* Ensure access to local model during `ChatOllama` instantiation
(#27720). This adds a new param `validate_model_on_init` (default:
`true`)
* Catch a few more errors from the Ollama client to assist users
## Summary
- Removes the `xslt_path` parameter from HTMLSectionSplitter to
eliminate XXE attack vector
- Hardens XML/HTML parsers with secure configurations to prevent XXE
attacks
- Adds comprehensive security tests to ensure the vulnerability is fixed
## Context
This PR addresses a critical XXE vulnerability discovered in the
HTMLSectionSplitter component. The vulnerability allowed attackers to:
- Read sensitive local files (SSH keys, passwords, configuration files)
- Perform Server-Side Request Forgery (SSRF) attacks
- Exfiltrate data to attacker-controlled servers
## Changes Made
1. **Removed `xslt_path` parameter** - This eliminates the primary
attack vector where users could supply malicious XSLT files
2. **Hardened XML parsers** - Added security configurations to prevent
XXE attacks even with the default XSLT:
- `no_network=True` - Blocks network access
- `resolve_entities=False` - Prevents entity expansion -
`load_dtd=False` - Disables DTD processing -
`XSLTAccessControl.DENY_ALL` - Blocks all file/network I/O in XSLT
transformations
3. **Added security tests** - New test file `test_html_security.py` with
comprehensive tests for various XXE attack vectors
4. **Updated existing tests** - Modified tests that were using the
removed `xslt_path` parameter
## Test Plan
- [x] All existing tests pass
- [x] New security tests verify XXE attacks are blocked
- [x] Code passes linting and formatting checks
- [x] Tested with both old and new versions of lxml
Twitter handle: @_colemurray
Recommend using context manager for FileCallbackHandler to avoid opening
too many file descriptors
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: 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 no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
- There was some ambiguous wording that has been updated to hopefully
clarify the functionality of `reasoning_format` in ChatGroq.
- Added support for `reasoning_effort`
- Added links to see models capable of `reasoning_format` and
`reasoning_effort`
- Other minor nits
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: 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 no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
- docs: for the Ollama notebooks, improve the specificity of some links,
add `homebrew` install info, update some wording
- tests: reduce number of local models needed to run in half from 4 → 2
(shedding 8gb of required installs)
- bump deps (non-breaking) in anticipation of upcoming "thinking" PR
Add additional hashing options to the indexing API, warn on SHA-1
Requires:
- Bumping langchain-core version
- bumping min langchain-core in langchain
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
**Description:** Updates ChatPerplexity documentation to replace
deprecated llama 3 model reference with the current sonar model in the
API key example code block.
**Issue:** N/A (maintenance update for deprecated model)
**Dependencies:** No new dependencies required
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
`Runnable`'s `Input` is contravariant so we need to enumerate all
possible inputs and it's not possible to put them in a `Union`.
Also, it's better to only require a runnable that
accepts`list[BaseMessage]` instead of a broader `Sequence[BaseMessage]`
as internally the runnable is only called with a list.
As part of core releases we run tests on the last released version of
some packages (including langchain-openai) using the new version of
langchain-core. We run langchain-openai's test suite as it was when it
was last released.
Our test for computer use started raising 500 error at some point during
the day today (test passed as part of scheduled test job in the
morning):
> InternalServerError: Error code: 500 - {'error': {'message': 'An error
occurred while processing your request. You can retry your request, or
contact us through our help center at help.openai.com if the error
persists.
Will revert this change after we release langchain-core.
**Description:**
Previously, when transitioning from a deeper Markdown header (e.g., ###)
to a shallower one (e.g., ##), the
ExperimentalMarkdownSyntaxTextSplitter retained the deeper header in the
metadata.
This commit updates the `_resolve_header_stack` method to remove headers
at the same or deeper levels before appending the current header. As a
result, each chunk now reflects only the active header context.
Fixes unexpected metadata leakage across sections in nested Markdown
documents.
Additionally, test cases have been updated to:
- Validate correct header resolution and metadata assignment.
- Cover edge cases with nested headers and horizontal rules.
**Issue:**
Fixes [#31596](https://github.com/langchain-ai/langchain/issues/31596)
**Dependencies:**
None
**Twitter handle:** -> [_RaghuKapur](https://twitter.com/_RaghuKapur)
**LinkedIn:** ->
[https://www.linkedin.com/in/raghukapur/](https://www.linkedin.com/in/raghukapur/)
## Description
<!-- What does this pull request accomplish? -->
- When parsing MistralAI chunk dicts to Langchain to `AIMessageChunk`
schemas via the `_convert_chunk_to_message_chunk` utility function, the
`finish_reason` was not being included in `response_metadata` as it is
for other providers.
- This PR adds a one-liner fix to include the finish reason.
- fixes: https://github.com/langchain-ai/langchain/issues/31666
* Simplified Pydantic handling since Pydantic v1 is not supported
anymore.
* Replace use of deprecated v1 methods by corresponding v2 methods.
* Remove use of other deprecated methods.
* Activate mypy errors on deprecated methods use.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**Description:**
This PR fixes an `IndexError` that occurs when `LLMListwiseRerank` is
called with an empty list of documents.
Earlier, the code assumed the presence of at least one document and
attempted to construct the context string based on `len(documents) - 1`,
which raises an error when documents is an empty list.
The fix works with gpt-4o-mini if I make the list empty, but fails
occasionally with gpt-3.5-turbo. In case of empty list, setting the
string to "empty list" seems to have the expected response.
**Issue:** #31192
Description:
This pull request corrects minor spelling mistakes in the comments
within the `chat_models.py` file of the MistralAI partner integration.
Specifically, it fixes the spelling of "equivalent" and "compatibility"
in two separate comments. These changes improve code readability and
maintain professional documentation standards. No functional code
changes are included.
`uv lock --upgrade-package langsmith
`
Original issue: The lock file (uv.lock) was constraining
langsmith>=0.1.125,<0.4, preventing LangSmith 0.4.1 installation. Even
though the pyproject.toml wasn't restricting langchain core.
Issue:
https://langchain.slack.com/archives/C050X0VTN56/p1750107176007629
This PR updates the tool runtime example notebook to replace the
deprecated `.schema()` method with `.model_json_schema()`, aligning it
with Pydantic V2.
### 🔧 Changes:
- Replaced:
```python
update_favorite_pets.get_input_schema().schema()
with
update_favorite_pets.get_input_schema().model_json_schema()
```
Fixes#31609
### Description
Add keep_separator arg to HTMLSemanticPreservingSplitter and pass value
to instance of RecursiveCharacterTextSplitter used under the hood.
### Issue
Documents returned by `HTMLSemanticPreservingSplitter.split_text(text)`
are defaulted to use separators at beginning of page_content. [See third
and fourth document in example output from how-to
guide](https://python.langchain.com/docs/how_to/split_html/#using-htmlsemanticpreservingsplitter):
```
[Document(metadata={'Header 1': 'Main Title'}, page_content='This is an introductory paragraph with some basic content.'),
Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content='This section introduces the topic'),
Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content='. Below is a list: First item Second item Third item with bold text and a link Subsection 1.1: Details This subsection provides additional details'),
Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content=". Here's a table: Header 1 Header 2 Header 3 Row 1, Cell 1 Row 1, Cell 2 Row 1, Cell 3 Row 2, Cell 1 Row 2, Cell 2 Row 2, Cell 3"),
Document(metadata={'Header 2': 'Section 2: Media Content'}, page_content='This section contains an image and a video:  '),
Document(metadata={'Header 2': 'Section 3: Code Example'}, page_content='This section contains a code block: <code:html> <div> <p>This is a paragraph inside a div.</p> </div> </code>'),
Document(metadata={'Header 2': 'Conclusion'}, page_content='This is the conclusion of the document.')]
```
### Dependencies
None
@ttrumper3
docs: update multimodal PDF and image usage for gpt-4.1
**Description:**
This update revises the LangChain documentation to support the new
GPT-4.1 multimodal API format. It fixes the previous broken example for
PDF uploads (which returned a 400 error: "Missing required parameter:
'messages[0].content[1].file'") and adds clear instructions on how to
include base64-encoded images for OpenAI models.
**Issue:**
error appointed in foruns for pdf load into api ->
'''
@[Albaeld](https://github.com/Albaeld)
Albaeld
[8 days
ago](https://github.com/langchain-ai/langchain/discussions/27702#discussioncomment-13369460)
This simply does not work with openai:gpt-4.1. I get:
Error code: 400 - {'error': {'message': "Missing required parameter:
'messages[0].content[1].file'.", 'type': 'invalid_request_error',
'param': 'messages[0].content[1].file', 'code':
'missing_required_parameter'}}
'''
**Dependencies:**
None
**Twitter handle:**
N/A
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Description:
This pull request corrects minor typographical errors in the
documentation notebooks for vectorstore integrations. Specifically, it
fixes the spelling of "datastore" in `llm_rails.ipynb` and
"pre-existent" in `redis.ipynb`. These changes improve the clarity and
professionalism of the documentation. No functional code changes are
included.
Thank you for contributing to LangChain!
[x] PR title: langchain_ollama: support custom headers for Ollama
partner APIs
Where "package" is whichever of langchain, core, etc. is being modified.
Use "docs: ..." for purely docs changes, "infra: ..." for CI changes.
Example: "core: add foobar LLM"
[x] PR message:
**Description: This PR adds support for passing custom HTTP headers to
Ollama models when used as a LangChain integration. This is especially
useful for enterprise users or partners who need to send authentication
tokens, API keys, or custom tracking headers when querying secured
Ollama servers.
Issue: N/A (new enhancement)
**Dependencies: No external dependencies introduced.
Twitter handle: @arunkumar_offl
[x] Add tests and docs: If you're adding a new integration, please
include
1.Added a unit test in test_chat_models.py to validate headers are
passed correctly.
2. Added an example notebook:
docs/docs/integrations/llms/ollama_custom_headers.ipynb showing how to
use custom headers.
[x] Lint and test: Ran make format, make lint, and make test to ensure
the code is clean and passing all checks.
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 no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
This MR is only for the docs. Added integration with Nebius AI Studio to
docs. The integration package is available at
[https://github.com/nebius/langchain-nebius](https://github.com/nebius/langchain-nebius).
---------
Co-authored-by: Akim Tsvigun <aktsvigun@nebius.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: 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 no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: 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 no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
- **Description:** Remove the outdated Gemini models and replace those
with the latest models.
- **Issue:** Earlier the code was not running, now the code runs.
- **Dependencies:** No
- **Twitter handle:** [soumendrak_](https://x.com/soumendrak_)
## Description
Updating Exa integration documentation to showcase the latest features
and best practices.
## Changes
- Added examples for `ExaSearchResults` tool with advanced search
options
- Added examples for `ExaFindSimilarResults` tool
- Updated agent example to use LangGraph
- Demonstrated text content options, summaries, and highlights
- Included examples of search type control and live crawling
## Additional Context
I'm from the Exa team updating our integration documentation to reflect
current capabilities and best practices.
Remove proxy imports to langchain_experimental.
Previously, these imports would work if a user manually installed
langchain_experimental. However, we want to drop support even for that
as langchain_experimental is generally not recommended to be run in
production.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
**Description:**
Fixed a small grammatical error in the `retrievers.mdx` documentation.
Replaced "we can be built retrievers on top of search APIs..." with
"we can build retrievers on top of search APIs..." for clarity and
correctness.
**Issue:**
N/A
**Dependencies:**
None
**Twitter handle:**
@hassan_zameel
OpenAI changed their API to require the `partial_images` parameter when
using image generation + streaming.
As described in https://github.com/langchain-ai/langchain/pull/31424, we
are ignoring partial images. Here, we accept the `partial_images`
parameter (as required by OpenAI), but emit a warning and continue to
ignore partial images.
**Description:**
`langchain_huggingface` has a very large installation size of around 600
MB (on a Mac with Python 3.11). This is due to its dependency on
`sentence-transformers`, which in turn depends on `torch`, which is 320
MB all by itself. Similarly, the depedency on `transformers` adds
another set of heavy dependencies. With those dependencies removed, the
installation of `langchain_huggingface` only takes up ~26 MB. This is
only 5 % of the full installation!
These libraries are not necessary to use `langchain_huggingface`'s API
wrapper classes, only for local inferences/embeddings. All import
statements for those two libraries already have import guards in place
(try/catch with a helpful "please install x" message).
This PR therefore moves those two libraries to an optional dependency
group `full`. So a `pip install langchain_huggingface` will only install
the lightweight version, and a `pip install
"langchain_huggingface[full]"` will install all dependencies.
I know this may break existing code, because `sentence-transformers` and
`transformers` are now no longer installed by default. Given that users
will see helpful error messages when that happens, and the major impact
of this small change, I hope that you will still consider this PR.
**Dependencies:** No new dependencies, but new optional grouping.
- **Description:**
- In _infer_arg_descriptions, the annotations dictionary contains string
representations of types instead of actual typing objects. This causes
_is_annotated_type to fail, preventing the correct description from
being generated.
- This is a simple fix using the get_type_hints method, which resolves
the annotations properly and is supported across all Python versions.
- **Issue:** #31051
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
https://github.com/langchain-ai/langchain/pull/31286 included an update
to the return type for `BaseChatModel.(a)stream`, from
`Iterator[BaseMessageChunk]` to `Iterator[BaseMessage]`.
This change is correct, because when streaming is disabled, the stream
methods return an iterator of `BaseMessage`, and the inheritance is such
that an `BaseMessage` is not a `BaseMessageChunk` (but the reverse is
true).
However, LangChain includes a pattern throughout its docs of [summing
BaseMessageChunks](https://python.langchain.com/docs/how_to/streaming/#llms-and-chat-models)
to accumulate a chat model stream. This pattern is implemented in tests
for most integration packages and appears in application code. So
https://github.com/langchain-ai/langchain/pull/31286 introduces mypy
errors throughout the ecosystem (or maybe more accurately, it reveals
that this pattern does not account for use of the `.stream` method when
streaming is disabled).
Here we revert just the change to the stream return type to unblock
things. A fix for this should address docs + integration packages (or if
we elect to just force people to update code, be explicit about that).
**Description:**
This PR updates approximately 4' occurences of the deprecated
`initialize_agent` function in LangChain documentation and examples,
replacing it with the recommended `create_react_agent` and pattern. It
also refactors related examples to align with current best practices.
**Issue:**
Partially Fixes#29277
**Dependencies:**
None
**X handle:**
@TK1475
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This PR adds documentation to our Microsft Provider page for LangChain
Azure AI. This PR does not add any extra dependencies or require any
tests besides passing CI.
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: add foobar LLM"
- [x] **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!
- [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. 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 no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
## PR Title
```
docs: enhance Salesforce Toolkit documentation
```
## PR Description
**Description:** Enhanced the Salesforce Toolkit documentation to
provide a more comprehensive overview of the `langchain-salesforce`
package. The updates include improved descriptions of the toolkit's
capabilities, detailed setup instructions for authentication using
environment variables, updated code snippets with consistent parameter
naming and improved readability, and additional resources with API
references for better user guidance.
**Issue:** N/A (documentation improvement)
**Dependencies:** None
**Twitter handle:** @colesmcintosh
---
### Changes Made:
- Improved description of the Salesforce Toolkit's capabilities and
features
- Added detailed setup instructions for authentication using environment
variables
- Updated code snippets to use consistent parameter naming and improved
readability
- Included additional resources and API references for better user
guidance
- Enhanced overall documentation structure and clarity
### Files Modified:
- `docs/docs/integrations/tools/salesforce.ipynb` (83 insertions, 36
deletions)
This is a documentation-only change that improves the user experience
for developers working with the Salesforce Toolkit. The changes are
backwards compatible and follow LangChain's documentation standards.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Does not support partial images during generation at the moment. Before
doing that I'd like to figure out how to specify the aggregation logic
without requiring changes in core.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
change `--no-cache-dirclear` -> `--no-cache-dir`.
pip throws `no such option: --no-cache-dirclear` since its invalid.
`--no-cache-dir` is the correct one.
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: 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 no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
…cs/integrations/tools/ All tools section
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: add foobar LLM"
- [x] **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!
- [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`
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Llama-3.1 started failing consistently with
> groq.BadRequestError: Error code: 400 - ***'error': ***'message':
"Failed to call a function. Please adjust your prompt. See
'failed_generation' for more details.", 'type': 'invalid_request_error',
'code': 'tool_use_failed', 'failed_generation':
'<function=brave_search>***"query": "Hello!"***</function>'***
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Co-authored-by: ccurme <chester.curme@gmail.com>
Release core 0.3.63
Small update just to expand the list of well known tools. This is
necessary while the logic lives in langchain-core.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Add image generation tool to the list of well known tools. This is needed for changes in the ChatOpenAI client.
TODO: Some of this logic needs to be moved from core directly into the client as changes in core should not be required to add a new tool to the openai chat client.
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Deleted two outdated phrases that were reflecting the current versions
of packages at the time i.e.: 1-"langchain-community is currently on
version 0.2.x." 2-langchain-"experimental is currently on version 0.0.x"
docs: update Valyu integration notebooks to reflect current
langchain-valyu package implementation
Updated the Valyu integration documentation notebooks to align with the
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- Updated ValyuContextRetriever to ValyuRetriever class name
- Changed parameter name from similarity_threshold to
relevance_threshold
- Removed query_rewrite parameter from search tool examples
- Added start_date and end_date parameters for time filtering
- Updated default values to match current implementation
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constraints
- Updated section titles to reflect "Deep Search" functionality
…integrations/retrievers/ All retrievers section
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…ngchain.com/docs/integrations/tools/ All tools section
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…gchain.com/docs/integrations/document_loaders/ All document loaders
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---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
…integrations/document_loaders/ All document loaders section
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…tionguard.ipynb
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### Description
Added a note above the retriever overview table to clarify that the
descriptions are truncated for readability and how to view the full
version (via hover or click).
### Issue
Fixes#31311 — Users were confused by incomplete retriever descriptions
in the integration docs.
### Dependencies
None
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
…rations/chat/ All chat models section
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As part of core releases we run tests on the last released version of
some packages (including langchain-openai) using the new version of
langchain-core. We run langchain-openai's test suite as it was when it
was last released.
OpenAI has since updated their API— relaxing constraints on what schemas
are supported when `strict=True`— causing these tests to break. They
have since been fixed. But the old tests will continue to fail.
Will revert this change after we release OpenAI today.
Added support for new Exa API features. Updated Exa docs and python
package (langchain-exa).
Description
Added support for new Exa API features in the langchain-exa package:
- Added max_characters option for text content
- Added support for summary and custom summary prompts
- Added livecrawl option with "always", "fallback", "never" settings
- Added "auto" option for search type
- Updated documentation and tests
Dependencies
- No new dependencies required. Using existing features from exa-py.
twitter: @theishangoswami
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** ConversationChain has been deprecated, and the
documentation says to use RunnableWithMessageHistory in its place, but
the link at the top of the page to RunnableWithMessageHistory is broken
(it's rendering as "html()"). See here at the top of the page:
https://python.langchain.com/api_reference/langchain/chains/langchain.chains.conversation.base.ConversationChain.html.
This PR fixes the link.
**Issue**: N/A
**Dependencies**: N/A
**Twitter handle:**: If you're on Bluesky, I'm @vikramsaraph.com
Scheduled testing started failing today because the Responses API
stopped raising `BadRequestError` for a schema that was previously
invalid when `strict=True`.
Although docs still say that [some type-specific keywords are not yet
supported](https://platform.openai.com/docs/guides/structured-outputs#some-type-specific-keywords-are-not-yet-supported)
(including `minimum` and `maximum` for numbers), the below appears to
run and correctly respect the constraints:
```python
import json
import openai
maximums = list(range(1, 11))
arg_values = []
for maximum in maximums:
tool = {
"type": "function",
"name": "magic_function",
"description": "Applies a magic function to an input.",
"parameters": {
"properties": {
"input": {"maximum": maximum, "minimum": 0, "type": "integer"}
},
"required": ["input"],
"type": "object",
"additionalProperties": False
},
"strict": True
}
client = openai.OpenAI()
response = client.responses.create(
model="gpt-4.1",
input=[{"role": "user", "content": "What is the value of magic_function(3)? Use the tool."}],
tools=[tool],
)
function_call = next(item for item in response.output if item.type == "function_call")
args = json.loads(function_call.arguments)
arg_values.append(args["input"])
print(maximums)
print(arg_values)
# [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# [1, 2, 3, 3, 3, 3, 3, 3, 3, 3]
```
Until yesterday this raised BadRequestError.
The same is not true of Chat Completions, which appears to still raise
BadRequestError
```python
tool = {
"type": "function",
"function": {
"name": "magic_function",
"description": "Applies a magic function to an input.",
"parameters": {
"properties": {
"input": {"maximum": 5, "minimum": 0, "type": "integer"}
},
"required": ["input"],
"type": "object",
"additionalProperties": False
},
"strict": True
}
}
response = client.chat.completions.create(
model="gpt-4.1",
messages=[{"role": "user", "content": "What is the value of magic_function(3)? Use the tool."}],
tools=[tool],
)
response # raises BadRequestError
```
Here we update tests accordingly.
Thank you for contributing to LangChain!
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**Description**:
This PR updates the documentation to address a potential issue when
using `hub.pull(...)` with non-US LangSmith endpoints (e.g.,
`https://eu.api.smith.langchain.com`).
By default, the `hub.pull` function assumes the non US-based API URL.
When the `LANGSMITH_ENDPOINT` environment variable is set to a non-US
region, this can lead to `LangSmithNotFoundError 404 not found` errors
when pulling public assets from the LangChain Hub.
Issue: #31191
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
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Update docs to add Featherless.ai Provider & Chat Model
- **Description:** Adding Featherless.ai as provider in teh
documentations giving access to over 4300+ open-source models
- **Twitter handle:** https://x.com/FeatherlessAI
DSPy removed their LangChain integration in version 2.6.6.
Here we remove the page and add a redirect to the LangChain v0.2 docs
for posterity.
We add an admonition to the v0.2 docs in
https://github.com/langchain-ai/langchain/pull/31277.
* It is possible to chain a `Runnable` with an `AsyncIterator` as seen
in `test_runnable.py`.
* Iterator and AsyncIterator Input/Output of Callables must be put
before `Callable[[Other], Any]` otherwise the pattern matching picks the
latter.
**PR message**: Not sure if I put the check at the right spot, but I
thought throwing the error before the loop made sense to me.
**Description:** Checks if there are only system messages using
AnthropicChat model and throws an error if it's the case. Check Issue
for more details
**Issue:** #30764
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Issue:**[
#309070](https://github.com/langchain-ai/langchain/issues/30970)
**Cause**
Arg type in python code
```
arg: Union[SubSchema1, SubSchema2]
```
is translated to `anyOf` in **json schema**
```
"anyOf" : [{sub schema 1 ...}, {sub schema 1 ...}]
```
The value of anyOf is a list sub schemas.
The bug is caused since the sub schemas inside `anyOf` list is not taken
care of.
The location where the issue happens is `convert_to_openai_function`
function -> `_recursive_set_additional_properties_false` function, that
recursively adds `"additionalProperties": false` to json schema which is
[required by OpenAI's strict function
calling](https://platform.openai.com/docs/guides/structured-outputs?api-mode=responses#additionalproperties-false-must-always-be-set-in-objects)
**Solution:**
This PR fixes this issue by iterating each sub schema inside `anyOf`
list.
A unit test is added.
**Twitter handle:** shengboma
If no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
`aindex` function should check not only `adelete` method, but `delete`
method too
**PR title**: "core: fix async indexing issue with adelete/delete
checking"
**PR message**: Currently `langchain.indexes.aindex` checks if vector
store has overrided adelete method. But due to `adelete` default
implementation store can have just `delete` overrided to make `adelete`
working.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Description: This document change concerns the document-loader
integration, specifically `Confluence`.
I am trying to use the ConfluenceLoader and came across deprecations
when I followed the instructions in the documentation. So I updated the
code blocks with the latest changes made to langchain, and also updated
the documentation for better readability
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
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- Where "package" is whichever of langchain, core, etc. is being
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changes.
- Example: "core: add foobar LLM"
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Additional guidelines:
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- Please do not add dependencies to pyproject.toml files (even optional
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If no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** The file ```docs/api_reference/create_api_rst.py```
uses a pyproject.toml check to remove partners which don't have a valid
pyproject.toml. This PR extends that check to ```/langchain/libs/*```
sub-directories as well. Without this the ```make api_docs_build```
command fails (see error).
- **Issue:** #31109
- **Dependencies:** none
- **Error Traceback:**
uv run --no-group test python docs/api_reference/create_api_rst.py
Starting to build API reference files.
Building package: community
pyproject.toml not found in /langchain/libs/community.
You are either attempting to build a directory which is not a package or
the package is missing a pyproject.toml file which should be
added.Aborting the build.
make: *** [Makefile:35: api_docs_build] Error 1
**Description**:
Add a `async_client_kwargs` field to ollama chat/llm/embeddings adapters
that is passed to async httpx client constructor.
**Motivation:**
In my use-case:
- chat/embedding model adapters may be created frequently, sometimes to
be called just once or to never be called at all
- they may be used in bots sunc and async mode (not known at the moment
they are created)
So, I want to keep a static transport instance maintaining connection
pool, so model adapters can be created and destroyed freely. But that
doesn't work when both sync and async functions are in use as I can only
pass one transport instance for both sync and async client, while
transport types must be different for them. So I can't make both sync
and async calls use shared transport with current model adapter
interfaces.
In this PR I add a separate `async_client_kwargs` that gets passed to
async client constructor, so it will be possible to pass a separate
transport instance. For sake of backwards compatibility, it is merged
with `client_kwargs`, so nothing changes when it is not set.
I am unable to run linter right now, but the changes look ok.
Bumps
[Ana06/get-changed-files](https://github.com/ana06/get-changed-files)
from 2.2.0 to 2.3.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/ana06/get-changed-files/releases">Ana06/get-changed-files's
releases</a>.</em></p>
<blockquote>
<h2>v2.3.0</h2>
<p>This project is a fork of <a
href="https://github.com/jitterbit/get-changed-files">jitterbit/get-changed-files</a>,
which:</p>
<ul>
<li>Supports <code>pull_request_target</code></li>
<li>Allows to filter files using regular expressions</li>
<li>Removes the ahead check</li>
<li>Considers renamed modified files as modified</li>
<li>Adds <code>added_modified_renamed</code> that includes renamed
non-modified files and all files in <code>added_modified</code></li>
<li>Uses Node 20</li>
</ul>
<h2>Changes</h2>
<ul>
<li>Update to Node 20</li>
<li>Update dependencies</li>
</ul>
<h2>Raw diff</h2>
<p><a
href="https://github.com/Ana06/get-changed-files/compare/v2.2.0...v2.3.0">https://github.com/Ana06/get-changed-files/compare/v2.2.0...v2.3.0</a></p>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="25f79e676e"><code>25f79e6</code></a>
Update version in package.json</li>
<li><a
href="6f5373eb01"><code>6f5373e</code></a>
Merge pull request <a
href="https://redirect.github.com/ana06/get-changed-files/issues/42">#42</a>
from Ana06/release2-3</li>
<li><a
href="64dffb46a4"><code>64dffb4</code></a>
Prepare 2.3.0 release</li>
<li><a
href="791f7645b7"><code>791f764</code></a>
[CI] Update actions/checkout</li>
<li><a
href="5a4a136e91"><code>5a4a136</code></a>
[CI] Ensure GH action uses node version 20</li>
<li><a
href="38bdb2e498"><code>38bdb2e</code></a>
Update to Node 20</li>
<li><a
href="5558be5781"><code>5558be5</code></a>
Merge pull request <a
href="https://redirect.github.com/ana06/get-changed-files/issues/30">#30</a>
from Ana06/dependabot/npm_and_yarn/decode-uri-componen...</li>
<li><a
href="6a376fdbb3"><code>6a376fd</code></a>
Merge pull request <a
href="https://redirect.github.com/ana06/get-changed-files/issues/31">#31</a>
from Ana06/dependabot/npm_and_yarn/qs-6.5.3</li>
<li><a
href="ace6e7bcbb"><code>ace6e7b</code></a>
Merge pull request <a
href="https://redirect.github.com/ana06/get-changed-files/issues/32">#32</a>
from brtrick/main</li>
<li><a
href="a102fae9bf"><code>a102fae</code></a>
Merge pull request <a
href="https://redirect.github.com/ana06/get-changed-files/issues/33">#33</a>
from Ana06/dependabot/npm_and_yarn/json5-2.2.3</li>
<li>Additional commits viewable in <a
href="https://github.com/ana06/get-changed-files/compare/v2.2.0...v2.3.0">compare
view</a></li>
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Updates dependencies to Chroma to integrate the major release of Chroma
with improved performance, and to fix issues users have been seeing
using the latest chroma docker image with langchain-chroma
https://github.com/langchain-ai/langchain/issues/31047#issuecomment-2850790841
Updates chromadb dependency to >=1.0.9
This also removes the dependency of chroma-hnswlib, meaning it can run
against python 3.13 runners for tests as well.
Tested this by pulling the latest Chroma docker image, running
langchain-chroma using client mode
```
httpClient = chromadb.HttpClient(host="localhost", port=8000)
vector_store = Chroma(
client=httpClient,
collection_name="test",
embedding_function=embeddings,
)
```
"Alanis Morissette" spelling error
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: 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
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- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
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If no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
Bumps [astral-sh/setup-uv](https://github.com/astral-sh/setup-uv) from 5
to 6.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/astral-sh/setup-uv/releases">astral-sh/setup-uv's
releases</a>.</em></p>
<blockquote>
<h2>v6.0.0 🌈 activate-environment and working-directory</h2>
<h2>Changes</h2>
<p>This version contains some breaking changes which have been gathering
up for a while. Lets dive into them:</p>
<ul>
<li><a
href="https://github.com/astral-sh/setup-uv/blob/HEAD/#activate-environment">Activate
environment</a></li>
<li><a
href="https://github.com/astral-sh/setup-uv/blob/HEAD/#working-directory">Working
Directory</a></li>
<li><a
href="https://github.com/astral-sh/setup-uv/blob/HEAD/#default-cache-dependency-glob">Default
<code>cache-dependency-glob</code></a></li>
<li><a
href="https://github.com/astral-sh/setup-uv/blob/HEAD/#use-default-cache-dir-on-self-hosted-runners">Use
default cache dir on self hosted runners</a></li>
</ul>
<h3>Activate environment</h3>
<p>In previous versions using the input <code>python-version</code>
automatically activated a venv at the repository root.
This led to some unwanted side-effects, was sometimes unexpected and not
flexible enough.</p>
<p>The venv activation is now explicitly controlled with the new input
<code>activate-environment</code> (false by default):</p>
<pre lang="yaml"><code>- name: Install the latest version of uv and
activate the environment
uses: astral-sh/setup-uv@v6
with:
activate-environment: true
- run: uv pip install pip
</code></pre>
<p>The venv gets created by the <a
href="https://docs.astral.sh/uv/pip/environments/"><code>uv
venv</code></a> command so the python version is controlled by the
<code>python-version</code> input or the files
<code>pyproject.toml</code>, <code>uv.toml</code>,
<code>.python-version</code> in the <code>working-directory</code>.</p>
<h3>Working Directory</h3>
<p>The new input <code>working-directory</code> controls where we look
for <code>pyproject.toml</code>, <code>uv.toml</code> and
<code>.python-version</code> files
which are used to determine the version of uv and python to install.</p>
<p>It can also be used to control where the venv gets created.</p>
<pre lang="yaml"><code>- name: Install uv based on the config files in
the working-directory
uses: astral-sh/setup-uv@v6
with:
working-directory: my/subproject/dir
</code></pre>
<blockquote>
<p>[!CAUTION]</p>
<p>The inputs <code>pyproject-file</code> and <code>uv-file</code> have
been removed.</p>
</blockquote>
<h3>Default <code>cache-dependency-glob</code></h3>
<p><a href="https://github.com/ssbarnea"><code>@ssbarnea</code></a>
found out that the default <code>cache-dependency-glob</code> was not
suitable for a lot of users.</p>
<p>The old default</p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="6b9c6063ab"><code>6b9c606</code></a>
Bump dependencies (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/389">#389</a>)</li>
<li><a
href="ef6bcdff59"><code>ef6bcdf</code></a>
Fix default cache dependency glob (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/388">#388</a>)</li>
<li><a
href="9a311713f4"><code>9a31171</code></a>
chore: update known checksums for 0.6.17 (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/384">#384</a>)</li>
<li><a
href="c7f87aa956"><code>c7f87aa</code></a>
bump to v6 in README (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/382">#382</a>)</li>
<li><a
href="aadfaf08d6"><code>aadfaf0</code></a>
Change default cache-dependency-glob (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/352">#352</a>)</li>
<li><a
href="a0f9da6273"><code>a0f9da6</code></a>
No default UV_CACHE_DIR on selfhosted runners (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/380">#380</a>)</li>
<li><a
href="ec4c691628"><code>ec4c691</code></a>
new inputs activate-environment and working-directory (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/381">#381</a>)</li>
<li><a
href="aa1290542e"><code>aa12905</code></a>
chore: update known checksums for 0.6.16 (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/378">#378</a>)</li>
<li><a
href="fcaddda076"><code>fcaddda</code></a>
chore: update known checksums for 0.6.15 (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/377">#377</a>)</li>
<li><a
href="fb3a0a97fa"><code>fb3a0a9</code></a>
log info on venv activation (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/375">#375</a>)</li>
<li>See full diff in <a
href="https://github.com/astral-sh/setup-uv/compare/v5...v6">compare
view</a></li>
</ul>
</details>
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**Description:** This is a document change regarding integration with
package `langchain-anthropic` for newly released websearch tool ([Claude
doc](https://docs.anthropic.com/en/docs/build-with-claude/tool-use/web-search-tool)).
Issue 1: The sample in [Web Search
section](https://python.langchain.com/docs/integrations/chat/anthropic/#web-search)
did not run. You would get an error as below:
```
File "my_file.py", line 170, in call
model_with_tools = model.bind_tools([websearch_tool])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/langchain_anthropic/chat_models.py", line 1363, in bind_tools
tool if _is_builtin_tool(tool) else convert_to_anthropic_tool(tool)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/langchain_anthropic/chat_models.py", line 1645, in convert_to_anthropic_tool
input_schema=oai_formatted["parameters"],
~~~~~~~~~~~~~^^^^^^^^^^^^^^
KeyError: 'parameters'
```
This is because websearch tool is only recently supported in
langchain-anthropic==0.3.13`, in [0.3.13
release](https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-anthropic%3D%3D0%22&expanded=true)
mentioning:
> anthropic[patch]: support web search
(https://github.com/langchain-ai/langchain/pull/31157)
Issue 2: The current doc has outdated package requirements for Websearch
tool: "This guide requires langchain-anthropic>=0.3.10".
Changes:
- Updated the required `langchain-anthropic` package version (0.3.10 ->
0.3.13).
- Added notes to user when using websearch sample.
I believe this will help avoid future confusion from readers.
**Issue:** N/A
**Dependencies:** N/A
**Twitter handle:** N/A
* Remove unnecessary cast of id -> str (can do with a field setting)
* Remove unnecessary `set_text` model validator (can be done with a
computed field - though we had to make some changes to the `Generation`
class to make this possible
Before: ~2.4s
Blue circles represent time spent in custom validators :(
<img width="1337" alt="Screenshot 2025-05-14 at 10 10 12 AM"
src="https://github.com/user-attachments/assets/bb4f477f-4ee3-4870-ae93-14ca7f197d55"
/>
After: ~2.2s
<img width="1344" alt="Screenshot 2025-05-14 at 10 11 03 AM"
src="https://github.com/user-attachments/assets/99f97d80-49de-462f-856f-9e7e8662adbc"
/>
We still want to optimize the backwards compatible tool calls model
validator, though I think this might involve breaking changes, so wanted
to separate that into a different PR. This is circled in green.
**Description:** Before this commit, if one record is batched in more
than 32k rows for sqlite3 >= 3.32 or more than 999 rows for sqlite3 <
3.31, the `record_manager.delete_keys()` will fail, as we are creating a
query with too many variables.
This commit ensures that we are batching the delete operation leveraging
the `cleanup_batch_size` as it is already done for `full` cleanup.
Added unit tests for incremental mode as well on different deleting
batch size.
2025-05-14 11:38:21 -04:00
615 changed files with 28587 additions and 16412 deletions
@@ -7,8 +7,8 @@ LangChain has a large ecosystem of integrations with various external resources
When building such applications developers should remember to follow good security practices:
* [**Limit Permissions**](https://en.wikipedia.org/wiki/Principle_of_least_privilege): Scope permissions specifically to the application's need. Granting broad or excessive permissions can introduce significant security vulnerabilities. To avoid such vulnerabilities, consider using read-only credentials, disallowing access to sensitive resources, using sandboxing techniques (such as running inside a container), specifying proxy configurations to control external requests, etc. as appropriate for your application.
* **Anticipate Potential Misuse**: Just as humans can err, so can Large Language Models (LLMs). Always assume that any system access or credentials may be used in any way allowed by the permissions they are assigned. For example, if a pair of database credentials allows deleting data, it’s safest to assume that any LLM able to use those credentials may in fact delete data.
* [**Defense in Depth**](https://en.wikipedia.org/wiki/Defense_in_depth_(computing)): No security technique is perfect. Fine-tuning and good chain design can reduce, but not eliminate, the odds that a Large Language Model (LLM) may make a mistake. It’s best to combine multiple layered security approaches rather than relying on any single layer of defense to ensure security. For example: use both read-only permissions and sandboxing to ensure that LLMs are only able to access data that is explicitly meant for them to use.
* **Anticipate Potential Misuse**: Just as humans can err, so can Large Language Models (LLMs). Always assume that any system access or credentials may be used in any way allowed by the permissions they are assigned. For example, if a pair of database credentials allows deleting data, it's safest to assume that any LLM able to use those credentials may in fact delete data.
* [**Defense in Depth**](https://en.wikipedia.org/wiki/Defense_in_depth_(computing)): No security technique is perfect. Fine-tuning and good chain design can reduce, but not eliminate, the odds that a Large Language Model (LLM) may make a mistake. It's best to combine multiple layered security approaches rather than relying on any single layer of defense to ensure security. For example: use both read-only permissions and sandboxing to ensure that LLMs are only able to access data that is explicitly meant for them to use.
Risks of not doing so include, but are not limited to:
* Data corruption or loss.
@@ -39,7 +39,7 @@ Before reporting a vulnerability, please review:
1) In-Scope Targets and Out-of-Scope Targets below.
2) The [langchain-ai/langchain](https://python.langchain.com/docs/contributing/repo_structure) monorepo structure.
3) The [Best practicies](#best-practices) above to
3) The [Best practices](#best-practices) above to
understand what we consider to be a security vulnerability vs. developer
"This guide demonstrates how Oracle AI Vector Search can be used with Langchain to serve an end-to-end RAG pipeline. This guide goes through examples of:\n",
"This guide demonstrates how Oracle AI Vector Search can be used with LangChain to serve an end-to-end RAG pipeline. This guide goes through examples of:\n",
"\n",
" * Loading the documents from various sources using OracleDocLoader\n",
" * Summarizing them within/outside the database using OracleSummary\n",
@@ -47,7 +47,19 @@
"source": [
"### Prerequisites\n",
"\n",
"Please install Oracle Python Client driver to use Langchain with Oracle AI Vector Search. "
"Please install the Oracle Database [python-oracledb driver](https://pypi.org/project/oracledb/) to use LangChain with Oracle AI Vector Search:\n",
"\n",
"```\n",
"$ python -m pip install --upgrade oracledb\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Demo User\n",
"First, connect as a privileged user to create a demo user with all the required privileges. Change the credentials for your environment. Also set the DEMO_PY_DIR path to a directory on the database host where your model file is located:"
]
},
{
@@ -56,65 +68,30 @@
"metadata": {},
"outputs": [],
"source": [
"# pip install oracledb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Demo User\n",
"First, create a demo user with all the required privileges. "
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Connection successful!\n",
"User setup done!\n"
]
}
],
"source": [
"import sys\n",
"\n",
"import oracledb\n",
"\n",
"# Update with your username, password, hostname, and service_name\n",
"username = \"\"\n",
"# Please update with your SYSTEM (or privileged user) username, password, and database connection string\n",
" execute immediate 'drop user if exists testuser cascade';\n",
"\n",
" -- Create user and grant privileges\n",
" execute immediate 'create user testuser identified by testuser';\n",
" execute immediate 'grant connect, unlimited tablespace, create credential, create procedure, create any index to testuser';\n",
" execute immediate 'create or replace directory DEMO_PY_DIR as ''/scratch/hroy/view_storage/hroy_devstorage/demo/orachain''';\n",
" execute immediate 'create or replace directory DEMO_PY_DIR as ''/home/yourname/demo/orachain''';\n",
" execute immediate 'grant read, write on directory DEMO_PY_DIR to public';\n",
" execute immediate 'grant create mining model to testuser';\n",
"\n",
"\n",
" -- Network access\n",
" begin\n",
" DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(\n",
@@ -127,15 +104,7 @@
" end;\n",
" \"\"\"\n",
" )\n",
" print(\"User setup done!\")\n",
" except Exception as e:\n",
" print(f\"User setup failed with error: {e}\")\n",
" finally:\n",
" cursor.close()\n",
" conn.close()\n",
"except Exception as e:\n",
" print(f\"Connection failed with error: {e}\")\n",
" sys.exit(1)"
" print(\"User setup done!\")"
]
},
{
@@ -143,13 +112,13 @@
"metadata": {},
"source": [
"## Process Documents using Oracle AI\n",
"Consider the following scenario: users possess documents stored either in an Oracle Database or a file system and intend to utilize this data with Oracle AI Vector Search powered by Langchain.\n",
"Consider the following scenario: users possess documents stored either in an Oracle Database or a file system and intend to utilize this data with Oracle AI Vector Search powered by LangChain.\n",
"\n",
"To prepare the documents for analysis, a comprehensive preprocessing workflow is necessary. Initially, the documents must be retrieved, summarized (if required), and chunked as needed. Subsequent steps involve generating embeddings for these chunks and integrating them into the Oracle AI Vector Store. Users can then conduct semantic searches on this data.\n",
"\n",
"The Oracle AI Vector Search Langchain library encompasses a suite of document processing tools that facilitate document loading, chunking, summary generation, and embedding creation.\n",
"The Oracle AI Vector Search LangChain library encompasses a suite of document processing tools that facilitate document loading, chunking, summary generation, and embedding creation.\n",
"\n",
"In the sections that follow, we will detail the utilization of Oracle AI Langchain APIs to effectively implement each of these processes."
