Poetry

Makefile

@@ -1,8 +1,8 @@
 .PHONY: start
 start:
-	uvicorn main:app --reload --port 9000
+	poetry run uvicorn main:app --reload --port 9000
 
 .PHONY: format
 format:
-	black .
-	isort .
+	poetry run black .
+	poetry run isort .

README.md

@@ -6,9 +6,10 @@ Built with [LangChain](https://github.com/hwchase17/langchain/) and [FastAPI](ht
 The app leverages LangChain's streaming support and async API to update the page in real time for multiple users.
 
 ## ✅ Running locally
-1. Install dependencies: `pip install -r requirements.txt`
+1. Install dependencies: `poetry install`
 1. Run `ingest.sh` to ingest LangChain docs data into the vectorstore (only needs to be done once).
    1. You can use other [Document Loaders](https://langchain.readthedocs.io/en/latest/modules/document_loaders.html) to load your own data into the vectorstore.
+1. Export your OpenAPI key, eg `export OPENAI_API_KEY=sk-...`
 1. Run the app: `make start`
    1. To enable tracing, make sure `langchain-server` is running locally and pass `tracing=True` to `get_chain` in `main.py`. You can find more documentation [here](https://langchain.readthedocs.io/en/latest/tracing.html).
 1. Open [localhost:9000](http://localhost:9000) in your browser.

ingest.sh

@@ -3,4 +3,4 @@
 # Error if any command fails
 set -e
 wget -r -A.html https://langchain.readthedocs.io/en/latest/
-python3 ingest.py
+poetry run python ingest.py

pyproject.toml

@@ -0,0 +1,30 @@
+[tool.poetry]
+name = "chat-langchain"
+version = "0.1.0"
+description = ""
+authors = ["Your Name <[email protected]>"]
+readme = "README.md"
+packages = [{include = "chat_langchain"}]
+
+[tool.poetry.dependencies]
+python = "^3.11"
+openai = "^0.27.5"
+fastapi = "^0.95.1"
+black = "^23.3.0"
+isort = "^5.12.0"
+websockets = "^11.0.2"
+pydantic = "^1.10.7"
+langchain = "^0.0.152"
+uvicorn = "^0.22.0"
+jinja2 = "^3.1.2"
+faiss-cpu = "^1.7.4"
+bs4 = "^0.0.1"
+unstructured = "^0.6.2"
+libmagic = "^1.0"
+tiktoken = "^0.3.3"
+
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

query_data.py

@@ -1,7 +1,7 @@
 """Create a ChatVectorDBChain for question/answering."""
 from langchain.callbacks.base import AsyncCallbackManager
 from langchain.callbacks.tracers import LangChainTracer
-from langchain.chains import ChatVectorDBChain
+from langchain.chains import ConversationalRetrievalChain
 from langchain.chains.chat_vector_db.prompts import (CONDENSE_QUESTION_PROMPT,
                                                      QA_PROMPT)
 from langchain.chains.llm import LLMChain
@@ -12,9 +12,9 @@
 
 def get_chain(
     vectorstore: VectorStore, question_handler, stream_handler, tracing: bool = False
-) -> ChatVectorDBChain:
-    """Create a ChatVectorDBChain for question/answering."""
-    # Construct a ChatVectorDBChain with a streaming llm for combine docs
+) -> ConversationalRetrievalChain:
+    """Create a ConversationalRetrievalChain for question/answering."""
+    # Construct a ConversationalRetrievalChain with a streaming llm for combine docs
     # and a separate, non-streaming llm for question generation
     manager = AsyncCallbackManager([])
     question_manager = AsyncCallbackManager([question_handler])
@@ -45,10 +45,11 @@ def get_chain(
         streaming_llm, chain_type="stuff", prompt=QA_PROMPT, callback_manager=manager
     )
 
-    qa = ChatVectorDBChain(
-        vectorstore=vectorstore,
+    qa = ConversationalRetrievalChain(
+        retriever=vectorstore.as_retriever(),
         combine_docs_chain=doc_chain,
         question_generator=question_generator,
         callback_manager=manager,
     )
+
     return qa