- **Description:** The HTMLHeaderTextSplitter Class now explicitly
specifies utf-8 encoding in the part of the split_text_from_file method
that calls the HTMLParser.
- **Issue:** Prevent garbled characters due to differences in encoding
of html files (except for English in particular, I noticed that problem
with Japanese).
- **Dependencies:** No dependencies,
- **Twitter handle:** @i_w__a
Adds the ability to return similarity scores when using
`RetrievalQA.from_chain_type` with `MongoDBAtlasVectorSearch`. Requires
that `return_source_documents=True` is set.
Example use:
```
vector_search = MongoDBAtlasVectorSearch.from_documents(...)
qa = RetrievalQA.from_chain_type(
llm=OpenAI(),
chain_type="stuff",
retriever=vector_search.as_retriever(search_kwargs={"additional": ["similarity_score"]}),
return_source_documents=True
)
...
docs = qa({"query": "..."})
docs["source_documents"][0].metadata["score"] # score will be here
```
I've tested this feature locally, using a MongoDB Atlas Cluster with a
vector search index.
- **Description:** Allow passing run_id to MLflowCallbackHandler to
resume a run instead of creating a new run. Support recording retriever
relevant metrics. Refactor the code to fix some bugs.
---------
Signed-off-by: Serena Ruan <serena.rxy@gmail.com>
In this PR I added a post-processing function to normalize the
embeddings. This happens only if the new `normalize` flag is `True`.
---------
Co-authored-by: taamedag <Davide.Menini@swisscom.com>
- **Description:** Baichuan Chat (with both Baichuan-Turbo and
Baichuan-Turbo-192K models) has updated their APIs. There are breaking
changes. For example, BAICHUAN_SECRET_KEY is removed in the latest API
but is still required in Langchain. Baichuan's Langchain integration
needs to be updated to the latest version.
- **Issue:** #15206
- **Dependencies:** None,
- **Twitter handle:** None
@hwchase17.
Co-authored-by: BaiChuanHelper <wintergyc@WinterGYCs-MacBook-Pro.local>
**Description:**
- Implement `SQLStrStore` and `SQLDocStore` classes that inherits from
`BaseStore` to allow to persist data remotely on a SQL server.
- SQL is widely used and sometimes we do not want to install a caching
solution like Redis.
- Multiple issues/comments complain that there is no easy remote and
persistent solution that are not in memory (users want to replace
InMemoryStore), e.g.,
https://github.com/langchain-ai/langchain/issues/14267,
https://github.com/langchain-ai/langchain/issues/15633,
https://github.com/langchain-ai/langchain/issues/14643,
https://stackoverflow.com/questions/77385587/persist-parentdocumentretriever-of-langchain
- This is particularly painful when wanting to use
`ParentDocumentRetriever `
- This implementation is particularly useful when:
* it's expensive to construct an InMemoryDocstore/dict
* you want to retrieve documents from remote sources
* you just want to reuse existing objects
- This implementation integrates well with PGVector, indeed, when using
PGVector, you already have a SQL instance running. `SQLDocStore` is a
convenient way of using this instance to store documents associated to
vectors. An integration example with ParentDocumentRetriever and
PGVector is provided in docs/docs/integrations/stores/sql.ipynb or
[here](https://github.com/gcheron/langchain/blob/sql-store/docs/docs/integrations/stores/sql.ipynb).
- It persists `str` and `Document` objects but can be easily extended.
**Issue:**
Provide an easy SQL alternative to `InMemoryStore`.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** this PR upgrades the `HuggingFaceHub` LLM:
* support more tasks (`translation` and `conversational`)
* replaced the deprecated `InferenceApi` with `InferenceClient`
* adjusted the overall logic to use the "recommended" model for each
task when no model is provided, and vice-versa.
- **Tag mainter(s)**: @baskaryan @hwchase17
For tracing, if a validation error occurs, currently it is attributed to
the previous step of the chain. It would be nice to have the on_start
and on_error callbacks called for tools when there is a validation error
that occurs to more easily attribute the root-cause
**Description** : New documents loader for visio files (with extension
.vsdx)
A [visio file](https://fr.wikipedia.org/wiki/Microsoft_Visio) (with
extension .vsdx) is associated with Microsoft Visio, a diagram creation
software. It stores information about the structure, layout, and
graphical elements of a diagram. This format facilitates the creation
and sharing of visualizations in areas such as business, engineering,
and computer science.
