Commiting updates

step3.py

@@ -1,4 +1,3 @@
-from helper import get_llama_cloud_api_key
 from helper import extract_html_content
 from IPython.display import display, HTML
 from llama_index.utils.workflow import draw_all_possible_flows
@@ -22,6 +21,7 @@ from llama_index.core.workflow import (
     Event,
     Context
 )
+import json
 from pathlib import Path
 from dotenv import load_dotenv
 import os
@@ -96,36 +96,49 @@ agent = FunctionCallingAgent.from_tools(
 response = agent.chat("How many years of experience does the applicant have?")
 print(response)
 
+print("\n\n" + "="*50, "\n\n")
+
+
+class ParseFormEvent(Event):
+    application_form: str
+
 
 class QueryEvent(Event):
     query: str
 
 
+class ResponseEvent(Event):
+    response: str
+
+
+    # the first step will be setup
 class RAGWorkflow(Workflow):
+    # define the LLM to work with
     storage_dir = "./storage"
     llm: Groq
     query_engine: VectorStoreIndex
 
-    # the first step will be setup
     @step
-    async def set_up(self, ctx: Context, ev: StartEvent) -> QueryEvent:
-
+    async def set_up(self, ctx: Context, ev: StartEvent) -> ParseFormEvent:
+        self.llm = global_llm
         if not ev.resume_file:
             raise ValueError("No resume file provided")
 
-        # define an LLM to work with
-        self.llm = global_llm
+        if not ev.application_form:
+            raise ValueError("No application form provided")
+
 
         # ingest the data and set up the query engine
         if os.path.exists(self.storage_dir):
-            # you've already ingested your documents
+            # you've already ingested the resume document
             storage_context = StorageContext.from_defaults(persist_dir=self.storage_dir)
             index = load_index_from_storage(storage_context)
         else:
-            # parse and load your documents
+            # parse and load the resume document
             documents = LlamaParse(
                 result_type="markdown",
-                content_guideline_instruction="This is a resume, gather related facts together and format it as bullet points with headers"
+                content_guideline_instruction="This is a resume, gather related facts together and format it as "
+                                              "bullet points with headers"
             ).load_data(ev.resume_file)
             # embed and index the documents
             index = VectorStoreIndex.from_documents(
@@ -134,33 +147,92 @@ class RAGWorkflow(Workflow):
             )
             index.storage_context.persist(persist_dir=self.storage_dir)
 
-        # either way, create a query engine
+        # create a query engine
         self.query_engine = index.as_query_engine(llm=self.llm, similarity_top_k=5)
 
-        # now fire off a query event to trigger the next step
-        return QueryEvent(query=ev.query)
+        # you no longer need a query to be passed in,
+        # you'll be generating the queries instead
+        # let's pass the application form to a new step to parse it
+        return ParseFormEvent(application_form=ev.application_form)
+
+    @step
+    async def parse_form(self, ctx: Context, ev: ParseFormEvent) -> QueryEvent:
+        parser = LlamaParse(
+            result_type="markdown",
+            content_guideline_instruction="This is a job application form. Create a list of all the fields that "
+                                          "need to be filled in.",
+            formatting_instruction="Return a bulleted list of the fields ONLY."
+        )
+
+        # get the LLM to convert the parsed form into JSON
+        result = parser.load_data(ev.application_form)[0]
+        raw_json = self.llm.complete(
+            f"""
+            This is a parsed form. 
+            Convert it into a JSON object containing only the list 
+            of fields to be filled in, in the form {{ fields: [...] }}. 
+            <form>{result.text}</form>. 
+            Return JSON ONLY, no markdown.
+            """)
+        fields = json.loads(raw_json.text)["fields"]
+
+        # new!
+        # generate one query for each of the fields, and fire them off
+        for field in fields:
+            ctx.send_event(QueryEvent(
+                field=field,
+                query=f"How would you answer this question about the candidate? {field}"
+            ))
+
+        # store the number of fields so we know how many to wait for later
+        await ctx.set("total_fields", len(fields))
+        return
+
+    @step
+    async def ask_question(self, ctx: Context, ev: QueryEvent) -> ResponseEvent:
+        response = self.query_engine.query(
+            f"This is a question about the specific resume we have in our database: {ev.query}")
+        return ResponseEvent(field=ev.field, response=response.response)
 
