Merge branch 'main' into voting-support

README.md

@@ -39,7 +39,7 @@ politics = Route(
     utterances=[
         "isn't politics the best thing ever",
         "why don't you tell me about your political opinions",
-        "don't you just love the president" "don't you just hate the president",
+        "don't you just love the president",
         "they're going to destroy this country!",
         "they will save the country!",
     ],
@@ -147,4 +147,4 @@ Daniel Avila, [Semantic Router: Enhancing Control in LLM Conversations](https://
 
 Yogendra Sisodia, [Stop Chat-GPT From Going Rogue In Production With Semantic Router](https://medium.com/@scholarly360/stop-chat-gpt-from-going-rogue-in-production-with-semantic-router-937a4768ae19), Medium
 
-Aniket Hingane, [LLM Apps: Why you Must Know Semantic Router in 2024: Part 1](https://medium.com/@learn-simplified/llm-apps-why-you-must-know-semantic-router-in-2024-part-1-bfbda81374c5), Medium
+Aniket Hingane, [LLM Apps: Why you Must Know Semantic Router in 2024: Part 1](https://medium.com/@learn-simplified/llm-apps-why-you-must-know-semantic-router-in-2024-part-1-bfbda81374c5), Medium

pyproject.toml

@@ -19,12 +19,12 @@ python = ">=3.9,<3.13"
 pydantic = "^2.5.3"
 openai = "^1.10.0"
 cohere = "^4.32"
-mistralai= "^0.0.12"
+mistralai= {version = "^0.0.12", optional = true}
 numpy = "^1.25.2"
 colorlog = "^6.8.0"
 pyyaml = "^6.0.1"
 pinecone-text = {version = "^0.7.1", optional = true}
-fastembed = {version = "^0.1.3", optional = true, python = "<3.12"}
+fastembed = {version = "^0.2.4", optional = true, python = "<3.12"}
 torch = {version = "^2.1.0", optional = true}
 transformers = {version = "^4.36.2", optional = true}
 llama-cpp-python = {version = "^0.2.28", optional = true}
@@ -44,6 +44,7 @@ local = ["torch", "transformers", "llama-cpp-python"]
 pinecone = ["pinecone-client"]
 vision = ["torch", "torchvision", "transformers", "pillow"]
 processing = ["matplotlib"]
+mistralai = ["mistralai"]
 
 [tool.poetry.group.dev.dependencies]
 ipykernel = "^6.25.0"
@@ -67,4 +68,4 @@ build-backend = "poetry.core.masonry.api"
 line-length = 88
 
 [tool.mypy]
-ignore_missing_imports = true
+ignore_missing_imports = true

semantic_router/encoders/fastembed.py

@@ -22,7 +22,7 @@ def __init__(
 
     def _initialize_client(self):
         try:
-            from fastembed.embedding import FlagEmbedding as Embedding
+            from fastembed import TextEmbedding
         except ImportError:
             raise ImportError(
                 "Please install fastembed to use FastEmbedEncoder. "
@@ -39,7 +39,7 @@ def _initialize_client(self):
 
         embedding_args = {k: v for k, v in embedding_args.items() if v is not None}
 
-        embedding = Embedding(**embedding_args)
+        embedding = TextEmbedding(**embedding_args)
         return embedding
 
     def __call__(self, docs: List[str]) -> List[List[float]]:

semantic_router/encoders/mistral.py

@@ -2,20 +2,19 @@
 
 import os
 from time import sleep
-from typing import List, Optional
+from typing import List, Optional, Any
 
-from mistralai.client import MistralClient
-from mistralai.exceptions import MistralException
-from mistralai.models.embeddings import EmbeddingResponse
 
 from semantic_router.encoders import BaseEncoder
 from semantic_router.utils.defaults import EncoderDefault
+from pydantic.v1 import PrivateAttr
 
 
 class MistralEncoder(BaseEncoder):
     """Class to encode text using MistralAI"""
 
