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guidance/langchain/__init__.py

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+from .embeddings import LlamaCppEmbeddings_Guidance
+from .llamacpp import LlamaCpp_LLM

guidance/langchain/embeddings.py

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+from typing import Any, Dict, List, Optional
+from langchain_core.embeddings import Embeddings
+from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator
+
+
+class LlamaCppEmbeddings_Guidance(BaseModel, Embeddings):
+    """llama.cpp embedding models using Guidance
+
+    To use, you should have the llama-cpp-python and langchain library installed.
+    LlamaCpp istance must have embedding = true.
+    
+    USAGE EXAMPLE (using Chroma database):
+        llama2 = guidance.models.LlamaCpp(model=modelPath,n_gpu_layers=-1,n_ctx=4096,embedding = true)
+        embeddings = GuidanceLlamaCppEmbeddings(client=llama2)
+        vectordb = Chroma(persist_directory={path_to_chromadb}, embedding_function=embeddings)
+    """
+    model: Any
+    client: Optional[Any]
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+
+        """Validate that llama-cpp-python library is installed."""
+        try:
+            if values["model"].engine.model_obj:
+                values["client"] = values["model"].engine.model_obj
+                return values
+
+            if values["model"].model_obj:
+                values["client"] = values["model"].model_obj
+                return values
+
+            raise ModuleNotFoundError("Could not import llama-cpp-python library or incompatible version.")
+        except ImportError:
+            raise ModuleNotFoundError(
+                "Could not import llama-cpp-python library. "
+                "Please install the llama-cpp-python library to "
+                "use this embedding model: pip install llama-cpp-python"
+            )
+        except Exception as e:
+            raise ValueError(
+                f"Received error {e}"
+            )
+        return values
+
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed a list of documents using the Llama model.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        embeddings = [self.client.embed(text) for text in texts]
+        return [list(map(float, e)) for e in embeddings]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed a query using the Llama model.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        embedding = self.client.embed(text)
+        return list(map(float, embedding))