feat: merge

.gitignore

@@ -1,11 +1,13 @@
 # See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
 __pycache__/
-.pyc
+*.pyc
 # dependencies
 /node_modules
 /.pnp
 .pnp.js
 
+server/temp/
+
 # testing
 /coverage
 
@@ -39,3 +41,4 @@ next-env.d.ts
 .yarn
 /server/.aws-sam/*
 .aws-sam/*
+

app/api/chat/retrieval/route.ts

@@ -7,10 +7,7 @@ import { ChatOpenAI } from 'langchain/chat_models/openai';
 import { PromptTemplate } from 'langchain/prompts';
 import { SupabaseVectorStore } from 'langchain/vectorstores/supabase';
 import { Document } from 'langchain/document';
-import {
-  RunnableSequence,
-  RunnablePassthrough,
-} from 'langchain/schema/runnable';
+import { RunnableSequence } from 'langchain/schema/runnable';
 import {
   BytesOutputParser,
   StringOutputParser,

server/Dockerfile.aws.lambda

@@ -1,11 +1,20 @@
 FROM public.ecr.aws/docker/library/python:3.12.0-slim-bullseye
 
+# Copy aws-lambda-adapter for Steaming response
 COPY --from=public.ecr.aws/awsguru/aws-lambda-adapter:0.8.1 /lambda-adapter /opt/extensions/lambda-adapter
 
+# Copy nltk_lambda_layer for using nltk in lambda
+COPY --from=public.ecr.aws/m5s2b0d4/nltk_lambda_layer:latest /nltk_data /opt/nltk_data
+
+
 # Copy function code
 COPY . ${LAMBDA_TASK_ROOT}
 # from your project folder.
 COPY requirements.txt .
 RUN pip3 install -r requirements.txt --target "${LAMBDA_TASK_ROOT}" -U --no-cache-dir
 
-CMD ["python", "main.py"]
+# Set NLTK_DATA to load nltk_data
+ENV NLTK_DATA=/opt/nltk_data
+
+
+CMD ["python", "main.py"]

server/data_class.py

@@ -1,3 +1,4 @@
+from typing import Optional
 from pydantic import BaseModel
 
 
@@ -17,4 +18,8 @@ class ChatData(BaseModel):
 class ExecuteMessage(BaseModel):
     type: str
     repo: str
-    path: str
+    path: str
+
+class S3Config(BaseModel):
+    s3_bucket: str
+    file_path: Optional[str] = None

server/main.py

@@ -1,5 +1,5 @@
 import os
-from rag import retrieval
+
 import uvicorn
 from fastapi import FastAPI
 from fastapi.responses import StreamingResponse
@@ -11,7 +11,11 @@
 from data_class import ChatData
 
 # Import fastapi routers
+<<<<<<< HEAD
 from routers import bot, health_checker, github
+=======
+from routers import bot, health_checker, github, rag
+>>>>>>> new-branch-name
 
 open_api_key = get_env_variable("OPENAI_API_KEY")
 is_dev = bool(get_env_variable("IS_DEV"))
@@ -33,6 +37,7 @@
 
 app.include_router(health_checker.router)
 app.include_router(github.router)
+app.include_router(rag.router)
 app.include_router(bot.router)
 
 
@@ -41,18 +46,10 @@ def run_agent_chat(input_data: ChatData):
     result = stream.agent_chat(input_data, open_api_key)
     return StreamingResponse(result, media_type="text/event-stream")
 
-@app.post("/api/rag/add_knowledge")
-def add_knowledge():
-    data=retrieval.add_knowledge()
-    return data
 
-@app.post("/api/rag/search_knowledge")
-def search_knowledge(query: str):
-    data=retrieval.search_knowledge(query)
-    return data
 
 if __name__ == "__main__":
     if is_dev:
         uvicorn.run("main:app", host="0.0.0.0", port=int(os.environ.get("PORT", "8080")), reload=True)
     else:
-        uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT", "8080")))
+        uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT", "8080")))

server/rag/retrieval.py

@@ -1,17 +1,22 @@
-import os
 import json
-from langchain_community.document_loaders import TextLoader
+import boto3
 from langchain_openai import OpenAIEmbeddings
 from langchain_text_splitters import CharacterTextSplitter
 from langchain_community.vectorstores import SupabaseVectorStore
 from db.supabase.client import get_client
+from data_class import S3Config
 from uilts.env import get_env_variable
+from langchain_community.document_loaders import S3DirectoryLoader
+
 
 supabase_url = get_env_variable("SUPABASE_URL")
 supabase_key = get_env_variable("SUPABASE_SERVICE_KEY")
+
+
 table_name="antd_knowledge"
 query_name="match_antd_knowledge"
-chunk_size=500
+chunk_size=2000
+
 
 def convert_document_to_dict(document):
     return {
@@ -32,36 +37,33 @@ def init_retriever():
 
     return db.as_retriever()
 
