fastembedder-fix (#1745)

## Description

Updated `get_embedding_and_usage` function of `FastEmbedEmbedder`. 

Fixes #1715 

## Type of change

Please check the options that are relevant:

- [ ] Bug fix (non-breaking change which fixes an issue)
- [ ] New feature (non-breaking change which adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to not work as expected)
- [ ] Model update
- [ ] Infrastructure change

## Checklist

- [ ] My code follows Phidata's style guidelines and best practices
- [ ] I have performed a self-review of my code
- [ ] I have added docstrings and comments for complex logic
- [ ] My changes generate no new warnings or errors
- [ ] I have added cookbook examples for my new addition (if needed)
- [ ] I have updated requirements.txt/pyproject.toml (if needed)
- [ ] I have verified my changes in a clean environment

Notes:

`FastEmbedEmbedder` currently does not provide usage information

cookbook/assistants/llms/groq/video_summary/app.py

@@ -89,7 +89,7 @@ def main() -> None:
         if num_chunks > 1:
             chunk_summaries = []
             for i in range(num_chunks):
-                with st.status(f"Summarizing chunk: {i+1}", expanded=False) as status:
+                with st.status(f"Summarizing chunk: {i + 1}", expanded=False) as status:
                     chunk_summary = ""
                     chunk_container = st.empty()
                     chunk_summarizer = get_chunk_summarizer(model=llm_model)
@@ -99,7 +99,7 @@ def main() -> None:
                         chunk_summary += delta  # type: ignore
                         chunk_container.markdown(chunk_summary)
                     chunk_summaries.append(chunk_summary)
-                    status.update(label=f"Chunk {i+1} summarized", state="complete", expanded=False)
+                    status.update(label=f"Chunk {i + 1} summarized", state="complete", expanded=False)
 
             with st.spinner("Generating Summary"):
                 summary = ""

cookbook/assistants/llms/ollama/video_summary/app.py

@@ -90,7 +90,7 @@ def main() -> None:
         if num_chunks > 1:
             chunk_summaries = []
             for i in range(num_chunks):
-                with st.status(f"Summarizing chunk: {i+1}", expanded=False) as status:
+                with st.status(f"Summarizing chunk: {i + 1}", expanded=False) as status:
                     chunk_summary = ""
                     chunk_container = st.empty()
                     chunk_summarizer = get_chunk_summarizer(model=llm_model)
@@ -100,7 +100,7 @@ def main() -> None:
                         chunk_summary += delta  # type: ignore
                         chunk_container.markdown(chunk_summary)
                     chunk_summaries.append(chunk_summary)
-                    status.update(label=f"Chunk {i+1} summarized", state="complete", expanded=False)
+                    status.update(label=f"Chunk {i + 1} summarized", state="complete", expanded=False)
 
             with st.spinner("Generating Summary"):
                 summary = ""

cookbook/assistants/tools/hackernews.py

@@ -10,7 +10,5 @@
     # debug_mode=True,
 )
 hn_assistant.print_response(
-    "Write an engaging summary of the "
-    "users with the top 2 stories on hackernews. "
-    "Please mention the stories as well.",
+    "Write an engaging summary of the users with the top 2 stories on hackernews. Please mention the stories as well.",
 )

cookbook/teams/02_news_reporter.py

@@ -18,8 +18,7 @@
     role="Searches the top URLs for a topic",
     instructions=[
         "Given a topic, first generate a list of 3 search terms related to that topic.",
-        "For each search term, search the web and analyze the results."
-        "Return the 10 most relevant URLs to the topic.",
+        "For each search term, search the web and analyze the results.Return the 10 most relevant URLs to the topic.",
         "You are writing for the New York Times, so the quality of the sources is important.",
     ],
     tools=[DuckDuckGo()],