-    # the second step will be to ask a question and return a result immediately
+    # new!
     @step
-    async def ask_question(self, ctx: Context, ev: QueryEvent) -> StopEvent:
-        response = self.query_engine.query(f"This is a question about the specific resume we have in our database: {ev.query}")
-        return StopEvent(result=response.response)
+    async def fill_in_application(self, ctx: Context, ev: ResponseEvent) -> StopEvent:
+        # get the total number of fields to wait for
+        total_fields = await ctx.get("total_fields")
+
+        responses = ctx.collect_events(ev, [ResponseEvent] * total_fields)
+        if responses is None:
+            return None  # do nothing if there's nothing to do yet
+
+        # we've got all the responses!
+        responseList = "\n".join("Field: " + r.field + "\n" + "Response: " + r.response for r in responses)
+
+        result = self.llm.complete(f"""
+            You are given a list of fields in an application form and responses to
+            questions about those fields from a resume. Combine the two into a list of
+            fields and succinct, factual answers to fill in those fields.
+
+            <responses>
+            {responseList}
+            </responses>
+        """)
+        return StopEvent(result=result)
 
 
 async def main():
     w = RAGWorkflow(timeout=120, verbose=False)
     result = await w.run(
-        resume_file="./data/fake_resume.pdf",
-        query="Where is the first place the applicant worked?"
+        resume_file="data/fake_resume.pdf",
+        application_form="data/fake_application_form.pdf"
     )
     print(result)
-
+    '''
     # Display of the workflow
     workflow_file = Path(__file__).parent / "workflows" / "rag_workflow.html"
     draw_all_possible_flows(w, filename=str(workflow_file))
     html_content = extract_html_content(str(workflow_file))
     display(HTML(html_content), metadata=dict(isolated=True))
-
+    '''
 
 if __name__ == "__main__":
     asyncio.run(main())

step4.py

@@ -0,0 +1,67 @@
+from helper import extract_html_content
+from IPython.display import display, HTML
+from llama_index.utils.workflow import draw_all_possible_flows
+from llama_index.core.tools import FunctionTool
+from llama_index.core.agent import FunctionCallingAgent
+from llama_index.core import Settings
+from llama_parse import LlamaParse
+from llama_index.llms.groq import Groq
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.core import (
+    VectorStoreIndex,
+    StorageContext,
+    load_index_from_storage
+)
+import nest_asyncio
+from llama_index.core.workflow import (
+    StartEvent,
+    StopEvent,
+    Workflow,
+    step,
+    Event,
+    Context
+)
+from pathlib import Path
+from dotenv import load_dotenv
+import os, json
+import asyncio
+
+
+storage_dir = "./storage"
+application_file = "./data/fake_application_form.pdf"
+nest_asyncio.apply()
+
+load_dotenv()
+llama_cloud_api_key = os.getenv("LLAMA_CLOUD_API_KEY")
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+LLAMA_CLOUD_BASE_URL = os.getenv("LLAMA_CLOUD_BASE_URL")
+
+global_llm = Groq(api_key=GROQ_API_KEY, model="llama3-70b-8192")
+global_embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+Settings.embed_model = global_embed_model
+
+# Parse the application form
+parser = LlamaParse(
+    result_type="markdown",
+    content_guideline_instruction="This is a job application form. Create a list of all the fields "
+                                  "that need to be filled in.",
+    formatting_instruction="Return a bulleted list of the fields ONLY."
+)
+
+result = parser.load_data(application_file)[0]
+#print(result.text)
+
+# Convert the above output into a JSON object
+raw_json = global_llm.complete(
+    f"""
+    This is a parsed form.
+    Convert it into a JSON object containing only the list 
+    of fields to be filled in, in the form {{ fields: [...] }}. 
+    <form>{result.text}</form>. 
+    Return JSON ONLY, no markdown."""
+)
+
+# print ou the fields one by one
+fields = json.loads(raw_json.text)["fields"]
+for field in fields:
+    print(field)