-def add_knowledge():
-    current_dir = os.path.dirname(os.path.abspath(__file__))
-    target_file_path = os.path.join(current_dir, "../docs/test.md")
-    loader = TextLoader(target_file_path)
-    documents = loader.load()
-    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-    docs = text_splitter.split_documents(documents)
-    embeddings = OpenAIEmbeddings()
 
+def add_knowledge(config: S3Config):    
     try:
-      SupabaseVectorStore.from_documents(
-        docs,
-        embeddings,
-        client=supabase,
-        table_name=table_name,
-        query_name=query_name,
-        chunk_size=chunk_size,
-      )
-      return json.dumps({
-        "success": True,
-        "message": "Knowledge added successfully!"
-      })
+        loader = S3DirectoryLoader(config.s3_bucket, prefix=config.file_path)
+        documents = loader.load()
+        text_splitter = CharacterTextSplitter(chunk_size=2000, chunk_overlap=0)
+        docs = text_splitter.split_documents(documents)
+        embeddings = OpenAIEmbeddings()
+        SupabaseVectorStore.from_documents(
+            docs,
+            embeddings,
+            client=get_client(),
+            table_name=table_name,
+            query_name=query_name,
+            chunk_size=chunk_size,
+        )
+        return json.dumps({
+            "success": True,
+            "message": "Knowledge added successfully!",
+            "docs_len": len(documents)
+        })
     except Exception as e:
-      return json.dumps({
-        "success": False,
-        "message": str(e)
-      })
+        return json.dumps({
+            "success": False,
+            "message": str(e)
+        })
 
-
-
 def search_knowledge(query: str):
     retriever = init_retriever()
     docs = retriever.get_relevant_documents(query)

server/requirements.txt

@@ -15,4 +15,5 @@ load_dotenv
 supabase
 boto3>=1.34.84
 pyjwt>=2.4.0
-pydantic>=2.7.0
+pydantic>=2.7.0
+unstructured[md]

server/routers/github.py

@@ -18,7 +18,7 @@
 
 router = APIRouter(
     prefix="/api/github",
-    tags=["health_checkers"],
+    tags=["github"],
     responses={404: {"description": "Not found"}},
 )
 

server/routers/health_checker.py

@@ -8,4 +8,4 @@
 
 @router.get("/health_checker")
 def health_checker():
-    return {"Hello": "World"}
+    return { "Hello": "World" }

server/routers/rag.py

@@ -0,0 +1,20 @@
+from fastapi import APIRouter
+from rag import retrieval
+from data_class import S3Config
+
+router = APIRouter(
+    prefix="/api",
+    tags=["rag"],
+    responses={404: {"description": "Not found"}},
+)
+
+
+@router.post("/rag/add_knowledge")
+def add_knowledge(config: S3Config):
+    data=retrieval.add_knowledge(config)
+    return data
+
+@router.post("/rag/search_knowledge")
+def search_knowledge(query: str):
+    data=retrieval.search_knowledge(query)
+    return data

server/tools/issue.py

@@ -2,7 +2,6 @@
 from typing import Optional
 from github import Github
 from langchain.tools import tool
-from uilts.env import get_env_variable
 
 DEFAULT_REPO_NAME = "ant-design/ant-design"
 
@@ -84,15 +83,15 @@ def search_issues(
         :param state: The state of the issue, e.g: open, closed, all
         """
         try:
-            search_query = f'{keyword} in:title,body,comments repo:{repo_name}'
+            search_query = f"{keyword} in:title,body,comments repo:{repo_name}"
             # Retrieve a list of open issues from the repository
             issues = g.search_issues(query=search_query, sort=sort, order=order)[:max_num]
             print(f"issues: {issues}")
 
             issues_list = [
                 {
-                    'issue_name': f'Issue #{issue.number} - {issue.title}',
-                    'issue_url': issue.html_url
+                    "issue_name": f"Issue #{issue.number} - {issue.title}",
+                    "issue_url": issue.html_url
                 }
                 for issue in issues
             ]

server/tools/sourcecode.py

@@ -2,8 +2,6 @@
 from github import Github
 from github.ContentFile import ContentFile
 from langchain.tools import tool
-from uilts.env import get_env_variable
-
 
 DEFAULT_REPO_NAME = "ant-design/ant-design"
 
@@ -29,7 +27,8 @@ def search_code(
 
         # Perform the search for code files containing the keyword
         code_files = g.search_code(query=query)[:max_num]
-        return code_files 
+
+        return code_files
     except Exception as e:
         print(f"An error occurred: {e}")
         return None