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fix#13356
Add supports following properties for metadata to NotionDBLoader.
- `checkbox`
- `email`
- `number`
- `select`
There are no relevant tests for this code to be updated.
- **Description:** Adds `limit_to_domains` param to the APIChain based
tools (open_meteo, TMDB, podcast_docs, and news_api)
- **Issue:** I didn't open an issue, but after upgrading to 0.0.328
using these tools would throw an error.
- **Dependencies:** N/A
- **Tag maintainer:** @baskaryan
**Note**: I included the trailing / simply because the docs here did
fc886cc303/docs/docs/use_cases/apis.ipynb (L246)
, but I checked the code and it is using `urlparse`. SoI followed the
docs since it comes down to stylee.
Hi,
this PR adds support for OpenAI API v1 for Azure OpenAI completion API.
@baskaryan @hwchase17
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Bumps [pyarrow](https://github.com/apache/arrow) from 13.0.0 to 14.0.1.
<details>
<summary>Commits</summary>
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MINOR: [Release] Update versions for 14.0.1</li>
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MINOR: [Release] Update .deb/.rpm changelogs for 14.0.1</li>
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MINOR: [Release] Update CHANGELOG.md for 14.0.1</li>
<li><a
href="f141709763"><code>f141709</code></a>
<a
href="https://redirect.github.com/apache/arrow/issues/38607">GH-38607</a>:
[Python] Disable PyExtensionType autoload (<a
href="https://redirect.github.com/apache/arrow/issues/38608">#38608</a>)</li>
<li><a
href="5a37e74198"><code>5a37e74</code></a>
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href="https://redirect.github.com/apache/arrow/issues/38455">#38455</a>)</li>
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<a
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[CI][Release] Resolve symlinks in RAT lint (<a
href="https://redirect.github.com/apache/arrow/issues/38337">#38337</a>)</li>
<li><a
href="bd61239a32"><code>bd61239</code></a>
<a
href="https://redirect.github.com/apache/arrow/issues/35531">GH-35531</a>:
[Python] C Data Interface PyCapsule Protocol (<a
href="https://redirect.github.com/apache/arrow/issues/37797">#37797</a>)</li>
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Hey @rlancemartin, @eyurtsev ,
I did some minimal changes to the `ElasticVectorSearch` client so that
it plays better with existing ES indices.
Main changes are as follows:
1. You can pass the dense vector field name into `_default_script_query`
2. You can pass a custom script query implementation and the respective
parameters to `similarity_search_with_score`
3. You can pass functions for building page content and metadata for the
resulting `Document`
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hi!
This is pretty straight-forward: The sdist package does not contain the
license file (which is needed by e.g. conda) because the package is
built from the subdir and can't see the license.
I _copied_ the license but since I'm unfamiliar with the projects
direction, I'm not sure that's correct.
thanks!
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Fixes: #8207
Description:
Pinecone returns scores (not distances) with cosine similarity. The
values according to the docs are [-1, 1], although I could never
reproduce negative values.
This PR ensures that the score returned from Pinecone is preserved,
rather than inverted, so the most relevant documents can be filtered (eg
when using similarity thresholds)
I'll leave this as a draft PR as I couldn't run the tests (my pinecone
account might not be enough - some errors were being thrown around
namespaces) so hopefully someone who _can_ will pick this up.
Maintainers:
@rlancemartin, @eyurtsev
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Fixed a serialization issue in the add_texts method
of the Matching Engine Vector Store caused by a typo, leading to an
attempt to serialize the json module itself.
- **Issue:** #12154
- **Dependencies:** ./.
- **Tag maintainer:**
Due to the possibility of external inputs including UUIDs, there may be
additional values in **kwargs, while Weaviate's `__init__` method does
not support passing extra **kwarg parameters.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
When calling max_marginal_relevance_search from PGVector the filter
param is not carried over to max_marginal_relevance_search_by_vector
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Uses `endpoint_url` if provided with a boto3 session.
When running dynamodb locally, credentials are required even if invalid.
With this change, it will be possible to pass a boto3 session with
credentials and specify an endpoint_url
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
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- **Description:** Add MyScaleWithoutJSON which allows user to wrap
columns into Document's Metadata
- **Tag maintainer:** @baskaryan
**Description**
Bumps the Momento dependency to the latest version and refactors the
usage of `SearchHit` in the Momento Vector Index (MVI) vector store
integration. This change is a one liner where we use the preferred
attribute `score` to read the query-document similarity instead of
`distance`. The latest versions of Momento clients will use this
attribute going forward.
**Dependencies**
Updated the Momento dependency to latest version.
**Tests**
💚 I re-ran the existing MVI integration tests
(`tests/integration_tests/vectorstores/test_momento_vector_index.py`)
and they pass.
**Review**
cc @baskaryan @eyurtsev
**Description**
the ollama api now supports passing system prompt and template directly
instead of modifying the model file , but the ollama integration in
langchain did not have this change updated . The update just adds these
two parameters to it ( there are 2 more parameters that are pending to
be updated, I was not sure about their utility wrt to langchain )
Refer :
8713ac23a8
**Issue** : None Applicable
**Dependencies** : None Changed
**Twitter handle** : https://twitter.com/violetto96
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
`_get_kwarg_value` function is useless, one can rely on python builtin
functionalities to do the exact same thing.
- **Description:** Removed `_get_kwarg_value`. Helps with code
readability.
- **Issue:** the issue # it fixes (if applicable),
- **Twitter handle:** @Guillem_96
Improve CSV reader which can't call .strip() on NoneType if there are
less cells in the row compared to the header
<!-- Thank you for contributing to LangChain!
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- **Description:**
I have a CSV file as followed
```
headerA,headerB,headerC
v1A,v1B,v1C,
v2A,v2B
v3A,v3B,v3C
```
In this case, row 2 is missing a value, which results in reading a None
type. The strip() method can not be called on None, hence raising. In
this PR I am making the change to only call strip if the value if not
None.
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
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Added a Docusaurus Loader
Issue: #6353
I had to implement this for working with the Ionic documentation, and
wanted to open this up as a draft to get some guidance on building this
out further. I wasn't sure if having it be a light extension of the
SitemapLoader was in the spirit of a proper feature for the library --
but I'm grateful for the opportunities Langchain has given me and I'd
love to build this out properly for the sake of the community.
Any feedback welcome!
- **Description:** `AzureMLChatOnlineEndpoint` object from
langchain/chat_models/azureml_endpoint.py safe to print
without having any secrets included in raw format in the string
representation.
- **Issue:** #12165,
- **Tag maintainer:** @eyurtsev
---------
Co-authored-by: Faysal Bougamale <faysal.bougamale@horiba.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Adding documentation to the runnable.
- Documentation is not organized in the best way for the runnable; i.e.,
in
terms of LCEL vs. other standard methods, will follow up with more
edits.
**Description:** Removing the single quote wrapper around the table
names in the SQL agent toolkit.py file as it misleads the LLM into
querying against tables with single quotes around their names.
**Issue:** #7457
**Dependencies:** None
**Tag maintainer:** @hwchase17
**Twitter handle:** None
- Implement config_specs to include session_id
- Remove Runnable method and update notebook
- Add more details to notebook, eg. show input schema and config schema
before and after adding message history
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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This commit fixes the issue that langchain.llms OpenAI completion
stopped working since the V1 openai client update.
Replace this entire comment with:
- **Description:** This PR fixes the issue [AttributeError: module
'openai' has no attribute
'Completion'](https://github.com/langchain-ai/langchain/issues/12967)
similar to
8e0cb2eb84
and https://github.com/langchain-ai/langchain/pull/12969,
- **Issue:** https://github.com/langchain-ai/langchain/issues/12967,
- **Dependencies:** `openai` v1.x.x client,
- **Tag maintainer:** @baskaryan,
- **Twitter handle:** @dosuken123
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@baskaryan, @eyurtsev, @hwchase17.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
This adds the response message as a document to the rag retriever so
users can choose to use this. Also drops document limit.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
We need to centralize the API we use to get the project name for our
tracers. This PR makes it so we always get this from a shared function
in the langsmith sdk.
## Dependencies
Upgraded langsmith from 0.52 to 0.62 to include the new API
`get_tracer_project`
- **Description:**
Recently Chroma rolled out a breaking change on the way we handle
embedding functions, in order to support multi-modal collections.
This broke the way LangChain's `Chroma` objects get created, because we
were passing the EF down into the Chroma collection:
https://docs.trychroma.com/migration#migration-to-0416---november-7-2023
However, internally, we are never actually using embeddings on the
chroma collection - LangChain's `Chroma` object calls it instead. Thus
we just don't pass an `embedding_function` to Chroma itself, which fixes
the issue.
- **Description:** The issue was not listing the proper import error for
amazon textract loader.
- **Issue:** Time wasted trying to figure out what to install...
(langchain docs don't list the dependency either)
- **Dependencies:** N/A
- **Tag maintainer:** @sbusso
- **Twitter handle:** @h9ste
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Astra DB Vector store integration
- **Description:** This PR adds a `VectorStore` implementation for
DataStax Astra DB using its HTTP API
- **Issue:** (no related issue)
- **Dependencies:** A new required dependency is `astrapy` (`>=0.5.3`)
which was added to pyptoject.toml, optional, as per guidelines
- **Tag maintainer:** I recently mentioned to @baskaryan this
integration was coming
- **Twitter handle:** `@rsprrs` if you want to mention me
This PR introduces the `AstraDB` vector store class, extensive
integration test coverage, a reworking of the documentation which
conflates Cassandra and Astra DB on a single "provider" page and a new,
completely reworked vector-store example notebook (common to the
Cassandra store, since parts of the flow is shared by the two APIs). I
also took care in ensuring docs (and redirects therein) are behaving
correctly.
All style, linting, typechecks and tests pass as far as the `AstraDB`
integration is concerned.
I could build the documentation and check it all right (but ran into
trouble with the `api_docs_build` makefile target which I could not
verify: `Error: Unable to import module
'plan_and_execute.agent_executor' with error: No module named
'langchain_experimental'` was the first of many similar errors)
Thank you for a review!
Stefano
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Cohere released the new embedding API (Embed v3:
https://txt.cohere.com/introducing-embed-v3/) that treats document and
query embeddings differently. This PR updated the `CohereEmbeddings` to
use them appropriately. It also works with the old models.
Description: This PR masks API key secrets for the Nebula model from
Symbl.ai
Issue: #12165
Maintainer: @eyurtsev
---------
Co-authored-by: Praveen Venkateswaran <praveen.venkateswaran@ibm.com>
* ChatAnyscale was missing coercion to SecretStr for anyscale api key
* The model inherits from ChatOpenAI so it should not force the openai
api key to be secret str until openai model has the same changes
https://github.com/langchain-ai/langchain/issues/12841
Qdrant was incorrectly calculating the cosine similarity and returning
`0.0` for the best match, instead of `1.0`. Internally Qdrant returns a
cosine score from `-1.0` (worst match) to `1.0` (best match), and the
current formula reflects it.
Possibility to pass on_artifacts to a conversation. It can be then
achieved by adding this way:
```python
result = agent.run(
input=message.text,
metadata={
"on_artifact": CALLBACK_FUNCTION
},
)
```
Calls uvicorn directly from cli:
Reload works if you define app by import string instead of object.
(was doing subprocess in order to get reloading)
Version bump to 0.0.14
Remove the need for [serve] for simplicity.
Readmes are updated in #12847 to avoid cluttering this PR
Previously we treated trace_on_chain_group as a command to always start
tracing. This is unintuitive (makes the function do 2 things), and makes
it harder to toggle tracing
When you use a MultiQuery it might be useful to use the original query
as well as the newly generated ones to maximise the changes to retriever
the correct document. I haven't created an issue, it seems a very small
and easy thing.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Correct number of elements in config list in
`batch()` and `abatch()` of `BaseLLM` in case `max_concurrency` is not
None.
