diff --git a/README.md b/README.md index e417cbda..c8d77991 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ # Your private ChatGPT alternative -**Securely unlock knowledge from your business documents. Give your employees' a second-brain.** +**Q&A with business documents securely. Depend less on other teams for answers.** ![Docq overview in a single diagram](https://docqai.github.io/docq/assets/docq-diag-apr2024.png) @@ -43,7 +43,7 @@ Abstractions to easily add custom data sources. Plugin system for extending appl We offer Docq as a **turnkey** solution to lower the barrier for your organisation to adopt the latest AI innovation safely. - Open-source (AGPLv3) to address your organisation's security and compliance needs. -- Commercial support available via a UK-based entity. +- Commercial license and support available (via Docq.AI based in London, UK). - Re-licensing option also available. ## Vision @@ -92,7 +92,7 @@ For more details see section [Deploy to Azure: 15 Minutes and Secure](./docs/use ## Roadmap -We aim to support the following features in the near future: +We aim to support the following features in the future: - More data ingestion options such as SaaS data connectors and network storage options - A plugin platform and ecosystem @@ -114,12 +114,15 @@ Project Link: [https://github.com/docqai/docq/](https://github.com/docqai/docq/) ## Licenses -Distributed under multiple licenses: +The code in the repo is distributed under the licenses below. If a file has a specific license and copyright notice displayed then that wins. + +- AGPLv3. See [`LICENSE`](./LICENSE.AGPL3) and [`NOTICE`](./NOTICE.AGPL3) for more information. +- Commercial licenses available via [Docq.AI](https://docq.ai) +- Copyright (C) 2023-2024 FABR Ltd t/a [Docq.AI](https://docq.ai). + +Made :heart: London. -- AGPLv3 See [`LICENSE`](https://github.com/docqai/docq/blob/main/LICENSE) for more information. -- Commercial licenses available -Copyright (C) 2023-2024 FABR Ltd t/a [Docq.AI](https://docq.ai) diff --git a/poetry.lock b/poetry.lock index 666ad5c6..578ddbbf 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2298,13 +2298,13 @@ proxy = ["PyJWT (>=2.8.0,<3.0.0)", "apscheduler (>=3.10.4,<4.0.0)", "backoff", " [[package]] name = "llama-index-core" -version = "0.10.27" +version = "0.10.39" description = "Interface between LLMs and your data" optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "llama_index_core-0.10.27-py3-none-any.whl", hash = "sha256:805b20a16a417180a32a31956710637af75e22cd0849fec2729447d182197d39"}, - {file = "llama_index_core-0.10.27.tar.gz", hash = "sha256:01881a1943cb7b37f9f8147212d4a55caeef2d68ec498a0a1b864f79cf9d2be4"}, + {file = "llama_index_core-0.10.39-py3-none-any.whl", hash = "sha256:4b68724936f09220e417951572739f041ec2184caf9eb4e45017f4419cb98bd9"}, + {file = "llama_index_core-0.10.39.tar.gz", hash = "sha256:67b417676ff9b50d89d9379251c7de51bd68da042c47db934ed5794824eb41a5"}, ] [package.dependencies] @@ -2314,7 +2314,7 @@ deprecated = ">=1.2.9.3" dirtyjson = ">=1.0.8,<2.0.0" fsspec = ">=2023.5.0" httpx = "*" -llamaindex-py-client = ">=0.1.16,<0.2.0" +llamaindex-py-client = ">=0.1.18,<0.2.0" nest-asyncio = ">=1.5.8,<2.0.0" networkx = ">=3.0" nltk = ">=3.8.1,<4.0.0" @@ -2332,14 +2332,6 @@ typing-extensions = ">=4.5.0" typing-inspect = ">=0.8.0" wrapt = "*" -[package.extras] -gradientai = ["gradientai (>=1.4.0)"] -html = ["beautifulsoup4 (>=4.12.2,<5.0.0)"] -langchain = ["langchain (>=0.0.303)"] -local-models = ["optimum[onnxruntime] (>=1.13.2,<2.0.0)", "sentencepiece (>=0.1.99,<0.2.0)", "transformers[torch] (>=4.33.1,<5.0.0)"] -postgres = ["asyncpg (>=0.29.0,<0.30.0)", "pgvector (>=0.2.4,<0.3.0)", "psycopg2-binary (>=2.9.9,<3.0.0)"] -query-tools = ["guidance (>=0.0.64,<0.0.65)", "jsonpath-ng (>=1.6.0,<2.0.0)", "lm-format-enforcer (>=0.4.3,<0.5.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "scikit-learn", "spacy (>=3.7.1,<4.0.0)"] - 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Your private ChatGPT alternative. Securely unlock knowledge from confidential documents." authors = ["Docq.AI Team "] maintainers = ["Docq.AI Team "] @@ -59,11 +59,12 @@ llama-index-llms-litellm = "^0.1.3" llama-index-embeddings-azure-openai = "^0.1.6" jwt = "^1.3.1" llama-index-embeddings-huggingface-optimum = "^0.1.5" -llama-index-core = "^0.10.21.post1" +llama-index-core = "^0.10.39" llama-index-readers-file = "^0.1.12" slack-bolt = "^1.18.1" llama-index-retrievers-bm25 = "^0.1.3" sentence-transformers = "^2.6.1" +llama-index-postprocessor-colbert-rerank = "^0.1.2" [tool.poetry.group.dev.dependencies] pre-commit = "^2.18.1" @@ -83,7 +84,7 @@ mkapi = "^1.0.14" pillow = "^10.2.0" cairosvg = "^2.7.0" python-dotenv = "^1.0.0" -ruff = "^0.2.2" +ruff = "^0.4.4" mkdocs = "^1.5.3" [tool.poetry.scripts] diff --git a/source/docq/config.py b/source/docq/config.py index 7f61ebc6..48d1487c 100644 --- a/source/docq/config.py +++ b/source/docq/config.py @@ -9,8 +9,17 @@ ENV_VAR_DOCQ_COOKIE_HMAC_SECRET_KEY = "DOCQ_COOKIE_HMAC_SECRET_KEY" ENV_VAR_DOCQ_API_SECRET = "DOCQ_API_SECRET" SESSION_COOKIE_NAME = "docqai/_docq" + ENV_VAR_DOCQ_GROQ_API_KEY = "DOCQ_GROQ_API_KEY" +ENV_VAR_DOCQ_AZURE_OPENAI_API_VERSION = "DOCQ_AZURE_OPENAI_API_VERSION" + +ENV_VAR_DOCQ_AZURE_OPENAI_API_BASE1 = "DOCQ_AZURE_OPENAI_API_BASE" +ENV_VAR_DOCQ_AZURE_OPENAI_API_KEY1 = "DOCQ_AZURE_OPENAI_API_KEY1" # key for base1 + +ENV_VAR_DOCQ_AZURE_OPENAI_API_BASE2 = "DOCQ_AZURE_OPENAI_API_BASE2" +ENV_VAR_DOCQ_AZURE_OPENAI_API_KEY2 = "DOCQ_AZURE_OPENAI_API_KEY2" # key for base2 + ENV_VAR_DOCQ_SLACK_CLIENT_ID = "DOCQ_SLACK_CLIENT_ID" ENV_VAR_DOCQ_SLACK_CLIENT_SECRET = "DOCQ_SLACK_CLIENT_SECRET" # noqa: S105 ENV_VAR_DOCQ_SLACK_SIGNING_SECRET = "DOCQ_SLACK_SIGNING_SECRET" # noqa: S105 diff --git a/source/docq/manage_indices.py b/source/docq/manage_indices.py new file mode 100644 index 00000000..6b7ce378 --- /dev/null +++ b/source/docq/manage_indices.py @@ -0,0 +1,89 @@ +"""Functions to manage indices.""" + +import logging as log +from typing import List + +from llama_index.core.indices import DocumentSummaryIndex, VectorStoreIndex +from llama_index.core.indices.base import BaseIndex +from llama_index.core.indices.loading import load_index_from_storage +from llama_index.core.schema import Document +from opentelemetry import trace +from opentelemetry.trace import Status, StatusCode + +import docq + +from .domain import SpaceKey +from .model_selection.main import LlmUsageSettingsCollection, ModelCapability, _get_service_context +from .support.store import _get_default_storage_context, _get_storage_context, get_index_dir + +tracer = trace.get_tracer(__name__, docq.__version_str__) + + +@tracer.start_as_current_span("manage_spaces._create_vector_index") +def _create_vector_index( + documents: List[Document], model_settings_collection: LlmUsageSettingsCollection +) -> VectorStoreIndex: + # Use default storage and service context to initialise index purely for persisting + return VectorStoreIndex.from_documents( + documents, + storage_context=_get_default_storage_context(), + service_context=_get_service_context(model_settings_collection), + kwargs=model_settings_collection.model_usage_settings[ModelCapability.CHAT].additional_args, + ) + + +@tracer.start_as_current_span("manage_spaces._create_document_summary_index") +def _create_document_summary_index( + documents: List[Document], model_settings_collection: LlmUsageSettingsCollection +) -> DocumentSummaryIndex: + """Create a an index of summaries for each document. This doen't create embedding for each node.""" + return DocumentSummaryIndex(embed_summaries=True).from_documents( + documents, + storage_context=_get_default_storage_context(), + service_context=_get_service_context(model_settings_collection), + kwargs=model_settings_collection.model_usage_settings[ModelCapability.CHAT].additional_args, + ) + + +@tracer.start_as_current_span("manage_spaces._persist_index") +def _persist_index(index: BaseIndex, space: SpaceKey) -> None: + """Persist an Space datasource index to disk.""" + index.storage_context.persist(persist_dir=get_index_dir(space)) + + +@tracer.start_as_current_span(name="_load_index_from_storage") +def _load_index_from_storage(space: SpaceKey, model_settings_collection: LlmUsageSettingsCollection) -> BaseIndex: + # set service context explicitly for multi model compatibility + sc = _get_service_context(model_settings_collection) + return load_index_from_storage( + storage_context=_get_storage_context(space), service_context=sc, callback_manager=sc.callback_manager + ) + + +def load_indices_from_storage( + spaces: List[SpaceKey], model_settings_collection: LlmUsageSettingsCollection +) -> List[BaseIndex]: + """Return a list of indices for the given list of spaces.""" + with tracer.start_as_current_span("indices_from_spaces") as span: + indices = [] + for space in spaces: + try: + index_ = _load_index_from_storage(space, model_settings_collection) + + log.debug("run_chat(): %s, %s", index_.index_id, space.summary) + indices.append(index_) + span.add_event( + name="index_appended", + attributes={"index_id": index_.index_id, "index_struct_cls": index_.index_struct_cls.__name__}, + ) + except Exception as e: + span.set_status(status=Status(StatusCode.ERROR)) + span.record_exception(e) + log.warning( + "Index for space '%s' failed to load, skipping. Maybe the index isn't created yet. Error message: %s", + space, + e, + ) + continue + span.add_event("indices_loaded", {"num_indices_loaded": len(indices), "num_spaces_given": len(spaces)}) + return indices diff --git a/source/docq/manage_spaces.py b/source/docq/manage_spaces.py index 1650a710..541a0058 100644 --- a/source/docq/manage_spaces.py +++ b/source/docq/manage_spaces.py @@ -8,9 +8,6 @@ from datetime import datetime from typing import Any, List, Optional -from llama_index.core.indices import DocumentSummaryIndex, VectorStoreIndex -from llama_index.core.indices.base import BaseIndex -from llama_index.core.schema import Document from opentelemetry import trace import docq @@ -19,9 +16,9 @@ from .config import SpaceType from .data_source.list import SpaceDataSources from .domain import DocumentListItem, SpaceKey -from .model_selection.main import LlmUsageSettingsCollection, ModelCapability, get_saved_model_settings_collection -from .support.llm import _get_default_storage_context, _get_service_context -from .support.store import get_index_dir, get_sqlite_shared_system_file +from .manage_indices import _create_vector_index, _persist_index +from .model_selection.main import get_saved_model_settings_collection +from .support.store import get_sqlite_shared_system_file tracer = trace.get_tracer(__name__, docq.__version_str__) @@ -67,35 +64,6 @@ def _init() -> None: connection.commit() -@tracer.start_as_current_span("manage_spaces._create_vector_index") -def _create_vector_index( - documents: List[Document], model_settings_collection: LlmUsageSettingsCollection -) -> VectorStoreIndex: - # Use default storage and service context to initialise index purely for persisting - return VectorStoreIndex.from_documents( - documents, - storage_context=_get_default_storage_context(), - service_context=_get_service_context(model_settings_collection), - kwargs=model_settings_collection.model_usage_settings[ModelCapability.CHAT].additional_args, - ) - -@tracer.start_as_current_span("manage_spaces._create_document_summary_index") -def _create_document_summary_index( - documents: List[Document], model_settings_collection: LlmUsageSettingsCollection -) -> DocumentSummaryIndex: - """Create a an index of summaries for each document. This doen't create embedding for each node.""" - return DocumentSummaryIndex(embed_summaries=True).from_documents( - documents, - storage_context=_get_default_storage_context(), - service_context=_get_service_context(model_settings_collection), - kwargs=model_settings_collection.model_usage_settings[ModelCapability.CHAT].additional_args, - ) - - -@tracer.start_as_current_span("manage_spaces._persist_index") -def _persist_index(index: BaseIndex, space: SpaceKey) -> None: - """Persist an Space datasource index to disk.""" - index.storage_context.persist(persist_dir=get_index_dir(space)) def _format_space(row: Any) -> SPACE: diff --git a/source/docq/model_selection/main.py b/source/docq/model_selection/main.py index 4032fc92..0639c5a4 100644 --- a/source/docq/model_selection/main.py +++ b/source/docq/model_selection/main.py @@ -12,10 +12,34 @@ from enum import Enum from typing import Any, Dict, Mapping, Optional +import docq +from llama_index.core.callbacks.base import CallbackManager +from llama_index.core.embeddings import BaseEmbedding +from llama_index.core.llms import LLM +from llama_index.core.node_parser import NodeParser, SentenceSplitter +from llama_index.core.service_context import ServiceContext +from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding +from llama_index.embeddings.huggingface_optimum import OptimumEmbedding +from llama_index.embeddings.openai import OpenAIEmbedding +from llama_index.llms.litellm import LiteLLM +from opentelemetry import trace from vertexai.preview.generative_models import HarmBlockThreshold, HarmCategory -from ..config import ENV_VAR_DOCQ_GROQ_API_KEY, OrganisationSettingsKey +from ..config import ( + ENV_VAR_DOCQ_AZURE_OPENAI_API_BASE1, + ENV_VAR_DOCQ_AZURE_OPENAI_API_BASE2, + ENV_VAR_DOCQ_AZURE_OPENAI_API_KEY1, + ENV_VAR_DOCQ_AZURE_OPENAI_API_KEY2, + ENV_VAR_DOCQ_AZURE_OPENAI_API_VERSION, + ENV_VAR_DOCQ_GROQ_API_KEY, + EXPERIMENTS, + OrganisationSettingsKey, +) from ..manage_settings import get_organisation_settings +from ..support.llama_index.callbackhandlers import OtelCallbackHandler +from ..support.store import get_models_dir + +tracer = trace.get_tracer(__name__, docq.