A Visio file can contain multiple pages. Some of them may serve as the
background for others, and this can occur across multiple layers. This
loader extracts the textual content from each page and its associated
pages, enabling the extraction of all visible text from each page,
similar to what an OCR algorithm would do.
**Dependencies** : xmltodict package
I also added LANGCHAIN_COMET_TRACING to enable the CometLLM tracing
integration similar to other tracing integrations. This is easier for
end-users to enable it rather than importing the callback and pass it
manually.
(This is the same content as
https://github.com/langchain-ai/langchain/pull/14650 but rebased and
squashed as something seems to confuse Github Action).
- **Description:** At the moment it's not possible to include in the
same project langchain-google-vertexai and boto3 (e.g. use bedrock and
vertex in the same application) because of the dependency resolutions
conflict. boto3 is still using urllib3 1.x, meanwhile
langchain-google-vertexai -> types-requests depends on urllib3 2.x. [the
last version of types-requests that allows urllib3 1.x is
2.31.0.6](https://pypi.org/project/types-requests/#description).
In this PR I allow the vertexai package to get that version also.
- **Twitter handle:** nicoloboschi
Description: Added support for asynchronous streaming in the Bedrock
class and corresponding tests.
Primarily:
async def aprepare_output_stream
async def _aprepare_input_and_invoke_stream
async def _astream
async def _acall
I've ensured that the code adheres to the project's linting and
formatting standards by running make format, make lint, and make test.
Issue: #12054, #11589
Dependencies: None
Tag maintainer: @baskaryan
Twitter handle: @dominic_lovric
---------
Co-authored-by: Piyush Jain <piyushjain@duck.com>
Replace this entire comment with:
- **Description:** allow user to define tVector length in PGVector when
creating the embedding store, this allows for later indexing
- **Issue:** #16132
- **Dependencies:** None
**Description:** Add support for querying TigerGraph databases through
the InquiryAI service.
**Issue**: N/A
**Dependencies:** N/A
**Twitter handle:** @TigerGraphDB
there is a case where "coords" does not exist in the "sentence"
therefore, the "split(";")" will lead to error.
we can fix that by adding "if sentence.get("coords") is not None:"
the resulting empty "sbboxes" from this scenario will raise error at
"sbboxes[0]["page"]" because sbboxes are empty.
the PDF from https://pubmed.ncbi.nlm.nih.gov/23970373/ can replicate
those errors.
This pull request integrates the TiDB database into LangChain for
storing message history, marking one of several steps towards a
comprehensive integration of TiDB with LangChain.
A simple usage
```python
from datetime import datetime
from langchain_community.chat_message_histories import TiDBChatMessageHistory
history = TiDBChatMessageHistory(
connection_string="mysql+pymysql://<host>:<PASSWORD>@<host>:4000/<db>?ssl_ca=/etc/ssl/cert.pem&ssl_verify_cert=true&ssl_verify_identity=true",
session_id="code_gen",
earliest_time=datetime.utcnow(), # Optional to set earliest_time to load messages after this time point.
)
history.add_user_message("hi! How's feature going?")
history.add_ai_message("It's almot done")
```
- **Description:** add support for kwargs in`MlflowEmbeddings`
`embed_document()` and `embed_query()` so that all the arguments
required by Cohere API (and others?) can be passed down to the server.
- **Issue:** #15234
- **Dependencies:** MLflow with MLflow Deployments (`pip install
mlflow[genai]`)
**Tests**
Now this code [adapted from the
docs](https://python.langchain.com/docs/integrations/providers/mlflow#embeddings-example)
for the Cohere API works locally.