- **Issue:** #12643
- **Twitter handle:** @akionux
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Zep now has the ability to search over chat history summaries. This PR
adds support for doing so. More here: https://blog.getzep.com/zep-v0-17/
@baskaryan @eyurtsev
…s present
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### Enabling `device_map` in HuggingFacePipeline
For multi-gpu settings with large models, the
[accelerate](https://huggingface.co/docs/accelerate/usage_guides/big_modeling#using--accelerate)
library provides the `device_map` parameter to automatically distribute
the model across GPUs / disk.
The [Transformers
pipeline](3520e37e86/src/transformers/pipelines/__init__.py (L543))
enables users to specify `device` (or) `device_map`, and handles cases
(with warnings) when both are specified.
However, Langchain's HuggingFacePipeline only supports specifying
`device` when calling transformers which limits large models and
multi-gpu use-cases.
Additionally, the [default
value](8bd3ce59cd/libs/langchain/langchain/llms/huggingface_pipeline.py (L72))
of `device` is initialized to `-1` , which is incompatible with the
transformers pipeline when `device_map` is specified.
This PR addresses the addition of `device_map` as a parameter , and
solves the incompatibility of `device = -1` when `device_map` is also
specified.
An additional test has been added for this feature.
Additionally, some existing tests no longer work since
1. `max_new_tokens` has to be specified under `pipeline_kwargs` and not
`model_kwargs`
2. The GPT2 tokenizer raises a `ValueError: Pipeline with tokenizer
without pad_token cannot do batching`, since the `tokenizer.pad_token`
is `None` ([related
issue](https://github.com/huggingface/transformers/issues/19853) on the
transformers repo).
This PR handles fixing these tests as well.
Co-authored-by: Praveen Venkateswaran <praveen.venkateswaran@ibm.com>
[The python
spec](https://docs.python.org/3/reference/datamodel.html#object.__getattr__)
requires that `__getattr__` throw `AttributeError` for missing
attributes but there are several places throwing `ImportError` in the
current code base. This causes a specific problem with `hasattr` since
it calls `__getattr__` then looks only for `AttributeError` exceptions.
At present, calling `hasattr` on any of these modules will raise an
unexpected exception that most code will not handle as `hasattr`
throwing exceptions is not expected.
In our case this is triggered by an exception tracker (Airbrake) that
attempts to collect the version of all installed modules with code that
looks like: `if hasattr(mod, "__version__"):`. With `HEAD` this is
causing our exception tracker to fail on all exceptions.
I only changed instances of unknown attributes raising `ImportError` and
left instances of known attributes raising `ImportError`. It feels a
little weird but doesn't seem to break anything.
- **Description:** Use all Google search results data in SerpApi.com
wrapper instead of the first one only
- **Tag maintainer:** @hwchase17
_P.S. `libs/langchain/tests/integration_tests/utilities/test_serpapi.py`
are not executed during the `make test`._
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It was passing in message instead of generation
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* Restrict the chain to specific domains by default
* This is a breaking change, but it will fail loudly upon object
instantiation -- so there should be no silent errors for users
* Resolves CVE-2023-32786
* This is an opt-in feature, so users should be aware of risks if using
jinja2.
* Regardless we'll add sandboxing by default to jinja2 templates -- this
sandboxing is a best effort basis.
* Best strategy is still to make sure that jinja2 templates are only
loaded from trusted sources.
**Description:** Update `langchain.document_loaders.pdf.PyPDFLoader` to
store url in metadata (instead of a temporary file path) if user
provides a web path to a pdf
- **Issue:** Related to #7034; the reporter on that issue submitted a PR
updating `PyMuPDFParser` for this behavior, but it has unresolved merge
issues as of 20 Oct 2023 #7077
- In addition to `PyPDFLoader` and `PyMuPDFParser`, these other classes
in `langchain.document_loaders.pdf` exhibit similar behavior and could
benefit from an update: `PyPDFium2Loader`, `PDFMinerLoader`,
`PDFMinerPDFasHTMLLoader`, `PDFPlumberLoader` (I'm happy to contribute
to some/all of that, including assisting with `PyMuPDFParser`, if my
work is agreeable)
- The root cause is that the underlying pdf parser classes, e.g.
`langchain.document_loaders.parsers.pdf.PyPDFParser`, never receive
information about the url; the parsers receive a
`langchain.document_loaders.blob_loaders.blob`, which contains the pdf
contents and local file path, but not the url
- This update passes the web path directly to the parser since it's
minimally invasive and doesn't require further changes to maintain
existing behavior for local files... bigger picture, I'd consider
extending `blob` so that extra information like this can be
communicated, but that has much bigger implications on the codebase
which I think warrants maintainer input
- **Dependencies:** None
```python
# old behavior
>>> from langchain.document_loaders import PyPDFLoader
>>> loader = PyPDFLoader('https://arxiv.org/pdf/1706.03762.pdf')
>>> docs = loader.load()
>>> docs[0].metadata
{'source': '/var/folders/w2/zx77z1cs01s1thx5dhshkd58h3jtrv/T/tmpfgrorsi5/tmp.pdf', 'page': 0}
# new behavior
>>> from langchain.document_loaders import PyPDFLoader
>>> loader = PyPDFLoader('https://arxiv.org/pdf/1706.03762.pdf')
>>> docs = loader.load()
>>> docs[0].metadata
{'source': 'https://arxiv.org/pdf/1706.03762.pdf', 'page': 0}
```
- **Description:** #12273 's suggestion PR
Like other PDFLoader, loading pdf per each page and giving page
metadata.
- **Issue:** #12273
- **Twitter handle:** @blue0_0hope
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This will allow you create the schema beforehand. The check was failing
and preventing importing into existing classes.
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- **Description:** implement [quip](https://quip.com) loader
- **Issue:** https://github.com/langchain-ai/langchain/issues/10352
- **Dependencies:** No
- pass make format, make lint, make test
---------
Co-authored-by: Hao Fan <h_fan@apple.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
latest release broken, this fixes it
---------
Co-authored-by: Roman Vasilyev <rvasilyev@mozilla.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Prior to this PR, `ruff` was used only for linting and not for
formatting, despite the names of the commands. This PR makes it be used
for both linting code and autoformatting it.
This input key was missed in the last update PR:
https://github.com/langchain-ai/langchain/pull/7391
The input/output formats are intended to be like this:
```
{"inputs": [<prompt>]}
{"outputs": [<output_text>]}
```
## Description
This PR adds support for
[lm-format-enforcer](https://github.com/noamgat/lm-format-enforcer) to
LangChain.
![image](https://raw.githubusercontent.com/noamgat/lm-format-enforcer/main/docs/Intro.webp)
The library is similar to jsonformer / RELLM which are supported in
Langchain, but has several advantages such as
- Batching and Beam search support
- More complete JSON Schema support
- LLM has control over whitespace, improving quality
- Better runtime performance due to only calling the LLM's generate()
function once per generate() call.
The integration is loosely based on the jsonformer integration in terms
of project structure.
## Dependencies
No compile-time dependency was added, but if `lm-format-enforcer` is not
installed, a runtime error will occur if it is trying to be used.
## Tests
Due to the integration modifying the internal parameters of the
underlying huggingface transformer LLM, it is not possible to test
without building a real LM, which requires internet access. So, similar
to the jsonformer and RELLM integrations, the testing is via the
notebook.
## Twitter Handle
[@noamgat](https://twitter.com/noamgat)
Looking forward to hearing feedback!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Best to review one commit at a time, since two of the commits are 100%
autogenerated changes from running `ruff format`:
- Install and use `ruff format` instead of black for code formatting.
- Output of `ruff format .` in the `langchain` package.
- Use `ruff format` in experimental package.
- Format changes in experimental package by `ruff format`.
- Manual formatting fixes to make `ruff .` pass.
I always take 20-30 seconds to re-discover where the
`convert_to_openai_function` wrapper lives in our codebase. Chat
langchain [has no
clue](https://smith.langchain.com/public/3989d687-18c7-4108-958e-96e88803da86/r)
what to do either. There's the older `create_openai_fn_chain` , but we
haven't been recommending it in LCEL. The example we show in the
[cookbook](https://python.langchain.com/docs/expression_language/how_to/binding#attaching-openai-functions)
is really verbose.
General function calling should be as simple as possible to do, so this
seems a bit more ergonomic to me (feel free to disagree). Another option
would be to directly coerce directly in the class's init (or when
calling invoke), if provided. I'm not 100% set against that. That
approach may be too easy but not simple. This PR feels like a decent
compromise between simple and easy.
```
from enum import Enum
from typing import Optional
from pydantic import BaseModel, Field
class Category(str, Enum):
"""The category of the issue."""
bug = "bug"
nit = "nit"
improvement = "improvement"
other = "other"
class IssueClassification(BaseModel):
"""Classify an issue."""
category: Category
other_description: Optional[str] = Field(
description="If classified as 'other', the suggested other category"
)
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI().bind_functions([IssueClassification])
llm.invoke("This PR adds a convenience wrapper to the bind argument")
# AIMessage(content='', additional_kwargs={'function_call': {'name': 'IssueClassification', 'arguments': '{\n "category": "improvement"\n}'}})
```
- Prefer lambda type annotations over inferred dict schema
- For sequences that start with RunnableAssign infer seq input type as
"input type of 2nd item in sequence - output type of runnable assign"
Replace this entire comment with:
-Add MultiOn close function and update key value and add async
functionality
- solved the key value TabId not found.. (updated to use latest key
value)
@hwchase17
- **Description:** This pull request removes secrets present in raw
format,
- **Issue:** Fireworks api key was exposed when printing out the
langchain object
[#12165](https://github.com/langchain-ai/langchain/issues/12165)
- **Maintainer:** @eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Textract PDF Loader generating linearized output,
meaning it will replicate the structure of the source document as close
as possible based on the features passed into the call (e. g. LAYOUT,
FORMS, TABLES). With LAYOUT reading order for multi-column documents or
identification of lists and figures is supported and with TABLES it will
generate the table structure as well. FORMS will indicate "key: value"
with columms.
- **Issue:** the issue fixes#12068
- **Dependencies:** amazon-textract-textractor is added, which provides
the linearization
- **Tag maintainer:** @3coins
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
can get the correct token count instead of using gpt-2 model
**Description:**
Implement get_num_tokens within VertexLLM to use google's count_tokens
function.
(https://cloud.google.com/vertex-ai/docs/generative-ai/get-token-count).
So we don't need to download gpt-2 model from huggingface, also when we
do the mapreduce chain we can get correct token count.
**Tag maintainer:**
@lkuligin
**Twitter handle:**
My twitter: @abehsu1992626
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Following this tutoral about using OpenAI Embeddings with FAISS
https://python.langchain.com/docs/integrations/vectorstores/faiss
```python
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader("../../../extras/modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
```
This works fine
```python
db = FAISS.from_documents(docs, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
```
But the async version is not
```python
db = await FAISS.afrom_documents(docs, embeddings) # NotImplementedError
query = "What did the president say about Ketanji Brown Jackson"
docs = await db.asimilarity_search(query) # this will use await asyncio.get_event_loop().run_in_executor under the hood and will not call OpenAIEmbeddings.aembed_query but call OpenAIEmbeddings.embed_query
```
So this PR add async/await supports for FAISS
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- Description: adding support to Activeloop's DeepMemory feature that
boosts recall up to 25%. Added Jupyter notebook showcasing the feature
and also made index params explicit.
- Twitter handle: will really appreciate if we could announce this on
twitter.
---------
Co-authored-by: adolkhan <adilkhan.sarsen@alumni.nu.edu.kz>
Hey, we're looking to invest more in adding cohere integrations to
langchain so would love to get more of an idea for how it's used.