__version_str__) class ModelProvider(str, Enum): @@ -122,9 +146,9 @@ class LlmUsageSettingsCollection: provider=ModelProvider.AZURE_OPENAI, model_name="gpt-35-turbo", model_deployment_name="gpt-35-turbo", - api_base=os.getenv("DOCQ_AZURE_OPENAI_API_BASE") or "", - api_key=os.getenv("DOCQ_AZURE_OPENAI_API_KEY1") or "", - api_version=os.environ.get("DOCQ_AZURE_OPENAI_API_VERSION", "2023-05-15"), + api_base=os.getenv(ENV_VAR_DOCQ_AZURE_OPENAI_API_BASE1) or "", + api_key=os.getenv(ENV_VAR_DOCQ_AZURE_OPENAI_API_KEY1) or "", + api_version=os.environ.get(ENV_VAR_DOCQ_AZURE_OPENAI_API_VERSION, "2023-05-15"), context_window_size=4096, license_="Commercial", ), @@ -132,17 +156,26 @@ class LlmUsageSettingsCollection: provider=ModelProvider.AZURE_OPENAI, model_name="gpt-4", model_deployment_name="gpt4-turbo-1106-preview", - api_base=os.getenv("DOCQ_AZURE_OPENAI_API_BASE") or "", - api_key=os.getenv("DOCQ_AZURE_OPENAI_API_KEY1") or "", - api_version=os.environ.get("DOCQ_AZURE_OPENAI_API_VERSION", "2023-05-15"), + api_base=os.getenv(ENV_VAR_DOCQ_AZURE_OPENAI_API_BASE1) or "", + api_key=os.getenv(ENV_VAR_DOCQ_AZURE_OPENAI_API_KEY1) or "", + api_version=os.environ.get(ENV_VAR_DOCQ_AZURE_OPENAI_API_VERSION, "2023-05-15"), + license_="Commercial", + ), + "azure-openai-gpt4o-2024-05-13": LlmServiceInstanceConfig( + provider=ModelProvider.AZURE_OPENAI, + model_name="gpt-4o", + model_deployment_name="gpt-4o-2024-05-13", + api_base=os.getenv(ENV_VAR_DOCQ_AZURE_OPENAI_API_BASE2) or "", + api_key=os.getenv(ENV_VAR_DOCQ_AZURE_OPENAI_API_KEY2) or "", + api_version=os.environ.get(ENV_VAR_DOCQ_AZURE_OPENAI_API_VERSION, "2023-05-15"), license_="Commercial", ), "azure-openai-ada-002": LlmServiceInstanceConfig( provider=ModelProvider.AZURE_OPENAI, model_name="text-embedding-ada-002", model_deployment_name="text-embedding-ada-002", - api_base=os.getenv("DOCQ_AZURE_OPENAI_API_BASE") or "", - api_key=os.getenv("DOCQ_AZURE_OPENAI_API_KEY1") or "", + api_base=os.getenv(ENV_VAR_DOCQ_AZURE_OPENAI_API_BASE1) or "", + api_key=os.getenv(ENV_VAR_DOCQ_AZURE_OPENAI_API_KEY1) or "", license_="Commercial", ), "google-vertexai-palm2": LlmServiceInstanceConfig( @@ -234,7 +267,7 @@ class LlmUsageSettingsCollection: ModelCapability.CHAT: LlmUsageSettings( model_capability=ModelCapability.CHAT, temperature=0.7, - service_instance_config=LLM_SERVICE_INSTANCES["azure-openai-gpt35turbo"], + service_instance_config=LLM_SERVICE_INSTANCES["azure-openai-gpt4o-2024-05-13"], ), ModelCapability.EMBEDDING: LlmUsageSettings( model_capability=ModelCapability.EMBEDDING, @@ -272,6 +305,21 @@ class LlmUsageSettingsCollection: ), }, ), + "azure_openai_gpt4o_with_local_embedding": LlmUsageSettingsCollection( + name="Azure OpenAI GPT-4o wth Local Embedding", + key="azure_openai_gpt4o_with_local_embedding", + model_usage_settings={ + ModelCapability.CHAT: LlmUsageSettings( + model_capability=ModelCapability.CHAT, + temperature=0.7, + service_instance_config=LLM_SERVICE_INSTANCES["azure-openai-gpt4o-2024-05-13"], + ), + ModelCapability.EMBEDDING: LlmUsageSettings( + model_capability=ModelCapability.EMBEDDING, + service_instance_config=LLM_SERVICE_INSTANCES["optimum-bge-small-en-v1.5"], + ), + }, + ), "groq_llama2_70b_with_local_embedding": LlmUsageSettingsCollection( name="Groq Llama2 70B wth Local Embedding", key="groq_llama2_70b_with_local_embedding", @@ -387,3 +435,164 @@ def get_saved_model_settings_collection(org_id: int) -> LlmUsageSettingsCollecti def list_available_model_settings_collections() -> dict: """List available models.""" return {k: v.name for k, v in LLM_MODEL_COLLECTIONS.items()} + +@tracer.start_as_current_span(name="_get_service_context") +def _get_service_context(model_settings_collection: LlmUsageSettingsCollection) -> ServiceContext: + log.debug( + "EXPERIMENTS['INCLUDE_EXTRACTED_METADATA']['enabled']: %s", EXPERIMENTS["INCLUDE_EXTRACTED_METADATA"]["enabled"] + ) + log.debug("EXPERIMENTS['ASYNC_NODE_PARSER']['enabled']: %s", EXPERIMENTS["ASYNC_NODE_PARSER"]["enabled"]) + + _node_parser = None # use default node parser + if EXPERIMENTS["INCLUDE_EXTRACTED_METADATA"]["enabled"]: + _node_parser = _get_node_parser(model_settings_collection) + if EXPERIMENTS["ASYNC_NODE_PARSER"]["enabled"]: + log.debug("loading async node parser.") + # _node_parser = _get_async_node_parser(model_settings_collection) + else: + _callback_manager = CallbackManager([OtelCallbackHandler(tracer_provider=trace.get_tracer_provider())]) + _node_parser = SentenceSplitter.from_defaults(callback_manager=_callback_manager) + + return ServiceContext.from_defaults( + llm=_get_generation_model(model_settings_collection), + node_parser=_node_parser, + embed_model=_get_embed_model(model_settings_collection), + callback_manager=_node_parser.callback_manager, + context_window=model_settings_collection.model_usage_settings[ + ModelCapability.CHAT + ].service_instance_config.context_window_size, + num_output=256, # default in lama-index but we need to be explicit here because it's not being set everywhere. + ) + + +@tracer.start_as_current_span(name="_get_generation_model") +def _get_generation_model(model_settings_collection: LlmUsageSettingsCollection) -> LLM | None: + import litellm + + litellm.telemetry = False + model = None + if model_settings_collection and model_settings_collection.model_usage_settings[ModelCapability.CHAT]: + chat_model_settings = model_settings_collection.model_usage_settings[ModelCapability.CHAT] + sc = chat_model_settings.service_instance_config + _callback_manager = CallbackManager([OtelCallbackHandler(tracer_provider=trace.get_tracer_provider())]) + if sc.provider == ModelProvider.AZURE_OPENAI: + _additional_kwargs: Dict[str, Any] = {} + _additional_kwargs["api_version"] = chat_model_settings.service_instance_config.api_version + model = LiteLLM( + temperature=chat_model_settings.temperature, + model=f"azure/{sc.model_deployment_name}", + additional_kwargs=_additional_kwargs, + api_base=sc.api_base, + api_key=sc.api_key, + set_verbose=True, + callback_manager=_callback_manager, + ) + log.info("Chat model: using Azure OpenAI") + _env_missing = not bool(sc.api_base and sc.api_key and sc.api_version) + if _env_missing: + log.warning("Chat model: env var values missing.") + elif sc.provider == ModelProvider.OPENAI: + model = LiteLLM( + temperature=chat_model_settings.temperature, + model=sc.model_name, + api_key=sc.api_key, + callback_manager=_callback_manager, + ) + log.info("Chat model: using OpenAI.") + _env_missing = not bool(sc.api_key) + if _env_missing: + log.warning("Chat model: env var values missing") + elif sc.provider == ModelProvider.GOOGLE_VERTEXAI_PALM2: + # GCP project_id is coming from the credentials json. + model = LiteLLM( + temperature=chat_model_settings.temperature, + model=sc.model_name, + callback_manager=_callback_manager, + ) + elif sc.provider == ModelProvider.GOOGLE_VERTEXTAI_GEMINI_PRO: + # GCP project_id is coming from the credentials json. + model = LiteLLM( + temperature=chat_model_settings.temperature, + model=sc.model_name, + callback_manager=_callback_manager, + max_tokens=2048, + kwargs={"telemetry": False}, + ) + litellm.VertexAIConfig() + litellm.vertex_location = sc.additional_properties["vertex_location"] + elif sc.provider == ModelProvider.GROQ: + model = LiteLLM( + temperature=chat_model_settings.temperature, + model=f"groq/{sc.model_name}", + api_key=sc.api_key, + # api_base=sc.api_base, + # max_tokens=4096, + callback_manager=_callback_manager, + kwargs={ + "set_verbose": True, + }, + ) + _env_missing = not bool(sc.api_key) + if _env_missing: + log.warning("Chat model: env var values missing.") + else: + raise ValueError("Chat model: model settings with a supported model provider not found.") + + model.max_retries = 3 + + print("model: ", model) + print("model_settings_collection: ", model_settings_collection) + + return model + + +@tracer.start_as_current_span(name="_get_embed_model") +def _get_embed_model(model_settings_collection: LlmUsageSettingsCollection) -> BaseEmbedding | None: + embedding_model = None + if model_settings_collection and model_settings_collection.model_usage_settings[ModelCapability.EMBEDDING]: + embedding_model_settings = model_settings_collection.model_usage_settings[ModelCapability.EMBEDDING] + _callback_manager = CallbackManager([OtelCallbackHandler(tracer_provider=trace.get_tracer_provider())]) + sc = embedding_model_settings.service_instance_config + with tracer.start_as_current_span(name=f"LangchainEmbedding.{sc.provider}"): + if sc.provider == ModelProvider.AZURE_OPENAI: + embedding_model = AzureOpenAIEmbedding( + model=sc.model_name, + azure_deployment=sc.model_deployment_name, # `deployment_name` is an alias + azure_endpoint=os.getenv("DOCQ_AZURE_OPENAI_API_BASE"), + api_key=os.getenv(ENV_VAR_DOCQ_AZURE_OPENAI_API_KEY1), + # openai_api_type="azure", + api_version=os.getenv(ENV_VAR_DOCQ_AZURE_OPENAI_API_VERSION), + callback_manager=_callback_manager, + ) + elif sc.provider == ModelProvider.OPENAI: + embedding_model = OpenAIEmbedding( + model=sc.model_name, + api_key=os.getenv("DOCQ_OPENAI_API_KEY"), + callback_manager=_callback_manager, + ) + elif sc.provider == ModelProvider.HUGGINGFACE_OPTIMUM_BAAI: + embedding_model = OptimumEmbedding( + folder_name=get_models_dir(sc.model_name), + callback_manager=_callback_manager, + ) + else: + # defaults + embedding_model = OpenAIEmbedding() + + return embedding_model + + +@tracer.start_as_current_span(name="_get_node_parser") +def _get_node_parser(model_settings_collection: LlmUsageSettingsCollection) -> NodeParser: + # metadata_extractor = MetadataExtractor( + # extractors=[ + + # KeywordExtractor(llm=_get_generation_model(model_settings_collection), keywords=5), + # EntityExtractor(label_entities=True, device="cpu"), + # # CustomExtractor() + # ], + # ) + _callback_manager = CallbackManager([OtelCallbackHandler(tracer_provider=trace.get_tracer_provider())]) + node_parser = SentenceSplitter.from_defaults() + + return node_parser diff --git a/source/docq/run_queries.py b/source/docq/run_queries.py index 4abaf5c3..9e2d8bdb 100644 --- a/source/docq/run_queries.py +++ b/source/docq/run_queries.py @@ -80,6 +80,18 @@ def _save_messages(data: list[tuple[str, bool, datetime, int]], feature: Feature def _retrieve_messages( cutoff: datetime, size: int, feature: FeatureKey, thread_id: int, sort_order: Literal["ASC", "DESC"] = "DESC" ) -> list[tuple[int, str, bool, datetime, int]]: + """Retrieve the history of messages up to certain size and cutoff. + + Args: + cutoff: The cutoff time. + size: The number of messages to retrieve. + feature: The feature key. + thread_id: The thread id. + sort_order: The order to sort the messages. + + Returns: + list pf tuples of (id:int, message:str, human:bool, timestamp, thread_id:int). + """ tablename = get_history_table_name(feature.type_) thread_tablename = get_history_thread_table_name(feature.type_) usage_file = ( @@ -253,7 +265,7 @@ def query( feature, spaces, ) - data = [(input_, True, datetime.now(), thread_id)] + data = [(input_, bool(True), datetime.now(), thread_id)] is_chat = feature.type_ == OrganisationFeatureType.CHAT_PRIVATE # history = _retrieve_last_n_history(feature, thread_id) @@ -278,7 +290,7 @@ def query( MESSAGE_TEMPLATE.format(message=response.response) if is_chat else MESSAGE_WITH_SOURCES_TEMPLATE.format( - message=response.response, source=format_document_sources(response.source_nodes) + message=response, source=format_document_sources(response.source_nodes) ), False, datetime.now(), @@ -288,6 +300,8 @@ def query( return _save_messages(data, feature) -def history(cutoff: datetime, size: int, feature: FeatureKey, thread_id: int) -> list[tuple[int, str, bool, datetime]]: +def history( + cutoff: datetime, size: int, feature: FeatureKey, thread_id: int +) -> list[tuple[int, str, bool, datetime, int]]: """Retrieve the history of messages up to certain size and cutoff.""" return _retrieve_messages(cutoff, size, feature, thread_id) diff --git a/source/docq/support/llama_index/__init__.py b/source/docq/support/llama_index/__init__.py new file mode 100644 index 00000000..f735fd25 --- /dev/null +++ b/source/docq/support/llama_index/__init__.py @@ -0,0 +1 @@ +"""Custom classes that inherit from Llama Index go in this module.""" diff --git a/source/docq/support/llamaindex_otel_callbackhandler.py b/source/docq/support/llama_index/callbackhandlers.py similarity index 76% rename from source/docq/support/llamaindex_otel_callbackhandler.py rename to source/docq/support/llama_index/callbackhandlers.py index 31be679f..aa33df9a 100644 --- a/source/docq/support/llamaindex_otel_callbackhandler.py +++ b/source/docq/support/llama_index/callbackhandlers.py @@ -1,19 +1,24 @@ """Llama Index callback handler for OpenTelemetry tracing.""" + import inspect import logging from typing import Any, Dict, List, Optional, Self, Tuple +from opentelemetry import trace +from opentelemetry.trace import NonRecordingSpan + import llama_index.core from llama_index.core.callbacks.base_handler import BaseCallbackHandler from llama_index.core.callbacks.schema import CBEventType, EventPayload -from opentelemetry import trace -from opentelemetry.trace import NonRecordingSpan logger = logging.getLogger(__name__) + class OtelCallbackHandler(BaseCallbackHandler): """Base callback handler that can be used to track event starts and ends.""" - _spans: dict[str, Any] = {} # track Otel spans so they can be ended on the end events. + + _spans: dict[str, Any] = {} # track Otel spans so they can be ended on the end events. + def __init__( self: Self, tracer_provider: trace.TracerProvider, @@ -26,7 +31,7 @@ def __init__( self.event_starts_to_ignore = tuple(start_ignore) self.event_ends_to_ignore = tuple(end_ignore) - #module_name, function_name = get_caller_function_and_module() + # module_name, function_name = get_caller_function_and_module() self._tracer = tracer_provider.get_tracer( instrumenting_module_name="docq.llama_index_otel_callbackhandler", instrumenting_library_version=llama_index.core.