Add documentation for ChromadbRM in DSPy retrievals documentation

docs/retrieval_models_client.md

@@ -8,6 +8,7 @@ This documentation provides an overview of the DSPy Retrieval Model Clients.
 | --- | --- |
 | ColBERTv2 | [ColBERTv2 Section](#ColBERTv2) |
 | AzureCognitiveSearch | [AzureCognitiveSearch Section](#AzureCognitiveSearch) |
+| ChromadbRM | [ChromadbRM Section](#ChromadbRM) |
 
 ## ColBERTv2
 
@@ -91,4 +92,66 @@ class AzureCognitiveSearch:
 
 Refer to [ColBERTv2](#ColBERTv2) documentation. Keep in mind there is no `simplify` flag for AzureCognitiveSearch.
 
-AzureCognitiveSearch supports sending queries and processing the received results, mapping content and scores to a correct format for the Azure Cognitive Search server.
+AzureCognitiveSearch supports sending queries and processing the received results, mapping content and scores to a correct format for the Azure Cognitive Search server.
+
+## ChromadbRM
+
+### Quickstart with OpenAI Embeddings
+
+ChromadbRM have the flexibility from a variety of embedding functions as outlined in the [chromadb embeddings documentation](https://docs.trychroma.com/embeddings). While different options are available, this example demonstrates how to utilize OpenAI embeddings specifically.
+
+```python
+from dspy.retrieve import ChromadbRM
+import os
+import openai
+from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
+
+embedding_function = OpenAIEmbeddingFunction(
+    api_key=os.environ.get('OPENAI_API_KEY'),
+    model_name="text-embedding-ada-002"
+)
+
+retriever_model = ChromadbRM(
+    'your_collection_name',
+    '/path/to/your/db',
+    embedding_function=embedding_function,
+    k=5
+)
+
+results = retriever_model("Explore the significance of quantum computing", k=5)
+
+for result in results:
+    print("Document:", result.long_text, "\n")
+```
+
+### Constructor
+
+Initialize an instance of the `ChromadbRM` class, with the option to use OpenAI's embeddings or any alternative supported by chromadb, as detailed in the official [chromadb embeddings documentation](https://docs.trychroma.com/embeddings).
+
+```python
+ChromadbRM(
+    collection_name: str,
+    persist_directory: str,
+    embedding_function: Optional[EmbeddingFunction[Embeddable]] = OpenAIEmbeddingFunction(),
+    k: int = 7,
+)
+```
+
+**Parameters:**
+- `collection_name` (_str_): The name of the chromadb collection.
+- `persist_directory` (_str_): Path to the directory where chromadb data is persisted.
+- `embedding_function` (_Optional[EmbeddingFunction[Embeddable]]_, _optional_): The function used for embedding documents and queries. Defaults to `DefaultEmbeddingFunction()` if not specified.
+- `k` (_int_, _optional_): The number of top passages to retrieve. Defaults to 7.
+
+### Methods
+
+#### `forward(self, query_or_queries: Union[str, List[str]], k: Optional[int] = None) -> dspy.Prediction`
+
+Search the chromadb collection for the top `k` passages matching the given query or queries, using embeddings generated via the specified `embedding_function`.
+
+**Parameters:**
+- `query_or_queries` (_Union[str, List[str]]_): The query or list of queries to search for.
+- `k` (_Optional[int]_, _optional_): The number of results to retrieve. If not specified, defaults to the value set during initialization.
+
+**Returns:**
+- `dspy.Prediction`: Contains the retrieved passages, each represented as a `dotdict` with a `long_text` attribute.