Next version of PrivateGPT (#1077)

* Dockerize private-gpt

* Use port 8001 for local development

* Add setup script

* Add CUDA Dockerfile

* Create README.md

* Make the API use OpenAI response format

* Truncate prompt

* refactor: add models and __pycache__ to .gitignore

* Better naming

* Update readme

* Move models ignore to it's folder

* Add scaffolding

* Apply formatting

* Fix tests

* Working sagemaker custom llm

* Fix linting

* Fix linting

* Enable streaming

* Allow all 3.11 python versions

* Use llama 2 prompt format and fix completion

* Restructure (#3)

Co-authored-by: Pablo Orgaz <pablo@Pablos-MacBook-Pro.local>

* Fix Dockerfile

* Use a specific build stage

* Cleanup

* Add FastAPI skeleton

* Cleanup openai package

* Fix DI and tests

* Split tests and tests with coverage

* Remove old scaffolding

* Add settings logic (#4)

* Add settings logic

* Add settings for sagemaker

---------

Co-authored-by: Pablo Orgaz <pablo@Pablos-MacBook-Pro.local>

* Local LLM (#5)

* Add settings logic

* Add settings for sagemaker

* Add settings-local-example.yaml

* Delete terraform files

* Refactor tests to use fixtures

* Join deltas

* Add local model support

---------

Co-authored-by: Pablo Orgaz <pablo@Pablos-MacBook-Pro.local>

* Update README.md

* Fix tests

* Version bump

* Enable simple llamaindex observability (#6)

* Enable simple llamaindex observability

* Improve code through linting

* Update README.md

* Move to async (#7)

* Migrate implementation to use asyncio

* Formatting

* Cleanup

* Linting

---------

Co-authored-by: Pablo Orgaz <pablo@Pablos-MacBook-Pro.local>

* Query Docs and gradio UI

* Remove unnecessary files

* Git ignore chromadb folder

* Async migration + DI Cleanup

* Fix tests

* Add integration test

* Use fastapi responses

* Retrieval service with partial implementation

* Cleanup

* Run formatter

* Fix types

* Fetch nodes asynchronously

* Install local dependencies in tests

* Install ui dependencies in tests

* Install dependencies for llama-cpp

* Fix sudo

* Attempt to fix cuda issues

* Attempt to fix cuda issues

* Try to reclaim some space from ubuntu machine

* Retrieval with context

* Fix lint and imports

* Fix mypy

* Make retrieval API a POST

* Make Completions body a dataclass

* Fix LLM chat message order

* Add Query Chunks to Gradio UI

* Improve rag query prompt

* Rollback CI Changes

* Move to sync code

* Using Llamaindex abstraction for query retrieval

* Fix types

* Default to CONDENSED chat mode for contextualized chat

* Rename route function

* Add Chat endpoint

* Remove webhooks

* Add IntelliJ run config to gitignore

* .gitignore applied

* Sync chat completion

* Refactor total

* Typo in context_files.py

* Add embeddings component and service

* Remove wrong dataclass from IngestService

* Filter by context file id implementation

* Fix typing

* Implement context_filter and separate from the bool use_context in the API

* Change chunks api to avoid conceptual class of the context concept

* Deprecate completions and fix tests

* Remove remaining dataclasses

* Use embedding component in ingest service

* Fix ingestion to have multipart and local upload

* Fix ingestion API

* Add chunk tests

* Add configurable paths

* Cleaning up

* Add more docs

* IngestResponse includes a list of IngestedDocs

* Use IngestedDoc in the Chunk document reference

* Rename ingest routes to ingest_router.py

* Fix test working directory for intellij

* Set testpaths for pytest

* Remove unused as_chat_engine

* Add .fleet ide to gitignore

* Make LLM and Embedding model configurable

* Fix imports and checks

* Let local_data folder exist empty in the repository

* Don't use certain metadata in LLM

* Remove long lines

* Fix windows installation

* Typos

* Update poetry.lock

* Add TODO for linux

* Script and first version of docs

* No jekill build

* Fix relative url to openapi json

* Change default docs values

* Move chromadb dependency to the general group

* Fix tests to use separate local_data

* Create CNAME

* Update CNAME

* Fix openapi.json relative path

* PrivateGPT logo

* WIP OpenAPI documentation metadata

* Add ingest script (#11)