__version__, @@ -38,7 +43,7 @@ def __init__( ) def on_event_start( - self:Self, + self: Self, event_type: CBEventType, payload: Optional[Dict[str, Any]] = None, event_id: str = "", @@ -47,11 +52,20 @@ def on_event_start( ) -> str: """Run when an event starts and return id of event.""" try: - #logging.debug("Starting event event_id: %s, event_type: %s, payload: %s", event_id, event_type, payload) - parent_span =self._spans[parent_id] if parent_id in self._spans else trace.get_current_span() + # logging.debug("Starting event event_id: %s, event_type: %s, payload: %s", event_id, event_type, payload) + parent_span = self._spans[parent_id] if parent_id in self._spans else trace.get_current_span() ctx = trace.set_span_in_context(NonRecordingSpan(parent_span.get_span_context())) - span = self._tracer.start_span(name=event_type.name, context=ctx, attributes=self._serialize_payload(payload)) - span.add_event(name="callback_handler.on_event_start", attributes={"cbevent.event_id": event_id, "cbevent.parent_id": parent_id, "cbevent.event_type": event_type}) + span = self._tracer.start_span( + name=event_type.name, context=ctx, attributes=self._serialize_payload(payload) + ) + span.add_event( + name="callback_handler.on_event_start", + attributes={ + "cbevent.event_id": event_id, + "cbevent.parent_id": parent_id, + "cbevent.event_type": event_type, + }, + ) self._spans[event_id] = span except Exception as e: @@ -67,17 +81,20 @@ def on_event_end( **kwargs: Any, ) -> None: """Run when an event ends.""" - #logger.debug("Ending event - event_id: '%s', event_type: '%s', event_payload: '%s'", event_id, event_type, payload) + # logger.debug("Ending event - event_id: '%s', event_type: '%s', event_payload: '%s'", event_id, event_type, payload) if event_id in self._spans: span = self._spans.pop(event_id) span.set_attributes(self._serialize_payload(payload)) - span.add_event(name="callback_handler.on_event_end", attributes={"cbevent.event_id": event_id, "cbevent.event_type": event_type}) + span.add_event( + name="callback_handler.on_event_end", + attributes={"cbevent.event_id": event_id, "cbevent.event_type": event_type}, + ) span.end() def start_trace(self: Self, trace_id: Optional[str] = None) -> None: """Run when an overall trace is launched.""" if trace_id: - #logger.debug("Starting trace - trace_id: '%s'", trace_id) + # logger.debug("Starting trace - trace_id: '%s'", trace_id) current_span = trace.get_current_span() ctx = trace.set_span_in_context(NonRecordingSpan(current_span.get_span_context())) span = self._tracer.start_span(name=trace_id, context=ctx) @@ -90,18 +107,17 @@ def end_trace( trace_map: Optional[Dict[str, List[str]]] = None, ) -> None: """Run when an overall trace is exited.""" - #logger.debug("Ending trace - trace_id: '%s'", trace_id) - #logger.debug("Ending trace - trace_map: '%s'", trace_map) + # logger.debug("Ending trace - trace_id: '%s'", trace_id) + # logger.debug("Ending trace - trace_map: '%s'", trace_map) if trace_id and trace_id in self._spans: span = self._spans.pop(trace_id) span.add_event(name="callback_handler.end_trace", attributes={"cbevent.trace_id": trace_id}) span.end() - @staticmethod def _serialize_payload(payload: Dict[str, Any] | None) -> Dict[str, str]: """Serialize payload.""" - _result: Dict[str, str] = {} + _result: Dict[str, str] = {} try: if payload: if EventPayload.SERIALIZED in payload: @@ -114,7 +130,6 @@ def _serialize_payload(payload: Dict[str, Any] | None) -> Dict[str, str]: return _result - @staticmethod def get_caller_function_and_module() -> Tuple[str, str]: """Get the caller function and module.""" @@ -134,4 +149,3 @@ def get_caller_function_and_module() -> Tuple[str, str]: module_name = module.__name__ return function_name, module_name - diff --git a/source/docq/support/node_parsers.py b/source/docq/support/llama_index/node_parsers.py similarity index 100% rename from source/docq/support/node_parsers.py rename to source/docq/support/llama_index/node_parsers.py diff --git a/source/docq/support/llama_index/node_post_processors.py b/source/docq/support/llama_index/node_post_processors.py new file mode 100644 index 00000000..ecc6145b --- /dev/null +++ b/source/docq/support/llama_index/node_post_processors.py @@ -0,0 +1,44 @@ +"""Functions that can post process nodes, typicaly after retrieval from an index using some search method like vector or BM25. + +These aren't always implementations of LlamaIndex's `BaseNodePostProcessor` interface, but they are used in a similar way. +""" + +from typing import Dict, List, Tuple + +from llama_index.core.schema import NodeWithScore + + +def reciprocal_rank_fusion(results: Dict[str, List[NodeWithScore]]) -> List[NodeWithScore]: + """Apply reciprocal rank fusion. + + The original paper uses k=60 for best results: + https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf + + Note: we cannot implement this as a `NodePostProcessor` because we need to pass in multiple lists of nodes. If we flatten the lists into one the ranking calc will be different there for a different result. + + Args: + results: A dictionary of results `NodeWithScore` from multiple search methods. + """ + k = 60.0 # `k` is a parameter used to control the impact of outlier rankings. + fused_scores = {} + text_to_node = {} + + # compute reciprocal rank scores + for nodes_with_scores in results.values(): + for rank, node_with_score in enumerate(sorted(nodes_with_scores, key=lambda x: x.score or 0.0, reverse=True)): + text = node_with_score.node.get_content() + text_to_node[text] = node_with_score + if text not in fused_scores: + fused_scores[text] = 0.0 + fused_scores[text] += 1.0 / (rank + k) + + # sort results + reranked_results = dict(sorted(fused_scores.items(), key=lambda x: x[1], reverse=True)) + + # adjust node scores + reranked_nodes: List[NodeWithScore] = [] + for text, score in reranked_results.items(): + reranked_nodes.append(text_to_node[text]) + reranked_nodes[-1].score = score + + return reranked_nodes diff --git a/source/docq/support/llama_index/query_pipeline_components.py b/source/docq/support/llama_index/query_pipeline_components.py new file mode 100644 index 00000000..8e53c3dd --- /dev/null +++ b/source/docq/support/llama_index/query_pipeline_components.py @@ -0,0 +1,285 @@ +"""Custom Llama Index query pipeline components.""" + +from abc import abstractmethod +from typing import Any, Dict, List, Optional, Self + +from llama_index.core.base.query_pipeline.query import ( + ChainableMixin, + InputKeys, + OutputKeys, + QueryComponent, + validate_and_convert_stringable, +) +from llama_index.core.bridge.pydantic import Field +from llama_index.core.llms import LLM, ChatMessage, MessageRole +from llama_index.core.prompts import BasePromptTemplate +from llama_index.core.prompts.default_prompts import DEFAULT_HYDE_PROMPT +from llama_index.core.prompts.mixin import ( + PromptDictType, + PromptMixin, + PromptMixinType, +) +from llama_index.core.query_pipeline import CustomQueryComponent +from llama_index.core.schema import MetadataMode, NodeWithScore, QueryBundle, QueryType +from llama_index.core.service_context_elements.llm_predictor import ( + LLMPredictorType, +) +from llama_index.core.settings import Settings + +DEFAULT_CONTEXT_PROMPT = ( + "Here is some context that may be relevant:\n" + "-----\n" + "{context_str}\n" + "-----\n" + "Please write a response to the following question, using the above context:\n" + "{query_str}\n" +) + + +class ResponseWithChatHistory(CustomQueryComponent): + """Response with chat history. + + Args: + system_prompt: Optional[str] - System prompt to use for the LLM + context_prompt: str - Context prompt to use for the LLM + Inputs: dict + chat_history: List[ChatMessage] - Chat history. Forms the message collection sent to the LLM for response generation. The context user message is appended to the end of this. + nodes: List[NodeWithScore] - Context nodes from retrieval. Optionally, after being reranked. Each nodes text is added to the context user prompt for final response generation. + query_str: str - the user query. Used added to the context user prompt for final response generation. + Output: dict + response: str - The generated response from the LLM + source_nodes: List[NodeWithScore] - The source nodes used to generate the response. + """ + + llm: LLM = Field(..., description="LLM") + system_prompt: Optional[str] = Field(default=None, description="System prompt to use for the LLM") + context_prompt: str = Field( + default=DEFAULT_CONTEXT_PROMPT, + description="Context prompt to use for the LLM", + ) + # query_str: Optional[str] = Field(default=None, description="The user query") + + # chat_history: Optional[List[ChatMessage]] = Field(default=None, description="Chat history") + + # nodes: Optional[List[NodeWithScore]] = Field( + # default=None, description="Context nodes from retrieval after being reranked." + # ) + + def _validate_component_inputs(self: Self, input: Dict[str, Any]) -> Dict[str, Any]: + """Validate component inputs during run_component.""" + # NOTE: this is OPTIONAL but we show you where to do validation as an example + return input + + @property + def _input_keys(self: Self) -> set: + """Input keys dict.""" + # NOTE: These are required inputs. If you have optional inputs please override + # `optional_input_keys_dict` + return {"chat_history", "nodes", "query_str"} + + @property + def _output_keys(self: Self) -> set: + return {"response", "source_nodes"} + + def _prepare_context( + self: Self, + chat_history: List[ChatMessage], + nodes: List[NodeWithScore], + query_str: str, + ) -> List[ChatMessage]: + node_context = "" + for idx, node in enumerate(nodes): + node_text = node.get_content(metadata_mode=MetadataMode.LLM) + node_context += f"Context Chunk {idx}:\n{node_text}\n\n" + + formatted_context = self.context_prompt.format(context_str=node_context, query_str=query_str) + user_message = ChatMessage(role=MessageRole.USER, content=formatted_context) + + chat_history.append(user_message) + + if self.system_prompt is not None: + chat_history = [ChatMessage(role=MessageRole.SYSTEM, content=self.system_prompt)] + chat_history + + return chat_history + + def _run_component(self: Self, **kwargs: Any) -> Dict[str, Any]: + """Run the component.""" + chat_history = kwargs["chat_history"] + nodes = kwargs["nodes"] + query_str = kwargs["query_str"] + + prepared_context = self._prepare_context(chat_history, nodes, query_str) + + response = self.llm.chat(prepared_context) + return {"response": response, "source_nodes": nodes} + + async def _arun_component(self: Self, **kwargs: Any) -> Dict[str, Any]: + """Run the component asynchronously.""" + # NOTE: Optional, but async LLM calls are easy to implement + chat_history = kwargs["chat_history"] + nodes = kwargs["nodes"] + query_str = kwargs["query_str"] + + prepared_context = self._prepare_context(chat_history, nodes, query_str) + + response = await self.llm.achat(prepared_context) + + return {"response": response, "source_nodes": nodes} + +class BaseQueryTransform(ChainableMixin, PromptMixin): + """Base class for query transform. + + A query transform augments a raw query string with associated transformations + to improve index querying. + + The query transformation is performed before the query is sent to the index. + FIXME: see note in QueryTransformComponent below + """ + + def _get_prompt_modules(self: Self) -> PromptMixinType: + """Get prompt modules.""" + # TODO: keep this for now since response synthesizers don't generally have sub-modules + return {} + + @abstractmethod + def _run(self: Self, query_bundle: QueryBundle, metadata: Dict) -> QueryBundle: + """Run query transform.""" + + def run( + self: Self, + query_bundle_or_str: QueryType, + metadata: Optional[Dict] = None, + ) -> QueryBundle: + """Run query transform.""" + metadata = metadata or {} + if isinstance(query_bundle_or_str, str): + query_bundle = QueryBundle( + query_str=query_bundle_or_str, + custom_embedding_strs=[query_bundle_or_str], + ) + else: + query_bundle = query_bundle_or_str + + return self._run(query_bundle, metadata=metadata) + + def __call__( + self: Self, + query_bundle_or_str: QueryType, + metadata: Optional[Dict] = None, + ) -> QueryBundle: + """Run query processor.""" + return self.run(query_bundle_or_str, metadata=metadata) + + def _as_query_component(self: Self, **kwargs: Any) -> QueryComponent: + """As query component.""" + return QueryTransformComponent(query_transform=self) + + + +class QueryTransformComponent(QueryComponent): + """Query transform component. + + FIXME: temp to fix a bug in _run_component. Remove once fix is merged upstream. + """ + + query_transform: BaseQueryTransform = Field(..., description="Query transform.") + + class Config: + arbitrary_types_allowed = True + + def set_callback_manager(self: Self, callback_manager: Any) -> None: + """Set callback manager.""" + # TODO: not implemented yet + + def _validate_component_inputs(self: Self, input: Dict[str, Any]) -> Dict[str, Any]: + """Validate component inputs during run_component.""" + if "query_str" not in input: + raise ValueError("Input must have key 'query_str'") + input["query_str"] = validate_and_convert_stringable(input["query_str"]) + + input["metadata"] = input.get("metadata", {}) + + return input + + def _run_component(self: Self, **kwargs: Any) -> Any: + """Run component.""" + output = self.query_transform.run( + kwargs["query_str"], + metadata=kwargs["metadata"], + ) + return {"query_str": output.query_str} + + async def _arun_component(self: Self, **kwargs: Any) -> Any: + """Run component.""" + # TODO: true async not implemented yet + return self._run_component(**kwargs) + + @property + def input_keys(self: Self) -> InputKeys: + """Input keys.""" + return InputKeys.from_keys({"query_str"}, optional_keys={"metadata"}) + + @property + def output_keys(self: Self) -> OutputKeys: + """Output keys.""" + return OutputKeys.from_keys({"query_str"}) + + +class HyDEQueryTransform(BaseQueryTransform): + """Hypothetical Document Embeddings (HyDE) query transform. + + It uses an LLM to generate hypothetical answer(s) to a given query, + and use the resulting documents as embedding strings. + + As described in `[Precise Zero-Shot Dense Retrieval without Relevance Labels] + (https://arxiv.org/abs/2212.10496)` + + Note: this has been customised from the original implementation in Llama Index + """ + + def __init__( + self, + llm: Optional[LLMPredictorType] = None, + hyde_prompt: Optional[BasePromptTemplate] = None, + include_original: bool = True, + prompt_args: Optional[Dict[str, Any]] = None, + ) -> None: + """Initialize HyDEQueryTransform. + + Args: + llm_predictor (Optional[LLM]): LLM for generating + hypothetical documents + hyde_prompt (Optional[BasePromptTemplate]): Custom prompt for HyDE. query_str in passed into the prompt for replacement by default. Use `{query_str}`. + include_original (bool): Whether to include original query + string as one of the embedding strings + prompt_args (Optional[Dict[str, Any]]): Additional arguments to be replaced in the prompt. key should be the variable in the template. + """ + super().