File Uploads

from honcho import Honcho

# Initialize client
honcho = Honcho()

# Create session and peer
session = honcho.session("research-session")
user = honcho.peer("researcher")

# Upload a PDF to a session
with open("research_paper.pdf", "rb") as file:
    messages = session.upload_file(
        file=file,
        peer_id=user.id,
    )

print(f"Created {len(messages)} messages from the PDF")

import { Honcho } from "@honcho-ai/sdk";
import fs from "fs";

(async () => {
    // Initialize client
    const honcho = new Honcho({});

    // Create session and peer
    const session = await honcho.session("research-session");
    const user = await honcho.peer("researcher");

    // Upload a PDF to a session
    const fileStream = fs.createReadStream("research_paper.pdf");
    const messages = await session.uploadFile(fileStream, user.id);

    console.log(`Created ${messages.length} messages from the PDF`);
})();

# Query what was learned from the uploaded documents
response = user.chat("What are the key findings from the research papers I uploaded?")
print(response)

# Ask about specific documents
response = user.chat("What does the quarterly report say about revenue growth?")
print(response)

# Get context from the uploaded documents for LLM integration
context = session.context(tokens=3000)
messages = context.to_openai(assistant=assistant)

(async () => {
    // Query what was learned from the uploaded documents
    const response = await user.chat("What are the key findings from the research papers I uploaded?");
    console.log(response);

    // Ask about specific documents
    const response2 = await user.chat("What does the quarterly report say about revenue growth?");
    console.log(response2);

    // Get context from the uploaded documents for LLM integration
    const context = await session.context({ tokens: 3000 });
    const messages = context.toOpenAI(assistant);
})();

from honcho import Honcho

# Initialize
honcho = Honcho()
session = honcho.session("document-analysis")
user = honcho.peer("analyst")
assistant = honcho.peer("analysis-bot")

def upload_document(file_path, description):
    """Upload a document and add it to the session"""
    with open(file_path, "rb") as file:
        messages = session.upload_file(
            file=file,
            peer_id=user.id,
        )
    return messages

def analyze_documents():
    """Get AI analysis of uploaded documents"""
    context = session.context(tokens=4000)
    messages = context.to_openai(assistant=assistant)
    # Add analysis request
    messages.append({
        "role": "user",
        "content": "Please analyze all the documents I've uploaded and provide a comprehensive summary of the key findings, trends, and recommendations."
    })

    # Call OpenAI (or your preferred LLM)
    # response = openai.chat.completions.create(model="gpt-4", messages=messages)
    # return response.choices[0].message.content

    return "Analysis would be generated here"

# Upload multiple documents
documents = [
    ("quarterly_report.pdf", "Q3 2024 Quarterly Financial Report"),
    ("market_research.pdf", "Market Analysis and Competitive Landscape"),
    ("product_roadmap.pdf", "Product Development Roadmap 2024-2025")
]

for file_path, description in documents:
    messages = upload_document(file_path, description)
    print(f"Uploaded {file_path}: {len(messages)} messages created")

# Get AI analysis
analysis = analyze_documents()
print("Document Analysis:", analysis)

import { Honcho } from "@honcho-ai/sdk";
import fs from "fs";

(async () => {
    // Initialize
    const honcho = new Honcho({});
    const session = await honcho.session("document-analysis");
    const user = await honcho.peer("analyst");
    const assistant = await honcho.peer("analysis-bot");

    async function uploadDocument(filePath: string, description: string) {
      const fileStream = fs.createReadStream(filePath);
      const messages = await session.uploadFile(fileStream, user.id);
      return messages;
    }

    async function analyzeDocuments() {
      const context = await session.context({ tokens: 4000 });
      const messages = context.toOpenAI(assistant);
      // Add analysis request
      messages.push({
        role: "user",
        content: "Please analyze all the documents I've uploaded and provide a comprehensive summary of the key findings, trends, and recommendations."
      });

      // Call OpenAI (or your preferred LLM)
      // const response = await openai.chat.completions.create({ model: "gpt-4", messages });
      // return response.choices[0].message.content;

      return "Analysis would be generated here";
    }

    // Upload multiple documents
    const documents = [
      ["quarterly_report.pdf", "Q3 2024 Quarterly Financial Report"],
      ["market_research.pdf", "Market Analysis and Competitive Landscape"],
      ["product_roadmap.pdf", "Product Development Roadmap 2024-2025"]
    ];

    for (const [filePath, description] of documents) {
      const messages = await uploadDocument(filePath, description);
      console.log(`Uploaded ${filePath}: ${messages.length} messages created`);
    }

    // Get AI analysis
    const analysis = await analyzeDocuments();
    console.log("Document Analysis:", analysis);
})();