storage/default__vector_store.json

@@ -1 +1 @@
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"7146b5c5-5c20-4f5d-a008-49571aa58334": "ec090e43-ef25-4902-b24b-a080d45f07b1"}, "metadata_dict": {"91c98c96-747f-4252-85ee-aeb0a25a8e94": {"_node_type": "TextNode", "document_id": "cc9d8436-8704-4296-96ff-18b2425417af", "doc_id": "cc9d8436-8704-4296-96ff-18b2425417af", "ref_doc_id": "cc9d8436-8704-4296-96ff-18b2425417af"}, "a14ffc58-aa00-481c-8261-b5120fa5b1d0": {"_node_type": "TextNode", "document_id": "b8a90649-5538-4fc0-826c-168a50587c4b", "doc_id": "b8a90649-5538-4fc0-826c-168a50587c4b", "ref_doc_id": "b8a90649-5538-4fc0-826c-168a50587c4b"}, "7146b5c5-5c20-4f5d-a008-49571aa58334": {"_node_type": "TextNode", "document_id": "ec090e43-ef25-4902-b24b-a080d45f07b1", "doc_id": "ec090e43-ef25-4902-b24b-a080d45f07b1", "ref_doc_id": "ec090e43-ef25-4902-b24b-a080d45f07b1"}}}