__init__() + + self._llm = llm or Settings.llm + self._hyde_prompt = hyde_prompt or DEFAULT_HYDE_PROMPT + self._include_original = include_original + self._promp_args = prompt_args or {} + + def _get_prompts(self: Self) -> PromptDictType: + """Get prompts.""" + return {"hyde_prompt": self._hyde_prompt} + + def _update_prompts(self: Self, prompts: PromptDictType) -> None: + """Update prompts.""" + if "hyde_prompt" in prompts: + self._hyde_prompt = prompts["hyde_prompt"] + + def _run(self: Self, query_bundle: QueryBundle, metadata: Dict) -> QueryBundle: + """Run query transform.""" + # TODO: support generating multiple hypothetical docs + query_str = query_bundle.query_str + hypothetical_doc = self._llm.predict(self._hyde_prompt, query_str=query_str, **self._promp_args) + print("hypothetical_doc: ", hypothetical_doc) + embedding_strs = [hypothetical_doc] + if self._include_original: + embedding_strs.extend(query_bundle.embedding_strs) + return QueryBundle( + query_str=query_str, + custom_embedding_strs=embedding_strs, + ) \ No newline at end of file diff --git a/source/docq/support/llama_index/query_transforms.py b/source/docq/support/llama_index/query_transforms.py new file mode 100644 index 00000000..a88677dd --- /dev/null +++ b/source/docq/support/llama_index/query_transforms.py @@ -0,0 +1,2 @@ +"""Custom Query Transforms.""" + diff --git a/source/docq/support/llm.py b/source/docq/support/llm.py index 0cc9b51c..13db3e26 100644 --- a/source/docq/support/llm.py +++ b/source/docq/support/llm.py @@ -2,49 +2,46 @@ import logging as log import os -from typing import Any, Dict, List +from typing import List, Optional from uu import Error import docq -from llama_index.core.base.llms.types import ChatMessage from llama_index.core.base.response.schema import RESPONSE_TYPE, Response -from llama_index.core.callbacks.base import CallbackManager from llama_index.core.chat_engine import SimpleChatEngine from llama_index.core.chat_engine.types import AGENT_CHAT_RESPONSE_TYPE, AgentChatResponse -from llama_index.core.embeddings import BaseEmbedding from llama_index.core.indices.base import BaseIndex -from llama_index.core.indices.loading import load_index_from_storage -from llama_index.core.llms import LLM -from llama_index.core.node_parser import NodeParser, SentenceSplitter +from llama_index.core.llms import ChatMessage +from llama_index.core.prompts import PromptTemplate, PromptType from llama_index.core.query_engine import RetrieverQueryEngine +from llama_index.core.query_pipeline import ( + InputComponent, + QueryPipeline, +) +from llama_index.core.query_pipeline.components import FnComponent +from llama_index.core.query_pipeline.components.argpacks import KwargPackComponent from llama_index.core.retrievers import BaseRetriever, QueryFusionRetriever from llama_index.core.retrievers.fusion_retriever import FUSION_MODES -from llama_index.core.service_context import ServiceContext # load_index_from_storage -from llama_index.core.storage import StorageContext -from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding from llama_index.embeddings.huggingface_optimum import OptimumEmbedding -from llama_index.embeddings.openai import OpenAIEmbedding -from llama_index.llms.litellm import LiteLLM from llama_index.retrievers.bm25 import BM25Retriever from opentelemetry import trace from opentelemetry.trace import Status, StatusCode -from ..config import EXPERIMENTS from ..domain import SpaceKey from ..manage_assistants import Assistant, llama_index_chat_prompt_template_from_assistant +from ..manage_indices import _load_index_from_storage, load_indices_from_storage from ..model_selection.main import ( LLM_MODEL_COLLECTIONS, LlmUsageSettingsCollection, ModelCapability, ModelProvider, + _get_service_context, ) -from .llamaindex_otel_callbackhandler import OtelCallbackHandler - -# from .metadata_extractors import DocqEntityExtractor, DocqMetadataExtractor -# from .node_parsers import AsyncSimpleNodeParser -from .store import get_index_dir, get_models_dir +from .ansi_colours import AnsiColours +from .llama_index.node_post_processors import reciprocal_rank_fusion +from .llama_index.query_pipeline_components import HyDEQueryTransform, ResponseWithChatHistory +from .store import get_models_dir tracer = trace.get_tracer(__name__, docq.__version_str__) @@ -57,6 +54,7 @@ ERROR: {error} """ + @tracer.start_as_current_span(name="_init_local_models") def _init_local_models() -> None: """Initialize local models.""" @@ -72,176 +70,6 @@ def _init_local_models() -> None: ) -@tracer.start_as_current_span(name="_get_generation_model") -def _get_generation_model(model_settings_collection: LlmUsageSettingsCollection) -> LLM | None: - import litellm - - litellm.telemetry = False - model = None - if model_settings_collection and model_settings_collection.model_usage_settings[ModelCapability.CHAT]: - chat_model_settings = model_settings_collection.model_usage_settings[ModelCapability.CHAT] - sc = chat_model_settings.service_instance_config - _callback_manager = CallbackManager([OtelCallbackHandler(tracer_provider=trace.get_tracer_provider())]) - if sc.provider == ModelProvider.AZURE_OPENAI: - _additional_kwargs: Dict[str, Any] = {} - _additional_kwargs["api_version"] = chat_model_settings.service_instance_config.api_version - model = LiteLLM( - temperature=chat_model_settings.temperature, - model=f"azure/{sc.model_deployment_name}", - additional_kwargs=_additional_kwargs, - api_base=sc.api_base, - api_key=sc.api_key, - set_verbose=True, - callback_manager=_callback_manager, - ) - log.info("Chat model: using Azure OpenAI") - _env_missing = not bool(sc.api_base and sc.api_key and sc.api_version) - if _env_missing: - log.warning("Chat model: env var values missing.") - elif sc.provider == ModelProvider.OPENAI: - model = LiteLLM( - temperature=chat_model_settings.temperature, - model=sc.model_name, - api_key=sc.api_key, - callback_manager=_callback_manager, - ) - log.info("Chat model: using OpenAI.") - _env_missing = not bool(sc.api_key) - if _env_missing: - log.warning("Chat model: env var values missing") - elif sc.provider == ModelProvider.GOOGLE_VERTEXAI_PALM2: - # GCP project_id is coming from the credentials json. - model = LiteLLM( - temperature=chat_model_settings.temperature, - model=sc.model_name, - callback_manager=_callback_manager, - ) - elif sc.provider == ModelProvider.GOOGLE_VERTEXTAI_GEMINI_PRO: - # GCP project_id is coming from the credentials json. - model = LiteLLM( - temperature=chat_model_settings.temperature, - model=sc.model_name, - callback_manager=_callback_manager, - max_tokens=2048, - kwargs={"telemetry": False}, - ) - litellm.VertexAIConfig() - litellm.vertex_location = sc.additional_properties["vertex_location"] - elif sc.provider == ModelProvider.GROQ: - model = LiteLLM( - temperature=chat_model_settings.temperature, - model=f"groq/{sc.model_name}", - api_key=sc.api_key, - # api_base=sc.api_base, - # max_tokens=4096, - callback_manager=_callback_manager, - kwargs={ - "set_verbose": True, - }, - ) - _env_missing = not bool(sc.api_key) - if _env_missing: - log.warning("Chat model: env var values missing.") - else: - raise ValueError("Chat model: model settings with a supported model provider not found.") - - model.max_retries = 3 - - log.info("model: ", model) - log.info("model_settings_collection: ", model_settings_collection) - - return model - - -@tracer.start_as_current_span(name="_get_embed_model") -def _get_embed_model(model_settings_collection: LlmUsageSettingsCollection) -> BaseEmbedding | None: - embedding_model = None - if model_settings_collection and model_settings_collection.model_usage_settings[ModelCapability.EMBEDDING]: - embedding_model_settings = model_settings_collection.model_usage_settings[ModelCapability.EMBEDDING] - _callback_manager = CallbackManager([OtelCallbackHandler(tracer_provider=trace.get_tracer_provider())]) - sc = embedding_model_settings.service_instance_config - with tracer.start_as_current_span(name=f"LangchainEmbedding.{sc.provider}"): - if sc.provider == ModelProvider.AZURE_OPENAI: - embedding_model = AzureOpenAIEmbedding( - model=sc.model_name, - azure_deployment=sc.model_deployment_name, # `deployment_name` is an alias - azure_endpoint=os.getenv("DOCQ_AZURE_OPENAI_API_BASE"), - api_key=os.getenv("DOCQ_AZURE_OPENAI_API_KEY1"), - # openai_api_type="azure", - api_version=os.getenv("DOCQ_AZURE_OPENAI_API_VERSION"), - callback_manager=_callback_manager, - ) - elif sc.provider == ModelProvider.OPENAI: - embedding_model = OpenAIEmbedding( - model=sc.model_name, - api_key=os.getenv("DOCQ_OPENAI_API_KEY"), - callback_manager=_callback_manager, - ) - elif sc.provider == ModelProvider.HUGGINGFACE_OPTIMUM_BAAI: - embedding_model = OptimumEmbedding( - folder_name=get_models_dir(sc.model_name), - callback_manager=_callback_manager, - ) - else: - # defaults - embedding_model = OpenAIEmbedding() - - return embedding_model - - -@tracer.start_as_current_span(name="_get_default_storage_context") -def _get_default_storage_context() -> StorageContext: - return StorageContext.from_defaults() - - -@tracer.start_as_current_span(name="_get_storage_context") -def _get_storage_context(space: SpaceKey) -> StorageContext: - return StorageContext.from_defaults(persist_dir=get_index_dir(space)) - - -@tracer.start_as_current_span(name="_get_service_context") -def _get_service_context(model_settings_collection: LlmUsageSettingsCollection) -> ServiceContext: - log.debug( - "EXPERIMENTS['INCLUDE_EXTRACTED_METADATA']['enabled']: %s", EXPERIMENTS["INCLUDE_EXTRACTED_METADATA"]["enabled"] - ) - log.debug("EXPERIMENTS['ASYNC_NODE_PARSER']['enabled']: %s", EXPERIMENTS["ASYNC_NODE_PARSER"]["enabled"]) - - _node_parser = None # use default node parser - if EXPERIMENTS["INCLUDE_EXTRACTED_METADATA"]["enabled"]: - _node_parser = _get_node_parser(model_settings_collection) - if EXPERIMENTS["ASYNC_NODE_PARSER"]["enabled"]: - log.debug("loading async node parser.") - # _node_parser = _get_async_node_parser(model_settings_collection) - else: - _callback_manager = CallbackManager([OtelCallbackHandler(tracer_provider=trace.get_tracer_provider())]) - _node_parser = SentenceSplitter.from_defaults(callback_manager=_callback_manager) - - return ServiceContext.from_defaults( - llm=_get_generation_model(model_settings_collection), - node_parser=_node_parser, - embed_model=_get_embed_model(model_settings_collection), - callback_manager=_node_parser.callback_manager, - context_window=model_settings_collection.model_usage_settings[ - ModelCapability.CHAT - ].service_instance_config.context_window_size, - num_output=256, # default in lama-index but we need to be explicit here because it's not being set everywhere. - ) - - -@tracer.start_as_current_span(name="_get_node_parser") -def _get_node_parser(model_settings_collection: LlmUsageSettingsCollection) -> NodeParser: - # metadata_extractor = MetadataExtractor( - # extractors=[ - - # KeywordExtractor(llm=_get_generation_model(model_settings_collection), keywords=5), - # EntityExtractor(label_entities=True, device="cpu"), - # # CustomExtractor() - # ], - # ) - _callback_manager = CallbackManager([OtelCallbackHandler(tracer_provider=trace.get_tracer_provider())]) - node_parser = SentenceSplitter.from_defaults() - - return node_parser # @tracer.start_as_current_span(name="_get_async_node_parser") @@ -263,13 +91,6 @@ def _get_node_parser(model_settings_collection: LlmUsageSettingsCollection) -> N # return node_parser -@tracer.start_as_current_span(name="_load_index_from_storage") -def _load_index_from_storage(space: SpaceKey, model_settings_collection: LlmUsageSettingsCollection) -> BaseIndex: - # set service context explicitly for multi model compatibility - sc = _get_service_context(model_settings_collection) - return load_index_from_storage( - storage_context=_get_storage_context(space), service_context=sc, callback_manager=sc.callback_manager - ) def get_hybrid_fusion_retriever_query( @@ -328,13 +149,23 @@ def run_chat( return output -@tracer.start_as_current_span(name="run_ask") def run_ask( input_: str, history: List[ChatMessage], model_settings_collection: LlmUsageSettingsCollection, assistant: Assistant, spaces: list[SpaceKey] | None = None, +) -> RESPONSE_TYPE | AGENT_CHAT_RESPONSE_TYPE: + return run_ask2(input_, history, model_settings_collection, assistant, spaces) + + +@tracer.start_as_current_span(name="run_ask") +def run_ask1( + input_: str, + history: List[ChatMessage], + model_settings_collection: LlmUsageSettingsCollection, + assistant: Assistant, + spaces: Optional[list[SpaceKey]] = None, ) -> RESPONSE_TYPE | AGENT_CHAT_RESPONSE_TYPE: """Ask questions against existing index(es) with history.""" log.debug("exec: runs_ask()") @@ -403,10 +234,165 @@ def run_ask( output = Response("You need to select a Space or upload a document to ask a questions.") span.add_event(name="ask_without_spaces", attributes={"question": input_, "answer": str(output)}) - return output +@tracer.start_as_current_span(name="run_ask2") +def run_ask2( + input_: str, + history: List[ChatMessage], + model_settings_collection: LlmUsageSettingsCollection, + assistant: Assistant, + spaces: Optional[list[SpaceKey]] = None, +) -> RESPONSE_TYPE | AGENT_CHAT_RESPONSE_TYPE: + """Implements logic of run_ask() using LlamaIndex query pipelines.""" + span = trace.get_current_span() + + try: + indices = [] + if spaces is not None and len(spaces) > 0: + indices = load_indices_from_storage(spaces, model_settings_collection) + # text_qa_template = llama_index_chat_prompt_template_from_assistant(assistant, history) + span.add_event(name="prompt_created") + similarity_top_k = 6 + vector_retriever = indices[0].as_retriever(similarity_top_k=similarity_top_k) + # retriever = get_hybrid_fusion_retriever_query(indices, model_settings_collection) + + bm25_retriever = BM25Retriever.from_defaults(docstore=indices[0].docstore, similarity_top_k=similarity_top_k) + + span.add_event(name="retriever_object_created", attributes={"retriever": vector_retriever.