mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
d01bad5169
This patch fixes the #18022 issue, converting the SimSIMD internal zero-copy outputs to NumPy. I've also noticed, that oftentimes `dtype=np.float32` conversion is used before passing to SimSIMD. Which numeric types do LangChain users generally care about? We support `float64`, `float32`, `float16`, and `int8` for cosine distances and `float16` seems reasonable for practically any kind of embeddings and any modern piece of hardware, so we can change that part as well 🤗 |
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langchain_elasticsearch | ||
scripts | ||
tests | ||
.gitignore | ||
LICENSE | ||
Makefile | ||
poetry.lock | ||
pyproject.toml | ||
README.md |
langchain-elasticsearch
This package contains the LangChain integration with Elasticsearch.
Installation
pip install -U langchain-elasticsearch
Elasticsearch setup
Elastic Cloud
You need a running Elasticsearch deployment. The easiest way to start one is through Elastic Cloud. You can sign up for a free trial.
- Create a deployment
- Get your Cloud ID:
- In the Elastic Cloud console, click "Manage" next to your deployment
- Copy the Cloud ID and paste it into the
es_cloud_id
parameter below
- Create an API key:
- In the Elastic Cloud console, click "Open" next to your deployment
- In the left-hand side menu, go to "Stack Management", then to "API Keys"
- Click "Create API key"
- Enter a name for the API key and click "Create"
- Copy the API key and paste it into the
es_api_key
parameter below
Elastic Cloud
Alternatively, you can run Elasticsearch via Docker as described in the docs.
Usage
ElasticsearchStore
The ElasticsearchStore
class exposes Elasticsearch as a vector store.
from langchain_elasticsearch import ElasticsearchStore
embeddings = ... # use a LangChain Embeddings class or ElasticsearchEmbeddings
vectorstore = ElasticsearchStore(
es_cloud_id="your-cloud-id",
es_api_key="your-api-key",
index_name="your-index-name",
embeddings=embeddings,
)
ElasticsearchEmbeddings
The ElasticsearchEmbeddings
class provides an interface to generate embeddings using a model
deployed in an Elasticsearch cluster.
from langchain_elasticsearch import ElasticsearchEmbeddings
embeddings = ElasticsearchEmbeddings.from_credentials(
model_id="your-model-id",
input_field="your-input-field",
es_cloud_id="your-cloud-id",
es_api_key="your-api-key",
)
ElasticsearchChatMessageHistory
The ElasticsearchChatMessageHistory
class stores chat histories in Elasticsearch.
from langchain_elasticsearch import ElasticsearchChatMessageHistory
chat_history = ElasticsearchChatMessageHistory(
index="your-index-name",
session_id="your-session-id",
es_cloud_id="your-cloud-id",
es_api_key="your-api-key",
)