Merge pull request #3 from stanfordnlp/main

Merge from main

Author: Anindyadeep

docs/api/language_model_clients/PremAI.md

@@ -0,0 +1,70 @@
+---
+sidebar_position: 5
+---
+
+# dsp.PremAI
+
+[PremAI](https://app.premai.io)  is an all-in-one platform that simplifies the process of creating robust, production-ready applications powered by Generative AI. By streamlining the development process, PremAI allows you to concentrate on enhancing user experience and driving overall growth for your application.
+
+### Prerequisites
+
+Refer to the [quick start](https://docs.premai.io/introduction) guide to getting started with the PremAI platform, create your first project and grab your API key.
+
+### Usage
+
+Please make sure you have premai python sdk installed. Otherwise you can do it using this command:
+
+```bash
+pip install -U premai
+```
+
+Here is a quick example on how to use premai python sdk with dspy
+
+```python
+from dspy import PremAI
+
+llm = PremAI(model='mistral-tiny', project_id=123, api_key="your-premai-api-key")
+print(llm("what is a large language model"))
+```
+
+> Please note: Project ID 123 is just an example. You can find your project ID inside our platform under which you created your project.
+
+### Constructor
+
+The constructor initializes the base class `LM` and verifies the `api_key` provided or defined through the `PREMAI_API_KEY` environment variable.
+
+```python
+class PremAI(LM):
+    def __init__(
+        self,
+        model: str,
+        project_id: int,
+        api_key: str,
+        base_url: Optional[str] = None,
+        session_id: Optional[int] = None,
+        **kwargs,
+    ) -> None:
+```
+
+**Parameters:**
+
+- `model` (_str_): Models supported by PremAI. Example: `mistral-tiny`. We recommend using the model selected in [project launchpad](https://docs.premai.io/get-started/launchpad).
+- `project_id` (_int_): The [project id](https://docs.premai.io/get-started/projects) which contains the model of choice.
+- `api_key` (_Optional[str]_, _optional_): API provider from PremAI. Defaults to None.
+- `session_id` (_Optional[int]_, _optional_): The ID of the session to use. It helps to track the chat history.
+- `**kwargs`: Additional language model arguments will be passed to the API provider.
+
+### Methods
+
+#### `__call__(self, prompt: str, **kwargs) -> List[Dict[str, Any]]`
+
+Retrieves completions from PremAI by calling `request`.
+
+Internally, the method handles the specifics of preparing the request prompt and corresponding payload to obtain the response.
+
+After generation, the completions are post-processed based on the `model_type` parameter.
+
+**Parameters:**
+
+- `prompt` (_str_): Prompt to send to PremAI.
+- `**kwargs`: Additional keyword arguments for completion request. Example: parameters like `temperature`, `max_tokens` etc. You can find all the additional kwargs [here](https://docs.premai.io/get-started/sdk#optional-parameters).

docs/api/language_model_clients/Snowflake.md

@@ -1,7 +1,3 @@
----
-sidebar_position: 
----
-
 # dspy.Snowflake
 
 ### Usage

docs/api/retrieval_model_clients/SnowflakeRM.md

@@ -1,7 +1,3 @@
----
-sidebar_position:
----
-
 # retrieve.SnowflakeRM
 
 ### Constructor

docs/docs/building-blocks/1-language_models.md

@@ -137,6 +137,7 @@ lm = dspy.{provider_listed_below}(model="your model", model_request_kwargs="..."
 
 4. `dspy.Together` for hosted various open source models.
 
+5. `dspy.PremAI` for hosted best open source and closed source models.
 
 ### Local LMs.
 
@@ -173,4 +174,4 @@ model = 'dist/prebuilt/mlc-chat-Llama-2-7b-chat-hf-q4f16_1'
 model_path = 'dist/prebuilt/lib/Llama-2-7b-chat-hf-q4f16_1-cuda.so'
 
 llama = dspy.ChatModuleClient(model=model, model_path=model_path)
-```
+```

docs/docs/cheatsheet.md

@@ -254,15 +254,15 @@ evaluate_program(your_dspy_program)
 
