@langchain4j Hi! I make some progress to integrate with Ollama, see
#244. Using `retrofit` to define REST API in [Ollama
doc](https://github.com/jmorganca/ollama/blob/main/docs/api.md#generate-a-completion).
Local test need local deployment of `Ollama`. Looking forward to get
your code review!
---------
Co-authored-by: Heezer <33568148+Heezer@users.noreply.github.com>
# Summary
I use https://github.com/pgvector/pgvector-java/ to implement
`PgVectorEmbeddingStore`.
1. Insertion works as regular SQL as long as a table (with namespace as
table name) contains type `vector`.
2. Query is upon the enhanced SQL by vector extension. The SQL returned
results are the final result with passing `minScore` and `maxResults`.
# Caveat
* The implementation leaves responsibility of installing `vector`
extension to users.
* Only cosine similarity is used.
* Only ivfflat index is used.
see #217, it's a good way to manage artifacts.
Now users can use code below to import dependency:
```xml
<dependencyManagement>
<dependencies>
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-bom</artifactId>
<version>${langchain4j.version}</version>
<type>pom</type>
<scope>import</scope>
</dependency>
</dependencies>
</dependencyManagement>
```
This PR contains the implementation of an integration with
[OpenSearch](https://opensearch.org/). As one of the growing vector
databases in the open source world, adding support for it to this
project makes total sense. This implementation includes:
1. A complete implementation of the `EmbeddingStore` interface.
2. Unit tests for the major use cases a store must implement correctly.
3. Usage of [TestContainers](https://testcontainers.com/) to automate
the execution of backends.
#### Context
Apache Cassandra is a popular open-source database created back in 2008.
This year with
[CEP30](https://cwiki.apache.org/confluence/display/CASSANDRA/CEP-30%3A+Approximate+Nearest+Neighbor%28ANN%29+Vector+Search+via+Storage-Attached+Indexes)
support for vector and similarity searches have been introduced.
Cassandra is very fast in read and write and is used as a cache by many
companies, it as an opportunity to implement the ChatMemoryStore. This
feature is expected for Cassandra 5 at the end of the year but some
docker images are already available.
DataStax AstraDb is a distribution of Apache Cassandra available as Saas
providing a free tier (free forever) of 80 millions queries/month.
[Registration](https://astra.datastax.com). The vector capability is
there production ready.
#### Data Modelling
With the proper data model in Cassandra we can perform both similarity
search, keyword search, metadata search.
```sql
CREATE TABLE sample_vector_table (
row_id text PRIMARY KEY,
attributes_blob text,
body_blob text,
metadata_s map<text, text>,
vector vector<float, 1536>
);
```
#### Implementation Throughts
- The **configuration** to connect to Astra and Cassandra are not
exactly the same so 2 different classes with associated builder are
provided:
[Astra](https://github.com/clun/langchain4j/blob/main/langchain4j/src/main/java/dev/langchain4j/store/embedding/cassandra/AstraDbEmbeddingConfiguration.java)
and [OSS
Cassandra](https://github.com/clun/langchain4j/blob/main/langchain4j/src/main/java/dev/langchain4j/store/embedding/cassandra/CassandraEmbeddingConfiguration.java).
A couple of fields are mutualized but creating a superclass to inherit
from lead to the use of Lombok `@SuperBuilder` and the Javadoc was not
able to found out what to do.
- Instead of passing a large number of arguments like other stores I
prefer to wrap them as a bean. With this trick you can add or remove
attributes, make then optional or mandatory at will. If you need to add
a new attribute in the configuration you do not have to change the
implementation of `XXXStore` and `XXXStoreImpl`
- I create an
[AstractEmbeddedStore<T>](https://github.com/clun/langchain4j/blob/main/langchain4j/src/main/java/dev/langchain4j/store/embedding/AbstractEmbeddingStore.java)
that could very well become the super class for any store. It handles
the different call of the real concrete implementation. (_delegate
pattern_). Some default implementation can be implemented
```java
/**
* Add a list of embeddings to the store.
*
* @param embeddings
* list of embeddings (hold vector)
* @return
* list of ids
*/
@Override
public List<String> addAll(List<Embedding> embeddings) {
Objects.requireNonNull(embeddings, "embeddings must not be null");
return embeddings.stream().map(this::add).collect(Collectors.toList());
}
```
The only method to implement at the Store level is:
```java
/**
* Initialize the concrete implementation.
* @return create implementation class for the store
*/
protected abstract EmbeddingStore<T> loadImplementation()
throws ClassNotFoundException, NoSuchMethodException, InstantiationException,
IllegalAccessException, InvocationTargetException;
```
-
[CassandraEmbeddedStore](https://github.com/clun/langchain4j/blob/main/langchain4j/src/main/java/dev/langchain4j/store/embedding/cassandra/CassandraEmbeddingStore.java#L30)
proposes 2 constructors, one could override the implementation class if
they want (extension point)
#### Tests
- Test classes are provided including some long form examples based on
classed found in `langchain4j-examples` but test are disabled.
- To start a local cassandra use docker and the
[docker-compose](https://github.com/clun/langchain4j/blob/main/langchain4j-cassandra/src/test/resources/docker-compose.yml)
```
docker compose up -d
```
- To run Test with Astra signin with your github account, create a token
(api Key) with role `Organization Administrator` following this
[procedure](https://awesome-astra.github.io/docs/pages/astra/create-token/#c-procedure)
<img width="926" alt="Screenshot 2023-09-06 at 18 14 12"
src="https://github.com/langchain4j/langchain4j/assets/726536/dfd2d9e5-09c9-4504-bfaa-31cfd87704a1">
- Pick the full value of the `token` from the json
<img width="713" alt="Screenshot 2023-09-06 at 18 15 53"
src="https://github.com/langchain4j/langchain4j/assets/726536/1be56234-dd98-4f59-af71-03df42ed6997">
- Create the environment variable `ASTRA_DB_APPLICATION_TOKEN`
```console
export ASTRA_DB_APPLICATION_TOKEN=AstraCS:....<your_token>
```
I've done integration with Elastic and do some local test to ensure it's
right!（some logic is translated from LangChain Python to Java）.
Elasticsearch do not support `Gson`. So we must have `Jackson`
dependency.
Qwen series models are provided by Alibaba Cloud. They are much better
in Asia languages then other LLMs.
DashScope is a model service platform. Qwen models are its primary
supported models. But it also supports other series like LLaMA2, Dolly,
ChatGLM, BiLLa(based on LLaMA)...These may be integrated sometime in the
future.
In-process embeddings are moved to their own repository
(https://github.com/langchain4j/langchain4j-embeddings) due to the size
of the involved files.
Note that once this PR is merged we'd ideally need a release so as to
have the `langchain4j-embeddings` related project depend on that
version.
- all-minilm-l6-v2
- all-minilm-l6-v2-q
- e5-small-v2
- e5-small-v2-q
The idea is to give users an option to embed documents/texts in the same
Java process without any external dependencies.
ONNX Runtime is used to run models inside JVM.
Each model resides in it's own maven module (inside the jar).
Have a parent pom that contains most/all common things for the
sub-projects.
Note that it is separate from the root aggregator pom: not mixing the
aggregator and the parents makes things slightly easier.
If this change makes it harder to do releases, there might be a
possibility to generate the effective poms for each subproject, but on
the other hand releasing everything should not be too problematic.
deep-learning-dynamo