__class__.__name__}) + + # query_engine = RetrieverQueryEngine.from_args( + # retriever=retriever, + # service_context=_get_service_context(model_settings_collection), + # text_qa_template=text_qa_template, + # ) + span.add_event(name="query_engine__object_created") + + # output = query_engine.query(input_) + # span.add_event(name="query_executed") + except Exception as e: + span.set_status(status=Status(StatusCode.ERROR)) + span.record_exception(e) + raise Error(f"Error: {e}") from e + + + + service_context = _get_service_context(model_settings_collection) + llm = service_context.llm + + # First, we create an input component to capture the user query + input_component = InputComponent() + + # Next, we use the LLM to rewrite a user query + HYDE_TMPL = ( + "Please write a passage to answer the \n" + "Use the current conversation available in \n" + "Try to include as many key details as possible.\n" + "\n" + "\n" + "{chat_history_str}\n" + "\n" + "\n" + "{query_str}\n" + "\n" + "\n" + "\n" + 'Passage:"""\n' + ) + history_str = "\n".join([str(x) for x in history]) + hyde_template = PromptTemplate(template=HYDE_TMPL, prompt_type=PromptType.SUMMARY) + hyde_query_transform_component = HyDEQueryTransform( + llm=llm, hyde_prompt=hyde_template, prompt_args={"chat_history_str": history_str} + ).as_query_component() + + # we will retrieve two times, so we need to pack the retrieved nodes into a single list + # argpack_component = ArgPackComponent() + kwargpack_component = KwargPackComponent() + + rerank_component = FnComponent(fn=reciprocal_rank_fusion) + + response_component = ResponseWithChatHistory( + llm=llm, + system_prompt=assistant.system_prompt_content, + ) + + # define query pipeline + pipeline = QueryPipeline( + modules={ + "input": input_component, + "hyde_query_transform": hyde_query_transform_component, + "v_rewrite_retriever": vector_retriever, + "v_query_retriever": vector_retriever, + # "bm25_rewrite_retriever": bm25_retriever, + "bm25_query_retriever": bm25_retriever, + "join": kwargpack_component, + "RRF_reranker": rerank_component, + "response_component": response_component, + }, + verbose=False, + ) + + # transform the user query using the HyDE technique + pipeline.add_link("input", "hyde_query_transform", src_key="query_str", dest_key="query_str") + + # run multiple retrievals (note we don't do BM25 with the hallucinated query. in a new thread it doesn't make sense) + pipeline.add_link( + "hyde_query_transform", "v_rewrite_retriever" + ) # vector search with the *hallucinated* query (HyDE) + pipeline.add_link("input", "v_query_retriever", src_key="query_str") # vector search with the *original* query + pipeline.add_link("input", "bm25_query_retriever", src_key="query_str") # BM25 search with the *original* query + + # each input to the Kwargpack component needs a dest key -- it's the key on the dict so can be anything. + # then, the kwargpack component will pack all the inputs into a dict of lists of nodes called 'join'. It's intentionally this for RRF rerank algo to work. + pipeline.add_link("v_rewrite_retriever", "join", dest_key="v_rewrite_nodes") + pipeline.add_link("v_query_retriever", "join", dest_key="v_query_nodes") + pipeline.add_link("bm25_query_retriever", "join", dest_key="bm25_query_nodes") + + # RRF reranker needs the packed dict of node list from each retrieval + # TODO: add top k + pipeline.add_link("join", "RRF_reranker", dest_key="results") + + # synthesizer needs the reranked nodes, query str, and chat history + pipeline.add_link("RRF_reranker", "response_component", dest_key="nodes") + pipeline.add_link("input", "response_component", src_key="query_str", dest_key="query_str") + pipeline.add_link( + "input", + "response_component", + src_key="chat_history", + dest_key="chat_history", + ) + + # from pyvis.network import Network # -- poetry add pyvis + + # net = Network(notebook=True, cdn_resources="in_line", directed=True) + # net.from_nx(pipeline.dag) + # net.show("web/rag_dag.html") + + # output, intermediates = pipeline.run_with_intermediates(input_) + + output, intermediates = pipeline.run_with_intermediates( + query_str=input_, + chat_history=history, + chat_history_str=history_str, + callback_manager=service_context.callback_manager, + ) + + # # debug code + # for k, v in intermediates.items(): + # print(f"{AnsiColours.BLUE.value}>>{k}{AnsiColours.RESET.value}:") + # for ki, vi in v.inputs.items(): + # print(f"{AnsiColours.GREEN.value}>>>>in: {ki} ({type(vi).__name__}) {AnsiColours.RESET.value}") + # for ko, vo in v.outputs.items(): + # print(f"{AnsiColours.GREEN.value}>>>>out: {ko} ({type(vo).__name__}) {AnsiColours.RESET.value}") + + # print("ANSWER:", output.get("response", "blah!").message) + + return Response( + response=output.get("response", "blah!").message.content, source_nodes=output.get("source_nodes", []) + ) + + @tracer.start_as_current_span(name="_default_response") def _default_response() -> Response: """A default response incase of any failure.""" diff --git a/source/docq/support/store.py b/source/docq/support/store.py index 9b10fb54..1badce4c 100644 --- a/source/docq/support/store.py +++ b/source/docq/support/store.py @@ -9,9 +9,15 @@ from threading import Timer from typing import Optional +import docq +from llama_index.core.storage import StorageContext +from opentelemetry import trace + from ..config import ENV_VAR_DOCQ_DATA, OrganisationFeatureType, SpaceType from ..domain import SpaceKey +tracer = trace.get_tracer(__name__, docq.__version_str__) + class _StoreDir(Enum): """Storage directories. This is based on the type and format of storage system used like SQLite, index, and models.""" @@ -211,6 +217,15 @@ def _clean_public_chat_history() -> None: log.info("Removing public chat history for session %s", dir_) shutil.rmtree(dir_path) +@tracer.start_as_current_span(name="_get_storage_context") +def _get_storage_context(space: SpaceKey) -> StorageContext: + return StorageContext.from_defaults(persist_dir=get_index_dir(space)) + + +@tracer.start_as_current_span(name="_get_default_storage_context") +def _get_default_storage_context() -> StorageContext: + return StorageContext.from_defaults() + def _init() -> None: """Initialise storage.""" diff --git a/storage/default__vector_store.json b/storage/default__vector_store.json deleted file mode 100644 index c61dc8ff..00000000 --- a/storage/default__vector_store.json +++ /dev/null @@ -1 +0,0 @@ -{"embedding_dict": {"7d4c028e-42ef-41c0-be81-4b41cc03dd7b": [-0.06410515308380127, -0.019321341067552567, -0.04472411796450615, 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I t i s p o w e r e d\nb y a n L L M f i n e - t u n e d w i t h t h e f i r m ' s o w n\nd a t a , t h a n k s t o D o c q m a k i n g i t h a p p e n\ns e a m l e s s l y b e h i n d t h e s c e n e .", "start_char_idx": 0, "end_char_idx": 1326, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "0bd85f0d-f4ff-407e-986f-3638998ae81a": {"__data__": {"id_": "0bd85f0d-f4ff-407e-986f-3638998ae81a", "embedding": null, "metadata": {"page_label": "12", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": ["file_path", "space_id", "space_type", "data_source_name", "data_source_type", "source_uri", "indexed_on", "page_label", "file_name"], "excluded_llm_metadata_keys": ["file_path", "space_id", "space_type", "data_source_name", "data_source_type", "indexed_on"], "relationships": {"1": {"node_id": "41433003-b668-4032-9fd0-b5679b6842dd", "node_type": "4", "metadata": {"page_label": "12", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "hash": "b405cc782373a5446947d97750b46a94df9d6a061d463aeee7c2cd729a465682", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "c1140940-9b58-44b1-89fa-6ac0074e2d52", "node_type": "1", "metadata": {"page_label": "12", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "hash": "499d18f627f352b15783aab2b553a9f2f2d849538910593572ba83e59ea06c0d", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "c26188e6-be2b-4981-b3e9-6871d338b092", "node_type": "1", "metadata": {}, "hash": "2499dfad936dc032c5d5b7b051920cf65278501adc89c4a7a4d77c9cb9b11bb6", "class_name": "RelatedNodeInfo"}}, "text": "I t i s p o w e r e d\nb y a n L L M f i n e - 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s s o f t w a r e s p e n d i n g = 5 0 % o f\nt o t a l b u s i n e s s s o f t w a r e s p e n d i n g f r o m 1 0 % o f\nt h e w o r l d ' s w o r k f o r c e .\nS A M\n1 5 0 M s e a t s l i c e n s e d a t $ 1 0 0 p e r s e a t ;\nE q u i v a l e n t t o 1 / 8 o f G e n A I b u s i n e s s s o f t w a r e\ns p e n d i n g o r 3 / 8 o f G e n A I b u s i n e s s u s e r s .\nS O M\nE q u i v a l e n t t o 0 . 4 % o f t h e G e n A I b u s i n e s s\ns o f t w a r e s p e n d i n g o r 1 / 8 0 o f G e n A I b u s i n e s s\nu s e r s .5 M s e a t s l i c e n s e d a t $ 1 0 0 p e r s e a t / y r ;\n* Given the nature of predicted\nexplosive growth in GenAI, this\nfigure is estimated in a 10-year\ntimeframe.", "start_char_idx": 0, "end_char_idx": 835, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "68949a99-01e1-4a40-87b8-c2fccd373101": {"__data__": {"id_": 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"data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "hash": "7a90d537f3e0d05f7537d0d5bfd8f78b909da7fea01e6081d3c502b2040c3d7d", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "bb87d425-3b71-47e7-b021-89b775b991fe", "node_type": "1", "metadata": {}, "hash": "482a087218ba4055b7b160aa240039d6060654fb3fe2d90cd6cd1fb0c3850777", "class_name": "RelatedNodeInfo"}}, "text": "O N T H E H O R I Z O N\nThe proliferation of open-source LLMs\nand performance vs cost optimisation.\nImminent AI regulations and building\ncompliance features to capture the value.As a turnkey application, Docq is well-\npositioned to step into:", "start_char_idx": 0, "end_char_idx": 244, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", 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"start_char_idx": 0, "end_char_idx": 215, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "102de2ee-532a-45b3-9b62-8cd005957bae": {"__data__": {"id_": "102de2ee-532a-45b3-9b62-8cd005957bae", "embedding": null, "metadata": {"page_label": "20", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": ["file_path", "space_id", "space_type", "data_source_name", "data_source_type", "source_uri", 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{"page_label": "19", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "hash": "ba23f691a3550301ce5b9b39ab642d42731f1a483bea488cbec5a075ce3060d0", "class_name": "RelatedNodeInfo"}}, "text": "Thank you!\nC h e n W a n g | c h e n @ d o c q a i . c o m\nP r o d u c t W a l k - t h r o u g h\nG i t H u b - h t t p s : / / g i t h u b . c o m / d o c q a i / d o c q\nW e b s i t e - h t t p s : / / d o c q . a i\nJoin us in helping businesses with\nprivate & secure AI adoption.", "start_char_idx": 0, "end_char_idx": 289, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "7d4c028e-42ef-41c0-be81-4b41cc03dd7b": {"__data__": {"id_": "7d4c028e-42ef-41c0-be81-4b41cc03dd7b", "embedding": null, "metadata": {"page_label": "1", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "549914f3-05c6-41ec-8cbf-764bd44309fb", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text appears to be the cover or introductory page of a pitch deck for a platform called Docq.AI, which is dated September 2023. The text highlights that this platform is focused on providing AI-driven knowledge insights while emphasizing privacy and security. \n\nSome questions that this text may be designed to answer could include:\n\n1. What is Docq.AI?\n2. What services or solutions does Docq.AI offer?\n3. How does Docq.AI ensure the privacy and security of its users' data?\n4. What makes Docq.AI unique or different from other AI knowledge platforms?\n5. What are the use cases or applications for Docq.AI's knowledge insight capabilities?\n6. Who is the intended audience or customer base for Docq.AI?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "25d2a6c0-ede1-4b90-b765-ad7828a3c6e2": {"__data__": {"id_": "25d2a6c0-ede1-4b90-b765-ad7828a3c6e2", "embedding": null, "metadata": {"page_label": "2", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "212dbfbb-068f-4667-92cc-8c473eab2eec", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text appears to be an excerpt from a section titled \"MEET THE TEAM\" in a pitch deck for a company named Docq.AI. This section introduces two key individuals from the company's leadership team, detailing their roles within the company and their previous professional experiences.\n\nThe first individual mentioned is Chen Wang, who is the Co-Founder & CEO of the company. Wang's background includes being the Founding CTO at Bondsmith during its Series A stage, a Tech Lead at Wise when it went public, and the Founding CTO at Volcube, a company that was bootstrapped and later acquired. Wang is described as an experienced engineering leader and exited founder with extensive exposure in sales, recruitment, and product growth. Personal interests such as being an abstract thinker and a runner are also noted.\n\nThe second individual is Janaka Abeywardhana, who holds the position of Co-Founder & CPTO. Abeywardhana's experience includes roles such as Head of Product, Platforms at TrueLayer during its Series E round, and Product Director, Platform Eng at Hudl during its Series D round. Prior to these roles, Abeywardhana was the CTO at Axonn and Adfero, both of which were bootstrapped ventures. Abeywardhana is described as an experienced technical product leader with a strong infrastructure background and personal interests in old towns and exotic food.