Batch and cache updates

docugami_kg_rag/config/__init__.py

@@ -1,13 +1,19 @@
+import hashlib
 import os
 from pathlib import Path
 
-from langchain_community.cache import SQLiteCache
+from gptcache import Cache
+from gptcache.manager.factory import manager_factory
+from gptcache.processor.pre import get_prompt
+from langchain.cache import GPTCache
 from langchain_core.globals import set_llm_cache
 
-# from docugami_kg_rag.config.fireworksai_llama3 import *
-# from docugami_kg_rag.config.huggingface import *
 from docugami_kg_rag.config.chromadb import *
-from docugami_kg_rag.config.openai import *
+from docugami_kg_rag.config.fireworksai_llama3 import *
+
+# from docugami_kg_rag.config.fireworksai_mixtral import *
+# from docugami_kg_rag.config.openai import *
+from docugami_kg_rag.config.huggingface import *
 
 # from docugami_kg_rag.config.redis import *
 
@@ -23,11 +29,26 @@
 INDEXING_LOCAL_REPORT_DBS_ROOT = os.environ.get("INDEXING_LOCAL_REPORT_DBS_ROOT", "/tmp/docugami/report_dbs")
 os.makedirs(Path(INDEXING_LOCAL_REPORT_DBS_ROOT).parent, exist_ok=True)
 
-LOCAL_LLM_CACHE_DB_FILE = os.environ.get("LOCAL_LLM_CACHE", "/tmp/docugami/.langchain.db")
-os.makedirs(Path(LOCAL_LLM_CACHE_DB_FILE).parent, exist_ok=True)
-set_llm_cache(SQLiteCache(database_path=LOCAL_LLM_CACHE_DB_FILE))
+LOCAL_LLM_CACHE_DIR = os.environ.get("LOCAL_LLM_CACHE", "/tmp/docugami/langchain_cache")
+os.makedirs(Path(LOCAL_LLM_CACHE_DIR).parent, exist_ok=True)
+
+
+def get_hashed_name(name: str) -> str:
+    return hashlib.sha256(name.encode()).hexdigest()
+
+
+def init_gptcache(cache_obj: Cache, llm: str) -> None:
+    hashed_llm = get_hashed_name(llm)
+    hashed_llm_dir = Path(LOCAL_LLM_CACHE_DIR) / hashed_llm
+    cache_obj.init(
+        pre_embedding_func=get_prompt,
+        data_manager=manager_factory(manager="map", data_dir=str(hashed_llm_dir.absolute())),
+    )
+
+
+set_llm_cache(GPTCache(init_gptcache))
 
 EXAMPLES_PATH = Path(__file__).parent.parent / "green_examples"
 
 DEFAULT_USE_REPORTS = True
-DEFAULT_USE_CONVERSATIONAL_TOOLS = False
+DEFAULT_USE_CONVERSATIONAL_TOOLS = True

docugami_kg_rag/config/fireworksai_llama3.py

@@ -16,13 +16,14 @@
 )
 SMALL_CONTEXT_INSTRUCT_LLM = LARGE_CONTEXT_INSTRUCT_LLM  # Use the same model for large and small context tasks
 SQL_GEN_LLM = LARGE_CONTEXT_INSTRUCT_LLM  # Use the same model for sql gen
+LLM_BATCH_SIZE = 2
 
 # Lengths for the Docugami loader are in terms of characters, 1 token ~= 4 chars in English
 # Reference: https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them
 MIN_LENGTH_TO_SUMMARIZE = 2048  # chunks and docs below this length are embedded as-is
 MAX_FULL_DOCUMENT_TEXT_LENGTH = int(1024 * 4 * 4.5)  # ~4.5k tokens
-MAX_CHUNK_TEXT_LENGTH = int(1024 * 4 * 4.5)  # ~4.5k tokens
-MIN_CHUNK_TEXT_LENGTH = int(1024 * 4 * 1.5)  # ~1.5k tokens
+MAX_CHUNK_TEXT_LENGTH = int(1024 * 4 * 1)  # ~1k tokens
+MIN_CHUNK_TEXT_LENGTH = int(1024 * 4 * 0.5)  # ~0.5k tokens
 SUB_CHUNK_TABLES = False
 INCLUDE_XML_TAGS = False
 PARENT_HIERARCHY_LEVELS = 2

docugami_kg_rag/config/fireworksai_mixtral.py

@@ -16,6 +16,7 @@
 )
 SMALL_CONTEXT_INSTRUCT_LLM = LARGE_CONTEXT_INSTRUCT_LLM  # Use the same model for large and small context tasks
 SQL_GEN_LLM = LARGE_CONTEXT_INSTRUCT_LLM  # Use the same model for sql gen
+LLM_BATCH_SIZE = 32
 
