## Change
Use awaitility in `EmbeddingStoreIT`
## General checklist
- [X] There are no breaking changes
- [X] I have added unit and integration tests for my change
- [x] I have manually run all the unit and integration tests in the
module I have added/changed, and they are all green
- [x] I have manually run all the unit and integration tests in the
[core]
## Issue
#1506
## Change
Enabled Maven Enforcer Plugin on modules without existing version
conflicts to ensure they remain conflict-free. The Maven Enforcer Plugin
will now cause the build to fail if new conflicts are introduced
guarding against these.
## Tests
`mvn clean test` passed
## Issue
Closes#1066
## Change
These are changes for each split package (each change was done in a
separate commit, so they can be reviewed in isolation):
- `dev.langchain4j.retriever` -> Moved `EmbeddingStoreRetriever` into
`langchain4j-core` module
- `dev.langchain4j.agent.tool` -> Moved `DefaultToolExecutor` and
`ToolExecutor` into `dev.langchain4j.service.tool` package
- `dev.langchain4j.classification` -> Moved `TextClassifier` into
`langchian4j` module
- `dev.langchain4j.chain` -> Moved `Chain` into `langchain4j` module
- `dev.langchain4j.model.embedding` -> [All in-process embedding models
should have unique package
name](https://github.com/langchain4j/langchain4j-embeddings/pull/33)
- `dev.langchain4j.model.output` -> Moved `OutputParser` and all it's
implementations into `dev.langchain4j.service.output` package of the
`langchain4j` module
More details can be found
[here](https://docs.google.com/spreadsheets/d/1U7f2MIfDgWA1tydPpzWpOGTHiBjBVZjsu0uZnXBT9qE/edit?usp=sharing).
## Breaking Changes
- All in-process ONNX model classes moved into their own unique
packages:
- `AllMiniLmL6V2EmbeddingModel` moved into
`dev.langchain4j.model.embedding.onnx.allminilml6v2`
- `AllMiniLmL6V2QuantizedEmbeddingModel` moved into
`dev.langchain4j.model.embedding.onnx.allminilml6v2q`
- `OnnxEmbeddingModel` moved into `dev.langchain4j.model.embedding.onnx`
package
- etc
- `ToolExecutor` and `DefaultToolExecutor` moved into
`dev.langchain4j.service.tool` package
- Moved `OutputParser` and all it's implementations into
`dev.langchain4j.service.output` package of the `langchain4j` module
- Moved `Chain` into `langchain4j` module
- Moved `TextClassifier` into `langchian4j` module
## General checklist
- [ ] There are no breaking changes
- [ ] I have added unit and integration tests for my change
- [X] I have manually run all the unit and integration tests in the
module I have added/changed, and they are all green
- [X] I have manually run all the unit and integration tests in the
[core](https://github.com/langchain4j/langchain4j/tree/main/langchain4j-core)
and
[main](https://github.com/langchain4j/langchain4j/tree/main/langchain4j)
modules, and they are all green
<!-- Before adding documentation and example(s) (below), please wait
until the PR is reviewed and approved. -->
- [ ] I have added/updated the
[documentation](https://github.com/langchain4j/langchain4j/tree/main/docs/docs)
- [ ] I have added an example in the [examples
repo](https://github.com/langchain4j/langchain4j-examples) (only for
"big" features)
- [ ] I have added/updated [Spring Boot
starter(s)](https://github.com/langchain4j/langchain4j-spring) (if
applicable)
For Issue #685
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Introduced a `Neo4jGraph` class for enhanced interaction with Neo4j
databases, including read/write operations and schema management.
- Added a `Neo4jContentRetriever` for generating and executing Cypher
queries from user questions, improving content retrieval from Neo4j
databases.
- **Tests**
- Implemented tests for Neo4j database interactions and content
retrieval functionalities, ensuring reliability and performance.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
## New EmbeddingStore (metadata) `Filter` API
Many embedding stores, such as
[Pinecone](https://docs.pinecone.io/docs/metadata-filtering) and
[Milvus](https://milvus.io/docs/boolean.md) support strict filtering
(think of an SQL "WHERE" clause) during similarity search.
So, if one has an embedding store with movies, for example, one could
search not only for the most semantically similar movies to the given
user query but also apply strict filtering by metadata fields like year,
genre, rating, etc. In this case, the similarity search will be
performed only on those movies that match the filter expression.
Since LangChain4j supports (and abstracts away) many embedding stores,
there needs to be an embedding-store-agnostic way for users to define
the filter expression.
This PR introduces a `Filter` interface, which can represent both simple
(e.g., `type = "documentation"`) and composite (e.g., `type in
("documentation", "tutorial") AND year > 2020`) filter expressions in an
embedding-store-agnostic manner.
