Ollama embeddings are useful to enhance langflow's support of Ollama,
allowing users to run LLMs such as Mistral and LLama locally. Langchain
documentation can be found via [this
link](https://python.langchain.com/docs/integrations/text_embedding/ollama).
Changes:
- New `OllamaEmbeddingsComponent` class
- Associated documentation in the `Embeddings` section
fix(authGuard): remove unnecessary promise return in logout function call
fix(headerComponent): remove unnecessary promise return in logout function call
fix(authContext): add navigation to login page after logout
fix(api): remove unnecessary promise return in logout function call and add navigation to login page after logout
In this pull request, I have:
- added Elasticsearch component on Vector Store
- functionality is basic to get started quickly
**Set up a Single-node Elasticsearch cluster on localhost on docker**
1. sudo docker network create elastic
2. sudo docker run --net elastic -p 9200:9200 -e
"discovery.type=single-node" -e "xpack.security.enabled=false" -e
"xpack.security.http.ssl.enabled=false" -d
docker.elastic.co/elasticsearch/elasticsearch:8.11.3
3. curl http://localhost:9200
{
"name" : "994a10c1dab5",
"cluster_name" : "docker-cluster",
"cluster_uuid" : "p_iQ88F-T2agFpIdzJN7Ow",
"version" : {
"number" : "8.11.3",
"build_flavor" : "default",
"build_type" : "docker",
"build_hash" : "64cf052f3b56b1fd4449f5454cb88aca7e739d9a",
"build_date" : "2023-12-08T11:33:53.634979452Z",
"build_snapshot" : false,
"lucene_version" : "9.8.0",
"minimum_wire_compatibility_version" : "7.17.0",
"minimum_index_compatibility_version" : "7.0.0"
},
"tagline" : "You Know, for Search"
}
4. curl -GET http://localhost:9200/_cat/indices
No indices are available
**Set up Langlow:**
1. make backend
2. make frontend
3. open localhost:3000
4. New Project -> Import from JSON: elasticsearch-langflow.json
**[elasticsearch-langflow.json](https://github.com/logspace-ai/langflow/files/13823444/elasticsearch-langflow.json)**
5. poetry add elasticsearch or pipenv install elasticsearch or pip
install elasticsearch
6. Select text file to load
7. Provide OpenAPI keys for OpenAIEmbeddings and ChatOpenAI
8. Build
9. Verify that document is indexed: curl -GET
http://localhost:9200/_cat/indices
health status index uuid pri rep docs.count docs.deleted store.size
pri.store.size dataset.size
yellow open test-index pt9_ZOACR8mWCNx7GO3scA 1 1 1 0 39.3kb 39.3kb
39.3kb
10. Open Chat and you can ask: "What is the document about?"