storage/docstore.json

@@ -1 +1 @@
-{"docstore/metadata": {"23e28b4b-62c3-4e17-b3a0-f830f4f1dca6": {"doc_hash": "ec7cb315d1b308d0ae022c78acc2f9bc5a2e011b6b9216e06e304394efcda964"}, "915e49b4-52d4-4b83-8234-6eb520aa40b7": {"doc_hash": "462ec5c5c45da617a23c1ba05c2ea8fdfb12232e5b2277ad17b2aa54393e610a"}, "786dc0d9-dd19-4be2-8027-005b54d09842": {"doc_hash": "ed022f5a93cadb684fc4406cd0f8c89e891cd27ab480fe74ddc16de7b928c698"}, "949fb843-d2e1-4df7-858d-6ea740348615": {"doc_hash": "49d6eef6279612434cc13da66dd423d713b7a940effd8c8f8a04540c92301988", "ref_doc_id": "23e28b4b-62c3-4e17-b3a0-f830f4f1dca6"}, "eda5445f-43af-4348-a05b-a7c16f03cc4c": {"doc_hash": "a33cba242c9cfef420917121ecfbd3cebcdac0922422f6231f1d12d125e0429b", "ref_doc_id": "915e49b4-52d4-4b83-8234-6eb520aa40b7"}, "3e40b9dc-8ae7-4942-8453-e13255a37580": {"doc_hash": "3ccf436e9b52028f19deac56f16026e88aaac1182998185346b04b2e4354cd83", "ref_doc_id": "786dc0d9-dd19-4be2-8027-005b54d09842"}}, "docstore/data": {"949fb843-d2e1-4df7-858d-6ea740348615": {"__data__": {"id_": "949fb843-d2e1-4df7-858d-6ea740348615", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "23e28b4b-62c3-4e17-b3a0-f830f4f1dca6", "node_type": "4", "metadata": {}, "hash": "ec7cb315d1b308d0ae022c78acc2f9bc5a2e011b6b9216e06e304394efcda964", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "# Sarah Chen\n\nEmail: sarah.chen@email.com\n\nLinkedIn: linkedin.com/in/sarahchen\n\nGitHub: github.com/sarahcodes\n\nPortfolio: sarahchen.dev\n\nLocation: San Francisco, CA\n\n# Professional Summary\n\nInnovative Full Stack Web Developer with 6+ years of experience crafting scalable web applications and microservices. Specialized in React, Node.js, and cloud architecture. Proven track record of leading technical teams and implementing CI/CD pipelines that reduced deployment time by 40%. Passionate about clean code, accessibility, and mentoring junior developers.\n\n# Professional Experience\n\n# Senior Full Stack Developer\n\nTechFlow Solutions | San Francisco, CA January 2022 - Present\n\n- Architected and implemented a microservices-based e-commerce platform serving 100K+ daily users\n- Led a team of 5 developers in rebuilding the company's flagship product using React and Node.js\n- Implemented GraphQL API gateway that reduced API response times by 60%\n- Established coding standards and review processes that improved code quality by 45%\n\n# Technical Skills\n\n# Frontend:\n\n- React.js, Redux, Next.js, TypeScript\n- Vue.js, Nuxt.js\n- HTML5, CSS3, SASS/SCSS\n- Jest, React Testing Library\n- WebPack, Babel\n\n# Backend:\n\n- Node.js, Express.js\n- Python, Django\n- GraphQL, REST APIs\n- PostgreSQL, MongoDB", "mimetype": "text/plain", "start_char_idx": 0, "end_char_idx": 1291, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "eda5445f-43af-4348-a05b-a7c16f03cc4c": {"__data__": {"id_": "eda5445f-43af-4348-a05b-a7c16f03cc4c", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "915e49b4-52d4-4b83-8234-6eb520aa40b7", "node_type": "4", "metadata": {}, "hash": "462ec5c5c45da617a23c1ba05c2ea8fdfb12232e5b2277ad17b2aa54393e610a", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "# Full Stack Developer\n\nInnovateSoft | Oakland, CA March 2019 - December 2021\n\n- Mentored 3 junior developers who were promoted to mid-level positions\n- Developed and maintained 10+ customer-facing applications using Vue.js and Django\n- Implemented automated testing suite that increased code coverage from 65% to 95%\n- Optimized database queries resulting in 30% faster page load times\n- Collaborated with UX team to implement accessibility features (WCAG 2.1 compliance)\n- Created documentation that reduced onboarding time for new developers by 50%\n\n# Tools & Others:\n\n- Docker, Kubernetes\n- AWS (EC2, S3, Lambda)\n- Git, GitHub Actions\n- Jenkins, CircleCI\n- Agile/Scrum methodology\n- Performance optimization\n\n# Junior Web Developer\n\nStartupHub | San Jose, CA June 2017 - February 2019\n\n- Built responsive web applications using React.js and Express.js\n- Implemented user authentication system using JWT and OAuth2.0\n- Contributed to migration of legacy PHP applications to modern JavaScript stack\n- Developed RESTful APIs consumed by mobile and web applications\n\n# Education\n\nBachelor of Science in Computer Science\n\nUniversity of California, Berkeley 2013 - 2017\n\n- GPA: 3.8/4.0\n- Minor in User Experience Design\n- President of Women in Tech Society", "mimetype": "text/plain", "start_char_idx": 0, "end_char_idx": 1254, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "3e40b9dc-8ae7-4942-8453-e13255a37580": {"__data__": {"id_": "3e40b9dc-8ae7-4942-8453-e13255a37580", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "786dc0d9-dd19-4be2-8027-005b54d09842", "node_type": "4", "metadata": {}, "hash": "ed022f5a93cadb684fc4406cd0f8c89e891cd27ab480fe74ddc16de7b928c698", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "# Projects\n\n# EcoTrack | GitHub\n\n- Built full-stack application for tracking carbon footprint using React, Node.js, and MongoDB\n- Implemented machine learning algorithm for providing personalized sustainability recommendations\n- Featured in TechCrunch's \"Top 10 Environmental Impact Apps of 2023\"\n\n# ChatFlow | Demo\n\n- Developed real-time chat application using WebSocket protocol and React\n- Implemented end-to-end encryption and message persistence\n- Serves 5000+ monthly active users\n\n# Certifications\n\n- AWS Certified Solutions Architect (2023)\n- Google Cloud Professional Developer (2022)\n- MongoDB Certified Developer (2021)\n\n# Languages\n\n- English (Native)\n- Mandarin Chinese (Fluent)\n- Spanish (Intermediate)\n\n# Interests\n\n- Open source contribution\n- Tech blogging (15K+ Medium followers)\n- Hackathon mentoring\n- Rock climbing", "mimetype": "text/plain", "start_char_idx": 0, "end_char_idx": 835, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}}, "docstore/ref_doc_info": {"23e28b4b-62c3-4e17-b3a0-f830f4f1dca6": {"node_ids": ["949fb843-d2e1-4df7-858d-6ea740348615"], "metadata": {}}, "915e49b4-52d4-4b83-8234-6eb520aa40b7": {"node_ids": ["eda5445f-43af-4348-a05b-a7c16f03cc4c"], "metadata": {}}, "786dc0d9-dd19-4be2-8027-005b54d09842": {"node_ids": ["3e40b9dc-8ae7-4942-8453-e13255a37580"], "metadata": {}}}}
