**Description:** Added revision_example prompt template to include the
revision request and revision examples in the revision chain.
**Issue:** Not Applicable
**Dependencies:** Not Applicable
**Twitter handle:** @nithinjp09
**Title**: "langchain: OpenAI Assistants v2 api support"
***Descriptions***
- [x] "attachments" support added along with backward compatibility of
"file_ids"
- [x] "tool_resources" support added while creating new assistant
- [ ] "tool_choice" parameter support
- [ ] Streaming support
- **Dependencies:** OpenAI v2 API (openai>=1.23.0)
- **Twitter handle:** @skanta_rath
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- `llm_chain` becomes `Union[LLMChain, Runnable]`
- `.from_llm` creates a runnable
tested by verifying that docs/how_to/MultiQueryRetriever.ipynb runs
unchanged with sync/async invoke (and that it runs if we specifically
instantiate with LLMChain).
Do not prefix function signature
---
* Reason for this is that information is already present with tool
calling models.
* This will save on tokens for those models, and makes it more obvious
what the description is!
* The @tool can get more parameters to allow a user to re-introduce the
the signature if we want
0.2 is not a breaking release for core (but it is for langchain and
community)
To keep the core+langchain+community packages in sync at 0.2, we will
relax deps throughout the ecosystem to tolerate `langchain-core` 0.2
0.2rc
migrations
- [x] Move memory
- [x] Move remaining retrievers
- [x] graph_qa chains
- [x] some dependency from evaluation code potentially on math utils
- [x] Move openapi chain from `langchain.chains.api.openapi` to
`langchain_community.chains.openapi`
- [x] Migrate `langchain.chains.ernie_functions` to
`langchain_community.chains.ernie_functions`
- [x] migrate `langchain/chains/llm_requests.py` to
`langchain_community.chains.llm_requests`
- [x] Moving `langchain_community.cross_enoders.base:BaseCrossEncoder`
->
`langchain_community.retrievers.document_compressors.cross_encoder:BaseCrossEncoder`
(namespace not ideal, but it needs to be moved to `langchain` to avoid
circular deps)
- [x] unit tests langchain -- add pytest.mark.community to some unit
tests that will stay in langchain
- [x] unit tests community -- move unit tests that depend on community
to community
- [x] mv integration tests that depend on community to community
- [x] mypy checks
Other todo
- [x] Make deprecation warnings not noisy (need to use warn deprecated
and check that things are implemented properly)
- [x] Update deprecation messages with timeline for code removal (likely
we actually won't be removing things until 0.4 release) -- will give
people more time to transition their code.
- [ ] Add information to deprecation warning to show users how to
migrate their code base using langchain-cli
- [ ] Remove any unnecessary requirements in langchain (e.g., is
SQLALchemy required?)
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
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, hwchase17.
Issue: `load_qa_chain` is placed in the __init__.py file. As a result,
it is not listed in the API Reference docs.
BTW `load_qa_chain` is heavily presented in the doc examples, but is
missed in API Ref.
Change: moved code from init.py into a new file. Related: #21266
**Description:**
The `LocalFileStore` class can be used to create an on-disk
`CacheBackedEmbeddings` cache. The number of files in these embeddings
caches can grow to be quite large over time (hundreds of thousands) as
embeddings are computed for new versions of content, but the embeddings
for old/deprecated content are not removed.
A *least-recently-used* (LRU) cache policy could be applied to the
`LocalFileStore` directory to delete cache entries that have not been
referenced for some time:
```bash
# delete files that have not been accessed in the last 90 days
find embeddings_cache_dir/ -atime 90 -print0 | xargs -0 rm
```
However, most filesystems in enterprise environments disable access time
modification on read to improve performance. As a result, the access
times of these cache entry files are not updated when their values are
read.
To resolve this, this pull request updates the `LocalFileStore`
constructor to offer an `update_atime` parameter that causes access
times to be updated when a cache entry is read.
For example,
```python
file_store = LocalFileStore(temp_dir, update_atime=True)
```
The default is `False`, which retains the original behavior.
**Testing:**
I updated the LocalFileStore unit tests to test the access time update.
## Description
Memory return could be set as `str` or `message` by `return_messages`
flag as mentioned in
https://python.langchain.com/docs/modules/memory/#whether-memory-is-a-string-or-a-list-of-messages,
where
`langchain.chains.conversation.memory.ConversationSummaryBufferMemory`
did not implement that.
This commit added `buffer_as_str` and `buffer_as_messages` function, and
`buffer` now affected by `return_messages` flag.
## Example Test Code and Output
```python
# Fix: ConversationSummaryBufferMemory with return_messages flag function
# Test code
from langchain.chains.conversation.memory import ConversationSummaryBufferMemory
from langchain_community.llms.ollama import Ollama
llm = Ollama()
# Create an instance of ConversationSummaryBufferMemory with return_messages set to True
memory = ConversationSummaryBufferMemory(return_messages=True, llm=llm)
# Add user and AI messages to the chat memory
memory.chat_memory.add_user_message("hi!")
memory.chat_memory.add_ai_message("what's up?")
# Print the buffer
print("Buffer:")
print(*map(type, memory.buffer), sep="\n")
print(memory.buffer, "\n")
# Print the buffer as a string
print("Buffer as String:")
print(type(memory.buffer_as_str))
print(memory.buffer_as_str, "\n")
# Print the buffer as messages
print("Buffer as Messages:")
print(*map(type, memory.buffer_as_messages), sep="\n")
print(memory.buffer_as_messages, "\n")
# Print the buffer after setting return_messages to False
memory.return_messages = False
print("Buffer after setting return_messages to False:")
print(type(memory.buffer))
print(memory.buffer, "\n")
```
```plaintext
Buffer:
<class 'langchain_core.messages.human.HumanMessage'>
<class 'langchain_core.messages.ai.AIMessage'>
[HumanMessage(content='hi!'), AIMessage(content="what's up?")]
Buffer as String:
<class 'str'>
Human: hi!
AI: what's up?
Buffer as Messages:
<class 'langchain_core.messages.human.HumanMessage'>
<class 'langchain_core.messages.ai.AIMessage'>
[HumanMessage(content='hi!'), AIMessage(content="what's up?")]
Buffer after setting return_messages to False:
<class 'str'>
Human: hi!
AI: what's up?
```
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Issue: `load_summarize_chain` is placed in the __init__.py file. As a
result, it doesn't listed in the API Reference docs.
Change: moved code from __init__.py into a new file.
**PR message**:
- **Description:** Corrected a syntax error in the code comments within
the `create_tool_calling_agent` function in the langchain package.
- **Issue:** N/A
- **Dependencies:** No additional dependencies required.
- **Twitter handle:** N/A