`Filter` currently supports the following operations:
- Comparison:
- `IsEqualTo`
- `IsNotEqualTo`
- `IsGreaterThan`
- `IsGreaterThanOrEqualTo`
- `IsLessThan`
- `IsLessThanOrEqualTo`
- `IsIn`
- `IsNotIn`
- Logical:
- `And`
- `Not`
- `Or`
These operations are supported by most embedding stores and serve as a
good starting point. However, the list of operations will expand over
time to include other operations (e.g., `Contains`) supported by
embedding stores.
Currently, the DSL looks like this:
```java
Filter onlyDocs = metadataKey("type").isEqualTo("documentation");
Filter docsAndTutorialsAfter2020 = metadataKey("type").isIn("documentation", "tutorial").and(metadataKey("year").isGreaterThan(2020));
// or
Filter docsAndTutorialsAfter2020 = and(
metadataKey("type").isIn("documentation", "tutorial"),
metadataKey("year").isGreaterThan(2020)
);
```
## Filter expression as a `String`
Filter expression can also be specified as a `String`. This might be
necessary, for example, if the filter expression is generated
dynamically by the application or by the LLM (as in [self
querying](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/)).
This PR introduces a `FilterParser` interface with a simple `Filter
parse(String)` API, allowing for future support of multiple syntaxes (if
this will be required).
For the out-of-the-box filter syntax, ANSI SQL's `WHERE` clause is
proposed as a suitable candidate for several reasons:
- SQL is well-known among Java developers
- There is extensive tooling available for SQL (e.g., parsers)
- LLMs are pretty good at generating valid SQL, as there are tons of SQL
queries on the internet, which are included in the LLM training
datasets. There are also specialized LLMs that are trained for
text-to-SQL task, such as [SQLCoder](https://huggingface.co/defog).
The downside is that SQL's `WHERE` clause might not support all
operations and data types that could be supported in the future by
various embedding stores. In such case, we could extend it to a superset
of ANSI SQL `WHERE` syntax and/or provide an option to express filters
in the native syntax of the store.
An out-of-the-box implementation of the SQL `FilterParser` is provided
as a `SqlFilterParser` in a separate module
`langchain4j-embedding-store-filter-parser-sql`, using
[JSqlParser](https://github.com/JSQLParser/JSqlParser) under the hood.
`SqlFilterParser` can parse SQL "SELECT" (or just "WHERE" clause)
statement into a `Filter` object:
- `SELECT * FROM fake_table WHERE userId = '123-456'` ->
`metadataKey("userId").isEqualTo("123-456")`
- `userId = '123-456'` -> `metadataKey("userId").isEqualTo("123-456")`
It can also resolve `CURDATE()` and
`CURRENT_DATE`/`CURRENT_TIME`/`CURRENT_TIMESTAMP`:
`SELECT * FROM fake_table WHERE year = EXTRACT(YEAR FROM CURRENT_DATE`
-> `metadataKey("year").isEqualTo(LocalDate.now().getYear())`
## Changes in `Metadata` API
Until now, `Metadata` supported only `String` values. This PR expands
the list of supported value types to `Integer`, `Long`, `Float` and
`Double`. In the future, more types may be added (if needed).
The method `String get(String key)` will be deprecated later in favor
of:
- `String getString(String key)`
- `Integer getInteger(String key)`
- `Long getLong(String key)`
- etc
New overloaded `put(key, value)` methods are introduced to support more
value types:
- `put(String key, int value)`
- `put(String key, long value)`
- etc
## Changes in `EmbeddingStore` API
New method `search` is added that will become the main entry point for
search in the future. All `findRelevant` methods will be deprecated
later.
New `search` method accepts `EmbeddingSearchRequest` and returns
`EmbeddingSearchResult`.
`EmbeddingSearchRequest` contains all search criteria (e.g.
`maxResults`, `minScore`), including new `Filter`.
`EmbeddingSearchResult` contains a list of `EmbeddingMatch`.
```java
EmbeddingSearchResult search(EmbeddingSearchRequest request);
```
## Changes in `EmbeddingStoreContentRetriever` API
`EmbeddingStoreContentRetriever` can now be configured with a static
`filter` as well as dynamic `dynamicMaxResults`, `dynamicMinScore` and
`dynamicFilter` in the builder:
```java
ContentRetriever contentRetriever = EmbeddingStoreContentRetriever.builder()
.embeddingStore(embeddingStore)
.embeddingModel(embeddingModel)
...
.maxResults(3)
// or
.dynamicMaxResults(query -> 3) // You can define maxResults dynamically. The value could, for example, depend on the query or the user associated with the query.
...
.minScore(0.3)
// or
.dynamicMinScore(query -> 0.3)
...
