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.
Decisions to discuss:
1. is a new attr needed or could additional_kwargs be used for this
2. is raw_output a good name for this attr
3. should raw_output default to {} or None
4. should raw_output be included in serialization
5. do we need to update repr/str to exclude raw_output
- add version of AIMessageChunk.__add__ that can add many chunks,
instead of only 2
- In agenerate_from_stream merge and parse chunks in bg thread
- In output parse base classes do more work in bg threads where
appropriate
---------
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
This PR moves the in memory implementation to langchain-core.
* The implementation remains importable from langchain-community.
* Supporting utilities are marked as private for now.
resolves https://github.com/langchain-ai/langchain/issues/23911
When an AIMessageChunk is instantiated, we attempt to parse tool calls
off of the tool_call_chunks.
Here we add a special-case to this parsing, where `""` will be parsed as
`{}`.
This is a reaction to how Anthropic streams tool calls in the case where
a function has no arguments:
```
{'id': 'toolu_01J8CgKcuUVrMqfTQWPYh64r', 'input': {}, 'name': 'magic_function', 'type': 'tool_use', 'index': 1}
{'partial_json': '', 'type': 'tool_use', 'index': 1}
```
The `partial_json` does not accumulate to a valid json string-- most
other providers tend to emit `"{}"` in this case.
This PR introduces a GraphStore component. GraphStore extends
VectorStore with the concept of links between documents based on
document metadata. This allows linking documents based on a variety of
techniques, including common keywords, explicit links in the content,
and other patterns.
This works with existing Documents, so it’s easy to extend existing
VectorStores to be used as GraphStores. The interface can be implemented
for any Vector Store technology that supports metadata, not only graph
DBs.
When retrieving documents for a given query, the first level of search
is done using classical similarity search. Next, links may be followed
using various traversal strategies to get additional documents. This
allows documents to be retrieved that aren’t directly similar to the
query but contain relevant information.
2 retrieving methods are added to the VectorStore ones :
* traversal_search which gets all linked documents up to a certain depth
* mmr_traversal_search which selects linked documents using an MMR
algorithm to have more diverse results.
If a depth of retrieval of 0 is used, GraphStore is effectively a
VectorStore. It enables an easy transition from a simple VectorStore to
GraphStore by adding links between documents as a second step.
An implementation for Apache Cassandra is also proposed.
See
https://github.com/datastax/ragstack-ai/blob/main/libs/knowledge-store/notebooks/astra_support.ipynb
for a notebook explaining how to use GraphStore and that shows that it
can answer correctly to questions that a simple VectorStore cannot.
**Twitter handle:** _cbornet
This PR rolls out part of the new proposed interface for vectorstores
(https://github.com/langchain-ai/langchain/pull/23544) to existing store
implementations.
The PR makes the following changes:
1. Adds standard upsert, streaming_upsert, aupsert, astreaming_upsert
methods to the vectorstore.
2. Updates `add_texts` and `aadd_texts` to be non required with a
default implementation that delegates to `upsert` and `aupsert` if those
have been implemented. The original `add_texts` and `aadd_texts` methods
are problematic as they spread object specific information across
document and **kwargs. (e.g., ids are not a part of the document)
3. Adds a default implementation to `add_documents` and `aadd_documents`
that delegates to `upsert` and `aupsert` respectively.
4. Adds standard unit tests to verify that a given vectorstore
implements a correct read/write API.
A downside of this implementation is that it creates `upsert` with a
very similar signature to `add_documents`.
The reason for introducing `upsert` is to:
* Remove any ambiguities about what information is allowed in `kwargs`.
Specifically kwargs should only be used for information common to all
indexed data. (e.g., indexing timeout).
*Allow inheriting from an anticipated generalized interface for indexing
that will allow indexing `BaseMedia` (i.e., allow making a vectorstore
for images/audio etc.)
`add_documents` can be deprecated in the future in favor of `upsert` to
make sure that users have a single correct way of indexing content.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
**Description**: After reviewing the prompts API, it is clear that the
only way a user can explicitly mark an input variable as optional is
through the `MessagePlaceholder.optional` attribute. Otherwise, the user
must explicitly pass in the `input_variables` expected to be used in the
`BasePromptTemplate`, which will be validated upon execution. Therefore,
to semantically handle a `MessagePlaceholder` `variable_name` as
optional, we will treat the `variable_name` of `MessagePlaceholder` as a
`partial_variable` if it has been marked as optional. This approach
aligns with how the `variable_name` of `MessagePlaceholder` is already
handled
[here](https://github.com/keenborder786/langchain/blob/optional_input_variables/libs/core/langchain_core/prompts/chat.py#L991).
Additionally, an attribute `optional_variable` has been added to
`BasePromptTemplate`, and the `variable_name` of `MessagePlaceholder` is
also made part of `optional_variable` when marked as optional.
Moreover, the `get_input_schema` method has been updated for
`BasePromptTemplate` to differentiate between optional and non-optional
variables.
