Use pydantic to infer nested schemas and all that fun.
Include bagatur's convenient docstring parser
Include annotation support
Previously we didn't adequately support many typehints in the
bind_tools() method on raw functions (like optionals/unions, nested
types, etc.)
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Added support for streaming in AI21 Jamba Model
- **Twitter handle:** https://github.com/AI21Labs
- [x] **Add tests and docs**: If you're adding a new integration, please
include
- [x] **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.
---------
Co-authored-by: Asaf Gardin <asafg@ai21.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [x] **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"
**Description:** Update docs content on agent memory
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
`ChatAnthropic` can get `stop_reason` from the resulting `AIMessage` in
`invoke` and `ainvoke`, but not in `stream` and `astream`.
This is a different behavior from `ChatOpenAI`.
It is possible to get `stop_reason` from `stream` as well, since it is
needed to determine the next action after the LLM call. This would be
easier to handle in situations where only `stop_reason` is needed.
- Issue: NA
- Dependencies: NA
- Twitter handle: https://x.com/kiarina37
- **Description:** Fix some issues in MiniMaxChat
- Fix `minimax_api_host` not in `values` error
- Remove `minimax_group_id` from reading environment variables, the
`minimax_group_id` no longer use in MiniMaxChat
- Invoke callback prior to yielding token, the issus #16913
The prompt template variable detection only worked for singly-nested
sections because we just kept track of whether we were in a section and
then set that to false as soon as we encountered an end block. i.e. the
following:
```
{{#outerSection}}
{{variableThatShouldntShowUp}}
{{#nestedSection}}
{{nestedVal}}
{{/nestedSection}}
{{anotherVariableThatShouldntShowUp}}
{{/outerSection}}
```
Would yield `['outerSection', 'anotherVariableThatShouldntShowUp']` as
input_variables (whereas it should just yield `['outerSection']`). This
fixes that by keeping track of the current depth and using a stack.
When `model_kwargs={"tools": tools}` are passed to `ChatLiteLLM`, they
are executed, but the response is not recognized correctly
Let's add `tool_calls` to the `additional_kwargs`
Thank you for contributing to LangChain!
## ChatAnthropic
I used the following example to verify the output of llm with tools:
```python
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_anthropic import ChatAnthropic
class GetWeather(BaseModel):
'''Get the current weather in a given location'''
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
class GetPopulation(BaseModel):
'''Get the current population in a given location'''
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
llm = ChatAnthropic(model="claude-3-sonnet-20240229")
llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
ai_msg = llm_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
print(ai_msg.tool_calls)
```
I get the following response:
```json
[{'name': 'GetWeather', 'args': {'location': 'Los Angeles, CA'}, 'id': 'toolu_01UfDA89knrhw3vFV9X47neT'}, {'name': 'GetWeather', 'args': {'location': 'New York, NY'}, 'id': 'toolu_01NrYVRYae7m7z7tBgyPb3Gd'}, {'name': 'GetPopulation', 'args': {'location': 'Los Angeles, CA'}, 'id': 'toolu_01EPFEpDgzL6vV2dTpD9SVP5'}, {'name': 'GetPopulation', 'args': {'location': 'New York, NY'}, 'id': 'toolu_01B5J6tPJXgwwfhQX9BHP2dt'}]
```
## LiteLLM
Based on https://litellm.vercel.app/docs/completion/function_call
```python
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.utils.function_calling import convert_to_openai_tool
import litellm
class GetWeather(BaseModel):
'''Get the current weather in a given location'''
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
class GetPopulation(BaseModel):
'''Get the current population in a given location'''
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
prompt = "Which city is hotter today and which is bigger: LA or NY?"
tools = [convert_to_openai_tool(GetWeather), convert_to_openai_tool(GetPopulation)]
response = litellm.completion(model="claude-3-sonnet-20240229", messages=[{'role': 'user', 'content': prompt}], tools=tools)
print(response.choices[0].message.tool_calls)
```
```python
[ChatCompletionMessageToolCall(function=Function(arguments='{"location": "Los Angeles, CA"}', name='GetWeather'), id='toolu_01HeDWV5vP7BDFfytH5FJsja', type='function'), ChatCompletionMessageToolCall(function=Function(arguments='{"location": "New York, NY"}', name='GetWeather'), id='toolu_01EiLesUSEr3YK1DaE2jxsQv', type='function'), ChatCompletionMessageToolCall(function=Function(arguments='{"location": "Los Angeles, CA"}', name='GetPopulation'), id='toolu_01Xz26zvkBDRxEUEWm9pX6xa', type='function'), ChatCompletionMessageToolCall(function=Function(arguments='{"location": "New York, NY"}', name='GetPopulation'), id='toolu_01SDqKnsLjvUXuBsgAZdEEpp', type='function')]
```
## ChatLiteLLM
When I try the following
```python
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.utils.function_calling import convert_to_openai_tool
from langchain_community.chat_models import ChatLiteLLM
class GetWeather(BaseModel):
'''Get the current weather in a given location'''
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
class GetPopulation(BaseModel):
'''Get the current population in a given location'''
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
prompt = "Which city is hotter today and which is bigger: LA or NY?"