```python
"""
Setup
-----
export COHERE_API_KEY=...
mlflow deployments start-server --config-path examples/deployments/cohere/config.yaml
Run
---
python /path/to/this/file.py
"""
embeddings = MlflowCohereEmbeddings(target_uri="http://127.0.0.1:5000", endpoint="embeddings")
print(embeddings.embed_query("hello")[:3])
print(embeddings.embed_documents(["hello", "world"])[0][:3])
```
Output
```
[0.060455322, 0.028793335, -0.025848389]
[0.031707764, 0.021057129, -0.009361267]
```
Titan Express model was not supported as a chat model because LangChain
messages were not "translated" to a text prompt.
Co-authored-by: Guillem Orellana Trullols <guillem.orellana_trullols@siemens.com>
Adjusted `deprecate` decorator to make sure decorated async functions
are still recognized as "coroutinefunction" by `inspect`.
Before change, functions such as `LLMChain.acall` which are decorated as
deprecated are not recognized as coroutine functions. After the change,
they are recognized:
```python
import inspect
from langchain import LLMChain
# Is false before change but true after.
inspect.iscoroutinefunction(LLMChain.acall)
```
- **Description:** I removed two queries to the database and left just
one whose results were formatted afterward into other type of schema
(avoided two calls to DB)
- **Issue:** /
- **Dependencies:** /
- **Twitter handle:** @supe_katarina
Enable max inner product for approximate retrieval strategy. For exact
strategy we lack the necessary `maxInnerProduct` function in the
Painless scripting language, this is why we do not add it there.
Similarity docs:
https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html#dense-vector-params
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Joe McElroy <joseph.mcelroy@elastic.co>
Implement similarity function selector for ElasticsearchStore. The
scores coming back from Elasticsearch are already similarities (not
distances) and they are already normalized (see
[docs](https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html#dense-vector-params)).
Hence we leave the scores untouched and just forward them.
This fixes#11539.
However, in hybrid mode (when keyword search and vector search are
involved) Elasticsearch currently returns no scores. This PR adds an
error message around this fact. We need to think a bit more to come up
with a solution for this case.
This PR also corrects a small error in the Elasticsearch integration
test.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Issue:** This is a PR about #16340
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@baskaryan, @eyurtsev, @hwchase17.
-->
Co-authored-by: yuhei.tsunoda <yuhei.tsunoda@brainpad.co.jp>
**Description:**
In this PR, I am adding a `PolygonLastQuote` Tool, which can be used to
get the latest price quote for a given ticker / stock.
Additionally, I've added a Polygon Toolkit, which we can use to
encapsulate future tools that we build for Polygon.
**Twitter handle:** [@virattt](https://twitter.com/virattt)
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- Used to be None, now is just the last chunk
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network access,
2. an example notebook showing its use. It lives in
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-->
fixed multi-query template for Vectara
added self-query template for Vectara
Also added prompt_name parameter to summarization
CC @efriis
**Twitter handle:** @ofermend
Add a version parameter while the method is in beta phase.
The idea is to make it possible to minimize making breaking changes for users while we're iterating on schema.
Once the API is stable we can assign a default version requirement.
- **Description:** Adds a text splitter based on
[Konlpy](https://konlpy.org/en/latest/#start) which is a Python package
for natural language processing (NLP) of the Korean language. (It is
like Spacy or NLTK for Korean)
- **Dependencies:** Konlpy would have to be installed before this
splitter is used,
- **Twitter handle:** @untilhamza
- **Description:** Some text-generation models on huggingface repeat the
prompt in their generated response, but not all do! The tests use "gpt2"
which DOES repeat the prompt and as such, the HuggingFaceHub class is
hardcoded to remove the first few characters of the response (to match
the len(prompt)). However, if you are using a model (such as the very
popular "meta-llama/Llama-2-7b-chat-hf") that DOES NOT repeat the prompt
in it's generated text, then the beginning of the generated text will be
cut off. This code change fixes that bug by first checking whether the
prompt is repeated in the generated response and removing it
conditionally.
- **Issue:** #16232
- **Dependencies:** N/A
- **Twitter handle:** N/A
This PR adds `astream_events` method to Runnables to make it easier to
stream data from arbitrary chains.