Hopefully this pr is acceptable. This week I'm also going to be looking
into adding our new [retrieval augmented generation
product](https://txt.cohere.com/chat-with-rag/) to langchain.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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## **Description:**
When building our own readthedocs.io scraper, we noticed a couple
interesting things:
1. Text lines with a lot of nested <span> tags would give unclean text
with a bunch of newlines. For example, for [Langchain's
documentation](https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.readthedocs.ReadTheDocsLoader.html#langchain.document_loaders.readthedocs.ReadTheDocsLoader),
a single line is represented in a complicated nested HTML structure, and
the naive `soup.get_text()` call currently being made will create a
newline for each nested HTML element. Therefore, the document loader
would give a messy, newline-separated blob of text. This would be true
in a lot of cases.
<img width="945" alt="Screenshot 2023-10-26 at 6 15 39 PM"
src="https://github.com/langchain-ai/langchain/assets/44193474/eca85d1f-d2bf-4487-a18a-e1e732fadf19">
<img width="1031" alt="Screenshot 2023-10-26 at 6 16 00 PM"
src="https://github.com/langchain-ai/langchain/assets/44193474/035938a0-9892-4f6a-83cd-0d7b409b00a3">
Additionally, content from iframes, code from scripts, css from styles,
etc. will be gotten if it's a subclass of the selector (which happens
more often than you'd think). For example, [this
page](https://pydeck.gl/gallery/contour_layer.html#) will scrape 1.5
million characters of content that looks like this:
<img width="1372" alt="Screenshot 2023-10-26 at 6 32 55 PM"
src="https://github.com/langchain-ai/langchain/assets/44193474/dbd89e39-9478-4a18-9e84-f0eb91954eac">
Therefore, I wrote a recursive _get_clean_text(soup) class function that
1. skips all irrelevant elements, and 2. only adds newlines when
necessary.
2. Index pages (like [this
one](https://api.python.langchain.com/en/latest/api_reference.html))
would be loaded, chunked, and eventually embedded. This is really bad
not just because the user will be embedding irrelevant information - but
because index pages are very likely to show up in retrieved content,
making retrieval less effective (in our tests). Therefore, I added a
bool parameter `exclude_index_pages` defaulted to False (which is the
current behavior — although I'd petition to default this to True) that
will skip all pages where links take up 50%+ of the page. Through manual
testing, this seems to be the best threshold.
## Other Information:
- **Issue:** n/a
- **Dependencies:** n/a
- **Tag maintainer:** n/a
- **Twitter handle:** @andrewthezhou
---------
Co-authored-by: Andrew Zhou <andrew@heykona.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:**
* Add unit tests for document_transformers/beautiful_soup_transformer.py
* Basic functionality is tested (extract tags, remove tags, drop lines)
* add a FIXME comment about the order of tags that is not preserved
(and a passing test, but with the expected tags now out-of-order)
- **Issue:** None
- **Dependencies:** None
- **Tag maintainer:** @rlancemartin
- **Twitter handle:** `peter_v`
Please make sure your PR is passing linting and testing before
submitting.
=> OK: I ran `make format`, `make test` (passing after install of
beautifulsoup4) and `make lint`.
- **Description:** Added masking of the API Key for AI21 LLM when
printed and improved the docstring for AI21 LLM.
- Updated the AI21 LLM to utilize SecretStr from pydantic to securely
manage API key.
- Made improvements in the docstring of AI21 LLM. It now mentions that
the API key can also be passed as a named parameter to the constructor.
- Added unit tests.
- **Issue:** #12165
- **Tag maintainer:** @eyurtsev
---------
Co-authored-by: Anirudh Gautam <anirudh@Anirudhs-Mac-mini.local>
Currently this gives a bug:
```
from langchain.schema.runnable import RunnableLambda
bound = RunnableLambda(lambda x: x).with_config({"callbacks": []})
# ConfigError: field "callbacks" not yet prepared so type is still a ForwardRef, you might need to call RunnableConfig.update_forward_refs().
```
Rather than deal with cyclic imports and extra load time, etc., I think
it makes sense to just have a separate Callbacks definition here that is
a relaxed typehint.
1. Allow run evaluators to return {"results": [list of evaluation
results]} in the evaluator callback.
2. Allows run evaluators to pick the target run ID to provide feedback
to
(1) means you could do something like a function call that populates a
full rubric in one go (not sure how reliable that is in general though)
rather than splitting off into separate LLM calls - cheaper and less
code to write
(2) means you can provide feedback to runs on subsequent calls.
Immediate use case is if you wanted to add an evaluator to a chat bot
and assign to assign to previous conversation turns
have a corresponding one in the SDK
In the GoogleSerperResults class, the name field is defined as
'google_serrper_results_json'. This looks like a typo, and perhaps
should be 'google_serper_results_json'.
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- **Description:** a description of the change,
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1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in `docs/extras`
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Add Redis langserve template! Eventually will add semantic caching to
this too. But I was struggling to get that to work for some reason with
the LCEL implementation here.
- **Description:** Introduces the Redis LangServe template. A simple RAG
based app built on top of Redis that allows you to chat with company's
public financial data (Edgar 10k filings)
- **Issue:** None
- **Dependencies:** The template contains the poetry project
requirements to run this template
- **Tag maintainer:** @baskaryan @Spartee
- **Twitter handle:** @tchutch94
**Note**: this requires the commit here that deletes the
`_aget_relevant_documents()` method from the Redis retriever class that
wasn't implemented. That was breaking the langserve app.
---------
Co-authored-by: Sam Partee <sam.partee@redis.com>
-**Description** Adds returning the reranking score when using semantic
search
-**Issue:* #12317
---------
Co-authored-by: Adam Law <adamlaw@microsoft.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Improve handling of empty queries in timescale-vector.
For timescale-vector it is more efficient to get a None embedding when
the embedding has no semantic meaning. It allows timescale-vector to
perform more optimizations. Thus, when the query is empty, use a None
embedding.
Also pass down constructor arguments to the timescale vector client.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This code path is hit in the following case:
- Start in langchain code and manually provide a tracer
- Handoff to the traceable
- Hand back to langchain code.
Which happens for evaluating `@traceable` functions unfortunately
- **Description: To handle the hybrid search with RRF(Reciprocal Rank
Fusion) in the Elasticsearch, rrf argument was added for adjusting
'rank_constant' and 'window_size' to combine multiple result sets with
different relevance indicators into a single result set. (ref:
https://www.elastic.co/kr/blog/whats-new-elastic-enterprise-search-8-9-0),
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** No dependencies changed,
- **Tag maintainer:** @baskaryan,
Nice to meet you,
I'm a newbie for contributions and it's my first PR.
I only changed the langchain/vectorstores/elasticsearch.py file.
I did make format&lint
I got this message,
```shell
make lint_diff
./scripts/check_pydantic.sh .
./scripts/check_imports.sh
poetry run ruff .
[ "langchain/vectorstores/elasticsearch.py" = "" ] || poetry run black langchain/vectorstores/elasticsearch.py --check
All done! ✨🍰✨
1 file would be left unchanged.
[ "langchain/vectorstores/elasticsearch.py" = "" ] || poetry run mypy langchain/vectorstores/elasticsearch.py
langchain/__init__.py: error: Source file found twice under different module names: "mvp.nlp.langchain.libs.langchain.langchain" and "langchain"
Found 1 error in 1 file (errors prevented further checking)
make: *** [lint_diff] Error 2
```
Thank you
---------
Co-authored-by: 황중원 <jwhwang@amorepacific.com>
My postgres out of connections after continuous PGVector usage, and the
reason because it constantly creates new connections, so adding a
reusable pre established connection seems like solves an issue
---------
Co-authored-by: Roman Vasilyev <rvasilyev@mozilla.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
See discussion here:
https://github.com/langchain-ai/langchain/discussions/11680
The code is available for usage from langchain_experimental. The reason
for the deprecation is that the agents are relying on a Python REPL. The
code can only be run safely with appropriate sandboxing.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
The changes introduced in #12267 and #12190 broke the cost computation
of the `completion` tokens for fine-tuned models because of the early
return. This PR aims at fixing this.
@baskaryan.
**Description:**
Revise `libs/langchain/langchain/document_loaders/async_html.py` to
store the HTML Title and Page Language in the `metadata` of
`AsyncHtmlLoader`.
Compare predicted json to reference. First canonicalize (sort keys, rm
whitespace separators), then return normalized string edit distance.
Not a silver bullet but maybe an easy way to capture structure
differences in a less flakey way
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Will run all CI because of _test change, but future PRs against CLI will
only trigger the new CLI one
Has a bunch of file changes related to formatting/linting.
No mypy yet - coming soon
**Description**
This small change will make chunk_size a configurable parameter for
loading documents into a Supabase database.
**Issue**
https://github.com/langchain-ai/langchain/issues/11422
**Dependencies**
No chanages
**Twitter**
@ j1philli
**Reminder**
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
---------
Co-authored-by: Greg Richardson <greg.nmr@gmail.com>
Description
* Add _generate and _agenerate to support Fireworks batching.
* Add stop words test cases
* Opt out retry mechanism
Issue - Not applicable
Dependencies - None
Tag maintainer - @baskaryan
- **Description:** refactors the redis vector field schema to properly
handle default values, includes a new unit test suite.
- **Issue:** N/A
- **Dependencies:** nothing new.
- **Tag maintainer:** @baskaryan @Spartee
- **Twitter handle:** this is a tiny fix/improvement :)
This issue was causing some clients/cuatomers issues when building a
vector index on Redis on smaller db instances (due to fault default
values in index configuration). It would raise an error like:
```redis.exceptions.ResponseError: Vector index initial capacity 20000 exceeded server limit (852 with the given parameters)```
This PR will address this moving forward.
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This PR replaces the previous `Intent` check with the new `Prompt
Safety` check. The logic and steps to enable chain moderation via the
Amazon Comprehend service, allowing you to detect and redact PII, Toxic,
and Prompt Safety information in the LLM prompt or answer remains
unchanged.
This implementation updates the code and configuration types with
respect to `Prompt Safety`.
### Usage sample
```python
from langchain_experimental.comprehend_moderation import (BaseModerationConfig,
ModerationPromptSafetyConfig,
ModerationPiiConfig,
ModerationToxicityConfig
)
pii_config = ModerationPiiConfig(
labels=["SSN"],
redact=True,
mask_character="X"
)
toxicity_config = ModerationToxicityConfig(
threshold=0.5
)
prompt_safety_config = ModerationPromptSafetyConfig(
threshold=0.5
)
moderation_config = BaseModerationConfig(
filters=[pii_config, toxicity_config, prompt_safety_config]
)
comp_moderation_with_config = AmazonComprehendModerationChain(
moderation_config=moderation_config, #specify the configuration
client=comprehend_client, #optionally pass the Boto3 Client
verbose=True
)
template = """Question: {question}
Answer:"""
prompt = PromptTemplate(template=template, input_variables=["question"])
responses = [
"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.",
"Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here."
]
llm = FakeListLLM(responses=responses)
llm_chain = LLMChain(prompt=prompt, llm=llm)
chain = (
prompt
| comp_moderation_with_config
| {llm_chain.input_keys[0]: lambda x: x['output'] }
| llm_chain
| { "input": lambda x: x['text'] }
| comp_moderation_with_config
)
try:
response = chain.invoke({"question": "A sample SSN number looks like this 123-456-7890. Can you give me some more samples?"})
except Exception as e:
print(str(e))
else:
print(response['output'])
```
### Output
```python
> Entering new AmazonComprehendModerationChain chain...
Running AmazonComprehendModerationChain...
Running pii Validation...
Running toxicity Validation...
Running prompt safety Validation...
> Finished chain.
> Entering new AmazonComprehendModerationChain chain...
Running AmazonComprehendModerationChain...
Running pii Validation...
Running toxicity Validation...
Running prompt safety Validation...
> Finished chain.
Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like XXXXXXXXXXXX John Doe's phone number is (999)253-9876.
