- Convert developer openai messages to SystemMessage
- store additional_kwargs={"__openai_role__": "developer"} so that the
correct role can be reconstructed if needed
- update ChatOpenAI to read in openai_role
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
…ent path given.
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
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/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** This PR introduces a `model` alias for the embedding
classes that contain the attribute `model_name`, to ensure consistency
across the codebase, as suggested by a moderator in a previous PR. The
change aligns the usage of attribute names across the project (see for
example
[here](65deeddd5d/libs/partners/groq/langchain_groq/chat_models.py (L304))).
**Issue:** This PR addresses the suggestion from the review of issue
#28269.
**Dependencies:** None
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Currently `_convert_TGI_message_to_LC_message` replaces `'` in the tool
arguments, so an argument like "It's" will be converted to `It"s` and
could cause a json parser to fail.
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Vadym Barda <vadym@langchain.dev>
- **Description:**: In the event of a Rate Limit Error from the
MistralAI server, the response JSON raises a KeyError. To address this,
a simple retry mechanism has been implemented to handle cases where the
request limit is exceeded.
- **Issue:** #27790
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- **Description:** Streaming response from Mistral model using Vertex AI
raises KeyError when trying to access `choices` key, that the last chunk
doesn't have. The fix is to access the key safely using `get()`.
- **Issue:** https://github.com/langchain-ai/langchain/issues/27886
- **Dependencies:**
- **Twitter handle:**
- Description: Azure AI takes an issue with the safe_mode parameter
being set to False instead of None. Therefore, this PR changes the
default value of safe_mode from False to None. This results in it being
filtered out before the request is sent - avoind the extra-parameter
issue described below.
- Issue: #26029
- Dependencies: /
---------
Co-authored-by: blaufink <sebastian.brueckner@outlook.de>
Co-authored-by: Erick Friis <erick@langchain.dev>
- Run standard integration tests in Chroma
- Add `get_by_ids` method
- Fix bug in `add_texts`: if a list of `ids` is passed but any of them
are None, Chroma will raise an exception. Here we assign a uuid.
Description:
* Added internal `Document.id` support to Chroma VectorStore
Dependencies:
* https://github.com/langchain-ai/langchain/pull/27968 should be merged
first and this PR should be re-based on top of those changes.
Tests:
* Modified/Added tests for `Document.id` support. All tests are passing.
Note: I am not a member of the Chroma team.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
This PR updates the Pinecone client to `5.4.0`, as well as its
dependencies (`pinecone-plugin-inference` and
`pinecone-plugin-interface`).
Note: `pinecone-client` is now simply called `pinecone`.
**Question for reviewer(s):** should this PR also update the `pinecone`
dep in [the root dir's `poetry.lock`
file](https://github.com/langchain-ai/langchain/blob/master/poetry.lock#L6729)?
Was unsure. (I don't believe so b/c it seems pinned to a lower version
likely based on 3rd-party deps (e.g. Unstructured).)
--
TW: @audrey_sage_
---
- To see the specific tasks where the Asana app for GitHub is being
used, see below:
- https://app.asana.com/0/0/1208693659122374
This PR adds an additional method to `Chroma` to retrieve the embedding
vectors, besides the most relevant Documents. This is sometimes of use
when you need to run a postprocessing algorithm on the retrieved results
based on the vectors, which has been the case for me lately.
Example issue (discussion) requesting this change:
https://github.com/langchain-ai/langchain/discussions/20383
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
## Description
This PR addresses the following:
**Fixes Issue #25343:**
- Adds additional logic to parse shallowly nested JSON-encoded strings
in tool call arguments, allowing for proper parsing of responses like
that of Llama3.1 and 3.2 with nested schemas.
**Adds Integration Test for Fix:**
- Adds a Ollama specific integration test to ensure the issue is
resolved and to prevent regressions in the future.
**Fixes Failing Integration Tests:**
- Fixes failing integration tests (even prior to changes) caused by
`llama3-groq-tool-use` model. Previously,
tests`test_structured_output_async` and
`test_structured_output_optional_param` failed due to the model not
issuing a tool call in the response. Resolved by switching to
`llama3.1`.