* Streaming only works properly in async right now
* One should use `astream()` with if mixing in imperative code as might
be done with tool implementations
* Astream_log has been modified with minimal additive changes, so no
breaking changes are expected
* Underlying callback code / tracing code should be refactored at some
point to handle things more consistently (OK for now)
- ~~[ ] verify event for on_retry~~ does not work until we implement
streaming for retry
- ~~[ ] Any rrenaming? Should we rename "event" to "hook"?~~
- [ ] Any other feedback from community?
- [x] throw NotImplementedError for `RunnableEach` for now
## Example
See this [Example
Notebook](dbbc7fa0d6/docs/docs/modules/agents/how_to/streaming_events.ipynb)
for an example with streaming in the context of an Agent
## Event Hooks Reference
Here is a reference table that shows some events that might be emitted
by the various Runnable objects.
Definitions for some of the Runnable are included after the table.
| event | name | chunk | input | output |
|----------------------|------------------|---------------------------------|-----------------------------------------------|-------------------------------------------------|
| on_chat_model_start | [model name] | | {"messages": [[SystemMessage,
HumanMessage]]} | |
| on_chat_model_stream | [model name] | AIMessageChunk(content="hello")
| | |
| on_chat_model_end | [model name] | | {"messages": [[SystemMessage,
HumanMessage]]} | {"generations": [...], "llm_output": None, ...} |
| on_llm_start | [model name] | | {'input': 'hello'} | |
| on_llm_stream | [model name] | 'Hello' | | |
| on_llm_end | [model name] | | 'Hello human!' |
| on_chain_start | format_docs | | | |
| on_chain_stream | format_docs | "hello world!, goodbye world!" | | |
| on_chain_end | format_docs | | [Document(...)] | "hello world!,
goodbye world!" |
| on_tool_start | some_tool | | {"x": 1, "y": "2"} | |
| on_tool_stream | some_tool | {"x": 1, "y": "2"} | | |
| on_tool_end | some_tool | | | {"x": 1, "y": "2"} |
| on_retriever_start | [retriever name] | | {"query": "hello"} | |
| on_retriever_chunk | [retriever name] | {documents: [...]} | | |
| on_retriever_end | [retriever name] | | {"query": "hello"} |
{documents: [...]} |
| on_prompt_start | [template_name] | | {"question": "hello"} | |
| on_prompt_end | [template_name] | | {"question": "hello"} |
ChatPromptValue(messages: [SystemMessage, ...]) |
Here are declarations associated with the events shown above:
`format_docs`:
```python
def format_docs(docs: List[Document]) -> str:
'''Format the docs.'''
return ", ".join([doc.page_content for doc in docs])
format_docs = RunnableLambda(format_docs)
```
`some_tool`:
```python
@tool
def some_tool(x: int, y: str) -> dict:
'''Some_tool.'''
return {"x": x, "y": y}
```
`prompt`:
```python
template = ChatPromptTemplate.from_messages(
[("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})
```
- **Description:** In Google Vertex AI, Gemini Chat models currently
doesn't have a support for SystemMessage. This PR adds support for it
only if a user provides additional convert_system_message_to_human flag
during model initialization (in this case, SystemMessage would be
prepended to the first HumanMessage). **NOTE:** The implementation is
similar to #14824
- **Twitter handle:** rajesh_thallam
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description:** Gemini model has quite annoying default safety_settings
settings. In addition, current VertexAI class doesn't provide a property
to override such settings.
So, this PR aims to
- add safety_settings property to VertexAI
- fix issue with incorrect LLM output parsing when LLM responds with
appropriate 'blocked' response
- fix issue with incorrect parsing LLM output when Gemini API blocks
prompt itself as inappropriate
- add safety_settings related tests
I'm not enough familiar with langchain code base and guidelines. So, any
comments and/or suggestions are very welcome.
**Issue:** it will likely fix#14841
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
* Removed some env vars not used in langchain package IT
* Added Astra DB env vars in langchain package, used for cache tests
* Added conftest.py to load env vars in langchain_community IT
* Added .env.example in langchain_community IT
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2. an example notebook showing its use. It lives in
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If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
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The timeout function comes in handy when you want to kill longrunning
queries.
The value sanitization removes all lists that are larger than 128
elements. The idea here is to remove embedding properties from results.