cookbook/tools/hackernews.py

@@ -8,7 +8,5 @@
     markdown=True,
 )
 agent.print_response(
-    "Write an engaging summary of the "
-    "users with the top 2 stories on hackernews. "
-    "Please mention the stories as well.",
+    "Write an engaging summary of the users with the top 2 stories on hackernews. Please mention the stories as well.",
 )

cookbook/workflows/content_creator_workflow/prompts.py

@@ -102,7 +102,7 @@
             "and business leaders while maintaining technical accuracy.\n\n"
         ),
         "expected_output": (
-            "A LinkedIn post plan containing:\n" "- content\n" "- a main blog URL that is associated with the post\n\n"
+            "A LinkedIn post plan containing:\n- content\n- a main blog URL that is associated with the post\n\n"
         ),
     },
 }

phi/agent/agent.py

@@ -493,8 +493,7 @@ def update_model(self) -> None:
             except ModuleNotFoundError as e:
                 logger.exception(e)
                 logger.error(
-                    "phidata uses `openai` as the default model provider. "
-                    "Please provide a `model` or install `openai`."
+                    "phidata uses `openai` as the default model provider. Please provide a `model` or install `openai`."
                 )
                 exit(1)
             self.model = OpenAIChat()  # We default to OpenAIChat as a base model

phi/assistant/assistant.py

@@ -275,9 +275,7 @@ def update_llm(self) -> None:
                 from phi.llm.openai import OpenAIChat
             except ModuleNotFoundError as e:
                 logger.exception(e)
-                logger.error(
-                    "phidata uses `openai` as the default LLM. " "Please provide an `llm` or install `openai`."
-                )
+                logger.error("phidata uses `openai` as the default LLM. Please provide an `llm` or install `openai`.")
                 exit(1)
 
             self.llm = OpenAIChat()
@@ -662,7 +660,7 @@ def get_system_prompt(self) -> Optional[str]:
                 )
             )
             for i, instruction in enumerate(instructions):
-                system_prompt_lines.append(f"{i+1}. {instruction}")
+                system_prompt_lines.append(f"{i + 1}. {instruction}")
             system_prompt_lines.append("</instructions>")
 
         # The add the expected output to the system prompt

phi/cli/config.py

@@ -269,7 +269,7 @@ def print_to_cli(self, show_all: bool = False):
         else:
             print_info("No active workspace found.")
             print_info(
-                "Please create a workspace using `phi ws create` " "or setup existing workspace using `phi ws setup`"
+                "Please create a workspace using `phi ws create` or setup existing workspace using `phi ws setup`"
             )
 
         if show_all and len(self.ws_config_map) > 0:

phi/document/chunking/agentic.py

@@ -30,7 +30,7 @@ def chunk(self, document: Document) -> List[Document]:
             Consider semantic completeness, paragraph boundaries, and topic transitions.
             Return only the character position number of where to break the text:
             
-            {remaining_text[:self.max_chunk_size]}"""
+            {remaining_text[: self.max_chunk_size]}"""
 
             try:
                 response = self.model.response([Message(role="user", content=prompt)])

phi/embedder/fastembed.py

@@ -27,4 +27,8 @@ def get_embedding(self, text: str) -> List[float]:
             return []
 
     def get_embedding_and_usage(self, text: str) -> Tuple[List[float], Optional[Dict]]:
-        return super().get_embedding_and_usage(text)
+        embedding = self.get_embedding(text=text)
+        # Currently, FastEmbed does not provide usage information
+        usage = None
+
+        return embedding, usage

phi/memory/classifier.py

@@ -23,8 +23,7 @@ def update_model(self) -> None:
             except ModuleNotFoundError as e:
                 logger.exception(e)
                 logger.error(
-                    "phidata uses `openai` as the default model provider. "
-                    "Please provide a `model` or install `openai`."
+                    "phidata uses `openai` as the default model provider. Please provide a `model` or install `openai`."
                 )
                 exit(1)
             self.model = OpenAIChat()