+{"docstore/metadata": {"cc9d8436-8704-4296-96ff-18b2425417af": {"doc_hash": "137681331020ba45370f85060fa7e57df067388da716943a2b1814f82d1a7aaf"}, "b8a90649-5538-4fc0-826c-168a50587c4b": {"doc_hash": "462ec5c5c45da617a23c1ba05c2ea8fdfb12232e5b2277ad17b2aa54393e610a"}, "ec090e43-ef25-4902-b24b-a080d45f07b1": {"doc_hash": "ed022f5a93cadb684fc4406cd0f8c89e891cd27ab480fe74ddc16de7b928c698"}, "91c98c96-747f-4252-85ee-aeb0a25a8e94": {"doc_hash": "d1749b9fd0ac387f090f97b52a516f23271a74a9afb93d281d05876baab03fdb", "ref_doc_id": "cc9d8436-8704-4296-96ff-18b2425417af"}, "a14ffc58-aa00-481c-8261-b5120fa5b1d0": {"doc_hash": "a33cba242c9cfef420917121ecfbd3cebcdac0922422f6231f1d12d125e0429b", "ref_doc_id": "b8a90649-5538-4fc0-826c-168a50587c4b"}, "7146b5c5-5c20-4f5d-a008-49571aa58334": {"doc_hash": "3ccf436e9b52028f19deac56f16026e88aaac1182998185346b04b2e4354cd83", "ref_doc_id": "ec090e43-ef25-4902-b24b-a080d45f07b1"}}, "docstore/data": {"91c98c96-747f-4252-85ee-aeb0a25a8e94": {"__data__": {"id_": "91c98c96-747f-4252-85ee-aeb0a25a8e94", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "cc9d8436-8704-4296-96ff-18b2425417af", "node_type": "4", "metadata": {}, "hash": "137681331020ba45370f85060fa7e57df067388da716943a2b1814f82d1a7aaf", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "Sarah Chen\n\nEmail: sarah.chen@email.com\n\nLinkedIn: linkedin.com/in/sarahchen\n\nFull Stack Web Developer\n\nGitHub: github.com/sarahcodes\n\nPortfolio: sarahchen.dev\n\nLocation: San Francisco, CA\n\n# Professional Summary\n\nInnovative Full Stack Web Developer with 6+ years of experience crafting scalable web applications and microservices. Specialized in React, Node.js, and cloud architecture. Proven track record of leading technical teams and implementing CI/CD pipelines that reduced deployment time by 40%. Passionate about clean code, accessibility, and mentoring junior developers.\n\n# Professional Experience\n\n# Senior Full Stack Developer\n\nTechFlow Solutions | San Francisco, CA January 2022 - Present\n\n- Architected and implemented a microservices-based e-commerce platform serving 100K+ daily users\n- Led a team of 5 developers in rebuilding the company's flagship product using React and Node.js\n- Implemented GraphQL API gateway that reduced API response times by 60%\n- Established coding standards and review processes that improved code quality by 45%\n\n# Technical Skills\n\n# Frontend:\n\n- React.js, Redux, Next.js, TypeScript\n- Vue.js, Nuxt.js\n- HTML5, CSS3, SASS/SCSS\n- Jest, React Testing Library\n- WebPack, Babel\n\n# Backend:\n\n- Node.js, Express.js\n- Python, Django\n- GraphQL, REST APIs\n- PostgreSQL, MongoDB", "mimetype": "text/plain", "start_char_idx": 0, "end_char_idx": 1315, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "a14ffc58-aa00-481c-8261-b5120fa5b1d0": {"__data__": {"id_": "a14ffc58-aa00-481c-8261-b5120fa5b1d0", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "b8a90649-5538-4fc0-826c-168a50587c4b", "node_type": "4", "metadata": {}, "hash": "462ec5c5c45da617a23c1ba05c2ea8fdfb12232e5b2277ad17b2aa54393e610a", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "# Full Stack Developer\n\nInnovateSoft | Oakland, CA March 2019 - December 2021\n\n- Mentored 3 junior developers who were promoted to mid-level positions\n- Developed and maintained 10+ customer-facing applications using Vue.js and Django\n- Implemented automated testing suite that increased code coverage from 65% to 95%\n- Optimized database queries resulting in 30% faster page load times\n- Collaborated with UX team to implement accessibility features (WCAG 2.1 compliance)\n- Created documentation that reduced onboarding time for new developers by 50%\n\n# Tools & Others:\n\n- Docker, Kubernetes\n- AWS (EC2, S3, Lambda)\n- Git, GitHub Actions\n- Jenkins, CircleCI\n- Agile/Scrum methodology\n- Performance optimization\n\n# Junior Web Developer\n\nStartupHub | San Jose, CA June 2017 - February 2019\n\n- Built responsive web applications using React.js and Express.js\n- Implemented user authentication system using JWT and OAuth2.0\n- Contributed to migration of legacy PHP applications to modern JavaScript stack\n- Developed RESTful APIs consumed by mobile and web applications\n\n# Education\n\nBachelor of Science in Computer Science\n\nUniversity of California, Berkeley 2013 - 2017\n\n- GPA: 3.8/4.0\n- Minor in User Experience Design\n- President of Women in Tech Society", "mimetype": "text/plain", "start_char_idx": 0, "end_char_idx": 1254, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "7146b5c5-5c20-4f5d-a008-49571aa58334": {"__data__": {"id_": "7146b5c5-5c20-4f5d-a008-49571aa58334", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "ec090e43-ef25-4902-b24b-a080d45f07b1", "node_type": "4", "metadata": {}, "hash": "ed022f5a93cadb684fc4406cd0f8c89e891cd27ab480fe74ddc16de7b928c698", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "# Projects\n\n# EcoTrack | GitHub\n\n- Built full-stack application for tracking carbon footprint using React, Node.js, and MongoDB\n- Implemented machine learning algorithm for providing personalized sustainability recommendations\n- Featured in TechCrunch's \"Top 10 Environmental Impact Apps of 2023\"\n\n# ChatFlow | Demo\n\n- Developed real-time chat application using WebSocket protocol and React\n- Implemented end-to-end encryption and message persistence\n- Serves 5000+ monthly active users\n\n# Certifications\n\n- AWS Certified Solutions Architect (2023)\n- Google Cloud Professional Developer (2022)\n- MongoDB Certified Developer (2021)\n\n# Languages\n\n- English (Native)\n- Mandarin Chinese (Fluent)\n- Spanish (Intermediate)\n\n# Interests\n\n- Open source contribution\n- Tech blogging (15K+ Medium followers)\n- Hackathon mentoring\n- Rock climbing", "mimetype": "text/plain", "start_char_idx": 0, "end_char_idx": 835, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}}, "docstore/ref_doc_info": {"cc9d8436-8704-4296-96ff-18b2425417af": {"node_ids": ["91c98c96-747f-4252-85ee-aeb0a25a8e94"], "metadata": {}}, "b8a90649-5538-4fc0-826c-168a50587c4b": {"node_ids": ["a14ffc58-aa00-481c-8261-b5120fa5b1d0"], "metadata": {}}, "ec090e43-ef25-4902-b24b-a080d45f07b1": {"node_ids": ["7146b5c5-5c20-4f5d-a008-49571aa58334"], "metadata": {}}}}