 ## DSPy Optimizers
 
-### dspy.LabeledFewShot 
+### LabeledFewShot 
 ```python
 from dspy.teleprompt import LabeledFewShot
 
-labeled_fewshot_optimizer = dspy.LabeledFewShot(k=8)
+labeled_fewshot_optimizer = LabeledFewShot(k=8)
 your_dspy_program_compiled = labeled_fewshot_optimizer.compile(student = your_dspy_program, trainset=trainset)
 ```
 
-### dspy.BootstrapFewShot 
+### BootstrapFewShot 
 ```python
 from dspy.teleprompt import BootstrapFewShot
 
@@ -302,7 +302,7 @@ loaded_program = YourProgramClass()
 loaded_program.load(path=save_path)
 ```
 
-### dspy.BootstrapFewShotWithRandomSearch
+### BootstrapFewShotWithRandomSearch
 
 ```python
 from dspy.teleprompt import BootstrapFewShotWithRandomSearch
@@ -312,10 +312,10 @@ fewshot_optimizer = BootstrapFewShotWithRandomSearch(metric=your_defined_metric,
 your_dspy_program_compiled = fewshot_optimizer.compile(student = your_dspy_program, trainset=trainset, valset=devset)
 
 ```
-Other custom configurations are similar to customizing the `dspy.BootstrapFewShot` optimizer. 
+Other custom configurations are similar to customizing the `BootstrapFewShot` optimizer. 
 
 
-### dspy.Ensemble
+### Ensemble
 
 ```python
 from dspy.teleprompt import BootstrapFewShotWithRandomSearch
@@ -329,7 +329,7 @@ programs = [x[-1] for x in your_dspy_program_compiled.candidate_programs]
 your_dspy_program_compiled_ensemble = ensemble_optimizer.compile(programs[:3])
 ```
 
-### dspy.BootstrapFinetune
+### BootstrapFinetune
 
 ```python
 from dspy.teleprompt import BootstrapFewShotWithRandomSearch, BootstrapFinetune
@@ -356,7 +356,7 @@ for p in finetune_program.predictors():
     p.activated = False
 ```
 
-### dspy.COPRO
+### COPRO
 
 ```python
 from dspy.teleprompt import COPRO
@@ -368,7 +368,7 @@ copro_teleprompter = COPRO(prompt_model=model_to_generate_prompts, task_model=mo
 compiled_program_optimized_signature = copro_teleprompter.compile(your_dspy_program, trainset=trainset, eval_kwargs=eval_kwargs)
 ```
 
-### dspy.MIPRO
+### MIPRO
 
 
 ```python
@@ -395,7 +395,7 @@ compiled_program = optimize_signature(
 ).program
 ```
 
-### dspy.KNNFewShot
+### KNNFewShot
 
 ```python
 from dspy.predict import KNN
@@ -406,7 +406,7 @@ knn_optimizer = KNNFewShot(KNN, k=3, trainset=trainset)
 your_dspy_program_compiled = knn_optimizer.compile(student=your_dspy_program, trainset=trainset, valset=devset)
 ```
 
