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davidmyriel
2024-04-15 17:56:01 -07:00
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commit 5d58909114
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qdrant-landing/content/documentation/examples/natural-language-search-oracle-cloud-infrastructure-cohere-langchain.md
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@@ -75,18 +75,25 @@ os.environ["COMPARTMENT_OCID"] = "<your-compartment-ocid>"
#### Qdrant Hybrid Cloud
Qdrant Hybrid Cloud running on Oracle Cloud helps you build a solution without sending your data to external services.
Our documentation provides a step-by-step guide on how to [deploy Qdrant Hybrid Cloud on Oracle
Qdrant Hybrid Cloud running on Oracle Cloud helps you build a solution without sending your data to external services. Our documentation provides a step-by-step guide on how to [deploy Qdrant Hybrid Cloud on Oracle
Cloud](/documentation/hybrid-cloud/platform-deployment-options/#oracle-cloud-infrastructure).
Qdrant will be running on a specific URL and access will be restricted by the API key. Make sure to store them both as
environment variables as well:
Qdrant will be running on a specific URL and access will be restricted by the API key. Make sure to store them both as environment variables as well:
```shell
export QDRANT_URL="https://qdrant.example.com"
export QDRANT_API_KEY="your-api-key"
```
*Optional:* You can also configure LangSmith, which will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith [here](https://smith.langchain.com/).
```shell
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY="your-api-key"
export LANGCHAIN_PROJECT="your-project" # if not specified, defaults to "default"
```
Now you can get started:
```python
import os
qdrant-landing/content/documentation/examples/rag-chatbot-scaleway.md
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@@ -39,6 +39,23 @@ A notebook for this tutorial is available on [GitHub](https://github.com/qdrant/
To prepare the environment for working with Qdrant and related libraries, it's necessary to install all required Python packages. This can be done using Poetry, a tool for dependency management and packaging in Python. The code snippet imports various libraries essential for the tasks ahead, including `bs4` for parsing HTML and XML documents, `langchain` and its community extensions for working with language models and document loaders, and `Qdrant` for vector storage and retrieval. These imports lay the groundwork for utilizing Qdrant alongside other tools for natural language processing and machine learning tasks.
Qdrant will be running on a specific URL and access will be restricted by the API key. Make sure to store them both as environment variables as well:
```shell
export QDRANT_URL="https://qdrant.example.com"
export QDRANT_API_KEY="your-api-key"
```
*Optional:* You can also configure LangSmith, which will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith [here](https://smith.langchain.com/).
```shell
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY="your-api-key"
export LANGCHAIN_PROJECT="your-project" # if not specified, defaults to "default"
```
Now you can get started:
```python
import getpass
import os
qdrant-landing/content/documentation/examples/rag-contract-management-stackit-aleph-alpha.md
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@@ -72,10 +72,20 @@ os.environ["QDRANT_URL"] = "https://qdrant.example.com"
os.environ["QDRANT_API_KEY"] = "your-api-key"
```
Qdrant will be running on a specific URL and access will be restricted by the API key. Make sure to store them both as environment variables as well:
*Optional:* You can also configure LangSmith, which will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith [here](https://smith.langchain.com/).
```shell
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY="your-api-key"
export LANGCHAIN_PROJECT="your-project" # if not specified, defaults to "default"
```
## Implementation
To build the application, we can use the official SDKs of Aleph Alpha and Qdrant. However, to streamline the process
let's use [Langchain](https://python.langchain.com/docs/get_started/introduction). This framework is already integrated with both services, so we can focus our efforts on
let's use [LangChain](https://python.langchain.com/docs/get_started/introduction). This framework is already integrated with both services, so we can focus our efforts on
developing business logic.
### Qdrant collection