\n\nThe text can answer questions such as:\n\n1. Who are the key members of the Docq.AI leadership team?\n2. What is the professional background of Chen Wang?\n3. What roles has Janaka Abeywardhana held in his career?\n4. What are the interests and specialties of the co-founders of Docq.AI?\n5. What is the experience level of the team leading Docq.AI in terms of previous companies and stages of growth?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "c7a35a66-8dfe-42d2-8144-3052430e878f": {"__data__": {"id_": "c7a35a66-8dfe-42d2-8144-3052430e878f", "embedding": null, "metadata": {"page_label": "3", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "c4b66901-90d9-4e79-bbfc-c25c8330838e", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text appears to be a section from a presentation, specifically a pitch deck for a company or product named \"Docq.AI,\" dated September 2023. The pitch deck seems to be addressing the impact of generative AI on business applications, suggesting a shift or change in how businesses use artificial intelligence to generate content or data.\n\nThe mention of an \"Infographic\" indicates that there is a visual representation or summary of the information related to generative AI in businesses, which can be accessed via a provided link. This infographic likely elaborates on the ways generative AI is being integrated into business processes or how it is transforming industries.\n\nSome questions the text might answer include:\n- How is generative AI changing business applications?\n- What new capabilities does generative AI bring to businesses?\n- What are some examples of generative AI being used in business settings?\n- How can businesses adapt to the generative AI era?\n- What are the benefits and challenges of implementing generative AI in businesses?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "5986759b-54ba-4a28-9a43-a2c7bce32817": {"__data__": {"id_": "5986759b-54ba-4a28-9a43-a2c7bce32817", "embedding": null, "metadata": {"page_label": "4", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "388e523c-c7bc-4ec4-bfbf-32699209f37a", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text appears to be an excerpt from a pitch deck for a company named Docq.AI, dated September 2023, suggesting that businesses are currently anticipating guidance on their artificial intelligence strategies. This interest is particularly heightened due to a recent ban on the use of ChatGPT.\n\nSome questions that this text can help answer include:\n\n1. What is the current climate for AI strategy development in businesses?\n2. Why is there an increased interest in AI strategies among businesses now?\n3. What impact has the ban on ChatGPT had on business planning for AI?\n4. What alternatives to ChatGPT are businesses considering for their AI needs?\n5. How might Docq.AI fit into the changing landscape of AI tools and strategies for businesses?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "e7038c9d-9e65-4689-964b-f012adcd6001": {"__data__": {"id_": "e7038c9d-9e65-4689-964b-f012adcd6001", "embedding": null, "metadata": {"page_label": "5", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "46504198-aca3-4fb0-8c56-673b3b299193", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text is about a shift in how private, organizational data is being used to achieve unprecedented levels of productivity. This indicates that there's a novel approach or methodology in place that leverages this type of data in a way that significantly enhances how work is done within organizations.\n\nSome of the questions that this text can answer include:\n\n1. What new methods are organizations employing to improve productivity?\n2. How is private organizational data being utilized in contemporary business practices?\n3. What kind of productivity gains are being realized through the use of this data?\n4. Is there a new technology or system that enables these productivity improvements?\n5. What changes have been introduced in the handling of organizational data to drive these gains?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "8483d640-6d7f-467a-a4fd-f157ff14cb9a": {"__data__": {"id_": "8483d640-6d7f-467a-a4fd-f157ff14cb9a", "embedding": null, "metadata": {"page_label": "6", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "926d5fda-d36c-40cd-9958-f310b49b960a", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text appears to be from a pitch deck for an AI product named Docq.AI, which discusses the application of large language models (LLMs) in business environments. The text outlines a new paradigm where LLMs can act as an extension of employees' capabilities, effectively serving as a \"second brain\" by providing knowledge and insights. It highlights the challenges modern businesses face with the production of heterogeneous data across various sources and emphasizes the importance of centralizing this data to leverage AI and LLMs effectively.\n\nThe text then progresses to describe the necessity of content ingestion from various sources into a centralized data repository as a preliminary step for utilizing AI and LLMs in business. The next step involves offering private and secure generative features, such as a writing and coding copilot, which would be based on organizational data and likely powered by a fine-tuned LLM trained on private, organizational data.\n\nLastly, the text anticipates significant productivity gains from AI agents designed to operate securely and privately within business contexts, highlighting the importance of meeting data security, privacy, and regulatory compliance requirements.\n\nThe text can answer questions such as:\n\n1. How can LLMs provide immediate value to businesses?\n2. What role do LLMs play in helping employees discover knowledge and research insights?\n3. Why is centralizing data from different sources important for the application of AI in businesses?\n4. What is the envisioned role of generative AI features in the context of organizational data?\n5. How might businesses achieve productivity gains through the use of AI agents?\n6. What considerations are there regarding data security, privacy, and regulatory compliance when implementing AI solutions in businesses?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "54397411-dd72-4566-87a1-0ec438841e2e": {"__data__": {"id_": "54397411-dd72-4566-87a1-0ec438841e2e", "embedding": null, "metadata": {"page_label": "7", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6a839623-359f-4468-bb55-35f6509de29a", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text describes Docq as an open-source, self-hosted application situated within the domain of artificial intelligence. It seems to be aimed at users or organizations looking to adopt a new AI-driven paradigm. This text appears to be part of a pitch deck, which suggests that it is likely used to attract potential investors, partners, or users by outlining the key features and value proposition of Docq.\n\nSome of the questions that this text can answer include:\n\n1. What is the nature of Docq? (It is an open-source, self-hosted application.)\n2. What is the intended use of Docq? (It is designed for adopting a new AI-induced paradigm.)\n3. How is Docq made available to users? (As a turnkey application.)\n4. In what context is this information being presented? (Within a pitch deck, potentially for investment or partnership opportunities.)", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "e7f86d56-9bec-45a0-a1f0-22cca55e9d26": {"__data__": {"id_": "e7f86d56-9bec-45a0-a1f0-22cca55e9d26", "embedding": null, "metadata": {"page_label": "8", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "06f7938c-6532-4ed5-99c4-8959ce7295ca", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text outlines a vision for integrating large language models (LLMs) into business operations to enhance productivity and knowledge management. It suggests that LLMs can serve as an immediate value-add by functioning as a \"second brain\" for employees, assisting in discovering knowledge and research insights. It addresses the need for modern businesses to handle the increasing volume and frequency of heterogeneous data across various sources. The text highlights the importance of moving away from data silos and towards a centralized data index/repository, which is crucial for effectively utilizing AI and LLMs.\n\nFurther, it proposes the next step of offering private and secure generative features, such as a writing and coding copilot, which would be based on organizational data and likely powered by an LLM fine-tuned with private, organizational data. It concludes by anticipating massive productivity gains from AI agents designed for private and secure business use, which suggests a future where AI can autonomously perform actions that benefit businesses.\n\nSome questions that this text can answer include:\n1. What role can large language models play in supporting employees' work?\n2. Why is centralizing data from different sources important for leveraging AI in business?\n3. How can AI provide generative assistance in writing and coding tasks?\n4. What type of data is needed to train an LLM for organizational use?\n5. What potential productivity benefits can businesses expect from using AI agents?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "61e19561-bdb0-4ce3-9c3c-7d5387688179": {"__data__": {"id_": "61e19561-bdb0-4ce3-9c3c-7d5387688179", "embedding": null, "metadata": {"page_label": "9", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "86fef575-be88-435c-b340-f8ff2d79c044", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text appears to be from a pitch deck for a product named Docq.AI. This product is being positioned as a private and secure alternative to ChatGPT for use within businesses. It aims to help businesses overcome the challenges associated with knowledge silos by providing employees with a tool that can function as a 'second brain'. The application is presented as a turnkey solution that carries a low risk for the organization implementing it.\n\nThe text outlines a short-term mission as part of a go-to-market strategy, indicating that the focus is on immediate business needs and providing a means for employees to access organizational knowledge securely and privately.\n\nSome of the questions that this text might answer include:\n\n1. What is Docq.AI?\n2. What is the purpose of Docq.AI?\n3. How does Docq.AI aim to help businesses?\n4. What are the key features or benefits of Docq.AI?\n5. What is the intended market for Docq.AI?\n6. How does Docq.AI position itself compared to ChatGPT?\n7. What is the short-term mission of Docq.AI in its go-to-market strategy?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "75e61215-dd3b-481b-b97c-0e9d125da3e3": {"__data__": {"id_": "75e61215-dd3b-481b-b97c-0e9d125da3e3", "embedding": null, "metadata": {"page_label": "10", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "98149271-9287-4b5b-9525-32c316b02eb9", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text describes the objectives of a platform aimed at enabling developers and domain experts to create extensions and plugins. This platform aims to simplify the infrastructure and model layers to cater to specific sector or vertical needs. Additionally, the platform's long-term mission is to transform the way individuals interact with generative assistance and autonomous actions, ensuring that the platform remains compliant with regulatory standards.\n\nSome questions that the text can answer are:\n\n1. What is the platform's goal for developers and domain experts?\n2. How does the platform intend to simplify the development process?\n3. What kind of assistance and actions does the platform aim to influence?\n4. How does the platform plan to maintain regulatory compliance?\n5. What is the long-term vision of the platform for the future of work?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "3f5258e8-9b91-448c-8bd0-6ed86bd2c447": {"__data__": {"id_": "3f5258e8-9b91-448c-8bd0-6ed86bd2c447", "embedding": null, "metadata": {"page_label": "11", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "c3eae0ef-6789-485d-87d3-6e6c572178fa", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text appears to be a section from a pitch deck for a product named Docq, which is likely a software platform or tool designed to enhance workplace productivity. The text outlines several key features and anticipated developments of the product:\n\n1. The product offers a free pricing tier for its basic version, suggesting that it aims to be accessible with low initial and operational costs.\n2. Docq is described as providing a \"second brain\" for employees, implying that it functions as a smart assistant that can understand and interact with the user's business-specific needs. This might be similar to a chatbot or AI assistant that assists with information retrieval and task management.\n3. The product is designed to integrate easily with documents and requires minimal setup (\"Hook up a Space to some documents and it's ready to go\").\n4. There is anticipation that open-source Large Language Models (LLMs) will improve significantly in terms of performance and cost, suggesting that Docq might utilize or integrate such models for its functionality.\n5. Docq intends to offer the capability to fine-tune these models, making them more adaptable to the specific needs of various businesses.\n6. The platform will support generative assistance for writing and coding tasks, as well as the creation of autonomous actions or agents through plugins and APIs. This indicates that Docq aims to assist with creating content and automating workflows.\n\nQuestions that this text can answer include:\n- What kind of pricing model does Docq offer for its basic version?