```
---------
Co-authored-by: Jha <nikjha@amazon.com>
Co-authored-by: Anjan Biswas <anjanavb@amazon.com>
Co-authored-by: Anjan Biswas <84933469+anjanvb@users.noreply.github.com>
**Description:**
This PR adds support for the [Pro version of Titan Takeoff
Server](https://docs.titanml.co/docs/category/pro-features). Users of
the Pro version will have to import the TitanTakeoffPro model, which is
different from TitanTakeoff.
**Issue:**
Also minor fixes to docs for Titan Takeoff (Community version)
**Dependencies:**
No additional dependencies
**Twitter handle:** @becoming_blake
@baskaryan @hwchase17
- **Description:**
This PR adds `allowd_operators` property to `QdrantTranslator` to fix
the `TypeError: can only join an iterable` bug. This property is
required in `get_query_constructor_prompt` in
`query_constructor\base.py`:
```
allowed_operators=" | ".join(allowed_operators),
```
- **Issue:**
#12061
---------
Co-authored-by: XIE Qihui <qihui.xie@bopufund.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Jacob Lee <jacoblee93@gmail.com>
If user function is wrapped as a traceable function, this will help hand
off the trace between the two.
Also update handling fields to reflect optional values
- **Description**: Fix for the SPARQL QA chain: fixed SPARQL queries for
retrieving information about relations in the graph to create a textual
description of the schema for the language model. This should resolve
#8907
- **Issue**: #8907
- **Dependencies**: None
- **Tag maintainer**: @baskaryan, @hwchase17
**Description:** When llms output leading or trailing whitespace for xml
(when using XMLOutputParser) the parser would raise a `ValueError: Could
not parse output: ...`. However, leading or trailing whitespace are
"ignorable" in the sense of XML standard.
**Issue:** I did not find an issue related.
**Dependencies:** None
**Tag maintainer:**
**Twitter handle:** donatoaz
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
Done, updated unit test and ran `make docker_test`.
- **Description:** Response parser for arcee retriever,
- **Issue:** follow-up pr on #11578 and
[discussion](https://github.com/arcee-ai/arcee-python/issues/15#issuecomment-1759874053),
- **Dependencies:** NA
This pr implements a parser for the response from ArceeRetreiver to
convert to langchain `Document`. This closes the loop of generation and
retrieval for Arcee DALMs in langchain.
The reference for the response parser is
[api-docs:retrieve](https://api.arcee.ai/docs#/v2/retrieve_model)
Attaching screenshot of working implementation:
<img width="1984" alt="Screenshot 2023-10-25 at 7 42 34 PM"
src="https://github.com/langchain-ai/langchain/assets/65639964/026987b9-34b2-4e4b-b87d-69fcd0c6641a">
\*api key deleted
---
Successful tests, lints, etc.
```shell
Re-run pytest with --snapshot-update to delete unused snapshots.
==================================================================================================================== slowest 5 durations =====================================================================================================================
1.56s call tests/unit_tests/schema/runnable/test_runnable.py::test_retrying
0.63s call tests/unit_tests/schema/runnable/test_runnable.py::test_map_astream
0.33s call tests/unit_tests/schema/runnable/test_runnable.py::test_map_stream_iterator_input
0.30s call tests/unit_tests/schema/runnable/test_runnable.py::test_map_astream_iterator_input
0.20s call tests/unit_tests/indexes/test_indexing.py::test_cleanup_with_different_batchsize
======================================================================================================= 1265 passed, 270 skipped, 32 warnings in 6.55s =======================================================================================================
[ "." = "" ] || poetry run black .
All done! ✨🍰✨
1871 files left unchanged.
[ "." = "" ] || poetry run ruff --select I --fix .
./scripts/check_pydantic.sh .
./scripts/check_imports.sh
poetry run ruff .
[ "." = "" ] || poetry run black . --check
All done! ✨🍰✨
1871 files would be left unchanged.
[ "." = "" ] || poetry run mypy .
Success: no issues found in 1868 source files
poetry run codespell --toml pyproject.toml
poetry run codespell --toml pyproject.toml -w
```
Co-authored-by: Shubham Kushwaha <shwu@Shubhams-MacBook-Pro.local>
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**Description:**
Documents further usage of RetrievalQAWithSourcesChain in an existing
test. I'd not found much documented usage of RetrievalQAWithSourcesChain
and how to get the sources out. This additional code will hopefully be
useful to other potential users of this retriever.
**Issue:** No raised issue
**Dependencies:** No new dependencies needed to run the test (it already
needs `open-ai`, `faiss-cpu` and `unstructured`).
Note - `make lint` showed 8 linting errors in unrelated files
---------
Co-authored-by: richarda23 <richard.c.adams@infinityworks.com>
If I go traceable -> runnable when the project is manually specified,
the runnable wont be logged. This makes sure the session/project is
threaded through appropriately.
This PR adds a data [E2B's](https://e2b.dev/) analysis/code interpreter
sandbox as a tool
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Jakub Novak <jakub@e2b.dev>
* Add a type literal for the generation and sub-classes for serialization purposes.
* Fix the root validator of ChatGeneration to return ValueError instead of KeyError or Attribute error if intialized improperly.
* This change is done for langserve to make sure that llm related callbacks can be serialized/deserialized properly.
Fix Description:
For Redis Vector integration in add_texts method, there were two issues
that lead to this bug.
1. Vector index is not being created leading to no such_index error
2. `doc:index` prefix was also missing for Redis Keys.
resolves#11197
Maintainer: @baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:**
Add cost calculation for fine tuned models (new and legacy), this is
required after OpenAI added new models for fine tuning and separated the
costs of I/O for fine tuned models.
Also I updated the relevant unit tests
see https://platform.openai.com/docs/guides/fine-tuning for more
information.
issue: https://github.com/langchain-ai/langchain/issues/11715
- **Issue:** 11715
- **Twitter handle:** @nirkopler
- replace `requests` package with `langchain.requests`
- add `_acall` support
- add `_stream` and `_astream`
- freshen up the documentation a bit
- update vendor doc
Allows for passing arguments into the LLM chains used by the
GraphCypherQAChain. This is to address a request by a user to include
memory in the Cypher creating chain. Will keep the prompt variables
as-is to be backward compatible. But, would be a good idea to deprecate
them and use the **kwargs variables. Added a test case.
In general, I think it would be good for any chain to automatically pass
in a readonlymemory(of its input) to its subchains whilist allowing for
an override. But, this would be a different change.
- **Description:**
Add missing apostrophe in `user's` in stuff_prompt's system_template.
The first sentence in the system template went from:
> Use the following pieces of context to answer the users question.
to
> Use the following pieces of context to answer the user's question.
- **Issue:**
- **Dependencies:** none
- **Tag maintainer:** @baskaryan
- **Twitter handle:** ojohnnyo
- This is used internally to gather aggregate usage metrics for the
LangChain integrations
- Note: This cannot be added to some of the Vertex AI integrations at
this time because the SDK doesn't allow overriding the
[`ClientInfo`](https://googleapis.dev/python/google-api-core/latest/client_info.html#module-google.api_core.client_info)
- Added to:
- BigQuery
- Google Cloud Storage
- Document AI
- Vertex AI Model Garden
- Document AI Warehouse
- Vertex AI Search
- Vertex AI Matching Engine (Cloud Storage Client)
@baskaryan, @eyurtsev, @hwchase17
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- **Description:** In the max_marginal_relevance_search function of the
ElasticsearchStore vector store, the name of the field corresponding to
the vector embedding of the document is hard coded in the delete
statement that drops the field from the document metadata. This results
in an exception if the vector embedding field is customized. This PR
changes the hard-coded "vector" into the vector_query_field variable.
- **Issue:** None
- **Dependencies:** None
- **Tag maintainer:** @hwchase17
Co-authored-by: Shilong Dai <sdai@viperfish.net>
**Description: Allow to inject boto3 client for Cross account access
type of scenarios in using SagemakerEndpointEmbeddings and also updated
the documentation for same in the sample notebook**
**Issue:SagemakerEndpointEmbeddings cross account capability #10634
#10184**
Dependencies: None
Tag maintainer:
Twitter handle:lethargicoder
Co-authored-by: Vikram(VS) <vssht@amazon.com>
- **Description:** sqlalchemy create_engine() does not take into account
connect_args which are mandatory for managed PGSQL instances on cloud
providers (ssl_context for example).
Also re-enabled create_vector_extension at post_init for using pgvector
class seamlessly
- **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17.
---------
Co-authored-by: Sami Bargaoui <bargaoui.sam@gmail.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
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If non-pickleable objects (like locks) get passed to the tracing
callback, they'll fail in the deepcopy. Fallback to a shallow copy in
these instances .
We don't use any of the new functionality at the moment. Just making
sure we don't fall back on versions and fail to benefit from new
patches. This is an easy upgrade and it's always harder to upgrade
across multiple major versions at once.
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Adding Tavily Search API as a tool. I will be the maintainer and
assaf_elovic is the twitter handler.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Current ChatTongyi is not compatible with DashScope API, which will
cause error when passing api key to chat model directly.
- **Description:** Update tongyi.py to be compatible with DashScope API.
Specifically, update parameter name "dashscope_api_key" to "api_key".
- **Issue:** None.
- **Dependencies:** Nothing new, Tongyi would require DashScope as
before.
- **Description:** Implementing the Google Scholar Tool as requested in
PR #11505. The tool will be using the [serpapi python
package](https://serpapi.com/integrations/python#search-google-scholar).
The main idea of the tool will be to return the results from a Google
Scholar search given a query as an input to the tool.
- **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17
- Fixes error:
```
ValueError: "GoogleVertexAISearchRetriever" object has no field "_serving_config"
```
Introduced in #11736
@baskaryan, @eyurtsev, @hwchase17 if you could review and merge quickly,
that would be appreciated :)
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- **Description:** The return info in the documentation for
similarity_search_by_vector and similarity_search_with_relevance_scores
is wrong
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This reverts commit a46eef64a7.
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- **Description:** Provide a way to use different text for embedding.
- For example, if you are ingesting stack-overflow Q&As for RAG, you
would want to embed the questions and return the answer(s) for the hits.
With this change, the consumer of langchain can implement that easily.
- I noticed the similar function is added on faiss.py with #1912 which
was for performance reason, but I see the same function can be used to
achieve what I thought. So instead of changing Document class to have
embedding_content, I mimicked the implementation of faiss.py.
- The test should provide some guidance on how to use it. It would be
more intuitive if I just pass texts and embedding_texts as separate
arguments, but I chose to use `zip`-ed object for the consistency with
faiss.py implementation.
- I plan to make similar pull request for OpenSearch.
- **Issue:** N/A
- **Dependencies:** None other than the existing ones.
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Adding Pydantic v2 support for OpenAPI Specs
- **Issue:**
- OpenAPI spec support was disabled because `openapi-schema-pydantic`
doesn't support Pydantic v2:
#9205
- Caused errors in `get_openapi_chain`
- This may be the cause of #9520.
- **Tag maintainer:** @eyurtsev
- **Twitter handle:** kreneskyp
The root cause was that `openapi-schema-pydantic` hasn't been updated in
some time but
[openapi-pydantic](https://github.com/mike-oakley/openapi-pydantic)
forked and updated the project.
Updated the elasticsearch self query retriever to use the match clause
for LIKE operator instead of the non-analyzed fuzzy search clause.
Other small updates include:
- fixing the stack inference integration test where the index's default
pipeline didn't use the inference pipeline created
- adding a user-agent to the old implementation to track usage
- improved the documentation for ElasticsearchStore filters
### Description:
To provide an eas llm service access methods in this pull request by
impletementing `PaiEasEndpoint` and `PaiEasChatEndpoint` classes in
`langchain.llms` and `langchain.chat_models` modules. Base on this pr,
langchain users can build up a chain to call remote eas llm service and
get the llm inference results.