-### dspy.BootstrapFewShotWithOptuna
+### BootstrapFewShotWithOptuna
 
 ```python
 from dspy.teleprompt import BootstrapFewShotWithOptuna

docs/docs/deep-dive/language_model_clients/remote_models/PremAI.mdx

@@ -0,0 +1,70 @@
+## PremAI
+
+[PremAI](https://app.premai.io)  is an all-in-one platform that simplifies the process of creating robust, production-ready applications powered by Generative AI. By streamlining the development process, PremAI allows you to concentrate on enhancing user experience and driving overall growth for your application.
+
+### Prerequisites
+
+Refer to the [quick start](https://docs.premai.io/introduction) guide to getting started with the PremAI platform, create your first project and grab your API key.
+
+### Setting up the PremAI Client
+
+The constructor initializes the base class `LM` to support prompting requests to supported PremAI hosted models. This requires the following parameters:
+
+- `model` (_str_): Models supported by PremAI. Example: `mistral-tiny`. We recommend using the model selected in [project launchpad](https://docs.premai.io/get-started/launchpad).
+- `project_id` (_int_): The [project id](https://docs.premai.io/get-started/projects) which contains the model of choice.
+- `api_key` (_Optional[str]_, _optional_): API provider from PremAI. Defaults to None.
+- `session_id` (_Optional[int]_, _optional_): The ID of the session to use. It helps to track the chat history.
+- `**kwargs`: Additional language model arguments will be passed to the API provider.
+
+Example of PremAI constructor:
+
+```python
+class PremAI(LM):
+    def __init__(
+        self,
+        model: str,
+        project_id: int,
+        api_key: str,
+        base_url: Optional[str] = None,
+        session_id: Optional[int] = None,
+        **kwargs,
+    ) -> None:
+```
+
+### Under the Hood
+
+#### `__call__(self, prompt: str, **kwargs) -> str`
+
+**Parameters:**
+- `prompt` (_str_): Prompt to send to PremAI.
+- `**kwargs`: Additional keyword arguments for completion request.
+
+**Returns:**
+- `str`: Completions string from the chosen LLM provider 
+
+Internally, the method handles the specifics of preparing the request prompt and corresponding payload to obtain the response.
+
+### Using the PremAI client
+
+```python
+premai_client = dspy.PremAI(project_id=1111)
+```
+
+Please note that, this is a dummy `project_id`. You need to change this to the project_id you are interested to use with dspy. 
+
+```python
+dspy.configure(lm=premai_client)
+
+#Example DSPy CoT QA program
+qa = dspy.ChainOfThought('question -> answer')
+
+response = qa(question="What is the capital of Paris?") 
+print(response.answer)
+```
+
+2) Generate responses using the client directly.
+
+```python
+response = premai_client(prompt='What is the capital of Paris?')
+print(response)
+```

docs/docs/quick-start/installation.mdx

@@ -30,37 +30,37 @@ import TabItem from '@theme/TabItem';
     </TabItem>
     <TabItem value="pinecone" label="Pinecone">
         ```text
-        pip install dspy-ai[pinecone]
+        pip install "dspy-ai[pinecone]"
         ```
     </TabItem>
     <TabItem value="qdrant" label="Qdrant">
         ```text
-        pip install dspy-ai[qdrant]
+        pip install "dspy-ai[qdrant]"
         ```
     </TabItem>
     <TabItem value="chromadb" label="ChromaDB">
         ```text
-        pip install dspy-ai[chromadb]
+        pip install "dspy-ai[chromadb]"
         ```
     </TabItem>
     <TabItem value="marqo" label="Marqo">
         ```text
-        pip install dspy-ai[marqo]
+        pip install "dspy-ai[marqo]"
         ```
     </TabItem>
     <TabItem value="mongodb" label="MongoDB">
         ```text
-        pip install dspy-ai[mongodb]
+        pip install "dspy-ai[mongodb]"
         ```
     </TabItem>
     <TabItem value="weaviate" label="Weaviate">
         ```text
-        pip install dspy-ai[weaviate]
+        pip install "dspy-ai[weaviate]"
         ```
     </TabItem>
     <TabItem value="milvus" label="Milvus">
         ```text
-        pip install dspy-ai[milvus]
+        pip install "dspy-ai[milvus]"
         ```
     </TabItem>
 

docs/docusaurus.config.ts

@@ -155,6 +155,23 @@ const config: Config = {
       }),
     ],
   ],
+  scripts: [
+    {
+      id: "runllm-widget-script",
+      type: "module",
+      src: "https://cdn.jsdelivr.net/npm/@runllm/search-widget@stable/dist/run-llm-search-widget.es.js",
+      "runllm-server-address": "https://api.runllm.com",
+      "runllm-assistant-id": "132",
+      "runllm-position": "BOTTOM_RIGHT",
+      "runllm-keyboard-shortcut": "Mod+j",
+      version: "stable",
+      "runllm-preset": "docusaurus",
+      "runllm-slack-community-url": "",
+      "runllm-name": "DSPy",
+      "runllm-theme-color": "#005EEC",
+      async: true,
+    },
+  ],
 };
 
 export default config;

dsp/modules/__init__.py

@@ -20,6 +20,7 @@
 from .hf_client import Anyscale, HFClientTGI, Together
 from .mistral import *
 from .ollama import *
+from .premai import PremAI
 from .pyserini import *
 from .sbert import *
 from .sentence_vectorizer import *

dsp/modules/cohere.py

@@ -58,7 +58,7 @@ def __init__(
             Additional arguments to pass to the API provider.
         """
         super().__init__(model)
-        self.co = cohere.Client(api_key)
+        self.co = cohere.Client(api_key, client_name='dspy')
         self.provider = "cohere"
         self.kwargs = {
             "model": model,