# 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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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
# 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
# Add QnA with sources example
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Fixes: see
https://stackoverflow.com/questions/76207160/langchain-doesnt-work-with-weaviate-vector-database-getting-valueerror/76210017#76210017
## Before submitting
<!-- If you're adding a new integration, include an integration test and
an example notebook showing its use! -->
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Community members can review the PR once tests pass. Tag
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@dev2049
# Bibtex integration
Wrap bibtexparser to retrieve a list of docs from a bibtex file.
* Get the metadata from the bibtex entries
* `page_content` get from the local pdf referenced in the `file` field
of the bibtex entry using `pymupdf`
* If no valid pdf file, `page_content` set to the `abstract` field of
the bibtex entry
* Support Zotero flavour using regex to get the file path
* Added usage example in
`docs/modules/indexes/document_loaders/examples/bibtex.ipynb`
---------
Co-authored-by: Sébastien M. Popoff <sebastien.popoff@espci.fr>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
I found an API key for `serpapi_api_key` while reading the docs. It
seems to have been modified very recently. Removed it in this PR
@hwchase17 - project lead
# Add Joplin document loader
[Joplin](https://joplinapp.org/) is an open source note-taking app.
Joplin has a [REST API](https://joplinapp.org/api/references/rest_api/)
for accessing its local database. The proposed `JoplinLoader` uses the
API to retrieve all notes in the database and their metadata. Joplin
needs to be installed and running locally, and an access token is
required.
- The PR includes an integration test.
- The PR includes an example notebook.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Beam
Calls the Beam API wrapper to deploy and make subsequent calls to an
instance of the gpt2 LLM in a cloud deployment. Requires installation of
the Beam library and registration of Beam Client ID and Client Secret.
Additional calls can then be made through the instance of the large
language model in your code or by calling the Beam API.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Vectara Integration
This PR provides integration with Vectara. Implemented here are:
* langchain/vectorstore/vectara.py
* tests/integration_tests/vectorstores/test_vectara.py
* langchain/retrievers/vectara_retriever.py
And two IPYNB notebooks to do more testing:
* docs/modules/chains/index_examples/vectara_text_generation.ipynb
* docs/modules/indexes/vectorstores/examples/vectara.ipynb
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# DOCS added missed document_loader examples
Added missed examples: `JSON`, `Open Document Format (ODT)`,
`Wikipedia`, `tomarkdown`.
Updated them to a consistent format.
## Who can review?
@hwchase17
@dev2049
# 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
# Add MosaicML inference endpoints
This PR adds support in langchain for MosaicML inference endpoints. We
both serve a select few open source models, and allow customers to
deploy their own models using our inference service. Docs are here
(https://docs.mosaicml.com/en/latest/inference.html), and sign up form
is here (https://forms.mosaicml.com/demo?utm_source=langchain). I'm not
intimately familiar with the details of langchain, or the contribution
process, so please let me know if there is anything that needs fixing or
this is the wrong way to submit a new integration, thanks!
I'm also not sure what the procedure is for integration tests. I have
tested locally with my api key.
## Who can review?
@hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This PR introduces a new module, `elasticsearch_embeddings.py`, which
provides a wrapper around Elasticsearch embedding models. The new
ElasticsearchEmbeddings class allows users to generate embeddings for
documents and query texts using a [model deployed in an Elasticsearch
cluster](https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-model-ref.html#ml-nlp-model-ref-text-embedding).
### Main features:
1. The ElasticsearchEmbeddings class initializes with an Elasticsearch
connection object and a model_id, providing an interface to interact
with the Elasticsearch ML client through
[infer_trained_model](https://elasticsearch-py.readthedocs.io/en/v8.7.0/api.html?highlight=trained%20model%20infer#elasticsearch.client.MlClient.infer_trained_model)
.
2. The `embed_documents()` method generates embeddings for a list of
documents, and the `embed_query()` method generates an embedding for a
single query text.
3. The class supports custom input text field names in case the deployed
model expects a different field name than the default `text_field`.
4. The implementation is compatible with any model deployed in
Elasticsearch that generates embeddings as output.
### Benefits:
1. Simplifies the process of generating embeddings using Elasticsearch
models.
2. Provides a clean and intuitive interface to interact with the
Elasticsearch ML client.