\n- What type of assistance does Docq provide to employees?\n- How does Docq integrate with existing documents or content?\n- What is the future outlook for LLMs in relation to Docq?\n- Will users be able to fine-tune the AI models used by Docq?\n- What kind of tasks can Docq assist with or automate?\n- Does Docq offer any extensibility options for developers or users to expand its capabilities?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "a90d8f5f-c89c-418d-a07c-325f0d07e1f2": {"__data__": {"id_": "a90d8f5f-c89c-418d-a07c-325f0d07e1f2", "embedding": null, "metadata": {"page_label": "12", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "41433003-b668-4032-9fd0-b5679b6842dd", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text describes a use case of Docq.AI within a law firm. The focus is on how employees at the firm can benefit from a secure and private AI assistant, akin to Chat GPT, to enhance their productivity. This AI assistant operates within the firm's existing Azure cloud environment, ensuring that no data is transmitted outside of it. Docq.AI offers an admin interface for monitoring and controlling employee access to AI features. It has capabilities such as real-time auto-completion and suggestion features for drafting legal documents, and autonomous actions to complete routine tasks during and after client meetings. The system is powered by a Large Language Model (LLM) that has been fine-tuned with the firm's own data, facilitating a seamless integration.\n\nSome questions that the text can answer include:\n\n1. How does Docq.AI ensure the privacy and security of a law firm\u2019s data?\n2. What cloud service does the law firm use in conjunction with Docq.AI?\n3. Is any data from Docq.AI transmitted outside of the firm's cloud account?\n4. Can the law firm use third-party AI products or APIs integrated with third-party AI models?\n5. What kind of features does Docq.AI provide to law firm employees?\n6. How does Docq.AI enhance the drafting of legal documents?\n7. What kind of administrative controls does Docq.AI offer for managing employee access to AI features?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "9cdf50b2-01f2-4d93-9447-ac5262b11432": {"__data__": {"id_": "9cdf50b2-01f2-4d93-9447-ac5262b11432", "embedding": null, "metadata": {"page_label": "13", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "f060d616-6e85-41ca-b664-988d7242c589", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text is from a pitch deck for a product called Docq. It discusses the importance of businesses being able to securely access and gain insights from the vast amounts of documents they store electronically. The text highlights the challenges of data security, privacy, and regulatory compliance in the context of leveraging artificial intelligence and large language models (AI/LLMs) for business productivity. It emphasizes the need for careful planning when adopting these technologies to prevent data leakage to third-party vendors.\n\nThe text also outlines how the product can deliver instant value to users with minimal maintenance and without the need for lengthy training. It suggests that Docq offers generative assistance and autonomous actions to improve productivity, and that it is designed for easy adoption by businesses, allowing them to get started within minutes. Furthermore, the product is described as extensible, with support for plugins and APIs, indicating its adaptability and readiness for future advancements.\n\nQuestions that this text can answer include:\n\n1. What challenges do businesses face when trying to extract insights from their electronic documents?\n2. How does Docq ensure the security and privacy of business data?\n3. What are the potential risks of using third-party AI/LLM vendors?\n4. How does Docq propose to deliver immediate value to its users?\n5. What measures does Docq take to make it easy for businesses to adopt its technology?\n6. How does Docq help businesses with the adoption of AI/LLMs?\n7. What features does Docq offer to ensure it is future-proof and adaptable?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "be3fabbb-2c8d-44f9-996f-fc441acd041a": {"__data__": {"id_": "be3fabbb-2c8d-44f9-996f-fc441acd041a", "embedding": null, "metadata": {"page_label": "14", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "af3d59bc-ff74-4f01-b61a-67ce212bbecf", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text appears to be a list of product features from a pitch deck for a software named Docq.AI. The features include:\n\n- Multi-user access control: This suggests that the software allows for different levels of access for different users.\n- Spaces as data compartmentation: This indicates that the software uses \"spaces\" to segregate and manage data.\n- Familiar chat User Interface: The software likely has a user interface that is similar to common chat applications, which could make it intuitive to use.\n- Cloud-hosted and self-hosted models: The product can be hosted on the cloud or on the user's own servers.\n- Support for multiple Machine Learning (ML) models: This suggests that the software can integrate with or use various ML models for its functionality.\n- Loom Video: Space-based Chat UI: This could refer to a feature within the software that utilizes Loom Video integration for a chat interface that is organized by \"spaces.\"\n- Battery-included Azure/AWS/GCP deployment help: This likely means that the software comes with built-in support to help with deploying on major cloud platforms like Azure, AWS, or GCP.\n- Design for easy onboarding and operating: The software is designed to be easy for new users to start using and for ongoing operation.\n\nSome of the questions that this text can answer include:\n- Does Docq.AI allow multiple users to work on it simultaneously?\n- How does Docq.AI manage and organize its data?\n- What does the user interface of Docq.AI look like?\n- Where can Docq.AI be hosted?\n- Can Docq.AI work with different machine learning models?\n- Does Docq.AI offer video-based communication features?\n- Does Docq.AI provide assistance for deployment on major cloud services?\n- Is the software user-friendly for new users and easy to operate?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "a3b165c2-8be5-4c00-8b63-4d3e8ff3d519": {"__data__": {"id_": "a3b165c2-8be5-4c00-8b63-4d3e8ff3d519", "embedding": null, "metadata": {"page_label": "15", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "eb67a438-abc4-47f8-9321-63026bbd6377", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text appears to be a snippet from a pitch deck of a company called Docq.AI, dated September 2023. The text discusses the competitive landscape in the field of artificial intelligence, particularly focusing on the opportunity for AI-first or native applications to compete with established players in the industry. It mentions that while incumbents are leveraging their large distribution networks, they often have only partial access to data repositories. This scenario suggests that there is a window of opportunity for AI-native applications to enter the market and offer Generation AI (GenAI) products, despite the fast pace at which established companies are moving.\n\nSome of the questions that this text could help answer include:\n1. What is the current competitive environment like in the AI industry?\n2. Do new AI companies have an opportunity to compete against established players?\n3. What challenges do incumbent companies face in the AI space?\n4. What is the significance of having full access to data repositories in AI development?\n5. What are GenAI products, and how are they relevant to the market competition?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "cb986916-4f56-443a-8af5-376356b82cff": {"__data__": {"id_": "cb986916-4f56-443a-8af5-376356b82cff", "embedding": null, "metadata": {"page_label": "16", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "8f2d758f-bc69-45c8-88bc-23080fda333c", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text appears to be part of a table from a pitch deck comparing different types of artificial intelligence or software products in terms of various attributes. It compares \"Developer Tools\" and \"Enterprise Apps\" with examples provided for each\u2014LangChain and LlamaIndex for Developer Tools, and Sana AI OSS turnkey app + platform for Enterprise Apps.\n\nKey aspects compared include:\n- Open Source Delivery: Whether the products are open-source.\n- Costs: Relative cost of using the products.\n- Audience: The target audience for each product type.\n- Enterprise Ready: If the product is ready for use in an enterprise setting.\n- Extensibility: The ability to extend or customize the product.\n- Time to value: How quickly one can realize value from the product.\n\nSome questions that this text can answer include:\n- Are the tools mentioned open-source?\n- What is the cost comparison between developer tools and enterprise apps?\n- Who are the intended users for each type of tool?\n- Are these products suitable for enterprise-level deployment?\n- How customizable are these tools?\n- How quickly can a user expect to see value from using these tools?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "7d2c05ee-a390-47b2-8e81-07945ab45e5f": {"__data__": {"id_": "7d2c05ee-a390-47b2-8e81-07945ab45e5f", "embedding": null, "metadata": {"page_label": "17", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "49d4ae96-4b7f-46cd-b192-46efe6738bc2", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text is a section from a pitch deck for a company named Docq.AI. It is discussing the market size and potential revenue opportunities for their product or service within the general AI (Gen AI) business software market. The text presents three key metrics commonly used in business planning and investment discussions:\n\n1. TAM (Total Addressable Market): The TAM is given as $120 billion, which represents the entire revenue opportunity available if the product or service were to be adopted by the entire market for Gen AI business software.\n\n2. SAM (Serviceable Available Market): The SAM is specified as $15 billion and is defined by the portion of the TAM that can be captured by the company's current business model or capabilities. It is further detailed as 150 million seats (user licenses) at $100 per seat, which implies that the product is a software solution requiring licensing.\n\n3. SOM (Serviceable Obtainable Market): The SOM is the segment of the SAM that the company realistically believes it can capture. It is noted as being equivalent to 0.4% of the Gen AI business software spending or 1/80 of Gen AI business users. This equates to 5 million seats licensed at $100 per seat per year.\n\nThe text also indicates that the Gen AI business software spending accounts for 50% of the total business software spending from 10% of the world's workforce. Additionally, there's a note suggesting that the $120 billion TAM is estimated over a 10-year timeframe due to expected explosive growth in the Gen AI sector.\n\nSome questions that this text can answer include:\n\n- What is the estimated total market size for Gen AI business software?\n- What percentage of the total business software spending does Gen AI represent?\n- What is the potential revenue from the Serviceable Available Market for the company's product?\n- How many user licenses does the company aim to sell, and at what price point?\n- What fraction of the overall Gen AI business software spending does the company's Serviceable Obtainable Market represent?\n- What growth is expected in the Gen AI sector over the next decade?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "0207b3a2-0cc0-4097-b467-c26851da7233": {"__data__": {"id_": "0207b3a2-0cc0-4097-b467-c26851da7233", "embedding": null, "metadata": {"page_label": "18", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "24a8d298-963e-4938-8b2a-714c94e57fac", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text appears to be a section from a pitch deck for an application named Docq. It discusses future considerations and strategic positioning for the application. The text outlines two key upcoming trends in the technology landscape:\n\n1. The increasing availability of open-source Large Language Models (LLMs), emphasizing the need to balance performance with cost.\n2. The anticipation of new AI regulations, highlighting the importance of building in compliance features to leverage upcoming changes in the legal framework.\n\nGiven these trends, the text suggests that Docq is in a good position to take advantage of these developments as a \"turnkey\" solution, which implies that it is ready to use without further installation or configuration.\n\nSome questions that the text can answer include:\n\n- What are some emerging trends in the AI industry that Docq.AI is preparing for?\n- How is Docq.AI positioning itself in response to the growth of open-source LLMs?\n- What strategic moves is Docq.AI planning to make in anticipation of new AI regulations?\n- What advantages does Docq.AI offer as a turnkey application in the context of these trends?\n- Why might it be important for an AI application to focus on compliance features going forward?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "2bce4e5c-8625-48f7-a71b-bab2e0a64837": {"__data__": {"id_": "2bce4e5c-8625-48f7-a71b-bab2e0a64837", "embedding": null, "metadata": {"page_label": "19", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6cbf6e51-1ecb-4f29-92a7-0bcf7a2fda56", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text appears to be a snapshot from a pitch deck for a company named Docq.AI. The text outlines key financial and operational details related to a fundraising initiative. Specifically, the company is raising between $1 million to $1.2 million to fund its operations for a duration of 18 months. The team size is noted to be between 6 to 8 people. Furthermore, the pitch highlights that the company's services are aimed at businesses, with over 1,000 clients (indicated by \"> 1K businesses\") and a monthly recurring revenue (MRR) of over $50,000.