storage/index_store.json

@@ -1 +1 @@
-{"index_store/data": {"53cc9987-aaba-4c79-8378-e72adbdc654f": {"__type__": "vector_store", "__data__": "{\"index_id\": \"53cc9987-aaba-4c79-8378-e72adbdc654f\", \"summary\": null, \"nodes_dict\": {\"949fb843-d2e1-4df7-858d-6ea740348615\": \"949fb843-d2e1-4df7-858d-6ea740348615\", \"eda5445f-43af-4348-a05b-a7c16f03cc4c\": \"eda5445f-43af-4348-a05b-a7c16f03cc4c\", \"3e40b9dc-8ae7-4942-8453-e13255a37580\": \"3e40b9dc-8ae7-4942-8453-e13255a37580\"}, \"doc_id_dict\": {}, \"embeddings_dict\": {}}"}}}
+{"index_store/data": {"38555257-a541-4ca0-bb9f-74a51eab93ae": {"__type__": "vector_store", "__data__": "{\"index_id\": \"38555257-a541-4ca0-bb9f-74a51eab93ae\", \"summary\": null, \"nodes_dict\": {\"91c98c96-747f-4252-85ee-aeb0a25a8e94\": \"91c98c96-747f-4252-85ee-aeb0a25a8e94\", \"a14ffc58-aa00-481c-8261-b5120fa5b1d0\": \"a14ffc58-aa00-481c-8261-b5120fa5b1d0\", \"7146b5c5-5c20-4f5d-a008-49571aa58334\": \"7146b5c5-5c20-4f5d-a008-49571aa58334\"}, \"doc_id_dict\": {}, \"embeddings_dict\": {}}"}}}