phi/memory/manager.py

@@ -31,8 +31,7 @@ def update_model(self) -> None:
             except ModuleNotFoundError as e:
                 logger.exception(e)
                 logger.error(
-                    "phidata uses `openai` as the default model provider. "
-                    "Please provide a `model` or install `openai`."
+                    "phidata uses `openai` as the default model provider. Please provide a `model` or install `openai`."
                 )
                 exit(1)
             self.model = OpenAIChat()

phi/memory/summarizer.py

@@ -21,8 +21,7 @@ def update_model(self) -> None:
             except ModuleNotFoundError as e:
                 logger.exception(e)
                 logger.error(
-                    "phidata uses `openai` as the default model provider. "
-                    "Please provide a `model` or install `openai`."
+                    "phidata uses `openai` as the default model provider. Please provide a `model` or install `openai`."
                 )
                 exit(1)
             self.model = OpenAIChat()

phi/tools/decorator.py

@@ -74,7 +74,7 @@ def another_function():
     invalid_kwargs = set(kwargs.keys()) - VALID_KWARGS
     if invalid_kwargs:
         raise ValueError(
-            f"Invalid tool configuration arguments: {invalid_kwargs}. " f"Valid arguments are: {sorted(VALID_KWARGS)}"
+            f"Invalid tool configuration arguments: {invalid_kwargs}. Valid arguments are: {sorted(VALID_KWARGS)}"
         )
 
     def decorator(func: F) -> Function:

phi/tools/github.py

@@ -24,7 +24,7 @@ def __init__(
         get_pull_request_changes: bool = True,
         create_issue: bool = True,
         create_repository: bool = True,
-        get_repository_languages: bool = True
+        get_repository_languages: bool = True,
     ):
         super().__init__(name="github")
 
@@ -136,7 +136,7 @@ def create_repository(
         logger.debug(f"Creating repository: {name}")
         try:
             description = description if description is not None else ""
-            
+
             if organization:
                 logger.debug(f"Creating in organization: {organization}")
                 org = self.g.get_organization(organization)
@@ -153,7 +153,7 @@ def create_repository(
                     description=description,
                     auto_init=auto_init,
                 )
-                
+
             repo_info = {
                 "name": repo.full_name,
                 "url": repo.html_url,

phi/tools/wikipedia.py

@@ -47,7 +47,7 @@ def search_wikipedia(self, query: str) -> str:
             import wikipedia  # noqa: F401
         except ImportError:
             raise ImportError(
-                "The `wikipedia` package is not installed. " "Please install it via `pip install wikipedia`."
+                "The `wikipedia` package is not installed. Please install it via `pip install wikipedia`."
             )
 
         logger.info(f"Searching wikipedia for: {query}")

phi/vectordb/chroma/chromadb.py

@@ -9,7 +9,7 @@
     from chromadb.api.types import QueryResult, GetResult
 
 except ImportError:
-    raise ImportError("The `chromadb` package is not installed. " "Please install it via `pip install chromadb`.")
+    raise ImportError("The `chromadb` package is not installed. Please install it via `pip install chromadb`.")
 
 from phi.document import Document
 from phi.embedder import Embedder

phi/vectordb/milvus/milvus.py

@@ -4,7 +4,7 @@
 try:
     from pymilvus import MilvusClient  # type: ignore
 except ImportError:
-    raise ImportError("The `pymilvus` package is not installed. " "Please install it via `pip install pymilvus`.")
+    raise ImportError("The `pymilvus` package is not installed. Please install it via `pip install pymilvus`.")
 
 from phi.document import Document
 from phi.embedder import Embedder

phi/vectordb/qdrant/qdrant.py

@@ -6,7 +6,7 @@
     from qdrant_client.http import models
 except ImportError:
     raise ImportError(
-        "The `qdrant-client` package is not installed. " "Please install it via `pip install qdrant-client`."
+        "The `qdrant-client` package is not installed. Please install it via `pip install qdrant-client`."
     )
 
 from phi.document import Document