- **Description:** As Shell tool is very versatile, while integrating it
into applications as openai functions, developers have no clue about
what command is being executed using the ShellTool. All one can see is:
Summarising my feature request:
1. There's no visibility about what command was executed.
2. There's no mechanism to prevent a command to be executed using
ShellTool, like a y/n human input which can be accepted from user to
proceed with executing the command.,
- **Issue:** the issue #15931 it fixes if applicable,
- **Dependencies:** There isn't any dependancy,
- **Twitter handle:** @krishnashed
- **Description:** Made a small fix for the `SQLDatabase` highlighted in
an issue. The issue pertains to switching schema for different SQL
engines.
- **Issue:** #16023
@baskaryan
- **Description:** Support IN and LIKE comparators in Milvus
self-querying retriever, based on [Boolean Expression
Rules](https://milvus.io/docs/boolean.md)
- **Issue:** No
- **Dependencies:** No
- **Twitter handle:** No
Signed-off-by: ChengZi <chen.zhang@zilliz.com>
- **Description:** This handles the cohere response when documents
aren't included in the response
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A
- bumps package post versions for packages without current unreleased
updates
- will bump package version in release prs associated with packages that
do have changes (mistral, vertex)
- **Description:** Adds MistralAIEmbeddings class for embeddings, using
the new official API.
- **Dependencies:** mistralai
- **Tag maintainer**: @efriis, @hwchase17
- **Twitter handle:** @LMS_David_RS
Create `integrations/text_embedding/mistralai.ipynb`: an example
notebook for MistralAIEmbeddings class
Modify `embeddings/__init__.py`: Import the class
Create `embeddings/mistralai.py`: The embedding class
Create `integration_tests/embeddings/test_mistralai.py`: The test file.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description:**
Implement `adelete` function from `VectorStore` in `Qdrant` to support
other asynchronous flows such as async indexing (`aindex`) which
requires `adelete` to be implemented. Since `Qdrant` can be passed an
async qdrant client, this can be supported easily.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR addresses an issue in OpenAIWhisperParserLocal where requesting
CUDA without availability leads to an AttributeError #15143
Changes:
- Refactored Logic for CUDA Availability: The initialization now
includes a check for CUDA availability. If CUDA is not available, the
code falls back to using the CPU. This ensures seamless operation
without manual intervention.
- Parameterizing Batch Size and Chunk Size: The batch_size and
chunk_size are now configurable parameters, offering greater flexibility
and optimization options based on the specific requirements of the use
case.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:** This new feature enhances the flexibility of pipeline
integration, particularly when working with RESTful APIs.
``JsonRequestsWrapper`` allows for the decoding of JSON output, instead
of the only option for text output.
---------
Co-authored-by: Zhichao HAN <hanzhichao2000@hotmail.com>
Fixed the issue mentioned in #15698 for SlackGetChannel Tool.
@baskaryan.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** add deprecated warning for ErnieBotChat and
ErnieEmbeddings.
- These two classes **lack maintenance** and do not use the sdk provided
by qianfan, which means hard to implement some key feature like
streaming.
- The alternative `langchain_community.chat_models.QianfanChatEndpoint`
and `langchain_community.embeddings.QianfanEmbeddingsEndpoint` can
completely replace these two classes, only need to change configuration
items.
- **Issue:** None,
- **Dependencies:** None,
- **Twitter handle:** None
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**: `zip` is iterator that will only produce result once,
so the previous code will cause the `embeddings` to be an empty list.
**Issue**: I could not find a related issue.
**Dependencies**: this PR does not introduce or affect dependencies.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** docs update following the changes introduced in
#15879
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2. an example notebook showing its use. It lives in
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BigQuery vector search lets you use GoogleSQL to do semantic search,
using vector indexes for fast but approximate results, or using brute
force for exact results.
This PR:
1. Add `metadata[_job_ib]` in Document returned by any similarity search
2. Add `explore_job_stats` to enable users to explore job statistics and
better the debuggability
3. Set the minimum row limit for running create vector index.
## Description
In this update, I addressed the missing implementation for
atransform_document, which is the asynchronous counterpart of
transform_document in Doctran.
