Looking at tokens / page of our docs, we see a few outliers:
<img width="761" alt="image"
src="https://github.com/langchain-ai/langchain/assets/122662504/677aa2d6-0a29-45e4-882a-db2bbf46d02b">
It is due to non-rendering images in one case, and output spamming.
Clean these, along with other cases of excessing output spamming in
docs.
All get sucked into chat-langchain for retrieval.
Thank you for contributing to LangChain!
bilibili-api-python use https://github.com/Nemo2011/bilibili-api repo.
Change to the correct address.
- [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, hwchase17.
**Description:** Update module imports for Fireworks documentation
**Issue:** Module imports not present or in incorrect location
**Dependencies:** None
**Description:** Update import paths and move to lcel for llama.cpp
examples
**Issue:** Update import paths to reflect package refactoring and move
chains to LCEL in examples
**Dependencies:** None
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:** Invoke callback prior to yielding token for BaseOpenAI
& OpenAIChat
**Issue:** [Callback for on_llm_new_token should be invoked before the
token is yielded by the model
#16913](https://github.com/langchain-ai/langchain/issues/16913)
**Dependencies:** None
**Description:** Invoke callback prior to yielding token for Fireworks
**Issue:** [Callback for on_llm_new_token should be invoked before the
token is yielded by the model
#16913](https://github.com/langchain-ai/langchain/issues/16913)
**Dependencies:** None
**Description:** Moving FireworksEmbeddings documentation to the
location docs/integration/text_embedding/ from langchain_fireworks/docs/
**Issue:** FireworksEmbeddings documentation was not in the correct
location
**Dependencies:** None
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
I have a small dataset, and I tried to use docarray:
``DocArrayHnswSearch ``. But when I execute, it returns:
```bash
raise ImportError(
ImportError: Could not import docarray python package. Please install it with `pip install "langchain[docarray]"`.
```
Instead of docarray it needs to be
```bash
docarray[hnswlib]
```
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
RecursiveUrlLoader does not currently provide an option to set
`base_url` other than the `url`, though it uses a function with such an
option.
For example, this causes it unable to parse the
`https://python.langchain.com/docs`, as it returns the 404 page, and
`https://python.langchain.com/docs/get_started/introduction` has no
child routes to parse.
`base_url` allows setting the `https://python.langchain.com/docs` to
filter by, while the starting URL is anything inside, that contains
relevant links to continue crawling.
I understand that for this case, the docusaurus loader could be used,
but it's a common issue with many websites.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [ ] **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, hwchase17.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Delete MistralAIEmbeddings usage document from folder
partners/mistralai/docs
**Issue:** The document is present in the folder docs/docs
**Dependencies:** None
**Description:** Invoke callback prior to yielding token for llama.cpp
**Issue:** [Callback for on_llm_new_token should be invoked before the
token is yielded by the model
#16913](https://github.com/langchain-ai/langchain/issues/16913)
**Dependencies:** None
```python
from langchain.agents import tool
from langchain_mistralai import ChatMistralAI
llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
@tool
def get_word_length(word: str) -> int:
"""Returns the length of a word."""
return len(word)
tools = [get_word_length]
llm_with_tools = llm.bind_tools(tools)
llm_with_tools.invoke("how long is the word chrysanthemum")
```
currently raises
```
AttributeError: 'dict' object has no attribute 'model_dump'
```
Same with `.with_structured_output`
```python
from langchain_mistralai import ChatMistralAI
from langchain_core.pydantic_v1 import BaseModel
class AnswerWithJustification(BaseModel):
"""An answer to the user question along with justification for the answer."""
answer: str
justification: str
llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
```
This appears to fix.
For prompt templates with only 1 variable (common in e.g.,
MessageGraph), it's convenient to wrap the incoming object in the
variable before formatting.
The downside of this, of course, would be that some number of
invocations will successfully format when the user may have intended to
format it properly before
