceExampleSelector` class, so the output is the same.
To fix it, I added an instantiation of the
`SemanticSimilarityExampleSelector` class, because this seems to be what
is intended.
## Who can review?
@hwchase17
# Resolve error in StructuredOutputParser docs
Documentation for `StructuredOutputParser` currently not reproducible,
that is, `output_parser.parse(output)` raises an error because the LLM
returns a response with an invalid format
```python
_input = prompt.format_prompt(question="what's the capital of france")
output = model(_input.to_string())
output
# ?
#
# ```json
# {
# "answer": "Paris",
# "source": "https://www.worldatlas.com/articles/what-is-the-capital-of-france.html"
# }
# ```
```
Was fixed by adding a question mark to the prompt
# Clarification of the reference to the "get_text_legth" function in
getting_started.md
Reference to the function "get_text_legth" in the documentation did not
make sense. Comment added for clarification.
@hwchase17
# Docs: updated getting_started.md
Just accommodating some unnecessary spaces in the example of "pass few
shot examples to a prompt template".
@vowelparrot
Updated `Getting Started` page of `Prompt Templates` to showcase more
features provided by the class. Might need some proof reading because
apparently English is not my first language.
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.
```
class Joke(BaseModel):
setup: str = Field(description="question to set up a joke")
punchline: str = Field(description="answer to resolve the joke")
joke_query = "Tell me a joke."
# Or, an example with compound type fields.
#class FloatArray(BaseModel):
# values: List[float] = Field(description="list of floats")
#
#float_array_query = "Write out a few terms of fiboacci."
model = OpenAI(model_name='text-davinci-003', temperature=0.0)
parser = PydanticOutputParser(pydantic_object=Joke)
prompt = PromptTemplate(
template="Answer the user query.\n{format_instructions}\n{query}\n",
input_variables=["query"],
partial_variables={"format_instructions": parser.get_format_instructions()}
)
_input = prompt.format_prompt(query=joke_query)
print("Prompt:\n", _input.to_string())
output = model(_input.to_string())
print("Completion:\n", output)
parsed_output = parser.parse(output)
print("Parsed completion:\n", parsed_output)
```
```
Prompt:
Answer the user query.
The output should be formatted as a JSON instance that conforms to the JSON schema below. For example, the object {"foo": ["bar", "baz"]} conforms to the schema {"foo": {"description": "a list of strings field", "type": "string"}}.
Here is the output schema:
---
{"setup": {"description": "question to set up a joke", "type": "string"}, "punchline": {"description": "answer to resolve the joke", "type": "string"}}
---
Tell me a joke.
Completion:
{"setup": "Why don't scientists trust atoms?", "punchline": "Because they make up everything!"}
Parsed completion:
setup="Why don't scientists trust atoms?" punchline='Because they make up everything!'
```
Ofc, works only with LMs of sufficient capacity. DaVinci is reliable but
not always.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
The YAML and JSON examples of prompt serialization now give a strange
`No '_type' key found, defaulting to 'prompt'` message when you try to
run them yourself or copy the format of the files. The reason for this
harmless warning is that the _type key was not in the config files,
which means they are parsed as a standard prompt.
This could be confusing to new users (like it was confusing to me after
upgrading from 0.0.85 to 0.0.86+ for my few_shot prompts that needed a
_type added to the example_prompt config), so this update includes the
_type key just for clarity.
Obviously this is not critical as the warning is harmless, but it could
be confusing to track down or be interpreted as an error by a new user,
so this update should resolve that.
On the [Getting Started
page](https://langchain.readthedocs.io/en/latest/modules/prompts/getting_started.html)
for prompt templates, I believe the very last example
```python
print(dynamic_prompt.format(adjective=long_string))
```
should actually be
```python
print(dynamic_prompt.format(input=long_string))
```
The existing example produces `KeyError: 'input'` as expected
***
On the [Create a custom prompt
template](https://langchain.readthedocs.io/en/latest/modules/prompts/examples/custom_prompt_template.html#id1)
page, I believe the line
```python
Function Name: {kwargs["function_name"]}
```
should actually be
```python
Function Name: {kwargs["function_name"].__name__}
```
The existing example produces the prompt:
```
Given the function name and source code, generate an English language explanation of the function.
Function Name: <function get_source_code at 0x7f907bc0e0e0>
Source Code:
def get_source_code(function_name):
# Get the source code of the function
return inspect.getsource(function_name)
Explanation:
```
***
On the [Example
Selectors](https://langchain.readthedocs.io/en/latest/modules/prompts/examples/example_selectors.html)
page, the first example does not define `example_prompt`, which is also
subtly different from previous example prompts used. For user
convenience, I suggest including
```python
example_prompt = PromptTemplate(
input_variables=["input", "output"],
template="Input: {input}\nOutput: {output}",
)
```
in the code to be copy-pasted
tl;dr: input -> word, output -> antonym, rename to dynamic_prompt
consistently
The provided code in this example doesn't run, because the keys are
`word` and `antonym`, rather than `input` and `output`.
Also, the `ExampleSelector`-based prompt is named `few_shot_prompt` when
defined and `dynamic_prompt` in the follow-up example. The former name
is less descriptive and collides with an earlier example, so I opted for
the latter.
Thanks for making a really cool library!
- Add support for local build and linkchecking of docs
- Add GitHub Action to automatically check links before prior to
publication
- Minor reformat of Contributing readme
- Fix existing broken links
Co-authored-by: Hunter Gerlach <hunter@huntergerlach.com>
Co-authored-by: Hunter Gerlach <HunterGerlach@users.noreply.github.com>
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>
