Merge branch 'main' of https://github.com/stanfordnlp/dspy

dsp/modules/aws_models.py

@@ -275,6 +275,8 @@ def __init__(
         self.aws_provider = aws_provider
         self.provider = aws_provider.get_provider_name()
 
+        self.kwargs["stop"] = ["<|eot_id|>"]
+
         for k, v in kwargs.items():
             self.kwargs[k] = v
 
@@ -290,25 +292,43 @@ def _create_body(self, prompt: str, **kwargs) -> tuple[int, dict[str, str | floa
         for k, v in kwargs.items():
             base_args[k] = v
 
-        n, query_args = self.aws_provider.sanitize_kwargs(base_args)
+        n, base_args = self.aws_provider.sanitize_kwargs(base_args)
 
         # Meta models do not support the following parameters
-        query_args.pop("frequency_penalty", None)
-        query_args.pop("num_generations", None)
-        query_args.pop("presence_penalty", None)
-        query_args.pop("model", None)
+        base_args.pop("frequency_penalty", None)
+        base_args.pop("num_generations", None)
+        base_args.pop("presence_penalty", None)
+        base_args.pop("model", None)
 
-        max_tokens = query_args.pop("max_tokens", None)
-        if max_tokens:
-            query_args["max_gen_len"] = max_tokens
+        max_tokens = base_args.pop("max_tokens", None)
+
+        query_args: dict[str, str | float] = {}
+        if isinstance(self.aws_provider, Bedrock):
+            if max_tokens:
+                base_args["max_gen_len"] = max_tokens
+            query_args = base_args
+            query_args["prompt"] = prompt
+        elif isinstance(self.aws_provider, Sagemaker):
+            if max_tokens:
+                base_args["max_new_tokens"] = max_tokens
+            query_args["parameters"] = base_args
+            query_args["inputs"] = prompt
+        else:
+            raise ValueError("Error - provider not recognized")
 
-        query_args["prompt"] = prompt
         return (n, query_args)
 
     def _call_model(self, body: str) -> str:
-        response = self.aws_provider.predictor.invoke_model(
-            modelId=self._model_name,
+        response = self.aws_provider.call_model(
+            model_id=self._model_name,
             body=body,
         )
-        response_body = json.loads(response["body"].read())
-        return response_body["generation"]
+        if isinstance(self.aws_provider, Bedrock):
+            response_body = json.loads(response["body"].read())
+            completion = response_body["generation"]
+        elif isinstance(self.aws_provider, Sagemaker):
+            response_body = json.loads(response["Body"].read())
+            completion = response_body["generated_text"]
+        else:
+            raise ValueError("Error - provider not recognized")
+        return completion

dsp/modules/hf_client.py

@@ -118,7 +118,7 @@ def send_hftgi_request_v00(arg, **kwargs):
 
 
 class HFClientVLLM(HFModel):
-    def __init__(self, model, port, model_type: Literal['chat', 'text'] = 'text', url="http://localhost", **kwargs):
+    def __init__(self, model, port, model_type: Literal['chat', 'text'] = 'text', url="http://localhost", http_request_kwargs=None, **kwargs):
         super().__init__(model=model, is_client=True)
 
         if isinstance(url, list):
@@ -132,6 +132,7 @@ def __init__(self, model, port, model_type: Literal['chat', 'text'] = 'text', ur
 
         self.urls_const = tuple(self.urls)
         self.port = port
+        self.http_request_kwargs = http_request_kwargs or {}
         self.model_type = model_type
         self.headers = {"Content-Type": "application/json"}
         self.kwargs |= kwargs
@@ -198,6 +199,7 @@ def _generate(self, prompt, **kwargs):
                 port=self.port,
                 json=payload,
                 headers=self.headers,
+                **self.http_request_kwargs,
             )
 
             try:
@@ -225,6 +227,7 @@ def _generate(self, prompt, **kwargs):
                 port=self.port,
                 json=payload,
                 headers=self.headers,
+                **self.http_request_kwargs,
             )
 
             try:
@@ -323,6 +326,7 @@ def __init__(self, model, api_base="https://api.together.xyz/v1", api_key=None,
         stop_default = "\n\n---"
 
         self.kwargs = {
+            "model": model,
             "temperature": 0.0,
             "max_tokens": 512,
             "top_p": 1,

dspy/propose/grounded_proposer.py

@@ -237,6 +237,7 @@ def __init__(
         prompt_model,
         trainset,
         program_code_string=None,
+        view_data_batch_size=10,
         use_dataset_summary=True,
         program_aware=True,
         use_task_demos=True,
@@ -257,7 +258,7 @@ def __init__(
         self.prompt_model = prompt_model
         self.program_code_string = program_code_string
         self.data_summary = create_dataset_summary(
-            trainset=trainset, view_data_batch_size=10, prompt_model=prompt_model,
+            trainset=trainset, view_data_batch_size=view_data_batch_size, prompt_model=prompt_model,
         )
         print(f"DATA SUMMARY: {self.data_summary}")
 

dspy/retrieve/qdrant_rm.py

@@ -38,7 +38,7 @@ class QdrantRM(dspy.Retrieve):
 
         Below is a code snippet that shows how to use Qdrant in the forward() function of a module
         ```python
-        self.retrieve = QdrantRM("my_collection_name", qdrant_client=qdrant_client, k=num_passages)
+        self.retrieve = QdrantRM(question, k=num_passages, filter=filter)
         ```
     """
 
@@ -62,12 +62,13 @@ def __init__(
 
         super().__init__(k=k)
 
-    def forward(self, query_or_queries: Union[str, list[str]], k: Optional[int] = None) -> dspy.Prediction:
+    def forward(self, query_or_queries: Union[str, list[str]], k: Optional[int] = None, filter: Optional[models.Filter]=None) -> dspy.Prediction:
         """Search with Qdrant for self.k top passages for query.
 
         Args:
             query_or_queries (Union[str, List[str]]): The query or queries to search for.
             k (Optional[int]): The number of top passages to retrieve. Defaults to self.k.
+            filter (Optional["Filter"]): "Look only for points which satisfies this conditions". Default: None.
 
         Returns:
             dspy.Prediction: An object containing the retrieved passages.
@@ -90,6 +91,7 @@ def forward(self, query_or_queries: Union[str, list[str]], k: Optional[int] = No
                 vector=vector,
                 limit=k or self.k,
                 with_payload=[self._document_field],
+                filter=filter,
             )
             for vector in vectors
         ]

dspy/teleprompt/mipro_optimizer_v2.py

@@ -220,6 +220,7 @@ def compile(
                 trainset=trainset,
                 prompt_model=self.prompt_model,
                 program_code_string=self.program_code_string,
+                view_data_batch_size=self.view_data_batch_size,
                 program_aware=program_aware_proposer,
             )