### Usage Example:
```py
# Instantiate DoctranPropertyExtractor with specified properties
property_extractor = DoctranPropertyExtractor(properties=properties)
# Asynchronously extract properties from a list of documents
extracted_document = await property_extractor.atransform_documents(
documents, properties=properties
)
# Display metadata of the first extracted document
print(json.dumps(extracted_document[0].metadata, indent=2))
```
## Issue
- Pull request #14525 has caused a break in the aforementioned code.
Instead of removing an asynchronous implementation of a function,
consider implementing a synchronous version alongside it.
- **Description:** Added parenthesis in return statement of
aembed_query() funtion to fix 'coroutine' object is not subscriptable
error.
- **Dependencies:** NA
Co-authored-by: H161961 <Raunak.Raunak@Honeywell.com>
## Feature
- Follow parameter structure as per official documentation
- top level parameters (e.g. model, system, template) will be passed as
top level parameters
- other parameters will be sent in options unless options is provided
## Tests
- Test if top level parameters handled properly
- Test if parameters that are not top level parameters are handled as
options
- Test if options is provided, it will be passed as is
**Description:** Added the new gpt-3.5-turbo-1106 for **finetuned** cost
calculation,
**Issue:** no issue found open
By the information in OpenAI the pricing is the same as the older model
(0613)
- **Description:** Added a `PolygonAPIWrapper` and an initial
`get_last_quote` endpoint, which allows us to get the last price quote
for a given `ticker`. Once merged, I can add a Polygon tool in `tools/`
for agents to use.
- **Twitter handle:** [@virattt](https://twitter.com/virattt)
The Polygon.io Stocks API provides REST endpoints that let you query the
latest market data from all US stock exchanges.
Support [Lantern](https://github.com/lanterndata/lantern) as a new
VectorStore type.
- Added Lantern as VectorStore.
It will support 3 distance functions `l2 squared`, `cosine` and
`hamming` and will use `HNSW` index.
- Added tests
- Added example notebook
**Description**: the "page" mode in the
AzureAIDocumentIntelligenceParser is not accessible due to a wrong
membership test. The mode argument can only be a string (also see the
assertion in the `__init__`: `assert self.mode in ["single", "page",
"object", "markdown"]`, so the check `elif self.mode == ["page"]:`
always fails.
As a result, effectively the "object" mode is used when selecting the
"page" mode, which may lead to errors.
The docstring of the `AzureAIDocumentIntelligenceLoader` also ommitted
the `mode` parameter alltogether, so I added it.
**Issue**: I could not find a related issue (this class is only 3 weeks
old anyways)
**Dependencies**: this PR does not introduce or affect dependencies.
The current demo notebook and examples are not affected because they all
use the default markdown mode.
- **Description:** Azure Cognitive Search vector DB store performs slow
embedding as it does not utilize the batch embedding functionality. This
PR provide a fix to improve the performance of Azure Search class when
adding documents to the vector search,
- **Issue:** #11313 ,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
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-->
- **Description:** Milvus's partition key is an important feature. It
can support multi-tenancy. We hope to introduce this feature.
https://milvus.io/docs/partition_key.md
- **Issue:** No
- **Dependencies:** No
- **Twitter handle:** No
---------
Signed-off-by: ChengZi <chen.zhang@zilliz.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Add support for end_point and transport parameters to the Gemini API
---------
Co-authored-by: yangenfeng <yangenfeng@xiaoniangao.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description:**
Added aembed_documents() and aembed_query() async functions in
HuggingFaceHubEmbeddings class in
langchain_community\embeddings\huggingface_hub.py file. It will support
to make async calls to HuggingFaceHub's
embedding endpoint and generate embeddings asynchronously.
Test Cases: Added test_huggingfacehub_embedding_async_documents() and
test_huggingfacehub_embedding_async_query()
functions in test_huggingface_hub.py file to test the two async
functions created in HuggingFaceHubEmbeddings class.
Documentation: Updated huggingfacehub.ipynb with steps to install
huggingface_hub package and use
HuggingFaceHubEmbeddings.