### About EAS Service
EAS is a Alicloud product on Alibaba Cloud Machine Learning Platform for
AI which is short for AliCloud PAI. EAS provides model inference
deployment services for the users. We build up a llm inference services
on EAS with a general llm docker images. Therefore, end users can
quickly setup their llm remote instances to load majority of the
hugginface llm models, and serve as a backend for most of the llm apps.
### Dependencies
This pr does't involve any new dependencies.
---------
Co-authored-by: 子洪 <gaoyihong.gyh@alibaba-inc.com>
Description: Supported RetryOutputParser & RetryWithErrorOutputParser
max_retries
- max_retries: Maximum number of retries to parser.
Issue: None
Dependencies: None
Tag maintainer: @baskaryan
Twitter handle:
We now require uses to have the pip package `llmonitor` installed. It
allows us to have cleaner code and avoid duplicates between our library
and our code in Langchain.
FAISS does not implement embeddings method and use embed_query to
embedding texts which is wrong for some embedding models.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
feat: Raise KeyError when 'prompt' key is missing in JSON response
This commit updates the error handling in the code to raise a KeyError
when the 'prompt' key is not found in the JSON response. This change
makes the code more explicit about the nature of the error, helping to
improve clarity and debugging.
@baskaryan, @eyurtsev.
I may be missing something but it seems like we inappropriately overrode
the 'stream()' method, losing callbacks in the process. I don't think
(?) it gave us anything in this case to customize it here?
See new trace:
https://smith.langchain.com/public/fbb82825-3a16-446b-8207-35622358db3b/r
and confirmed it streams.
Also fixes the stopwords issues from #12000
- **Description:** According to the document
https://cloud.baidu.com/doc/WENXINWORKSHOP/s/clntwmv7t, add ERNIE-Bot-4
model support for ErnieBotChat.
- **Dependencies:** Before using the ERNIE-Bot-4, you should have the
model's access authority.
By default replace input_variables with the correct value
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.dict() is a Pydantic method that cannot raise exceptions, as it is used
eg. in `__eq__`
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Type hinting `*args` as `List[Any]` means that each positional argument
should be a list. Type hinting `**kwargs` as `Dict[str, Any]` means that
each keyword argument should be a dict of strings.
This is almost never what we actually wanted, and doesn't seem to be
what we want in any of the cases I'm replacing here.
- Update Zep Memory and Retriever docstrings
- Zep Memory Retriever: Add support for native MMR
- Add MMR example to existing ZepRetriever Notebook
@baskaryan
Example
```
from langchain.schema.runnable import RunnableLambda
from langsmith import traceable
chain = RunnableLambda(lambda x: x)
@traceable(run_type = "chain")
def my_traceable(a):
chain.invoke(a)
my_traceable(5)
```
Would have a nested result.
This would NOT work for interleaving chains and traceables. E.g., things
like thiswould still not work well
```
from langchain.schema.runnable import RunnableLambda
from langsmith import traceable
@traceable()
def other_traceable(a):
return a
def foo(x):
return other_traceable(x)
chain = RunnableLambda(foo)
@traceable(run_type = "chain")
def my_traceable(a):
chain.invoke(a)
my_traceable(5)
```
Minor lint dependency version upgrade to pick up latest functionality.
Ruff's new v0.1 version comes with lots of nice features, like
fix-safety guarantees and a preview mode for not-yet-stable features:
https://astral.sh/blog/ruff-v0.1.0
- **Description:** Chroma >= 0.4.10 added support for batch sizes
validation of add/upsert. This batch size is dependent on the SQLite
limits of the target system and varies. In this change, for
Chroma>=0.4.10 batch splitting was added as the aforementioned
validation is starting to surface in the Chroma community (users using
LC)
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** @eyurtsev
- **Twitter handle:** t_azarov
Description: A large language models developed by Baichuan Intelligent
Technology,https://www.baichuan-ai.com/home
Issue: None
Dependencies: None
Tag maintainer:
Twitter handle:
This adds security notices to toolkits init, and to several toolkits.
We'll need to continue documenting the rest of the toolkits.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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the updated value was:
` Criteria.MISOGYNY: "Is the submission misogynistic? If so, respond Y."
`
The " If so, respond Y." should not be here. This sub-string is not
presented in any other criteria and should not be presented here.
I also added a synonym to "misogynistic" as it done in many other
criteria.
**Description**
- Added the `SingleStoreDBChatMessageHistory` class that inherits
`BaseChatMessageHistory` and allows to use of a SingleStoreDB database
as a storage for chat message history.
- Added integration test to check that everything works (requires
`singlestoredb` to be installed)
- Added notebook with usage example
- Removed custom retriever for SingleStoreDB vector store (as it is
useless)
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
## Description
| Tool | Original Tool Name |
|-----------------------------|---------------------------|
| open-meteo-api | Open Meteo API |
| news-api | News API |
| tmdb-api | TMDB API |
| podcast-api | Podcast API |
| golden_query | Golden Query |
| dall-e-image-generator | Dall-E Image Generator |
| twilio | Text Message |
| searx_search_results | Searx Search Results |
| dataforseo | DataForSeo Results JSON |
When using these tools through `load_tools`, I encountered the following
validation error:
```console
openai.error.InvalidRequestError: 'TMDB API' does not match '^[a-zA-Z0-9_-]{1,64}$' - 'functions.0.name'
```
In order to avoid this error, I replaced spaces with hyphens in the tool
names:
| Tool | Corrected Tool Name |
|-----------------------------|---------------------------|
| open-meteo-api | Open-Meteo-API |
| news-api | News-API |
| tmdb-api | TMDB-API |
| podcast-api | Podcast-API |
| golden_query | Golden-Query |
| dall-e-image-generator | Dall-E-Image-Generator |
| twilio | Text-Message |
| searx_search_results | Searx-Search-Results |
| dataforseo | DataForSeo-Results-JSON |
This correction resolved the validation error.
Additionally, a unit test,
`tests/unit_tests/schema/runnable/test_runnable.py::test_stream_log_retriever`,
was failing at random. Upon further investigation, I confirmed that the
failure was not related to the above-mentioned changes. The `stream_log`
variable was generating the order of logs in two ways at random The
reason for this behavior is unclear, but in the assertion, I included
both possible orders to account for this variability.
Hello Folks,
Alibaba Cloud OpenSearch has released a new version of the vector
storage engine, which has significantly improved performance compared to
the previous version. At the same time, the sdk has also undergone
changes, requiring adjustments alibaba opensearch vector store code to
adapt.
This PR includes:
Adapt to the latest version of Alibaba Cloud OpenSearch API.
More comprehensive unit testing.
Improve documentation.
I have read your contributing guidelines. And I have passed the tests
below
- [x] make format
- [x] make lint
- [x] make coverage
- [x] make test
---------
Co-authored-by: zhaoshengbo <shengbo.zsb@alibaba-inc.com>
**Description:**
While working on the Docusaurus site loader #9138, I noticed some
outdated docs and tests for the Sitemap Loader.
**Issue:**
This is tangentially related to #6691 in reference to doc links. I plan
on digging in to a few of these issue when I find time next.
- **Description:** added examples to Vertex chat models as optional
class attributes, so that a model with examples can be used inside a
chain
- **Twitter handle:** lkuligin
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---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Related to #10800
- Errors in the Docstring of GradientLLM / Gradient.ai LLM
- Renamed the `model_id` to `model` and adapting this in all tests.
Reason to so is to be in Sync with `GradientEmbeddings` and other LLM's.
- inmproving tests so they check the headers in the sent request.
- making the aiosession a private attribute in the docs, as in the
future `pip install gradientai` will be replacing aiosession.
- adding a example how to fine-tune on the Prompt Template as suggested
in #10800
- Description: Adds the ChatEverlyAI class with llama-2 7b on [EverlyAI
Hosted
Endpoints](https://everlyai.xyz/)
- It inherits from ChatOpenAI and requires openai (probably unnecessary
but it made for a quick and easy implementation)
---------
Co-authored-by: everly-studio <127131037+everly-studio@users.noreply.github.com>
- **Description:**
- If the Elasticsearch field used for Langchain > Document.page_content
is missing because the specific document is
somehow malformed fail gracefully.
- **Tag maintainer:**
- @joemcelroy
Reverts langchain-ai/langchain#11714
This has linting and formatting issues, plus it's added to chat models
folder but doesn't subclass Chat Model base class
Motivation and Context
At present, the Baichuan Large Language Model is relatively popular and
efficient in performance. Due to widespread market recognition, this
model has been added to enhance the scalability of Langchain's ability
to access the big language model, so as to facilitate application access
and usage for interested users.
System Info
langchain: 0.0.295
python:3.8.3
IDE:vs code
Description
Add the following files:
1. Add baichuan_baichuaninc_endpoint.py in the
libs/langchain/langchain/chat_models
2. Modify the __init__.py file,which is located in the
libs/langchain/langchain/chat_models/__init__.py:
a. Add "from langchain.chat_models.baichuan_baichuaninc_endpoint import
BaichuanChatEndpoint"
b. Add "BaichuanChatEndpoint" In the file's __ All__ method
Your contribution
I am willing to help implement this feature and submit a PR, but I would
appreciate guidance from the maintainers or community to ensure the
changes are made correctly and in line with the project's standards and
practices.
- **Description:** Add `TrainableLLM` for those LLM support fine-tuning
- **Tag maintainer:** @hwchase17
This PR add training methods to `GradientLLM`
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Hi there
This PR is aim to implement chat model for Alibaba Tongyi LLM model. It
contains work below:
1.Implement ChatTongyi chat model in langchain.chat_models.tongyi. Note
this is different with tongyi llm model to another PR
https://github.com/langchain-ai/langchain/pull/10878.
For detail it implements _generate() and _stream() function in
ChatTongyi.
2. Add some examples in chat/tongyi.ipynb.
3. Add integration test in chat_models/test_tongyi.py
Note async completion for the Text API is not yet supported.
Dependencies: dashscope. It will be installed manually cause it is not
need by everyone.
**Description**
This PR adds the `ElasticsearchChatMessageHistory` implementation that
stores chat message history in the configured
[Elasticsearch](https://www.elastic.co/elasticsearch/) deployment.
```python
from langchain.memory.chat_message_histories import ElasticsearchChatMessageHistory
history = ElasticsearchChatMessageHistory(
es_url="https://my-elasticsearch-deployment-url:9200", index="chat-history-index", session_id="123"
)
history.add_ai_message("This is me, the AI")
history.add_user_message("This is me, the human")
```
**Dependencies**
- [elasticsearch client](https://elasticsearch-py.readthedocs.io/)
required
Co-authored-by: Bagatur <baskaryan@gmail.com>
Instead of accessing `langchain.debug`, `langchain.verbose`, or
`langchain.llm_cache`, please use the new getter/setter functions in
`langchain.globals`:
- `langchain.globals.set_debug()` and `langchain.globals.get_debug()`
- `langchain.globals.set_verbose()` and
`langchain.globals.get_verbose()`
- `langchain.globals.set_llm_cache()` and
`langchain.globals.get_llm_cache()`
Using the old globals directly will now raise a warning.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:**
Add a document loader for the RSpace Electronic Lab Notebook
(www.researchspace.com), so that scientific documents and research notes
can be easily pulled into Langchain pipelines.
**Issue**
This is an new contribution, rather than an issue fix.
**Dependencies:**
There are no new required dependencies.
In order to use the loader, clients will need to install rspace_client
SDK using `pip install rspace_client`
---------
Co-authored-by: richarda23 <richard.c.adams@infinityworks.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Update Indexing API docs to specify vectorstores that
are compatible with the Indexing API. I add a unit test to remind
developers to update the documentation whenever they add or change a
vectorstore in a way that affects compatibility. For the unit test I
repurposed existing code from
[here](https://github.com/langchain-ai/langchain/blob/v0.0.311/libs/langchain/langchain/indexes/_api.py#L245-L257).