tools = [convert_to_openai_tool(GetWeather), convert_to_openai_tool(GetPopulation)]
llm = ChatLiteLLM(model="claude-3-sonnet-20240229", model_kwargs={"tools": tools})
ai_msg = llm.invoke(prompt)
print(ai_msg)
print(ai_msg.tool_calls)
```
```python
content="Okay, let's find out the current weather and populations for Los Angeles and New York City:" response_metadata={'token_usage': Usage(prompt_tokens=329, completion_tokens=193, total_tokens=522), 'model': 'claude-3-sonnet-20240229', 'finish_reason': 'tool_calls'} id='run-748b7a84-84f4-497e-bba1-320bd4823937-0'
[]
```
---
When I apply the changes of this PR, the output is
```json
[{'name': 'GetWeather', 'args': {'location': 'Los Angeles, CA'}, 'id': 'toolu_017D2tGjiaiakB1HadsEFZ4e'}, {'name': 'GetWeather', 'args': {'location': 'New York, NY'}, 'id': 'toolu_01WrDpJfVqLkPejWzonPCbLW'}, {'name': 'GetPopulation', 'args': {'location': 'Los Angeles, CA'}, 'id': 'toolu_016UKyYrVAV9Pz99iZGgGU7V'}, {'name': 'GetPopulation', 'args': {'location': 'New York, NY'}, 'id': 'toolu_01Sgv1imExFX1oiR1Cw88zKy'}]
```
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
Co-authored-by: Igor Drozdov <idrozdov@gitlab.com>
Description:
1. partners/HuggingFace module support reading params from env. Not
adjust langchain_community/.../huggingfaceXX modules since they are
deprecated.
2. pydantic 2 @root_validator migration.
Issue: #22448#22819
---------
Co-authored-by: gongwn1 <gongwn1@lenovo.com>
**Description**: Milvus vectorstore supports both `add_documents` via
the base class and `upsert` method which deletes and re-adds documents
based on their ids
**Issue**: Due to mismatch in the interfaces the ids used by `upsert`
are neglected in `add_documents`, as `ids` are passed as argument in
`upsert` but via `kwargs` is `add_documents`
This caused exceptions and inconsistency in the DB, tested with
`auto_id=False`
**Fix**: pass `ids` via `kwargs` to `add_documents`
added pre-filtering documentation
Thank you for contributing to LangChain!
- [x] **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"
- [x] **PR message**:
- **Description:** added filter vector search
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:**: n/a
- [x] **Add tests and docs**: If you're adding a new integration, please
include - No need for tests, just a simple doc update
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. 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.
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.
# Fix streaming in mistral with ainvoke
- [x] **PR title**
- [x] **PR message**
- [x] **Add tests and docs**:
1. [x] Added a test for the fixed integration.
2. [x] An example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Ran `make format`, `make lint` and `make test`
from the root of the package(s) I've modified.
Hello
* I Identified an issue in the mistral package where the callback
streaming (see on_llm_new_token) was not functioning correctly when the
streaming parameter was set to True and call with `ainvoke`.
* The root cause of the problem was the streaming not taking into
account. ( I think it's an oversight )
* To resolve the issue, I added the `streaming` attribut.
* Now, the callback with streaming works as expected when the streaming
parameter is set to True.
## How to reproduce
```
from langchain_mistralai.chat_models import ChatMistralAI
chain = ChatMistralAI(streaming=True)
# Add a callback
chain.ainvoke(..)
# Oberve on_llm_new_token
# Now, the callback is given as streaming tokens, before it was in grouped format.
```
Co-authored-by: Erick Friis <erick@langchain.dev>
This PR implements a BaseContent object from which Document and Blob
objects will inherit proposed here:
https://github.com/langchain-ai/langchain/pull/23544
Alternative: Create a base object that only has an identifier and no
metadata.
For now decided against it, since that refactor can be done at a later
time. It also feels a bit odd since our IDs are optional at the moment.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This fix is for #21726. When having other packages installed that
require the `openai_api_base` environment variable, users are not able
to instantiate the AzureChatModels or AzureEmbeddings.
This PR adds a new value `ignore_openai_api_base` which is a bool. When
set to True, it sets `openai_api_base` to `None`
Two new tests were added for the `test_azure` and a new file
`test_azure_embeddings`
A different approach may be better for this. If you can think of better
logic, let me know and I can adjust it.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
