docs: Added DSPy integration docs (#390)

* docs: Added dspy docs

* chore: Added social preview

* fix: python formatting

qdrant-landing/content/documentation/integrations/dspy.md

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+---
+title: Stanford DSPy
+weight: 1500
+---
+
+# Stanford DSPy
+
+[DSPy](https://github.com/stanfordnlp/dspy) is the framework for solving advanced tasks with language models (LMs) and retrieval models (RMs). It unifies techniques for prompting and fine-tuning LMs — and approaches for reasoning, self-improvement, and augmentation with retrieval and tools.
+
+- Provides composable and declarative modules for instructing LMs in a familiar Pythonic syntax.
+
+- Introduces an automatic compiler that teaches LMs how to conduct the declarative steps in your program.
+
+Qdrant can be used as a retrieval mechanism in the DSPy flow.
+
+## Installation
+
+For the Qdrant retrieval integration, include `dspy-ai` with the `qdrant` extra:
+```bash
+pip install dspy-ai[qdrant]
+```
+
+## Usage
+
+We can configure `DSPy` settings to use the Qdrant retriever model like so:
+```python
+import dspy
+from dspy.retrieve.qdrant_rm import QdrantRM
+
+from qdrant_client import QdrantClient
+
+turbo = dspy.OpenAI(model="gpt-3.5-turbo")
+qdrant_client = QdrantClient()  # Defaults to a local instance at http://localhost:6333/
+qdrant_retriever_model = QdrantRM("collection-name", qdrant_client, k=3)
+
+dspy.settings.configure(lm=turbo, rm=qdrant_retriever_model)
+```
+Using the retriever is pretty simple. The `dspy.Retrieve(k)` module will search for the top-k passages that match a given query.
+
+```python
+retrieve = dspy.Retrieve(k=3)
+question = "Some question about my data"
+topK_passages = retrieve(question).passages
+
+print(f"Top {retrieve.k} passages for question: {question} \n", "\n")
+
+for idx, passage in enumerate(topK_passages):
+    print(f"{idx+1}]", passage, "\n")
+```
+
+With Qdrant configured as the retriever for contexts, you can set up a DSPy module like so:
+```python
+class RAG(dspy.Module):
+    def __init__(self, num_passages=3):
+        super().__init__()
+
+        self.retrieve = dspy.Retrieve(k=num_passages)
+        ...
+
+    def forward(self, question):
+        context = self.retrieve(question).passages
+        ...
+
+```
+
+With the generic RAG blueprint now in place, you can add the many interactions offered by DSPy with context retrieval powered by Qdrant.
+
+## Next steps
+
+Find DSPy usage docs and examples [here](https://github.com/stanfordnlp/dspy#4-documentation--tutorials).