3. Allows users to easily integrate Elasticsearch-generated embeddings.
Related issue https://github.com/hwchase17/langchain/issues/3400
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Fix typo + add wikipedia package installation part in
human_input_llm.ipynb
This PR
1. Fixes typo ("the the human input LLM"),
2. Addes wikipedia package installation part (in accordance with
`WikipediaQueryRun`
[documentation](https://python.langchain.com/en/latest/modules/agents/tools/examples/wikipedia.html))
in `human_input_llm.ipynb`
(`docs/modules/models/llms/examples/human_input_llm.ipynb`)
# Add link to Psychic from document loaders documentation page
In my previous PR I forgot to update `document_loaders.rst` to link to
`psychic.ipynb` to make it discoverable from the main documentation.
# Add AzureCognitiveServicesToolkit to call Azure Cognitive Services
API: achieve some multimodal capabilities
This PR adds a toolkit named AzureCognitiveServicesToolkit which bundles
the following tools:
- AzureCogsImageAnalysisTool: calls Azure Cognitive Services image
analysis API to extract caption, objects, tags, and text from images.
- AzureCogsFormRecognizerTool: calls Azure Cognitive Services form
recognizer API to extract text, tables, and key-value pairs from
documents.
- AzureCogsSpeech2TextTool: calls Azure Cognitive Services speech to
text API to transcribe speech to text.
- AzureCogsText2SpeechTool: calls Azure Cognitive Services text to
speech API to synthesize text to speech.
This toolkit can be used to process image, document, and audio inputs.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
OpenLM is a zero-dependency OpenAI-compatible LLM provider that can call
different inference endpoints directly via HTTP. It implements the
OpenAI Completion class so that it can be used as a drop-in replacement
for the OpenAI API. This changeset utilizes BaseOpenAI for minimal added
code.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add Mastodon toots loader.
Loader works either with public toots, or Mastodon app credentials. Toot
text and user info is loaded.
I've also added integration test for this new loader as it works with
public data, and a notebook with example output run now.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Improve pinecone hybrid search retriever adding metadata support
I simply remove the hardwiring of metadata to the existing
implementation allowing one to pass `metadatas` attribute to the
constructors and in `get_relevant_documents`. I also add one missing pip
install to the accompanying notebook (I am not adding dependencies, they
were pre-existing).
First contribution, just hoping to help, feel free to critique :)
my twitter username is `@andreliebschner`
While looking at hybrid search I noticed #3043 and #1743. I think the
former can be closed as following the example right now (even prior to
my improvements) works just fine, the latter I think can be also closed
safely, maybe pointing out the relevant classes and example. Should I
reply those issues mentioning someone?
@dev2049, @hwchase17
---------
Co-authored-by: Andreas Liebschner <a.liebschner@shopfully.com>
### Submit Multiple Files to the Unstructured API
Enables batching multiple files into a single Unstructured API requests.
Support for requests with multiple files was added to both
`UnstructuredAPIFileLoader` and `UnstructuredAPIFileIOLoader`. Note that
if you submit multiple files in "single" mode, the result will be
concatenated into a single document. We recommend using this feature in
"elements" mode.
### Testing
The following should load both documents, using two of the example docs
from the integration tests folder.
```python
from langchain.document_loaders import UnstructuredAPIFileLoader
file_paths = ["examples/layout-parser-paper.pdf", "examples/whatsapp_chat.txt"]
loader = UnstructuredAPIFileLoader(
file_paths=file_paths,
api_key="FAKE_API_KEY",
strategy="fast",
mode="elements",
)
docs = loader.load()
```
# Corrected Misspelling in agents.rst Documentation
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In the
[documentation](https://python.langchain.com/en/latest/modules/agents.html)
it says "in fact, it is often best to have an Action Agent be in
**change** of the execution for the Plan and Execute agent."
**Suggested Change:** I propose correcting change to charge.
Fix for issue: #5039
# Add documentation for Databricks integration
This is a follow-up of https://github.com/hwchase17/langchain/pull/4702
It documents the details of how to integrate Databricks using langchain.
It also provides examples in a notebook.
## Who can review?
@dev2049 @hwchase17 since you are aware of the context. We will promote
the integration after this doc is ready. Thanks in advance!
# Fixes an annoying typo in docs
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Fixes Annoying typo in docs - "Therefor" -> "Therefore". It's so
annoying to read that I just had to make this PR.
# Streaming only final output of agent (#2483)
As requested in issue #2483, this Callback allows to stream only the
final output of an agent (ie not the intermediate steps).
Fixes#2483
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add self query translator for weaviate vectorstore
Adds support for the EQ comparator and the AND/OR operators.
