feat: Add new agents docs embbeddings functionality

agents-api/agents_api/embed_models_registry.py

@@ -0,0 +1,124 @@
+import numpy as np
+from typing import TypedDict, Any
+from dataclasses import dataclass
+from transformers import PreTrainedTokenizer
+from agents_api.clients.model import openai_client
+from agents_api.clients.embed import embed
+
+
+def normalize_l2(x):
+    x = np.array(x)
+    if x.ndim == 1:
+        norm = np.linalg.norm(x)
+        if norm == 0:
+            return x
+        return x / norm
+    else:
+        norm = np.linalg.norm(x, 2, axis=1, keepdims=True)
+        return np.where(norm == 0, x, x / norm)
+
+
+class ModelNotSupportedError(Exception):
+    def __init__(self, model_name):
+        super().__init__(f"model {model_name} is not supporrted")
+
+
+class EmbeddingInput(TypedDict):
+    instruction: str | None
+    text: str
+
+
+@dataclass
+class EmbeddingModel:
+    embedding_provider: str
+    embedding_model_name: str
+    original_embedding_dimensions: int
+    output_embedding_dimensions: int
+    context_window: int
+    tokenizer: Any
+
+    @classmethod
+    def from_model_name(cls, model_name: str):
+        try:
+            return _embedding_model_registry[model_name]
+        except KeyError:
+            raise ModelNotSupportedError(model_name)
+
+    def preprocess(self, inputs: list[EmbeddingInput]):
+        """Maybe use this function from embed() to truncate (if needed) or raise an error"""
+        pass
+
+    async def embed(
+        self, inputs: list[EmbeddingInput]
+    ) -> list[np.NDArray | list[float]]:
+        embeddings: list[np.NDArray | list[float]] = []
+        input = [f"{input.get('instruction', '')} {input['text']}" for input in inputs]
+
+        if self.embedding_provider == "julep":
+            embeddings = await embed(input)
+        elif self.embedding_provider == "openai":
+            embeddings = (
+                await openai_client.embeddings.create(
+                    input=input, model=self.embedding_model_name
+                )
+                .data[0]
+                .embedding
+            )
+
+        return self.normalize(embeddings)
+
+    def normalize(
+        self, embeddings: list[np.NDArray | list[float]]
+    ) -> list[np.NDArray | list[float]]:
+        return [
+            (
+                e
+                if len(e) <= self.original_embedding_dimensions
+                else normalize_l2(e[: self.original_embedding_dimensions])
+            )
+            for e in embeddings
+        ]
+
+
+_embedding_model_registry = {
+    "text-embeddins-3-small": EmbeddingModel(
+        embedding_provider="openai",
+        embedding_model_name="text-embeddins-3-small",
+        original_embedding_dimensions=1024,
+        output_embedding_dimensions=1024,
+        context_window=8192,
+        tokenizer=PreTrainedTokenizer.from_pretrained("text-embeddins-3-small"),
+    ),
+    "text-embeddins-3-large": EmbeddingModel(
+        embedding_provider="openai",
+        embedding_model_name="text-embeddins-3-large",
+        original_embedding_dimensions=1024,
+        output_embedding_dimensions=1024,
+        context_window=8192,
+        tokenizer=PreTrainedTokenizer.from_pretrained("text-embeddins-3-large"),
+    ),
+    "Alibaba-NLP/gte-large-en-v1.5": EmbeddingModel(
+        embedding_provider="julep",
+        embedding_model_name="Alibaba-NLP/gte-large-en-v1.5",
+        original_embedding_dimensions=1024,
+        output_embedding_dimensions=1024,
+        context_window=8192,
+        tokenizer=PreTrainedTokenizer.from_pretrained("Alibaba-NLP/gte-large-en-v1.5"),
+    ),
+    "BAAI/bge-m3": EmbeddingModel(
+        embedding_provider="julep",
+        embedding_model_name="BAAI/bge-m3",
+        original_embedding_dimensions=1024,
+        output_embedding_dimensions=1024,
+        context_window=8192,
+        tokenizer=PreTrainedTokenizer.from_pretrained("BAAI/bge-m3"),
+    ),
+    "BAAI/llm-embedder": EmbeddingModel(
+        embedding_provider="julep",
+        embedding_model_name="BAAI/llm-embedder",
+        original_embedding_dimensions=1024,
+        output_embedding_dimensions=1024,
+        context_window=8192,
+        tokenizer=PreTrainedTokenizer.from_pretrained("BAAI/llm-embedder"),
+    ),
+}

