feat: configurable embedding/llms

app/config.py

@@ -1,6 +1,7 @@
 from typing import Any, Optional
 
-from pydantic import RedisDsn
+from pydantic import RedisDsn, ValidationInfo, field_validator
+from pydantic_core import Url
 from pydantic_settings import BaseSettings
 
 from app.constants import Environment
@@ -16,19 +17,80 @@ class Config:
 
         env_file = ".env"
         env_file_encoding = "utf-8"
-        env_prefix = "KB_"
 
     ENVIRONMENT: Environment = Environment.PRODUCTION
     """The environment the application is running in."""
 
     REDIS: Optional[RedisDsn] = None
     """The Redis service to use for queueing, indexing and document storage."""
 
+    EMBEDDING_MODEL: str = ""
+    """The embedding model to use.
+
+    This is a string of the form `<kind>[:<model>]` with the following options:
+
+    1. `local[:<path or repository>]` -- Run a model locally. If this is a path
+       it will attempt to load a model from that location. Otherwise, it should
+       be a Hugging Face repository from which to retrieve the model.
+    2. `openai[:<name>]` -- The named OpenAI model. `OPENAI_API_KEY` must be set.
+    3. `ollama:<name>` -- The named Ollama model. `OLLAMA_BASE_URL` must be set.
+
+    In each of these cases, you can omit the second part for the default model of the
+    given kind.
+
+    If unset, this will default to `"openai"` if an OpenAI API KEY is available and
+    otherwise will use `"local"`.
+
+    NOTE: Changing embedding models is not currently supported.
+    """
+
+    LLM_MODEL: str = ""
+    """The LLM model to use.
+
+    This is a string of the form `<kind>:<model>` with the following options:
+
+    1. `local[:<path or repository>]` -- Run a model locally. If this is a path
+       it will attempt to load a model from that location. Otherwise, it should
+       be a Hugging Face repository from which to retrieve the model.
+    2. `openai[:<name>]` -- The named OpenAI model. `OPENAI_API_KEY` must be set.
+    3. `ollama:<name>` -- The named Ollama model. `OLLAMA_BASE_URL` must be set.
+
+    In each of these cases, you can omit the second part for the default model of the
+    given kind.
+
+    If unset, this will default to `"openai"` if an OpenAI API KEY is available and
+    otherwise will use `"local"`.
+    """
+
+    OPENAI_API_KEY: Optional[str] = None
+    """ The OpenAI API Key to use for OpenAI models.
+
+    This is required for using openai models.
+    """
+
+    OLLAMA_BASE_URL: Optional[Url] = None
+    """The Base URL for Ollama.
+
+    This is required for using ollama models.
+    """
+
+    @field_validator("OLLAMA_BASE_URL")
+    def validate_ollama_base_url(cls, v, info: ValidationInfo):
+        MODELS = ["LLM_MODEL", "EMBEDDING_MODEL"]
+        if v is None:
+            for model in MODELS:
+                value = info.get(model, "")
+                if value.startswith("ollama"):
+                    raise ValueError(
+                        f"{info.field_name} must be set to use '{model}={value}'"
+                    )
+        return v
+
 
 settings = Config()
 
 app_configs: dict[str, Any] = {
-    "title": "Knowledge Bases API",
+    "title": "Dewy Knowledge Base API",
 }
 
 if not settings.ENVIRONMENT.is_debug:

app/ingest/store.py

@@ -2,29 +2,93 @@
 
 from fastapi import Depends, Request
 from llama_index import ServiceContext, StorageContext, VectorStoreIndex
+from llama_index.embeddings import BaseEmbedding
 from llama_index.ingestion import DocstoreStrategy, IngestionPipeline
 from llama_index.ingestion.cache import IngestionCache, RedisCache
 from llama_index.storage.docstore.redis_docstore import RedisDocumentStore
 from llama_index.vector_stores import RedisVectorStore
+from loguru import logger
 
 from app.config import settings
-from app.ingest.embed import EMBED_MODEL
+
+DEFAULT_OPENAI_EMBEDDING_MODEL: str = "text-embedding-ada-002"
+DEFAULT_HF_EMBEDDING_MODEL: str = "BAAI/bge-small-en"
+DEFAULT_OPENAI_LLM_MODEL: str = "gpt-3.5-turbo"
+DEFAULT_HF_LLM_MODEL: str = "StabilityAI/stablelm-tuned-alpha-3b"
+
+
+def _embedding_model(model: str) -> BaseEmbedding:
+    if not model:
+        if settings.OPENAI_API_KEY:
+            model = "openai"
+        else:
+            model = "local"
+
+    split = model.split(":", 2)
+    if split[0] == "openai":
+        from llama_index.embeddings import OpenAIEmbedding
+
+        model = DEFAULT_OPENAI_EMBEDDING_MODEL
+        if len(split) == 2:
+            model = split[1]
+        return OpenAIEmbedding(model=model)
+    elif split[0] == "local":
+        from llama_index.embeddings import HuggingFaceEmbedding
+
+        model = DEFAULT_HF_EMBEDDING_MODEL
+        if len(split) == 2:
+            model = split[1]
+        return HuggingFaceEmbedding(model)
+    elif split[0] == "ollama":
+        from llama_index.embeddings import OllamaEmbedding
+
+        model = split[1]
+        return OllamaEmbedding(
+            model=model, base_url=settings.OLLAMA_BASE_URL.unicode_string()
+        )
+    else:
+        raise ValueError(f"Unrecognized embedding model '{model}'")
+
+
+def _llm_model(model: str) -> BaseEmbedding:
+    if not model:
+        if settings.OPENAI_API_KEY:
+            model = "openai"
+        else:
+            model = "local"
+
+    split = model.split(":", 2)
+    if split[0] == "openai":
+        from llama_index.llms import OpenAI
+
+        model = DEFAULT_OPENAI_LLM_MODEL
+        if len(split) == 2:
+            model = split[1]
+        return OpenAI(model=model)
+    elif split[0] == "local":
+        from llama_index.llms import HuggingFaceLLM
+
+        model = DEFAULT_HF_LLM_MODEL
+        if len(split) == 2:
+            model = split[1]
+        return HuggingFaceLLM(model_name=model, tokenizer_name=model)
+    elif split[0] == "ollama":
+        from llama_index.llms import Ollama
+
+        model = split[1]
+        return Ollama(model=model, base_url=settings.OLLAMA_BASE_URL.unicode_string())
+    else:
+        raise ValueError(f"Unrecognized LLM model '{model}")
 