Co-authored-by: Dominic Chan <dchan@cppib.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Improve Evernote Document Loader
When exporting from Evernote you may export more than one note.
Currently the Evernote loader concatenates the content of all notes in
the export into a single document and only attaches the name of the
export file as metadata on the document.
This change ensures that each note is loaded as an independent document
and all available metadata on the note e.g. author, title, created,
updated are added as metadata on each document.
It also uses an existing optional dependency of `html2text` instead of
`pypandoc` to remove the need to download the pandoc application via
`download_pandoc()` to be able to use the `pypandoc` python bindings.
Fixes#4493
Co-authored-by: Mike McGarry <mike.mcgarry@finbourne.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Remove autoreload in examples
Remove the `autoreload` in examples since it is not necessary for most
users:
```
%load_ext autoreload,
%autoreload 2
```
# Powerbi API wrapper bug fix + integration tests
- Bug fix by removing `TYPE_CHECKING` in in utilities/powerbi.py
- Added integration test for power bi api in
utilities/test_powerbi_api.py
- Added integration test for power bi agent in
agent/test_powerbi_agent.py
- Edited .env.examples to help set up power bi related environment
variables
- Updated demo notebook with working code in
docs../examples/powerbi.ipynb - AzureOpenAI -> ChatOpenAI
Notes:
Chat models (gpt3.5, gpt4) are much more capable than davinci at writing
DAX queries, so that is important to getting the agent to work properly.
Interestingly, gpt3.5-turbo needed the examples=DEFAULT_FEWSHOT_EXAMPLES
to write consistent DAX queries, so gpt4 seems necessary as the smart
llm.
Fixes#4325
## Before submitting
Azure-core and Azure-identity are necessary dependencies
check integration tests with the following:
`pytest tests/integration_tests/utilities/test_powerbi_api.py`
`pytest tests/integration_tests/agent/test_powerbi_agent.py`
You will need a power bi account with a dataset id + table name in order
to test. See .env.examples for details.
## Who can review?
@hwchase17
@vowelparrot
---------
Co-authored-by: aditya-pethe <adityapethe1@gmail.com>
This PR adds support for Databricks runtime and Databricks SQL by using
[Databricks SQL Connector for
Python](https://docs.databricks.com/dev-tools/python-sql-connector.html).
As a cloud data platform, accessing Databricks requires a URL as follows
`databricks://token:{api_token}@{hostname}?http_path={http_path}&catalog={catalog}&schema={schema}`.
**The URL is **complicated** and it may take users a while to figure it
out**. Since the fields `api_token`/`hostname`/`http_path` fields are
known in the Databricks notebook, I am proposing a new method
`from_databricks` to simplify the connection to Databricks.
## In Databricks Notebook
After changes, Databricks users only need to specify the `catalog` and
`schema` field when using langchain.
<img width="881" alt="image"
src="https://github.com/hwchase17/langchain/assets/1097932/984b4c57-4c2d-489d-b060-5f4918ef2f37">
## In Jupyter Notebook
The method can be used on the local setup as well:
<img width="678" alt="image"
src="https://github.com/hwchase17/langchain/assets/1097932/142e8805-a6ef-4919-b28e-9796ca31ef19">
# Add Spark SQL support
* Add Spark SQL support. It can connect to Spark via building a
local/remote SparkSession.
* Include a notebook example
I tried some complicated queries (window function, table joins), and the
tool works well.
Compared to the [Spark Dataframe
agent](https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark.html),
this tool is able to generate queries across multiple tables.