\n\nThe company's platform focuses on providing \"Generative Assistance,\" \"Autonomous Actions,\" and ensuring \"Regulatory Compliance\" for its clients.\n\nSome questions that this text can answer include:\n- How much funding is Docq.AI looking to raise?\n- What is the intended runway duration for the funds being raised?\n- How many people are on the Docq.AI team?\n- What is the company's current monthly recurring revenue?\n- How many businesses does Docq.AI currently serve?\n- What are the key features or services provided by the Docq.AI platform?", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "394f0972-e1d0-4bd8-9e0c-11284e44f18c": {"__data__": {"id_": "394f0972-e1d0-4bd8-9e0c-11284e44f18c", "embedding": null, "metadata": {"page_label": "20", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "ca86892b-5aaa-4277-99a1-b6ad258a4290", "node_type": null, "metadata": {}, "hash": null, "class_name": "RelatedNodeInfo"}}, "text": "The provided text appears to be contact and informational content from a pitch deck for a company named Docq.AI. The text includes the name and email address of an individual, Chen Wang, who is likely associated with Docq.AI, possibly in a role that could involve product demonstrations or client interactions, as indicated by the term \"Product Walk-through.\" The text also includes web resources for additional information or interaction with the company, such as a GitHub URL where one might find code repositories or projects related to Docq.AI and a website URL for the company's homepage.\n\nThis text can answer questions such as:\n- Who can I contact at Docq.AI for more information or a product demonstration?\n- Where can I find the code or repositories related to Docq.AI's projects?\n- What is the website to visit for more information about Docq.AI?\n- How can I get involved with Docq.AI's mission to assist businesses with private and secure AI adoption?\n\nAdditionally, the invitation to \"Join us\" suggests that the company might be looking for partners, collaborators, or possibly new team members interested in their mission.", "start_char_idx": null, "end_char_idx": null, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}}, "docstore/ref_doc_info": {"549914f3-05c6-41ec-8cbf-764bd44309fb": {"node_ids": ["023dd416-7918-4742-bdf1-ece6e5e44d6b", "7d4c028e-42ef-41c0-be81-4b41cc03dd7b"], "metadata": {"page_label": "1", "file_name": "Docq.AI Pitch Deck, Sept 2023.pdf", "file_path": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "space_id": 5, "space_type": "SHARED", "data_source_name": "Manual Upload", "data_source_type": "SpaceDataSourceFileBased", "source_uri": "/Users/janakaabeywardhana/code-projects/docqai/docq-workspace/docq/.persisted/upload/SHARED/1000/5/Docq.AI Pitch Deck, Sept 2023.pdf", "indexed_on": 1711307184.053143}}, "212dbfbb-068f-4667-92cc-8c473eab2eec": {"node_ids": 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unittest.mock import MagicMock, Mock, patch from docq.config import SpaceType from docq.domain import SpaceKey -from docq.manage_spaces import _create_vector_index, _persist_index +from docq.manage_spaces import _create_vector_index, _persist_index, reindex from docq.model_selection.main import ( LLM_SERVICE_INSTANCES, LlmUsageSettings, LlmUsageSettingsCollection, ModelCapability, + get_model_settings_collection, ) from docq.support import llm +from llama_index.core.indices import VectorStoreIndex from llama_index.core.schema import Document class TestCreateIndex(unittest.TestCase): - def setUp(self) -> None: - logging.getLogger().setLevel(logging.ERROR) + """Integration tests for indexing related functionality.""" - self.MODEL_SETTINGS_COLLECTION = LlmUsageSettingsCollection( - name="Azure OpenAI wth Local Embedding", - key="azure_openai_with_local_embedding", - model_usage_settings={ - ModelCapability.CHAT: LlmUsageSettings( - model_capability=ModelCapability.CHAT, - temperature=0.7, - service_instance_config=LLM_SERVICE_INSTANCES["azure-openai-gpt35turbo"], - ), - ModelCapability.EMBEDDING: LlmUsageSettings( - model_capability=ModelCapability.EMBEDDING, - service_instance_config=LLM_SERVICE_INSTANCES["optimum-bge-small-en-v1.5"], - ), - }, - ) + def setUp(self: Self) -> None: + logging.getLogger().setLevel(logging.ERROR) llm._init_local_models() # download local models. this is run on app setup. def test_create_vector_index( - self, - ): + self: Self, + ) -> None: + """Test creating a vector index. Indexes docs using local model. then checks retrieval.""" # Arrange document1 = Document(doc_id="1", text="This is the first document.") document2 = Document(doc_id="2", text="This is the second document.") documents = [document1, document2] - model_settings_collection = self.MODEL_SETTINGS_COLLECTION + model_settings_collection = get_model_settings_collection("azure_openai_with_local_embedding") # Act # result_index = _create_document_summary_index(documents, model_settings_collection) @@ -58,3 +49,43 @@ def test_create_vector_index( # Assert assert len(result_nodes) == 2 assert result_nodes[0].text == "This is the first document." + + # def test_reindex_vector_index() -> None: + # """Test reindex. Check's that _persist_index() is called.""" + # from docq.manage_spaces import reindex + + # with patch("docq.manage_indices._persist_index") as mock_persist_index, patch( + # "docq.manage_indices._create_vector_index" + # ) as mock_create_vector_index, patch( + # "docq.manage_spaces.get_space_data_source" + # ) as mock_get_space_data_source, patch( + # "docq.data_source.manual_upload.ManualUpload.load" + # ) as mock_ManualUpload_load, patch( # noqa: N806 + # "docq.manage_spaces.get_saved_model_settings_collection" # note the reference to the file where the function is called, not defined. + # ) as mock_get_saved_model_settings_collection, patch( + # "docq.manage_spaces.SpaceDataSources", new_callable=MagicMock + # ) as mock_SpaceDataSources: + # # ... + + # mock_vector_index = Mock(VectorStoreIndex) + # mock_create_vector_index.return_value = mock_vector_index + + # mock_get_space_data_source.return_value = ("MANUAL_UPLOAD", {}) + + # # mock_ManualUpload_load.return_value = [ + + # # ] + + # mock_SpaceDataSources.__getitem__.return_value.value.load.return_value = [ + # Document(doc_id="testid", text="test", extra_info={"source_uri": "https://example.com}"}) + # ] + + # arg_space_key = SpaceKey(SpaceType.SHARED, 1234, 4567, "this is a test space with mocked data") + + # mock_get_saved_model_settings_collection.return_value = get_model_settings_collection( + # "azure_openai_with_local_embedding" + # ) + # # print({mock_get_saved_model_settings_collection.return_value.__str__()}) + # reindex(arg_space_key) + + # mock_persist_index.assert_called_once_with(mock_vector_index, arg_space_key) diff --git a/tests/unit/docq/manage_spaces_test.py b/tests/unit/docq/manage_spaces_test.py index 52cfc111..a57b97dc 100644 --- a/tests/unit/docq/manage_spaces_test.py +++ b/tests/unit/docq/manage_spaces_test.py @@ -8,11 +8,10 @@ from unittest.mock import MagicMock, Mock, patch import pytest +from docq import manage_spaces from docq.access_control.main import SpaceAccessor, SpaceAccessType from docq.config import SpaceType from docq.domain import SpaceKey -from docq.model_selection.main import ModelCapability, get_model_settings_collection -from llama_index.core.indices import VectorStoreIndex from llama_index.core.schema import Document TEST_ORG_ID = 1000 @@ -29,7 +28,7 @@ def manage_spaces_test_dir() -> Generator: sqlite_system_file = f"{temp_dir}/sql_system.db" mock_get_sqlite_shared_system_file.return_value = sqlite_system_file - _init() + manage_spaces._init() yield temp_dir, sqlite_system_file, mock_get_sqlite_shared_system_file @@ -59,63 +58,57 @@ def test_db_init(manage_spaces_test_dir: tuple) -> None: result = cursor.fetchone() assert result is not None, f"Table {table} not found." - -@patch("docq.manage_spaces._get_default_storage_context") -@patch("docq.manage_spaces._get_service_context") -def test_create_index(get_service_context: MagicMock, get_default_storage_context: MagicMock) -> None: - """Test create index.""" - from docq.manage_spaces import _create_vector_index - - with patch("docq.manage_spaces.VectorStoreIndex", Mock(from_documents=MagicMock())): - documents = Mock([Document]) - model_settings_collection = Mock() - mocked_model_usage_settings = Mock() - mocked_model_usage_settings.additional_args = {"arg1": "value1", "arg2": "value2"} - model_settings_collection.model_usage_settings = {ModelCapability.CHAT: mocked_model_usage_settings} - _create_vector_index(documents, model_settings_collection) - - get_service_context.assert_called_once_with(model_settings_collection) - get_default_storage_context.assert_called_once() - - -def test_reindex_vector_index() -> None: - """Test reindex.""" - from docq.manage_spaces import reindex - - with patch("docq.manage_spaces._persist_index") as mock_persist_index, patch( - "docq.manage_spaces._create_vector_index" - ) as mock_create_vector_index, patch( - "docq.manage_spaces.get_space_data_source" - ) as mock_get_space_data_source, patch( - "docq.data_source.manual_upload.ManualUpload.load" - ) as mock_ManualUpload_load, patch( # noqa: N806 - "docq.manage_spaces.get_saved_model_settings_collection" # note the reference to the file where the function is called, not defined. - ) as mock_get_saved_model_settings_collection: # noqa: N806 - # mock_index = Mock(DocumentSummaryIndex) - # mock_create_document_summary_index.return_value = mock_index - - mock_vector_index = Mock(VectorStoreIndex) - mock_create_vector_index.return_value = mock_vector_index - - mock_get_space_data_source.return_value = ("MANUAL_UPLOAD", {}) - - mock_ManualUpload_load.return_value = [ - Document(doc_id="testid", text="test", extra_info={"source_uri": "https://example.com}"}) + # @patch("docq.support.store._get_default_storage_context") + # @patch("docq.model_selection.main._get_service_context") + # def test_create_index(get_service_context: MagicMock, get_default_storage_context: MagicMock) -> None: + # """Test create index.""" + # from docq.manage_spaces import _create_vector_index + + # with patch("llama_index.core.VectorStoreIndex", Mock(from_documents=MagicMock())): + # documents = [Document(text=f"Document {_}") for _ in range(10)] + # # model_settings_collection = Mock() + # # mocked_model_usage_settings = Mock() + # # mocked_model_usage_settings.additional_args = {"arg1": "value1", "arg2": "value2"} + # model_settings_collection = get_model_settings_collection( + # "azure_openai_latest" + # ) # {ModelCapability.CHAT: mocked_model_usage_settings} + # _create_vector_index(documents, model_settings_collection) + + # get_service_context.assert_called_once_with(model_settings_collection) + # get_default_storage_context.assert_called_once() + + @patch("docq.manage_spaces._persist_index") + @patch("docq.manage_spaces._create_vector_index") + @patch("docq.manage_spaces.get_saved_model_settings_collection") + @patch("docq.manage_spaces.get_space_data_source") + @patch("docq.manage_spaces.SpaceDataSources") + def test_reindex( + self, + mock_SpaceDataSources, + mock_get_space_data_source, + mock_get_saved_model_settings_collection, + mock_create_vector_index, + mock_persist_index, + ): + # Arrange + mock_space = MagicMock(spec=SpaceKey, id_="test_id", org_id="test_org_id") + mock_get_space_data_source.return_value = ("ds_type", "ds_configs") + mock_SpaceDataSources.__getitem__.return_value.value.load.return_value = [ + Document(doc_id="testid", text="test", extra_info={"source_uri": "https://example.com"}) ] + mock_create_vector_index.return_value = "vector_index" - arg_space_key = SpaceKey(SpaceType.SHARED, 1234, 4567, "this is a test space with mocked data") - - mock_get_saved_model_settings_collection.return_value = get_model_settings_collection("openai_latest") - # print({mock_get_saved_model_settings_collection.return_value.__str__()}) - reindex(arg_space_key) + # Act + manage_spaces.reindex(mock_space) - mock_persist_index.assert_called_once_with(mock_vector_index, arg_space_key) + # Assert + mock_persist_index.assert_called_once_with("vector_index", mock_space) -@patch("docq.manage_spaces.get_index_dir") +@patch("docq.manage_indices.get_index_dir") def test_persist_index(get_index_dir: MagicMock) -> None: """Test persist index.""" - from docq.manage_spaces import _persist_index + from docq.manage_indices import _persist_index def _persist (persist_dir: str) -> None: with open(persist_dir, "w") as f: diff --git a/web/api/threads_handler.py b/web/api/threads_handler.py index d3185faa..ef2c94a5 100644 --- a/web/api/threads_handler.py +++ b/web/api/threads_handler.py @@ -5,7 +5,8 @@ import docq.run_queries as rq from docq.domain import SpaceKey from docq.model_selection.main import LlmUsageSettingsCollection, get_saved_model_settings_collection -from docq.support.llm import _get_service_context, _get_storage_context +from docq.support.llm import _get_service_context +from docq.support.store import _get_storage_context from llama_index.core.indices import DocumentSummaryIndex, load_index_from_storage from llama_index.core.indices.base import BaseIndex from pydantic import ValidationError @@ -157,8 +158,10 @@ class TopQuestionsHandler(BaseRequestHandler): def _load_index(self: Self, space: SpaceKey, model_settings_collection: LlmUsageSettingsCollection) -> BaseIndex: """Load index from storage.""" - storage_context = _get_storage_context(space) - service_context = _get_service_context(model_settings_collection) + storage_context = _get_storage_context(space) # FIXME: _get_storage_context should not be called directly here. + service_context = _get_service_context( + model_settings_collection + ) # FIXME: _get_storage_context should not be called directly here. return load_index_from_storage(storage_context=storage_context, service_context=service_context) def get_summary_questions(self: Self, thread_space: SpaceKey) -> dict: