**Description:** Added a cookbook that showcase how to build a RAG agent
pipeline locally using open-source LLM and embedding models on Intel
Xeon CPU. It uses Llama 3.1:8B model from Ollama for LLM and
nomic-embed-text-v1.5 from NomicEmbeddings for embeddings. The whole
experiment is developed and tested on Intel 4th Gen Xeon Scalable CPU.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
## Description
This PR addresses the following:
**Fixes Issue #25343:**
- Adds additional logic to parse shallowly nested JSON-encoded strings
in tool call arguments, allowing for proper parsing of responses like
that of Llama3.1 and 3.2 with nested schemas.
**Adds Integration Test for Fix:**
- Adds a Ollama specific integration test to ensure the issue is
resolved and to prevent regressions in the future.
**Fixes Failing Integration Tests:**
- Fixes failing integration tests (even prior to changes) caused by
`llama3-groq-tool-use` model. Previously,
tests`test_structured_output_async` and
`test_structured_output_optional_param` failed due to the model not
issuing a tool call in the response. Resolved by switching to
`llama3.1`.
## Issue
Fixes#25343.
## Dependencies
No dependencies.
____
Done in collaboration with @ishaan-upadhyay @mirajismail @ZackSteine.
**Description:** Fixed a grammatical error in the documentation section
on embedding vectors. Replaced "Embedding vectors can be comparing" with
"Embedding vectors can be compared."
**Issue:** N/A (This is a minor documentation fix with no linked issue.)
**Dependencies:** None.
- **Description:** `add_texts` was using `get_setting` for marqo client
which was being used according to 1.5.x API version. However, this PR
updates the `add_text` accounting for updated response payload for 2.x
and later while maintaining backward compatibility. Plus I have verified
this was the only place where marqo client was not accounting for
updated API version.
- **Issue:** #28323
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
_This should only be merged once neo4j is included under libs/partners._
# **Description:**
Neo4jVector from langchain-community is being moved to langchain-neo4j:
[see
link](https://github.com/langchain-ai/langchain-neo4j/blob/main/libs/neo4j/langchain_neo4j/vectorstores/neo4j_vector.py#L436).
To solve the issue below, this PR adds an attempt to import
`Neo4jVector` from the partner package `langchain-neo4j`, similarly to
the other partner packages.
# **Issue:**
When initializing `SelfQueryRetriever`, the following error is raised:
```
ValueError: Self query retriever with Vector Store type <class 'langchain_neo4j.vectorstores.neo4j_vector.Neo4jVector'> not supported.
```
[See related
issue](https://github.com/langchain-ai/langchain/issues/19748).
# **Dependencies:**
- langchain-neo4j
v0.4 of the Python SDK is already installed via the lock file in CI, but
our current implementation is not compatible with it.
This also addresses an issue introduced in
https://github.com/langchain-ai/langchain/pull/28299. @RyanMagnuson
would you mind explaining the motivation for that change? From what I
can tell the Ollama SDK [does not support
kwargs](6c44bb2729/ollama/_client.py (L286)).
Previously, unsupported kwargs were ignored, but they currently raise
`TypeError`.
Some of LangChain's standard test suite expects `tool_choice` to be
supported, so here we catch it in `bind_tools` so it is ignored and not
passed through to the client.
Adds deprecation notices for Neo4j components moving to the
`langchain_neo4j` partner package.
- Adds deprecation warnings to all Neo4j-related classes and functions
that have been migrated to the new `langchain_neo4j` partner package
- Updates documentation to reference the new `langchain_neo4j` package
instead of `langchain_community`
**Description:**
Currently, the docstring for `LanceDB.__init__()` provides the default
value for `mode`, but not the list of valid values. This PR adds that
list to the docstring.
**Issue:**
N/A
**Dependencies:**
N/A
**Twitter handle:**
`@metadaddy`
[Leaving as a reminder: If no one reviews your PR within a few days,
please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda,
hwchase17.]
We have a test
[test_structured_few_shot_examples](ad4333ca03/libs/standard-tests/langchain_tests/integration_tests/chat_models.py (L546))
in standard integration tests that implements a version of tool-calling
few shot examples that works with ~all tested providers. The formulation
supported by ~all providers is: `human message, tool call, tool message,
AI reponse`.
Here we update
`langchain_core.utils.function_calling.tool_example_to_messages` to
support this formulation.
The `tool_example_to_messages` util is undocumented outside of our API
reference. IMO, if we are testing that this function works across all
providers, it can be helpful to feature it in our guides. The structured
few-shot examples we document at the moment require users to implement
this function and can be simplified.
- **Description:** Corrected the parameter name in the
HuggingFaceEmbeddings documentation under integrations/text_embedding/
from model to model_name to align with the actual code usage in the
langchain_huggingface package.
- **Issue:** Fixes#28231
- **Dependencies:** None
Thank you for contributing to LangChain!
Ctrl+F to find instances of `langchain-databricks` and replace with
`databricks-langchain`.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
Signed-off-by: Prithvi Kannan <prithvi.kannan@databricks.com>