* Add ingest script

* Fix broken name refactor

* Add ingest docs and Makefile script

* Linting

* Move transformers to main dependency

* Move torch to main dependencies

* Don't load HuggingFaceEmbedding in tests

* Fix lint

---------

Co-authored-by: Pablo Orgaz <pablo@Pablos-MacBook-Pro.local>

* Rename file to camel_case

* Commit settings-local.yaml

* Move documentation to public docs

* Fix docker image for linux

* Installation and Running the Server documentation

* Move back to docs folder, as it is the only supported by github pages

* Delete CNAME

* Create CNAME

* Delete CNAME

* Create CNAME

* Improved API documentation

* Fix lint

* Completions documentation

* Updated openapi scheme

* Ingestion API doc

* Minor doc changes

* Updated openapi scheme

* Chunks API documentation

* Embeddings and Health API, and homogeneous responses

* Revamp README with new skeleton of content

* More docs

* PrivateGPT logo

* Improve UI

* Update ingestion docu

* Update README with new sections

* Use context window in the retriever

* Gradio Documentation

* Add logo to UI

* Include Contributing and Community sections to README

* Update links to resources in the README

* Small README.md updates

* Wrap lines of README.md

* Don't put health under /v1

* Add copy button to Chat

* Architecture documentation

* Updated openapi.json

* Updated openapi.json

* Updated openapi.json

* Change UI label

* Update documentation

* Add releases link to README.md

* Gradio avatar and stop debug

* Readme update

* Clean old files

* Remove unused terraform checks

* Update twitter link.

* Disable minimum coverage

* Clean install message in README.md

---------

Co-authored-by: Pablo Orgaz <pablo@Pablos-MacBook-Pro.local>
Co-authored-by: Iván Martínez <ivanmartit@gmail.com>
Co-authored-by: RubenGuerrero <ruben.guerrero@boopos.com>
Co-authored-by: Daniel Gallego Vico <daniel.gallego@bq.com>

private_gpt/components/vector_store/vector_store_component.py

@@ -0,0 +1,61 @@
+import typing
+
+import chromadb
+from injector import inject, singleton
+from llama_index import VectorStoreIndex
+from llama_index.indices.vector_store import VectorIndexRetriever
+from llama_index.vector_stores import ChromaVectorStore
+from llama_index.vector_stores.types import VectorStore
+
+from private_gpt.open_ai.extensions.context_filter import ContextFilter
+from private_gpt.paths import local_data_path
+
+
+@typing.no_type_check
+def _chromadb_doc_id_metadata_filter(
+    context_filter: ContextFilter | None,
+) -> dict | None:
+    if context_filter is None or context_filter.docs_ids is None:
+        return {}  # No filter
+    elif len(context_filter.docs_ids) < 1:
+        return {"doc_id": "-"}  # Effectively filtering out all docs
+    else:
+        doc_filter_items = []
+        if len(context_filter.docs_ids) > 1:
+            doc_filter = {"$or": doc_filter_items}
+            for doc_id in context_filter.docs_ids:
+                doc_filter_items.append({"doc_id": doc_id})
+        else:
+            doc_filter = {"doc_id": context_filter.docs_ids[0]}
+        return doc_filter
+
+
+@singleton
+class VectorStoreComponent:
+    vector_store: VectorStore
+
+    @inject
+    def __init__(self) -> None:
+        db = chromadb.PersistentClient(
+            path=str((local_data_path / "chroma_db").absolute())
+        )
+        chroma_collection = db.get_or_create_collection(
+            "make_this_parameterizable_per_api_call"
+        )  # TODO
+
+        self.vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
+
+    @staticmethod
+    def get_retriever(
+        index: VectorStoreIndex,
+        context_filter: ContextFilter | None = None,
+        similarity_top_k: int = 2,
+    ) -> VectorIndexRetriever:
+        # TODO this 'where' is specific to chromadb. Implement other vector stores
+        return VectorIndexRetriever(
+            index=index,
+            similarity_top_k=similarity_top_k,
+            vector_store_kwargs={
+                "where": _chromadb_doc_id_metadata_filter(context_filter)
+            },
+        )