**Dependencies:** None,
**Twitter handle:** I do not have a Twitter account
---------
Co-authored-by: H161961 <Raunak.Raunak@Honeywell.com>
- **Description:** This PR defines the output parser of
OpenAIFunctionsAgent as an attribute, enabling customization and
subclassing of the parser logic.
- **Issue:** Subclassing is currently impossible as the
`OpenAIFunctionsAgentOutputParser` class is hard coded into the `plan`
and `aplan` methods
- **Dependencies:** None
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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## Feature
- Set additional headers in constructor
- Headers will be sent in post request
This feature is useful if deploying Ollama on a cloud service such as
hugging face, which requires authentication tokens to be passed in the
request header.
## Tests
- Test if header is passed
- Test if header is not passed
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Major changes:
- Rename `wasm_chat.py` to `llama_edge.py`
- Rename the `WasmChatService` class to `ChatService`
- Implement the `stream` interface for `ChatService`
- Add `test_chat_wasm_service_streaming` in the integration test
- Update `llama_edge.ipynb`
---------
Signed-off-by: Xin Liu <sam@secondstate.io>
- **Description:** `AmadeusToolkit` and `AmadeusClosestAirport`
contained a hardcoded call to `ChatOpenAI`. This PR makes it
LLM-independent, while guaranteeing backward compatibility.
- **Issue:** #15847
- **Dependencies:** None
@baskaryan
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**Description:**
Fixes OutputParserException thrown by the output_parser when 'query' is
'Null'.
Replace this entire comment with:
- **Description:** Current implentation of output_parser throws
OutputParserException if the response from the LLM contains `query:
null`. This unfortunately happens for my use case. And since there is no
way to modify the prompt used in SelfQueryRetriever, then we have to fix
it here, so it doesn't crash.
- **Issue:** https://github.com/langchain-ai/langchain/issues/15914
Didn't run tests. `make test` is not working. There is no `test` rule in
the `Makefile`.
Co-authored-by: Jan Horcicka <jhorcick@amazon.com>
- **Description:** The pinecone docstring instructs to pass the
embedding query text causing the warning below. It should be the
embeddings object.
warning message: UserWarning: Passing in `embedding` as a Callable is
deprecated. Please pass in an Embeddings object instead.
- **Issue:** NA
- **Dependencies:** None
@baskaryan
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Community : Modified doc strings and example notebook for Clarifai
Description:
1. Modified doc strings inside clarifai vectorstore class and
embeddings.
2. Modified notebook examples.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This PR fixes an issue where AgentExecutor with RunnableAgent does not allow users to see individual llm tokens if streaming=True is not set explicitly on the underlying chat model.
The majority of this PR is testing code:
1. Create a test chat model that makes it easier to test streaming and
supports AIMessages that include function invocation information.
2. Tests for the chat model
3. Tests for RunnableAgent (previously untested)
4. Tests for openai agent (previously untested)
- **Description:**
`QianfanChatEndpoint` extends `BaseChatModel` as a super class, which
has a default stream implement might concat the MessageChunk with
`__add__`. When call stream(), a ValueError for duplicated key will be
raise.
- **Issues:**
* #13546
* #13548
* merge two single test file related to qianfan.
- **Dependencies:** no
- **Tag maintainer:**
---------
Co-authored-by: root <liujun45@baidu.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
experimental relies on `from langchain_core.runnables.config import
run_in_executor` which was introduced in core 0.1.5.
Updated pyproject dependency as well as minimum version test.
Now the SQL used to delete vector doc from myscale is as follow:
```sql
DELETE FROM collection WHERE id = '1' AND id = '2' AND id = '3'
```
But the expected one should be
```sql
DELETE FROM collection WHERE id IN ('1', '2', '3')
```
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This change fixes the AstraDB logical operator filtering (`$and,`
`$or`).
The `metadata` prefix must not be added if the key is `$and` or `$or`.
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See preview :
https://langchain-git-fork-cbornet-astra-loader-doc-langchain.vercel.app/docs/integrations/document_loaders/astradb
This means that users of astream_log() now get streamed output of
virtually all requested runs, whereas before the only streamed output
would be for the root run and raw llm runs
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- **Description:** This update rectifies an error in the notebook by
changing the input variable from `zhipu_api_key` to `api_key`. It also
includes revisions to comments to improve program readability.