## Issue
Fixes#25343.
## Dependencies
No dependencies.
____
Done in collaboration with @ishaan-upadhyay @mirajismail @ZackSteine.
v0.4 of the Python SDK is already installed via the lock file in CI, but
our current implementation is not compatible with it.
This also addresses an issue introduced in
https://github.com/langchain-ai/langchain/pull/28299. @RyanMagnuson
would you mind explaining the motivation for that change? From what I
can tell the Ollama SDK [does not support
kwargs](6c44bb2729/ollama/_client.py (L286)).
Previously, unsupported kwargs were ignored, but they currently raise
`TypeError`.
Some of LangChain's standard test suite expects `tool_choice` to be
supported, so here we catch it in `bind_tools` so it is ignored and not
passed through to the client.
From what I can tell response using SDK is not deterministic:
```python
import numpy as np
import openai
documents = ["disallowed special token '<|endoftext|>'"]
model = "text-embedding-ada-002"
direct_output_1 = (
openai.OpenAI()
.embeddings.create(input=documents, model=model)
.data[0]
.embedding
)
for i in range(10):
direct_output_2 = (
openai.OpenAI()
.embeddings.create(input=documents, model=model)
.data[0]
.embedding
)
print(f"{i}: {np.isclose(direct_output_1, direct_output_2).all()}")
```
```
0: True
1: True
2: True
3: True
4: False
5: True
6: True
7: True
8: True
9: True
```
See related discussion here:
https://community.openai.com/t/can-text-embedding-ada-002-be-made-deterministic/318054
Found the same result using `"text-embedding-3-small"`.
This change refines the handling of _model_kwargs in POST requests.
Instead of nesting _model_kwargs as a dictionary under the parameters
key, it is now directly unpacked and merged into the request's JSON
payload. This ensures that the model parameters are passed correctly and
avoids unnecessary nesting.E. g.:
```python
import asyncio
from langchain_huggingface.embeddings import HuggingFaceEndpointEmbeddings
embedding_input = ["This input will get multiplied" * 10000]
embeddings = HuggingFaceEndpointEmbeddings(
model="http://127.0.0.1:8081/embed",
model_kwargs={"truncate": True},
)
# Truncated parameters in synchronized methods are handled correctly
embeddings.embed_documents(texts=embedding_input)
# The truncate parameter is not handled correctly in the asynchronous method,
# and 413 Request Entity Too Large is returned.
asyncio.run(embeddings.aembed_documents(texts=embedding_input))
```
Co-authored-by: af su <saf@zjuici.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Description:
* I'm planning to add `Document.id` support to the Chroma VectorStore,
but first I wanted to make sure all the integration tests were passing
first. They weren't. This PR fixes the broken tests.
* I found 2 issues:
* This change (from a year ago, exactly :) ) for supporting multi-modal
embeddings:
https://docs.trychroma.com/deployment/migration#migration-to-0.4.16---november-7,-2023
* This change https://github.com/langchain-ai/langchain/pull/27827 due
to an update in the chroma client.
Also ran `format` and `lint` on the changes.
Note: I am not a member of the Chroma team.
**Description:** The issue concerns the unexpected behavior observed
using the bind_tools method in LangChain's ChatOllama. When tools are
not bound, the llm.stream() method works as expected, returning
incremental chunks of content, which is crucial for real-time
applications such as conversational agents and live feedback systems.
However, when bind_tools([]) is used, the streaming behavior changes,
causing the output to be delivered in full chunks rather than
incrementally. This change negatively impacts the user experience by
breaking the real-time nature of the streaming mechanism.
**Issue:** #26971
---------
Co-authored-by: 4meyDam1e <amey.damle@mail.utoronto.ca>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Last week Anthropic released version 0.39.0 of its python sdk, which
enabled support for Python 3.13. This release deleted a legacy
`client.count_tokens` method, which we currently access during init of
the `Anthropic` LLM. Anthropic has replaced this functionality with the
[client.beta.messages.count_tokens()
API](https://github.com/anthropics/anthropic-sdk-python/pull/726).