---------
# Your PR Title (What it does)
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Fixes # (issue)
## Before submitting
<!-- If you're adding a new integration, include an integration test and
an example notebook showing its use! -->
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Community members can review the PR once tests pass. Tag
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Tracing / Callbacks
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- @hwchase17
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Agents / Tools / Toolkits
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VectorStores / Retrievers / Memory
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---------
Co-authored-by: Gengliang Wang <gengliang@apache.org>
Co-authored-by: Mike W <62768671+skcoirz@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: UmerHA <40663591+UmerHA@users.noreply.github.com>
Co-authored-by: 张城铭 <z@hyperf.io>
Co-authored-by: assert <zhangchengming@kkguan.com>
Co-authored-by: blob42 <spike@w530>
Co-authored-by: Yuekai Zhang <zhangyuekai@foxmail.com>
Co-authored-by: Richard He <he.yucheng@outlook.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Co-authored-by: Leonid Ganeline <leo.gan.57@gmail.com>
Co-authored-by: Alexey Nominas <60900649+Chae4ek@users.noreply.github.com>
Co-authored-by: elBarkey <elbarkey@gmail.com>
Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com>
Co-authored-by: Jeffrey D <1289344+verygoodsoftwarenotvirus@users.noreply.github.com>
Co-authored-by: so2liu <yangliu35@outlook.com>
Co-authored-by: Viswanadh Rayavarapu <44315599+vishwa-rn@users.noreply.github.com>
Co-authored-by: Chakib Ben Ziane <contact@blob42.xyz>
Co-authored-by: Daniel Chalef <131175+danielchalef@users.noreply.github.com>
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
Co-authored-by: Jari Bakken <jari.bakken@gmail.com>
Co-authored-by: escafati <scafatieugenio@gmail.com>
# Zep Retriever - Vector Search Over Chat History with the Zep Long-term
Memory Service
More on Zep: https://github.com/getzep/zep
Note: This PR is related to and relies on
https://github.com/hwchase17/langchain/pull/4834. I did not want to
modify the `pyproject.toml` file to add the `zep-python` dependency a
second time.
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
# docs: updated `Supabase` notebook
- the title of the notebook was inconsistent (included redundant
"Vectorstore"). Removed this "Vectorstore"
- added `Postgress` to the title. It is important. The `Postgres` name
is much more popular than `Supabase`.
- added description for the `Postrgress`
- added more info to the `Supabase` description
# Update GPT4ALL integration
GPT4ALL have completely changed their bindings. They use a bit odd
implementation that doesn't fit well into base.py and it will probably
be changed again, so it's a temporary solution.
Fixes#3839, #4628
# Docs: compound ecosystem and integrations
**Problem statement:** We have a big overlap between the
References/Integrations and Ecosystem/LongChain Ecosystem pages. It
confuses users. It creates a situation when new integration is added
only on one of these pages, which creates even more confusion.
- removed References/Integrations page (but move all its information
into the individual integration pages - in the next PR).
- renamed Ecosystem/LongChain Ecosystem into Integrations/Integrations.
I like the Ecosystem term. It is more generic and semantically richer
than the Integration term. But it mentally overloads users. The
`integration` term is more concrete.
UPDATE: after discussion, the Ecosystem is the term.
Ecosystem/Integrations is the page (in place of Ecosystem/LongChain
Ecosystem).
As a result, a user gets a single place to start with the individual
integration.
# Fix bilibili api import error
bilibili-api package is depracated and there is no sync module.
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Fixes#2673#2724
## Before submitting
<!-- If you're adding a new integration, include an integration test and
an example notebook showing its use! -->
## Who can review?
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maintainers/contributors who might be interested:
@vowelparrot @liaokongVFX
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# TextLoader auto detect encoding and enhanced exception handling
- Add an option to enable encoding detection on `TextLoader`.
- The detection is done using `chardet`
- The loading is done by trying all detected encodings by order of
confidence or raise an exception otherwise.
### New Dependencies:
- `chardet`
Fixes#4479
## Before submitting
<!-- If you're adding a new integration, include an integration test and
an example notebook showing its use! -->
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
- @eyurtsev
---------
Co-authored-by: blob42 <spike@w530>
# Load specific file types from Google Drive (issue #4878)
Add the possibility to define what file types you want to load from
Google Drive.
```
loader = GoogleDriveLoader(
folder_id="1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5",
file_types=["document", "pdf"]
recursive=False
)
```
Fixes ##4878
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
DataLoaders
- @eyurtsev
Twitter: [@UmerHAdil](https://twitter.com/@UmerHAdil) | Discord:
RicChilligerDude#7589
---------
Co-authored-by: UmerHA <40663591+UmerHA@users.noreply.github.com>
#docs: text splitters improvements
Changes are only in the Jupyter notebooks.
- added links to the source packages and a short description of these
packages
- removed " Text Splitters" suffixes from the TOC elements (they made
the list of the text splitters messy)
- moved text splitters, based on the length function into a separate
list. They can be mixed with any classes from the "Text Splitters", so
it is a different classification.
## Who can review?
@hwchase17 - project lead
@eyurtsev
@vowelparrot
NOTE: please, check out the results of the `Python code` text splitter
example (text_splitters/examples/python.ipynb). It looks suboptimal.