"In the sections that follow, we will detail the utilization of Oracle AI LangChain APIs to effectively implement each of these processes."
]
},
{
@@ -157,38 +126,24 @@
"metadata": {},
"source": [
"### Connect to Demo User\n",
"The following sample code will show how to connect to Oracle Database. By default, python-oracledb runs in a ‘Thin’ mode which connects directly to Oracle Database. This mode does not need Oracle Client libraries. However, some additional functionality is available when python-oracledb uses them. Python-oracledb is said to be in ‘Thick’ mode when Oracle Client libraries are used. Both modes have comprehensive functionality supporting the Python Database API v2.0 Specification. See the following [guide](https://python-oracledb.readthedocs.io/en/latest/user_guide/appendix_a.html#featuresummary) that talks about features supported in each mode. You might want to switch to thick-mode if you are unable to use thin-mode."
"The following sample code shows how to connect to Oracle Database using the python-oracledb driver. By default, python-oracledb runs in a ‘Thin’ mode which connects directly to Oracle Database. This mode does not need Oracle Client libraries. However, some additional functionality is available when python-oracledb uses them. Python-oracledb is said to be in ‘Thick’ mode when Oracle Client libraries are used. Both modes have comprehensive functionality supporting the Python Database API v2.0 Specification. See the following [guide](https://python-oracledb.readthedocs.io/en/latest/user_guide/appendix_a.html#featuresummary) that talks about features supported in each mode. You can switch to Thickmode if you are unable to use Thinmode."
]
},
{
"cell_type": "code",
"execution_count": 45,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Connection successful!\n"
]
}
],
"outputs": [],
"source": [
"import sys\n",
"\n",
"import oracledb\n",
"\n",
"# please update with your username, password, hostname and service_name\n",
"username = \"\"\n",
"# please update with your username, password, and database connection string\n",
"Oracle accommodates a variety of embedding providers, enabling users to choose between proprietary database solutions and third-party services such as OCIGENAI and HuggingFace. This selection dictates the methodology for generating and managing embeddings.\n",
"Oracle accommodates a variety of embedding providers, enabling you to choose between proprietary database solutions and third-party services such as Oracle Generative AI Service and HuggingFace. This selection dictates the methodology for generating and managing embeddings.\n",
"\n",
"***Important*** : Should users opt for the database option, they must upload an ONNX model into the Oracle Database. Conversely, if a third-party provider is selected for embedding generation, uploading an ONNX model to Oracle Database is not required.\n",
"***Important*** : Should you opt for the database option, you must upload an ONNX model into the Oracle Database. Conversely, if a third-party provider is selected for embedding generation, uploading an ONNX model to Oracle Database is not required.\n",
"\n",
"A significant advantage of utilizing an ONNX model directly within Oracle is the enhanced security and performance it offers by eliminating the need to transmit data to external parties. Additionally, this method avoids the latency typically associated with network or REST API calls.\n",
"A significant advantage of utilizing an ONNX model directly within Oracle Database is the enhanced security and performance it offers by eliminating the need to transmit data to external parties. Additionally, this method avoids the latency typically associated with network or REST API calls.\n",
"\n",
"Below is the example code to upload an ONNX model into Oracle Database:"
"Users have the flexibility to load documents from either the Oracle Database, a file system, or both, by appropriately configuring the loader parameters. For comprehensive details on these parameters, please consult the [Oracle AI Vector Search Guide](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-73397E89-92FB-48ED-94BB-1AD960C4EA1F).\n",
"You have the flexibility to load documents from either the Oracle Database, a file system, or both, by appropriately configuring the loader parameters. For comprehensive details on these parameters, please consult the [Oracle AI Vector Search Guide](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-73397E89-92FB-48ED-94BB-1AD960C4EA1F).\n",
"\n",
"A significant advantage of utilizing OracleDocLoader is its capability to process over 150 distinct file formats, eliminating the need for multiple loaders for different document types. For a complete list of the supported formats, please refer to the [Oracle Text Supported Document Formats](https://docs.oracle.com/en/database/oracle/oracle-database/23/ccref/oracle-text-supported-document-formats.html).\n",
"\n",
"Below is a sample code snippet that demonstrates how to use OracleDocLoader"
"Below is a sample code snippet that demonstrates how to use OracleDocLoader:"
"Now that the user loaded the documents, they may want to generate a summary for each document. The Oracle AI Vector Search Langchain library offers a suite of APIs designed for document summarization. It supports multiple summarization providers such as Database, OCIGENAI, HuggingFace, among others, allowing users to select the provider that best meets their needs. To utilize these capabilities, users must configure the summary parameters as specified. For detailed information on these parameters, please consult the [Oracle AI Vector Search Guide book](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-EC9DDB58-6A15-4B36-BA66-ECBA20D2CE57)."
"Now that you have loaded the documents, you may want to generate a summary for each document. The Oracle AI Vector Search LangChain library offers a suite of APIs designed for document summarization. It supports multiple summarization providers such as Database, Oracle Generative AI Service, HuggingFace, among others, allowing you to select the provider that best meets their needs. To utilize these capabilities, you must configure the summary parameters as specified. For detailed information on these parameters, please consult the [Oracle AI Vector Search Guide book](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-EC9DDB58-6A15-4B36-BA66-ECBA20D2CE57)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Note:*** The users may need to set proxy if they want to use some 3rd party summary generation providers other than Oracle's in-house and default provider: 'database'. If you don't have proxy, please remove the proxy parameter when you instantiate the OracleSummary."
"***Note:*** You may need to set proxy if you want to use some 3rd party summary generation providers other than Oracle's in-house and default provider: 'database'. If you don't have proxy, please remove the proxy parameter when you instantiate the OracleSummary."
]
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# proxy to be used when we instantiate summary and embedder object\n",
"# proxy to be used when we instantiate summary and embedder objects\n",
"proxy = \"\""
]
},
@@ -433,22 +345,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"The following sample code will show how to generate summary:"
"The following sample code shows how to generate a summary:"
"Now that the documents are chunked as per requirements, the users may want to generate embeddings for these chunks. Oracle AI Vector Search provides multiple methods for generating embeddings, utilizing either locally hosted ONNX models or third-party APIs. For comprehensive instructions on configuring these alternatives, please refer to the [Oracle AI Vector Search Guide](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-C6439E94-4E86-4ECD-954E-4B73D53579DE)."
"Now that the documents are chunked as per requirements, you may want to generate embeddings for these chunks. Oracle AI Vector Search provides multiple methods for generating embeddings, utilizing either locally hosted ONNX models or third-party APIs. For comprehensive instructions on configuring these alternatives, please refer to the [Oracle AI Vector Search Guide](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-C6439E94-4E86-4ECD-954E-4B73D53579DE)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Note:*** Users may need to configure a proxy to utilize third-party embedding generation providers, excluding the 'database' provider that utilizes an ONNX model."
"***Note:*** You may need to configure a proxy to utilize third-party embedding generation providers, excluding the 'database' provider that utilizes an ONNX model."
]
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -547,22 +443,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"The following sample code will show how to generate embeddings:"
"The following sample code shows how to generate embeddings:"
"Now that you know how to use Oracle AI Langchain library APIs individually to process the documents, let us show how to integrate with Oracle AI Vector Store to facilitate the semantic searches."
"Now that you know how to use Oracle AI LangChain library APIs individually to process the documents, let us show how to integrate with Oracle AI Vector Store to facilitate the semantic searches."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, let's import all the dependencies."
