# docs: `ecosystem_integrations` update 3
Next cycle of updating the `ecosystem/integrations`
* Added an integration `template` file
* Added missed integration files
* Fixed several document_loaders/notebooks
## Who can review?
Is it possible to assign somebody to review PRs on docs? Thanks.
# Fix wrong class instantiation in docs MMR example
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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
# Update Unstructured docs to remove the `detectron2` install
instructions
Removes `detectron2` installation instructions from the Unstructured
docs because installing `detectron2` is no longer required for
`unstructured>=0.7.0`. The `detectron2` model now runs using the ONNX
runtime.
## Who can review?
@hwchase17
@eyurtsev
# Add Managed Motorhead
This change enabled MotorheadMemory to utilize Metal's managed version
of Motorhead. We can easily enable this by passing in a `api_key` and
`client_id` in order to hit the managed url and access the memory api on
Metal.
Twitter: [@softboyjimbo](https://twitter.com/softboyjimbo)
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@dev2049 @hwchase17
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Bedrock LLM and Embeddings
This PR adds a new LLM and an Embeddings class for the
[Bedrock](https://aws.amazon.com/bedrock) service. The PR also includes
example notebooks for using the LLM class in a conversation chain and
embeddings usage in creating an embedding for a query and document.
**Note**: AWS is doing a private release of the Bedrock service on
05/31/2023; users need to request access and added to an allowlist in
order to start using the Bedrock models and embeddings. Please use the
[Bedrock Home Page](https://aws.amazon.com/bedrock) to request access
and to learn more about the models available in Bedrock.
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# Support Qdrant filters
Qdrant has an [extensive filtering
system](https://qdrant.tech/documentation/concepts/filtering/) with rich
type support. This PR makes it possible to use the filters in Langchain
by passing an additional param to both the
`similarity_search_with_score` and `similarity_search` methods.
## Who can review?
@dev2049 @hwchase17
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# SQLite-backed Entity Memory
Following the initiative of
https://github.com/hwchase17/langchain/pull/2397 I think it would be
helpful to be able to persist Entity Memory on disk by default
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
This PR adds a new method `from_es_connection` to the
`ElasticsearchEmbeddings` class allowing users to use Elasticsearch
clusters outside of Elastic Cloud.
Users can create an Elasticsearch Client object and pass that to the new
function.
The returned object is identical to the one returned by calling
`from_credentials`
```
# Create Elasticsearch connection
es_connection = Elasticsearch(
hosts=['https://es_cluster_url:port'],
basic_auth=('user', 'password')
)
# Instantiate ElasticsearchEmbeddings using es_connection
embeddings = ElasticsearchEmbeddings.from_es_connection(
model_id,
es_connection,
)
```
I also added examples to the elasticsearch jupyter notebook
Fixes # https://github.com/hwchase17/langchain/issues/5239
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
As the title says, I added more code splitters.
The implementation is trivial, so i don't add separate tests for each
splitter.
Let me know if any concerns.
Fixes # (issue)
https://github.com/hwchase17/langchain/issues/5170
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@eyurtsev @hwchase17
---------
Signed-off-by: byhsu <byhsu@linkedin.com>
Co-authored-by: byhsu <byhsu@linkedin.com>
# Creates GitHubLoader (#5257)
GitHubLoader is a DocumentLoader that loads issues and PRs from GitHub.
Fixes#5257
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Added New Trello loader class and documentation
Simple Loader on top of py-trello wrapper.
With a board name you can pull cards and to do some field parameter
tweaks on load operation.
I included documentation and examples.
Included unit test cases using patch and a fixture for py-trello client
class.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add ToolException that a tool can throw
This is an optional exception that tool throws when execution error
occurs.