This is my first PR to an open source project. This is a trivially
simple PR whose main purpose is to make me more comfortable submitting
Langchain PRs. If this PR goes through I plan to submit PRs with more
substantive changes in the near future.
**Issue:** Resolves
[10482](https://github.com/langchain-ai/langchain/discussions/10482).
**Dependencies:** No new dependencies.
**Twitter handle:** None.
Allows MMR functionality only for the case where we have access to the
embedding function. Also allows for users to request for fields from
elasticsearch store. These are added to the document metadata.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: Introducing an ability to load a transcription document of
audio file using [Yandex
SpeechKit](https://cloud.yandex.com/en-ru/services/speechkit)
Issue: None
Dependencies: yandex-speechkit
Tag maintainer: @rlancemartin, @eyurtsev
**Description**
This PR implements the usage of the correct tokenizer in Bedrock LLMs,
if using anthropic models.
**Issue:** #11560
**Dependencies:** optional dependency on `anthropic` python library.
**Twitter handle:** jtolgyesi
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Modify Anyscale integration to work with [Anyscale
Endpoint](https://docs.endpoints.anyscale.com/)
and it supports invoke, async invoke, stream and async invoke features
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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2. an example notebook showing its use. It lives in `docs/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
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Should delegate to parse_result, not to aparse, as parse_result is a
method that some output parsers override
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**Description:** Avoid huggingfacepipeline to truncate the response if
user setup return_full_text as False within huggingface pipeline.
**Dependencies:** : None
**Tag maintainer:** Maybe @sam-h-bean ?
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** implements a retriever on top of DocAI Warehouse (to
interact with existing enterprise documents)
https://cloud.google.com/document-ai-warehouse?hl=en
- **Issue:** new functionality
@baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
No relevant documents may be found for a given question. In some use
cases, we could directly respond with a fixed message instead of doing
an LLM call with an empty context. This PR exposes this as an option:
response_if_no_docs_found.
---------
Co-authored-by: Sudharsan Rangarajan <sudranga@nile-global.com>
Replace this entire comment with:
- **Description:** In this modified version of the function, if the
metadatas parameter is not None, the function includes the corresponding
metadata in the JSON object for each text. This allows the metadata to
be stored alongside the text's embedding in the vector store.
-
- **Issue:** #10924
- **Dependencies:** None
- **Tag maintainer:** @hwchase17
@agola11
- **Twitter handle:** @MelliJoaco
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** fixed a bug in pal-chain when it reports Python
code validation errors. When node.func does not have any ids, the
original code tried to print node.func.id in raising ValueError.
- **Issue:** n/a,
- **Dependencies:** no dependencies,
- **Tag maintainer:** @hazzel-cn, @eyurtsev
- **Twitter handle:** @lazyswamp
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
I am merely making some minor adjustments to the function documentation.
I hope to provide a small assistance to LangChain.
- **Description:** Change the docs of JSONAgentOutputParser. It will be
`JSON` better,
- **Issue:** no,
- **Dependencies:** no,
- **Tag maintainer:** @hwchase17,
- **Twitter handle:** Not worth mentioning.
**Description:** This PR adds support for ChatOpenAI models in the
Infino callback handler. In particular, this PR implements
`on_chat_model_start` callback, so that ChatOpenAI models are supported.
With this change, Infino callback handler can be used to track latency,
errors, and prompt tokens for ChatOpenAI models too (in addition to the
support for OpenAI and other non-chat models it has today). The existing
example notebook is updated to show how to use this integration as well.
cc/ @naman-modi @savannahar68
**Issue:** https://github.com/langchain-ai/langchain/issues/11607
**Dependencies:** None
**Tag maintainer:** @hwchase17
**Twitter handle:** [@vkakade](https://twitter.com/vkakade)
This PR adds support for the Azure Cosmos DB MongoDB vCore Vector Store
https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/vector-search
Summary:
- **Description:** added vector store integration for Azure Cosmos DB
MongoDB vCore Vector Store,
- **Issue:** the issue # it fixes#11627,
- **Dependencies:** pymongo dependency,
- **Tag maintainer:** @hwchase17,
- **Twitter handle:** @izzyacademy
---------
Co-authored-by: Israel Ekpo <israel.ekpo@gmail.com>
Co-authored-by: Israel Ekpo <44282278+izzyacademy@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
* Should use non chunked messages for Invoke/Batch
* After this PR, stream output type is not represented, do we want to
use the union?
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Adds standard `type` field for all messages that will be
serialized/validated by pydantic.
* The presence of `type` makes it easier for developers consuming
schemas to write client code to serialize/deserialize.
* In LangServe `type` will be used for both validation and will appear
in the generated openapi specs
Preventing error caused by attempting to move the model that was already
loaded on the GPU using the Accelerate module to the same or another
device. It is not possible to load model with Accelerate/PEFT to CPU for
now
Addresses:
[#10985](https://github.com/langchain-ai/langchain/issues/10985)
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- **Description:** This is an update to OctoAI LLM provider that adds
support for llama2 endpoints hosted on OctoAI and updates MPT-7b url
with the current one.
@baskaryan
Thanks!
---------
Co-authored-by: ML Wiz <bassemgeorgi@gmail.com>
**Description:** I noticed the metadata returned by the url_selenium
loader was missing several values included by the web_base loader. (The
former returned `{source: ...}`, the latter returned `{source: ...,
title: ..., description: ..., language: ...}`.) This change fixes it so
both loaders return all 4 key value pairs.
Files have been properly formatted and all tests are passing. Note,
however, that I am not much of a python expert, so that whole "Adding
the imports inside the code so that tests pass" thing seems weird to me.
Please LMK if I did anything wrong.
- **Description:** Assigning the custom_llm_provider to the default
params function so that it will be passed to the litellm
- **Issue:** Even though the custom_llm_provider argument is being
defined it's not being assigned anywhere in the code and hence its not
being passed to litellm, therefore any litellm call which uses the
custom_llm_provider as required parameter is being failed. This
parameter is mainly used by litellm when we are doing inference via
Custom API server.
https://docs.litellm.ai/docs/providers/custom_openai_proxy
- **Dependencies:** No dependencies are required
@krrishdholakia , @baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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- **Description:** This PR introduces a new LLM and Retriever API to
https://arcee.ai for the python client
- **Issue:** implements the integrations as requested in #11578 ,
- **Dependencies:** no dependencies are required,
- **Tag maintainer:** @hwchase17
- **Twitter handle:** shwooobham
**✅ `make format`, `make lint` and `make test` runs locally.**
```shell
=========== 1245 passed, 277 skipped, 20 warnings in 16.26s ===========
./scripts/check_pydantic.sh .
./scripts/check_imports.sh
poetry run ruff .
[ "." = "" ] || poetry run black . --check
All done! ✨🍰✨
1818 files would be left unchanged.
[ "." = "" ] || poetry run mypy .
Success: no issues found in 1815 source files
[ "." = "" ] || poetry run black .
All done! ✨🍰✨
1818 files left unchanged.
[ "." = "" ] || poetry run ruff --select I --fix .
poetry run codespell --toml pyproject.toml
poetry run codespell --toml pyproject.toml -w
```
**Contributions**
1. Arcee (langchain/llms), ArceeRetriever (langchain/retrievers),
ArceeWrapper (langchain/utilities)
2. docs for Arcee (llms/arcee.py) and
ArceeRetriever(retrievers/arcee.py)
3.
cc: @jacobsolawetz @ben-epstein
---------
Co-authored-by: Shubham <shubham@sORo.local>
jinja2 templates are not sandboxed and are at risk for arbitrary code
execution. To mitigate this risk:
- We no longer support loading jinja2-formatted prompt template files.
- `PromptTemplate` with jinja2 may still be constructed manually, but
the class carries a security warning reminding the user to not pass
untrusted input into it.
Resolves#4394.
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- **Issue:** the issue # it fixes (if applicable),
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tests, lint, etc:
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network access,
2. an example notebook showing its use. It lives in `docs/extras`
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**Description:** CohereRerank is missing `cohere_api_key` as a field and
since extras are forbidden, it is not possible to pass-in the key. The
only way is to use an env variable named `COHERE_API_KEY`.
For example, if trying to create a compressor like this:
```python
cohere_api_key = "......Cohere api key......"
compressor = CohereRerank(cohere_api_key=cohere_api_key)
```
you will get the following error:
```
File "/langchain/.venv/lib/python3.10/site-packages/pydantic/v1/main.py", line 341, in __init__
raise validation_error
pydantic.v1.error_wrappers.ValidationError: 1 validation error for CohereRerank
cohere_api_key
extra fields not permitted (type=value_error.extra)
```
- **Description:** Fixes minor typo for the
query_sql_database_tool_description in the db toolkit
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** @nfcampos
- **Twitter handle:** N/A
LangChain relies on NumPy to compute cosine distances, which becomes a
bottleneck with the growing dimensionality and number of embeddings. To
avoid this bottleneck, in our libraries at
[Unum](https://github.com/unum-cloud), we have created a specialized
package - [SimSIMD](https://github.com/ashvardanian/simsimd), that knows
how to use newer hardware capabilities. Compared to SciPy and NumPy, it
reaches 3x-200x performance for various data types. Since publication,
several LangChain users have asked me if I can integrate it into
LangChain to accelerate their workflows, so here I am 🤗
## Benchmarking
To conduct benchmarks locally, run this in your Jupyter:
```py
import numpy as np
import scipy as sp
import simsimd as simd
import timeit as tt
def cosine_similarity_np(X: np.ndarray, Y: np.ndarray) -> np.ndarray:
X_norm = np.linalg.norm(X, axis=1)
Y_norm = np.linalg.norm(Y, axis=1)
with np.errstate(divide="ignore", invalid="ignore"):
similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm)
similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0
return similarity
def cosine_similarity_sp(X: np.ndarray, Y: np.ndarray) -> np.ndarray:
return 1 - sp.spatial.distance.cdist(X, Y, metric='cosine')
def cosine_similarity_simd(X: np.ndarray, Y: np.ndarray) -> np.ndarray:
return 1 - simd.cdist(X, Y, metric='cosine')
X = np.random.randn(1, 1536).astype(np.float32)
Y = np.random.randn(1, 1536).astype(np.float32)
repeat = 1000
print("NumPy: {:,.0f} ops/s, SciPy: {:,.0f} ops/s, SimSIMD: {:,.0f} ops/s".format(
repeat / tt.timeit(lambda: cosine_similarity_np(X, Y), number=repeat),
repeat / tt.timeit(lambda: cosine_similarity_sp(X, Y), number=repeat),
repeat / tt.timeit(lambda: cosine_similarity_simd(X, Y), number=repeat),
))
```
## Results
I ran this on an M2 Pro Macbook for various data types and different
number of rows in `X` and reformatted the results as a table for
readability:
| Data Type | NumPy | SciPy | SimSIMD |
| :--- | ---: | ---: | ---: |
| `f32, 1` | 59,114 ops/s | 80,330 ops/s | 475,351 ops/s |
| `f16, 1` | 32,880 ops/s | 82,420 ops/s | 650,177 ops/s |
| `i8, 1` | 47,916 ops/s | 115,084 ops/s | 866,958 ops/s |
| `f32, 10` | 40,135 ops/s | 24,305 ops/s | 185,373 ops/s |
| `f16, 10` | 7,041 ops/s | 17,596 ops/s | 192,058 ops/s |
| `f16, 10` | 21,989 ops/s | 25,064 ops/s | 619,131 ops/s |
| `f32, 100` | 3,536 ops/s | 3,094 ops/s | 24,206 ops/s |
| `f16, 100` | 900 ops/s | 2,014 ops/s | 23,364 ops/s |
| `i8, 100` | 5,510 ops/s | 3,214 ops/s | 143,922 ops/s |
It's important to note that SimSIMD will underperform if both matrices
are huge.
