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Merge branch 'master' into cc/retriever_score

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pull/20800/head
Chester Curme 1 month ago
parent bc7af5fd7e 078c5d9bc6
commit 267ee9db4c
4 changed files with 79 additions and 41 deletions
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9
.github/workflows/_release.yml vendored
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@ -13,6 +13,11 @@ on:
required: true
type: string
default: 'libs/langchain'
dangerous-nonmaster-release:
required: false
type: boolean
default: false
description: "Release from a non-master branch (danger!)"
env:
PYTHON_VERSION: "3.11"
@ -20,7 +25,7 @@ env:
jobs:
build:
if: github.ref == 'refs/heads/master'
if: github.ref == 'refs/heads/master' || inputs.dangerous-nonmaster-release
environment: Scheduled testing
runs-on: ubuntu-latest
@ -301,4 +306,4 @@ jobs:
draft: false
generateReleaseNotes: true
tag: v${{ needs.build.outputs.version }}
commit: master
commit: ${{ github.sha }}

57
docs/docs/integrations/platforms/google.mdx
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@ -163,16 +163,16 @@ from langchain_google_alloydb_pg import AlloyDBEngine, AlloyDBLoader
> [Google Cloud BigQuery](https://cloud.google.com/bigquery) is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data in Google Cloud.
We need to install `google-cloud-bigquery` python package.
We need to install `langchain-google-community` with Big Query dependencies:
```bash
pip install google-cloud-bigquery
pip install langchain-google-community[bigquery]
```
See a [usage example](/docs/integrations/document_loaders/google_bigquery).
```python
from langchain_community.document_loaders import BigQueryLoader
from langchain_google_community import BigQueryLoader
```
### Bigtable
@ -239,10 +239,10 @@ from langchain_google_cloud_sql_pg import PostgresEngine, PostgresLoader
>[Cloud Storage](https://en.wikipedia.org/wiki/Google_Cloud_Storage) is a managed service for storing unstructured data in Google Cloud.
We need to install `google-cloud-storage` python package.
We need to install `langchain-google-community` with Google Cloud Storage dependencies.
```bash
pip install google-cloud-storage
pip install langchain-google-community[gcs]
```
There are two loaders for the `Google Cloud Storage`: the `Directory` and the `File` loaders.
@ -250,12 +250,12 @@ There are two loaders for the `Google Cloud Storage`: the `Directory` and the `F
See a [usage example](/docs/integrations/document_loaders/google_cloud_storage_directory).
```python
from langchain_community.document_loaders import GCSDirectoryLoader
from langchain_google_community import GCSDirectoryLoader
```
See a [usage example](/docs/integrations/document_loaders/google_cloud_storage_file).
```python
from langchain_community.document_loaders import GCSFileLoader
from langchain_google_community import GCSFileLoader
```
### El Carro for Oracle Workloads
@ -280,16 +280,16 @@ from langchain_google_el_carro import ElCarroLoader
Currently, only `Google Docs` are supported.
We need to install several python packages.
We need to install `langchain-google-community` with Google Drive dependencies.
```bash
pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib
pip install langchain-google-community[drive]
```
See a [usage example and authorization instructions](/docs/integrations/document_loaders/google_drive).
```python
from langchain_community.document_loaders import GoogleDriveLoader
from langchain_google_community import GoogleDriveLoader
```
### Firestore (Native Mode)
@ -359,16 +359,16 @@ from langchain_google_spanner import SpannerLoader
This document loader transcribes audio files and outputs the text results as Documents.
First, we need to install the python package.
First, we need to install `langchain-google-community` with speech-to-text dependencies.
```bash
pip install google-cloud-speech
pip install langchain-google-community[speech]
```
See a [usage example and authorization instructions](/docs/integrations/document_loaders/google_speech_to_text).
```python
from langchain_community.document_loaders import GoogleSpeechToTextLoader
from langchain_google_community import SpeechToTextLoader
```
## Document Transformers
@ -386,15 +386,14 @@ We can get it either programmatically or copy from the `Prediction endpoint` sec
tab in the Google Cloud Console.
```bash
pip install google-cloud-documentai
pip install google-cloud-documentai-toolbox
pip install langchain-google-community[docai]
```
See a [usage example](/docs/integrations/document_transformers/google_docai).
```python
from langchain_community.document_loaders.blob_loaders import Blob
from langchain_community.document_loaders.parsers import DocAIParser
from langchain_core.document_loaders.blob_loaders import Blob
from langchain_google_community import DocAIParser
```
### Google Translate
@ -405,18 +404,16 @@ from langchain_community.document_loaders.parsers import DocAIParser
The `GoogleTranslateTransformer` allows you to translate text and HTML with the [Google Cloud Translation API](https://cloud.google.com/translate).
To use it, you should have the `google-cloud-translate` python package installed, and a Google Cloud project with the [Translation API enabled](https://cloud.google.com/translate/docs/setup). This transformer uses the [Advanced edition (v3)](https://cloud.google.com/translate/docs/intro-to-v3).
First, we need to install the python package.
First, we need to install the `langchain-google-community` with translate dependencies.
```bash
pip install google-cloud-translate
pip install langchain-google-community[translate]
```
See a [usage example and authorization instructions](/docs/integrations/document_transformers/google_translate).
```python
from langchain_community.document_transformers import GoogleTranslateTransformer
from langchain_google_community import GoogleTranslateTransformer
```
## Vector Stores
@ -646,7 +643,7 @@ pip install google-cloud-text-to-speech
See a [usage example and authorization instructions](/docs/integrations/tools/google_cloud_texttospeech).
```python
from langchain.tools import GoogleCloudTextToSpeechTool
from langchain_google_community import TextToSpeechTool
```
### Google Drive
@ -739,7 +736,7 @@ from langchain_community.utilities.google_scholar import GoogleScholarAPIWrapper
`GOOGLE_API_KEY` and `GOOGLE_CSE_ID` respectively.
```python
from langchain_community.utilities import GoogleSearchAPIWrapper
from langchain_google_community import GoogleSearchAPIWrapper
```
For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/google_search).
@ -773,16 +770,16 @@ from langchain_community.utilities.google_trends import GoogleTrendsAPIWrapper
> [Google Gmail](https://en.wikipedia.org/wiki/Gmail) is a free email service provided by Google.
This toolkit works with emails through the `Gmail API`.
We need to install several python packages.
We need to install `langchain-google-community` with required dependencies:
```bash
pip install google-api-python-client google-auth-oauthlib google-auth-httplib2
pip install langchain-google-community[gmail]
```
See a [usage example and authorization instructions](/docs/integrations/toolkits/gmail).
```python
from langchain_community.agent_toolkits import GmailToolkit
from langchain_google_community import GmailToolkit
```
## Memory
@ -948,16 +945,16 @@ from langchain_google_el_carro import ElCarroChatMessageHistory
> [Gmail](https://en.wikipedia.org/wiki/Gmail) is a free email service provided by Google.
This loader works with emails through the `Gmail API`.
We need to install several python packages.
We need to install `langchain-google-community` with underlying dependencies.
```bash
pip install google-api-python-client google-auth-oauthlib google-auth-httplib2
pip install langchain-google-community[gmail]
```
See a [usage example and authorization instructions](/docs/integrations/chat_loaders/gmail).
```python
from langchain_community.chat_loaders.gmail import GMailLoader
from langchain_google_community import GMailLoader
```
## 3rd Party Integrations