"First, let's import all the dependencies:"
]
},
{
"cell_type": "code",
"execution_count": 52,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -626,100 +514,80 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, let's combine all document processing stages together. Here is the sample code below:"
"Next, let's combine all document processing stages together. Here is the sample code:"
]
},
{
"cell_type": "code",
"execution_count": 53,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Connection successful!\n",
"ONNX model loaded.\n",
"Number of total chunks with metadata: 3\n"
]
}
],
"outputs": [],
"source": [
"\"\"\"\n",
"In this sample example, we will use 'database' provider for both summary and embeddings.\n",
"So, we don't need to do the followings:\n",
"In this sample example, we will use 'database' provider for both summary and embeddings\n",
"so, we don't need to do the following:\n",
" - set proxy for 3rd party providers\n",
" - create credential for 3rd party providers\n",
"\n",
"If you choose to use 3rd party provider, \n",
"please follow the necessary steps for proxy and credential.\n",
"If you choose to use 3rd party provider, please follow the necessary steps for proxy and credential.\n",
"\"\"\"\n",
"\n",
"# oracle connection\n",
"# please update with your username, password, hostname, and service_name\n",
"# please update with your username, password, and database connection string\n",
"This example demonstrates the creation of a default HNSW index on embeddings within the 'oravs' table. Users may adjust various parameters according to their specific needs. For detailed information on these parameters, please consult the [Oracle AI Vector Search Guide book](https://docs.oracle.com/en/database/oracle/oracle-database/23/vecse/manage-different-categories-vector-indexes.html).\n",
"This example demonstrates the creation of a default HNSW index on embeddings within the 'oravs' table. You may adjust various parameters according to your specific needs. For detailed information on these parameters, please consult the [Oracle AI Vector Search Guide book](https://docs.oracle.com/en/database/oracle/oracle-database/23/vecse/manage-different-categories-vector-indexes.html).\n",
"\n",
"Additionally, various types of vector indices can be created to meet diverse requirements. More details can be found in our [comprehensive guide](https://python.langchain.com/v0.1/docs/integrations/vectorstores/oracle/).\n"
]
@@ -805,29 +665,16 @@
"## Perform Semantic Search\n",
"All set!\n",
"\n",
"We have successfully processed the documents and stored them in the vector store, followed by the creation of an index to enhance query performance. We are now prepared to proceed with semantic searches.\n",
"You have successfully processed the documents and stored them in the vector store, followed by the creation of an index to enhance query performance. You are now prepared to proceed with semantic searches.\n",
"\n",
"Below is the sample code for this process:"
]
},
{
"cell_type": "code",
"execution_count": 58,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Document(page_content='The database stores LOBs differently from other data types. Creating a LOB column implicitly creates a LOB segment and a LOB index. The tablespace containing the LOB segment and LOB index, which are always stored together, may be different from the tablespace containing the table. Sometimes the database can store small amounts of LOB data in the table itself rather than in a separate LOB segment.', metadata={'_oid': '662f2f257677f3c2311a8ff999fd34e5', '_rowid': 'AAAR/xAAEAAAAAnAAC', 'id': '662f2f257677f3c2311a8ff999fd34e5$3$1', 'document_id': '3', 'document_summary': 'Sometimes the database can store small amounts of LOB data in the table itself rather than in a separate LOB segment.\\n\\n'})]\n",
"[]\n",
"[(Document(page_content='The database stores LOBs differently from other data types. Creating a LOB column implicitly creates a LOB segment and a LOB index. The tablespace containing the LOB segment and LOB index, which are always stored together, may be different from the tablespace containing the table. Sometimes the database can store small amounts of LOB data in the table itself rather than in a separate LOB segment.', metadata={'_oid': '662f2f257677f3c2311a8ff999fd34e5', '_rowid': 'AAAR/xAAEAAAAAnAAC', 'id': '662f2f257677f3c2311a8ff999fd34e5$3$1', 'document_id': '3', 'document_summary': 'Sometimes the database can store small amounts of LOB data in the table itself rather than in a separate LOB segment.\\n\\n'}), 0.055675752460956573)]\n",
"[]\n",
"[Document(page_content='If the answer to any preceding questions is yes, then the database stops the search and allocates space from the specified tablespace; otherwise, space is allocated from the database default shared temporary tablespace.', metadata={'_oid': '662f2f253acf96b33b430b88699490a2', '_rowid': 'AAAR/xAAEAAAAAnAAA', 'id': '662f2f253acf96b33b430b88699490a2$1$1', 'document_id': '1', 'document_summary': 'If the answer to any preceding questions is yes, then the database stops the search and allocates space from the specified tablespace; otherwise, space is allocated from the database default shared temporary tablespace.\\n\\n'})]\n",
"[Document(page_content='If the answer to any preceding questions is yes, then the database stops the search and allocates space from the specified tablespace; otherwise, space is allocated from the database default shared temporary tablespace.', metadata={'_oid': '662f2f253acf96b33b430b88699490a2', '_rowid': 'AAAR/xAAEAAAAAnAAA', 'id': '662f2f253acf96b33b430b88699490a2$1$1', 'document_id': '1', 'document_summary': 'If the answer to any preceding questions is yes, then the database stops the search and allocates space from the specified tablespace; otherwise, space is allocated from the database default shared temporary tablespace.\\n\\n'})]\n"
@@ -48,7 +48,7 @@ From the opposite direction, scientists use `LangChain` in research and referenc
| `2205.12654v1` [Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages](http://arxiv.org/abs/2205.12654v1) | Kevin Heffernan, Onur Çelebi, Holger Schwenk | 2022‑05‑25 | `API:` [langchain_community...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
| `2204.00498v1` [Evaluating the Text-to-SQL Capabilities of Large Language Models](http://arxiv.org/abs/2204.00498v1) | Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau | 2022‑03‑15 | `Docs:` [docs/tutorials/sql_qa](https://python.langchain.com/docs/tutorials/sql_qa), `API:` [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
| `2202.00666v5` [Locally Typical Sampling](http://arxiv.org/abs/2202.00666v5) | Clara Meister, Tiago Pimentel, Gian Wiher, et al. | 2022‑02‑01 | `API:` [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
| `2112.01488v3` [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](http://arxiv.org/abs/2112.01488v3) | Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, et al. | 2021‑12‑02 | `Docs:` [docs/integrations/retrievers/ragatouille](https://python.langchain.com/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/docs/concepts), [docs/integrations/providers/dspy](https://python.langchain.com/docs/integrations/providers/dspy)
| `2112.01488v3` [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](http://arxiv.org/abs/2112.01488v3) | Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, et al. | 2021‑12‑02 | `Docs:` [docs/integrations/retrievers/ragatouille](https://python.langchain.com/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/docs/concepts)
| `2103.00020v1` [Learning Transferable Visual Models From Natural Language Supervision](http://arxiv.org/abs/2103.00020v1) | Alec Radford, Jong Wook Kim, Chris Hallacy, et al. | 2021‑02‑26 | `API:` [langchain_experimental.open_clip](https://python.langchain.com/api_reference/experimental/open_clip.html)
| `2005.14165v4` [Language Models are Few-Shot Learners](http://arxiv.org/abs/2005.14165v4) | Tom B. Brown, Benjamin Mann, Nick Ryder, et al. | 2020‑05‑28 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
| `2005.11401v4` [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](http://arxiv.org/abs/2005.11401v4) | Patrick Lewis, Ethan Perez, Aleksandra Piktus, et al. | 2020‑05‑22 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
@@ -9,7 +9,7 @@ LLM based applications often involve a lot of I/O-bound operations, such as maki
:::note
You are expected to be familiar with asynchronous programming in Python before reading this guide. If you are not, please find appropriate resources online to learn how to program asynchronously in Python.
This guide specifically focuses on what you need to know to work with LangChain in an asynchronous context, assuming that you are already familiar with asynch
This guide specifically focuses on what you need to know to work with LangChain in an asynchronous context, assuming that you are already familiar with asynchronous programming.
LangChain provides a callback system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.
You can subscribe to these events by using the `callbacks` argument available throughout the API. This argument is list of handler objects, which are expected to implement one or more of the methods described below in more detail.
You can subscribe to these events by using the `callbacks` argument available throughout the API. This argument is a list of handler objects, which are expected to implement one or more of the methods described below in more detail.
@@ -32,7 +32,7 @@ The only requirement for a retriever is the ability to accepts a query and retur
In particular, [LangChain's retriever class](https://python.langchain.com/api_reference/core/retrievers/langchain_core.retrievers.BaseRetriever.html#) only requires that the `_get_relevant_documents` method is implemented, which takes a `query: str` and returns a list of [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) objects that are most relevant to the query.
The underlying logic used to get relevant documents is specified by the retriever and can be whatever is most useful for the application.
A LangChain retriever is a [runnable](/docs/how_to/lcel_cheatsheet/), which is a standard interface is for LangChain components.
A LangChain retriever is a [runnable](/docs/how_to/lcel_cheatsheet/), which is a standard interface for LangChain components.
This means that it has a few common methods, including `invoke`, that are used to interact with it. A retriever can be invoked with a query:
```python
@@ -57,7 +57,7 @@ Despite the flexibility of the retriever interface, a few common types of retrie
### Search apis
It's important to note that retrievers don't need to actually *store* documents.
For example, we can be built retrievers on top of search APIs that simply return search results!
For example, we can build retrievers on top of search APIs that simply return search results!
See our retriever integrations with [Amazon Kendra](/docs/integrations/retrievers/amazon_kendra_retriever/) or [Wikipedia Search](/docs/integrations/retrievers/wikipedia/).
### Relational or graph database
@@ -68,8 +68,8 @@ For example, you can build a retriever for a SQL database using text-to-SQL conv
:::info[Further reading]
* See our [tutorial](/docs/tutorials/sql_qa/) for context on how to build a retreiver using a SQL database and text-to-SQL.
* See our [tutorial](/docs/tutorials/graph/) for context on how to build a retreiver using a graph database and text-to-Cypher.
* See our [tutorial](/docs/tutorials/sql_qa/) for context on how to build a retriever using a SQL database and text-to-SQL.
* See our [tutorial](/docs/tutorials/graph/) for context on how to build a retriever using a graph database and text-to-Cypher.
@@ -11,8 +11,8 @@ This need motivates the concept of structured output, where models can be instru
## Key concepts
**(1) Schema definition:** The output structure is represented as a schema, which can be defined in several ways.
**(2) Returning structured output:** The model is given this schema, and is instructed to return output that conforms to it.
1. **Schema definition:** The output structure is represented as a schema, which can be defined in several ways.<br/>
2. **Returning structured output:** The model is given this schema, and is instructed to return output that conforms to it.
## Recommended usage
@@ -109,11 +109,11 @@ ai_msg
There are a few challenges when producing structured output with the above methods:
(1) When tool calling is used, tool call arguments needs to be parsed from a dictionary back to the original schema.
1. When tool calling is used, tool call arguments needs to be parsed from a dictionary back to the original schema.<br/>
(2) In addition, the model needs to be instructed to *always* use the tool when we want to enforce structured output, which is a provider specific setting.
2. In addition, the model needs to be instructed to *always* use the tool when we want to enforce structured output, which is a provider specific setting.<br/>
(3) When JSON mode is used, the output needs to be parsed into a JSON object.
3. When JSON mode is used, the output needs to be parsed into a JSON object.
With these challenges in mind, LangChain provides a helper function (`with_structured_output()`) to streamline the process.
@@ -21,10 +21,10 @@ You will sometimes hear the term `function calling`. We use this term interchang
## Key concepts
**(1) Tool Creation:** Use the [@tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html) decorator to create a [tool](/docs/concepts/tools). A tool is an association between a function and its schema.
**(2) Tool Binding:** The tool needs to be connected to a model that supports tool calling. This gives the model awareness of the tool and the associated input schema required by the tool.
**(3) Tool Calling:** When appropriate, the model can decide to call a tool and ensure its response conforms to the tool's input schema.
**(4) Tool Execution:** The tool can be executed using the arguments provided by the model.
1. **Tool Creation:** Use the [@tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html) decorator to create a [tool](/docs/concepts/tools). A tool is an association between a function and its schema.<br/>
2. **Tool Binding:** The tool needs to be connected to a model that supports tool calling. This gives the model awareness of the tool and the associated input schema required by the tool.<br/>
3. **Tool Calling:** When appropriate, the model can decide to call a tool and ensure its response conforms to the tool's input schema.<br/>
4. **Tool Execution:** The tool can be executed using the arguments provided by the model.
@@ -114,12 +114,12 @@ result = llm_with_tools.invoke("What is 2 multiplied by 3?")
```
As before, the output `result` will be an `AIMessage`.
But, if the tool was called, `result` will have a `tool_calls` attribute.
But, if the tool was called, `result` will have a `tool_calls` [attribute](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.tool_calls).
This attribute includes everything needed to execute the tool, including the tool name and input arguments:
For more details on usage, see our [how-to guides](/docs/how_to/#tools)!
@@ -137,6 +137,16 @@ For more details on usage, see our [how-to guides](/docs/how_to/#tools)!
:::
## Forcing tool use
By default, the model has the freedom to choose which tool to use based on the user's input. However, in certain scenarios, you might want to influence the model's decision-making process. LangChain allows you to enforce tool choice (using `tool_choice`), ensuring the model uses either a particular tool or *any* tool from a given list. This is useful for structuring the model's behavior and guiding it towards a desired outcome.
:::info[Further reading]
* See our [how-to guide](/docs/how_to/tool_choice) on forcing tool use.
