From ddf22413e727c2456bda17df601b945d44d6b96b Mon Sep 17 00:00:00 2001
From: Anush008 <anushshetty90@gmail.com>
Date: Wed, 11 Dec 2024 11:27:36 +0530
Subject: [PATCH] feat: Camel integration

Signed-off-by: Anush008 <anushshetty90@gmail.com>
---
 .../documentation/frameworks/_index.md        |   1 +
 .../content/documentation/frameworks/camel.md | 100 ++++++++++++++++++
 2 files changed, 101 insertions(+)
 create mode 100644 qdrant-landing/content/documentation/frameworks/camel.md

diff --git a/qdrant-landing/content/documentation/frameworks/_index.md b/qdrant-landing/content/documentation/frameworks/_index.md
index c6ae7b413..532ed29cc 100644
--- a/qdrant-landing/content/documentation/frameworks/_index.md
+++ b/qdrant-landing/content/documentation/frameworks/_index.md
@@ -9,6 +9,7 @@ partition: build
 | Framework                                                     | Description                                                                                                          |
 | ------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------- |
 | [AutoGen](/documentation/frameworks/autogen/)                 | Framework from Microsoft building LLM applications using multiple conversational agents.                             |
+| [Camel](/documentation/frameworks/camel/)                     | Framework to build and use LLM-based agents for real-world task solving                                              |
 | [Canopy](/documentation/frameworks/canopy/)                   | Framework from Pinecone for building RAG applications using LLMs and knowledge bases.                                |
 | [Cheshire Cat](/documentation/frameworks/cheshire-cat/)       | Framework to create personalized AI assistants using custom data.                                                    |
 | [CrewAI](/documentation/frameworks/crewai/)                   | CrewAI is a framework to build automated workflows using multiple AI agents that perform complex tasks.              |
diff --git a/qdrant-landing/content/documentation/frameworks/camel.md b/qdrant-landing/content/documentation/frameworks/camel.md
new file mode 100644
index 000000000..9f5d28189
--- /dev/null
+++ b/qdrant-landing/content/documentation/frameworks/camel.md
@@ -0,0 +1,100 @@
+---
+title: CamelAI
+---
+
+# Camel
+
+[Camel](https://www.camel-ai.org) is a Python framework to build and use LLM-based agents for real-world task solving.
+
+Qdrant is available as a storage mechanism in Camel for ingesting and retrieving semantically similar data.
+
+## Usage With Qdrant
+
+- Install Camel with the `vector-databases` extra.
+
+```bash
+pip install "camel[vector-databases]"
+```
+
+- Configure the `QdrantStorage` class.
+
+```python
+from camel.storages import QdrantStorage, VectorDBQuery, VectorRecord
+from camel.types import VectorDistance
+
+qdrant_storage = QdrantStorage(
+    url_and_api_key=(
+        "https://xyz-example.eu-central.aws.cloud.qdrant.io:6333",
+        "<provide-your-own-key>",
+    ),
+    collection_name="{collection_name}",
+    distance=VectorDistance.COSINE,
+    vector_dim=384,
+)
+```
+
+The `QdrantStorage` class implements methods to read and write to a Qdrant instance. An instance of this class can now be passed to retrievers for interfacing with your Qdrant collections.
+
+```python
+qdrant_storage.add([VectorRecord(
+            vector=[-0.1, 0.1, ...],
+            payload={'key1': 'value1'},
+        ),
+        VectorRecord(
+            vector=[-0.1, 0.1, ...],
+            payload={'key2': 'value2'},
+        ),])
+
+query_results = qdrant_storage.query(VectorDBQuery(query_vector=[0.1, 0.2, ...], top_k=10))
+for result in query_results:
+    print(result.record.payload, result.similarity)
+
+qdrant_storage.clear()
+```
+
+- Use the `QdrantStorage` in Camel's Vector Retriever.
+
+```python
+from camel.embeddings import OpenAIEmbedding
+from camel.retrievers import VectorRetriever
+
+# Initialize the VectorRetriever with an embedding model
+vr = VectorRetriever(embedding_model=OpenAIEmbedding())
+
+content_input_path = "<URL-TO-SOME-RESOURCE>"
+
+vr.process(content_input_path, qdrant_storage)
+
+# Execute the query and retrieve results
+results = vr.query("<SOME_USER_QUERY>", vector_storage)
+```
+
+- Camel also provides an Auto Retriever implementation that handles both embedding and storing data and executing queries.
+
+```python
+from camel.retrievers import AutoRetriever
+from camel.types import StorageType
+
+ar = AutoRetriever(
+    url_and_api_key=(
+        "https://xyz-example.eu-central.aws.cloud.qdrant.io:6333",
+        "<provide-your-own-key>",
+    ),
+    storage_type=StorageType.QDRANT,
+)
+
+retrieved_info = ar.run_vector_retriever(
+    contents=["<URL-TO-SOME-RESOURCE>"],
+    query=""<SOME_USER_QUERY>"",
+    return_detailed_info=True, 
+)
+
+print(retrieved_info)
+```
+
+You can refer to the Camel [documentation](https://docs.camel-ai.org/index.html) for more information about the retrieval mechansims.
+
+## End-To-End Examples
+
+- [Camel RAG Cookbook](https://docs.camel-ai.org/cookbooks/agents_with_rag.html)
+- [Customer Service Discord Bot with Agentic RAG](https://docs.camel-ai.org/cookbooks/customer_service_Discord_bot_with_agentic_RAG.html)