That, however, seems to be an uncommon usage pattern for LangChain
users.
You can find a much more detailed performance report for different
hardware models here:
- [Apple M2
Pro](https://ashvardanian.com/posts/simsimd-faster-scipy/#appendix-1-performance-on-apple-m2-pro).
- [4th Gen Intel Xeon
Platinum](https://ashvardanian.com/posts/simsimd-faster-scipy/#appendix-2-performance-on-4th-gen-intel-xeon-platinum-8480).
- [AWS Graviton
3](https://ashvardanian.com/posts/simsimd-faster-scipy/#appendix-3-performance-on-aws-graviton-3).
## Additional Notes
1. Previous version used `X = np.array(X)`, to repackage lists of lists.
It's an anti-pattern, as it will use double-precision floating-point
numbers, which are slow on both CPUs and GPUs. I have replaced it with
`X = np.array(X, dtype=np.float32)`, but a more selective approach
should be discussed.
2. In numerical computations, it's recommended to explicitly define
tolerance levels, which were previously avoided in
`np.allclose(expected, actual)` calls. For now, I've set absolute
tolerance to distance computation errors as 0.01: `np.allclose(expected,
actual, atol=1e-2)`.
---
- **Dependencies:** adds `simsimd` dependency
- **Tag maintainer:** @hwchase17
- **Twitter handle:** @ashvardanian
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
#### Description
This PR adds the option to specify additional metadata columns in the
CSVLoader beyond just `Source`.
The current CSV loader includes all columns in `page_content` and if we
want to have columns specified for `page_content` and `metadata` we have
to do something like the below.:
```
csv = pd.read_csv(
"path_to_csv"
).to_dict("records")
documents = [
Document(
page_content=doc["content"],
metadata={
"last_modified_by": doc["last_modified_by"],
"point_of_contact": doc["point_of_contact"],
}
) for doc in csv
]
```
#### Usage
Example Usage:
```
csv_test = CSVLoader(
file_path="path_to_csv",
metadata_columns=["last_modified_by", "point_of_contact"]
)
```
Example CSV:
```
content, last_modified_by, point_of_contact
"hello world", "Person A", "Person B"
```
Example Result:
```
Document {
page_content: "hello world"
metadata: {
row: '0',
source: 'path_to_csv',
last_modified_by: 'Person A',
point_of_contact: 'Person B',
}
```
---------
Co-authored-by: Ben Chello <bchello@dropbox.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Fixes the comments in the ConvoOutputParser. Because
the \\\\ is escaping a single \\, they render something like:
`"action_input": string \ The input to the action` in the prompt.
Changing this to \\\\\\\\ lets it escape two slashes so that it renders
a proper comment: `"action_input": string \\ The input to the action`
- **Issue:** N/A
- **Dependencies:**
- **Tag maintainer:** @hwchase17
- **Twitter handle:**
**Description**:
- Added Momento Vector Index (MVI) as a vector store provider. This
includes an implementation with docstrings, integration tests, a
notebook, and documentation on the docs pages.
- Updated the Momento dependency in pyproject.toml and the lock file to
enable access to MVI.
- Refactored the Momento cache and chat history session store to prefer
using "MOMENTO_API_KEY" over "MOMENTO_AUTH_TOKEN" for consistency with
MVI. This change is backwards compatible with the previous "auth_token"
variable usage. Updated the code and tests accordingly.
**Dependencies**:
- Updated Momento dependency in pyproject.toml.
**Testing**:
- Run the integration tests with a Momento API key. Get one at the
[Momento Console](https://console.gomomento.com) for free. MVI is
available in AWS us-west-2 with a superuser key.
- `MOMENTO_API_KEY=<your key> poetry run pytest
tests/integration_tests/vectorstores/test_momento_vector_index.py`
**Tag maintainer:**
@eyurtsev
**Twitter handle**:
Please mention @momentohq for this addition to langchain. With the
integration of Momento Vector Index, Momento caching, and session store,
Momento provides serverless support for the core langchain data needs.
Also mention @mlonml for the integration.
Wraps every callback handler method in error handlers to avoid breaking
users' programs when an error occurs inside the handler.
Thanks @valdo99 for the suggestion 🙂
[The `duckduckgo-search` v3.9.2 was removed from
PyPi](https://pypi.org/project/duckduckgo-search/#history). That breaks
the build.
- **Description:** refreshes the Poetry dependency to v3.9.3
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @ashvardanian
updating query constructor and self query retriever to
- make it easier to pass in examples
- validate attributes used in query
- remove invalid parts of query
- make it easier to get + edit prompt
- make query constructor a runnable
- make self query retriever use as runnable
- keep alias for RunnableMap
- update docs to use RunnableParallel and RunnablePassthrough.assign
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1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in `docs/extras`
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- **Description:** Add support for a SQLRecordManager in async
environments. It includes the creation of `RecorManagerAsync` abstract
class.
- **Issue:** None
- **Dependencies:** Optional `aiosqlite`.
- **Tag maintainer:** @nfcampos
- **Twitter handle:** @jvelezmagic
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Use `.copy()` to fix the bug that the first `llm_inputs` element is
overwritten by the second `llm_inputs` element in `intermediate_steps`.
***Problem description:***
In [line 127](
c732d8fffd/libs/experimental/langchain_experimental/sql/base.py (L127C17-L127C17)),
the `llm_inputs` of the sql generation step is appended as the first
element of `intermediate_steps`:
```
intermediate_steps.append(llm_inputs) # input: sql generation
```
However, `llm_inputs` is a mutable dict, it is updated in [line
179](https://github.com/langchain-ai/langchain/blob/master/libs/experimental/langchain_experimental/sql/base.py#L179)
for the final answer step:
```
llm_inputs["input"] = input_text
```
Then, the updated `llm_inputs` is appended as another element of
`intermediate_steps` in [line
180](c732d8fffd/libs/experimental/langchain_experimental/sql/base.py (L180)):
```
intermediate_steps.append(llm_inputs) # input: final answer
```
As a result, the final `intermediate_steps` returned in [line
189](c732d8fffd/libs/experimental/langchain_experimental/sql/base.py (L189C43-L189C43))
actually contains two same `llm_inputs` elements, i.e., the `llm_inputs`
for the sql generation step overwritten by the one for final answer step
by mistake. Users are not able to get the actual `llm_inputs` for the
sql generation step from `intermediate_steps`
Simply calling `.copy()` when appending `llm_inputs` to
`intermediate_steps` can solve this problem.
### Description
This pull request involves modifications to the extraction method for
abstracts/summaries within the PubMed utility. A condition has been
added to verify the presence of unlabeled abstracts. Now an abstract
will be extracted even if it does not have a subtitle. In addition, the
extraction of the abstract was extended to books.
### Issue
The PubMed utility occasionally returns an empty result when extracting
abstracts from articles, despite the presence of an abstract for the
paper on PubMed. This issue arises due to the varying structure of
articles; some articles follow a "subtitle/label: text" format, while
others do not include subtitles in their abstracts. An example of the
latter case can be found at:
[https://pubmed.ncbi.nlm.nih.gov/37666905/](url)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Description
SelfQueryRetriever is missing async support, so I am adding it.
I also removed deprecated predict_and_parse method usage here, and added
some tests.
### Issue
N/A
### Tag maintainer
Not yet
### Twitter handle
N/A
**Description**
It is for #10423 that it will be a useful feature if we can extract
images from pdf and recognize text on them. I have implemented it with
`PyPDFLoader`, `PyPDFium2Loader`, `PyPDFDirectoryLoader`,
`PyMuPDFLoader`, `PDFMinerLoader`, and `PDFPlumberLoader`.
[RapidOCR](https://github.com/RapidAI/RapidOCR.git) is used to recognize
text on extracted images. It is time-consuming for ocr so a boolen
parameter `extract_images` is set to control whether to extract and
recognize. I have tested the time usage for each parser on my own laptop
thinkbook 14+ with AMD R7-6800H by unit test and the result is:
| extract_images | PyPDFParser | PDFMinerParser | PyMuPDFParser |
PyPDFium2Parser | PDFPlumberParser |
| ------------- | ------------- | ------------- | ------------- |
------------- | ------------- |
| False | 0.27s | 0.39s | 0.06s | 0.08s | 1.01s |
| True | 17.01s | 20.67s | 20.32s | 19,75s | 20.55s |
**Issue**
#10423
**Dependencies**
rapidocr_onnxruntime in
[RapidOCR](https://github.com/RapidAI/RapidOCR/tree/main)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: The previous version of the MarkdownHeaderTextSplitter
did not take into account the possibility of '#' appearing within code
blocks, which caused segmentation anomalies in these situations. This PR
has fixed this issue.
- Issue:
- Dependencies: No
- Tag maintainer:
- Twitter handle:
cc @baskaryan @eyurtsev @rlancemartin
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: This PR adds a new chain `rl_chain.PickBest` for learned
prompt variable injection, detailed description and usage can be found
in the example notebook added. It essentially adds a
[VowpalWabbit](https://github.com/VowpalWabbit/vowpal_wabbit) layer
before the llm call in order to learn or personalize prompt variable
selections.
Most of the code is to make the API simple and provide lots of defaults
and data wrangling that is needed to use Vowpal Wabbit, so that the user
of the chain doesn't have to worry about it.
- Dependencies:
[vowpal-wabbit-next](https://pypi.org/project/vowpal-wabbit-next/),
- sentence-transformers (already a dep)
- numpy (already a dep)
- tagging @ataymano who contributed to this chain
- Tag maintainer: @baskaryan
- Twitter handle: @olgavrou
Added example notebook and unit tests
Replace this entire comment with:
- **Description:** minor update to constructor to allow for
specification of "source"
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @ofermend
# Description
Attempts to fix RedisCache for ChatGenerations using `loads` and `dumps`
used in SQLAlchemy cache by @hwchase17 . this is better than pickle
dump, because this won't execute any arbitrary code during
de-serialisation.
# Issues
#7722 & #8666
# Dependencies
None, but removes the warning introduced in #8041 by @baskaryan
Handle: @jaikanthjay46
- Description: Updated output parser for mrkl to remove any
hallucination actions after the final answer; this was encountered when
using Anthropic claude v2 for planning; reopening PR with updated unit
tests
- Issue: #10278
- Dependencies: N/A
- Twitter handle: @johnreynolds
Description: this PR changes the `ArcGISLoader` to set
`return_all_records` to `False` when `result_record_count` is provided
as a keyword argument. Previously, `return_all_records` was `True` by
default and this made the API ignore `result_record_count`.
Issue: `ArcGISLoader` would ignore `result_record_count` unless user
also passed `return_all_records=False`.
- **Description:** Fix the `PyMuPDFLoader` to accept `loader_kwargs`
from the document loader's `loader_kwargs` option. This provides more
flexibility in formatting the output from documents.
- **Issue:** The `loader_kwargs` is not passed into the `load` method
from the document loader, which limits configuration options.
- **Dependencies:** None
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
I have restructured the code to ensure uniform handling of ImportError.
In place of previously used ValueError, I've adopted the standard
practice of raising ImportError with explanatory messages. This
modification enhances code readability and clarifies that any problems
stem from module importation.
### Description
Add instance anonymization - if `John Doe` will appear twice in the
text, it will be treated as the same entity.
The difference between `PresidioAnonymizer` and
`PresidioReversibleAnonymizer` is that only the second one has a
built-in memory, so it will remember anonymization mapping for multiple
texts:
```
>>> anonymizer = PresidioAnonymizer()
>>> anonymizer.anonymize("My name is John Doe. Hi John Doe!")
'My name is Noah Rhodes. Hi Noah Rhodes!'
>>> anonymizer.anonymize("My name is John Doe. Hi John Doe!")