To enable support for `anthropic >= 0.39.0` and Python 3.13, here we
drop support for the legacy token counting method, and add support for
the new method via `ChatAnthropic.get_num_tokens_from_messages`.
To fully support the token counting API, we update the signature of
`get_num_tokens_from_message` to accept tools everywhere.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Now `encode_kwargs` used for both for documents and queries and this
leads to wrong embeddings. E. g.:
```python
model_kwargs = {"device": "cuda", "trust_remote_code": True}
encode_kwargs = {"normalize_embeddings": False, "prompt_name": "s2p_query"}
model = HuggingFaceEmbeddings(
model_name="dunzhang/stella_en_400M_v5",
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
)
query_embedding = np.array(
model.embed_query("What are some ways to reduce stress?",)
)
document_embedding = np.array(
model.embed_documents(
[
"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
]
)
)
print(model._client.similarity(query_embedding, document_embedding)) # output: tensor([[0.8421, 0.3317]], dtype=torch.float64)
```
But from the [model
card](https://huggingface.co/dunzhang/stella_en_400M_v5#sentence-transformers)
expexted like this:
```python
model_kwargs = {"device": "cuda", "trust_remote_code": True}
encode_kwargs = {"normalize_embeddings": False}
query_encode_kwargs = {"normalize_embeddings": False, "prompt_name": "s2p_query"}
model = HuggingFaceEmbeddings(
model_name="dunzhang/stella_en_400M_v5",
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_encode_kwargs=query_encode_kwargs,
)
query_embedding = np.array(
model.embed_query("What are some ways to reduce stress?", )
)
document_embedding = np.array(
model.embed_documents(
[
"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
]
)
)
print(model._client.similarity(query_embedding, document_embedding)) # tensor([[0.8398, 0.2990]], dtype=torch.float64)
```
There was a change of attribute name which was "max_batch_size". It's
now "get_max_batch_size" method.
I want to use "create_batches" which is right down below.
Please check this PR link.
reference: https://github.com/chroma-core/chroma/pull/2305
---------
Signed-off-by: Prithvi Kannan <prithvi.kannan@databricks.com>
Co-authored-by: Prithvi Kannan <46332835+prithvikannan@users.noreply.github.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Jun Yamog <jkyamog@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: ono-hiroki <86904208+ono-hiroki@users.noreply.github.com>
Co-authored-by: Dobiichi-Origami <56953648+Dobiichi-Origami@users.noreply.github.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Co-authored-by: Duy Huynh <vndee.huynh@gmail.com>
Co-authored-by: Rashmi Pawar <168514198+raspawar@users.noreply.github.com>
Co-authored-by: sifatj <26035630+sifatj@users.noreply.github.com>
Co-authored-by: Eric Pinzur <2641606+epinzur@users.noreply.github.com>
Co-authored-by: Daniel Vu Dao <danielvdao@users.noreply.github.com>
Co-authored-by: Ofer Mendelevitch <ofermend@gmail.com>
Co-authored-by: Stéphane Philippart <wildagsx@gmail.com>
**Description:** Fixes None addition issues when an empty value is
passed on
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
**Description:** Returns the document id along with the Vector Search
results
**Issue:** Fixes https://github.com/langchain-ai/langchain/issues/26860
for CouchbaseVectorStore
- [x] **Add tests and docs**: If you're adding a new integration, please
include
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/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified.
Co-authored-by: Erick Friis <erick@langchain.dev>
## Description
I encountered an error while using the` gemma-2-2b-it model` with the
`HuggingFacePipeline` class and have implemented a fix to resolve this
issue.