.filter(metadataKey("userId").isEqualTo("123-456")) // Assuming your TextSegments contain Metadata with key "userId"
// or
.dynamicFilter(query -> metadataKey("userId").isEqualTo(query.metadata().chatMemoryId().toString()))
...
.build();
```
So now you can define `maxResults`, `minScore` and `filter` both
statically and dynamically (they can depend on the query, user, etc.).
These values will be propagated to the underlying `EmbeddingStore`.
##
["Self-querying"](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/)
This PR also introduces `LanguageModelSqlFilterBuilder` in
`langchain4j-embedding-store-filter-parser-sql` module which can be used
with `EmbeddingStoreContentRetriever`'s `dynamicFilter` to automatically
build a `Filter` object from the `Query` using language model and
`SqlFilterParser`.
For example:
```java
TextSegment groundhogDay = TextSegment.from("Groundhog Day", new Metadata().put("genre", "comedy").put("year", 1993));
TextSegment forrestGump = TextSegment.from("Forrest Gump", new Metadata().put("genre", "drama").put("year", 1994));
TextSegment dieHard = TextSegment.from("Die Hard", new Metadata().put("genre", "action").put("year", 1998));
// describe metadata keys as if they were columns in the SQL table
TableDefinition tableDefinition = TableDefinition.builder()
.name("movies")
.addColumn("genre", "VARCHAR", "one of [comedy, drama, action]")
.addColumn("year", "INT")
.build();
LanguageModelSqlFilterBuilder sqlFilterBuilder = new LanguageModelSqlFilterBuilder(model, tableDefinition);
ContentRetriever contentRetriever = EmbeddingStoreContentRetriever.builder()
.embeddingStore(embeddingStore)
.embeddingModel(embeddingModel)
.dynamicFilter(sqlFilterBuilder::build)
.build();
String answer = assistant.answer("Recommend me a good drama from 90s"); // Forrest Gump
```
## Which embedding store integrations will support `Filter`?
In the long run, all (provided the embedding store itself supports it).
In the first iteration, I aim to add support to just a few:
- `InMemoryEmbeddingStore`
- Elasticsearch
- Milvus
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Summary by CodeRabbit
- **New Features**
- Introduced filters for checking key's value existence in a collection
for improved data handling.
- **Enhancements**
- Updated `InMemoryEmbeddingStoreTest` to extend a different class for
improved testing coverage and added a new test method.
- **Refactor**
- Made minor formatting adjustments in the assertion block for better
readability.
- **Documentation**
- Updated class hierarchy information for clarity.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Hi! Noticed _almost all_ tests used AssertJ, but in some cases JUnit was
still used. In addition to that some tests don't use the most expressive
assertions. Figured clean that up such that you get better assertions if
any tests were to fail. Compare for instance
```diff
- assertThat(document.metadata().asMap().size()).isEqualTo(4);
+ assertThat(document.metadata().asMap()).hasSize(4);
```
The first one will print expected 5 to be equal to 4, whereas the second
one shows the contents of the map involved.
Being consistent with your test library also stops bad patterns from
repeating accidentally through copy-and-paste. If you want to enforce
these best practices through an automated pull request check that's also
an option. Let me know if you'd want that as well. Hope that helps!
with such a setting, you can safely build only once the whole project
with JDK 17 or even 21 without fearing any wrong API being injected in
.class files
Fixes#241: Added support for Neo4j Vector Index
This commit brings support for Neo4j graph database in general, and uses
the vector index functionality, generally available since version 5.13.
Mostly aligned with the existing WeaviateEmbeddingStoreImpl
implementation and tests.
The tests have some additional Neo4j node assertion to check that the
nodes involved are correctly created.
The module creates indexes, i.e. `"CALL
db.index.vector.createNodeIndex(<indexName>, <label>,
<embeddingProperty>, <dimension>, <distanceType>)"`, if needed, for the
vector search .
The required configurations are:
- the Neo4j index dimension parameter
- the Neo4j Java Driver connection instance
- as an alternative to the Neo4j Java Driver, we can create a
`Neo4jEmbeddingStore.builder().withBasicAuth(<url>, <username>,
<password>)`, which will create a Driver connection instance under the
hood
It is possible to customize, via the builder:
- the index name (with default `langchain-embedding-index`)
- the Neo4j node label (with default `Document`)
- the Neo4j property key which save the embeddings (with default
`embeddingProp`)
- the Neo4j index distanceType parameter
- the metadata prefix (with default `metadata.`)
- the text property key (with default `text`), which store the text
field of the `TextSegment.java`
Created an example PR as well, on `langchain4j-examples` repo:
https://github.com/langchain4j/langchain4j-examples/pull/23