# Added another helpful way for developers who want to set OpenAI API
Key dynamically
Previous methods like exporting environment variables are good for
project-wide settings.
But many use cases need to assign API keys dynamically, recently.
```python
from langchain.llms import OpenAI
llm = OpenAI(openai_api_key="OPENAI_API_KEY")
```
## Before submitting
```bash
export OPENAI_API_KEY="..."
```
Or,
```python
import os
os.environ["OPENAI_API_KEY"] = "..."
```
<hr>
Thank you.
Cheers,
Bongsang
# Documentation for Azure OpenAI embeddings model
- OPENAI_API_VERSION environment variable is needed for the endpoint
- The constructor does not work with model, it works with deployment.
I fixed it in the notebook.
(This is my first contribution)
## Who can review?
@hwchase17
@agola
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Docs: improvements in the `retrievers/examples/` notebooks
Its primary purpose is to make the Jupyter notebook examples
**consistent** and more suitable for first-time viewers.
- add links to the integration source (if applicable) with a short
description of this source;
- removed `_retriever` suffix from the file names (where it existed) for
consistency;
- removed ` retriever` from the notebook title (where it existed) for
consistency;
- added code to install necessary Python package(s);
- added code to set up the necessary API Key.
- very small fixes in notebooks from other folders (for consistency):
- docs/modules/indexes/vectorstores/examples/elasticsearch.ipynb
- docs/modules/indexes/vectorstores/examples/pinecone.ipynb
- docs/modules/models/llms/integrations/cohere.ipynb
- fixed misspelling in langchain/retrievers/time_weighted_retriever.py
comment (sorry, about this change in a .py file )
## Who can review
@dev2049
# Fixed typos (issues #4818 & #4668 & more typos)
- At some places, it said `model = ChatOpenAI(model='gpt-3.5-turbo')`
but should be `model = ChatOpenAI(model_name='gpt-3.5-turbo')`
- Fixes some other typos
Fixes#4818, #4668
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @vowelparrot
# Removed usage of deprecated methods
Replaced `SQLDatabaseChain` deprecated direct initialisation with
`from_llm` method
## Who can review?
@hwchase17
@agola11
---------
Co-authored-by: imeckr <chandanroutray2012@gmail.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
- Installation of non-colab packages
- Get API keys
# Added dependencies to make notebook executable on hosted notebooks
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@hwchase17
@vowelparrot
- Installation of non-colab packages
- Get API keys
- Get rid of warnings
# Cleanup and added dependencies to make notebook executable on hosted
notebooks
@hwchase17
@vowelparrot
The current example in
https://python.langchain.com/en/latest/modules/agents/plan_and_execute.html
has inconsistent reasoning step (observing 28 years and thinking it's 26
years):
```
Observation: 28 years
Thought:Based on my search, Gigi Hadid's current age is 26 years old.
Action:
{
"action": "Final Answer",
"action_input": "Gigi Hadid's current age is 26 years old."
}
```
Guessing this is model noise. Rerunning seems to give correct answer of
28 years.
# Fix Telegram API loader + add tests.
I was testing this integration and it was broken with next error:
```python
message_threads = loader._get_message_threads(df)
KeyError: False
```
Also, this particular loader didn't have any tests / related group in
poetry, so I added those as well.
@hwchase17 / @eyurtsev please take a look on this fix PR.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Cassandra support for chat history
### Description
- Store chat messages in cassandra
### Dependency
- cassandra-driver - Python Module
## Before submitting
- Added Integration Test
## Who can review?
@hwchase17
@agola11
# Your PR Title (What it does)
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Fixes # (issue)
## Before submitting
<!-- If you're adding a new integration, include an integration test and
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Tracing / Callbacks
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- @agola11
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- @hwchase17
- @agola11
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- @vowelparrot
VectorStores / Retrievers / Memory
- @dev2049
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Co-authored-by: Jinto Jose <129657162+jj701@users.noreply.github.com>
# Add GraphQL Query Support
This PR introduces a GraphQL API Wrapper tool that allows LLM agents to
query GraphQL databases. The tool utilizes the httpx and gql Python
packages to interact with GraphQL APIs and provides a simple interface
for running queries with LLM agents.