-    client: Optional[MistralClient]
+    _client: Any = PrivateAttr()
+    _mistralai: Any = PrivateAttr()
     type: str = "mistral"
 
     def __init__(
@@ -27,33 +26,53 @@ def __init__(
         if name is None:
             name = EncoderDefault.MISTRAL.value["embedding_model"]
         super().__init__(name=name, score_threshold=score_threshold)
-        api_key = mistralai_api_key or os.getenv("MISTRALAI_API_KEY")
+        self._client, self._mistralai = self._initialize_client(mistralai_api_key)
+
+    def _initialize_client(self, api_key):
+        try:
+            import mistralai
+            from mistralai.client import MistralClient
+        except ImportError:
+            raise ImportError(
+                "Please install MistralAI to use MistralEncoder. "
+                "You can install it with: "
+                "`pip install 'semantic-router[mistralai]'`"
+            )
+
+        api_key = api_key or os.getenv("MISTRALAI_API_KEY")
         if api_key is None:
             raise ValueError("Mistral API key not provided")
         try:
-            self.client = MistralClient(api_key=api_key)
+            client = MistralClient(api_key=api_key)
         except Exception as e:
             raise ValueError(f"Unable to connect to MistralAI {e.args}: {e}") from e
+        return client, mistralai
 
     def __call__(self, docs: List[str]) -> List[List[float]]:
-        if self.client is None:
+        if self._client is None:
             raise ValueError("Mistral client not initialized")
         embeds = None
         error_message = ""
 
         # Exponential backoff
         for _ in range(3):
             try:
-                embeds = self.client.embeddings(model=self.name, input=docs)
+                embeds = self._client.embeddings(model=self.name, input=docs)
                 if embeds.data:
                     break
-            except MistralException as e:
+            except self._mistralai.exceptions.MistralException as e:
                 sleep(2**_)
                 error_message = str(e)
             except Exception as e:
                 raise ValueError(f"Unable to connect to MistralAI {e.args}: {e}") from e
 
-        if not embeds or not isinstance(embeds, EmbeddingResponse) or not embeds.data:
+        if (
+            not embeds
+            or not isinstance(
+                embeds, self._mistralai.models.embeddings.EmbeddingResponse
+            )
+            or not embeds.data
+        ):
             raise ValueError(f"No embeddings returned from MistralAI: {error_message}")
         embeddings = [embeds_obj.embedding for embeds_obj in embeds.data]
         return embeddings

semantic_router/encoders/openai.py

@@ -20,6 +20,7 @@ class OpenAIEncoder(BaseEncoder):
     def __init__(
         self,
         name: Optional[str] = None,
+        openai_base_url: Optional[str] = None,
         openai_api_key: Optional[str] = None,
         openai_org_id: Optional[str] = None,
         score_threshold: float = 0.82,
@@ -29,11 +30,14 @@ def __init__(
             name = EncoderDefault.OPENAI.value["embedding_model"]
         super().__init__(name=name, score_threshold=score_threshold)
         api_key = openai_api_key or os.getenv("OPENAI_API_KEY")
+        base_url = openai_base_url or os.getenv("OPENAI_BASE_URL")
         openai_org_id = openai_org_id or os.getenv("OPENAI_ORG_ID")
         if api_key is None:
             raise ValueError("OpenAI API key cannot be 'None'.")
         try:
-            self.client = openai.Client(api_key=api_key, organization=openai_org_id)
+            self.client = openai.Client(
+                base_url=base_url, api_key=api_key, organization=openai_org_id
+            )
         except Exception as e:
             raise ValueError(
                 f"OpenAI API client failed to initialize. Error: {e}"

semantic_router/llms/__init__.py

@@ -1,5 +1,6 @@
 from semantic_router.llms.base import BaseLLM
 from semantic_router.llms.cohere import CohereLLM
+from semantic_router.llms.llamacpp import LlamaCppLLM
 from semantic_router.llms.mistral import MistralAILLM
 from semantic_router.llms.openai import OpenAILLM
 from semantic_router.llms.openrouter import OpenRouterLLM
@@ -8,6 +9,7 @@
 __all__ = [
     "BaseLLM",
     "OpenAILLM",
+    "LlamaCppLLM",
     "OpenRouterLLM",
     "CohereLLM",
     "AzureOpenAILLM",

semantic_router/llms/llamacpp.py

@@ -2,26 +2,27 @@
 from pathlib import Path
 from typing import Any, Optional
 
-from llama_cpp import Llama, LlamaGrammar
-
 from semantic_router.llms.base import BaseLLM
 from semantic_router.schema import Message
 from semantic_router.utils.logger import logger
 
