Merge branch 'stanfordnlp:main' into main

.pre-commit-config.yaml

@@ -5,15 +5,12 @@ default_stages: [commit]
 default_install_hook_types: [pre-commit, commit-msg]
 
 repos:
-  #  - repo: https://github.com/astral-sh/ruff-pre-commit
-  #    # Ruff version.
-  #    rev: v0.1.11
-  #    hooks:
-  #      # Run the linter.
-  #      - id: ruff
-  #        args: [--fix]
-  #      # Run the formatter.
-  #      - id: ruff-format
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.1.11
+    hooks:
+      - id: ruff
+        args: [--fix]
+      - id: ruff-format
 
   - repo: https://github.com/timothycrosley/isort
     rev: 5.12.0
@@ -50,14 +47,15 @@ repos:
         args:
           - "--autofix"
           - "--indent=2"
-  #  - repo: local
-  #    hooks:
-  #      - id: validate-commit-msg
-  #        name: Commit Message is Valid
-  #        language: pygrep
-  #        entry: ^(break|build|ci|docs|feat|fix|perf|refactor|style|test|ops|hotfix|release|maint|init|enh|revert)\([\w,\.,\-,\(,\),\/]+\)(!?)(:)\s{1}([\w,\W,:]+)
-  #        stages: [commit-msg]
-  #        args: [--negate]
+
+  - repo: local
+    hooks:
+      - id: validate-commit-msg
+        name: Commit Message is Valid
+        language: pygrep
+        entry: ^(break|build|ci|docs|feat|fix|perf|refactor|style|test|ops|hotfix|release|maint|init|enh|revert)\([\w,\.,\-,\(,\),\/]+\)(!?)(:)\s{1}([\w,\W,:]+)
+        stages: [commit-msg]
+        args: [--negate]
 
   - repo: https://github.com/pre-commit/mirrors-prettier
     rev: v3.0.3

dsp/modules/cache_utils.py

@@ -6,7 +6,7 @@
 
 from dsp.utils import dotdict
 
-cache_turn_on = True
+cache_turn_on = os.environ.get('DSP_CACHEBOOL', 'True').lower() != 'false'
 
 
 def noop_decorator(arg=None, *noop_args, **noop_kwargs):

dsp/modules/gpt3.py

@@ -62,12 +62,15 @@ def __init__(
         api_provider: Literal["openai"] = "openai",
         api_base: Optional[str] = None,
         model_type: Literal["chat", "text"] = None,
+        system_prompt: Optional[str] = None,
         **kwargs,
     ):
         super().__init__(model)
         self.provider = "openai"
         openai.api_type = api_provider
 
+        self.system_prompt = system_prompt
+
         assert (
             api_provider != "azure"
         ), "Azure functionality with base OpenAI has been deprecated, please use dspy.AzureOpenAI instead."
@@ -118,6 +121,9 @@ def basic_request(self, prompt: str, **kwargs):
         kwargs = {**self.kwargs, **kwargs}
         if self.model_type == "chat":
             # caching mechanism requires hashable kwargs
+            messages = [{"role": "user", "content": prompt}]
+            if self.system_prompt:
+                messages.insert(0, {"role": "system", "content": self.system_prompt})
             kwargs["messages"] = [{"role": "user", "content": prompt}]
             kwargs = {"stringify_request": json.dumps(kwargs)}
             response = chat_request(**kwargs)

dsp/primitives/predict.py

@@ -98,11 +98,20 @@ def do_generate(
             completion[field_names[last_field_idx]] = ""
 
             # Recurse with greedy decoding and a shorter length.
-            max_tokens = kwargs.get("max_tokens", dsp.settings.lm.kwargs["max_tokens"])
+            max_tokens = (kwargs.get("max_tokens") or 
+                        kwargs.get("max_output_tokens") or
+                        dsp.settings.lm.kwargs.get("max_tokens") or 
+                        dsp.settings.lm.kwargs.get('max_output_tokens'))
+
+
+            if max_tokens is None:
+                raise ValueError("Required 'max_tokens' or 'max_output_tokens' not specified in settings.")
             max_tokens = min(max(75, max_tokens // 2), max_tokens)
+            keys = list(kwargs.keys()) + list(dsp.settings.lm.kwargs.keys()) 
+            max_tokens_key = "max_tokens" if "max_tokens" in keys else "max_output_tokens"
             new_kwargs = {
                 **kwargs,
-                "max_tokens": max_tokens,
+                max_tokens_key: max_tokens,
                 "n": 1,
                 "temperature": 0.0,
             }

dsp/utils/settings.py

@@ -36,7 +36,7 @@ def __new__(cls):
                 force_reuse_cached_compilation=False,
                 compiling=False,
                 skip_logprobs=False,
-                trace=None,
+                trace=[],
                 release=0,
                 log_openai_usage=False,
                 bypass_assert=False,