**Issue**: #22832, #21425
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This PR should fix the following issue:
https://github.com/langchain-ai/langchain/issues/23824
Introduced as part of this PR:
https://github.com/langchain-ai/langchain/pull/23416
I am unable to reproduce the issue locally though it's clear that we're
getting a `serialized` object which is not a dictionary somehow.
The test below passes for me prior to the PR as well
```python
def test_cache_with_sqllite() -> None:
from langchain_community.cache import SQLiteCache
from langchain_core.globals import set_llm_cache
cache = SQLiteCache(database_path=".langchain.db")
set_llm_cache(cache)
chat_model = FakeListChatModel(responses=["hello", "goodbye"], cache=True)
assert chat_model.invoke("How are you?").content == "hello"
assert chat_model.invoke("How are you?").content == "hello"
```
- Description: Add support for `path` and `detail` keys in
`ImagePromptTemplate`. Previously, only variables associated with the
`url` key were considered. This PR allows for the inclusion of a local
image path and a detail parameter as input to the format method.
- Issues:
- fixes#20820
- related to #22024
- Dependencies: None
- Twitter handle: @DeschampsTho5
---------
Co-authored-by: tdeschamps <tdeschamps@kameleoon.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
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.)
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.
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>
Fix#23716
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: Eugene Yurtsev <eyurtsev@gmail.com>
This PR introduces a maxsize parameter for the InMemoryCache class,
allowing users to specify the maximum number of items to store in the
cache. If the cache exceeds the specified maximum size, the oldest items
are removed. Additionally, comprehensive unit tests have been added to
ensure all functionalities are thoroughly tested. The tests are written
using pytest and cover both synchronous and asynchronous methods.
Twitter: @spyrosavl
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Fix LLM string representation for serializable objects.
Fix for issue: https://github.com/langchain-ai/langchain/issues/23257
The llm string of serializable chat models is the serialized
representation of the object. LangChain serialization dumps some basic
information about non serializable objects including their repr() which
includes an object id.
This means that if a chat model has any non serializable fields (e.g., a
cache), then any new instantiation of the those fields will change the
llm representation of the chat model and cause chat misses.
i.e., re-instantiating a postgres cache would result in cache misses!
**Description:** In the chat_models module of the language model, the
import statement for BaseModel has been moved from the conditionally
imported section to the main import area, fixing `NameError `.
**Issue:** fix `NameError `
This PR adds a part of the indexing API proposed in this RFC
https://github.com/langchain-ai/langchain/pull/23544/files.
It allows rolling out `get_by_ids` which should be uncontroversial to
existing vectorstores without introducing new abstractions.
The semantics for this method depend on the ability of identifying
returned documents using the new optional ID field on documents:
https://github.com/langchain-ai/langchain/pull/23411
Alternatives are:
1. Relax the sequence requirement
```python
def get_by_ids(self, ids: Iterable[str], /) -> Iterable[Document]:
```
Rejected:
- implementations are more likley to start batching with bad defaults
- users would need to call list() or we'd need to introduce another
convenience method
2. Support more kwargs
```python
def get_by_ids(self, ids: Sequence[str], /, **kwargs) -> List[Document]:
...
```
Rejected:
- No need for `batch` parameter since IDs is a sequence
- Output cannot be customized since `Document` is fixed. (e.g.,
parameters could be useful to grab extra metadata like the vector that
was indexed with the Document or to project a part of the document)
This change adds a new message type `RemoveMessage`. This will enable
`langgraph` users to manually modify graph state (or have the graph
nodes modify the state) to remove messages by `id`
Examples:
* allow users to delete messages from state by calling
```python
graph.update_state(config, values=[RemoveMessage(id=state.values[-1].id)])
```
* allow nodes to delete messages
```python
graph.add_node("delete_messages", lambda state: [RemoveMessage(id=state[-1].id)])
```
This PR adds an optional ID field to the document schema.
# 1. Optional or Required
- An optional field will will requrie additional checking for the type
in user code (annoying).
- However, vectorstores currently don't respect this field. So if we
make it
required and start returning random UUIDs that might be even more
confusing
to users.
**Proposal**: Start with Optional and convert to Required (with default
set to uuid4()) in 1-2 major releases.
# 2. Override __str__ or generic solution in prompts
Overriding __str__ as a simple way to avoid changing user code that
relies on
default str(document) in prompts.
I considered rolling out a more general solution in prompts
(https://github.com/langchain-ai/langchain/pull/8685),
but to do that we need to:
1. Make things serializable
2. The more general solution would likely need to be backwards
compatible as well
3. It's unclear that one wants to format a List[int] in the same way as
List[Document]. The former should be `,` seperated (likely), the latter
should be `---` separated (likely).
**Proposal** Start with __str__ override and focus on the vectorstore
APIs, we generalize prompts later
These currently read off AIMessage.tool_calls, and only fall back to
OpenAI parsing if tool calls aren't populated.
Importing these from `openai_tools` (e.g., in our [tool calling
docs](https://python.langchain.com/v0.2/docs/how_to/tool_calling/#tool-calls))
can lead to confusion.
After landing, would need to release core and update docs.
