Commit Graph

5 Commits

Author SHA1 Message Date
Eugene Yurtsev
844955d6e1 community[patch]: assign missed default (#26326)
Assigning missed defaults in various classes. Most clients were being
assigned during the `model_validator(mode="before")` step, so this
change should amount to a no-op in those cases.

---

This PR was autogenerated using gritql

```shell

grit apply 'class_definition(name=$C, $body, superclasses=$S) where {    
    $C <: ! "Config", // Does not work in this scope, but works after class_definition
    $body <: block($statements),
    $statements <: some bubble assignment(left=$x, right=$y, type=$t) as $A where {
        or {
            $y <: `Field($z)`,
            $x <: "model_config"
        }
    },
    // And has either Any or Optional fields without a default
    $statements <: some bubble assignment(left=$x, right=$y, type=$t) as $A where {
        $t <: or {
            r"Optional.*",
            r"Any",
            r"Union[None, .*]",
            r"Union[.*, None, .*]",
            r"Union[.*, None]",
        },
        $y <: ., // Match empty node        
        $t => `$t = None`,
    },    
}
' --language python .

```
2024-09-11 11:13:11 -04:00
Harrison Chase
8516a03a02 langchain-community[major]: Upgrade community to pydantic 2 (#26011)
This PR upgrades langchain-community to pydantic 2.


* Most of this PR was auto-generated using code mods with gritql
(https://github.com/eyurtsev/migrate-pydantic/tree/main)
* Subsequently, some code was fixed manually due to accommodate
differences between pydantic 1 and 2

Breaking Changes:

- Use TEXTEMBED_API_KEY and TEXTEMBEB_API_URL for env variables for text
embed integrations:
cbea780492

Other changes:

- Added pydantic_settings as a required dependency for community. This
may be removed if we have enough time to convert the dependency into an
optional one.

---------

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2024-09-05 14:07:10 -04:00
Eugene Yurtsev
bf5193bb99 community[patch]: Upgrade pydantic extra (#25185)
Upgrade to using a literal for specifying the extra which is the
recommended approach in pydantic 2.

This works correctly also in pydantic v1.

```python
from pydantic.v1 import BaseModel

class Foo(BaseModel, extra="forbid"):
    x: int

Foo(x=5, y=1)
```

And 


```python
from pydantic.v1 import BaseModel

class Foo(BaseModel):
    x: int

    class Config:
      extra = "forbid"

Foo(x=5, y=1)
```


## Enum -> literal using grit pattern:

```
engine marzano(0.1)
language python
or {
    `extra=Extra.allow` => `extra="allow"`,
    `extra=Extra.forbid` => `extra="forbid"`,
    `extra=Extra.ignore` => `extra="ignore"`
}
```

Resorted attributes in config and removed doc-string in case we will
need to deal with going back and forth between pydantic v1 and v2 during
the 0.3 release. (This will reduce merge conflicts.)


## Sort attributes in Config:

```
engine marzano(0.1)
language python


function sort($values) js {
    return $values.text.split(',').sort().join("\n");
}


class_definition($name, $body) as $C where {
    $name <: `Config`,
    $body <: block($statements),
    $values = [],
    $statements <: some bubble($values) assignment() as $A where {
        $values += $A
    },
    $body => sort($values),
}

```
2024-08-08 17:20:39 +00:00
Eugene Yurtsev
d084172b63 community[patch]: root validator set explicit pre=False or pre=True (#23764)
See issue: https://github.com/langchain-ai/langchain/issues/22819
2024-07-02 09:42:05 -04:00
Robert Caulk
54adcd9e82 community[minor]: add AskNews retriever and AskNews tool (#21581)
We add a tool and retriever for the [AskNews](https://asknews.app)
platform with example notebooks.

The retriever can be invoked with:

```py
from langchain_community.retrievers import AskNewsRetriever

retriever = AskNewsRetriever(k=3)

retriever.invoke("impact of fed policy on the tech sector")
```

To retrieve 3 documents in then news related to fed policy impacts on
the tech sector. The included notebook also includes deeper details
about controlling filters such as category and time, as well as
including the retriever in a chain.

The tool is quite interesting, as it allows the agent to decide how to
obtain the news by forming a query and deciding how far back in time to
look for the news:

```py
from langchain_community.tools.asknews import AskNewsSearch
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_openai import ChatOpenAI

tool = AskNewsSearch()

instructions = """You are an assistant."""
base_prompt = hub.pull("langchain-ai/openai-functions-template")
prompt = base_prompt.partial(instructions=instructions)
llm = ChatOpenAI(temperature=0)
asknews_tool = AskNewsSearch()
tools = [asknews_tool]
agent = create_openai_functions_agent(llm, tools, prompt)
agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    verbose=True,
)

agent_executor.invoke({"input": "How is the tech sector being affected by fed policy?"})
```

---------

Co-authored-by: Emre <e@emre.pm>
2024-05-20 18:23:06 -07:00