'My name is Brett Russell. Hi Brett Russell!'
```
```
>>> anonymizer = PresidioReversibleAnonymizer()
>>> anonymizer.anonymize("My name is John Doe. Hi John Doe!")
'My name is Noah Rhodes. Hi Noah Rhodes!'
>>> anonymizer.anonymize("My name is John Doe. Hi John Doe!")
'My name is Noah Rhodes. Hi Noah Rhodes!'
```
### Twitter handle
@deepsense_ai / @MaksOpp
### Tag maintainer
@baskaryan @hwchase17 @hinthornw
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
PyPDF does not chunk at the character level to my understanding.
Description: PyPDF does not chunk at the character level, but instead
breaks up content by page. Fixup comment
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: There are cases when the output from the LLM comes fine
(i.e. function_call["arguments"] is a valid JSON object), but it does
not contain the key "actions". So I split the validation in 2 steps:
loading arguments as JSON and then checking for "actions" in it.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: Google Cloud Enterprise Search was renamed to Vertex AI
Search
-
https://cloud.google.com/blog/products/ai-machine-learning/vertex-ai-search-and-conversation-is-now-generally-available
- This PR updates the documentation and Retriever class to use the new
terminology.
- Changed retriever class from `GoogleCloudEnterpriseSearchRetriever` to
`GoogleVertexAISearchRetriever`
- Updated documentation to specify that `extractive_segments` requires
the new [Enterprise
edition](https://cloud.google.com/generative-ai-app-builder/docs/about-advanced-features#enterprise-features)
to be enabled.
- Fixed spelling errors in documentation.
- Change parameter for Retriever from `search_engine_id` to
`data_store_id`
- When this retriever was originally implemented, there was no
distinction between a data store and search engine, but now these have
been split.
- Fixed an issue blocking some users where the api_endpoint can't be set
### Description
When using Weaviate Self-Retrievers, certain common filter comparators
generated by user queries were unimplemented, resulting in errors. This
PR implements some of them. All linting and format commands have been
run and tests passed.
### Issue
#10474
### Dependencies
timestamp module
---------
Co-authored-by: Patrick Randell <prandell@deloitte.com.au>
**Description:** Previously if the access to Azure Cognitive Search was
not done via an API key, the default credential was called which doesn't
allow to use an interactive login. I simply added the option to use
"INTERACTIVE" as a key name, and this will launch a login window upon
initialization of the AzureSearch object.
I was hoping this would pick up numpy 1.26, which is required to support
the new Python 3.12 release, but it didn't. It seems that some
transitive dependency requirement on numpy is preventing that, and the
highest we can currently go is 1.24.x.
But to find this out required a 15min `poetry lock`, so I figured we
might as well upgrade the dependencies we can and hopefully make the
next dependency upgrade a bit smaller.
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Consolidating to a single README for now, will be easier to maintain we
can differentiate between poetry and pip later. Does not seem critical.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
First version of CLI command to create a new langchain project template
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
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## Description
Currently SQLAlchemy >=1.4.0 is a hard requirement. We are unable to run
`from langchain.vectorstores import FAISS` with SQLAlchemy <1.4.0 due to
top-level imports, even if we aren't even using parts of the library
that use SQLAlchemy. See Testing section for repro. Let's make it so
that langchain is still compatible with SQLAlchemy <1.4.0, especially if
we aren't using parts of langchain that require it.
The main conflict is that SQLAlchemy removed `declarative_base` from
`sqlalchemy.ext.declarative` in 1.4.0 and moved it to `sqlalchemy.orm`.
We can fix this by try-catching the import. This is the same fix as
applied in https://github.com/langchain-ai/langchain/pull/883.
(I see that there seems to be some refactoring going on about isolating
dependencies, e.g.
c87e9fb2ce,
so if this issue will be eventually fixed by isolating imports in
langchain.vectorstores that also works).
## Issue
I can't find a matching issue.
## Dependencies
No additional dependencies
## Maintainer
@hwchase17 since you reviewed
https://github.com/langchain-ai/langchain/pull/883
## Testing
I didn't add a test, but I manually tested this.
1. Current failure:
```
langchain==0.0.305
sqlalchemy==1.3.24
```
``` python
python -i
>>> from langchain.vectorstores import FAISS
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/pay/src/zoolander/vendor3/lib/python3.8/site-packages/langchain/vectorstores/__init__.py", line 58, in <module>
from langchain.vectorstores.pgembedding import PGEmbedding
File "/pay/src/zoolander/vendor3/lib/python3.8/site-packages/langchain/vectorstores/pgembedding.py", line 10, in <module>
from sqlalchemy.orm import Session, declarative_base, relationship
ImportError: cannot import name 'declarative_base' from 'sqlalchemy.orm' (/pay/src/zoolander/vendor3/lib/python3.8/site-packages/sqlalchemy/orm/__init__.py)
```
2. This fix:
```
langchain==<this PR>
sqlalchemy==1.3.24
```
``` python
python -i
>>> from langchain.vectorstores import FAISS
<succeeds>
```
- Make logs a dictionary keyed by run name (and counter for repeats)
- Ensure no output shows up in lc_serializable format
- Fix up repr for RunLog and RunLogPatch
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- default MessagesPlaceholder one to list of messages
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Removes human prompt prefix before system message for anthropic models
Bedrock anthropic api enforces that Human and Assistant messages must be
interleaved (cannot have same type twice in a row). We currently treat
System Messages as human messages when converting messages -> string
prompt. Our validation when using Bedrock/BedrockChat raises an error
when this happens. For ChatAnthropic we don't validate this so no error
is raised, but perhaps the behavior is still suboptimal
**Description:**
Added support for Cohere command model via Bedrock.
With this change it is now possible to use the `cohere.command-text-v14`
model via Bedrock API.
About Streaming: Cohere model outputs 2 additional chunks at the end of
the text being generated via streaming: a chunk containing the text
`<EOS_TOKEN>`, and a chunk indicating the end of the stream. In this
implementation I chose to ignore both chunks. An alternative solution
could be to replace `<EOS_TOKEN>` with `\n`
Tests: manually tested that the new model work with both
`llm.generate()` and `llm.stream()`.
Tested with `temperature`, `p` and `stop` parameters.
**Issue:** #11181
**Dependencies:** No new dependencies
**Tag maintainer:** @baskaryan
**Twitter handle:** mangelino
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Description: Similar in concept to the `MarkdownHeaderTextSplitter`, the
`HTMLHeaderTextSplitter` is a "structure-aware" chunker that splits text
at the element level and adds metadata for each header "relevant" to any
given chunk. It can return chunks element by element or combine elements
with the same metadata, with the objectives of (a) keeping related text
grouped (more or less) semantically and (b) preserving context-rich
information encoded in document structures. It can be used with other
text splitters as part of a chunking pipeline.
Dependency: lxml python package
Maintainer: @hwchase17
Twitter handle: @MartinZirulnik
---------
Co-authored-by: PresidioVantage <github@presidiovantage.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
I've refactored the code to ensure that ImportError is consistently
handled. Instead of using ValueError as before, I've now followed the
standard practice of raising ImportError along with clear and
informative error messages. This change enhances the code's clarity and
explicitly signifies that any problems are associated with module
imports.
Add device to GPT4All
- **Description:** GPT4All now supports GPU. This commit adds the option
to enable it.
- **Issue:** It closes
https://github.com/langchain-ai/langchain/issues/10486
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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- **Description:** Adds Kotlin language to `TextSplitter`
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
For external libraries that depend on `type_to_cls_dict`, adds a
workaround to continue using the old format.
Recommend people use `get_type_to_cls_dict()` instead and only resolve
the imports when they're used.
- **Description:** use term keyword according to the official python doc
glossary, see https://docs.python.org/3/glossary.html
- **Issue:** not applicable
- **Dependencies:** not applicable
- **Tag maintainer:** @hwchase17
- **Twitter handle:** vreyespue
The previous API of the `_execute()` function had a few rough edges that
this PR addresses:
- The `fetch` argument was type-hinted as being able to take any string,
but any string other than `"all"` or `"one"` would `raise ValueError`.
The new type hints explicitly declare that only those values are
supported.
- The return type was type-hinted as `Sequence` but using `fetch =
"one"` would actually return a single result item. This was incorrectly
suppressed using `# type: ignore`. We now always return a list.
- Using `fetch = "one"` would return a single item if data was found, or
an empty *list* if no data was found. This was confusing, and we now
always return a list to simplify.
- The return type was `Sequence[Any]` which was a bit difficult to use
since it wasn't clear what one could do with the returned rows. I'm
making the new type `Dict[str, Any]` that corresponds to the column
names and their values in the query.
I've updated the use of this method elsewhere in the file to match the
new behavior.
continuation of PR #8550
@hwchase17 please see and merge. And also close the PR #8550.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Instead of:
```
client = Client()
with collect_runs() as cb:
chain.invoke()
run = cb.traced_runs[0]
client.get_run_url(run)
```
it's
```
with tracing_v2_enabled() as cb:
chain.invoke()
cb.get_run_url()
```
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---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Similarly to Vertex classes, PaLM classes weren't marked as
serialisable. Should be working fine with LangSmith.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
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This PR uses 2 dedicated LangChain warnings types for deprecations
(mirroring python's built in deprecation and pending deprecation
warnings).
These deprecation types are unslienced during initialization in
langchain achieving the same default behavior that we have with our
current warnings approach. However, because these warnings have a
dedicated type, users will be able to silence them selectively (I think
this is strictly better than our current handling of warnings).
The PR adds a deprecation warning to llm symbolic math.
---------
Co-authored-by: Predrag Gruevski <2348618+obi1kenobi@users.noreply.github.com>
- Also move RunnableBranch to its own file
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### Description
renamed several repository links from `hwchase17` to `langchain-ai`.
### Why
I discovered that the README file in the devcontainer contains an old
repository name, so I took the opportunity to rename the old repository
name in all files within the repository, excluding those that do not
require changes.
### Dependencies
none
### Tag maintainer
@baskaryan
### Twitter handle
[kzk_maeda](https://twitter.com/kzk_maeda)
**Description:** Adds streaming and many more sampling parameters to the
DeepSparse interface
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Fix a code injection vuln by adding one more keyword
into the filtering list
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:**
- **Twitter handle:**
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Passes through dict input and assigns additional keys
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- **Twitter handle:** we announce bigger features on Twitter. If your PR
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Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
See contribution guidelines for more information on how to write/run
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https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
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If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
<img width="1728" alt="Screenshot 2023-09-28 at 20 15 01"
src="https://github.com/langchain-ai/langchain/assets/56902/ed0644c3-6db7-41b9-9543-e34fce46d3e5">
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- **Issue:** the issue # it fixes (if applicable),
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Suppress warnings in interactive environments that can arise from users
relying on tab completion (without even using deprecated modules).
jupyter seems to filter warnings by default (at least for me), but
ipython surfaces them all
- **Description:** A Document Loader for MongoDB
- **Issue:** n/a
- **Dependencies:** Motor, the async driver for MongoDB
- **Tag maintainer:** n/a
- **Twitter handle:** pigpenblue
Note that an initial mongodb document loader was created 4 months ago,
but the [PR ](https://github.com/langchain-ai/langchain/pull/4285)was
never pulled in. @leo-gan had commented on that PR, but given it is
extremely far behind the master branch and a ton has changed in
Langchain since then (including repo name and structure), I rewrote the
branch and issued a new PR with the expectation that the old one can be
closed.
Please reference that old PR for comments/context, but it can be closed
in favor of this one. Thanks!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
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```
ChatPromptTemplate(messages=[SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=[], template='You are a nice assistant.')), HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['question'], template='{question}'))])
| RunnableLambda(lambda x: x)
| {
chat: FakeListChatModel(responses=["i'm a chatbot"]),
llm: FakeListLLM(responses=["i'm a textbot"])
}
```
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