### What is Problem
```python
model_id="google/gemma-2-2b-it"
gemma_2_model = AutoModelForCausalLM.from_pretrained(model_id)
gemma_2_tokenizer = AutoTokenizer.from_pretrained(model_id)
gen = pipeline(
task='text-generation',
model=gemma_2_model,
tokenizer=gemma_2_tokenizer,
max_new_tokens=1024,
device=0 if torch.cuda.is_available() else -1,
temperature=.5,
top_p=0.7,
repetition_penalty=1.1,
do_sample=True,
)
llm = HuggingFacePipeline(pipeline=gen)
for chunk in llm.stream("Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World."):
print(chunk, end="", flush=True)
```
This code outputs the following error message:
```
/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1258: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.
warnings.warn(
Exception in thread Thread-19 (generate):
Traceback (most recent call last):
File "/usr/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
self.run()
File "/usr/lib/python3.10/threading.py", line 953, in run
self._target(*self._args, **self._kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 1874, in generate
self._validate_generated_length(generation_config, input_ids_length, has_default_max_length)
File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 1266, in _validate_generated_length
raise ValueError(
ValueError: Input length of input_ids is 31, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_length` or, better yet, setting `max_new_tokens`.
```
In addition, the following error occurs when the number of tokens is
reduced.
```python
for chunk in llm.stream("Hello World"):
print(chunk, end="", flush=True)
```
```
/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1258: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.
warnings.warn(
/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1885: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.
warnings.warn(
Exception in thread Thread-20 (generate):
Traceback (most recent call last):
File "/usr/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
self.run()
File "/usr/lib/python3.10/threading.py", line 953, in run
self._target(*self._args, **self._kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 2024, in generate
result = self._sample(
File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 2982, in _sample
outputs = self(**model_inputs, return_dict=True)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/transformers/models/gemma2/modeling_gemma2.py", line 994, in forward
outputs = self.model(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/transformers/models/gemma2/modeling_gemma2.py", line 803, in forward
inputs_embeds = self.embed_tokens(input_ids)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/sparse.py", line 164, in forward
return F.embedding(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py", line 2267, in embedding
return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument index in method wrapper_CUDA__index_select)
```
On the other hand, in the case of invoke, the output is normal:
```
llm.invoke("Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World.")
```
```
'Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World.\n\nThis is a simple program that prints the phrase "Hello World" to the console. \n\n**Here\'s how it works:**\n\n* **`print("Hello World")`**: This line of code uses the `print()` function, which is a built-in function in most programming languages (like Python). The `print()` function takes whatever you put inside its parentheses and displays it on the screen.\n* **`"Hello World"`**: The text within the double quotes (`"`) is called a string. It represents the message we want to print.\n\n\nLet me know if you\'d like to explore other programming concepts or see more examples! \n'
```
### Problem Analysis
- Apparently, I put kwargs in while generating pipelines and it applied
to `invoke()`, but it's not applied in the `stream()`.
- When using the stream, `inputs = self.pipeline.tokenizer (prompt,
return_tensors = "pt")` enters cpu.
- This can crash when the model is in gpu.
### Solution
Just use `self.pipeline` instead of `self.pipeline.model.generate`.
- **Original Code**
```python
stopping_criteria = StoppingCriteriaList([StopOnTokens()])
inputs = self.pipeline.tokenizer(prompt, return_tensors="pt")
streamer = TextIteratorStreamer(
self.pipeline.tokenizer,
timeout=60.0,
skip_prompt=skip_prompt,
skip_special_tokens=True,
)
generation_kwargs = dict(
inputs,
streamer=streamer,
stopping_criteria=stopping_criteria,
**pipeline_kwargs,
)
t1 = Thread(target=self.pipeline.model.generate, kwargs=generation_kwargs)
t1.start()
```
- **Updated Code**
```python
stopping_criteria = StoppingCriteriaList([StopOnTokens()])
streamer = TextIteratorStreamer(
self.pipeline.tokenizer,
timeout=60.0,
skip_prompt=skip_prompt,
skip_special_tokens=True,
)
generation_kwargs = dict(
text_inputs= prompt,
streamer=streamer,
stopping_criteria=stopping_criteria,
**pipeline_kwargs,
)
t1 = Thread(target=self.pipeline, kwargs=generation_kwargs)
t1.start()
```
By using the `pipeline` directly, the `kwargs` of the pipeline are
applied, and there is no need to consider the `device` of the `tensor`
made with the `tokenizer`.
> According to the change to use `pipeline`, it was modified to put
`text_inputs=prompts` directly into `generation_kwargs`.
## Issue
None
## Dependencies
None
## Twitter handle
None
---------
Co-authored-by: Vadym Barda <vadym@langchain.dev>