:::
## Best practices
When designing [tools](/docs/concepts/tools/) to be used by a model, it is important to keep in mind that:
"LangChain integrates with a host of PDF parsers. Some are simple and relatively low-level; others will support OCR and image-processing, or perform advanced document layout analysis. The right choice will depend on your needs. Below we enumerate the possibilities.\n",
"\n",
"We will demonstrate these approaches on a [sample file](https://github.com/langchain-ai/langchain/blob/master/libs/community/tests/integration_tests/examples/layout-parser-paper.pdf):"
"We will demonstrate these approaches on a [sample file](https://github.com/langchain-ai/langchain-community/blob/main/libs/community/tests/examples/layout-parser-paper.pdf):"
"[Google Gemini](/docs/integrations/chat/google_generative_ai/)) will accept PDF documents.\n",
"\n",
":::note\n",
"OpenAI requires file-names be specified for PDF inputs. When using LangChain's format, include the `filename` key. See [example below](#example-openai-file-names).\n",
":::\n",
"\n",
"### Documents from base64 data\n",
"\n",
"To pass documents in-line, format them as content blocks of the following form:\n",
"category_chain.invoke({\"input\": \"What are all the genres of Alanis Morisette songs\"})"
"category_chain.invoke({\"input\": \"What are all the genres of Alanis Morissette songs\"})"
]
},
{
@@ -261,7 +261,7 @@
"\n",
"\n",
"table_chain = category_chain | get_tables\n",
"table_chain.invoke({\"input\": \"What are all the genres of Alanis Morisette songs\"})"
"table_chain.invoke({\"input\": \"What are all the genres of Alanis Morissette songs\"})"
]
},
{
@@ -313,7 +313,7 @@
],
"source": [
"query = full_chain.invoke(\n",
" {\"question\": \"What are all the genres of Alanis Morisette songs\"}\n",
" {\"question\": \"What are all the genres of Alanis Morissette songs\"}\n",
")\n",
"print(query)"
]
@@ -346,7 +346,7 @@
"source": [
"We can see the LangSmith trace for this run [here](https://smith.langchain.com/public/4fbad408-3554-4f33-ab47-1e510a1b52a3/r).\n",
"\n",
"We've seen how to dynamically include a subset of table schemas in a prompt within a chain. Another possible approach to this problem is to let an Agent decide for itself when to look up tables by giving it a Tool to do so. You can see an example of this in the [SQL: Agents](/docs/tutorials/agents) guide."
"We've seen how to dynamically include a subset of table schemas in a prompt within a chain. Another possible approach to this problem is to let an Agent decide for itself when to look up tables by giving it a Tool to do so. You can see an example of this in the [SQL: Agents](/docs/tutorials/sql_qa/#agents) guide."
]
},
{
@@ -555,7 +555,7 @@
"source": [
"We can see that with retrieval we're able to correct the spelling from \"Elenis Moriset\" to \"Alanis Morissette\" and get back a valid result.\n",
"\n",
"Another possible approach to this problem is to let an Agent decide for itself when to look up proper nouns. You can see an example of this in the [SQL: Agents](/docs/tutorials/agents) guide."
"Another possible approach to this problem is to let an Agent decide for itself when to look up proper nouns. You can see an example of this in the [SQL: Agents](/docs/tutorials/sql_qa/#agents) guide."
"We can also just force our tool to select at least one of our tools by passing in the \"any\" (or \"required\" which is OpenAI specific) keyword to the `tool_choice` parameter."
"We can also just force our tool to select at least one of our tools by passing in the \"any\" (or \"required\" [which is OpenAI specific](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.BaseChatOpenAI.html#langchain_openai.chat_models.base.BaseChatOpenAI.bind_tools)) keyword to the `tool_choice` parameter."
"If you do not have a PromptLayer account, create one on [promptlayer.com](https://www.promptlayer.com). Then get an API key by clicking on the settings cog in the navbar and\n",
"set it as an environment variabled called `PROMPTLAYER_API_KEY`\n"
"set it as an environment variable called `PROMPTLAYER_API_KEY`\n"
"This notebook showcases the UpTrain callback handler seamlessly integrating into your pipeline, facilitating diverse evaluations. We have chosen a few evaluations that we deemed apt for evaluating the chains. These evaluations run automatically, with results displayed in the output. More details on UpTrain's evaluations can be found [here](https://github.com/uptrain-ai/uptrain?tab=readme-ov-file#pre-built-evaluations-we-offer-). \n",
"\n",
"Selected retievers from Langchain are highlighted for demonstration:\n",
"Selected retrievers from Langchain are highlighted for demonstration:\n",
"\n",
"### 1. **Vanilla RAG**:\n",
"RAG plays a crucial role in retrieving context and generating responses. To ensure its performance and response quality, we conduct the following evaluations:\n",
"This will help you getting started with ChatAbso [chat models](https://python.langchain.com/docs/concepts/chat_models/). For detailed documentation of all ChatAbso features and configurations head to the [API reference](https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.html).\n",
"This will help you get started with ChatAbso [chat models](https://python.langchain.com/docs/concepts/chat_models/). For detailed documentation of all ChatAbso features and configurations, head to the [API reference](https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.html).\n",
"\n",
"- You can find the full documentation for the Abso router [here] (https://abso.ai)\n",
"This notebook covers how to get started with AI21 chat models.\n",
"Note that different chat models support different parameters. See the [AI21 documentation](https://docs.ai21.com/reference) to learn more about the parameters in your chosen model.\n",
"[See all AI21's LangChain components.](https://pypi.org/project/langchain-ai21/)\n",
@@ -68,7 +66,9 @@
"cell_type": "markdown",
"id": "f6844fff-3702-4489-ab74-732f69f3b9d7",
"metadata": {},
"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
"source": [
"To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
]
},
{
"cell_type": "code",
@@ -198,13 +198,17 @@
"cell_type": "markdown",
"id": "39c0ccd229927eab",
"metadata": {},
"source": "# Tool Calls / Function Calling"
"source": [
"# Tool Calls / Function Calling"
]
},
{
"cell_type": "markdown",
"id": "2bf6b40be07fe2d4",
"metadata": {},
"source": "This example shows how to use tool calling with AI21 models:"
"source": [
"This example shows how to use tool calling with AI21 models:"
":::info This guide requires ``langchain-anthropic>=0.3.10``\n",
":::info This guide requires ``langchain-anthropic>=0.3.13``\n",
"\n",
":::"
]
@@ -325,6 +325,102 @@
"ai_msg.tool_calls"
]
},
{
"cell_type": "markdown",
"id": "535a16e4-cd5a-479f-b315-37c816ec4387",
"metadata": {},
"source": [
"## Multimodal\n",
"\n",
"Claude supports image and PDF inputs as content blocks, both in Anthropic's native format (see docs for [vision](https://docs.anthropic.com/en/docs/build-with-claude/vision#base64-encoded-image-example) and [PDF support](https://docs.anthropic.com/en/docs/build-with-claude/pdf-support)) as well as LangChain's [standard format](/docs/how_to/multimodal_inputs/).\n",
"\n",
"### Files API\n",
"\n",
"Claude also supports interactions with files through its managed [Files API](https://docs.anthropic.com/en/docs/build-with-claude/files). See examples below.\n",
"\n",
"The Files API can also be used to upload files to a container for use with Claude's built-in code-execution tools. See the [code execution](#code-execution) section below, for details.\n",
" The cache lifetime is 5 minutes by default. If this is too short, you can apply one hour caching by enabling the `\"extended-cache-ttl-2025-04-11\"` beta header:\n",
"\n",
" ```python\n",
" llm = ChatAnthropic(\n",
" model=\"claude-3-7-sonnet-20250219\",\n",
" # highlight-next-line\n",
" betas=[\"extended-cache-ttl-2025-04-11\"],\n",
" )\n",
" ```\n",
" and specifying `\"cache_control\": {\"type\": \"ephemeral\", \"ttl\": \"1h\"}`.\n",
"\n",
" Details of cached token counts will be included on the `InputTokenDetails` of response's `usage_metadata`:\n",
"\n",
" ```python\n",
" response = llm.invoke(messages)\n",
" response.usage_metadata\n",
" ```\n",
" ```\n",
" {\n",
" \"input_tokens\": 1500,\n",
" \"output_tokens\": 200,\n",
" \"total_tokens\": 1700,\n",
" \"input_token_details\": {\n",
" \"cache_read\": 0,\n",
" \"cache_creation\": 1000,\n",
" \"ephemeral_1h_input_tokens\": 750,\n",
" \"ephemeral_5m_input_tokens\": 250,\n",
" }\n",
" }\n",
" ```\n",
"\n",
":::"
]
},
{
"cell_type": "markdown",
"id": "141ce9c5-012d-4502-9d61-4a413b5d959a",
@@ -756,7 +893,7 @@
"source": [
"## Citations\n",
"\n",
"Anthropic supports a [citations](https://docs.anthropic.com/en/docs/build-with-claude/citations) feature that lets Claude attach context to its answers based on source documents supplied by the user. When [document content blocks](https://docs.anthropic.com/en/docs/build-with-claude/citations#document-types) with `\"citations\": {\"enabled\": True}` are included in a query, Claude may generate citations in its response.\n",
"Anthropic supports a [citations](https://docs.anthropic.com/en/docs/build-with-claude/citations) feature that lets Claude attach context to its answers based on source documents supplied by the user. When [document](https://docs.anthropic.com/en/docs/build-with-claude/citations#document-types) or `search result` content blocks with `\"citations\": {\"enabled\": True}` are included in a query, Claude may generate citations in its response.\n",
"Claude supports a [search_result](https://docs.anthropic.com/en/docs/build-with-claude/search-results) content block representing citable results from queries against a knowledge base or other custom source. These content blocks can be passed to claude both top-line (as in the above example) and within a tool result. This allows Claude to cite elements of its response using the result of a tool call.\n",
"\n",
"To pass search results in response to tool calls, define a tool that returns a list of `search_result` content blocks in Anthropic's native format. For example:\n",
"We also need to specify the `search-results-2025-06-09` beta when instantiating ChatAnthropic. You can see an end-to-end example below.\n",
"\n",
"<details>\n",
"<summary>End to end example with LangGraph</summary>\n",
"\n",
"Here we demonstrate an end-to-end example in which we populate a LangChain [vector store](/docs/concepts/vectorstores/) with sample documents and equip Claude with a tool that queries those documents.\n",
"The tool here takes a search query and a `category` string literal, but any valid tool signature can be used.\n",
" \"content\": \"How do I request vacation days?\",\n",
"}\n",
"async for step in agent.astream(\n",
" {\"messages\": [input_message]},\n",
" config,\n",
" stream_mode=\"values\",\n",
"):\n",
" step[\"messages\"][-1].pretty_print()\n",
"```\n",
"\n",
"</details>"
]
},
{
"cell_type": "markdown",
"id": "69956596-0e6c-492b-934d-c08ed3c9de9a",
@@ -926,6 +1200,16 @@
"Claude can use a [web search tool](https://docs.anthropic.com/en/docs/build-with-claude/tool-use/web-search-tool) to run searches and ground its responses with citations."