4
libs/langchain/langchain/chains/openai_functions/base.py
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@ -34,7 +34,7 @@ from langchain.chains.structured_output.base import (
__all__ = [
"get_openai_output_parser",
"create_openai_fn_runnable",
"create_structured_output_runnable",
"create_structured_output_runnable", # deprecated
"create_openai_fn_chain", # deprecated
"create_structured_output_chain", # deprecated
"PYTHON_TO_JSON_TYPES", # backwards compatibility
@ -144,7 +144,7 @@ def create_openai_fn_chain(
@deprecated(
since="0.1.1", removal="0.2.0", alternative="create_structured_output_runnable"
since="0.1.1", removal="0.2.0", alternative="ChatOpenAI.with_structured_output"
)
def create_structured_output_chain(
output_schema: Union[Dict[str, Any], Type[BaseModel]],

50
libs/langchain/langchain/chains/structured_output/base.py
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@ -30,15 +30,15 @@ from langchain.output_parsers import (
@deprecated(
since="0.1.14",
message=(
"LangChain has introduced a method called `with_structured_output` that"
"is available on ChatModels capable of tool calling."
"LangChain has introduced a method called `with_structured_output` that "
"is available on ChatModels capable of tool calling. "
"You can read more about the method here: "
"https://python.langchain.com/docs/modules/model_io/chat/structured_output/"
"Please follow our extraction use case documentation for more guidelines"
"on how to do information extraction with LLMs."
"https://python.langchain.com/docs/use_cases/extraction/."
"https://python.langchain.com/docs/modules/model_io/chat/structured_output/ "
"Please follow our extraction use case documentation for more guidelines "
"on how to do information extraction with LLMs. "
"https://python.langchain.com/docs/use_cases/extraction/. "
"If you notice other issues, please provide "
"feedback here:"
"feedback here: "
"https://github.com/langchain-ai/langchain/discussions/18154"
),
removal="0.3.0",
@ -146,6 +146,42 @@ def create_openai_fn_runnable(
return llm.bind(**llm_kwargs_) | output_parser
@deprecated(
since="0.1.17",
message=(
"LangChain has introduced a method called `with_structured_output` that "
"is available on ChatModels capable of tool calling. "
"You can read more about the method here: "
"https://python.langchain.com/docs/modules/model_io/chat/structured_output/ "
"Please follow our extraction use case documentation for more guidelines "
"on how to do information extraction with LLMs. "
"https://python.langchain.com/docs/use_cases/extraction/. "
"If you notice other issues, please provide "
"feedback here: "
"https://github.com/langchain-ai/langchain/discussions/18154"
),
removal="0.3.0",
pending=True,
alternative=(
"""
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_anthropic import ChatAnthropic
class Joke(BaseModel):
setup: str = Field(description="The setup of the joke")
punchline: str = Field(description="The punchline to the joke")
# Or any other chat model that supports tools.
# Please reference to to the documentation of structured_output
# to see an up to date list of which models support
# with_structured_output.
model = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
structured_llm = model.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about cats.
Make sure to call the Joke function.")
"""
),
)
def create_structured_output_runnable(
output_schema: Union[Dict[str, Any], Type[BaseModel]],
llm: Runnable,

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