Doomgrave patch 1

README.md

@@ -5,6 +5,7 @@
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+ORIGINAL GIT: https://github.com/guidance-ai/guidance
 
 > *Note that v0.1 is a dramatically new version developed while releases had to be paused over the summer. If you are looking for the old version based on handlebars, you can use v0.0.64, but you should instead try porting over to the much better new version :)*
 

guidance/langchain/__init__.py

@@ -0,0 +1,2 @@
+from .embeddings import LlamaCppEmbeddings_Guidance
+from .llamacpp import LlamaCpp_LLM

guidance/langchain/embeddings.py

@@ -0,0 +1,74 @@
+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))