- **Issue:** The input variable in the notebook example should be
`api_key` instead of `zhipu_api_key`.
- **Dependencies:** No additional dependencies are required for this
change.
To ensure quality and standards, we have performed extensive linting and
testing. Commands such as make format, make lint, and make test have
been run from the root of the modified package to ensure compliance with
LangChain's coding standards.
- ArgillaCallbackHandler does not properly set the default values while
initializing. This PR corrects the line.
- Issue: #15531
- Dependencies: Argilla
- Also corrected some dead links.
- **Description:** The `delete_collection` method deletes an entire
collection regardless of custom ID. The `delete` method deletes
everything with the provided custom IDs regardless of collection. It can
be useful to restrict deletion to both the collection and a set of
custom IDs. This change adds support for that by allowing you to
optionally specify that `delete` should be restricted to the collection
defined on the `PGVector` instance.
- **Description:** Includes the PDF ID in the MathPix document metadata.
This is useful in case you need to re-request a processed PDF from the
MathPix API later.
- **Description:** The `error_info['id']` can be cross-referenced with
the MathPix API documentation to get very specific information about why
an error occurred.
- **Description:** This PR is to fix a bug of "system message check" in
langchain_community/ chat_models/tongyi.py
- **Issue:** In term of current logic, if there's no system message in
the chat messages, an error of "System message can only be the first
message." will be wrongly raised.
- **Dependencies:** No.
- **Twitter handle:** I don't have a Twitter account.
- **Description:** This PR is to fix a bug in
semantic_hybrid_search_with_score_and_rerank() function in
langchain_community/vectorstores/azuresearch.py. The hardcoded
"metadata" name is replaced with FIELDS_METADATA variable with an if
block to check if the metadata column exists or not.
- **Issue:** Fixed#15581
- **Dependencies:** No
- **Twitter handle:** None
Co-authored-by: H161961 <Raunak.Raunak@Honeywell.com>
Updates docs and cookbooks to import ChatOpenAI, OpenAI, and OpenAI
Embeddings from `langchain_openai`
There are likely more
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Todo
- [x] copy over integration tests
- [x] update docs with new instructions in #15513
- [x] add linear ticket to bump core -> community, community->langchain,
and core->openai deps
- [ ] (optional): add `pip install langchain-openai` command to each
notebook using it
- [x] Update docstrings to not need `openai` install
- [x] Add serialization
- [x] deprecate old models
Contributor steps:
- [x] Add secret names to manual integrations workflow in
.github/workflows/_integration_test.yml
- [x] Add secrets to release workflow (for pre-release testing) in
.github/workflows/_release.yml
Maintainer steps (Contributors should not do these):
- [x] set up pypi and test pypi projects
- [x] add credential secrets to Github Actions
- [ ] add package to conda-forge
Functional changes to existing classes:
- now relies on openai client v1 (1.6.1) via concrete dep in
langchain-openai package
Codebase organization
- some function calling stuff moved to
`langchain_core.utils.function_calling` in order to be used in both
community and langchain-openai
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Removes unused `Params` in `libs/langchain/langchain/llms/mlflow.py`.
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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The example code for `llms.Mlflow` is outdated.
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** `MarkdownHeaderTextSplitter` currently strips header
lines from chunked content. Many applications require these header lines
are preserved. This adds an optional parameter to preserve those headers
in the chunked content.
- **Issue:** #2836 (relevant)
- **Dependencies:** -
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @finnless
Unit tests and new examples in notebook included.
cc @rlancemartin
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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Adds `WasmChat` integration. `WasmChat` runs GGUF models locally or via
chat service in lightweight and secure WebAssembly containers. In this
PR, `WasmChatService` is introduced as the first step of the
integration. `WasmChatService` is driven by
[llama-api-server](https://github.com/second-state/llama-utils) and
[WasmEdge Runtime](https://wasmedge.org/).
---------
Signed-off-by: Xin Liu <sam@secondstate.io>