 # Lengths for the Docugami loader are in terms of characters, 1 token ~= 4 chars in English
 # Reference: https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them

docugami_kg_rag/config/openai.py

@@ -21,6 +21,7 @@
     max_tokens=2 * 1024,  # only output tokens
 )
 SQL_GEN_LLM = SMALL_CONTEXT_INSTRUCT_LLM  # Use the same model for sql gen
+LLM_BATCH_SIZE = 256
 
 EMBEDDINGS = OpenAIEmbeddings(model="text-embedding-ada-002")
 
@@ -36,3 +37,4 @@
 INCLUDE_XML_TAGS = True
 PARENT_HIERARCHY_LEVELS = 2
 RETRIEVER_K = 8
+

docugami_kg_rag/indexing.py

@@ -34,6 +34,7 @@
     INDEXING_LOCAL_REPORT_DBS_ROOT,
     INDEXING_LOCAL_STATE_PATH,
     LARGE_CONTEXT_INSTRUCT_LLM,
+    LLM_BATCH_SIZE,
     MAX_CHUNK_TEXT_LENGTH,
     MAX_FULL_DOCUMENT_TEXT_LENGTH,
     MIN_CHUNK_TEXT_LENGTH,
@@ -143,6 +144,7 @@ def index_docset(docset_id: str, name: str) -> None:
         max_length_cutoff=MAX_FULL_DOCUMENT_TEXT_LENGTH,
         include_xml_tags=INCLUDE_XML_TAGS,
         summarize_document_examples_file=EXAMPLES_PATH / "summarize_document_examples.yaml",
+        batch_size=LLM_BATCH_SIZE,
     )
     chunk_summaries_by_id = build_chunk_summary_mappings(
         docs_by_id=parent_chunks_by_id,
@@ -152,6 +154,7 @@ def index_docset(docset_id: str, name: str) -> None:
         max_length_cutoff=MAX_FULL_DOCUMENT_TEXT_LENGTH,
         include_xml_tags=INCLUDE_XML_TAGS,
         summarize_chunk_examples_file=EXAMPLES_PATH / "summarize_chunk_examples.yaml",
+        batch_size=LLM_BATCH_SIZE,
     )
 
     direct_tool_function_name = docset_name_to_direct_retrieval_tool_function_name(name)

pyproject.toml

@@ -11,7 +11,7 @@ readme = "README.md"
 python = ">=3.9,<4.0"
 langchain = ">=0.1.17"
 langchain-community = ">=0.0.36"
-docugami-langchain = "^0.0.9rc2"
+docugami-langchain = "^0.0.9rc4"
 # docugami-langchain = {git = "https://github.com/docugami/docugami-langchain.git"}
 # docugami-langchain = {git = "https://github.com/docugami/docugami-langchain.git", rev = "tjaffri/rel"}
 typer = ">=0.9.0"
@@ -21,6 +21,7 @@ langsmith = ">=0.1.52"
 openpyxl = ">=3.1.2"
 faiss-cpu = ">=1.8.0"
 torch = "2.2.0"
+gptcache = "^0.1.43"
 rerankers = { extras = ["all"], version = ">=0.2.0" }
 langchain-openai = { version = ">=0.1.6", optional = true }
 langchain-fireworks = { version = ">=0.1.2", optional = true } 
@@ -65,7 +66,7 @@ exclude = "/temp/"
 
 [[tool.mypy.overrides]]
 module = [
-    "torch", "langchain_openai.*", "langchain_fireworks.*", "docugami_langchain.*", "rerankers.*",
+    "torch", "langchain_openai.*", "langchain_fireworks.*", "docugami_langchain.*", "rerankers.*", "gptcache.*"
 ]
 ignore_missing_imports = true