agents-api/agents_api/routers/agents/routers.py

@@ -66,6 +66,8 @@
     PatchToolRequest,
     PatchAgentRequest,
 )
+from agents_api.env import embedding_model_id
+from agents_api.embed_models_registry import EmbeddingModel
 
 
 class AgentList(BaseModel):
@@ -319,9 +321,13 @@ async def create_docs(agent_id: UUID4, request: CreateDoc) -> ResourceCreatedRes
     )
 
     indices, snippets = list(zip(*enumerate(content)))
-    embeddings = await embed(
+    model = EmbeddingModel.from_model_name(embedding_model_id)
+    embeddings = await model.embed(
         [
-            snippet_embed_instruction + request.title + "\n\n" + snippet
+            {
+                "instruction": snippet_embed_instruction,
+                "text": request.title + "\n\n" + snippet,
+            }
             for snippet in snippets
         ]
     )

agents-api/migrations/migrate_1714566760_change_embeddings_dimensions.py

@@ -0,0 +1,111 @@
+# /usr/bin/env python3
+
+MIGRATION_ID = "change_embeddings_dimensions"
+CREATED_AT = 1714566760.731964
+
+
+change_dimensions = {
+    "up": """
+    ?[
+        doc_id,
+        snippet_idx,
+        title,
+        snippet,
+        embed_instruction,
+        embedding,
+    ] :=
+        *information_snippets{
+            snippet_idx,
+            title,
+            snippet,
+            embed_instruction,
+            embedding,
+            additional_info_id: doc_id,
+        }
+
+    :replace information_snippets {
+        doc_id: Uuid,
+        snippet_idx: Int,
+        =>
+        title: String,
+        snippet: String,
+        embed_instruction: String default 'Encode this passage for retrieval: ',
+        embedding: <F32; 1024>? default null,
+    }
+    """,
+    "down": """
+    ?[
+        doc_id,
+        snippet_idx,
+        title,
+        snippet,
+        embed_instruction,
+        embedding,
+    ] :=
+        *information_snippets{
+            snippet_idx,
+            title,
+            snippet,
+            embed_instruction,
+            embedding,
+            additional_info_id: doc_id,
+        }
+
+    :replace information_snippets {
+        doc_id: Uuid,
+        snippet_idx: Int,
+        =>
+        title: String,
+        snippet: String,
+        embed_instruction: String default 'Encode this passage for retrieval: ',
+        embedding: <F32; 768>? default null,
+    }
+    """,
+}
+
+information_snippets_hnsw_index = dict(
+    up="""
+    ::hnsw drop information_snippets:embedding_space
+
+    ::hnsw create information_snippets:embedding_space {
+        fields: [embedding],
+        filter: !is_null(embedding),
+        dim: 1024,
+        distance: Cosine,
+        m: 64,
+        ef_construction: 256,
+        extend_candidates: false,
+        keep_pruned_connections: false,
+    }
+    """,
+    down="""
+    ::hnsw drop information_snippets:embedding_space
+    
+    ::hnsw create information_snippets:embedding_space {
+        fields: [embedding],
+        filter: !is_null(embedding),
+        dim: 768,
+        distance: Cosine,
+        m: 64,
+        ef_construction: 256,
+        extend_candidates: false,
+        keep_pruned_connections: false,
+    }
+    """,
+)
+
+
+queries_to_run = [
+    change_dimensions,
+    information_snippets_hnsw_index,
+]
+
+
+def up(client):
+    for q in queries_to_run:
+        client.run(q["up"])
+
+
+def down(client):
+    for q in reversed(queries_to_run):
+        client.run(q["down"])