When this exception is thrown, the agent will not stop working,but will
handle the exception according to the handle_tool_error variable of the
tool,and the processing result will be returned to the agent as
observation,and printed in pink on the console.It can be used like this:
```python
from langchain.schema import ToolException
from langchain import LLMMathChain, SerpAPIWrapper, OpenAI
from langchain.agents import AgentType, initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.tools import BaseTool, StructuredTool, Tool, tool
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(temperature=0)
llm_math_chain = LLMMathChain(llm=llm, verbose=True)
class Error_tool:
def run(self, s: str):
raise ToolException('The current search tool is not available.')
def handle_tool_error(error) -> str:
return "The following errors occurred during tool execution:"+str(error)
search_tool1 = Error_tool()
search_tool2 = SerpAPIWrapper()
tools = [
Tool.from_function(
func=search_tool1.run,
name="Search_tool1",
description="useful for when you need to answer questions about current events.You should give priority to using it.",
handle_tool_error=handle_tool_error,
),
Tool.from_function(
func=search_tool2.run,
name="Search_tool2",
description="useful for when you need to answer questions about current events",
return_direct=True,
)
]
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,
handle_tool_errors=handle_tool_error)
agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
```
## Who can review?
- @vowelparrot
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# docs: ecosystem/integrations update
It is the first in a series of `ecosystem/integrations` updates.
The ecosystem/integrations list is missing many integrations.
I'm adding the missing integrations in a consistent format:
1. description of the integrated system
2. `Installation and Setup` section with 'pip install ...`, Key setup,
and other necessary settings
3. Sections like `LLM`, `Text Embedding Models`, `Chat Models`... with
links to correspondent examples and imports of the used classes.
This PR keeps new docs, that are presented in the
`docs/modules/models/text_embedding/examples` but missed in the
`ecosystem/integrations`. The next PRs will cover the next example
sections.
Also updated `integrations.rst`: added the `Dependencies` section with a
link to the packages used in LangChain.
## Who can review?
@hwchase17
@eyurtsev
@dev2049
# docs: ecosystem/integrations update 2
#5219 - part 1
The second part of this update (parts are independent of each other! no
overlap):
- added diffbot.md
- updated confluence.ipynb; added confluence.md
- updated college_confidential.md
- updated openai.md
- added blackboard.md
- added bilibili.md
- added azure_blob_storage.md
- added azlyrics.md
- added aws_s3.md
## Who can review?
@hwchase17@agola11
@agola11
@vowelparrot
@dev2049
# Update llamacpp demonstration notebook
Add instructions to install with BLAS backend, and update the example of
model usage.
Fixes#5071. However, it is more like a prevention of similar issues in
the future, not a fix, since there was no problem in the framework
functionality
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
- @hwchase17
- @agola11
# Fix for `update_document` Function in Chroma
## Summary
This pull request addresses an issue with the `update_document` function
in the Chroma class, as described in
[#5031](https://github.com/hwchase17/langchain/issues/5031#issuecomment-1562577947).
The issue was identified as an `AttributeError` raised when calling
`update_document` due to a missing corresponding method in the
`Collection` object. This fix refactors the `update_document` method in
`Chroma` to correctly interact with the `Collection` object.
## Changes
1. Fixed the `update_document` method in the `Chroma` class to correctly
call methods on the `Collection` object.
2. Added the corresponding test `test_chroma_update_document` in
`tests/integration_tests/vectorstores/test_chroma.py` to reflect the
updated method call.
3. Added an example and explanation of how to use the `update_document`
function in the Jupyter notebook tutorial for Chroma.
## Test Plan
All existing tests pass after this change. In addition, the
`test_chroma_update_document` test case now correctly checks the
functionality of `update_document`, ensuring that the function works as
expected and updates the content of documents correctly.
## Reviewers
@dev2049
This fix will ensure that users are able to use the `update_document`
function as expected, without encountering the previous
`AttributeError`. This will enhance the usability and reliability of the
Chroma class for all users.
Thank you for considering this pull request. I look forward to your
feedback and suggestions.
# Add SKLearnVectorStore
This PR adds SKLearnVectorStore, a simply vector store based on
NearestNeighbors implementations in the scikit-learn package. This
provides a simple drop-in vector store implementation with minimal
dependencies (scikit-learn is typically installed in a data scientist /
ml engineer environment). The vector store can be persisted and loaded
from json, bson and parquet format.