@vowelparrot
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
### Adds a document loader for Docugami
Specifically:
1. Adds a data loader that talks to the [Docugami](http://docugami.com)
API to download processed documents as semantic XML
2. Parses the semantic XML into chunks, with additional metadata
capturing chunk semantics
3. Adds a detailed notebook showing how you can use additional metadata
returned by Docugami for techniques like the [self-querying
retriever](https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/self_query_retriever.html)
4. Adds an integration test, and related documentation
Here is an example of a result that is not possible without the
capabilities added by Docugami (from the notebook):
<img width="1585" alt="image"
src="https://github.com/hwchase17/langchain/assets/749277/bb6c1ce3-13dc-4349-a53b-de16681fdd5b">
---------
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
Co-authored-by: Taqi Jaffri <tjaffri@gmail.com>
[OpenWeatherMapAPIWrapper](f70e18a5b3/docs/modules/agents/tools/examples/openweathermap.ipynb)
works wonderfully, but the _tool_ itself can't be used in master branch.
- added OpenWeatherMap **tool** to the public api, to be loadable with
`load_tools` by using "openweathermap-api" tool name (that name is used
in the existing
[docs](aff33d52c5/docs/modules/agents/tools/getting_started.md),
at the bottom of the page)
- updated OpenWeatherMap tool's **description** to make the input format
match what the API expects (e.g. `London,GB` instead of `'London,GB'`)
- added [ecosystem documentation page for
OpenWeatherMap](f9c41594fe/docs/ecosystem/openweathermap.md)
- added tool usage example to [OpenWeatherMap's
notebook](f9c41594fe/docs/modules/agents/tools/examples/openweathermap.ipynb)
Let me know if there's something I missed or something needs to be
updated! Or feel free to make edits yourself if that makes it easier for
you 🙂
[RELLM](https://github.com/r2d4/rellm) is a library that wraps local
HuggingFace pipeline models for structured decoding.
RELLM works by generating tokens one at a time. At each step, it masks
tokens that don't conform to the provided partial regular expression.
[JSONFormer](https://github.com/1rgs/jsonformer) is a bit different, where it sequentially adds the keys then decodes each value directly
**Problem statement:** the
[document_loaders](https://python.langchain.com/en/latest/modules/indexes/document_loaders.html#)
section is too long and hard to comprehend.
**Proposal:** group document_loaders by 3 classes: (see `Files changed`
tab)
UPDATE: I've completely reworked the document_loader classification.
Now this PR changes only one file!
FYI @eyurtsev @hwchase17
[Text Generation
Inference](https://github.com/huggingface/text-generation-inference) is
a Rust, Python and gRPC server for generating text using LLMs.
This pull request add support for self hosted Text Generation Inference
servers.
feature: #4280
---------
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add option to `load_huggingface_tool`
Expose a method to load a huggingface Tool from the HF hub
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Thanks to @anna-charlotte and @jupyterjazz for the contribution! Made
few small changes to get it across the finish line
---------
Signed-off-by: anna-charlotte <charlotte.gerhaher@jina.ai>
Signed-off-by: jupyterjazz <saba.sturua@jina.ai>
Co-authored-by: anna-charlotte <charlotte.gerhaher@jina.ai>
Co-authored-by: jupyterjazz <saba.sturua@jina.ai>
Co-authored-by: Saba Sturua <45267439+jupyterjazz@users.noreply.github.com>
# ODF File Loader
Adds a data loader for handling Open Office ODT files. Requires
`unstructured>=0.6.3`.
### Testing
The following should work using the `fake.odt` example doc from the
[`unstructured` repo](https://github.com/Unstructured-IO/unstructured).
```python
from langchain.document_loaders import UnstructuredODTLoader
loader = UnstructuredODTLoader(file_path="fake.odt", mode="elements")
loader.load()
loader = UnstructuredODTLoader(file_path="fake.odt", mode="single")
loader.load()
```
- added `Wikipedia` retriever. It is effectively a wrapper for
`WikipediaAPIWrapper`. It wrapps load() into get_relevant_documents()
- sorted `__all__` in the `retrievers/__init__`
- added integration tests for the WikipediaRetriever
- added an example (as Jupyter notebook) for the WikipediaRetriever
# Minor Wording Documentation Change
```python
agent_chain.run("When's my friend Eric's surname?")
# Answer with 'Zhu'
```
is change to
```python
agent_chain.run("What's my friend Eric's surname?")
# Answer with 'Zhu'
```
I think when is a residual of the old query that was "When’s my friends
Eric`s birthday?".
# Fix grammar in Text Splitters docs
Just a small fix of grammar in the documentation:
"That means there two different axes" -> "That means there are two
different axes"