+from pydantic.v1 import PrivateAttr
+
 
 class LlamaCppLLM(BaseLLM):
-    llm: Llama
+    llm: Any
     temperature: float
     max_tokens: Optional[int] = 200
-    grammar: Optional[LlamaGrammar] = None
+    grammar: Optional[Any] = None
+    _llama_cpp: Any = PrivateAttr()
 
     def __init__(
         self,
-        llm: Llama,
+        llm: Any,
         name: str = "llama.cpp",
         temperature: float = 0.2,
         max_tokens: Optional[int] = 200,
-        grammar: Optional[LlamaGrammar] = None,
+        grammar: Optional[Any] = None,
     ):
         super().__init__(
             name=name,
@@ -30,6 +31,18 @@ def __init__(
             max_tokens=max_tokens,
             grammar=grammar,
         )
+
+        try:
+            import llama_cpp
+        except ImportError:
+            raise ImportError(
+                "Please install LlamaCPP to use Llama CPP llm. "
+                "You can install it with: "
+                "`pip install 'semantic-router[local]'`"
+            )
+        self._llama_cpp = llama_cpp
+        llm = self._llama_cpp.Llama
+        grammar = self._llama_cpp.LlamaGrammar
         self.llm = llm
         self.temperature = temperature
         self.max_tokens = max_tokens
@@ -62,7 +75,7 @@ def _grammar(self):
         grammar_path = Path(__file__).parent.joinpath("grammars", "json.gbnf")
         assert grammar_path.exists(), f"{grammar_path}\ndoes not exist"
         try:
-            self.grammar = LlamaGrammar.from_file(grammar_path)
+            self.grammar = self._llama_cpp.LlamaGrammar.from_file(grammar_path)
             yield
         finally:
             self.grammar = None

semantic_router/llms/mistral.py

@@ -1,18 +1,20 @@
 import os
-from typing import List, Optional
+from typing import List, Optional, Any
 
-from mistralai.client import MistralClient
 
 from semantic_router.llms import BaseLLM
 from semantic_router.schema import Message
 from semantic_router.utils.defaults import EncoderDefault
 from semantic_router.utils.logger import logger
 
+from pydantic.v1 import PrivateAttr
+
 
 class MistralAILLM(BaseLLM):
-    client: Optional[MistralClient]
+    _client: Any = PrivateAttr()
     temperature: Optional[float]
     max_tokens: Optional[int]
+    _mistralai: Any = PrivateAttr()
 
     def __init__(
         self,
@@ -24,25 +26,45 @@ def __init__(
         if name is None:
             name = EncoderDefault.MISTRAL.value["language_model"]
         super().__init__(name=name)
-        api_key = mistralai_api_key or os.getenv("MISTRALAI_API_KEY")
+        self._client, self._mistralai = self._initialize_client(mistralai_api_key)
+        self.temperature = temperature
+        self.max_tokens = max_tokens
+
+    def _initialize_client(self, api_key):
+        try:
+            import mistralai
+            from mistralai.client import MistralClient
+        except ImportError:
+            raise ImportError(
+                "Please install MistralAI to use MistralAI LLM. "
+                "You can install it with: "
+                "`pip install 'semantic-router[mistralai]'`"
+            )
+        api_key = api_key or os.getenv("MISTRALAI_API_KEY")
         if api_key is None:
             raise ValueError("MistralAI API key cannot be 'None'.")
         try:
-            self.client = MistralClient(api_key=api_key)
+            client = MistralClient(api_key=api_key)
         except Exception as e:
             raise ValueError(
                 f"MistralAI API client failed to initialize. Error: {e}"
             ) from e
-        self.temperature = temperature
-        self.max_tokens = max_tokens
+        return client, mistralai
 
     def __call__(self, messages: List[Message]) -> str:
-        if self.client is None:
+        if self._client is None:
             raise ValueError("MistralAI client is not initialized.")
+
+        chat_messages = [
+            self._mistralai.models.chat_completion.ChatMessage(
+                role=m.role, content=m.content
+            )
+            for m in messages
+        ]
         try:
-            completion = self.client.chat(
+            completion = self._client.chat(
                 model=self.name,
-                messages=[m.to_mistral() for m in messages],
+                messages=chat_messages,
                 temperature=self.temperature,
                 max_tokens=self.max_tokens,
             )