SKLearnVectorStore has soft (dynamic) dependency on the scikit-learn,
numpy and pandas packages. Persisting to bson requires the bson package,
persisting to parquet requires the pyarrow package.
## Before submitting
Integration tests are provided under
`tests/integration_tests/vectorstores/test_sklearn.py`
Sample usage notebook is provided under
`docs/modules/indexes/vectorstores/examples/sklear.ipynb`
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Sample Notebook for DynamoDB Chat Message History
@dev2049
Adding a sample notebook for the DynamoDB Chat Message History class.
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# docs: improve flow of llm caching notebook
The notebook `llm_caching` demos various caching providers. In the
previous version, there was setup common to all examples but under the
`In Memory Caching` heading.
If a user comes and only wants to try a particular example, they will
run the common setup, then the cells for the specific provider they are
interested in. Then they will get import and variable reference errors.
This commit moves the common setup to the top to avoid this.
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@dev2049
# Better docs for weaviate hybrid search
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<!-- Remove if not applicable -->
Fixes: NA
## Before submitting
<!-- If you're adding a new integration, include an integration test and
an example notebook showing its use! -->
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@dev2049
This PR adds LLM wrapper for Databricks. It supports two endpoint types:
* serving endpoint
* cluster driver proxy app
An integration notebook is included to show how it works.
Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com>
Co-authored-by: Gengliang Wang <gengliang@apache.org>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Fixed typo: 'ouput' to 'output' in all documentation
In this instance, the typo 'ouput' was amended to 'output' in all
occurrences within the documentation. There are no dependencies required
for this change.
# Add Momento as a standard cache and chat message history provider
This PR adds Momento as a standard caching provider. Implements the
interface, adds integration tests, and documentation. We also add
Momento as a chat history message provider along with integration tests,
and documentation.
[Momento](https://www.gomomento.com/) is a fully serverless cache.
Similar to S3 or DynamoDB, it requires zero configuration,
infrastructure management, and is instantly available. Users sign up for
free and get 50GB of data in/out for free every month.
## Before submitting
✅ We have added documentation, notebooks, and integration tests
demonstrating usage.
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Add Multi-CSV/DF support in CSV and DataFrame Toolkits
* CSV and DataFrame toolkits now accept list of CSVs/DFs
* Add default prompts for many dataframes in `pandas_dataframe` toolkit
Fixes#1958
Potentially fixes#4423
## Testing
* Add single and multi-dataframe integration tests for
`pandas_dataframe` toolkit with permutations of `include_df_in_prompt`
* Add single and multi-CSV integration tests for csv toolkit
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Add C Transformers for GGML Models
I created Python bindings for the GGML models:
https://github.com/marella/ctransformers
Currently it supports GPT-2, GPT-J, GPT-NeoX, LLaMA, MPT, etc. See
[Supported
Models](https://github.com/marella/ctransformers#supported-models).
It provides a unified interface for all models:
```python
from langchain.llms import CTransformers
llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2')
print(llm('AI is going to'))
```
It can be used with models hosted on the Hugging Face Hub:
```py
llm = CTransformers(model='marella/gpt-2-ggml')
```
It supports streaming:
```py
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
llm = CTransformers(model='marella/gpt-2-ggml', callbacks=[StreamingStdOutCallbackHandler()])
```
Please see [README](https://github.com/marella/ctransformers#readme) for
more details.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
zep-python's sync methods no longer need an asyncio wrapper. This was
causing issues with FastAPI deployment.
Zep also now supports putting and getting of arbitrary message metadata.
Bump zep-python version to v0.30
Remove nest-asyncio from Zep example notebooks.
Modify tests to include metadata.
---------
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
Co-authored-by: Daniel Chalef <131175+danielchalef@users.noreply.github.com>
For most queries it's the `size` parameter that determines final number
of documents to return. Since our abstractions refer to this as `k`, set
this to be `k` everywhere instead of expecting a separate param. Would
be great to have someone more familiar with OpenSearch validate that
this is reasonable (e.g. that having `size` and what OpenSearch calls
`k` be the same won't lead to any strange behavior). cc @naveentatikonda
Closes#5212