 
 class Store:
     """Class managing the vector and document store."""
 
     def __init__(self) -> None:
-        from llama_index.llms import HuggingFaceLLM
-
-        model = "mistralai/Mistral-7B-v0.1"
-        model_kwargs = {}
-        self.llm = HuggingFaceLLM(
-            model_name=model,
-            model_kwargs=model_kwargs,
-            tokenizer_name=model,
-            tokenizer_kwargs=model_kwargs,
-        )
+        self.embedding = _embedding_model(settings.EMBEDDING_MODEL)
+        self.llm = _llm_model(settings.LLM_MODEL)
+        logger.info("Embedding: {}", self.embedding.to_dict())
+        logger.info("LLM: {}", self.llm.to_dict())
 
         vector_store = RedisVectorStore(
             index_name="vector_store",
@@ -50,20 +114,20 @@ def __init__(self) -> None:
             HierarchicalNodeParser.from_defaults(chunk_sizes=[2048, 512, 128]),
         ]
 
-        # if self.llm:
-        #     # Transformations that require an LLM.
-        #     from llama_index.extractors import SummaryExtractor, TitleExtractor
+        if self.llm:
+            # Transformations that require an LLM.
+            from llama_index.extractors import SummaryExtractor, TitleExtractor
 
-        #     transformations.extend(
-        #         [
-        #             TitleExtractor(self.llm),
-        #             SummaryExtractor(self.llm),
-        #         ]
-        #     )
+            transformations.extend(
+                [
+                    TitleExtractor(self.llm),
+                    SummaryExtractor(self.llm),
+                ]
+            )
 
         self.service_context = ServiceContext.from_defaults(
             llm=self.llm,
-            embed_model=EMBED_MODEL,
+            embed_model=self.embedding,
             transformations=transformations,
         )
 
@@ -74,7 +138,7 @@ def __init__(self) -> None:
         )
 
         self.ingestion_pipeline = IngestionPipeline(
-            transformations=transformations + [EMBED_MODEL],
+            transformations=transformations + [self.embedding],
             vector_store=vector_store,
             docstore=docstore,
             cache=cache,

docker-compose.yml

@@ -4,8 +4,8 @@ services:
   dewy:
     image: app_image
     environment:
-      KB_ENVIRONMENT: LOCAL
-      KB_REDIS: "redis://default:testing123@redis:6379"
+      ENVIRONMENT: LOCAL
+      REDIS: "redis://default:testing123@redis:6379"
       LLAMA_INDEX_CACHE_DIR: "/tmp/cache/llama_index"
       HF_HOME: "/tmp/cache/hf"
     env_file:

env.template

@@ -0,0 +1,46 @@
+# The embedding model to use.
+#
+# This is a string of the form `<kind>[:<model>]` with the following options:
+#
+# 1. `local:<path>` -- A local model from the given path.
+# 2. `hf:<repository>` -- A hugging face model from the given repository.
+# 3. `ollama:<name>` -- The named Ollama model. `OLLAMA_BASE_URL` must be set.
+#
+# In each of these cases, you can omit the second part for the default model of the
+# given kind.
+#
+# If unset, this will default to `"openai"` if an OpenAI API KEY is available and
+# otherwise will use `"local"`.
+#
+# NOTE: Changing embedding models is not currently supported.
+EMBEDDING_MODEL="local"
+
+# The LLM model to use.
+#
+# This is a string of the form `<kind>:<model>` with the following options:
+#
+# 1. `local:<path or repository>` -- Run a model locally. If this is a path
+#     it will attempt to load a model from that location. Otherwise, it should
+#     be a Hugging Face repository from which to retrieve the model.
+# 2. `openai:<name>` -- The named OpenAI model. `OPENAI_API_KEY` must be set.
+# 3. `ollama:<name>` -- The named Ollama model. `OLLAMA_BASE_URL` must be set.
+#
+# In each of these cases, you can omit the second part for the default model of the
+# given kind.
+#
+# If unset, this will default to `"openai"` if an OpenAI API KEY is available and
+# otherwise will use `"local"`.
+#
+LLM_MODEL="local"
+
+# The OpenAI API Key to use for OpenAI models.
+#
+# This is required for using openai models.
+#
+# OPENAI_API_KEY="<your key here>"
+
+# The Base URL for Ollama.
+#
+# This is required for using ollama models.
+#
+OLLAMA_BASE_URL="<host>:11434"