]
},
{
"cell_type": "markdown",
"id": "6a0e8fd5",
"metadata": {},
"source": [
":::info Web search tool is supported since ``langchain-anthropic>=0.3.13``\n",
"\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": 1,
@@ -943,6 +1227,159 @@
"response = llm_with_tools.invoke(\"How do I update a web app to TypeScript 5.5?\")"
]
},
{
"cell_type": "markdown",
"id": "1478cdc6-2e52-4870-80f9-b4ddf88f2db2",
"metadata": {},
"source": [
"### Code execution\n",
"\n",
"Claude can use a [code execution tool](https://docs.anthropic.com/en/docs/agents-and-tools/tool-use/code-execution-tool) to execute Python code in a sandboxed environment.\n",
"\n",
":::info Code execution is supported since ``langchain-anthropic>=0.3.14``\n",
"Claude can use a [MCP connector tool](https://docs.anthropic.com/en/docs/agents-and-tools/mcp-connector) for model-generated calls to remote MCP servers.\n",
"\n",
":::info Remote MCP is supported since ``langchain-anthropic>=0.3.14``\n",
"This will help you getting started with AzureAIChatCompletionsModel [chat models](/docs/concepts/chat_models). For detailed documentation of all AzureAIChatCompletionsModel features and configurations head to the [API reference](https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html)\n",
"This will help you get started with AzureAIChatCompletionsModel [chat models](/docs/concepts/chat_models). For detailed documentation of all AzureAIChatCompletionsModel features and configurations, head to the [API reference](https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html)\n",
"\n",
"The AzureAIChatCompletionsModel class uses the Azure AI Foundry SDK. AI Foundry has several chat models including AzureOpenAI, Cohere, Llama, Phi-3/4, and DeepSeek-R1 to name a few. You can find information about their latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/azure/ai-studio/how-to/model-catalog-overview).\n",
"The AzureAIChatCompletionsModel class uses the Azure AI Foundry SDK. AI Foundry has several chat models, including AzureOpenAI, Cohere, Llama, Phi-3/4, and DeepSeek-R1, among others. You can find information about their latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/azure/ai-studio/how-to/model-catalog-overview).\n",
"\n",
"\n",
"## Overview\n",
@@ -37,12 +37,12 @@
"\n",
"## Setup\n",
"\n",
"To access AzureAIChatCompletionsModel models you'll need to create an [Azure account](https://azure.microsoft.com/pricing/purchase-options/azure-account), get an API key, and install the `langchain-azure-ai` integration package.\n",
"To access AzureAIChatCompletionsModel models, you'll need to create an [Azure account](https://azure.microsoft.com/pricing/purchase-options/azure-account), get an API key, and install the `langchain-azure-ai` integration package.\n",
"\n",
"### Credentials\n",
"\n",
"\n",
"Head to the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/develop/sdk-overview?tabs=sync&pivots=programming-language-python) to see how to create your deployment and generate an API key. Once your model is deployed you click the 'get endpoint' button in AI Foundry. This will show you your endpoint and api key. Once you've done this set the AZURE_INFERENCE_CREDENTIAL and AZURE_INFERENCE_ENDPOINT environment variables:"
"Head to the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/develop/sdk-overview?tabs=sync&pivots=programming-language-python) to see how to create your deployment and generate an API key. Once your model is deployed, you click the 'get endpoint' button in AI Foundry. This will show you your endpoint and api key. Once you've done this, set the AZURE_INFERENCE_CREDENTIAL and AZURE_INFERENCE_ENDPOINT environment variables:"
]
},
{
@@ -71,7 +71,7 @@
"id": "72ee0c4b-9764-423a-9dbf-95129e185210",
"metadata": {},
"source": [
"If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
"If you want to get automated tracing of your model calls, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
]
},
{
@@ -247,13 +247,13 @@
"source": [
"## API reference\n",
"\n",
"For detailed documentation of all AzureAIChatCompletionsModel features and configurations head to the API reference: https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html"
"For detailed documentation of all AzureAIChatCompletionsModel features and configurations, head to the API reference: https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html"
"This will help you getting started with CloudflareWorkersAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatCloudflareWorkersAI features and configurations head to the [API reference](https://python.langchain.com/docs/integrations/chat/cloudflare_workersai/).\n",
"This will help you get started with CloudflareWorkersAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatCloudflareWorkersAI features and configurations head to the [API reference](https://python.langchain.com/docs/integrations/chat/cloudflare_workersai/).\n",
"This will help you getting started with DeepSeek's hosted [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatDeepSeek features and configurations head to the [API reference](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html).\n",
"This will help you get started with DeepSeek's hosted [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatDeepSeek features and configurations head to the [API reference](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html).\n",
"This will help you get started with FeatherlessAi [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatFeatherlessAi features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.ChatFeatherlessAi.html).\n",
"\n",
"- See https://featherless.ai/ for an example.\n",
"\n",
"## Overview\n",
"### Integration details\n",
"\n",
"\n",
"| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
"To access Featherless AI models you'll need to create a/an Featherless AI account, get an API key, and install the `langchain-featherless-ai` integration package.\n",
"\n",
"### Credentials\n",
"\n",
"\n",
"Head to https://featherless.ai/ to sign up to FeatherlessAI and generate an API key. Once you've done this set the FEATHERLESSAI_API_KEY environment variable:"
"If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
" \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
" ),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")\n",
"\n",
"chain = prompt | llm\n",
"chain.invoke(\n",
" {\n",
" \"input_language\": \"English\",\n",
" \"output_language\": \"German\",\n",
" \"input\": \"I love programming.\",\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
"metadata": {},
"source": [
"## API reference\n",
"\n",
"For detailed documentation of all ChatFeatherlessAi features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/.chat_models.ChatFeatherlessAi.html)"
"This doc help you get started with Fireworks AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatFireworks features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html).\n",
"This doc helps you get started with Fireworks AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatFireworks features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html).\n",
"\n",
"Fireworks AI is an AI inference platform to run and customize models. For a list of all models served by Fireworks see the [Fireworks docs](https://fireworks.ai/models).\n",
"\n",
@@ -39,7 +39,7 @@
"\n",
"### Credentials\n",
"\n",
"Head to (ttps://fireworks.ai/login to sign up to Fireworks and generate an API key. Once you've done this set the FIREWORKS_API_KEY environment variable:"
"Head to (https://fireworks.ai/login to sign up to Fireworks and generate an API key. Once you've done this set the FIREWORKS_API_KEY environment variable:"
"To get GigaChat credentials you need to [create account](https://developers.sber.ru/studio/login) and [get access to API](https://developers.sber.ru/docs/ru/gigachat/individuals-quickstart)\n",
"\n",
"## Example"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"from getpass import getpass\n",
"\n",
"if \"GIGACHAT_CREDENTIALS\" not in os.environ:\n",
"This will help you getting started with Goodfire [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoodfire features and configurations head to the [PyPI project page](https://pypi.org/project/langchain-goodfire/), or go directly to the [Goodfire SDK docs](https://docs.goodfire.ai/sdk-reference/example). All of the Goodfire-specific functionality (e.g. SAE features, variants, etc.) is available via the main `goodfire` package. This integration is a wrapper around the Goodfire SDK.\n",
"This will help you get started with Goodfire [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoodfire features and configurations head to the [PyPI project page](https://pypi.org/project/langchain-goodfire/), or go directly to the [Goodfire SDK docs](https://docs.goodfire.ai/sdk-reference/example). All of the Goodfire-specific functionality (e.g. SAE features, variants, etc.) is available via the main `goodfire` package. This integration is a wrapper around the Goodfire SDK.\n",
"This page provides a quick overview for getting started with VertexAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatVertexAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html).\n",
"\n",
"ChatVertexAI exposes all foundational models available in Google Cloud, like `gemini-1.5-pro`, `gemini-1.5-flash`, etc. For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/overview).\n",
"\n",
":::info Google Cloud VertexAI vs Google PaLM\n",
"\n",
"The Google Cloud VertexAI integration is separate from the [Google PaLM integration](/docs/integrations/chat/google_generative_ai/). Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there.\n",
"\n",
":::\n",
"\n",
"## Overview\n",
"### Integration details\n",
"\n",
"| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/google_vertex_ai) | Package downloads | Package latest |\n",
"To access VertexAI models you'll need to create a Google Cloud Platform account, set up credentials, and install the `langchain-google-vertexai` integration package.\n",
"\n",
"### Credentials\n",
"\n",
"To use the integration you must:\n",
"- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
"- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
"\n",
"This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.\n",
" \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
" ),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")\n",
"\n",
"chain = prompt | llm\n",
"chain.invoke(\n",
" {\n",
" \"input_language\": \"English\",\n",
" \"output_language\": \"German\",\n",
" \"input\": \"I love programming.\",\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
"metadata": {},
"source": [
"## API reference\n",
"\n",
"For detailed documentation of all ChatVertexAI features and configurations, like how to send multimodal inputs and configure safety settings, head to the API reference: https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "poetry-venv-2",
"language": "python",
"name": "poetry-venv-2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
"cells": [
{
"cell_type": "raw",
"id": "afaf8039",
"metadata": {},
"source": [
"---\n",
"sidebar_label: Google Cloud Vertex AI\n",
"---"
]
},
"nbformat": 4,
"nbformat_minor": 5
{
"cell_type": "markdown",
"id": "e49f1e0d",
"metadata": {},
"source": [
"# ChatVertexAI\n",
"\n",
"This page provides a quick overview for getting started with VertexAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatVertexAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html).\n",
"\n",
"ChatVertexAI exposes all foundational models available in Google Cloud, like `gemini-1.5-pro`, `gemini-1.5-flash`, etc. For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/overview).\n",
"\n",
":::info Google Cloud VertexAI vs Google PaLM\n",
"\n",
"The Google Cloud VertexAI integration is separate from the [Google PaLM integration](/docs/integrations/chat/google_generative_ai/). Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there.\n",
"\n",
":::\n",
"\n",
"## Overview\n",
"### Integration details\n",
"\n",
"| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/google_vertex_ai) | Package downloads | Package latest |\n",
"To access VertexAI models you'll need to create a Google Cloud Platform account, set up credentials, and install the `langchain-google-vertexai` integration package.\n",
"\n",
"### Credentials\n",
"\n",
"To use the integration you must:\n",
"- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
"- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
"\n",
"This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.\n",
" \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
" ),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")\n",
"\n",
"chain = prompt | llm\n",
"chain.invoke(\n",
" {\n",
" \"input_language\": \"English\",\n",
" \"output_language\": \"German\",\n",
" \"input\": \"I love programming.\",\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
"metadata": {},
"source": [
"## API reference\n",
"\n",
"For detailed documentation of all ChatVertexAI features and configurations, like how to send multimodal inputs and configure safety settings, head to the API reference: https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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