dspy/experimental/synthesizer/synthesizer.py

@@ -202,7 +202,9 @@ def generate(
             }
 
             if self.config.num_example_for_optim:
-                kwargs["ground_source"] = random.sample(ground_source, self.config.num_example_for_optim)
+                if not isinstance(ground_source, list):
+                    raise ValueError("Ground source must be a list of examples when `num_example_for_optim` is provided.")
+                kwargs["ground_source"] = random.sample(ground_source, k=self.config.num_example_for_optim)
 
             with dspy.context(lm=self.input_lm):
                 inputs = self.input_predictor(**kwargs)

dspy/retrieve/pgvector_rm.py

@@ -1,6 +1,5 @@
-from typing import List, Optional
-
-import openai
+import warnings
+from typing import Callable, Optional
 
 import dspy
 
@@ -12,6 +11,11 @@
     raise ImportError(
         "The 'pgvector' extra is required to use PgVectorRM. Install it with `pip install dspy-ai[pgvector]`",
     )
+try:
+    import openai
+except ImportError:
+    warnings.warn("`openai` is not installed. Install it with `pip install openai` to use OpenAI embedding models.",
+                  category=ImportWarning)
 
 
 class PgVectorRM(dspy.Retrieve):
@@ -26,7 +30,8 @@ class PgVectorRM(dspy.Retrieve):
     Args:
         db_url (str): A PostgreSQL database URL in psycopg2's DSN format
         pg_table_name (Optional[str]): name of the table containing passages
-        openai_client (openai.OpenAI): OpenAI client to use for computing query embeddings
+        openai_client (openai.OpenAI): OpenAI client to use for computing query embeddings. Either openai_client or embedding_func must be provided.
+        embedding_func (Callable): A function to use for computing query embeddings. Either openai_client or embedding_func must be provided.
         k (Optional[int]): Default number of top passages to retrieve. Defaults to 20
         embedding_field (str = "embedding"): Field containing passage embeddings. Defaults to "embedding"
         fields (List[str] = ['text']): Fields to retrieve from the table. Defaults to "text"
@@ -41,10 +46,10 @@ class PgVectorRM(dspy.Retrieve):
 
         openai.api_key = os.environ.get("OPENAI_API_KEY", None)
         openai_client = openai.OpenAI()
-        
+
         llm = dspy.OpenAI(model="gpt-3.5-turbo")
-        
-        DATABASE_URL should be in the format postgresql://user:password@host/database 
+
+        DATABASE_URL should be in the format postgresql://user:password@host/database
         db_url=os.getenv("DATABASE_URL")
 
         retriever_model = PgVectorRM(conn, openai_client=openai_client, "paragraphs", fields=["text", "document_id"], k=20)
@@ -60,16 +65,19 @@ def __init__(
             self,
             db_url: str,
             pg_table_name: str,
-            openai_client: openai.OpenAI,
-            k: Optional[int]=20,
+            openai_client: Optional[openai.OpenAI] = None,
+            embedding_func: Optional[Callable] = None,
+            k: Optional[int] = 20,
             embedding_field: str = "embedding",
-            fields: List[str] = ['text'],
+            fields: list[str] = ['text'],
     ):
         """
         k = 20 is the number of paragraphs to retrieve
         """
+        assert openai_client or embedding_func, "Either openai_client or embedding_func must be provided."
         self.openai_client = openai_client
-
+        self.embedding_func = embedding_func
+
         self.conn = psycopg2.connect(db_url)
         register_vector(self.conn)
         self.pg_table_name = pg_table_name
@@ -80,19 +88,15 @@ def __init__(
 
     def forward(self, query: str, k: Optional[int]=20):
         """Search with PgVector for self.k top passages for query
-        
+
         Args:
             query  (str): The query to search for
             k (Optional[int]): The number of top passages to retrieve. Defaults to self.k
-        Returns: 
+        Returns:
             dspy.Prediction: an object containing the retrieved passages.
         """
         # Embed query
-        query_embedding = self.openai_client.embeddings.create(
-            model="text-embedding-ada-002",
-            input=query,
-            encoding_format="float",
-        ).data[0].embedding
+        query_embedding = self._get_embeddings(query)
 
         related_paragraphs = []
 
@@ -115,4 +119,14 @@ def forward(self, query: str, k: Optional[int]=20):
                 for row in rows:
                     related_paragraphs.append(dspy.Example(long_text=row[0], document_id=row[1]))
         # Return Prediction
-        return related_paragraphs
+        return related_paragraphs
+
+    def _get_embeddings(self, query: str) -> list[float]:
+        if self.openai_client is not None:
+            return self.openai_client.embeddings.create(
+                model="text-embedding-ada-002",
+                input=query,
+                encoding_format="float",
+            ).data[0].embedding
+        else:
+            return self.embedding_func(query)